system

Abstract

The system according to the embodiment aims to analyze product information and develop and implement an optimal promotion strategy. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a formulation unit, a generation unit, and a distribution unit. The collection unit collects information about each product. The analysis unit analyzes the information collected by the collection unit. The formulation unit formulates a promotion strategy based on the analysis results obtained by the analysis unit. The generation unit generates advertisements based on the promotion strategy formulated by the formulation unit. The distribution unit distributes the advertisements generated by the generation unit.

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] Conventional technologies have not been able to effectively collect and analyze product information and formulate optimal promotion strategies, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze product information and develop and implement an optimal promotion strategy. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a formulation unit, a generation unit, and a distribution unit. The collection unit collects information about each product. The analysis unit analyzes the information collected by the collection unit. The formulation unit formulates a promotion strategy based on the analysis results obtained by the analysis unit. The generation unit generates an advertisement based on the promotion strategy formulated by the formulation unit. The distribution unit distributes the advertisement generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze product information and formulate and implement an optimal promotion strategy. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A promotion system according to an embodiment of the present invention promotes Japan's traditional culture, high-quality craftsmanship, and various products made using those craftsmanship worldwide. This promotion system collects information about each product and registers it in a database. Next, AI analyzes the collected information and formulates an optimal promotion strategy. Furthermore, based on the formulated promotion strategy, advertisements tailored to each country's market are generated and distributed. This allows for increased awareness of Japan's traditional culture and craftsmanship and increased demand. For example, the promotion system collects information about each product. This information includes detailed information about the product's characteristics, manufacturing process, history, and so on. For example, for traditional crafts, information on the manufacturing method, materials used, and historical background is collected. This maximizes the appeal of the product. Next, the promotion system uses AI to analyze the collected information. Based on the collected information, the AI ​​analyzes the characteristics and market needs of each product. For example, it analyzes products that are popular in specific countries or regions, and the characteristics that are easily accepted by specific demographics. This allows for the formulation of an optimal promotion strategy. Furthermore, the promotion system generates advertisements tailored to each country's market based on the formulated promotion strategy. The AI ​​generates advertisements tailored to each country's culture, language, and consumer preferences. For example, if traditional designs are preferred in a particular country, advertisements for that country can incorporate traditional designs. This allows for effective promotion. Finally, the promotion system distributes the generated advertisements to each country's market. AI distributes the advertisements using major advertising media and social media in each country. This makes it possible to raise awareness of Japan's traditional culture and technology and expand demand. This system can promote Japan's traditional culture and high-quality technology around the world and increase awareness, thereby revitalizing the Japanese economy. This promotion system can promote Japan's traditional culture and high-quality technology around the world and increase awareness, thereby revitalizing the Japanese economy.

[0029] A promotion system according to an embodiment includes a collection unit, an analysis unit, a formulation unit, a generation unit, and a distribution unit. The collection unit collects information about each product. The collection unit collects detailed information, such as product characteristics, manufacturing process, and history. For example, the collection unit can collect information about traditional craft manufacturing methods, materials used, and historical background. The collection unit can also monitor the product manufacturing process in real time and collect the latest information. For example, the collection unit can acquire data from sensors installed on the production line and collect real-time information about the progress of the manufacturing process. The collection unit can also collect detailed background information by interviewing manufacturers or conducting on-site surveys. For example, the collection unit can collect details about the product manufacturing process and materials used through interviews with manufacturers. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the characteristics and market needs of each product based on the collected information. For example, the analysis unit can analyze products that are popular in a particular country or region, or characteristics that are easily accepted by a particular demographic. The analysis unit can also perform a competitive analysis of the product and compare it with competing products. For example, the analysis unit can analyze the characteristics of competing products and clarify the strengths and weaknesses of the company's products. Furthermore, the analysis unit can predict market trends for products and estimate future demand. For example, the analysis unit can predict market trends for products based on past sales data. The formulation unit formulates a promotion strategy based on the analysis results obtained by the analysis unit. The formulation unit formulates an optimal promotion strategy based on the analysis results. For example, the formulation unit can select a target market and design an advertising campaign. The formulation unit can also customize the strategy taking into account the legal regulations and cultural background of each country. For example, the formulation unit can formulate an appropriate promotion strategy in compliance with each country's advertising regulations. The generation unit generates advertisements based on the promotion strategy formulated by the formulation unit. For example, the generation unit generates advertisements tailored to the culture, language, and consumer preferences of each country. For example, if traditional designs are preferred in a particular country, the generation unit can incorporate traditional designs into advertisements for that country.The generation unit can also generate visuals and video content for the product to visually appeal to the user. For example, the generation unit can generate high-resolution images of the product to visually appeal to the user. The distribution unit distributes the advertisements generated by the generation unit. The distribution unit distributes the advertisements, for example, by utilizing major advertising media and social media in each country. For example, the distribution unit can analyze the effectiveness of major advertising media in each country and select the optimal distribution channel. The distribution unit can also monitor the effectiveness of the advertisements in real time and adjust the distribution content as necessary. For example, the distribution unit can monitor the click-through rate of the advertisements in real time and adjust the distribution content if the effectiveness is low. As a result, the promotion system according to the embodiment can promote Japan's traditional culture and high-quality technology worldwide and increase awareness among many people, thereby revitalizing the Japanese economy.

[0030] The collection unit can collect detailed information about the product's characteristics, manufacturing process, and history. For example, the collection unit can collect detailed information about the product's characteristics, manufacturing process, and history. For example, the collection unit can collect information about the manufacturing method, materials used, and historical background of traditional crafts. The collection unit can also monitor the product's manufacturing process in real time and collect the latest information. For example, the collection unit can acquire data from sensors installed on the production line and collect the progress of the manufacturing process in real time. Furthermore, the collection unit can interview manufacturers or conduct on-site surveys to collect detailed background information. For example, the collection unit can collect details about the product's manufacturing process and materials used through interviews with manufacturers. By collecting detailed information about the product, the product's appeal can be maximized. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input interviews with manufacturers into a generation AI and have the generation AI execute a summary of the interview content.

[0031] The analysis unit can analyze the characteristics of each product and market needs based on the collected information. The analysis unit can, for example, analyze the characteristics of each product and market needs based on the collected information. For example, the analysis unit can analyze products that are popular in a particular country or region, or the characteristics that are easily accepted by a particular demographic. The analysis unit can also perform competitive analysis of the product and compare it with competing products. For example, the analysis unit can analyze the characteristics of competing products and clarify the strengths and weaknesses of the company's own products. Furthermore, the analysis unit can predict market trends for the product and estimate future demand. For example, the analysis unit can predict market trends for the product based on past sales data. This allows for the development of an optimal promotion strategy by analyzing the product's characteristics and market needs. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected information to a generation AI and have the generation AI output the analysis results.

[0032] The formulation unit can formulate a promotion strategy based on the results of the analysis unit. The formulation unit, for example, formulates an optimal promotion strategy based on the analysis results. For example, the formulation unit can select a target market and design an advertising campaign. The formulation unit can also customize the strategy taking into account the legal regulations and cultural background of each country. For example, the formulation unit can formulate an appropriate promotion strategy in compliance with each country's advertising regulations. Furthermore, the formulation unit can select an optimal strategy by referring to past successful promotion strategies. For example, the formulation unit can analyze past successful cases and formulate a promotion strategy for a similar product. This enables effective promotion by formulating an optimal promotion strategy based on the analysis results. Some or all of the above-mentioned processing in the formulation unit may be performed using, for example, AI, or may be performed without AI. For example, the formulation unit can input the analysis results into a generation AI and have the generation AI formulate a promotion strategy.

[0033] The generation unit can generate advertisements tailored to the culture, language, and consumer preferences of each country. For example, if traditional designs are preferred in a particular country, the generation unit can incorporate traditional designs into advertisements for that country. The generation unit can also generate visuals and video content for products to appeal visually. For example, the generation unit can generate high-resolution images of products to appeal visually. Furthermore, the generation unit can generate advertisements that incorporate product storytelling. For example, the generation unit can generate storytelling advertisements that introduce the history and background of the products. This enables effective promotion by generating advertisements tailored to the culture, language, and consumer preferences of each country. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate advertisements.

[0034] The distribution unit can distribute advertisements using major advertising media or social media in each country. For example, the distribution unit can distribute advertisements using major advertising media or social media in each country. For example, the distribution unit can analyze the effectiveness of major advertising media in each country and select the optimal distribution channel. The distribution unit can also monitor the effectiveness of advertisements in real time and adjust the distribution content as necessary. For example, the distribution unit can monitor the click-through rate of advertisements in real time and adjust the distribution content if the effectiveness is low. Furthermore, the distribution unit can estimate user emotions and adjust the timing of advertisement distribution based on those emotions. For example, the distribution unit can distribute advertisements during times when users are relaxing. By distributing advertisements using major advertising media and social media in each country, Japan's traditional culture and technology can be recognized by many people. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or without AI. For example, the distribution unit can have a generation AI determine the timing of advertisement distribution.

[0035] The collection unit can monitor the product manufacturing process in real time and collect the latest information. For example, the collection unit can monitor the product manufacturing process in real time and collect the latest information. For example, the collection unit can acquire data from sensors installed on the production line and collect the progress of the manufacturing process in real time. The collection unit can also analyze camera footage from the manufacturing site to collect detailed information about the manufacturing process. Furthermore, the collection unit can collect interviews from manufacturers in real time to supplement background information about the manufacturing process. For example, the collection unit can collect details about the product manufacturing process and the materials used through interviews with manufacturers. This allows the latest information to be collected by monitoring the manufacturing process in real time. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input camera footage from the manufacturing site into a generation AI and have the generation AI analyze the manufacturing process.

[0036] The collection unit may conduct interviews with manufacturers or on-site investigations to collect detailed background information when collecting product information. For example, the collection unit may conduct interviews with manufacturers or on-site investigations to collect detailed background information when collecting product information. For example, the collection unit may collect details about the product's manufacturing process and the materials used through interviews with manufacturers. The collection unit may also conduct on-site investigations to record in detail the environment and work procedures at the manufacturing site. Furthermore, the collection unit may collect information about the manufacturer's history and traditional techniques to complement the product's background information. For example, the collection unit may collect information about the manufacturer's history and traditional techniques to complement the product's background information. In this way, detailed product background information can be collected by conducting interviews with manufacturers or on-site investigations. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the content of an interview with a manufacturer into a generation AI and cause the generation AI to execute a summary of the interview content.

[0037] The collection unit can supplement technical details by referring to related patent information and academic papers when collecting product information. For example, the collection unit can supplement technical details by referring to related patent information and academic papers when collecting product information. For example, the collection unit can collect patent information related to the product to supplement technical details. The collection unit can also refer to academic papers related to the product to incorporate the latest research results. Furthermore, the collection unit can collect related technical literature to understand the technical background of the product. For example, the collection unit can collect patent information related to the product to supplement technical details. This allows the technical details of the product to be supplemented by referring to related patent information and academic papers. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input patent information and academic papers into a generation AI to supplement technical details.

[0038] The collection unit can analyze social media or online reviews to collect consumer feedback when collecting product information. For example, the collection unit can analyze social media or online reviews to collect consumer feedback when collecting product information. For example, the collection unit can analyze social media posts to collect product reputations and consumer opinions. The collection unit can also analyze online reviews to identify product strengths and areas for improvement. Furthermore, the collection unit can collect information to maximize the appeal of the product based on consumer feedback. For example, the collection unit can analyze social media posts to collect product reputations and consumer opinions. In this way, consumer feedback can be collected by analyzing social media and online reviews. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media posts into a generation AI to collect consumer feedback.

[0039] The analysis unit can perform a competitive analysis of the product and compare it with competing products during the analysis. The analysis unit, for example, can perform a competitive analysis of the product and compare it with competing products during the analysis. For example, the analysis unit can analyze the characteristics of competing products to clarify the strengths and weaknesses of the company's product. The analysis unit can also compare the prices and sales strategies of competing products to formulate an optimal promotion strategy. Furthermore, the analysis unit can analyze the market share of competing products and determine the positioning of the company's product. For example, the analysis unit can analyze the characteristics of competing products to clarify the strengths and weaknesses of the company's product. As a result, the strengths and weaknesses of the company's product can be clarified by comparing it with competing products. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on competing products into the generation AI and have the generation AI perform the competitive analysis.

[0040] The analysis unit can predict the market trend of a product and estimate future demand during analysis. The analysis unit, for example, predicts the market trend of a product and estimates future demand during analysis. For example, the analysis unit can predict the market trend of a product based on past sales data. The analysis unit can also analyze consumer purchasing behavior and estimate future demand. Furthermore, the analysis unit can monitor market trends and forecast demand for a product. For example, the analysis unit can predict the market trend of a product based on past sales data. As a result, future demand can be estimated by predicting the market trend. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past sales data into a generation AI and cause the generation AI to predict a market trend.

[0041] The analysis unit can evaluate the ecological footprint of a product during analysis and analyze its impact on the environment. For example, the analysis unit can evaluate the ecological footprint of a product during analysis and analyze its impact on the environment. For example, the analysis unit can evaluate the amount of carbon dioxide emitted during the manufacturing process of the product. The analysis unit can also analyze the environmental impact of the product throughout its life cycle. Furthermore, the analysis unit can formulate an environmentally friendly promotion strategy based on the ecological footprint of the product. For example, the analysis unit can evaluate the amount of carbon dioxide emitted during the manufacturing process of the product. Thus, by evaluating the ecological footprint of the product, its impact on the environment can be analyzed. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the ecological footprint data of the product into a generation AI to analyze its impact on the environment.

[0042] The analysis unit can apply different analysis methods based on the price range or sales channel of the product during analysis. For example, the analysis unit can apply different analysis methods based on the price range or sales channel of the product during analysis. For example, the analysis unit can apply a premium promotion strategy to products in a high price range. Furthermore, the analysis unit can apply a promotion strategy that emphasizes cost performance to products in a low price range. Furthermore, the analysis unit can apply an analysis method that emphasizes digital marketing to products for online sales channels. For example, the analysis unit can apply a premium promotion strategy to products in a high price range. This enables more effective analysis by applying different analysis methods based on the price range or sales channel of the product. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input product price range and sales channel data into a generation AI and apply different analysis methods.

[0043] The formulation department can select the optimal strategy by referring to successful examples of past promotional strategies when formulating a strategy. For example, the formulation department can select the optimal strategy by referring to successful examples of past promotional strategies when formulating a strategy. For example, the formulation department can analyze past successful examples and formulate a promotion strategy for a similar product. The formulation department can also learn from the successful examples and adopt effective promotion techniques. Furthermore, the formulation department can analyze past unsuccessful examples and formulate a strategy to avoid the same mistakes. For example, the formulation department can analyze past successful examples and formulate a promotion strategy for a similar product. In this way, an effective promotion strategy can be formulated by referring to past successful examples. Some or all of the above-mentioned processing in the formulation department may be performed, for example, using AI, or may be performed without using AI. For example, the formulation department can input data of past promotional strategies into a generation AI to select the optimal strategy.

[0044] The formulation department can customize the strategy by taking into account the legal regulations and cultural background of each country during formulation. For example, the formulation department can customize the strategy by taking into account the legal regulations and cultural background of each country during formulation. For example, the formulation department can formulate an appropriate promotion strategy by complying with each country's advertising regulations. The formulation department can also formulate a strategy that is easily accepted by local consumers by taking into account the cultural background of each country. Furthermore, the formulation department can analyze the preferences of consumers in each country and formulate an optimal promotion strategy. For example, the formulation department can formulate an appropriate promotion strategy by complying with each country's advertising regulations. This allows for the formulation of a strategy that is easily accepted by local consumers by taking into account the legal regulations and cultural background of each country. Some or all of the above-mentioned processing in the formulation department may be performed using, for example, AI, or may be performed without using AI. For example, the formulation department can input data on the legal regulations and cultural background of each country into the generation AI to customize the strategy.

[0045] The formulation unit can formulate a strategy tailored to the seasonality or event of a product during formulation. For example, the formulation unit formulates a strategy tailored to the seasonality or event of a product during formulation. For example, the formulation unit can analyze seasonal demand and formulate an optimal promotion strategy. The formulation unit can also formulate a promotion strategy tailored to a specific event. Furthermore, the formulation unit can formulate a promotion strategy that emphasizes the characteristics of a product according to the season or event. For example, the formulation unit can analyze seasonal demand and formulate an optimal promotion strategy. This enables effective promotion by formulating a strategy tailored to the seasonality or event of a product. Some or all of the above-mentioned processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can input data related to seasons and events into a generation AI and formulate a strategy.

[0046] The formulation department can formulate different strategies depending on the target demographic of the product during formulation. For example, the formulation department formulates different strategies depending on the target demographic of the product during formulation. For example, the formulation department can formulate a promotion strategy that utilizes social media for products targeted at younger generations. Furthermore, the formulation department can formulate a promotion strategy that emphasizes reliability for products targeted at older generations. Furthermore, the formulation department can formulate a promotion strategy that the whole family can enjoy for products targeted at families. For example, the formulation department can formulate a promotion strategy that utilizes social media for products targeted at younger generations. This enables effective promotion by formulating a strategy depending on the target demographic of the product. Some or all of the above-mentioned processing in the formulation department may be performed using, for example, AI, or may be performed without using AI. For example, the formulation department can input data about the target demographic into a generation AI to formulate a strategy.

[0047] The generation unit can generate visuals or video content of the product at the time of generation to visually appeal to the user. The generation unit can generate visuals or video content of the product at the time of generation to visually appeal to the user. For example, the generation unit can generate high-resolution images of the product to visually appeal to the user. The generation unit can also generate videos that simulate usage scenes of the product. Furthermore, the generation unit can generate videos that introduce the manufacturing process of the product to convey the appeal of the product. For example, the generation unit can generate high-resolution images of the product to visually appeal to the user. In this way, the visuals and video content of the product can be generated to visually appeal to the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate the visuals and video content of the product.

[0048] The generation unit can generate an advertisement incorporating product storytelling at the time of generation. The generation unit, for example, generates an advertisement incorporating product storytelling at the time of generation. For example, the generation unit can generate a storytelling advertisement that introduces the history and background of the product. The generation unit can also generate a storytelling advertisement that introduces the product's manufacturing process and the craftsmanship of the artisans. Furthermore, the generation unit can generate a storytelling advertisement that introduces product usage scenes in a narrative format. For example, the generation unit can generate a storytelling advertisement that introduces the history and background of the product. In this way, by generating an advertisement incorporating product storytelling, the appeal of the product can be effectively conveyed. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate a product storytelling advertisement.

[0049] The generation unit can generate an advertisement that simulates a product usage scene at the time of generation. The generation unit, for example, generates an advertisement that simulates a product usage scene at the time of generation. For example, the generation unit can generate an advertisement that recreates a product usage scene using a 3D simulation. The generation unit can also generate an advertisement that provides a step-by-step introduction to how to use the product. Furthermore, the generation unit can generate an advertisement that simulates a product usage scene from the perspective of an actual user. For example, the generation unit can generate an advertisement that recreates a product usage scene using a 3D simulation. In this way, by generating an advertisement that simulates a product usage scene, the appeal of the product can be effectively conveyed. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to execute a simulation of a product usage scene.

[0050] The generation unit can generate an advertisement that incorporates product word-of-mouth or reviews at the time of generation. The generation unit, for example, generates an advertisement that incorporates product word-of-mouth or reviews at the time of generation. For example, the generation unit can generate an advertisement that quotes actual user word-of-mouth. The generation unit can also generate an advertisement that incorporates online review ratings. Furthermore, the generation unit can generate an advertisement that introduces user testimonials in video format. For example, the generation unit can generate an advertisement that quotes actual user word-of-mouth. In this way, by generating an advertisement that incorporates product word-of-mouth or reviews, the reliability of the product can be increased. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input word-of-mouth or review data into a generation AI to generate an advertisement.

[0051] The distribution unit can analyze the effectiveness of major advertising media or SNS in each country at the time of distribution and select the optimal distribution channel. For example, the distribution unit can analyze the effectiveness of major advertising media or SNS in each country at the time of distribution and select the optimal distribution channel. For example, the distribution unit can analyze the effectiveness of major advertising media in each country and select the optimal distribution channel. The distribution unit can also analyze the usage status of SNS in each country and select an effective distribution channel. Furthermore, the distribution unit can analyze consumer preferences in each country and select the optimal distribution channel. For example, the distribution unit can analyze the effectiveness of major advertising media in each country and select the optimal distribution channel. In this way, the optimal distribution channel can be selected by analyzing the effectiveness of major advertising media and SNS in each country. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input effectiveness data of advertising media and SNS into a generation AI and select the optimal distribution channel.

[0052] The distribution unit can monitor the effectiveness of the advertisement in real time during distribution and adjust the distribution content as necessary. For example, the distribution unit can monitor the effectiveness of the advertisement in real time during distribution and adjust the distribution content as necessary. For example, the distribution unit can monitor the click-through rate of the advertisement in real time and adjust the distribution content if the effectiveness is low. The distribution unit can also monitor the viewing time of the advertisement in real time and adjust the distribution content if the effectiveness is low. Furthermore, the distribution unit can monitor the conversion rate of the advertisement in real time and adjust the distribution content if the effectiveness is low. For example, the distribution unit can monitor the click-through rate of the advertisement in real time and adjust the distribution content if the effectiveness is low. This enables effective advertisement distribution by monitoring the effectiveness of the advertisement in real time. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input advertisement effectiveness data into a generation AI and adjust the distribution content.

[0053] The distribution unit can analyze the purchasing behavior of consumers in each country at the time of distribution and formulate a distribution schedule. The distribution unit, for example, analyzes the purchasing behavior of consumers in each country at the time of distribution and formulates a distribution schedule. For example, the distribution unit can analyze the purchasing behavior of consumers in each country and determine the optimal distribution time. The distribution unit can also analyze the purchasing patterns of consumers in each country and determine the optimal distribution date. Furthermore, the distribution unit can analyze the purchasing trends of consumers in each country and formulate an optimal distribution schedule. For example, the distribution unit can analyze the purchasing behavior of consumers in each country and determine the optimal distribution time. In this way, an optimal distribution schedule can be formulated by analyzing the purchasing behavior of consumers in each country. Some or all of the above-mentioned processing in the distribution unit may be performed, for example, using AI, or may be performed without using AI. For example, the distribution unit can input consumer purchasing behavior data into a generation AI and formulate a distribution schedule.

[0054] The distribution unit can apply different distribution methods depending on the target demographic of the advertisement during distribution. For example, the distribution unit can apply different distribution methods depending on the target demographic of the advertisement during distribution. For example, the distribution unit can apply a distribution method that utilizes SNS to advertisements aimed at younger generations. Furthermore, the distribution unit can apply a distribution method that emphasizes reliability to advertisements aimed at older generations. Furthermore, the distribution unit can apply a distribution method that the whole family can enjoy to advertisements aimed at families. For example, the distribution unit can apply a distribution method that utilizes SNS to advertisements aimed at younger generations. This enables effective advertisement delivery by applying a distribution method depending on the target demographic of the advertisement. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input data related to the target demographic into a generation AI and apply a distribution method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When collecting product information, the collection unit can supplement technical details by referring to related patent information and academic papers. For example, the collection unit can collect patent information related to the product to supplement technical details. The collection unit can also refer to academic papers related to the product to incorporate the latest research results. Furthermore, the collection unit can collect related technical literature to understand the technical background of the product. This allows the technical details of the product to be supplemented by referring to related patent information and academic papers. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input patent information and academic papers into the generation AI to supplement technical details.

[0057] During analysis, the analysis unit can evaluate the ecological footprint of the product and analyze its impact on the environment. For example, the analysis unit can evaluate the amount of carbon dioxide emitted during the manufacturing process of the product. The analysis unit can also analyze the environmental impact of the product throughout its life cycle. Furthermore, the analysis unit can formulate an environmentally friendly promotion strategy based on the ecological footprint of the product. In this way, the environmental impact can be analyzed by evaluating the ecological footprint of the product. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the ecological footprint data of the product into a generation AI to analyze its impact on the environment.

[0058] During the planning process, the planning department can formulate a strategy tailored to the seasonality of a product or an event. For example, the planning department can analyze seasonal demand and formulate an optimal promotion strategy. The planning department can also formulate a promotion strategy tailored to a specific event. Furthermore, the planning department can formulate a promotion strategy that emphasizes the characteristics of a product according to the season or event. This enables effective promotion by formulating a strategy tailored to the seasonality of a product or an event. Some or all of the above-described processing in the planning department may be performed using, for example, AI, or may be performed without using AI. For example, the planning department can input data related to seasons and events into a generation AI to formulate a strategy.

[0059] During generation, the generation unit can generate visuals and video content for the product to visually appeal. For example, the generation unit can generate high-resolution images of the product to visually appeal. The generation unit can also generate videos that simulate usage scenes of the product. Furthermore, the generation unit can generate videos that introduce the manufacturing process of the product to convey the appeal of the product. In this way, by generating visuals and video content for the product, visual appeal can be achieved. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate visuals and video content for the product.

[0060] The distribution unit can analyze the purchasing behavior of consumers in each country at the time of distribution and formulate a distribution schedule. For example, the distribution unit can analyze the purchasing behavior of consumers in each country and determine the optimal distribution time. The distribution unit can also analyze the purchasing patterns of consumers in each country and determine the optimal distribution date. Furthermore, the distribution unit can analyze the purchasing trends of consumers in each country and formulate an optimal distribution schedule. In this way, an optimal distribution schedule can be formulated by analyzing the purchasing behavior of consumers in each country. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input consumer purchasing behavior data into a generation AI and formulate a distribution schedule.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection department collects information about each product. The collection department collects detailed information about the product's characteristics, manufacturing process, history, etc. For example, they can collect information about the manufacturing method of a traditional craft, the materials used, and historical background. The collection department can also monitor the manufacturing process in real time and collect the latest information. Furthermore, the collection department can interview manufacturers and conduct on-site surveys to gather detailed background information. Step 2: The analysis unit analyzes the information collected by the collection unit. Based on the collected information, the analysis unit analyzes the characteristics of each product and market needs. For example, it can analyze which products are popular in specific countries or regions, or which characteristics are likely to be accepted by specific demographics. It can also perform competitive analysis of the product and compare it with competing products. It can also forecast market trends for the product and estimate future demand. Step 3: The Planning Department formulates a promotion strategy based on the analysis results obtained by the Analysis Department. The Planning Department formulates the optimal promotion strategy based on the analysis results, selects the target market, designs advertising campaigns, etc. The strategy can also be customized taking into account the legal regulations and cultural background of each country. Step 4: The generation department generates advertisements based on the promotion strategy formulated by the formulation department. The generation department creates advertisements that are tailored to the culture, language, and consumer preferences of each country, and generates product visuals and video content to appeal to the viewer. Step 5: The distribution unit distributes the advertisements generated by the generation unit. The distribution unit distributes advertisements using major advertising media and social media in each country, monitors the effectiveness of the advertisements in real time, and adjusts the content of the advertisements as necessary.

[0063] (Example 2) A promotion system according to an embodiment of the present invention promotes Japan's traditional culture, high-quality craftsmanship, and various products made using those craftsmanship worldwide. This promotion system collects information about each product and registers it in a database. Next, AI analyzes the collected information and formulates an optimal promotion strategy. Furthermore, based on the formulated promotion strategy, advertisements tailored to each country's market are generated and distributed. This allows for increased awareness of Japan's traditional culture and craftsmanship and increased demand. For example, the promotion system collects information about each product. This information includes detailed information about the product's characteristics, manufacturing process, history, and so on. For example, for traditional crafts, information on the manufacturing method, materials used, and historical background is collected. This maximizes the appeal of the product. Next, the promotion system uses AI to analyze the collected information. Based on the collected information, the AI ​​analyzes the characteristics and market needs of each product. For example, it analyzes products that are popular in specific countries or regions, and the characteristics that are easily accepted by specific demographics. This allows for the formulation of an optimal promotion strategy. Furthermore, the promotion system generates advertisements tailored to each country's market based on the formulated promotion strategy. The AI ​​generates advertisements tailored to each country's culture, language, and consumer preferences. For example, if traditional designs are preferred in a particular country, advertisements for that country can incorporate traditional designs. This allows for effective promotion. Finally, the promotion system distributes the generated advertisements to each country's market. AI distributes the advertisements using major advertising media and social media in each country. This makes it possible to raise awareness of Japan's traditional culture and technology and expand demand. This system can promote Japan's traditional culture and high-quality technology around the world and increase awareness, thereby revitalizing the Japanese economy. This promotion system can promote Japan's traditional culture and high-quality technology around the world and increase awareness, thereby revitalizing the Japanese economy.

[0064] A promotion system according to an embodiment includes a collection unit, an analysis unit, a formulation unit, a generation unit, and a distribution unit. The collection unit collects information about each product. The collection unit collects detailed information, such as product characteristics, manufacturing process, and history. For example, the collection unit can collect information about traditional craft manufacturing methods, materials used, and historical background. The collection unit can also monitor the product manufacturing process in real time and collect the latest information. For example, the collection unit can acquire data from sensors installed on the production line and collect real-time information about the progress of the manufacturing process. The collection unit can also collect detailed background information by interviewing manufacturers or conducting on-site surveys. For example, the collection unit can collect details about the product manufacturing process and materials used through interviews with manufacturers. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the characteristics and market needs of each product based on the collected information. For example, the analysis unit can analyze products that are popular in a particular country or region, or characteristics that are easily accepted by a particular demographic. The analysis unit can also perform a competitive analysis of the product and compare it with competing products. For example, the analysis unit can analyze the characteristics of competing products and clarify the strengths and weaknesses of the company's products. Furthermore, the analysis unit can predict market trends for products and estimate future demand. For example, the analysis unit can predict market trends for products based on past sales data. The formulation unit formulates a promotion strategy based on the analysis results obtained by the analysis unit. The formulation unit formulates an optimal promotion strategy based on the analysis results. For example, the formulation unit can select a target market and design an advertising campaign. The formulation unit can also customize the strategy taking into account the legal regulations and cultural background of each country. For example, the formulation unit can formulate an appropriate promotion strategy in compliance with each country's advertising regulations. The generation unit generates advertisements based on the promotion strategy formulated by the formulation unit. For example, the generation unit generates advertisements tailored to the culture, language, and consumer preferences of each country. For example, if traditional designs are preferred in a particular country, the generation unit can incorporate traditional designs into advertisements for that country.The generation unit can also generate visuals and video content for the product to visually appeal to the user. For example, the generation unit can generate high-resolution images of the product to visually appeal to the user. The distribution unit distributes the advertisements generated by the generation unit. The distribution unit distributes the advertisements, for example, by utilizing major advertising media and social media in each country. For example, the distribution unit can analyze the effectiveness of major advertising media in each country and select the optimal distribution channel. The distribution unit can also monitor the effectiveness of the advertisements in real time and adjust the distribution content as necessary. For example, the distribution unit can monitor the click-through rate of the advertisements in real time and adjust the distribution content if the effectiveness is low. As a result, the promotion system according to the embodiment can promote Japan's traditional culture and high-quality technology worldwide and increase awareness among many people, thereby revitalizing the Japanese economy.

[0065] The collection unit can collect detailed information about the product's characteristics, manufacturing process, and history. For example, the collection unit can collect detailed information about the product's characteristics, manufacturing process, and history. For example, the collection unit can collect information about the manufacturing method, materials used, and historical background of traditional crafts. The collection unit can also monitor the product's manufacturing process in real time and collect the latest information. For example, the collection unit can acquire data from sensors installed on the production line and collect the progress of the manufacturing process in real time. Furthermore, the collection unit can interview manufacturers or conduct on-site surveys to collect detailed background information. For example, the collection unit can collect details about the product's manufacturing process and materials used through interviews with manufacturers. By collecting detailed information about the product, the product's appeal can be maximized. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input interviews with manufacturers into a generation AI and have the generation AI execute a summary of the interview content.

[0066] The analysis unit can analyze the characteristics of each product and market needs based on the collected information. The analysis unit can, for example, analyze the characteristics of each product and market needs based on the collected information. For example, the analysis unit can analyze products that are popular in a particular country or region, or the characteristics that are easily accepted by a particular demographic. The analysis unit can also perform competitive analysis of the product and compare it with competing products. For example, the analysis unit can analyze the characteristics of competing products and clarify the strengths and weaknesses of the company's own products. Furthermore, the analysis unit can predict market trends for the product and estimate future demand. For example, the analysis unit can predict market trends for the product based on past sales data. This allows for the development of an optimal promotion strategy by analyzing the product's characteristics and market needs. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected information to a generation AI and have the generation AI output the analysis results.

[0067] The formulation unit can formulate a promotion strategy based on the results of the analysis unit. The formulation unit, for example, formulates an optimal promotion strategy based on the analysis results. For example, the formulation unit can select a target market and design an advertising campaign. The formulation unit can also customize the strategy taking into account the legal regulations and cultural background of each country. For example, the formulation unit can formulate an appropriate promotion strategy in compliance with each country's advertising regulations. Furthermore, the formulation unit can select an optimal strategy by referring to past successful promotion strategies. For example, the formulation unit can analyze past successful cases and formulate a promotion strategy for a similar product. This enables effective promotion by formulating an optimal promotion strategy based on the analysis results. Some or all of the above-mentioned processing in the formulation unit may be performed using, for example, AI, or may be performed without AI. For example, the formulation unit can input the analysis results into a generation AI and have the generation AI formulate a promotion strategy.

[0068] The generation unit can generate advertisements tailored to the culture, language, and consumer preferences of each country. For example, if traditional designs are preferred in a particular country, the generation unit can incorporate traditional designs into advertisements for that country. The generation unit can also generate visuals and video content for products to appeal visually. For example, the generation unit can generate high-resolution images of products to appeal visually. Furthermore, the generation unit can generate advertisements that incorporate product storytelling. For example, the generation unit can generate storytelling advertisements that introduce the history and background of the products. This enables effective promotion by generating advertisements tailored to the culture, language, and consumer preferences of each country. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate advertisements.

[0069] The distribution unit can distribute advertisements using major advertising media or social media in each country. For example, the distribution unit can distribute advertisements using major advertising media or social media in each country. For example, the distribution unit can analyze the effectiveness of major advertising media in each country and select the optimal distribution channel. The distribution unit can also monitor the effectiveness of advertisements in real time and adjust the distribution content as necessary. For example, the distribution unit can monitor the click-through rate of advertisements in real time and adjust the distribution content if the effectiveness is low. Furthermore, the distribution unit can estimate user emotions and adjust the timing of advertisement distribution based on those emotions. For example, the distribution unit can distribute advertisements during times when users are relaxing. By distributing advertisements using major advertising media and social media in each country, Japan's traditional culture and technology can be recognized by many people. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or without AI. For example, the distribution unit can have a generation AI determine the timing of advertisement distribution.

[0070] The collection unit can estimate the user's emotions and adjust the timing of product information collection based on the emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of product information collection based on the emotions. For example, if the user is excited, the collection unit can instantly collect product information and provide it in real time. Furthermore, if the user is relaxed, the collection unit can collect information at a slower pace and provide detailed information. Furthermore, if the user is stressed, the collection unit can prioritize collecting simple, to-the-point information. This enables more effective information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI execute the timing of information collection.

[0071] The collection unit can monitor the product manufacturing process in real time and collect the latest information. For example, the collection unit can monitor the product manufacturing process in real time and collect the latest information. For example, the collection unit can acquire data from sensors installed on the production line and collect the progress of the manufacturing process in real time. The collection unit can also analyze camera footage from the manufacturing site to collect detailed information about the manufacturing process. Furthermore, the collection unit can collect interviews from manufacturers in real time to supplement background information about the manufacturing process. For example, the collection unit can collect details about the product manufacturing process and the materials used through interviews with manufacturers. This allows the latest information to be collected by monitoring the manufacturing process in real time. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input camera footage from the manufacturing site into a generation AI and have the generation AI analyze the manufacturing process.

[0072] The collection unit may conduct interviews with manufacturers or on-site investigations to collect detailed background information when collecting product information. For example, the collection unit may conduct interviews with manufacturers or on-site investigations to collect detailed background information when collecting product information. For example, the collection unit may collect details about the product's manufacturing process and the materials used through interviews with manufacturers. The collection unit may also conduct on-site investigations to record in detail the environment and work procedures at the manufacturing site. Furthermore, the collection unit may collect information about the manufacturer's history and traditional techniques to complement the product's background information. For example, the collection unit may collect information about the manufacturer's history and traditional techniques to complement the product's background information. In this way, detailed product background information can be collected by conducting interviews with manufacturers or on-site investigations. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the content of an interview with a manufacturer into a generation AI and cause the generation AI to execute a summary of the interview content.

[0073] The collection unit can estimate the user's emotions and determine the priority of products to be collected based on the emotions. The collection unit, for example, estimates the user's emotions and determines the priority of products to be collected based on the emotions. For example, when the user is excited, the collection unit can prioritize collecting popular products. Furthermore, when the user is relaxed, the collection unit can prioritize collecting products for which detailed information is required. Furthermore, when the user is stressed, the collection unit can prioritize collecting information on simple and easy-to-understand products. This enables more effective information collection by determining the priority of products according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI to determine the priority of products.

[0074] The collection unit can supplement technical details by referring to related patent information and academic papers when collecting product information. For example, the collection unit can supplement technical details by referring to related patent information and academic papers when collecting product information. For example, the collection unit can collect patent information related to the product to supplement technical details. The collection unit can also refer to academic papers related to the product to incorporate the latest research results. Furthermore, the collection unit can collect related technical literature to understand the technical background of the product. For example, the collection unit can collect patent information related to the product to supplement technical details. This allows the technical details of the product to be supplemented by referring to related patent information and academic papers. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input patent information and academic papers into a generation AI to supplement technical details.

[0075] The collection unit can analyze social media or online reviews to collect consumer feedback when collecting product information. For example, the collection unit can analyze social media or online reviews to collect consumer feedback when collecting product information. For example, the collection unit can analyze social media posts to collect product reputations and consumer opinions. The collection unit can also analyze online reviews to identify product strengths and areas for improvement. Furthermore, the collection unit can collect information to maximize the appeal of the product based on consumer feedback. For example, the collection unit can analyze social media posts to collect product reputations and consumer opinions. In this way, consumer feedback can be collected by analyzing social media and online reviews. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media posts into a generation AI to collect consumer feedback.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This enables more effective information provision by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the display method of the analysis results.

[0077] The analysis unit can perform a competitive analysis of the product and compare it with competing products during the analysis. The analysis unit, for example, can perform a competitive analysis of the product and compare it with competing products during the analysis. For example, the analysis unit can analyze the characteristics of competing products to clarify the strengths and weaknesses of the company's product. The analysis unit can also compare the prices and sales strategies of competing products to formulate an optimal promotion strategy. Furthermore, the analysis unit can analyze the market share of competing products and determine the positioning of the company's product. For example, the analysis unit can analyze the characteristics of competing products to clarify the strengths and weaknesses of the company's product. As a result, the strengths and weaknesses of the company's product can be clarified by comparing it with competing products. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on competing products into the generation AI and have the generation AI perform the competitive analysis.

[0078] The analysis unit can predict the market trend of a product and estimate future demand during analysis. The analysis unit, for example, predicts the market trend of a product and estimates future demand during analysis. For example, the analysis unit can predict the market trend of a product based on past sales data. The analysis unit can also analyze consumer purchasing behavior and estimate future demand. Furthermore, the analysis unit can monitor market trends and forecast demand for a product. For example, the analysis unit can predict the market trend of a product based on past sales data. As a result, future demand can be estimated by predicting the market trend. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past sales data into a generation AI and cause the generation AI to predict a market trend.

[0079] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priorities based on the emotions. For example, if the user is excited, the analysis unit can prioritize analyzing popular products. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing products that require detailed analysis. Furthermore, if the user is stressed, the analysis unit can prioritize analyzing products that are simple and easy to understand. This enables more effective analysis by determining the analysis priorities based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and determine the analysis priorities.

[0080] The analysis unit can evaluate the ecological footprint of a product during analysis and analyze its impact on the environment. For example, the analysis unit can evaluate the ecological footprint of a product during analysis and analyze its impact on the environment. For example, the analysis unit can evaluate the amount of carbon dioxide emitted during the manufacturing process of the product. The analysis unit can also analyze the environmental impact of the product throughout its life cycle. Furthermore, the analysis unit can formulate an environmentally friendly promotion strategy based on the ecological footprint of the product. For example, the analysis unit can evaluate the amount of carbon dioxide emitted during the manufacturing process of the product. Thus, by evaluating the ecological footprint of the product, its impact on the environment can be analyzed. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the ecological footprint data of the product into a generation AI to analyze its impact on the environment.

[0081] The analysis unit can apply different analysis methods based on the price range or sales channel of the product during analysis. For example, the analysis unit can apply different analysis methods based on the price range or sales channel of the product during analysis. For example, the analysis unit can apply a premium promotion strategy to products in a high price range. Furthermore, the analysis unit can apply a promotion strategy that emphasizes cost performance to products in a low price range. Furthermore, the analysis unit can apply an analysis method that emphasizes digital marketing to products for online sales channels. For example, the analysis unit can apply a premium promotion strategy to products in a high price range. This enables more effective analysis by applying different analysis methods based on the price range or sales channel of the product. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input product price range and sales channel data into a generation AI and apply different analysis methods.

[0082] The formulation unit can estimate the user's emotions and adjust the content of the promotion strategy based on the emotions. For example, the formulation unit can estimate the user's emotions and adjust the content of the promotion strategy based on the emotions. For example, if the user is excited, the formulation unit can formulate a visually stimulating promotion strategy. Furthermore, if the user is relaxed, the formulation unit can formulate a promotion strategy that provides detailed information. Furthermore, if the user is stressed, the formulation unit can formulate a simple and to-the-point promotion strategy. This enables more effective promotion by adjusting the content of the promotion strategy according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the formulation unit can be performed using, for example, AI, or without AI. For example, the formulation unit can input the user's emotion data into the generation AI and adjust the content of the promotion strategy.

[0083] The formulation department can select the optimal strategy by referring to successful examples of past promotional strategies when formulating a strategy. For example, the formulation department can select the optimal strategy by referring to successful examples of past promotional strategies when formulating a strategy. For example, the formulation department can analyze past successful examples and formulate a promotion strategy for a similar product. The formulation department can also learn from the successful examples and adopt effective promotion techniques. Furthermore, the formulation department can analyze past unsuccessful examples and formulate a strategy to avoid the same mistakes. For example, the formulation department can analyze past successful examples and formulate a promotion strategy for a similar product. In this way, an effective promotion strategy can be formulated by referring to past successful examples. Some or all of the above-mentioned processing in the formulation department may be performed, for example, using AI, or may be performed without using AI. For example, the formulation department can input data of past promotional strategies into a generation AI to select the optimal strategy.

[0084] The formulation department can customize the strategy by taking into account the legal regulations and cultural background of each country during formulation. For example, the formulation department can customize the strategy by taking into account the legal regulations and cultural background of each country during formulation. For example, the formulation department can formulate an appropriate promotion strategy by complying with each country's advertising regulations. The formulation department can also formulate a strategy that is easily accepted by local consumers by taking into account the cultural background of each country. Furthermore, the formulation department can analyze the preferences of consumers in each country and formulate an optimal promotion strategy. For example, the formulation department can formulate an appropriate promotion strategy by complying with each country's advertising regulations. This allows for the formulation of a strategy that is easily accepted by local consumers by taking into account the legal regulations and cultural background of each country. Some or all of the above-mentioned processing in the formulation department may be performed using, for example, AI, or may be performed without using AI. For example, the formulation department can input data on the legal regulations and cultural background of each country into the generation AI to customize the strategy.

[0085] The formulation unit can estimate the user's emotions and prioritize promotion strategies based on the emotions. For example, the formulation unit can estimate the user's emotions and prioritize promotion strategies based on the emotions. For example, if the user is excited, the formulation unit can prioritize a visually stimulating promotion strategy. Furthermore, if the user is relaxed, the formulation unit can prioritize a promotion strategy that provides detailed information. Furthermore, if the user is stressed, the formulation unit can prioritize a simple and to-the-point promotion strategy. This enables more effective promotions by prioritizing promotion strategies based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the formulation unit can be performed using, for example, AI, or without AI. For example, the formulation unit can input the user's emotion data into the generation AI to prioritize promotion strategies.

[0086] The formulation unit can formulate a strategy tailored to the seasonality or event of a product during formulation. For example, the formulation unit formulates a strategy tailored to the seasonality or event of a product during formulation. For example, the formulation unit can analyze seasonal demand and formulate an optimal promotion strategy. The formulation unit can also formulate a promotion strategy tailored to a specific event. Furthermore, the formulation unit can formulate a promotion strategy that emphasizes the characteristics of a product according to the season or event. For example, the formulation unit can analyze seasonal demand and formulate an optimal promotion strategy. This enables effective promotion by formulating a strategy tailored to the seasonality or event of a product. Some or all of the above-mentioned processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can input data related to seasons and events into a generation AI and formulate a strategy.

[0087] The formulation department can formulate different strategies depending on the target demographic of the product during formulation. For example, the formulation department formulates different strategies depending on the target demographic of the product during formulation. For example, the formulation department can formulate a promotion strategy that utilizes social media for products targeted at younger generations. Furthermore, the formulation department can formulate a promotion strategy that emphasizes reliability for products targeted at older generations. Furthermore, the formulation department can formulate a promotion strategy that the whole family can enjoy for products targeted at families. For example, the formulation department can formulate a promotion strategy that utilizes social media for products targeted at younger generations. This enables effective promotion by formulating a strategy depending on the target demographic of the product. Some or all of the above-mentioned processing in the formulation department may be performed using, for example, AI, or may be performed without using AI. For example, the formulation department can input data about the target demographic into a generation AI to formulate a strategy.

[0088] The generation unit can estimate the user's emotions and adjust the advertisement presentation style based on the user's emotions. For example, the generation unit can estimate the user's emotions and adjust the advertisement presentation style based on the user's emotions. For example, if the user is excited, the generation unit can generate a visually stimulating advertisement. Furthermore, if the user is relaxed, the generation unit can generate a calming advertisement. Furthermore, if the user is stressed, the generation unit can generate a simple and to-the-point advertisement. This allows for the generation of more effective advertisements by adjusting the advertisement presentation style according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input user emotion data into the generation AI and adjust the advertisement presentation style.

[0089] The generation unit can generate visuals or video content of the product at the time of generation to visually appeal to the user. The generation unit can generate visuals or video content of the product at the time of generation to visually appeal to the user. For example, the generation unit can generate high-resolution images of the product to visually appeal to the user. The generation unit can also generate videos that simulate usage scenes of the product. Furthermore, the generation unit can generate videos that introduce the manufacturing process of the product to convey the appeal of the product. For example, the generation unit can generate high-resolution images of the product to visually appeal to the user. In this way, the visuals and video content of the product can be generated to visually appeal to the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate the visuals and video content of the product.

[0090] The generation unit can generate an advertisement incorporating product storytelling at the time of generation. The generation unit, for example, generates an advertisement incorporating product storytelling at the time of generation. For example, the generation unit can generate a storytelling advertisement that introduces the history and background of the product. The generation unit can also generate a storytelling advertisement that introduces the product's manufacturing process and the craftsmanship of the artisans. Furthermore, the generation unit can generate a storytelling advertisement that introduces product usage scenes in a narrative format. For example, the generation unit can generate a storytelling advertisement that introduces the history and background of the product. In this way, by generating an advertisement incorporating product storytelling, the appeal of the product can be effectively conveyed. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate a product storytelling advertisement.

[0091] The generation unit can estimate the user's emotions and adjust the length of the advertisement based on the user's emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the advertisement based on the user's emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point advertisement. Furthermore, if the user is relaxed, the generation unit can generate a longer advertisement with detailed explanations. Furthermore, if the user is excited, the generation unit can generate an advertisement with visually stimulating effects. This allows for the generation of more effective advertisements by adjusting the length of the advertisement according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using AI, or can be performed without AI. For example, the generation unit can input user emotion data into the generation AI and adjust the length of the advertisement.

[0092] The generation unit can generate an advertisement that simulates a product usage scene at the time of generation. The generation unit, for example, generates an advertisement that simulates a product usage scene at the time of generation. For example, the generation unit can generate an advertisement that recreates a product usage scene using a 3D simulation. The generation unit can also generate an advertisement that provides a step-by-step introduction to how to use the product. Furthermore, the generation unit can generate an advertisement that simulates a product usage scene from the perspective of an actual user. For example, the generation unit can generate an advertisement that recreates a product usage scene using a 3D simulation. In this way, by generating an advertisement that simulates a product usage scene, the appeal of the product can be effectively conveyed. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to execute a simulation of a product usage scene.

[0093] The generation unit can generate an advertisement that incorporates product word-of-mouth or reviews at the time of generation. The generation unit, for example, generates an advertisement that incorporates product word-of-mouth or reviews at the time of generation. For example, the generation unit can generate an advertisement that quotes actual user word-of-mouth. The generation unit can also generate an advertisement that incorporates online review ratings. Furthermore, the generation unit can generate an advertisement that introduces user testimonials in video format. For example, the generation unit can generate an advertisement that quotes actual user word-of-mouth. In this way, by generating an advertisement that incorporates product word-of-mouth or reviews, the reliability of the product can be increased. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input word-of-mouth or review data into a generation AI to generate an advertisement.

[0094] The distribution unit can estimate a user's emotions and adjust the timing of advertisement delivery based on the user's emotions. The distribution unit, for example, estimates a user's emotions and adjusts the timing of advertisement delivery based on the user's emotions. For example, the distribution unit can deliver an advertisement during a time period when the user is relaxed. Furthermore, if the user is excited, the distribution unit can immediately deliver an advertisement. Furthermore, if the user is stressed, the distribution unit can deliver an advertisement during a time period when the user is calm. This enables more effective advertisement delivery by adjusting the timing of advertisement delivery according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input user's emotion data into the generation AI and adjust the timing of advertisement delivery.

[0095] The distribution unit can analyze the effectiveness of major advertising media or SNS in each country at the time of distribution and select the optimal distribution channel. For example, the distribution unit can analyze the effectiveness of major advertising media or SNS in each country at the time of distribution and select the optimal distribution channel. For example, the distribution unit can analyze the effectiveness of major advertising media in each country and select the optimal distribution channel. The distribution unit can also analyze the usage status of SNS in each country and select an effective distribution channel. Furthermore, the distribution unit can analyze consumer preferences in each country and select the optimal distribution channel. For example, the distribution unit can analyze the effectiveness of major advertising media in each country and select the optimal distribution channel. In this way, the optimal distribution channel can be selected by analyzing the effectiveness of major advertising media and SNS in each country. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input effectiveness data of advertising media and SNS into a generation AI and select the optimal distribution channel.

[0096] The distribution unit can monitor the effectiveness of the advertisement in real time during distribution and adjust the distribution content as necessary. For example, the distribution unit can monitor the effectiveness of the advertisement in real time during distribution and adjust the distribution content as necessary. For example, the distribution unit can monitor the click-through rate of the advertisement in real time and adjust the distribution content if the effectiveness is low. The distribution unit can also monitor the viewing time of the advertisement in real time and adjust the distribution content if the effectiveness is low. Furthermore, the distribution unit can monitor the conversion rate of the advertisement in real time and adjust the distribution content if the effectiveness is low. For example, the distribution unit can monitor the click-through rate of the advertisement in real time and adjust the distribution content if the effectiveness is low. This enables effective advertisement distribution by monitoring the effectiveness of the advertisement in real time. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input advertisement effectiveness data into a generation AI and adjust the distribution content.

[0097] The delivery unit can estimate a user's emotions and adjust the delivery order of advertisements based on the user's emotions. For example, the delivery unit can estimate a user's emotions and adjust the delivery order of advertisements based on the user's emotions. For example, if the user is excited, the delivery unit can prioritize delivering visually stimulating advertisements. Furthermore, if the user is relaxed, the delivery unit can prioritize delivering advertisements that provide detailed information. Furthermore, if the user is stressed, the delivery unit can prioritize delivering advertisements that are simple and to the point. This enables more effective advertisement delivery by adjusting the delivery order of advertisements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the delivery unit may be performed using, for example, AI, or without AI. For example, the delivery unit can input user emotion data into the generation AI and adjust the delivery order of advertisements.

[0098] The distribution unit can analyze the purchasing behavior of consumers in each country at the time of distribution and formulate a distribution schedule. The distribution unit, for example, analyzes the purchasing behavior of consumers in each country at the time of distribution and formulates a distribution schedule. For example, the distribution unit can analyze the purchasing behavior of consumers in each country and determine the optimal distribution time. The distribution unit can also analyze the purchasing patterns of consumers in each country and determine the optimal distribution date. Furthermore, the distribution unit can analyze the purchasing trends of consumers in each country and formulate an optimal distribution schedule. For example, the distribution unit can analyze the purchasing behavior of consumers in each country and determine the optimal distribution time. In this way, an optimal distribution schedule can be formulated by analyzing the purchasing behavior of consumers in each country. Some or all of the above-mentioned processing in the distribution unit may be performed, for example, using AI, or may be performed without using AI. For example, the distribution unit can input consumer purchasing behavior data into a generation AI and formulate a distribution schedule.

[0099] The distribution unit can apply different distribution methods depending on the target demographic of the advertisement during distribution. For example, the distribution unit can apply different distribution methods depending on the target demographic of the advertisement during distribution. For example, the distribution unit can apply a distribution method that utilizes SNS to advertisements aimed at younger generations. Furthermore, the distribution unit can apply a distribution method that emphasizes reliability to advertisements aimed at older generations. Furthermore, the distribution unit can apply a distribution method that the whole family can enjoy to advertisements aimed at families. For example, the distribution unit can apply a distribution method that utilizes SNS to advertisements aimed at younger generations. This enables effective advertisement delivery by applying a distribution method depending on the target demographic of the advertisement. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input data related to the target demographic into a generation AI and apply a distribution method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, formulation unit, generation unit, and distribution unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects product information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes product features and market needs based on the collected information. The formulation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and formulates an optimal promotion strategy based on the analysis results. The generation unit, for example, is realized by the control unit 46A of the smart device 14 and generates advertisements based on the formulated promotion strategy. The distribution unit, for example, distributes the generated advertisements to markets in various countries using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, formulation unit, generation unit, and distribution unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects product information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes product features and market needs based on the collected information. The formulation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, formulates an optimal promotion strategy based on the analysis results. The generation unit, realized, for example, by the control unit 46A of the smart glasses 214, generates advertisements based on the formulated promotion strategy. The distribution unit, for example, distributes the generated advertisements to markets in various countries using the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, formulation unit, generation unit, and distribution unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects product information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes product features and market needs based on the collected information. The formulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and formulates an optimal promotion strategy based on the analysis results. The generation unit is realized, for example, by the control unit 46A of the headset terminal 314 and generates advertisements based on the formulated promotion strategy. The distribution unit distributes the generated advertisements to markets in various countries using, for example, the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, formulation unit, generation unit, and distribution unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects product information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes product features and market needs based on the collected information. The formulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and formulates an optimal promotion strategy based on the analysis results. The generation unit is realized, for example, by the control unit 46A of the robot 414 and generates advertisements based on the formulated promotion strategy. The distribution unit distributes the generated advertisements to markets in various countries using, for example, the communication I / F 44 of the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] When collecting product information, the collection unit can supplement technical details by referring to related patent information and academic papers. For example, the collection unit can collect patent information related to the product to supplement technical details. The collection unit can also refer to academic papers related to the product to incorporate the latest research results. Furthermore, the collection unit can collect related technical literature to understand the technical background of the product. This allows the technical details of the product to be supplemented by referring to related patent information and academic papers. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input patent information and academic papers into the generation AI to supplement technical details.

[0102] During analysis, the analysis unit can evaluate the ecological footprint of the product and analyze its impact on the environment. For example, the analysis unit can evaluate the amount of carbon dioxide emitted during the manufacturing process of the product. The analysis unit can also analyze the environmental impact of the product throughout its life cycle. Furthermore, the analysis unit can formulate an environmentally friendly promotion strategy based on the ecological footprint of the product. In this way, the environmental impact can be analyzed by evaluating the ecological footprint of the product. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the ecological footprint data of the product into a generation AI to analyze its impact on the environment.

[0103] During the planning process, the planning department can formulate a strategy tailored to the seasonality of a product or an event. For example, the planning department can analyze seasonal demand and formulate an optimal promotion strategy. The planning department can also formulate a promotion strategy tailored to a specific event. Furthermore, the planning department can formulate a promotion strategy that emphasizes the characteristics of a product according to the season or event. This enables effective promotion by formulating a strategy tailored to the seasonality of a product or an event. Some or all of the above-described processing in the planning department may be performed using, for example, AI, or may be performed without using AI. For example, the planning department can input data related to seasons and events into a generation AI to formulate a strategy.

[0104] During generation, the generation unit can generate visuals and video content for the product to visually appeal. For example, the generation unit can generate high-resolution images of the product to visually appeal. The generation unit can also generate videos that simulate usage scenes of the product. Furthermore, the generation unit can generate videos that introduce the manufacturing process of the product to convey the appeal of the product. In this way, by generating visuals and video content for the product, visual appeal can be achieved. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to generate visuals and video content for the product.

[0105] The distribution unit can analyze the purchasing behavior of consumers in each country at the time of distribution and formulate a distribution schedule. For example, the distribution unit can analyze the purchasing behavior of consumers in each country and determine the optimal distribution time. The distribution unit can also analyze the purchasing patterns of consumers in each country and determine the optimal distribution date. Furthermore, the distribution unit can analyze the purchasing trends of consumers in each country and formulate an optimal distribution schedule. In this way, an optimal distribution schedule can be formulated by analyzing the purchasing behavior of consumers in each country. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input consumer purchasing behavior data into a generation AI and formulate a distribution schedule.

[0106] The collection unit can estimate the user's emotions and prioritize the products to be collected based on the emotions. For example, if the user is excited, the collection unit can prioritize popular products. Furthermore, if the user is relaxed, the collection unit can prioritize products requiring detailed information. Furthermore, if the user is stressed, the collection unit can prioritize products that are simple and easy to understand. This enables more effective information collection by prioritizing products according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI to prioritize products.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This enables more effective information provision by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the display method of the analysis results.

[0108] The formulation unit can estimate the user's emotions and adjust the content of the promotion strategy based on the emotions. For example, if the user is excited, the formulation unit can formulate a visually stimulating promotion strategy. Furthermore, if the user is relaxed, the formulation unit can formulate a promotion strategy that provides detailed information. Furthermore, if the user is stressed, the formulation unit can formulate a simple and to-the-point promotion strategy. This enables more effective promotion by adjusting the content of the promotion strategy according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can input the user's emotion data into the generation AI and adjust the content of the promotion strategy.

[0109] The generation unit can estimate the user's emotions and adjust the advertisement's presentation style based on the user's emotions. For example, if the user is excited, the generation unit can generate a visually stimulating advertisement. Furthermore, if the user is relaxed, the generation unit can generate a calming advertisement. Furthermore, if the user is stressed, the generation unit can generate a simple and to-the-point advertisement. This allows for the generation of more effective advertisements by adjusting the advertisement's presentation style according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the user's emotion data into the generation AI and adjust the advertisement's presentation style.

[0110] The distribution unit can estimate a user's emotions and adjust the timing of advertisement delivery based on the user's emotions. For example, the distribution unit can deliver an advertisement during a time when the user is relaxed. Furthermore, if the user is excited, the distribution unit can immediately deliver an advertisement. Furthermore, if the user is feeling stressed, the distribution unit can deliver an advertisement during a time when the user is calm. This enables more effective advertisement delivery by adjusting the timing of advertisement delivery according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input user's emotion data into the generation AI and adjust the timing of advertisement delivery.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection department collects information about each product. The collection department collects detailed information about the product's characteristics, manufacturing process, history, etc. For example, they can collect information about the manufacturing method of a traditional craft, the materials used, and historical background. The collection department can also monitor the manufacturing process in real time and collect the latest information. Furthermore, the collection department can interview manufacturers and conduct on-site surveys to gather detailed background information. Step 2: The analysis unit analyzes the information collected by the collection unit. Based on the collected information, the analysis unit analyzes the characteristics of each product and market needs. For example, it can analyze which products are popular in specific countries or regions, or which characteristics are likely to be accepted by specific demographics. It can also perform competitive analysis of the product and compare it with competing products. It can also forecast market trends for the product and estimate future demand. Step 3: The Planning Department formulates a promotion strategy based on the analysis results obtained by the Analysis Department. The Planning Department formulates the optimal promotion strategy based on the analysis results, selects the target market, designs advertising campaigns, etc. The strategy can also be customized taking into account the legal regulations and cultural background of each country. Step 4: The generation department generates advertisements based on the promotion strategy formulated by the formulation department. The generation department creates advertisements that are tailored to the culture, language, and consumer preferences of each country, and generates product visuals and video content to appeal to the viewer. Step 5: The distribution unit distributes the advertisements generated by the generation unit. The distribution unit distributes advertisements using major advertising media and social media in each country, monitors the effectiveness of the advertisements in real time, and adjusts the content of the advertisements as necessary.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0136] 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0152] 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] 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.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0157] 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.

[0158] 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.

[0159] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0168] 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.

[0169] 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).

[0170] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] 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."

[0172] 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.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects information on each product; an analysis unit that analyzes the information collected by the collection unit; a formulation unit that formulates a promotion strategy based on the analysis results obtained by the analysis unit; a generation unit that generates an advertisement based on the promotion strategy formulated by the formulation unit; a distribution unit that distributes the advertisement generated by the generation unit. A system characterized by:

2. The collecting unit Collect detailed information about the product's characteristics, manufacturing process, and history 2. The system of claim 1.

3. The analysis unit Based on the collected information, analyze the characteristics of each product and market needs.

2. The system of claim 1.

4. The formulation unit Formulating a promotion strategy based on the results of the analysis section 2. The system of claim 1.

5. The generation unit Generate ads tailored to each country's culture, language, and consumer preferences 2. The system of claim 1.

6. The distribution unit Distribute advertisements using major advertising media or social media in each country 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of product information gathering based on those emotions 2. The system of claim 1.

8. The collecting unit Monitor the product manufacturing process in real time and collect the latest information 2. The system of claim 1.

Images