system

Abstract

The system according to this embodiment aims to enable effective communication with customers and to predict future trends and competitive situations. [Solution] The system according to the embodiment comprises a generation unit, a contact unit, a prediction unit, and a provision unit. The generation unit generates customized campaigns based on social trends and expertise. The contact unit increases customer contact points through the campaigns generated by the generation unit. The prediction unit analyzes customer data obtained by the contact unit and predicts future trends and competitive situations. The provision unit provides optimal content in real time based on the prediction results obtained by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, campaign generation for realizing effective communication with customers and prediction of future trends have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to realize effective communication with customers and predict future trends and competitive situations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a contact unit, a prediction unit, and a provision unit. The generation unit generates customized campaigns based on social trends and expertise. The contact unit increases customer contact points through the campaigns generated by the generation unit. The prediction unit analyzes customer data obtained by the contact unit and predicts future trends and competitive situations. The provision unit provides optimal content in real time based on the prediction results obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable effective communication with customers and predict future trends and competitive situations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The marketing system according to an embodiment of the present invention is a system that proposes a new marketing method for effectively communicating with customers by utilizing customer preference prediction AI and generation AI. This marketing system generates customized campaigns based on social trends and expertise, increasing points of contact with customers. Furthermore, it enhances the brand forecasting function to evaluate not only future trends but also future competitive situations and risks. Finally, the generation AI provides optimal content in real time by utilizing communication data with customers. For example, the marketing system generates customized campaigns based on social trends and expertise. In this process, the generation AI analyzes the latest trend data and expertise to generate the most effective campaign for customers. For example, it can generate campaigns tailored to seasonal trends or specific events. Next, the marketing system increases points of contact with customers through the generated campaigns. The generation AI analyzes customer behavior data and preference data and delivers campaigns at the optimal timing. This increases points of contact with customers and enables effective communication. For example, when a customer shows interest in a particular product, a campaign related to that product can be delivered. Furthermore, the marketing system enhances the brand forecasting function to evaluate not only future trends but also future competitive situations and risks. Generative AI analyzes historical data and current market conditions to predict future trends and competitive landscapes. This allows companies to anticipate future risks and take appropriate measures. For example, it can predict when competitors will launch new products and prepare counter-campaigns in advance. Finally, marketing systems leverage customer communication data, and the generative AI provides optimal content in real time. The generative AI analyzes past customer communication data to understand customer preferences and behavioral patterns. Based on this, it can provide the most relevant content to customers in real time. For example, it can provide information on relevant products based on products a customer has purchased in the past.In this way, by utilizing AI for customer preference prediction and generation, it is possible to not only predict customer preferences but also to achieve effective communication, significantly improving a company's marketing activities. As a result, marketing systems can achieve customer preference prediction and effective communication, significantly improving a company's marketing activities.

[0029] The marketing system according to this embodiment comprises a generation unit, a contact unit, a prediction unit, and a delivery unit. The generation unit generates customized campaigns based on social trends and expertise. For example, the generation unit analyzes the latest trend data and expertise to generate the most effective campaigns for customers. For example, the generation unit can generate campaigns tailored to seasonal trends or specific events. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the latest trend data and expertise into the generation AI, which performs the analysis and generates the optimal campaign. The contact unit increases customer contact points through the campaigns generated by the generation unit. For example, the contact unit analyzes customer behavior data and preference data and delivers campaigns at the optimal timing. For example, the contact unit can deliver campaigns related to a product when a customer shows interest in that product. Some or all of the above-described processes in the contact unit may be performed using AI or not. For example, the contact unit inputs customer behavior data and preference data into the AI, which performs the analysis and delivers campaigns at the optimal timing. The prediction unit analyzes customer data obtained by the contact unit to predict future trends and competitive landscapes. For example, the prediction unit analyzes past data and current market conditions to predict future trends and competitive landscapes. For example, the prediction unit can predict when competitors will launch new products and prepare counter-campaigns in advance. Some or all of the above processing in the prediction unit is performed using AI. For example, the prediction unit inputs past data and current market conditions into the AI, which analyzes the data and predicts future trends and competitive landscapes. The delivery unit provides optimal content in real time based on the prediction results obtained by the prediction unit. For example, the delivery unit analyzes past communication data with customers to understand customer preferences and behavioral patterns. For example, the delivery unit can provide information on relevant products based on products that customers have purchased in the past. Some or all of the above processing in the delivery unit is performed using generative AI.For example, the service provider inputs past communication data with customers into a generating AI, which then analyzes the data and provides optimal content in real time. As a result, the marketing system according to this embodiment can predict customer preferences and enable effective communication, significantly improving the company's marketing activities.

[0030] The generation unit generates customized campaigns based on social trends and expertise. For example, it analyzes the latest trend data and expertise to generate the most effective campaigns for clients. Specifically, the generation unit collects trend data from online news articles, social media posts, industry reports, etc., and analyzes this data. The analysis uses natural language processing technology and machine learning algorithms to detect trend changes and the emergence of new keywords. Furthermore, as expertise, it databases the opinions of industry experts and past success stories, and customizes the content of campaigns based on this information. Some or all of the above processes in the generation unit are performed using generation AI. For example, the generation unit inputs the latest trend data and expertise into the generation AI, which analyzes it and generates the optimal campaign. Based on the input data, the generation AI automatically generates messages and visuals that resonate most with the target audience. For example, it can generate campaigns tailored to seasonal trends or specific events. The generation AI learns from the effectiveness of past campaigns and proposes new campaigns that incorporate successful elements. This allows the generation unit to quickly generate effective campaigns based on the latest information, supporting companies' marketing activities.

[0031] The contact unit increases customer touchpoints through campaigns generated by the generation unit. For example, the contact unit analyzes customer behavior and preference data to deliver campaigns at the optimal time. Specifically, the contact unit collects data such as customer website visit history, purchase history, and social media activity, and analyzes this data. Machine learning algorithms are used in the analysis to identify customer behavior patterns and preferences. For example, a campaign related to a product can be delivered when a customer shows interest in that product. Some or all of the above processing in the contact unit may be performed using AI, or not. For example, the contact unit inputs customer behavior and preference data into AI, which analyzes the data and delivers campaigns at the optimal time. Based on the customer's past behavior data, the AI ​​predicts what action they will take next and delivers campaigns based on that prediction. This allows the contact unit to provide customers with effective campaigns at the optimal time and increase customer touchpoints. Furthermore, the contact unit can deliver campaigns through multiple channels. For example, email, SMS, social media, and push notifications can be used to deliver consistent messages to customers. This allows the point of contact to enhance customer engagement and maximize the effectiveness of marketing activities.

[0032] The forecasting unit analyzes customer data obtained by the contact unit to predict future trends and competitive landscapes. Specifically, the forecasting unit collects data such as customer purchase history, website visit history, and social media activity, and analyzes this data. Statistical methods such as time series analysis and regression analysis are used in the analysis to predict future trends. For example, it can predict how sales of a specific product category will fluctuate seasonally, and use this information to plan campaigns. Some or all of the above processing in the forecasting unit is performed using AI. For example, the forecasting unit inputs historical data and current market conditions into the AI, which then analyzes and predicts future trends and competitive landscapes. The AI ​​learns from historical data and can predict future trends with high accuracy. For example, it can predict when competitors will launch new products and prepare counter-campaigns in advance. This allows the forecasting unit to understand future market trends and provide information that enables companies to take quick and appropriate actions. Furthermore, the forecasting unit can continuously revise its prediction results based on real-time updated data, enabling it to adapt to the latest situations. This allows the forecasting unit to consistently provide highly accurate predictions based on the most up-to-date information, supporting companies' marketing activities.

[0033] The content delivery unit provides optimal content in real time based on prediction results obtained by the prediction unit. For example, the content delivery unit analyzes past communication data with customers to understand their preferences and behavioral patterns. Specifically, the content delivery unit can provide information on relevant products based on products previously purchased and content viewed by customers. Some or all of the above processing in the content delivery unit is performed using generative AI. For example, the content delivery unit inputs past communication data with customers into the generative AI, which analyzes the data and provides optimal content in real time. The generative AI learns customer preferences and behavioral patterns and automatically generates content best suited to each individual customer. For example, it can provide information on relevant products based on products previously purchased by customers. This allows the content delivery unit to provide personalized content to customers and increase customer satisfaction. Furthermore, the content delivery unit can continuously optimize content based on data updated in real time. For example, if customer behavioral data changes, the content delivery unit immediately incorporates the new data and updates the content it provides. The content delivery unit can also deliver content through multiple channels. For example, it can utilize websites, email, social media, and mobile apps to deliver consistent messages to customers. This allows the service provider to consistently deliver the latest and most relevant content to customers, supporting the company's marketing activities.

[0034] The generation unit can analyze the latest trend data and expertise to generate the most effective campaigns for customers. For example, the generation unit collects and analyzes the latest trend data in real time. For example, the generation unit analyzes social media trends and news articles to generate the most effective campaigns for customers. For example, the generation unit can also generate campaigns tailored to specific industries or markets based on expertise. Some or all of the above processes in the generation unit are performed using generation AI. For example, the generation unit inputs the latest trend data and expertise into the generation AI, which analyzes it and generates the optimal campaign. This maximizes marketing effectiveness by generating the most effective campaigns for customers.

[0035] The contact unit can analyze customer behavior and preference data to deliver campaigns at the optimal time. For example, the contact unit can analyze a customer's website browsing history and purchase history to deliver campaigns at the optimal time. The contact unit can also analyze customer survey results and social media posts to deliver campaigns at the optimal time. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit can input customer behavior and preference data into AI, which will analyze it and deliver campaigns at the optimal time. This increases customer contact points and enables effective communication.

[0036] The forecasting unit can analyze past data and current market conditions to predict future trends and competitive landscapes. For example, it can analyze past sales data and customer purchase history to predict future trends. It can also analyze current market share and competitor activities to predict future competitive landscapes. Some or all of the above processes in the forecasting unit are performed using AI. For example, the forecasting unit inputs past data and current market conditions into the AI, which then analyzes the data to predict future trends and competitive landscapes. This allows companies to anticipate future risks and take appropriate measures.

[0037] The service provider can analyze past communication data with customers to understand their preferences and behavioral patterns. For example, the service provider can analyze email exchanges and phone call records with customers to understand their preferences. The service provider can also analyze customer survey results and social media posts to understand their behavioral patterns. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider inputs past communication data with customers into the generative AI, which then analyzes it to understand the customer's preferences and behavioral patterns. This allows the service provider to deliver the most suitable content to customers in real time.

[0038] The generation unit can customize campaigns by referencing the customer's past purchase history. For example, the generation unit can generate campaigns related to products the customer has previously purchased. For example, the generation unit can generate campaigns that reflect the customer's preferences for a specific brand based on their purchase history. For example, the generation unit can generate campaigns that reflect seasonal purchasing trends based on the customer's purchase history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the customer's past purchase history into the generation AI, which analyzes it and generates the optimal campaign. This allows the system to attract customer interest by providing campaigns that reflect the customer's past purchase history.

[0039] The generation unit can generate campaign content tailored to specific regions and cultures. For example, it can generate campaigns that are aligned with local festivals or events. It can also generate campaigns that highlight products or services related to a particular culture. Furthermore, it can generate campaigns that reflect the preferences of consumers in different regions. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs data about a specific region or culture into the generation AI, which then analyzes the data and generates the optimal campaign. This allows for the creation of campaigns tailored to specific regions and cultures, thereby gaining customer engagement.

[0040] The generation unit can analyze a customer's social media activity and generate relevant content when creating a campaign. For example, the generation unit can generate a campaign based on what the customer has shared on social media. The generation unit can also generate a campaign related to brands or influencers that the customer follows. The generation unit can also generate a campaign that matches the time of day when the customer is active on social media. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the customer's social media activity data into the generation AI, which analyzes it and generates the optimal campaign. This allows the system to attract customer interest by providing campaigns that reflect the customer's social media activity.

[0041] The generation unit can generate campaigns in the optimal format, taking into account the customer's device information. For example, if the customer is using a smartphone, the generation unit will generate a mobile-friendly campaign. If the customer is using a tablet, the generation unit can also generate a campaign optimized for large screens. If the customer is using a desktop, the generation unit can also generate a campaign with detailed information. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the customer's device information into the generation AI, which analyzes it and generates the campaign in the optimal format. This improves usability by providing campaigns optimized for the customer's device.

[0042] The contact unit can select the optimal contact method by referring to the customer's past response data at the time of contact. For example, the contact unit may prioritize contact methods in which the customer has shown a favorable response in the past. The contact unit may also avoid contact methods in which the customer has shown no response in the past. For example, the contact unit may select the optimal contact timing based on the customer's past response data. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs the customer's past response data into AI, which analyzes it and determines the optimal contact method. This enables effective communication by providing contact methods that reflect the customer's past response data.

[0043] The contact unit can customize the content of the interaction based on the customer's current living situation at the time of contact. For example, if the customer is traveling, the contact unit can provide information related to their travel destination. For example, if the customer is moving, the contact unit can also provide information related to their new home. For example, if the customer is raising children, the contact unit can also provide information related to childcare. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs the customer's current living situation data into the AI, which analyzes it and determines the optimal content of the interaction. This allows the contact unit to attract the customer's interest by providing content that is tailored to the customer's living situation.

[0044] The contact unit can select the optimal contact method at the time of contact, taking into account the customer's geographical location information. For example, if the customer is in a specific area, the contact unit can provide information relevant to that area. For example, if the customer is on the move, the contact unit can also select a mobile-friendly contact method. For example, if the customer is at home, the contact unit can also select a contact method that includes detailed information. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs the customer's geographical location information into the AI, which analyzes it and determines the optimal contact method. This enables effective communication by providing contact methods based on the customer's geographical location information.

[0045] The contact unit can analyze the customer's social media activity and provide relevant content at the time of contact. For example, the contact unit can provide information based on what the customer has shared on social media. The contact unit can also provide information related to brands and influencers that the customer follows. The contact unit can also provide information tailored to the time of day when the customer is active on social media. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs the customer's social media activity data into AI, which analyzes it and provides optimal information. This allows the contact unit to attract the customer's attention by providing contact content that reflects the customer's social media activity.

[0046] The forecasting unit can analyze not only historical data but also current market conditions in real time during the forecasting process. For example, the forecasting unit can analyze current market trends in real time and reflect them in the forecast. For example, the forecasting unit can also analyze the current actions of competitors in real time and reflect them in the forecast. For example, the forecasting unit can analyze current consumer behavior patterns in real time and reflect them in the forecast. Some or all of the above processing in the forecasting unit is performed using AI. For example, the forecasting unit inputs historical data and current market conditions into the AI, which then performs the analysis and makes the optimal forecast. This allows for more accurate forecasts by analyzing current market conditions in real time.

[0047] The prediction unit can apply prediction algorithms specific to particular industries or regions during the prediction process. For example, it can apply a prediction algorithm that emphasizes trends in a particular industry. It can also apply a prediction algorithm that emphasizes consumer behavior in a particular region. It can also apply a prediction algorithm that combines data from a particular industry and region. Some or all of the above processes in the prediction unit are performed using AI. For example, the prediction unit inputs data related to a particular industry or region into the AI, which then analyzes the data and makes the optimal prediction. This improves the accuracy of predictions by making predictions specific to particular industries or regions.

[0048] The forecasting unit can analyze the actions of competitors in real time and incorporate them into its forecasts. For example, the forecasting unit can analyze information on new product launches by competitors in real time and incorporate it into its forecasts. The forecasting unit can also analyze marketing campaigns by competitors in real time and incorporate them into its forecasts. The forecasting unit can also analyze fluctuations in the market share of competitors in real time and incorporate them into its forecasts. Some or all of the above processes in the forecasting unit are performed using AI. For example, the forecasting unit inputs competitor activity data into the AI, which then analyzes the data and makes the optimal forecast. This allows for more accurate forecasts by analyzing competitor activity in real time.

[0049] The forecasting unit can improve the accuracy of its predictions by referring to relevant market data during the forecasting process. For example, the forecasting unit can improve the accuracy of its predictions by referring to relevant market sales data. The forecasting unit can also improve the accuracy of its predictions by referring to relevant market consumer behavior data. The forecasting unit can also improve the accuracy of its predictions by referring to relevant market trend data. Some or all of the above processing in the forecasting unit is performed using AI. For example, the forecasting unit inputs relevant market data into the AI, which then analyzes it and makes the optimal prediction. This allows the forecasting unit to improve its accuracy by referring to relevant market data.

[0050] The content delivery unit can select the most suitable content by referring to the customer's past behavior patterns at the time of delivery. For example, the delivery unit can provide content related to products the customer has previously viewed. The delivery unit can also provide content related to products the customer has previously purchased. For example, the delivery unit can analyze the customer's past behavior patterns and provide the most interesting content. Some or all of the above processing in the delivery unit is performed using generative AI. For example, the delivery unit inputs the customer's past behavior pattern data into the generative AI, which analyzes it and provides the most suitable content. This allows the delivery unit to attract the customer's interest by providing content that reflects the customer's past behavior patterns.

[0051] The service provider can deliver content tailored to specific times of day or events. For example, if a customer is active during a particular time period, the service provider will deliver content tailored to that time. The service provider can also deliver content tailored to specific events (e.g., Black Friday). The service provider can also deliver content tailored to a customer's birthday or anniversary. Some or all of the above processes in the service provider are performed using generative AI. For example, the service provider inputs data about specific times of day or events into the generative AI, which analyzes the data and delivers the most suitable content. This allows the service provider to attract customer interest by delivering content tailored to specific times of day or events.

[0052] The service provider can provide optimal content by considering the customer's geographical location at the time of delivery. For example, if the customer is in a specific region, the service provider can provide content related to that region. For example, if the customer is traveling, the service provider can also provide content related to their travel destination. For example, if the customer is at home, the service provider can also provide content that can be enjoyed at home. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider inputs the customer's geographical location information into the generative AI, which analyzes it and provides optimal content. This enables effective communication by providing content based on the customer's geographical location.

[0053] The service provider can analyze the customer's social media activity and provide relevant content at the time of delivery. For example, the service provider can provide content based on what the customer has shared on social media. The service provider can also provide content related to brands or influencers that the customer follows. The service provider can also provide content tailored to the customer's social media activity times. Some or all of the above processes in the service provider are performed using generative AI. For example, the service provider inputs the customer's social media activity data into the generative AI, which analyzes it and provides the most suitable content. This allows the service provider to attract the customer's interest by providing content that reflects the customer's social media activity.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Marketing systems can predict products customers are likely to purchase in the future based on their purchase history and generate campaigns related to those products. For example, if a customer has previously purchased cosmetics from a particular brand, a special campaign can be offered when that brand launches a new product. Similarly, if a customer tends to purchase certain products seasonally, campaigns tailored to that season can be generated. Furthermore, if a customer purchases products related to a specific event (e.g., a birthday or anniversary), a campaign tailored to that event can be offered. This allows for more personalized campaigns by leveraging customer purchase history.

[0056] Marketing systems can analyze customers' social media activity and generate campaigns based on topics and trends that customers are interested in. For example, if a customer frequently mentions a particular brand or product on social media, the system can provide campaigns related to that brand or product. Similarly, if a customer shows interest in a specific event or trend, the system can generate campaigns related to that event or trend. Furthermore, it can provide relevant campaigns based on the influencers and brands that customers follow. This allows marketing systems to leverage customers' social media activity to deliver more engaging campaigns.

[0057] Marketing systems can leverage customers' geographical location information to generate campaigns tailored to specific regions and cultures. For example, they can offer campaigns aligned with events and festivals held in a particular area. They can also generate campaigns that highlight products and services related to specific cultures and customs. Furthermore, they can provide campaigns that reflect the preferences and purchasing trends of consumers in each region. By offering campaigns tailored to local areas and cultures, companies can gain customer engagement.

[0058] Marketing systems can generate campaigns in the most optimal format, taking into account customer device information. For example, if a customer uses a smartphone, a mobile-friendly campaign can be generated. Similarly, if a customer uses a tablet, a campaign optimized for larger screens can be generated. Furthermore, if a customer uses a desktop, a campaign with detailed information can be generated. This improves usability by providing campaigns optimized for each customer's device.

[0059] Marketing systems can customize their interactions based on a customer's current life situation. For example, if a customer is traveling, they can be provided with information related to their destination. If a customer is moving, they can be provided with information related to their new home. Furthermore, if a customer is raising children, they can be provided with information related to childcare. This allows for greater customer engagement by providing interactions tailored to the customer's life circumstances.

[0060] Marketing systems can select the optimal contact method by referring to past customer response data. For example, they can prioritize contact methods in which customers have responded positively in the past. They can also avoid contact methods in which customers have not responded. Furthermore, they can select the optimal timing for contact based on past customer response data. This allows for effective communication by providing contact methods that reflect past customer response data.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The generation unit generates customized campaigns based on social trends and expertise. The generation unit analyzes the latest trend data and expertise to generate the most effective campaigns for customers. For example, it can generate campaigns tailored to seasonal trends or specific events. Some or all of the processing in the generation unit is performed using generation AI. Step 2: The contact unit increases customer contact points through campaigns generated by the generation unit. The contact unit analyzes customer behavior data and preference data to deliver campaigns at the optimal time. For example, when a customer shows interest in a particular product, a campaign related to that product can be delivered. Some or all of the processing in the contact unit may be performed using AI, or it may not be performed using AI. Step 3: The prediction unit analyzes customer data obtained by the contact unit to predict future trends and competitive landscapes. The prediction unit analyzes past data and current market conditions to predict future trends and competitive landscapes. For example, it can predict when competitors will launch new products and prepare counter-campaigns in advance. Some or all of the processing in the prediction unit is performed using AI. Step 4: The delivery unit provides optimal content in real time based on the prediction results obtained by the prediction unit. The delivery unit analyzes past communication data with customers to understand customer preferences and behavioral patterns. For example, it can provide information on relevant products based on products that customers have purchased in the past. Some or all of the processing in the delivery unit is performed using generative AI.

[0063] (Example of form 2) The marketing system according to an embodiment of the present invention is a system that proposes a new marketing method for effectively communicating with customers by utilizing customer preference prediction AI and generation AI. This marketing system generates customized campaigns based on social trends and expertise, increasing points of contact with customers. Furthermore, it enhances the brand forecasting function to evaluate not only future trends but also future competitive situations and risks. Finally, the generation AI provides optimal content in real time by utilizing communication data with customers. For example, the marketing system generates customized campaigns based on social trends and expertise. In this process, the generation AI analyzes the latest trend data and expertise to generate the most effective campaign for customers. For example, it can generate campaigns tailored to seasonal trends or specific events. Next, the marketing system increases points of contact with customers through the generated campaigns. The generation AI analyzes customer behavior data and preference data and delivers campaigns at the optimal timing. This increases points of contact with customers and enables effective communication. For example, when a customer shows interest in a particular product, a campaign related to that product can be delivered. Furthermore, the marketing system enhances the brand forecasting function to evaluate not only future trends but also future competitive situations and risks. Generative AI analyzes historical data and current market conditions to predict future trends and competitive landscapes. This allows companies to anticipate future risks and take appropriate measures. For example, it can predict when competitors will launch new products and prepare counter-campaigns in advance. Finally, marketing systems leverage customer communication data, and the generative AI provides optimal content in real time. The generative AI analyzes past customer communication data to understand customer preferences and behavioral patterns. Based on this, it can provide the most relevant content to customers in real time. For example, it can provide information on relevant products based on products a customer has purchased in the past.In this way, by utilizing AI for customer preference prediction and generation, it is possible to not only predict customer preferences but also to achieve effective communication, significantly improving a company's marketing activities. As a result, marketing systems can achieve customer preference prediction and effective communication, significantly improving a company's marketing activities.

[0064] The marketing system according to this embodiment comprises a generation unit, a contact unit, a prediction unit, and a delivery unit. The generation unit generates customized campaigns based on social trends and expertise. For example, the generation unit analyzes the latest trend data and expertise to generate the most effective campaigns for customers. For example, the generation unit can generate campaigns tailored to seasonal trends or specific events. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the latest trend data and expertise into the generation AI, which performs the analysis and generates the optimal campaign. The contact unit increases customer contact points through the campaigns generated by the generation unit. For example, the contact unit analyzes customer behavior data and preference data and delivers campaigns at the optimal timing. For example, the contact unit can deliver campaigns related to a product when a customer shows interest in that product. Some or all of the above-described processes in the contact unit may be performed using AI or not. For example, the contact unit inputs customer behavior data and preference data into the AI, which performs the analysis and delivers campaigns at the optimal timing. The prediction unit analyzes customer data obtained by the contact unit to predict future trends and competitive landscapes. For example, the prediction unit analyzes past data and current market conditions to predict future trends and competitive landscapes. For example, the prediction unit can predict when competitors will launch new products and prepare counter-campaigns in advance. Some or all of the above processing in the prediction unit is performed using AI. For example, the prediction unit inputs past data and current market conditions into the AI, which analyzes the data and predicts future trends and competitive landscapes. The delivery unit provides optimal content in real time based on the prediction results obtained by the prediction unit. For example, the delivery unit analyzes past communication data with customers to understand customer preferences and behavioral patterns. For example, the delivery unit can provide information on relevant products based on products that customers have purchased in the past. Some or all of the above processing in the delivery unit is performed using generative AI.For example, the service provider inputs past communication data with customers into a generating AI, which then analyzes the data and provides optimal content in real time. As a result, the marketing system according to this embodiment can predict customer preferences and enable effective communication, significantly improving the company's marketing activities.

[0065] The generation unit generates customized campaigns based on social trends and expertise. For example, it analyzes the latest trend data and expertise to generate the most effective campaigns for clients. Specifically, the generation unit collects trend data from online news articles, social media posts, industry reports, etc., and analyzes this data. The analysis uses natural language processing technology and machine learning algorithms to detect trend changes and the emergence of new keywords. Furthermore, as expertise, it databases the opinions of industry experts and past success stories, and customizes the content of campaigns based on this information. Some or all of the above processes in the generation unit are performed using generation AI. For example, the generation unit inputs the latest trend data and expertise into the generation AI, which analyzes it and generates the optimal campaign. Based on the input data, the generation AI automatically generates messages and visuals that resonate most with the target audience. For example, it can generate campaigns tailored to seasonal trends or specific events. The generation AI learns from the effectiveness of past campaigns and proposes new campaigns that incorporate successful elements. This allows the generation unit to quickly generate effective campaigns based on the latest information, supporting companies' marketing activities.

[0066] The contact unit increases customer touchpoints through campaigns generated by the generation unit. For example, the contact unit analyzes customer behavior and preference data to deliver campaigns at the optimal time. Specifically, the contact unit collects data such as customer website visit history, purchase history, and social media activity, and analyzes this data. Machine learning algorithms are used in the analysis to identify customer behavior patterns and preferences. For example, a campaign related to a product can be delivered when a customer shows interest in that product. Some or all of the above processing in the contact unit may be performed using AI, or not. For example, the contact unit inputs customer behavior and preference data into AI, which analyzes the data and delivers campaigns at the optimal time. Based on the customer's past behavior data, the AI ​​predicts what action they will take next and delivers campaigns based on that prediction. This allows the contact unit to provide customers with effective campaigns at the optimal time and increase customer touchpoints. Furthermore, the contact unit can deliver campaigns through multiple channels. For example, email, SMS, social media, and push notifications can be used to deliver consistent messages to customers. This allows the point of contact to enhance customer engagement and maximize the effectiveness of marketing activities.

[0067] The forecasting unit analyzes customer data obtained by the contact unit to predict future trends and competitive landscapes. Specifically, the forecasting unit collects data such as customer purchase history, website visit history, and social media activity, and analyzes this data. Statistical methods such as time series analysis and regression analysis are used in the analysis to predict future trends. For example, it can predict how sales of a specific product category will fluctuate seasonally, and use this information to plan campaigns. Some or all of the above processing in the forecasting unit is performed using AI. For example, the forecasting unit inputs historical data and current market conditions into the AI, which then analyzes and predicts future trends and competitive landscapes. The AI ​​learns from historical data and can predict future trends with high accuracy. For example, it can predict when competitors will launch new products and prepare counter-campaigns in advance. This allows the forecasting unit to understand future market trends and provide information that enables companies to take quick and appropriate actions. Furthermore, the forecasting unit can continuously revise its prediction results based on real-time updated data, enabling it to adapt to the latest situations. This allows the forecasting unit to consistently provide highly accurate predictions based on the most up-to-date information, supporting companies' marketing activities.

[0068] The content delivery unit provides optimal content in real time based on prediction results obtained by the prediction unit. For example, the content delivery unit analyzes past communication data with customers to understand their preferences and behavioral patterns. Specifically, the content delivery unit can provide information on relevant products based on products previously purchased and content viewed by customers. Some or all of the above processing in the content delivery unit is performed using generative AI. For example, the content delivery unit inputs past communication data with customers into the generative AI, which analyzes the data and provides optimal content in real time. The generative AI learns customer preferences and behavioral patterns and automatically generates content best suited to each individual customer. For example, it can provide information on relevant products based on products previously purchased by customers. This allows the content delivery unit to provide personalized content to customers and increase customer satisfaction. Furthermore, the content delivery unit can continuously optimize content based on data updated in real time. For example, if customer behavioral data changes, the content delivery unit immediately incorporates the new data and updates the content it provides. The content delivery unit can also deliver content through multiple channels. For example, it can utilize websites, email, social media, and mobile apps to deliver consistent messages to customers. This allows the service provider to consistently deliver the latest and most relevant content to customers, supporting the company's marketing activities.

[0069] The generation unit can analyze the latest trend data and expertise to generate the most effective campaigns for customers. For example, the generation unit collects and analyzes the latest trend data in real time. For example, the generation unit analyzes social media trends and news articles to generate the most effective campaigns for customers. For example, the generation unit can also generate campaigns tailored to specific industries or markets based on expertise. Some or all of the above processes in the generation unit are performed using generation AI. For example, the generation unit inputs the latest trend data and expertise into the generation AI, which analyzes it and generates the optimal campaign. This maximizes marketing effectiveness by generating the most effective campaigns for customers.

[0070] The contact unit can analyze customer behavior and preference data to deliver campaigns at the optimal time. For example, the contact unit can analyze a customer's website browsing history and purchase history to deliver campaigns at the optimal time. The contact unit can also analyze customer survey results and social media posts to deliver campaigns at the optimal time. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit can input customer behavior and preference data into AI, which will analyze it and deliver campaigns at the optimal time. This increases customer contact points and enables effective communication.

[0071] The forecasting unit can analyze past data and current market conditions to predict future trends and competitive landscapes. For example, it can analyze past sales data and customer purchase history to predict future trends. It can also analyze current market share and competitor activities to predict future competitive landscapes. Some or all of the above processes in the forecasting unit are performed using AI. For example, the forecasting unit inputs past data and current market conditions into the AI, which then analyzes the data to predict future trends and competitive landscapes. This allows companies to anticipate future risks and take appropriate measures.

[0072] The service provider can analyze past communication data with customers to understand their preferences and behavioral patterns. For example, the service provider can analyze email exchanges and phone call records with customers to understand their preferences. The service provider can also analyze customer survey results and social media posts to understand their behavioral patterns. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider inputs past communication data with customers into the generative AI, which then analyzes it to understand the customer's preferences and behavioral patterns. This allows the service provider to deliver the most suitable content to customers in real time.

[0073] The generation unit can estimate customer emotions and adjust campaign content based on those emotions. For example, if a customer is stressed, the generation unit can generate a campaign with relaxing content. If a customer is excited, the generation unit can also generate an energetic campaign. If a customer is depressed, the generation unit can also generate a campaign that includes encouraging messages. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs customer emotion data into the generation AI, which analyzes it and generates the optimal campaign. This allows for improved customer satisfaction by providing campaigns that respond to customer emotions.

[0074] The generation unit can customize campaigns by referencing the customer's past purchase history. For example, the generation unit can generate campaigns related to products the customer has previously purchased. For example, the generation unit can generate campaigns that reflect the customer's preferences for a specific brand based on their purchase history. For example, the generation unit can generate campaigns that reflect seasonal purchasing trends based on the customer's purchase history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the customer's past purchase history into the generation AI, which analyzes it and generates the optimal campaign. This allows the system to attract customer interest by providing campaigns that reflect the customer's past purchase history.

[0075] The generation unit can generate campaign content tailored to specific regions and cultures. For example, it can generate campaigns that are aligned with local festivals or events. It can also generate campaigns that highlight products or services related to a particular culture. Furthermore, it can generate campaigns that reflect the preferences of consumers in different regions. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs data about a specific region or culture into the generation AI, which then analyzes the data and generates the optimal campaign. This allows for the creation of campaigns tailored to specific regions and cultures, thereby gaining customer engagement.

[0076] The generation unit can estimate customer emotions and adjust the timing of campaign delivery based on those estimated emotions. For example, the generation unit can deliver campaigns during times when customers are relaxed. It can also deliver energetic campaigns during times when customers are excited. It can also deliver relaxing campaigns during times when customers are stressed. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs customer emotion data into the generation AI, which analyzes it and determines the optimal delivery timing. This enables effective communication by delivering campaigns at times that match customer emotions.

[0077] The generation unit can analyze a customer's social media activity and generate relevant content when creating a campaign. For example, the generation unit can generate a campaign based on what the customer has shared on social media. The generation unit can also generate a campaign related to brands or influencers that the customer follows. The generation unit can also generate a campaign that matches the time of day when the customer is active on social media. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the customer's social media activity data into the generation AI, which analyzes it and generates the optimal campaign. This allows the system to attract customer interest by providing campaigns that reflect the customer's social media activity.

[0078] The generation unit can generate campaigns in the optimal format, taking into account the customer's device information. For example, if the customer is using a smartphone, the generation unit will generate a mobile-friendly campaign. If the customer is using a tablet, the generation unit can also generate a campaign optimized for large screens. If the customer is using a desktop, the generation unit can also generate a campaign with detailed information. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the customer's device information into the generation AI, which analyzes it and generates the campaign in the optimal format. This improves usability by providing campaigns optimized for the customer's device.

[0079] The contact unit can estimate the customer's emotions and adjust the contact method based on the estimated emotions. For example, if the customer is relaxed, the contact unit may prioritize contact via email. If the customer is excited, for example, the contact unit may prioritize contact via social media. If the customer is stressed, for example, the contact unit may avoid contact via telephone. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs customer emotion data into AI, which analyzes it and determines the optimal contact method. This can improve customer satisfaction by providing contact methods that match the customer's emotions.

[0080] The contact unit can select the optimal contact method by referring to the customer's past response data at the time of contact. For example, the contact unit may prioritize contact methods in which the customer has shown a favorable response in the past. The contact unit may also avoid contact methods in which the customer has shown no response in the past. For example, the contact unit may select the optimal contact timing based on the customer's past response data. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs the customer's past response data into AI, which analyzes it and determines the optimal contact method. This enables effective communication by providing contact methods that reflect the customer's past response data.

[0081] The contact unit can customize the content of the interaction based on the customer's current living situation at the time of contact. For example, if the customer is traveling, the contact unit can provide information related to their travel destination. For example, if the customer is moving, the contact unit can also provide information related to their new home. For example, if the customer is raising children, the contact unit can also provide information related to childcare. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs the customer's current living situation data into the AI, which analyzes it and determines the optimal content of the interaction. This allows the contact unit to attract the customer's interest by providing content that is tailored to the customer's living situation.

[0082] The contact unit can estimate the customer's emotions and determine the priority of contact based on those emotions. For example, if the customer is excited, the contact unit may attempt contact immediately. If the customer is relaxed, for example, the contact unit may postpone contact. If the customer is stressed, for example, the contact unit may refrain from contacting them. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs customer emotion data into the AI, which analyzes it and determines the optimal priority of contact. This enables effective communication by prioritizing contact according to the customer's emotions.

[0083] The contact unit can select the optimal contact method at the time of contact, taking into account the customer's geographical location information. For example, if the customer is in a specific area, the contact unit can provide information relevant to that area. For example, if the customer is on the move, the contact unit can also select a mobile-friendly contact method. For example, if the customer is at home, the contact unit can also select a contact method that includes detailed information. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs the customer's geographical location information into the AI, which analyzes it and determines the optimal contact method. This enables effective communication by providing contact methods based on the customer's geographical location information.

[0084] The contact unit can analyze the customer's social media activity and provide relevant content at the time of contact. For example, the contact unit can provide information based on what the customer has shared on social media. The contact unit can also provide information related to brands and influencers that the customer follows. The contact unit can also provide information tailored to the time of day when the customer is active on social media. Some or all of the above processing in the contact unit may be performed using AI or not. For example, the contact unit inputs the customer's social media activity data into AI, which analyzes it and provides optimal information. This allows the contact unit to attract the customer's attention by providing contact content that reflects the customer's social media activity.

[0085] The prediction unit can estimate the customer's emotions and improve the accuracy of its predictions based on those emotions. For example, if the customer is relaxed, the prediction unit will prioritize past data in its predictions. If the customer is excited, the prediction unit can also prioritize real-time data in its predictions. If the customer is stressed, the prediction unit can also combine past and real-time data in its predictions. Some or all of the above processes in the prediction unit are performed using AI. For example, the prediction unit inputs customer emotion data into the AI, which analyzes it and makes the optimal prediction. This improves the accuracy of predictions by making predictions that are tailored to the customer's emotions.

[0086] The forecasting unit can analyze not only historical data but also current market conditions in real time during the forecasting process. For example, the forecasting unit can analyze current market trends in real time and reflect them in the forecast. For example, the forecasting unit can also analyze the current actions of competitors in real time and reflect them in the forecast. For example, the forecasting unit can analyze current consumer behavior patterns in real time and reflect them in the forecast. Some or all of the above processing in the forecasting unit is performed using AI. For example, the forecasting unit inputs historical data and current market conditions into the AI, which then performs the analysis and makes the optimal forecast. This allows for more accurate forecasts by analyzing current market conditions in real time.

[0087] The prediction unit can apply prediction algorithms specific to particular industries or regions during the prediction process. For example, it can apply a prediction algorithm that emphasizes trends in a particular industry. It can also apply a prediction algorithm that emphasizes consumer behavior in a particular region. It can also apply a prediction algorithm that combines data from a particular industry and region. Some or all of the above processes in the prediction unit are performed using AI. For example, the prediction unit inputs data related to a particular industry or region into the AI, which then analyzes the data and makes the optimal prediction. This improves the accuracy of predictions by making predictions specific to particular industries or regions.

[0088] The prediction unit can estimate the customer's emotions and adjust how the prediction results are displayed based on the estimated emotions. For example, if the customer is relaxed, the prediction unit can display detailed prediction results. If the customer is excited, for example, the prediction unit can display concise prediction results. If the customer is stressed, for example, the prediction unit can display simple and easy-to-understand prediction results. Some or all of the above processing in the prediction unit is performed using AI. For example, the prediction unit inputs customer emotion data into the AI, which analyzes it and determines the optimal display method. This facilitates understanding of the prediction results by providing a display method that is appropriate to the customer's emotions.

[0089] The forecasting unit can analyze the actions of competitors in real time and incorporate them into its forecasts. For example, the forecasting unit can analyze information on new product launches by competitors in real time and incorporate it into its forecasts. The forecasting unit can also analyze marketing campaigns by competitors in real time and incorporate them into its forecasts. The forecasting unit can also analyze fluctuations in the market share of competitors in real time and incorporate them into its forecasts. Some or all of the above processes in the forecasting unit are performed using AI. For example, the forecasting unit inputs competitor activity data into the AI, which then analyzes the data and makes the optimal forecast. This allows for more accurate forecasts by analyzing competitor activity in real time.

[0090] The forecasting unit can improve the accuracy of its predictions by referring to relevant market data during the forecasting process. For example, the forecasting unit can improve the accuracy of its predictions by referring to relevant market sales data. The forecasting unit can also improve the accuracy of its predictions by referring to relevant market consumer behavior data. The forecasting unit can also improve the accuracy of its predictions by referring to relevant market trend data. Some or all of the above processing in the forecasting unit is performed using AI. For example, the forecasting unit inputs relevant market data into the AI, which then analyzes it and makes the optimal prediction. This allows the forecasting unit to improve its accuracy by referring to relevant market data.

[0091] The service provider can estimate the customer's emotions and adjust the content provided based on those emotions. For example, if the customer is relaxed, the service provider can provide relaxing content. If the customer is excited, the service provider can also provide energetic content. If the customer is depressed, the service provider can also provide content that includes encouraging messages. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider inputs customer emotion data into the generative AI, which analyzes it and provides the most suitable content. This improves customer satisfaction by providing content that matches the customer's emotions.

[0092] The content delivery unit can select the most suitable content by referring to the customer's past behavior patterns at the time of delivery. For example, the delivery unit can provide content related to products the customer has previously viewed. The delivery unit can also provide content related to products the customer has previously purchased. For example, the delivery unit can analyze the customer's past behavior patterns and provide the most interesting content. Some or all of the above processing in the delivery unit is performed using generative AI. For example, the delivery unit inputs the customer's past behavior pattern data into the generative AI, which analyzes it and provides the most suitable content. This allows the delivery unit to attract the customer's interest by providing content that reflects the customer's past behavior patterns.

[0093] The service provider can deliver content tailored to specific times of day or events. For example, if a customer is active during a particular time period, the service provider will deliver content tailored to that time. The service provider can also deliver content tailored to specific events (e.g., Black Friday). The service provider can also deliver content tailored to a customer's birthday or anniversary. Some or all of the above processes in the service provider are performed using generative AI. For example, the service provider inputs data about specific times of day or events into the generative AI, which analyzes the data and delivers the most suitable content. This allows the service provider to attract customer interest by delivering content tailored to specific times of day or events.

[0094] The delivery unit can estimate customer emotions and adjust the timing of content delivery based on those estimated emotions. For example, the delivery unit can deliver content when customers are relaxed. For example, the delivery unit can deliver energetic content when customers are excited. For example, the delivery unit can deliver relaxing content when customers are stressed. Some or all of the above processing in the delivery unit is performed using generative AI. For example, the delivery unit inputs customer emotion data into the generative AI, which analyzes it and determines the optimal delivery timing. This enables effective communication by delivering content at a time that matches the customer's emotions.

[0095] The service provider can provide optimal content by considering the customer's geographical location at the time of delivery. For example, if the customer is in a specific region, the service provider can provide content related to that region. For example, if the customer is traveling, the service provider can also provide content related to their travel destination. For example, if the customer is at home, the service provider can also provide content that can be enjoyed at home. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider inputs the customer's geographical location information into the generative AI, which analyzes it and provides optimal content. This enables effective communication by providing content based on the customer's geographical location.

[0096] The service provider can analyze the customer's social media activity and provide relevant content at the time of delivery. For example, the service provider can provide content based on what the customer has shared on social media. The service provider can also provide content related to brands or influencers that the customer follows. The service provider can also provide content tailored to the customer's social media activity times. Some or all of the above processes in the service provider are performed using generative AI. For example, the service provider inputs the customer's social media activity data into the generative AI, which analyzes it and provides the most suitable content. This allows the service provider to attract the customer's interest by providing content that reflects the customer's social media activity.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] Marketing systems can predict products customers are likely to purchase in the future based on their purchase history and generate campaigns related to those products. For example, if a customer has previously purchased cosmetics from a particular brand, a special campaign can be offered when that brand launches a new product. Similarly, if a customer tends to purchase certain products seasonally, campaigns tailored to that season can be generated. Furthermore, if a customer purchases products related to a specific event (e.g., a birthday or anniversary), a campaign tailored to that event can be offered. This allows for more personalized campaigns by leveraging customer purchase history.

[0099] Marketing systems can estimate customer emotions and adjust the tone and content of marketing messages based on those estimates. For example, if a customer is stressed, a relaxing message can be sent. If a customer is excited, an energetic message can be sent. Furthermore, if a customer is depressed, an encouraging message can be sent. This allows for improved customer satisfaction by providing marketing messages tailored to the customer's emotions.

[0100] Marketing systems can analyze customers' social media activity and generate campaigns based on topics and trends that customers are interested in. For example, if a customer frequently mentions a particular brand or product on social media, the system can provide campaigns related to that brand or product. Similarly, if a customer shows interest in a specific event or trend, the system can generate campaigns related to that event or trend. Furthermore, it can provide relevant campaigns based on the influencers and brands that customers follow. This allows marketing systems to leverage customers' social media activity to deliver more engaging campaigns.

[0101] Marketing systems can leverage customers' geographical location information to generate campaigns tailored to specific regions and cultures. For example, they can offer campaigns aligned with events and festivals held in a particular area. They can also generate campaigns that highlight products and services related to specific cultures and customs. Furthermore, they can provide campaigns that reflect the preferences and purchasing trends of consumers in each region. By offering campaigns tailored to local areas and cultures, companies can gain customer engagement.

[0102] Marketing systems can estimate customer emotions and adjust the timing of campaign delivery based on those estimates. For example, campaigns can be delivered when customers are relaxed, energetic campaigns when they are excited, and relaxing campaigns when they are stressed. This allows for more effective communication by delivering campaigns at times that align with customer emotions.

[0103] Marketing systems can generate campaigns in the most optimal format, taking into account customer device information. For example, if a customer uses a smartphone, a mobile-friendly campaign can be generated. Similarly, if a customer uses a tablet, a campaign optimized for larger screens can be generated. Furthermore, if a customer uses a desktop, a campaign with detailed information can be generated. This improves usability by providing campaigns optimized for each customer's device.

[0104] Marketing systems can estimate customer emotions and adjust how they interact with customers based on those estimates. For example, if a customer is relaxed, email contact can be prioritized. If a customer is excited, social media contact can be prioritized. Furthermore, if a customer is stressed, phone contact can be avoided. By providing contact methods tailored to the customer's emotions, customer satisfaction can be improved.

[0105] Marketing systems can customize their interactions based on a customer's current life situation. For example, if a customer is traveling, they can be provided with information related to their destination. If a customer is moving, they can be provided with information related to their new home. Furthermore, if a customer is raising children, they can be provided with information related to childcare. This allows for greater customer engagement by providing interactions tailored to the customer's life circumstances.

[0106] Marketing systems can estimate customer emotions and adjust how prediction results are displayed based on those estimates. For example, if a customer is relaxed, detailed prediction results can be displayed. If a customer is excited, concise prediction results can be displayed. Furthermore, if a customer is stressed, simple and easy-to-understand prediction results can be displayed. This allows for a more emotionally responsive presentation of prediction results, thereby facilitating their understanding.

[0107] Marketing systems can select the optimal contact method by referring to past customer response data. For example, they can prioritize contact methods in which customers have responded positively in the past. They can also avoid contact methods in which customers have not responded. Furthermore, they can select the optimal timing for contact based on past customer response data. This allows for effective communication by providing contact methods that reflect past customer response data.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The generation unit generates customized campaigns based on social trends and expertise. The generation unit analyzes the latest trend data and expertise to generate the most effective campaigns for customers. For example, it can generate campaigns tailored to seasonal trends or specific events. Some or all of the processing in the generation unit is performed using generation AI. Step 2: The contact unit increases customer contact points through campaigns generated by the generation unit. The contact unit analyzes customer behavior data and preference data to deliver campaigns at the optimal time. For example, when a customer shows interest in a particular product, a campaign related to that product can be delivered. Some or all of the processing in the contact unit may be performed using AI, or it may not be performed using AI. Step 3: The prediction unit analyzes customer data obtained by the contact unit to predict future trends and competitive landscapes. The prediction unit analyzes past data and current market conditions to predict future trends and competitive landscapes. For example, it can predict when competitors will launch new products and prepare counter-campaigns in advance. Some or all of the processing in the prediction unit is performed using AI. Step 4: The delivery unit provides optimal content in real time based on the prediction results obtained by the prediction unit. The delivery unit analyzes past communication data with customers to understand customer preferences and behavioral patterns. For example, it can provide information on relevant products based on products that customers have purchased in the past. Some or all of the processing in the delivery unit is performed using generative AI.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the generation unit, contact unit, prediction unit, and delivery unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14, which analyzes the latest trend data and expertise to generate the most effective campaigns for customers. The contact unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes customer behavior data and preference data to deliver campaigns at the optimal timing. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes past data and current market conditions to predict future trends and competitive situations. The delivery unit is implemented by the control unit 46A of the smart device 14, which analyzes past communication data with customers to provide optimal content in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the generation unit, contact unit, prediction unit, and delivery unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the latest trend data and expertise to generate the most effective campaigns for customers. The contact unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes customer behavior data and preference data to deliver campaigns at the optimal timing. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes past data and current market conditions to predict future trends and competitive situations. The delivery unit is implemented by the control unit 46A of the smart glasses 214, which analyzes past communication data with customers to provide optimal content in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the generation unit, contact unit, prediction unit, and delivery unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the latest trend data and expertise to generate the most effective campaigns for customers. The contact unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes customer behavior data and preference data to deliver campaigns at the optimal timing. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes past data and current market conditions to predict future trends and competitive situations. The delivery unit is implemented by the control unit 46A of the headset terminal 314, which analyzes past communication data with customers to provide optimal content in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the generation unit, contact unit, prediction unit, and delivery unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414, which analyzes the latest trend data and expertise to generate the most effective campaigns for customers. The contact unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes customer behavior data and preference data to deliver campaigns at the optimal timing. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes past data and current market conditions to predict future trends and competitive situations. The delivery unit is implemented by, for example, the control unit 46A of the robot 414, which analyzes past communication data with customers to provide optimal content in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A generation unit that generates customized campaigns based on social trends and expertise, A contact unit that increases customer contact points through campaigns generated by the aforementioned generation unit, A prediction unit analyzes customer data obtained by the aforementioned contact unit to predict future trends and competitive situations, The system includes a provisioning unit that provides optimal content in real time based on the prediction results obtained by the prediction unit. A system characterized by the following features. (Note 2) The generating unit is We analyze the latest trend data and expertise to generate the most effective campaigns for our customers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned contact portion is We analyze customer behavior and preference data to deliver campaigns at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, We analyze past data and current market conditions to predict future trends and competitive landscapes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Analyze past communication data with customers to understand their preferences and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We estimate customer sentiment and adjust campaign content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When generating a campaign, customize it by referencing the customer's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When creating a campaign, generate content tailored to specific regions and cultures. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is We estimate customer sentiment and adjust campaign delivery timing based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating campaigns, we analyze customers' social media activity to generate relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating campaigns, the system takes customer device information into consideration and generates them in the most optimal format. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned contact portion is Estimate customer emotions and adjust contact methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned contact portion is When contacting a customer, the system selects the optimal contact method by referring to the customer's past response data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned contact portion is During contact, customize the content of the contact based on the customer's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned contact portion is Estimate customer emotions and prioritize contact based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned contact portion is When making contact, the optimal contact method is selected considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned contact portion is When contacting a customer, analyze their social media activity and provide relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, We estimate customer emotions and improve the accuracy of predictions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, When making predictions, we analyze not only historical data but also current market conditions in real time. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, we apply prediction algorithms that are specific to particular industries or regions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, It estimates customer sentiment and adjusts how the prediction results are displayed based on the estimated customer sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, During the forecasting process, the movements of competitors are analyzed in real time and reflected in the forecast. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, we refer to relevant market data to improve the accuracy of the forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate customer emotions and adjust the content we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, the system selects the most suitable content by referring to the customer's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing content, we will deliver content tailored to specific time slots or events. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates customer sentiment and adjusts the timing of content delivery based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing content, we take into account the customer's geographical location to deliver the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, we analyze the customer's social media activity and deliver relevant content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that generates customized campaigns based on social trends and expertise, A contact unit that increases customer contact points through campaigns generated by the aforementioned generation unit, A prediction unit analyzes customer data obtained by the aforementioned contact unit to predict future trends and competitive situations, The system includes a provisioning unit that provides optimal content in real time based on the prediction results obtained by the prediction unit. A system characterized by the following features.

2. The generating unit is We analyze the latest trend data and expertise to generate the most effective campaigns for our customers. The system according to feature 1.

3. The aforementioned contact portion is We analyze customer behavior and preference data to deliver campaigns at the optimal time. The system according to feature 1.

4. The prediction unit, We analyze past data and current market conditions to predict future trends and competitive landscapes. The system according to feature 1.

5. The aforementioned supply unit is, Analyze past communication data with customers to understand their preferences and behavioral patterns. The system according to feature 1.

6. The generating unit is We estimate customer sentiment and adjust campaign content based on that estimated sentiment. The system according to feature 1.

7. The generating unit is When generating a campaign, customize it by referencing the customer's past purchase history. The system according to feature 1.

8. The generating unit is When creating a campaign, generate content tailored to specific regions and cultures. The system according to feature 1.

9. The generating unit is We estimate customer sentiment and adjust campaign delivery timing based on that estimated sentiment. The system according to feature 1.

10. The generating unit is When generating campaigns, we analyze customers' social media activity to generate relevant content. The system according to feature 1.

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