system

Abstract

The system according to this embodiment aims to provide users with appropriate notifications based on demand forecasts and to facilitate payments. [Solution] The system according to the embodiment comprises a forecasting unit, a notification unit, a payment unit, and an analysis unit. The forecasting unit performs demand forecasting. The notification unit notifies the user based on the demand forecasted by the forecasting unit. The payment unit makes payments based on the information notified by the notification unit. The analysis unit collects data such as product inventory status, trend information from social media, climate and disasters, and performs trend analysis.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, an appropriate notification and payment process based on demand prediction has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately notify the user based on demand prediction and smooth the payment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a forecasting unit, a notification unit, a payment unit, and an analysis unit. The forecasting unit performs demand forecasting. The notification unit notifies the user based on the demand forecasted by the forecasting unit. The payment unit makes payments based on the information notified by the notification unit. The analysis unit collects data such as product inventory status, trend information from social media, and data on climate and disasters, and performs trend analysis. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with appropriate notifications based on demand forecasts and facilitate payments. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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 demand forecasting notification system according to an embodiment of the present invention is a notification service that utilizes an AI demand forecasting service to respond to buying demand such as rice shortages. The demand forecasting notification system uses AI to forecast demand, taking into account factors such as abnormal weather, the effects of earthquakes, the aging of farmers, and shortages in the supply of imported rice. Next, it notifies the user based on the predicted buying demand. Furthermore, it collects product inventory status, trend information from social media, and analysis results from climate and disasters, and the AI ​​performs trend analysis. This service allows users to understand buying demand in advance and take appropriate measures. For example, if a rice shortage is predicted, users can purchase rice in advance to prepare for the supply shortage. Similarly, appropriate measures can be taken for other products based on demand forecasts. In this way, by utilizing the AI ​​demand forecasting service, it is possible to respond quickly to buying demand and improve user convenience. As a result, the demand forecasting notification system allows users to respond to buying demand in advance.

[0029] The demand forecasting notification system according to this embodiment comprises a forecasting unit, a notification unit, a payment unit, and an analysis unit. The forecasting unit performs demand forecasting. The forecasting unit performs demand forecasting considering factors such as extreme weather, the effects of earthquakes, the aging of farmers, and shortages of imported rice. The forecasting unit can use AI to perform demand forecasting based on these factors. For example, the forecasting unit collects data on extreme weather, and the AI ​​analyzes that data to perform demand forecasting. The forecasting unit can also perform demand forecasting considering the effects of earthquakes. Furthermore, the forecasting unit can also perform demand forecasting considering factors such as the aging of farmers and shortages of imported rice. The notification unit notifies the user based on the demand forecasted by the forecasting unit. The notification unit can notify the user, for example, through a messaging service. The notification unit can use AI to notify the user at an appropriate time. For example, the notification unit analyzes the user's behavior patterns and notifies at the optimal time. The notification unit can also customize the content of the notification according to the user's preferences. The payment unit makes payments based on the information notified by the notification unit. The payment unit can, for example, make payments through an electronic payment system. The payment unit can use AI to suggest the most suitable payment method to the user. For example, the payment unit can analyze the user's past payment history and suggest the most suitable payment method. The payment unit can also suggest a payment method considering the user's current financial situation. The analysis unit collects data such as product inventory status, trend information from social media, and data such as climate and disasters, and performs trend analysis. For example, the analysis unit monitors product inventory status in real time, and the AI ​​analyzes that data to perform trend analysis. The analysis unit can also collect trend information from social media, and the AI ​​can analyze that data to perform trend analysis. Furthermore, the analysis unit can also collect data such as climate and disasters, and the AI ​​can analyze that data to perform trend analysis. As a result, the demand forecasting notification system according to this embodiment allows users to respond to purchase demand in advance.

[0030] The forecasting unit performs demand forecasting. The forecasting unit makes demand forecasts by considering factors such as extreme weather, the impact of earthquakes, the aging of farmers, and shortages of imported rice. The forecasting unit can use AI to make demand forecasts based on these factors. Specifically, the forecasting unit collects data on extreme weather, and the AI ​​analyzes this data to make demand forecasts. Extreme weather data includes meteorological elements such as temperature, precipitation, wind speed, and humidity, and this data is obtained from the Japan Meteorological Agency and satellite data. The AI ​​analyzes this data using time series analysis and machine learning algorithms to predict the impact of extreme weather on demand. The forecasting unit can also make demand forecasts by considering the impact of earthquakes. Earthquake data includes the epicenter, seismic intensity, and time of occurrence, and this data is obtained from earthquake observation agencies. The AI ​​analyzes earthquake data, evaluates the impact of earthquakes on logistics and production, and predicts fluctuations in demand. Furthermore, the forecasting unit can also make demand forecasts by considering factors such as the aging of farmers and shortages of imported rice. Data related to the aging of farmers includes the age distribution of farmers and the rate of decline in the workforce, and this data is obtained from agricultural statistics. The AI ​​analyzes this data to predict declines in agricultural production and assess fluctuations in demand. Data on shortages in imported rice includes import volume, production status in importing countries, and transportation costs, and this data is obtained from trade statistics and international organizations. The AI ​​analyzes this data to predict the impact of shortages on demand. This allows the forecasting unit to make highly accurate demand forecasts that take various factors into account.

[0031] The notification unit notifies users based on the demand predicted by the forecasting unit. The notification unit can, for example, send notifications to users through messaging services. The notification unit uses AI to deliver notifications to users at the appropriate time. Specifically, the notification unit analyzes user behavior patterns and sends notifications at the optimal time. This analysis uses data such as the user's past purchase history, app usage history, and location information. The AI ​​analyzes this data to identify the time of day and situation in which the user is most likely to receive notifications. Furthermore, the notification unit can customize the content of notifications according to user preferences. For example, if a user is interested in a particular product, it will prioritize notifications related to that product. In addition, the notification unit can customize the format and method of notifications. For example, if a user prefers messaging apps, notifications will be sent through messaging apps; if they prefer email, notifications will be sent via email. This allows the notification unit to provide users with demand forecast information at the optimal time and in the optimal way, improving user convenience.

[0032] The payment unit makes payments based on information notified by the notification unit. The payment unit can make payments, for example, through an electronic payment system. The payment unit can use AI to suggest the most suitable payment method to the user. Specifically, the payment unit analyzes the user's past payment history and suggests the most suitable payment method. Past payment history includes payment methods used, payment amounts, and payment frequency, and this data is obtained from the electronic payment system. The AI ​​analyzes this data to identify the most convenient payment method for the user. The payment unit can also suggest payment methods considering the user's current financial situation. Data related to financial situation includes income, expenses, savings, and credit score, and this data is obtained from financial institutions and credit card companies. The AI ​​analyzes this data and suggests the most suitable payment method according to the user's financial situation. For example, if the user is temporarily in a financially difficult situation, installment payments can be suggested. In this way, the payment unit can provide users with flexible and appropriate payment methods, reducing the user's burden.

[0033] The analysis department collects data on product inventory status, trend information from social media, and data on climate and disasters, and performs trend analysis. For example, the analysis department monitors product inventory status in real time, and AI analyzes this data to perform trend analysis. Data on product inventory status includes inventory quantity, inbound and outbound history, and sales speed, and this data is obtained from the inventory management system. The AI ​​analyzes this data to predict inventory surpluses and shortages and fluctuations in demand. The analysis department can also collect trend information from social media, and AI can analyze this data to perform trend analysis. Trend information from social media includes the number of posts, likes, and shares related to specific products or services, and this data is obtained from social media platforms. The AI ​​analyzes this data to understand consumer interest and trend changes. Furthermore, the analysis department can also collect data on climate and disasters, and AI can analyze this data to perform trend analysis. Data on climate includes temperature, precipitation, and wind speed, and data on disasters includes earthquakes, typhoons, and floods, and this data is obtained from the Japan Meteorological Agency and disaster prevention organizations. The AI ​​analyzes this data and predicts the impact of climate and disasters on demand. This allows the analytics department to perform highly accurate trend analysis based on diverse data, improving the accuracy and reliability of the demand forecasting notification system.

[0034] The forecasting unit can make demand forecasts by taking into account factors such as extreme weather, the impact of earthquakes, the aging of farmers, and shortages in the supply of imported rice. For example, the forecasting unit collects data on extreme weather, and AI analyzes that data to make demand forecasts. The forecasting unit needs to clearly define the specific definition and criteria of extreme weather. For example, this includes abnormal temperatures and abnormal rainfall. The forecasting unit can also make demand forecasts by taking into account the impact of earthquakes. For example, this includes the specific scope and criteria of the impact of earthquakes. For example, this includes the seismic intensity and the extent of damage. The forecasting unit can also make demand forecasts by taking into account factors such as the aging of farmers and shortages in the supply of imported rice. For example, this includes the specific definition and criteria of the aging of farmers. For example, this includes the average age and the decrease in the workforce. For example, this includes the specific criteria and impact of shortages in the supply of imported rice. For example, this includes a decrease in supply and an increase in price. By taking into account a variety of factors, the accuracy of demand forecasts can be improved.

[0035] The notification unit can send notifications to users through messaging services. For example, the notification unit uses messaging services such as SMS, email, and app notifications to send notifications to users. The notification unit needs to clearly define the specific type of messaging service and how it will be implemented. For example, SMS is a means of sending short text messages, while email is a means of sending longer messages. App notifications are a means of sending notifications to users through smartphone applications. This allows for rapid notification to users.

[0036] The payment unit can make payments through electronic payment systems. For example, the payment unit can use electronic payment systems such as credit card payments, e-money, and mobile payments. The payment unit needs to clearly define the specific type and method of implementation of the electronic payment system. For example, credit card payments are a method where the user enters their credit card information to make a payment, and e-money is a method where the user makes a payment using e-money that they have pre-charged. Mobile payments are a method where the user makes a payment using a smartphone. This allows users to make payments easily.

[0037] The analysis department can collect data on product inventory status, trend information from social media, and data on climate and disasters, and perform trend analysis. For example, the analysis department can monitor product inventory status in real time, and AI can analyze that data to perform trend analysis. It is necessary to clarify the specific types of data and collection methods for product inventory status. For example, this may include inventory counts and inbound / outbound history. The analysis department can also collect trend information from social media, and AI can analyze that data to perform trend analysis. It is necessary to clarify the specific methods for collecting and analyzing trend information from social media. For example, this may include hashtag analysis and post content analysis. Furthermore, the analysis department can collect data on climate and disasters, and AI can analyze that data to perform trend analysis. It is necessary to clarify the specific types of data and collection methods for climate and disaster data. For example, this may include weather data and disaster occurrence information. By collecting diverse data and performing trend analysis, the accuracy of demand forecasting can be improved.

[0038] The forecasting unit can optimize its forecasting algorithm by referencing historical demand data. For example, the forecasting unit can analyze demand data from the past five years, allowing the AI ​​to learn seasonal demand patterns. The forecasting unit can also detect outliers from historical demand data, and the AI ​​can adjust the forecasting algorithm accordingly. Based on historical demand data, the forecasting unit can also predict demand fluctuations during specific events or disasters. It is necessary to clarify the specific types and collection methods of historical demand data. Examples include sales history and consumer trend data. This will improve the accuracy of the forecasting algorithm by utilizing historical data.

[0039] The forecasting unit can make predictions while considering the characteristics of each region. For example, the forecasting unit uses AI to analyze regional population density and consumption trends and reflect them in demand forecasts. The forecasting unit can also use AI to make demand forecasts while considering regional climate conditions. The forecasting unit can also use AI to analyze regional economic conditions and reflect them in demand forecasts. It is necessary to clarify the specific definitions and criteria of regional characteristics. For example, these may include population density, consumption trends, and climate conditions. By considering regional characteristics, it becomes possible to make more accurate demand forecasts.

[0040] The forecasting unit can perform individual demand forecasts by considering the user's purchase history. For example, the forecasting unit analyzes the user's past purchase history to perform individual demand forecasts. The forecasting unit can also have AI learn the user's purchasing patterns and reflect them in the demand forecast. The forecasting unit can also predict the demand for specific products from the user's purchase history. It is necessary to clarify the specific methods for collecting and analyzing the user's purchase history. For example, this may include purchase date and time, purchased items, and purchase frequency. This will enable individual demand forecasts by considering the user's purchase history.

[0041] The forecasting unit can improve the accuracy of demand forecasts by analyzing users' social media activity. For example, the forecasting unit can analyze users' social media posts and reflect them in demand forecasts. The forecasting unit can also improve the accuracy of demand forecasts by analyzing posts from users' followers and friends. The forecasting unit can also have AI analyze trends on social media and reflect them in demand forecasts. It is necessary to clarify the specific methods for collecting and analyzing social media activity. For example, this may include post content, the number of likes, and the number of followers. By analyzing social media activity in this way, the accuracy of demand forecasts will improve.

[0042] The notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit may prioritize notification methods that the user has preferred to receive in the past. The notification unit can also select the optimal notification timing based on the user's past notification history. The notification unit can also analyze the user's past notification history and select the optimal notification content. It is necessary to clarify the specific methods for collecting and analyzing past notification history. For example, this includes notification content, notification timing, and notification responses. This will allow the optimal notification method to be selected by referring to past notification history.

[0043] The notification system can adjust the timing of notifications based on the user's current situation. For example, if the user is busy, the notification system may postpone the notification. If the user is relaxed, the notification system may also send an immediate notification. If the user is on the move, the notification system may refrain from sending a notification. It is necessary to clarify the specific methods for collecting and analyzing the user's current situation. This may include location information, activity status, and device status. This will enable more appropriate notifications by adjusting the timing of notifications according to the user's situation.

[0044] The notification system can prioritize highly relevant notifications by considering the user's geographical location. For example, if the user is in a specific region, the notification system will prioritize notifications related to that region. If the user is on the move, the notification system can also prioritize notifications related to their destination. If the user is at home, the notification system can also prioritize notifications related to their home. It is necessary to clarify the specific methods for collecting and analyzing geographical location information. For example, this could include GPS data and location services. This will enable more relevant notifications by considering geographical location.

[0045] The notification unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. If the user is using a tablet, the notification unit may also prioritize email notifications. If the user is using a smartwatch, the notification unit may also prioritize vibration notifications. It is necessary to clarify the specific methods for collecting and analyzing device information. This includes, for example, the type of device, the operating system, and usage patterns. By considering this device information, the optimal notification method can be selected.

[0046] The payment system can select the optimal payment method by referring to the user's past payment history. For example, the payment system might prioritize payment methods the user has used in the past. The payment system can also select the optimal payment timing based on the user's past payment history. The payment system can also analyze the user's past payment history and select the optimal payment content. It is necessary to clarify the specific methods for collecting and analyzing past payment history. For example, this includes payment date and time, payment amount, and payment method. This will allow the optimal payment method to be selected by referring to past payment history.

[0047] The payment system can customize payment methods based on the user's current financial situation. For example, it can consider the user's current income and suggest the most suitable payment method. It can also analyze the user's current spending habits and customize payment methods. It can also consider the user's current savings and suggest payment methods. It is necessary to clarify the specific methods for collecting and analyzing the user's current financial situation. This includes, for example, income, spending, and savings. By customizing payment methods according to the financial situation, more appropriate payments can be made.

[0048] The payment system can select the optimal payment method by considering the user's geographical location. For example, if the user is in a specific region, the payment system will prioritize payment methods related to that region. If the user is on the move, the payment system can also prioritize payment methods related to the user's destination. If the user is at home, the payment system can also prioritize payment methods related to the user's home. It is necessary to clarify the specific methods for collecting and analyzing geographical location information. For example, this could include GPS data and location services. This will allow the system to select the optimal payment method by considering geographical location information.

[0049] The payment function can analyze a user's social media activity and suggest payment methods. For example, it can analyze a user's social media posts and suggest the most suitable payment method. It can also analyze posts from a user's followers and friends and suggest payment methods. The payment function can also use AI to analyze social media trends and suggest payment methods. It is necessary to clarify the specific methods for collecting and analyzing social media activity. For example, this includes post content, the number of likes, and the number of followers. By analyzing social media activity, the most suitable payment method can be suggested.

[0050] The analysis department can optimize its analysis algorithms by referring to historical data. For example, the analysis department can analyze data from the past five years, allowing the AI ​​to learn seasonal trends. The analysis department can also detect outliers from historical data, and the AI ​​can adjust the analysis algorithm accordingly. Based on historical data, the analysis department can also have the AI ​​analyze trends during specific events or disasters. It is necessary to clarify the specific types of historical data and how they are collected. Examples include sales history and consumer trend data. This will improve the accuracy of the analysis algorithms by utilizing historical data.

[0051] The analysis department can perform analyses while considering the characteristics of each region. For example, the analysis department can use AI to analyze regional population density and consumption trends and reflect them in trend analysis. The analysis department can also use AI to perform trend analysis while considering regional climate conditions. The analysis department can also analyze regional economic conditions and reflect them in trend analysis. It is necessary to clarify the specific definitions and criteria of regional characteristics. For example, these may include population density, consumption trends, and climate conditions. By considering regional characteristics, it will be possible to perform more accurate trend analysis.

[0052] The analytics department can perform individual trend analyses by considering users' purchase history. For example, the analytics department can analyze a user's past purchase history and perform individual trend analyses. The analytics department can also have AI learn users' purchasing patterns and incorporate them into trend analyses. The analytics department can also analyze trends for specific products from users' purchase history. It is necessary to clarify the specific methods for collecting and analyzing users' purchase history. For example, this should include purchase date and time, purchased items, and purchase frequency. This will enable individual trend analyses by considering users' purchase history.

[0053] The analytics department can improve the accuracy of trend analysis by analyzing users' social media activity. For example, the analytics department can analyze users' social media posts and reflect the findings in trend analysis. The analytics department can also improve the accuracy of trend analysis by analyzing posts from users' followers and friends. The analytics department can also have AI analyze social media trends and reflect the findings in trend analysis. It is necessary to clarify the specific methods for collecting and analyzing social media activity. For example, this includes post content, the number of likes, and the number of followers. By doing so, the accuracy of trend analysis will improve by analyzing 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] The forecasting unit can perform demand forecasting while taking user health data into consideration. For example, the forecasting unit analyzes the user's dietary history and exercise level and makes demand forecasts based on their health status. Based on the user's health data, the forecasting unit can also predict the demand for products containing specific nutrients if the user is deficient in those nutrients. Based on the user's health data, the forecasting unit can also analyze seasonal health trends and reflect them in the demand forecast. This makes it possible to perform more personalized demand forecasting by taking the user's health status into consideration.

[0056] The payment department can suggest discounts and promotions for specific products based on the user's purchase history. For example, it can offer discounts on products that the user has frequently purchased in the past. The payment department can also suggest promotions related to specific seasons or events based on the user's purchase history. The payment department can also analyze the user's purchasing patterns and suggest promotions at the optimal time. This allows for more effective promotions by leveraging the user's purchase history.

[0057] The forecasting unit can perform demand forecasts by taking into account the user's lifestyle data. For example, the forecasting unit analyzes the user's work schedule and hobbies and performs demand forecasts based on their lifestyle. The forecasting unit can also predict demand for specific times of day or days of the week based on the user's lifestyle data. Based on the user's lifestyle data, the forecasting unit can also predict demand for products related to specific events or activities. This allows for more accurate demand forecasts by taking the user's lifestyle into consideration.

[0058] The payment department can propose subscription services for specific products based on the user's purchase history. For example, the payment department can offer subscription services for products that the user purchases regularly. The payment department can also propose subscription services related to specific seasons or events based on the user's purchase history. The payment department can also analyze the user's purchasing patterns and propose subscription services at the optimal time. This makes it possible to provide more effective subscription services by utilizing the user's purchase history.

[0059] The forecasting unit can predict demand for specific products based on the user's purchase history. For example, it can predict demand for products that a user has frequently purchased in the past. The forecasting unit can also predict demand for products related to specific seasons or events based on the user's purchase history. The forecasting unit can also analyze the user's purchasing patterns and make demand forecasts at the optimal timing. This makes it possible to make more accurate demand forecasts by utilizing the user's purchase history.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The forecasting unit performs demand forecasting. The forecasting unit performs demand forecasting considering factors such as extreme weather, the impact of earthquakes, the aging of farmers, and shortages in the supply of imported rice. The forecasting unit can use AI to perform demand forecasting based on these factors. For example, it can collect data on extreme weather and use AI to analyze that data and perform demand forecasting. It can also perform demand forecasting considering the impact of earthquakes. Furthermore, it can also perform demand forecasting considering factors such as the aging of farmers and shortages in the supply of imported rice. Step 2: The notification unit notifies the user based on the demand predicted by the forecasting unit. The notification unit can send notifications to the user through a messaging service. The notification unit can use AI to send notifications to the user at the appropriate time. For example, it can analyze the user's behavior patterns and send notifications at the optimal time. It can also customize the content of notifications according to the user's preferences. Step 3: The payment unit makes the payment based on the information notified by the notification unit. The payment unit can make the payment through an electronic payment system. The payment unit can use AI to suggest the best payment method to the user. For example, it can analyze the user's past payment history and suggest the best payment method. It can also suggest a payment method considering the user's current financial situation. Step 4: The analysis department collects data on product inventory status, trend information from social media, and data on climate and disasters, and performs trend analysis. The analysis department monitors product inventory status in real time, and AI analyzes that data to perform trend analysis. Furthermore, it can also collect trend information from social media, data on climate and disasters, and AI analyzes that data to perform trend analysis.

[0062] (Example of form 2) The demand forecasting notification system according to an embodiment of the present invention is a notification service that utilizes an AI demand forecasting service to respond to buying demand such as rice shortages. The demand forecasting notification system uses AI to forecast demand, taking into account factors such as abnormal weather, the effects of earthquakes, the aging of farmers, and shortages in the supply of imported rice. Next, it notifies the user based on the predicted buying demand. Furthermore, it collects product inventory status, trend information from social media, and analysis results from climate and disasters, and the AI ​​performs trend analysis. This service allows users to understand buying demand in advance and take appropriate measures. For example, if a rice shortage is predicted, users can purchase rice in advance to prepare for the supply shortage. Similarly, appropriate measures can be taken for other products based on demand forecasts. In this way, by utilizing the AI ​​demand forecasting service, it is possible to respond quickly to buying demand and improve user convenience. As a result, the demand forecasting notification system allows users to respond to buying demand in advance.

[0063] The demand forecasting notification system according to this embodiment comprises a forecasting unit, a notification unit, a payment unit, and an analysis unit. The forecasting unit performs demand forecasting. The forecasting unit performs demand forecasting considering factors such as extreme weather, the effects of earthquakes, the aging of farmers, and shortages of imported rice. The forecasting unit can use AI to perform demand forecasting based on these factors. For example, the forecasting unit collects data on extreme weather, and the AI ​​analyzes that data to perform demand forecasting. The forecasting unit can also perform demand forecasting considering the effects of earthquakes. Furthermore, the forecasting unit can also perform demand forecasting considering factors such as the aging of farmers and shortages of imported rice. The notification unit notifies the user based on the demand forecasted by the forecasting unit. The notification unit can notify the user, for example, through a messaging service. The notification unit can use AI to notify the user at an appropriate time. For example, the notification unit analyzes the user's behavior patterns and notifies at the optimal time. The notification unit can also customize the content of the notification according to the user's preferences. The payment unit makes payments based on the information notified by the notification unit. The payment unit can, for example, make payments through an electronic payment system. The payment unit can use AI to suggest the most suitable payment method to the user. For example, the payment unit can analyze the user's past payment history and suggest the most suitable payment method. The payment unit can also suggest a payment method considering the user's current financial situation. The analysis unit collects data such as product inventory status, trend information from social media, and data such as climate and disasters, and performs trend analysis. For example, the analysis unit monitors product inventory status in real time, and the AI ​​analyzes that data to perform trend analysis. The analysis unit can also collect trend information from social media, and the AI ​​can analyze that data to perform trend analysis. Furthermore, the analysis unit can also collect data such as climate and disasters, and the AI ​​can analyze that data to perform trend analysis. As a result, the demand forecasting notification system according to this embodiment allows users to respond to purchase demand in advance.

[0064] The forecasting unit performs demand forecasting. The forecasting unit makes demand forecasts by considering factors such as extreme weather, the impact of earthquakes, the aging of farmers, and shortages of imported rice. The forecasting unit can use AI to make demand forecasts based on these factors. Specifically, the forecasting unit collects data on extreme weather, and the AI ​​analyzes this data to make demand forecasts. Extreme weather data includes meteorological elements such as temperature, precipitation, wind speed, and humidity, and this data is obtained from the Japan Meteorological Agency and satellite data. The AI ​​analyzes this data using time series analysis and machine learning algorithms to predict the impact of extreme weather on demand. The forecasting unit can also make demand forecasts by considering the impact of earthquakes. Earthquake data includes the epicenter, seismic intensity, and time of occurrence, and this data is obtained from earthquake observation agencies. The AI ​​analyzes earthquake data, evaluates the impact of earthquakes on logistics and production, and predicts fluctuations in demand. Furthermore, the forecasting unit can also make demand forecasts by considering factors such as the aging of farmers and shortages of imported rice. Data related to the aging of farmers includes the age distribution of farmers and the rate of decline in the workforce, and this data is obtained from agricultural statistics. The AI ​​analyzes this data to predict declines in agricultural production and assess fluctuations in demand. Data on shortages in imported rice includes import volume, production status in importing countries, and transportation costs, and this data is obtained from trade statistics and international organizations. The AI ​​analyzes this data to predict the impact of shortages on demand. This allows the forecasting unit to make highly accurate demand forecasts that take various factors into account.

[0065] The notification unit notifies users based on the demand predicted by the forecasting unit. The notification unit can, for example, send notifications to users through messaging services. The notification unit uses AI to deliver notifications to users at the appropriate time. Specifically, the notification unit analyzes user behavior patterns and sends notifications at the optimal time. This analysis uses data such as the user's past purchase history, app usage history, and location information. The AI ​​analyzes this data to identify the time of day and situation in which the user is most likely to receive notifications. Furthermore, the notification unit can customize the content of notifications according to user preferences. For example, if a user is interested in a particular product, it will prioritize notifications related to that product. In addition, the notification unit can customize the format and method of notifications. For example, if a user prefers messaging apps, notifications will be sent through messaging apps; if they prefer email, notifications will be sent via email. This allows the notification unit to provide users with demand forecast information at the optimal time and in the optimal way, improving user convenience.

[0066] The payment unit makes payments based on information notified by the notification unit. The payment unit can make payments, for example, through an electronic payment system. The payment unit can use AI to suggest the most suitable payment method to the user. Specifically, the payment unit analyzes the user's past payment history and suggests the most suitable payment method. Past payment history includes payment methods used, payment amounts, and payment frequency, and this data is obtained from the electronic payment system. The AI ​​analyzes this data to identify the most convenient payment method for the user. The payment unit can also suggest payment methods considering the user's current financial situation. Data related to financial situation includes income, expenses, savings, and credit score, and this data is obtained from financial institutions and credit card companies. The AI ​​analyzes this data and suggests the most suitable payment method according to the user's financial situation. For example, if the user is temporarily in a financially difficult situation, installment payments can be suggested. In this way, the payment unit can provide users with flexible and appropriate payment methods, reducing the user's burden.

[0067] The analysis department collects data on product inventory status, trend information from social media, and data on climate and disasters, and performs trend analysis. For example, the analysis department monitors product inventory status in real time, and AI analyzes this data to perform trend analysis. Data on product inventory status includes inventory quantity, inbound and outbound history, and sales speed, and this data is obtained from the inventory management system. The AI ​​analyzes this data to predict inventory surpluses and shortages and fluctuations in demand. The analysis department can also collect trend information from social media, and AI can analyze this data to perform trend analysis. Trend information from social media includes the number of posts, likes, and shares related to specific products or services, and this data is obtained from social media platforms. The AI ​​analyzes this data to understand consumer interest and trend changes. Furthermore, the analysis department can also collect data on climate and disasters, and AI can analyze this data to perform trend analysis. Data on climate includes temperature, precipitation, and wind speed, and data on disasters includes earthquakes, typhoons, and floods, and this data is obtained from the Japan Meteorological Agency and disaster prevention organizations. The AI ​​analyzes this data and predicts the impact of climate and disasters on demand. This allows the analytics department to perform highly accurate trend analysis based on diverse data, improving the accuracy and reliability of the demand forecasting notification system.

[0068] The forecasting unit can make demand forecasts by taking into account factors such as extreme weather, the impact of earthquakes, the aging of farmers, and shortages in the supply of imported rice. For example, the forecasting unit collects data on extreme weather, and AI analyzes that data to make demand forecasts. The forecasting unit needs to clearly define the specific definition and criteria of extreme weather. For example, this includes abnormal temperatures and abnormal rainfall. The forecasting unit can also make demand forecasts by taking into account the impact of earthquakes. For example, this includes the specific scope and criteria of the impact of earthquakes. For example, this includes the seismic intensity and the extent of damage. The forecasting unit can also make demand forecasts by taking into account factors such as the aging of farmers and shortages in the supply of imported rice. For example, this includes the specific definition and criteria of the aging of farmers. For example, this includes the average age and the decrease in the workforce. For example, this includes the specific criteria and impact of shortages in the supply of imported rice. For example, this includes a decrease in supply and an increase in price. By taking into account a variety of factors, the accuracy of demand forecasts can be improved.

[0069] The notification unit can send notifications to users through messaging services. For example, the notification unit uses messaging services such as SMS, email, and app notifications to send notifications to users. The notification unit needs to clearly define the specific type of messaging service and how it will be implemented. For example, SMS is a means of sending short text messages, while email is a means of sending longer messages. App notifications are a means of sending notifications to users through smartphone applications. This allows for rapid notification to users.

[0070] The payment unit can make payments through electronic payment systems. For example, the payment unit can use electronic payment systems such as credit card payments, e-money, and mobile payments. The payment unit needs to clearly define the specific type and method of implementation of the electronic payment system. For example, credit card payments are a method where the user enters their credit card information to make a payment, and e-money is a method where the user makes a payment using e-money that they have pre-charged. Mobile payments are a method where the user makes a payment using a smartphone. This allows users to make payments easily.

[0071] The analysis department can collect data on product inventory status, trend information from social media, and data on climate and disasters, and perform trend analysis. For example, the analysis department can monitor product inventory status in real time, and AI can analyze that data to perform trend analysis. It is necessary to clarify the specific types of data and collection methods for product inventory status. For example, this may include inventory counts and inbound / outbound history. The analysis department can also collect trend information from social media, and AI can analyze that data to perform trend analysis. It is necessary to clarify the specific methods for collecting and analyzing trend information from social media. For example, this may include hashtag analysis and post content analysis. Furthermore, the analysis department can collect data on climate and disasters, and AI can analyze that data to perform trend analysis. It is necessary to clarify the specific types of data and collection methods for climate and disaster data. For example, this may include weather data and disaster occurrence information. By collecting diverse data and performing trend analysis, the accuracy of demand forecasting can be improved.

[0072] The forecasting unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on the estimated emotions. For example, if the user is feeling anxious, the AI ​​can improve the accuracy of the demand forecast and provide more detailed information. If the user is relaxed, the AI ​​can maintain the accuracy of the demand forecast at a normal level and provide concise information. If the user is excited, the AI ​​can adjust the accuracy of the demand forecast and provide information in a visually easy-to-understand format. This allows for more appropriate forecasts by adjusting the accuracy of the demand forecast according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The forecasting unit can optimize its forecasting algorithm by referencing historical demand data. For example, the forecasting unit can analyze demand data from the past five years, allowing the AI ​​to learn seasonal demand patterns. The forecasting unit can also detect outliers from historical demand data, and the AI ​​can adjust the forecasting algorithm accordingly. Based on historical demand data, the forecasting unit can also predict demand fluctuations during specific events or disasters. It is necessary to clarify the specific types and collection methods of historical demand data. Examples include sales history and consumer trend data. This will improve the accuracy of the forecasting algorithm by utilizing historical data.

[0074] The forecasting unit can make predictions while considering the characteristics of each region. For example, the forecasting unit uses AI to analyze regional population density and consumption trends and reflect them in demand forecasts. The forecasting unit can also use AI to make demand forecasts while considering regional climate conditions. The forecasting unit can also use AI to analyze regional economic conditions and reflect them in demand forecasts. It is necessary to clarify the specific definitions and criteria of regional characteristics. For example, these may include population density, consumption trends, and climate conditions. By considering regional characteristics, it becomes possible to make more accurate demand forecasts.

[0075] The forecasting unit can estimate the user's emotions and adjust the order in which it displays the demand forecast results based on the estimated user emotions. For example, if the user is feeling anxious, the forecasting unit will display the most important information first. If the user is relaxed, the forecasting unit can also display the information in stages. If the user is excited, the forecasting unit can also display the information in a visually easy-to-understand format. This allows for more appropriate information to be provided by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The forecasting unit can perform individual demand forecasts by considering the user's purchase history. For example, the forecasting unit analyzes the user's past purchase history to perform individual demand forecasts. The forecasting unit can also have AI learn the user's purchasing patterns and reflect them in the demand forecast. The forecasting unit can also predict the demand for specific products from the user's purchase history. It is necessary to clarify the specific methods for collecting and analyzing the user's purchase history. For example, this may include purchase date and time, purchased items, and purchase frequency. This will enable individual demand forecasts by considering the user's purchase history.

[0077] The forecasting unit can improve the accuracy of demand forecasts by analyzing users' social media activity. For example, the forecasting unit can analyze users' social media posts and reflect them in demand forecasts. The forecasting unit can also improve the accuracy of demand forecasts by analyzing posts from users' followers and friends. The forecasting unit can also have AI analyze trends on social media and reflect them in demand forecasts. It is necessary to clarify the specific methods for collecting and analyzing social media activity. For example, this may include post content, the number of likes, and the number of followers. By analyzing social media activity in this way, the accuracy of demand forecasts will improve.

[0078] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. For example, if the user is feeling anxious, the notification unit will use calm language. If the user is relaxed, the notification unit can use casual language. If the user is excited, the notification unit can use visually stimulating language. By adjusting the way notifications are presented according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit may prioritize notification methods that the user has preferred to receive in the past. The notification unit can also select the optimal notification timing based on the user's past notification history. The notification unit can also analyze the user's past notification history and select the optimal notification content. It is necessary to clarify the specific methods for collecting and analyzing past notification history. For example, this includes notification content, notification timing, and notification responses. This will allow the optimal notification method to be selected by referring to past notification history.

[0080] The notification system can adjust the timing of notifications based on the user's current situation. For example, if the user is busy, the notification system may postpone the notification. If the user is relaxed, the notification system may also send an immediate notification. If the user is on the move, the notification system may refrain from sending a notification. It is necessary to clarify the specific methods for collecting and analyzing the user's current situation. This may include location information, activity status, and device status. This will enable more appropriate notifications by adjusting the timing of notifications according to the user's situation.

[0081] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is feeling anxious, the notification unit will prioritize important notifications. If the user is relaxed, the notification unit may also prioritize normal notifications. If the user is excited, the notification unit may also prioritize visually clear notifications. This allows important information to be delivered preferentially by determining notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The notification system can prioritize highly relevant notifications by considering the user's geographical location. For example, if the user is in a specific region, the notification system will prioritize notifications related to that region. If the user is on the move, the notification system can also prioritize notifications related to their destination. If the user is at home, the notification system can also prioritize notifications related to their home. It is necessary to clarify the specific methods for collecting and analyzing geographical location information. For example, this could include GPS data and location services. This will enable more relevant notifications by considering geographical location.

[0083] The notification unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. If the user is using a tablet, the notification unit may also prioritize email notifications. If the user is using a smartwatch, the notification unit may also prioritize vibration notifications. It is necessary to clarify the specific methods for collecting and analyzing device information. This includes, for example, the type of device, the operating system, and usage patterns. By considering this device information, the optimal notification method can be selected.

[0084] The payment unit can estimate the user's emotions and adjust the payment method based on those emotions. For example, if the user is feeling anxious, the payment unit can suggest a simple and secure payment method. If the user is relaxed, the payment unit can suggest a standard payment method. If the user is excited, the payment unit can suggest a visually easy-to-understand payment method. This allows for more appropriate payments by adjusting the payment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The payment system can select the optimal payment method by referring to the user's past payment history. For example, the payment system might prioritize payment methods the user has used in the past. The payment system can also select the optimal payment timing based on the user's past payment history. The payment system can also analyze the user's past payment history and select the optimal payment content. It is necessary to clarify the specific methods for collecting and analyzing past payment history. For example, this includes payment date and time, payment amount, and payment method. This will allow the optimal payment method to be selected by referring to past payment history.

[0086] The payment system can customize payment methods based on the user's current financial situation. For example, it can consider the user's current income and suggest the most suitable payment method. It can also analyze the user's current spending habits and customize payment methods. It can also consider the user's current savings and suggest payment methods. It is necessary to clarify the specific methods for collecting and analyzing the user's current financial situation. This includes, for example, income, spending, and savings. By customizing payment methods according to the financial situation, more appropriate payments can be made.

[0087] The payment unit can estimate the user's emotions and determine payment priorities based on those emotions. For example, if the user is feeling anxious, the payment unit will prioritize important payments. If the user is relaxed, the payment unit may also prioritize regular payments. If the user is excited, the payment unit may also prioritize visually easy-to-understand payments. This allows important payments to be prioritized by determining payment priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The payment system can select the optimal payment method by considering the user's geographical location. For example, if the user is in a specific region, the payment system will prioritize payment methods related to that region. If the user is on the move, the payment system can also prioritize payment methods related to the user's destination. If the user is at home, the payment system can also prioritize payment methods related to the user's home. It is necessary to clarify the specific methods for collecting and analyzing geographical location information. For example, this could include GPS data and location services. This will allow the system to select the optimal payment method by considering geographical location information.

[0089] The payment function can analyze a user's social media activity and suggest payment methods. For example, it can analyze a user's social media posts and suggest the most suitable payment method. It can also analyze posts from a user's followers and friends and suggest payment methods. The payment function can also use AI to analyze social media trends and suggest payment methods. It is necessary to clarify the specific methods for collecting and analyzing social media activity. For example, this includes post content, the number of likes, and the number of followers. By analyzing social media activity, the most suitable payment method can be suggested.

[0090] The analysis unit can estimate the user's emotions and adjust the trend analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can perform a detailed trend analysis. If the user is relaxed, the analysis unit can also perform a normal trend analysis. If the user is excited, the analysis unit can also perform a trend analysis in a visually easy-to-understand format. This allows for more appropriate analysis by adjusting the trend analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The analysis department can optimize its analysis algorithms by referring to historical data. For example, the analysis department can analyze data from the past five years, allowing the AI ​​to learn seasonal trends. The analysis department can also detect outliers from historical data, and the AI ​​can adjust the analysis algorithm accordingly. Based on historical data, the analysis department can also have the AI ​​analyze trends during specific events or disasters. It is necessary to clarify the specific types of historical data and how they are collected. Examples include sales history and consumer trend data. This will improve the accuracy of the analysis algorithms by utilizing historical data.

[0092] The analysis department can perform analyses while considering the characteristics of each region. For example, the analysis department can use AI to analyze regional population density and consumption trends and reflect them in trend analysis. The analysis department can also use AI to perform trend analysis while considering regional climate conditions. The analysis department can also analyze regional economic conditions and reflect them in trend analysis. It is necessary to clarify the specific definitions and criteria of regional characteristics. For example, these may include population density, consumption trends, and climate conditions. By considering regional characteristics, it will be possible to perform more accurate trend analysis.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can highlight important information. If the user is relaxed, the analysis unit can also display information in stages. If the user is excited, the analysis unit can also display information in a visually easy-to-understand format. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The analytics department can perform individual trend analyses by considering users' purchase history. For example, the analytics department can analyze a user's past purchase history and perform individual trend analyses. The analytics department can also have AI learn users' purchasing patterns and incorporate them into trend analyses. The analytics department can also analyze trends for specific products from users' purchase history. It is necessary to clarify the specific methods for collecting and analyzing users' purchase history. For example, this should include purchase date and time, purchased items, and purchase frequency. This will enable individual trend analyses by considering users' purchase history.

[0095] The analytics department can improve the accuracy of trend analysis by analyzing users' social media activity. For example, the analytics department can analyze users' social media posts and reflect the findings in trend analysis. The analytics department can also improve the accuracy of trend analysis by analyzing posts from users' followers and friends. The analytics department can also have AI analyze social media trends and reflect the findings in trend analysis. It is necessary to clarify the specific methods for collecting and analyzing social media activity. For example, this includes post content, the number of likes, and the number of followers. By doing so, the accuracy of trend analysis will improve by analyzing social media activity.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The forecasting unit can perform demand forecasting while taking user health data into consideration. For example, the forecasting unit analyzes the user's dietary history and exercise level and makes demand forecasts based on their health status. Based on the user's health data, the forecasting unit can also predict the demand for products containing specific nutrients if the user is deficient in those nutrients. Based on the user's health data, the forecasting unit can also analyze seasonal health trends and reflect them in the demand forecast. This makes it possible to perform more personalized demand forecasting by taking the user's health status into consideration.

[0098] The notification unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications. If the user is relaxed, the notification unit can maintain a normal level of notification frequency. If the user is excited, the notification unit can prioritize important notifications. This allows for more appropriate information to be provided by adjusting the frequency of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The payment department can suggest discounts and promotions for specific products based on the user's purchase history. For example, it can offer discounts on products that the user has frequently purchased in the past. The payment department can also suggest promotions related to specific seasons or events based on the user's purchase history. The payment department can also analyze the user's purchasing patterns and suggest promotions at the optimal time. This allows for more effective promotions by leveraging the user's purchase history.

[0100] The analytics unit can estimate the user's emotions and adjust the data visualization method based on the estimated emotions. For example, if the user is feeling anxious, the analytics unit might use a simple and intuitive graph. If the user is relaxed, the analytics unit might use a complex graph with detailed data. If the user is excited, the analytics unit might use a visually appealing infographic. This allows for more easily understandable information to be provided by adjusting the data visualization method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The forecasting unit can perform demand forecasts by taking into account the user's lifestyle data. For example, the forecasting unit analyzes the user's work schedule and hobbies and performs demand forecasts based on their lifestyle. The forecasting unit can also predict demand for specific times of day or days of the week based on the user's lifestyle data. Based on the user's lifestyle data, the forecasting unit can also predict demand for products related to specific events or activities. This allows for more accurate demand forecasts by taking the user's lifestyle into consideration.

[0102] The notification unit can estimate the user's emotions and personalize the content of notifications based on those emotions. For example, if the user is feeling anxious, the notification unit will send a reassuring notification. If the user is relaxed, the notification unit can send a casual notification. If the user is excited, the notification unit can send an energetic notification. This allows for more effective information delivery by personalizing notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The payment department can propose subscription services for specific products based on the user's purchase history. For example, the payment department can offer subscription services for products that the user purchases regularly. The payment department can also propose subscription services related to specific seasons or events based on the user's purchase history. The payment department can also analyze the user's purchasing patterns and propose subscription services at the optimal time. This makes it possible to provide more effective subscription services by utilizing the user's purchase history.

[0104] The analysis unit can estimate the user's emotions and filter the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can highlight and display only important information. If the user is relaxed, the analysis unit can also display analysis results that include detailed information. If the user is excited, the analysis unit can also display analysis results in a visually appealing format. This allows for more appropriate information to be provided by filtering the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The forecasting unit can predict demand for specific products based on the user's purchase history. For example, it can predict demand for products that a user has frequently purchased in the past. The forecasting unit can also predict demand for products related to specific seasons or events based on the user's purchase history. The forecasting unit can also analyze the user's purchasing patterns and make demand forecasts at the optimal timing. This makes it possible to make more accurate demand forecasts by utilizing the user's purchase history.

[0106] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is feeling anxious, the notification unit will prioritize important notifications. If the user is relaxed, the notification unit may also prioritize normal notifications. If the user is excited, the notification unit may also prioritize visually clear notifications. This allows important information to be delivered preferentially by determining notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The forecasting unit performs demand forecasting. The forecasting unit performs demand forecasting considering factors such as extreme weather, the impact of earthquakes, the aging of farmers, and shortages in the supply of imported rice. The forecasting unit can use AI to perform demand forecasting based on these factors. For example, it can collect data on extreme weather and use AI to analyze that data and perform demand forecasting. It can also perform demand forecasting considering the impact of earthquakes. Furthermore, it can also perform demand forecasting considering factors such as the aging of farmers and shortages in the supply of imported rice. Step 2: The notification unit notifies the user based on the demand predicted by the forecasting unit. The notification unit can send notifications to the user through a messaging service. The notification unit can use AI to send notifications to the user at the appropriate time. For example, it can analyze the user's behavior patterns and send notifications at the optimal time. It can also customize the content of notifications according to the user's preferences. Step 3: The payment unit makes the payment based on the information notified by the notification unit. The payment unit can make the payment through an electronic payment system. The payment unit can use AI to suggest the best payment method to the user. For example, it can analyze the user's past payment history and suggest the best payment method. It can also suggest a payment method considering the user's current financial situation. Step 4: The analysis department collects data on product inventory status, trend information from social media, and data on climate and disasters, and performs trend analysis. The analysis department monitors product inventory status in real time, and AI analyzes that data to perform trend analysis. Furthermore, it can also collect trend information from social media, data on climate and disasters, and AI analyzes that data to perform trend analysis.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the prediction unit, notification unit, payment unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the prediction unit is implemented by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12. The payment unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing unit 12. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the prediction unit, notification unit, payment unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the prediction unit is implemented by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The payment unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the prediction unit, notification unit, payment unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the prediction unit is implemented by the processor 46 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The payment unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the processor 46 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the prediction unit, notification unit, payment unit, and analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the prediction unit is implemented by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12. The payment unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing unit 12. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A forecasting unit that performs demand forecasting, A notification unit that notifies the user based on the demand predicted by the forecasting unit, A payment unit that makes payments based on the information notified by the aforementioned notification unit, It includes an analysis department that collects data on product inventory status, trend information from social media, climate, disasters, etc., and performs trend analysis. A system characterized by the following features. (Note 2) The prediction unit, Demand forecasts are made by taking into account factors such as extreme weather, the effects of earthquakes, the aging of farmers, and shortages in the supply of imported rice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Notify users via messaging services. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned payment section is, Make a payment through an electronic payment system. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is We collect data on product inventory status, social media trends, climate, and natural disasters, and then perform trend analysis. The system described in Appendix 1, characterized by the features described herein. (Note 6) The prediction unit, It estimates user sentiment and adjusts the accuracy of demand forecasts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The prediction unit, Optimize the forecasting algorithm by referring to historical demand data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The prediction unit, Predictions are made by taking into account the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 9) The prediction unit, It estimates user sentiment and adjusts the order in which demand forecast results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The prediction unit, Perform individual demand forecasts by taking into account the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The prediction unit, Analyzing users' social media activity improves the accuracy of demand forecasting. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, The system selects the most suitable notification method by referring to the user's past notification history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, Adjust notification timing based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, Prioritize relevant notifications by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, The optimal notification method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned payment section is, It estimates the user's emotions and adjusts the payment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned payment section is, The system selects the most suitable payment method by referring to the user's past payment history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned payment section is, Customize payment methods based on the user's current financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned payment section is, It estimates the user's emotions and determines payment priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned payment section is, The system selects the optimal payment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned payment section is, Analyze users' social media activity to suggest payment methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is We estimate the user's emotions and adjust the trend analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is Optimize the analysis algorithm by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is The analysis will be conducted taking into account the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is Perform individual trend analysis by considering the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is Analyzing users' social media activity improves the accuracy of trend analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 forecasting unit that performs demand forecasting, A notification unit that notifies the user based on the demand predicted by the forecasting unit, A payment unit that makes payments based on the information notified by the aforementioned notification unit, It includes an analysis department that collects data on product inventory status, trend information from social media, climate, disasters, etc., and performs trend analysis. A system characterized by the following features.

2. The prediction unit, Demand forecasts are made by taking into account factors such as extreme weather, the effects of earthquakes, the aging of farmers, and shortages in the supply of imported rice. The system according to feature 1.

3. The aforementioned notification unit, Notify users via messaging services. The system according to feature 1.

4. The aforementioned payment section is, Make a payment through an electronic payment system. The system according to feature 1.

5. The aforementioned analysis unit is We collect data on product inventory status, social media trends, climate, and natural disasters, and then perform trend analysis. The system according to feature 1.

6. The prediction unit, It estimates user sentiment and adjusts the accuracy of demand forecasts based on the estimated user sentiment. The system according to feature 1.

7. The prediction unit, Optimize the forecasting algorithm by referring to historical demand data. The system according to feature 1.

8. The prediction unit, Predictions are made by taking into account the characteristics of each region. The system according to feature 1.

9. The prediction unit, It estimates user sentiment and adjusts the order in which demand forecast results are displayed based on the estimated user sentiment. The system according to feature 1.

10. The prediction unit, Perform individual demand forecasts by taking into account the user's purchase history. The system according to feature 1.

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