system

Abstract

The system according to this embodiment aims to maximize supply chain efficiency, reduce costs, and improve operational effectiveness. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit makes optimal suggestions based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 conventional technology, it cannot be said that maximizing the efficiency of the supply chain, reducing costs, and improving the operation effect have been fully achieved, and there is room for improvement.

[0005] The system according to the embodiment aims to maximize the efficiency of the supply chain, reduce costs, and improve the operation effect.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit makes an optimal proposal based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can maximize supply chain efficiency, reduce costs, and improve operational effectiveness. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI-Enhanced Supply Chain Synergy Optimizer, according to an embodiment of the present invention, is an advanced platform that innovates supply chain management. This system leverages advanced AI algorithms, real-time data integration, and predictive analytics to identify and leverage supply chain efficiencies, thereby reducing costs and improving overall group operational efficiency. The AI-Enhanced Supply Chain Synergy Optimizer identifies resource sharing opportunities within the group, forecasts demand, optimizes inventory levels, performs dynamic supplier evaluation, and provides real-time logistics tracking. For example, the AI-Enhanced Supply Chain Synergy Optimizer identifies resource sharing opportunities between group companies. For example, the AI-Enhanced Supply Chain Synergy Optimizer uses AI to forecast demand and optimize inventory levels. For example, the AI-Enhanced Supply Chain Synergy Optimizer performs dynamic supplier evaluation to improve supply chain quality. For example, the AI-Enhanced Supply Chain Synergy Optimizer provides real-time logistics tracking to improve supply chain transparency and efficiency. This enables the AI-Enhanced Supply Chain Synergy Optimizer to revolutionize supply chain management and improve operational efficiency across the entire group.

[0029] The AI-Enhanced Supply Chain Synergy Optimizer according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. For example, the data collection unit collects data to identify resource sharing opportunities between group companies. The data collection unit can also collect data using AI, for example. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes data to perform demand forecasting and optimize inventory levels. The analysis unit can also analyze data using AI, for example. The data provision unit makes optimal suggestions based on the analysis results obtained by the analysis unit. For example, the data provision unit performs dynamic supplier evaluation. For example, the data provision unit performs real-time logistics tracking. The data provision unit can also make optimal suggestions using AI, for example. This enables the AI-Enhanced Supply Chain Synergy Optimizer according to this embodiment to efficiently collect, analyze, and provide data.

[0030] The data collection unit collects data. For example, it collects data to identify opportunities for resource sharing among group companies. Specifically, it collects inventory status, manufacturing capacity, logistics information, and demand forecast data for each group company. This data is obtained from each company's WMS (Warehouse Management System), TMS (Transportation Management System), etc. Furthermore, the data collection unit can also collect data using AI. The AI ​​automatically extracts data from each system, verifies data integrity, and converts it into the required format. For example, it can use natural language processing (NLP) technology to extract useful information from emails and reports. It can also utilize IoT devices to monitor machine operation status and logistics progress in real time and collect data. This allows the data collection unit to efficiently collect a wide range of data from diverse data sources and strengthen the data infrastructure of the entire system. In addition, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, during periods of rapid demand surges, the frequency of data collection can be increased to obtain more detailed information, enabling a quicker response. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The Analysis Department analyzes data collected by the Data Collection Department. For example, the Analysis Department analyzes data to forecast demand and optimize inventory levels. Specifically, it uses AI to analyze past sales data, market trends, and seasonal fluctuations to predict future demand. Using machine learning algorithms, the AI ​​learns data patterns and can predict demand fluctuations with high accuracy. For example, it uses time series analysis and regression analysis to predict how the demand for a particular product will fluctuate. In addition, for inventory level optimization, the AI ​​calculates the optimal inventory level by considering inventory turnover rate, storage costs, lead time, etc. This prevents excess inventory and stockouts, enabling efficient inventory management. Furthermore, the Analysis Department also optimizes resource allocation and production planning to improve the efficiency of the entire supply chain. For example, it considers the production capacity and operating status of each factory to create an optimal production schedule. In addition, for logistics optimization, the AI ​​analyzes transportation routes and means of transportation and proposes an optimal logistics plan. In this way, the Analysis Department can quickly and accurately analyze the collected data and improve the efficiency of the entire supply chain. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The service department makes optimal proposals based on the analysis results obtained by the analysis department. Specifically, it performs dynamic supplier evaluation and real-time logistics tracking. In dynamic supplier evaluation, AI analyzes supplier performance data and ranks suppliers based on evaluation criteria such as quality, delivery time, and cost. This allows for the selection of optimal suppliers and improves supply chain efficiency. In real-time logistics tracking, GPS and RFID technology are used to track the location of goods in transit in real time. AI analyzes this data to detect transportation delays and problems early and propose appropriate countermeasures. For example, this could include changing transportation routes or arranging additional transportation methods. Furthermore, the service department can also make optimal proposals using AI. Based on the collected data and analysis results, AI proposes optimal production plans, inventory management, and logistics plans. This maximizes the efficiency of the entire supply chain, enabling cost reduction and shorter lead times. The service department quickly communicates these proposals to each department and stakeholder and supports their implementation. For example, it provides real-time information through dashboards and reports to support decision-making. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the service provider to quickly and reliably provide users with optimal proposals, thereby improving the efficiency and reliability of the entire supply chain.

[0033] The supply department can perform dynamic supplier evaluations. For example, by dynamically evaluating suppliers, the supply department can improve the quality of the supply chain. The supply department can also use AI to evaluate suppliers. For example, the supply department can collect supplier performance data in real time and incorporate it into the evaluation. For example, the supply department can evaluate suppliers based on their on-time delivery rate and quality data. For example, the supply department can incorporate suppliers' cost performance into the evaluation. In this way, the supply department can improve the quality of the supply chain by dynamically evaluating suppliers.

[0034] The supply unit can perform real-time logistics tracking. For example, by tracking logistics in real time, the supply unit can improve the transparency and efficiency of the supply chain. The supply unit can also use AI to track logistics. For example, the supply unit can collect real-time location information of logistics and provide tracking information. For example, the supply unit can optimize logistics routes and perform efficient logistics management. For example, the supply unit can detect logistics delays and problems in real time and respond quickly. In this way, the supply unit can improve the transparency and efficiency of the supply chain by tracking logistics in real time.

[0035] The data collection unit can identify resource sharing opportunities among group companies. For example, the data collection unit collects data to identify resource sharing opportunities among group companies. The data collection unit can also identify resource sharing opportunities using AI, for example. For example, the data collection unit analyzes the resource usage of each company to identify shareable resources. For example, the data collection unit identifies sharing opportunities based on resource usage frequency and cost. For example, the data collection unit identifies sharing opportunities by considering the balance between resource supply and demand. This allows the data collection unit to improve the overall efficiency of the group by identifying resource sharing opportunities among group companies.

[0036] The analytics department can perform demand forecasting and optimize inventory levels. For example, by forecasting demand, the analytics department can optimize inventory levels and reduce waste. The analytics department can also perform demand forecasting using AI, for example. The analytics department can analyze historical demand data to predict future demand, for example. The analytics department can perform demand forecasting considering seasonal fluctuations and market trends, for example. The analytics department can adjust inventory levels based on demand forecasts to achieve optimal inventory management, for example. In this way, the analytics department can optimize inventory levels and reduce waste by forecasting demand.

[0037] The service provider can use predictive analytics to manage risks and mitigate disruptions. For example, the service provider can effectively manage risks and mitigate disruptions by using predictive analytics. The service provider can also use AI for predictive analytics. For example, the service provider can analyze historical risk data to predict future risks. For example, the service provider can evaluate the probability and impact of risks and develop risk management plans. For example, the service provider can detect risks early and respond quickly to minimize disruptions. Thus, by using predictive analytics, the service provider can effectively manage risks and mitigate disruptions.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. For example, the data collection unit can analyze past data collection history to find areas for improvement in collection methods and optimize them. For example, the data collection unit can find patterns in collection methods based on past data collection history and select the optimal method. In this way, the data collection unit can select the optimal collection method and improve efficiency by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter data based on current market conditions and trends during data collection. For example, the data collection unit can analyze current market conditions and collect only highly relevant data. For example, the data collection unit can filter the data to be collected based on trend information and prioritize important data. For example, the data collection unit can adjust the type and amount of data to be collected in accordance with market fluctuations. In this way, the data collection unit can prioritize the collection of important data by filtering data based on market conditions and trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current market conditions and trend information into a generating AI and have the generating AI perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can prioritize the collection of data related to a specific region based on geographical location information. For example, the data collection unit can collect highly relevant data by considering the user's travel history. In this way, the data collection unit can efficiently collect highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media activity and collect relevant data. For example, the data collection unit can select data to collect based on social media trends. For example, the data collection unit can analyze the content of users' social media posts and collect highly relevant data. In this way, the data collection unit can collect highly relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on highly important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit adjusts the depth and scope of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can apply a customer behavior analysis algorithm to customer data. For example, the analysis unit can apply a logistics optimization algorithm to logistics data. This allows the analysis unit to perform highly accurate analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the appropriate analysis algorithm.

[0044] The analysis department can prioritize analyses based on the data submission date. For example, the analysis department can prioritize the analysis of the most recent data to enable a quick response. For example, the analysis department can postpone the analysis of older data. For example, the analysis department can adjust the analysis schedule based on the submission date. This allows the analysis department to respond quickly by prioritizing analyses based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant data to gain important insights early. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can optimize the order of analysis based on the relevance of the data. This allows the analysis unit to gain important insights early by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0046] The service provider can adjust the level of detail in a proposal based on its importance. For example, it can provide detailed proposals for high-importance proposals, and simplified proposals for low-importance proposals. The service provider can adjust the depth and scope of a proposal according to its importance. This allows the service provider to make efficient proposals by adjusting the level of detail based on the importance of the proposal. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the importance of the proposal into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0047] The service provider can apply different proposal algorithms depending on the category of the proposal content when making a proposal. For example, the service provider can apply a specific financial proposal algorithm to a financial proposal. For example, the service provider can apply a customer behavior proposal algorithm to a customer proposal. For example, the service provider can apply a logistics optimization proposal algorithm to a logistics proposal. This allows the service provider to make highly accurate proposals by applying the appropriate proposal algorithm according to the category of the proposal content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of the proposal content into a generating AI and have the generating AI execute the application of the appropriate proposal algorithm.

[0048] The service provider can determine the priority of proposals based on when they are submitted. For example, the service provider can prioritize the most recent proposals to enable a quick response. For example, the service provider can postpone submitting older proposals. For example, the service provider can adjust the proposal schedule based on when the proposals are submitted. This allows the service provider to respond quickly by prioritizing proposals based on when they are submitted. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the submission dates of the proposals into a generating AI and have the generating AI determine the priority of the proposals.

[0049] The service provider can adjust the order of proposals based on their relevance. For example, the service provider can prioritize highly relevant proposals to gain important insights early. For example, the service provider can postpone proposing less relevant proposals. For example, the service provider can optimize the order of proposals based on their relevance. This allows the service provider to gain important insights early by adjusting the order of proposals based on their relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of the proposals into a generating AI and have the generating AI adjust the order of the proposals.

[0050] The service provider can improve the accuracy of its evaluations by analyzing the supplier's past performance data when conducting dynamic supplier evaluations. For example, the service provider can analyze the supplier's past on-time delivery rate and reflect it in the evaluation. For example, the service provider can analyze the supplier's past quality data and reflect it in the evaluation. For example, the service provider can analyze the supplier's past cost performance and reflect it in the evaluation. In this way, the service provider can improve the accuracy of its evaluations by analyzing the supplier's past performance data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the supplier's past performance data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0051] The supply unit can perform dynamic supplier evaluations while taking into account the supplier's geographical location information. For example, the supply unit can reflect logistics costs in the evaluation based on the supplier's geographical location information. For example, the supply unit can reflect delivery risk in the evaluation based on the supplier's geographical location information. For example, the supply unit can reflect region-specific risks in the evaluation based on the supplier's geographical location information. In this way, the supply unit can reflect logistics costs and delivery risk in the evaluation by taking into account the supplier's geographical location information. Some or all of the above processing in the supply unit may be performed using AI, for example, or without using AI. For example, the supply unit can input the supplier's geographical location information into a generating AI and have the generating AI perform the evaluation.

[0052] The service provider can analyze historical logistics data to improve tracking accuracy when performing real-time logistics tracking. For example, the service provider can analyze historical logistics data to improve tracking accuracy. For example, the service provider can optimize the tracking algorithm based on historical logistics data. For example, the service provider can find tracking patterns by referring to historical logistics data to improve accuracy. In this way, the service provider can improve tracking accuracy by analyzing historical logistics data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input historical logistics data into a generating AI and have the generating AI perform the task of improving tracking accuracy.

[0053] The service provider can perform real-time logistics tracking while considering the geographical location of the logistics. For example, the service provider can select the optimal tracking method based on the geographical location of the logistics. For example, the service provider can improve the accuracy of tracking based on the geographical location of the logistics. For example, the service provider can display tracking information while considering the geographical location of the logistics. In this way, the service provider can select the optimal tracking method and improve accuracy by considering the geographical location of the logistics. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the geographical location of the logistics into a generating AI and have the generating AI perform the tracking.

[0054] The data collection unit can analyze past resource sharing data to improve the accuracy of resource sharing opportunities when identifying resource sharing opportunities among group companies. For example, the data collection unit can analyze past resource sharing data to improve the accuracy of resource sharing opportunities. For example, the data collection unit can find patterns in resource sharing opportunities based on past resource sharing data to improve accuracy. For example, the data collection unit can identify optimal resource sharing opportunities by referring to past resource sharing data. In this way, the data collection unit can improve the accuracy of resource sharing opportunities by analyzing past resource sharing data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past resource sharing data into a generating AI and have the generating AI perform the task of improving the accuracy of resource sharing opportunities.

[0055] The data collection unit can identify resource sharing opportunities among group companies by considering the geographical location information of each company. For example, the data collection unit identifies the optimal sharing opportunity based on the geographical location information of each company. For example, the data collection unit improves the accuracy of the sharing opportunities based on the geographical location information of each company. For example, the data collection unit identifies sharing opportunities by considering the geographical location information of each company. In this way, the data collection unit can identify the optimal sharing opportunities and improve accuracy by considering the geographical location information of each company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of each company into a generating AI and have the generating AI perform the identification of sharing opportunities.

[0056] The analysis unit can improve the accuracy of its forecasts by analyzing historical demand data when forecasting demand and optimizing inventory levels. For example, the analysis unit can improve the accuracy of its forecasts by analyzing historical demand data. For example, the analysis unit can optimize its demand forecasting algorithm based on historical demand data. For example, the analysis unit can identify patterns in demand forecasting by referring to historical demand data and improve accuracy. In this way, the analysis unit can improve the accuracy of its forecasts by analyzing historical demand data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input historical demand data into a generating AI and have the generating AI perform the task of improving the accuracy of its forecasts.

[0057] The analysis unit can perform demand forecasting and optimize inventory levels by taking geographical location information into consideration. For example, the analysis unit can perform optimal demand forecasting based on geographical location information. For example, the analysis unit can improve the accuracy of demand forecasting based on geographical location information. For example, the analysis unit can perform demand forecasting by taking geographical location information into consideration. In this way, the analysis unit can improve the accuracy of demand forecasting by taking geographical location information into consideration. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input geographical location information into a generating AI and have the generating AI perform demand forecasting.

[0058] The service provider can improve the accuracy of risk management by analyzing historical risk data when using predictive analytics to manage risks and mitigate disruptions. For example, the service provider can analyze historical risk data to improve the accuracy of management. For example, the service provider can optimize risk management algorithms based on historical risk data. For example, the service provider can identify risk management patterns and improve accuracy by referring to historical risk data. In this way, the service provider can improve the accuracy of management by analyzing historical risk data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input historical risk data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0059] The service provider can perform risk management and mitigation of disruptions using predictive analytics, taking geographical location information into consideration. For example, the service provider can perform optimal risk management based on geographical location information. For example, the service provider can improve the accuracy of risk management based on geographical location information. For example, the service provider can perform risk management considering geographical location information. In this way, the service provider can improve the accuracy of risk management by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform risk management.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. For example, the data collection unit can analyze past data collection history to find areas for improvement in collection methods and optimize them. For example, the data collection unit can find patterns in collection methods based on past data collection history and select the optimal method. In this way, the data collection unit can select the optimal collection method and improve efficiency by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0062] The data collection unit can filter data based on current market conditions and trends during data collection. For example, the data collection unit can analyze current market conditions and collect only highly relevant data. For example, the data collection unit can filter the data to be collected based on trend information and prioritize important data. For example, the data collection unit can adjust the type and amount of data to be collected in accordance with market fluctuations. In this way, the data collection unit can prioritize the collection of important data by filtering data based on market conditions and trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current market conditions and trend information into a generating AI and have the generating AI perform data filtering.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on highly important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit adjusts the depth and scope of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0064] The service provider can adjust the level of detail in a proposal based on its importance. For example, it can provide detailed proposals for high-importance proposals, and simplified proposals for low-importance proposals. The service provider can adjust the depth and scope of a proposal according to its importance. This allows the service provider to make efficient proposals by adjusting the level of detail based on the importance of the proposal. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the importance of the proposal into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0065] The service provider can improve the accuracy of risk management by analyzing historical risk data when using predictive analytics to manage risks and mitigate disruptions. For example, the service provider can analyze historical risk data to improve the accuracy of management. For example, the service provider can optimize risk management algorithms based on historical risk data. For example, the service provider can identify risk management patterns and improve accuracy by referring to historical risk data. In this way, the service provider can improve the accuracy of management by analyzing historical risk data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input historical risk data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data. For example, it collects data to identify resource sharing opportunities between group companies. The data collection unit can also use AI to collect data. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they analyze data to forecast demand and optimize inventory levels. The analysis department can also use AI to analyze the data. Step 3: The service department makes optimal proposals based on the analysis results obtained by the analysis department. For example, this includes dynamic supplier evaluation and real-time logistics tracking. The service department can also use AI to make optimal proposals.

[0068] (Example of form 2) The AI-Enhanced Supply Chain Synergy Optimizer, according to an embodiment of the present invention, is an advanced platform that innovates supply chain management. This system leverages advanced AI algorithms, real-time data integration, and predictive analytics to identify and leverage supply chain efficiencies, thereby reducing costs and improving overall group operational efficiency. The AI-Enhanced Supply Chain Synergy Optimizer identifies resource sharing opportunities within the group, forecasts demand, optimizes inventory levels, performs dynamic supplier evaluation, and provides real-time logistics tracking. For example, the AI-Enhanced Supply Chain Synergy Optimizer identifies resource sharing opportunities between group companies. For example, the AI-Enhanced Supply Chain Synergy Optimizer uses AI to forecast demand and optimize inventory levels. For example, the AI-Enhanced Supply Chain Synergy Optimizer performs dynamic supplier evaluation to improve supply chain quality. For example, the AI-Enhanced Supply Chain Synergy Optimizer provides real-time logistics tracking to improve supply chain transparency and efficiency. This enables the AI-Enhanced Supply Chain Synergy Optimizer to revolutionize supply chain management and improve operational efficiency across the entire group.

[0069] The AI-Enhanced Supply Chain Synergy Optimizer according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. For example, the data collection unit collects data to identify resource sharing opportunities between group companies. The data collection unit can also collect data using AI, for example. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes data to perform demand forecasting and optimize inventory levels. The analysis unit can also analyze data using AI, for example. The data provision unit makes optimal suggestions based on the analysis results obtained by the analysis unit. For example, the data provision unit performs dynamic supplier evaluation. For example, the data provision unit performs real-time logistics tracking. The data provision unit can also make optimal suggestions using AI, for example. This enables the AI-Enhanced Supply Chain Synergy Optimizer according to this embodiment to efficiently collect, analyze, and provide data.

[0070] The data collection unit collects data. For example, it collects data to identify opportunities for resource sharing among group companies. Specifically, it collects inventory status, manufacturing capacity, logistics information, and demand forecast data for each group company. This data is obtained from each company's WMS (Warehouse Management System), TMS (Transportation Management System), etc. Furthermore, the data collection unit can also collect data using AI. The AI ​​automatically extracts data from each system, verifies data integrity, and converts it into the required format. For example, it can use natural language processing (NLP) technology to extract useful information from emails and reports. It can also utilize IoT devices to monitor machine operation status and logistics progress in real time and collect data. This allows the data collection unit to efficiently collect a wide range of data from diverse data sources and strengthen the data infrastructure of the entire system. In addition, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, during periods of rapid demand surges, the frequency of data collection can be increased to obtain more detailed information, enabling a quicker response. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0071] The Analysis Department analyzes data collected by the Data Collection Department. For example, the Analysis Department analyzes data to forecast demand and optimize inventory levels. Specifically, it uses AI to analyze past sales data, market trends, and seasonal fluctuations to predict future demand. Using machine learning algorithms, the AI ​​learns data patterns and can predict demand fluctuations with high accuracy. For example, it uses time series analysis and regression analysis to predict how the demand for a particular product will fluctuate. In addition, for inventory level optimization, the AI ​​calculates the optimal inventory level by considering inventory turnover rate, storage costs, lead time, etc. This prevents excess inventory and stockouts, enabling efficient inventory management. Furthermore, the Analysis Department also optimizes resource allocation and production planning to improve the efficiency of the entire supply chain. For example, it considers the production capacity and operating status of each factory to create an optimal production schedule. In addition, for logistics optimization, the AI ​​analyzes transportation routes and means of transportation and proposes an optimal logistics plan. In this way, the Analysis Department can quickly and accurately analyze the collected data and improve the efficiency of the entire supply chain. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The service department makes optimal proposals based on the analysis results obtained by the analysis department. Specifically, it performs dynamic supplier evaluation and real-time logistics tracking. In dynamic supplier evaluation, AI analyzes supplier performance data and ranks suppliers based on evaluation criteria such as quality, delivery time, and cost. This allows for the selection of optimal suppliers and improves supply chain efficiency. In real-time logistics tracking, GPS and RFID technology are used to track the location of goods in transit in real time. AI analyzes this data to detect transportation delays and problems early and propose appropriate countermeasures. For example, this could include changing transportation routes or arranging additional transportation methods. Furthermore, the service department can also make optimal proposals using AI. Based on the collected data and analysis results, AI proposes optimal production plans, inventory management, and logistics plans. This maximizes the efficiency of the entire supply chain, enabling cost reduction and shorter lead times. The service department quickly communicates these proposals to each department and stakeholder and supports their implementation. For example, it provides real-time information through dashboards and reports to support decision-making. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the service provider to quickly and reliably provide users with optimal proposals, thereby improving the efficiency and reliability of the entire supply chain.

[0073] The supply department can perform dynamic supplier evaluations. For example, by dynamically evaluating suppliers, the supply department can improve the quality of the supply chain. The supply department can also use AI to evaluate suppliers. For example, the supply department can collect supplier performance data in real time and incorporate it into the evaluation. For example, the supply department can evaluate suppliers based on their on-time delivery rate and quality data. For example, the supply department can incorporate suppliers' cost performance into the evaluation. In this way, the supply department can improve the quality of the supply chain by dynamically evaluating suppliers.

[0074] The supply unit can perform real-time logistics tracking. For example, by tracking logistics in real time, the supply unit can improve the transparency and efficiency of the supply chain. The supply unit can also use AI to track logistics. For example, the supply unit can collect real-time location information of logistics and provide tracking information. For example, the supply unit can optimize logistics routes and perform efficient logistics management. For example, the supply unit can detect logistics delays and problems in real time and respond quickly. In this way, the supply unit can improve the transparency and efficiency of the supply chain by tracking logistics in real time.

[0075] The data collection unit can identify resource sharing opportunities among group companies. For example, the data collection unit collects data to identify resource sharing opportunities among group companies. The data collection unit can also identify resource sharing opportunities using AI, for example. For example, the data collection unit analyzes the resource usage of each company to identify shareable resources. For example, the data collection unit identifies sharing opportunities based on resource usage frequency and cost. For example, the data collection unit identifies sharing opportunities by considering the balance between resource supply and demand. This allows the data collection unit to improve the overall efficiency of the group by identifying resource sharing opportunities among group companies.

[0076] The analytics department can perform demand forecasting and optimize inventory levels. For example, by forecasting demand, the analytics department can optimize inventory levels and reduce waste. The analytics department can also perform demand forecasting using AI, for example. The analytics department can analyze historical demand data to predict future demand, for example. The analytics department can perform demand forecasting considering seasonal fluctuations and market trends, for example. The analytics department can adjust inventory levels based on demand forecasts to achieve optimal inventory management, for example. In this way, the analytics department can optimize inventory levels and reduce waste by forecasting demand.

[0077] The service provider can use predictive analytics to manage risks and mitigate disruptions. For example, the service provider can effectively manage risks and mitigate disruptions by using predictive analytics. The service provider can also use AI for predictive analytics. For example, the service provider can analyze historical risk data to predict future risks. For example, the service provider can evaluate the probability and impact of risks and develop risk management plans. For example, the service provider can detect risks early and respond quickly to minimize disruptions. Thus, by using predictive analytics, the service provider can effectively manage risks and mitigate disruptions.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. In this way, the data collection unit can reduce the user's burden by adjusting the timing of data collection 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. For example, the data collection unit can analyze past data collection history to find areas for improvement in collection methods and optimize them. For example, the data collection unit can find patterns in collection methods based on past data collection history and select the optimal method. In this way, the data collection unit can select the optimal collection method and improve efficiency by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0080] The data collection unit can filter data based on current market conditions and trends during data collection. For example, the data collection unit can analyze current market conditions and collect only highly relevant data. For example, the data collection unit can filter the data to be collected based on trend information and prioritize important data. For example, the data collection unit can adjust the type and amount of data to be collected in accordance with market fluctuations. In this way, the data collection unit can prioritize the collection of important data by filtering data based on market conditions and trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current market conditions and trend information into a generating AI and have the generating AI perform data filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit will prioritize the collection of detailed data. For example, if the user is in a hurry, the data collection unit will prioritize data that can be collected quickly. This allows the data collection unit to efficiently collect data by prioritizing data 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can prioritize the collection of data related to a specific region based on geographical location information. For example, the data collection unit can collect highly relevant data by considering the user's travel history. In this way, the data collection unit can efficiently collect highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0083] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media activity and collect relevant data. For example, the data collection unit can select data to collect based on social media trends. For example, the data collection unit can analyze the content of users' social media posts and collect highly relevant data. In this way, the data collection unit can collect highly relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and highly visual presentation. For example, if the user is relaxed, the analysis unit provides a presentation that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a presentation that gets straight to the point. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on highly important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit adjusts the depth and scope of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can apply a customer behavior analysis algorithm to customer data. For example, the analysis unit can apply a logistics optimization algorithm to logistics data. This allows the analysis unit to perform highly accurate analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the appropriate analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. If the user is relaxed, the analysis unit will provide a longer analysis with detailed explanations. If the user is excited, the analysis unit will provide an analysis with visually stimulating effects. In this way, the analysis unit can provide the user with the most optimal analysis results by adjusting the length of the analysis 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis department can prioritize analyses based on the data submission date. For example, the analysis department can prioritize the analysis of the most recent data to enable a quick response. For example, the analysis department can postpone the analysis of older data. For example, the analysis department can adjust the analysis schedule based on the submission date. This allows the analysis department to respond quickly by prioritizing analyses based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant data to gain important insights early. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can optimize the order of analysis based on the relevance of the data. This allows the analysis unit to gain important insights early by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0090] The service provider can estimate the user's emotions and adjust the presentation of suggestions based on the estimated emotions. For example, if the user is nervous, the service provider will provide a simple and easily understandable presentation. If the user is relaxed, the service provider will provide a presentation that includes detailed information. If the user is in a hurry, the service provider will provide a presentation that gets straight to the point. In this way, by adjusting the presentation of suggestions according to the user's emotions, the service provider can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0091] The service provider can adjust the level of detail in a proposal based on its importance. For example, it can provide detailed proposals for high-importance proposals, and simplified proposals for low-importance proposals. The service provider can adjust the depth and scope of a proposal according to its importance. This allows the service provider to make efficient proposals by adjusting the level of detail based on the importance of the proposal. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the importance of the proposal into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0092] The service provider can apply different proposal algorithms depending on the category of the proposal content when making a proposal. For example, the service provider can apply a specific financial proposal algorithm to a financial proposal. For example, the service provider can apply a customer behavior proposal algorithm to a customer proposal. For example, the service provider can apply a logistics optimization proposal algorithm to a logistics proposal. This allows the service provider to make highly accurate proposals by applying the appropriate proposal algorithm according to the category of the proposal content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of the proposal content into a generating AI and have the generating AI execute the application of the appropriate proposal algorithm.

[0093] The service provider can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the service provider will provide short, concise suggestions. If the user is relaxed, the service provider will provide longer suggestions with detailed explanations. If the user is excited, the service provider will provide suggestions with visually stimulating effects. In this way, the service provider can provide the user with the most suitable suggestions by adjusting the length of suggestions 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0094] The service provider can determine the priority of proposals based on when they are submitted. For example, the service provider can prioritize the most recent proposals to enable a quick response. For example, the service provider can postpone submitting older proposals. For example, the service provider can adjust the proposal schedule based on when the proposals are submitted. This allows the service provider to respond quickly by prioritizing proposals based on when they are submitted. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the submission dates of the proposals into a generating AI and have the generating AI determine the priority of the proposals.

[0095] The service provider can adjust the order of proposals based on their relevance. For example, the service provider can prioritize highly relevant proposals to gain important insights early. For example, the service provider can postpone proposing less relevant proposals. For example, the service provider can optimize the order of proposals based on their relevance. This allows the service provider to gain important insights early by adjusting the order of proposals based on their relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of the proposals into a generating AI and have the generating AI adjust the order of the proposals.

[0096] The supply unit can estimate a supplier's emotions when conducting dynamic supplier evaluations and adjust evaluation criteria based on those emotions. For example, if a supplier is stressed, the supply unit can relax the evaluation criteria to maintain a cooperative relationship. For example, if a supplier is relaxed, the supply unit can apply strict evaluation criteria to ensure quality. For example, if a supplier is in a hurry, the supply unit can conduct a rapid evaluation to enable a quick response. In this way, the supply unit can ensure quality while maintaining a cooperative relationship by adjusting evaluation criteria according to the supplier's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the supply unit may be performed using AI or not using AI. For example, the supply unit can input supplier emotion data into a generative AI and have the generative AI perform the adjustment of evaluation criteria.

[0097] The service provider can improve the accuracy of its evaluations by analyzing the supplier's past performance data when conducting dynamic supplier evaluations. For example, the service provider can analyze the supplier's past on-time delivery rate and reflect it in the evaluation. For example, the service provider can analyze the supplier's past quality data and reflect it in the evaluation. For example, the service provider can analyze the supplier's past cost performance and reflect it in the evaluation. In this way, the service provider can improve the accuracy of its evaluations by analyzing the supplier's past performance data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the supplier's past performance data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0098] The service provider can estimate the supplier's emotions when performing dynamic supplier evaluations and adjust the display method of the evaluation results based on the estimated emotions. For example, if the supplier is tense, the service provider can provide a simple and highly visible display method. For example, if the supplier is relaxed, the service provider can provide a display method that includes detailed information. For example, if the supplier is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by adjusting the display method of the evaluation results according to the supplier's emotions, the service provider can provide evaluation results that are easy for suppliers to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input supplier emotion data into the generative AI and have the generative AI adjust the display method of the evaluation results.

[0099] The supply unit can perform dynamic supplier evaluations while taking into account the supplier's geographical location information. For example, the supply unit can reflect logistics costs in the evaluation based on the supplier's geographical location information. For example, the supply unit can reflect delivery risk in the evaluation based on the supplier's geographical location information. For example, the supply unit can reflect region-specific risks in the evaluation based on the supplier's geographical location information. In this way, the supply unit can reflect logistics costs and delivery risk in the evaluation by taking into account the supplier's geographical location information. Some or all of the above processing in the supply unit may be performed using AI, for example, or without using AI. For example, the supply unit can input the supplier's geographical location information into a generating AI and have the generating AI perform the evaluation.

[0100] The service provider can estimate the emotions of logistics personnel when performing real-time logistics tracking and adjust the display method of tracking information based on the estimated emotions. For example, if the logistics personnel are stressed, the service provider can provide a simple and highly visible display method. For example, if the logistics personnel are relaxed, the service provider can provide a display method that includes detailed information. For example, if the logistics personnel are in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by adjusting the display method of tracking information according to the emotions of the logistics personnel, the service provider can provide information that is easy for the personnel to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the emotions of logistics personnel into a generative AI and have the generative AI adjust the display method of tracking information.

[0101] The service provider can analyze historical logistics data to improve tracking accuracy when performing real-time logistics tracking. For example, the service provider can analyze historical logistics data to improve tracking accuracy. For example, the service provider can optimize the tracking algorithm based on historical logistics data. For example, the service provider can find tracking patterns by referring to historical logistics data to improve accuracy. In this way, the service provider can improve tracking accuracy by analyzing historical logistics data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input historical logistics data into a generating AI and have the generating AI perform the task of improving tracking accuracy.

[0102] The service provider can estimate the emotions of logistics personnel when performing real-time logistics tracking and prioritize tracking information based on the estimated emotions. For example, if a logistics personnel is stressed, the service provider will prioritize displaying high-priority tracking information. For example, if a logistics personnel is relaxed, the service provider will prioritize displaying detailed tracking information. For example, if a logistics personnel is in a hurry, the service provider will prioritize displaying tracking information that can be quickly checked. In this way, the service provider can quickly provide important information by prioritizing tracking information according to the emotions of logistics personnel. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input logistics personnel's emotion data into a generative AI and have the generative AI perform the determination of tracking information prioritization.

[0103] The service provider can perform real-time logistics tracking while considering the geographical location of the logistics. For example, the service provider can select the optimal tracking method based on the geographical location of the logistics. For example, the service provider can improve the accuracy of tracking based on the geographical location of the logistics. For example, the service provider can display tracking information while considering the geographical location of the logistics. In this way, the service provider can select the optimal tracking method and improve accuracy by considering the geographical location of the logistics. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the geographical location of the logistics into a generating AI and have the generating AI perform the tracking.

[0104] The data collection unit can estimate the emotions of the representatives at each company when identifying resource sharing opportunities between group companies, and adjust the method of proposing resource sharing based on the estimated emotions. For example, if the representatives at each company are tense, the data collection unit can provide a simple and highly visible proposal. For example, if the representatives at each company are relaxed, the data collection unit can provide a proposal that includes detailed information. For example, if the representatives at each company are in a hurry, the data collection unit can provide a proposal that gets straight to the point. In this way, the data collection unit can improve the acceptability of proposals by adjusting the method of proposing resource sharing according to the emotions of the representatives at each company. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the emotion data of the representatives at each company into a generative AI and have the generative AI perform the adjustment of the proposal method.

[0105] The data collection unit can analyze past resource sharing data to improve the accuracy of resource sharing opportunities when identifying resource sharing opportunities among group companies. For example, the data collection unit can analyze past resource sharing data to improve the accuracy of resource sharing opportunities. For example, the data collection unit can find patterns in resource sharing opportunities based on past resource sharing data to improve accuracy. For example, the data collection unit can identify optimal resource sharing opportunities by referring to past resource sharing data. In this way, the data collection unit can improve the accuracy of resource sharing opportunities by analyzing past resource sharing data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past resource sharing data into a generating AI and have the generating AI perform the task of improving the accuracy of resource sharing opportunities.

[0106] The data collection unit can estimate the emotions of the representatives at each company when identifying resource sharing opportunities between group companies, and prioritize sharing opportunities based on the estimated emotions. For example, if the representatives at each company are stressed, the data collection unit will prioritize suggesting high-priority sharing opportunities. For example, if the representatives at each company are relaxed, the data collection unit will prioritize suggesting detailed sharing opportunities. For example, if the representatives at each company are in a hurry, the data collection unit will prioritize suggesting sharing opportunities that can be quickly reviewed. In this way, the data collection unit can prioritize suggesting important sharing opportunities by prioritizing sharing opportunities according to the emotions of the representatives at each company. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the emotion data of the representatives at each company into a generative AI and have the generative AI perform the determination of the priority of sharing opportunities.

[0107] The data collection unit can identify resource sharing opportunities among group companies by considering the geographical location information of each company. For example, the data collection unit identifies the optimal sharing opportunity based on the geographical location information of each company. For example, the data collection unit improves the accuracy of the sharing opportunities based on the geographical location information of each company. For example, the data collection unit identifies sharing opportunities by considering the geographical location information of each company. In this way, the data collection unit can identify the optimal sharing opportunities and improve accuracy by considering the geographical location information of each company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of each company into a generating AI and have the generating AI perform the identification of sharing opportunities.

[0108] The analysis unit can estimate user emotions and adjust the demand forecasting method based on the estimated emotions when forecasting demand and optimizing inventory levels. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand demand forecasting method. For example, if the user is relaxed, the analysis unit provides a demand forecasting method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a concise demand forecasting method. In this way, the analysis unit can provide forecast results that are easy for the user to understand by adjusting the demand forecasting 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the demand forecasting method.

[0109] The analysis unit can improve the accuracy of its forecasts by analyzing historical demand data when forecasting demand and optimizing inventory levels. For example, the analysis unit can improve the accuracy of its forecasts by analyzing historical demand data. For example, the analysis unit can optimize its demand forecasting algorithm based on historical demand data. For example, the analysis unit can identify patterns in demand forecasting by referring to historical demand data and improve accuracy. In this way, the analysis unit can improve the accuracy of its forecasts by analyzing historical demand data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input historical demand data into a generating AI and have the generating AI perform the task of improving the accuracy of its forecasts.

[0110] The analytics unit can estimate user emotions and prioritize inventory levels based on the estimated emotions when forecasting demand and optimizing inventory levels. For example, if the user is stressed, the analytics unit will prioritize adjusting high-priority inventory levels. If the user is relaxed, the analytics unit will prioritize adjusting detailed inventory levels. If the user is in a hurry, the analytics unit will prioritize adjusting inventory levels that can be quickly accessed. This allows the analytics unit to prioritize important inventory by determining inventory levels according to user emotions. Emotion estimation is achieved using emotion estimation functions, 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. Some or all of the above processing in the analytics unit may be performed using AI or not. For example, the analytics unit can input user emotion data into a generative AI and have the generative AI determine the priority of inventory levels.

[0111] The analysis unit can perform demand forecasting and optimize inventory levels by taking geographical location information into consideration. For example, the analysis unit can perform optimal demand forecasting based on geographical location information. For example, the analysis unit can improve the accuracy of demand forecasting based on geographical location information. For example, the analysis unit can perform demand forecasting by taking geographical location information into consideration. In this way, the analysis unit can improve the accuracy of demand forecasting by taking geographical location information into consideration. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input geographical location information into a generating AI and have the generating AI perform demand forecasting.

[0112] The service provider can estimate the user's emotions when performing risk management and mitigating confusion using predictive analytics, and adjust the risk management method based on the estimated emotions. For example, if the user is tense, the service provider can provide a simple and highly visible risk management method. For example, if the user is relaxed, the service provider can provide a risk management method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a risk management method that is easy for the user to understand by adjusting the risk management 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the risk management method.

[0113] The service provider can improve the accuracy of risk management by analyzing historical risk data when using predictive analytics to manage risks and mitigate disruptions. For example, the service provider can analyze historical risk data to improve the accuracy of management. For example, the service provider can optimize risk management algorithms based on historical risk data. For example, the service provider can identify risk management patterns and improve accuracy by referring to historical risk data. In this way, the service provider can improve the accuracy of management by analyzing historical risk data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input historical risk data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0114] The service provider can estimate the user's emotions when performing risk management and mitigating confusion using predictive analytics, and determine risk management priorities based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize high-priority risk management. For example, if the user is relaxed, the service provider will prioritize detailed risk management. For example, if the user is in a hurry, the service provider will prioritize risk management that can be quickly verified. In this way, the service provider can prioritize important risks by determining risk management 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the determination of risk management priorities.

[0115] The service provider can perform risk management and mitigation of disruptions using predictive analytics, taking geographical location information into consideration. For example, the service provider can perform optimal risk management based on geographical location information. For example, the service provider can improve the accuracy of risk management based on geographical location information. For example, the service provider can perform risk management considering geographical location information. In this way, the service provider can improve the accuracy of risk management by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform risk management.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. In this way, the data collection unit can reduce the user's burden by adjusting the timing of data collection 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and highly visual presentation. For example, if the user is relaxed, the analysis unit provides a presentation that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a presentation that gets straight to the point. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0119] The service provider can estimate the user's emotions and adjust the presentation of suggestions based on the estimated emotions. For example, if the user is nervous, the service provider will provide a simple and easily understandable presentation. If the user is relaxed, the service provider will provide a presentation that includes detailed information. If the user is in a hurry, the service provider will provide a presentation that gets straight to the point. In this way, by adjusting the presentation of suggestions according to the user's emotions, the service provider can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0120] The supply unit can estimate a supplier's emotions when conducting dynamic supplier evaluations and adjust evaluation criteria based on those emotions. For example, if a supplier is stressed, the supply unit can relax the evaluation criteria to maintain a cooperative relationship. For example, if a supplier is relaxed, the supply unit can apply strict evaluation criteria to ensure quality. For example, if a supplier is in a hurry, the supply unit can conduct a rapid evaluation to enable a quick response. In this way, the supply unit can ensure quality while maintaining a cooperative relationship by adjusting evaluation criteria according to the supplier's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the supply unit may be performed using AI or not using AI. For example, the supply unit can input supplier emotion data into a generative AI and have the generative AI perform the adjustment of evaluation criteria.

[0121] The service provider can estimate the emotions of logistics personnel when performing real-time logistics tracking and adjust the display method of tracking information based on the estimated emotions. For example, if the logistics personnel are stressed, the service provider can provide a simple and highly visible display method. For example, if the logistics personnel are relaxed, the service provider can provide a display method that includes detailed information. For example, if the logistics personnel are in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by adjusting the display method of tracking information according to the emotions of the logistics personnel, the service provider can provide information that is easy for the personnel to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the emotions of logistics personnel into a generative AI and have the generative AI adjust the display method of tracking information.

[0122] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. For example, the data collection unit can analyze past data collection history to find areas for improvement in collection methods and optimize them. For example, the data collection unit can find patterns in collection methods based on past data collection history and select the optimal method. In this way, the data collection unit can select the optimal collection method and improve efficiency by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0123] The data collection unit can filter data based on current market conditions and trends during data collection. For example, the data collection unit can analyze current market conditions and collect only highly relevant data. For example, the data collection unit can filter the data to be collected based on trend information and prioritize important data. For example, the data collection unit can adjust the type and amount of data to be collected in accordance with market fluctuations. In this way, the data collection unit can prioritize the collection of important data by filtering data based on market conditions and trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current market conditions and trend information into a generating AI and have the generating AI perform data filtering.

[0124] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on highly important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit adjusts the depth and scope of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0125] The service provider can adjust the level of detail in a proposal based on its importance. For example, it can provide detailed proposals for high-importance proposals, and simplified proposals for low-importance proposals. The service provider can adjust the depth and scope of a proposal according to its importance. This allows the service provider to make efficient proposals by adjusting the level of detail based on the importance of the proposal. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the importance of the proposal into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0126] The service provider can improve the accuracy of risk management by analyzing historical risk data when using predictive analytics to manage risks and mitigate disruptions. For example, the service provider can analyze historical risk data to improve the accuracy of management. For example, the service provider can optimize risk management algorithms based on historical risk data. For example, the service provider can identify risk management patterns and improve accuracy by referring to historical risk data. In this way, the service provider can improve the accuracy of management by analyzing historical risk data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input historical risk data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects data. For example, it collects data to identify resource sharing opportunities between group companies. The data collection unit can also use AI to collect data. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they analyze data to forecast demand and optimize inventory levels. The analysis department can also use AI to analyze the data. Step 3: The service department makes optimal proposals based on the analysis results obtained by the analysis department. For example, this includes dynamic supplier evaluation and real-time logistics tracking. The service department can also use AI to make optimal proposals.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, and supply unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects data to identify resource sharing opportunities among group companies. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI to forecast demand and optimize inventory levels. The supply unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which performs dynamic supplier evaluation and real-time logistics tracking to make optimal proposals. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, and supply unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects data to identify resource sharing opportunities among group companies. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI to forecast demand and optimize inventory levels. The supply unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which performs dynamic supplier evaluation and real-time logistics tracking to make optimal proposals. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects data to identify resource sharing opportunities among group companies. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI to forecast demand and optimize inventory levels. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which performs dynamic supplier evaluation and real-time logistics tracking to make optimal proposals. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the data collection unit, analysis unit, and supply unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects data to identify resource sharing opportunities among group companies. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI to forecast demand and optimize inventory levels. The supply unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which performs dynamic supplier evaluation and real-time logistics tracking to make optimal proposals. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides optimal suggestions based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned supply unit is, Conduct dynamic supplier evaluations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Real-time logistics tracking The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Identify opportunities for resource sharing among group companies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is We forecast demand and optimize inventory levels. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Use predictive analytics to manage risk and mitigate disruptions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on current market conditions and trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When making a proposal, adjust the level of detail based on the importance of the proposed content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When submitting a proposal, a different proposal algorithm is applied depending on the category of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When submitting a proposal, the priority of the proposals will be determined based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When submitting proposals, adjust the order of the proposals based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When conducting dynamic supplier evaluations, we estimate the supplier's sentiment and adjust the evaluation criteria based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When conducting dynamic supplier evaluations, we analyze the supplier's past performance data to improve the accuracy of the evaluation. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When conducting dynamic supplier evaluations, we estimate the supplier's sentiment and adjust how the evaluation results are displayed based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When conducting dynamic supplier evaluations, the supplier's geographical location information should be taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When performing real-time logistics tracking, the system estimates the emotions of logistics personnel and adjusts how tracking information is displayed based on these estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When performing real-time logistics tracking, we analyze past logistics data to improve tracking accuracy. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When performing real-time logistics tracking, the system estimates the emotions of logistics personnel and prioritizes tracking information based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When performing real-time logistics tracking, the tracking process takes into account the geographical location of the logistics. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned collection unit is When identifying resource sharing opportunities between group companies, we estimate the sentiments of the representatives at each company and adjust the method of proposing resource sharing based on those estimated sentiments. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned collection unit is When identifying resource sharing opportunities between group companies, we analyze past resource sharing data to improve the accuracy of identifying such opportunities. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned collection unit is When identifying resource sharing opportunities between group companies, we estimate the sentiments of the representatives at each company and prioritize sharing opportunities based on those estimated sentiments. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned collection unit is When identifying resource sharing opportunities among group companies, consider the geographical location of each company to identify these opportunities. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned analysis unit is When forecasting demand and optimizing inventory levels, we estimate user sentiment and adjust the demand forecasting method based on that estimated sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned analysis unit is Analyzing historical demand data improves the accuracy of forecasts when forecasting demand and optimizing inventory levels. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned analysis unit is When forecasting demand and optimizing inventory levels, the system estimates user sentiment and prioritizes inventory levels based on that estimated sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned analysis unit is When forecasting demand and optimizing inventory levels, geographical location information should be taken into consideration during the demand forecasting process. The system described in Appendix 5, characterized by the features described herein. (Note 41) The aforementioned supply unit is, When using predictive analytics for risk management and disruption mitigation, we estimate user sentiment and adjust risk management methods based on that estimated sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When using predictive analytics for risk management and disruption mitigation, historical risk data is analyzed to improve the accuracy of management. The system described in Appendix 6, characterized by the features described herein. (Note 43) The aforementioned supply unit is, When using predictive analytics for risk management and disruption mitigation, we estimate user sentiment and determine risk management priorities based on that estimated sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 44) The aforementioned supply unit is, When using predictive analytics for risk management and disruption mitigation, consider geographical location information when managing risks. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0201] 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 data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides optimal suggestions based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned supply unit is, Conduct dynamic supplier evaluations. The system according to feature 1.

3. The aforementioned supply unit is, Real-time logistics tracking The system according to feature 1.

4. The aforementioned collection unit is Identify opportunities for resource sharing among group companies. The system according to feature 1.

5. The aforementioned analysis unit is We forecast demand and optimize inventory levels. The system according to feature 1.

6. The aforementioned supply unit is, Use predictive analytics to manage risk and mitigate disruptions. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on current market conditions and trends. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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