system

Abstract

The system according to this embodiment aims to reduce food waste and achieve efficient food recovery. [Solution] The system according to the embodiment comprises a linking unit, a collection unit, a matching unit, and a proposal unit. The linking unit links with logistics management software. The collection unit collects and analyzes the data linked by the linking unit in real time. The matching unit matches the recipient with an appropriate food bank or organization based on the data collected by the collection unit. The proposal unit proposes the optimal delivery route based on the data matched by the matching unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to effectively reduce food loss and ensure rapid delivery to food banks, and there is an efficiency decline due to the mismatch between demand and supply.

[0005] The system according to the embodiment aims to reduce food loss and achieve efficient food recovery.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a linking unit, a data collection unit, a matching unit, and a proposal unit. The linking unit links with logistics management software. The data collection unit collects and analyzes the data linked by the linking unit in real time. The matching unit matches the data collected by the data collection unit with appropriate food banks or organizations. The proposal unit proposes the optimal delivery route based on the data matched by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce food waste and achieve efficient food recovery. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 three or more matters are expressed by connecting them with "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The food loss reduction system according to an embodiment of the present invention is a system that reduces food loss and enables rapid delivery to food banks. This system integrates with existing logistics systems using APIs to optimize food distribution. Furthermore, it uses a cloud platform to collect and analyze surplus food and demand data in real time. It uses AI to match with appropriate food banks and organizations and proposes the optimal delivery route using a map information provision API. This mechanism reduces food loss and enables efficient food recovery. For example, by integrating with logistics management software using APIs, the inventory and delivery status of food can be grasped in real time. This enables efficient management of food distribution. Next, by using a cloud platform, the occurrence of surplus food and demand data can be collected in real time and analyzed using AI. This enables efficient matching of surplus food and demand. Furthermore, AI can be used to match the location of surplus food generation with the location of demand, and the optimal delivery route can be proposed using a map information provision API. This enables efficient food delivery. As a result, food loss can be reduced and efficient food recovery can be achieved. For example, food loss can be reduced by quickly delivering surplus food to food banks. Furthermore, AI-powered route optimization reduces time and costs, enabling immediate response. It also strengthens support for local food banks and promotes social contribution. For example, providing surplus food to local food banks contributes to the community. Thus, this invention is a system for reducing food waste and enabling rapid delivery to food banks. It integrates with existing logistics systems using APIs, collects and analyzes surplus food and demand data in real time using a cloud platform, matches appropriate food banks and organizations using AI, and proposes optimal delivery routes using a map information API, thereby reducing food waste and achieving efficient food recovery. As a result, this food waste reduction system can reduce food waste and achieve efficient food recovery.

[0029] The food loss reduction system according to this embodiment comprises a linking unit, a collection unit, a matching unit, and a proposal unit. The linking unit links with logistics management software. The linking unit can grasp the inventory status and delivery status of food in real time by linking with the logistics management software, for example, using an API. The linking unit can efficiently manage the distribution of food by linking with the logistics management software, for example. The collection unit collects and analyzes the data linked by the linking unit in real time. The collection unit collects and analyzes surplus food and demand data in real time, for example, using a cloud platform. The collection unit can collect the occurrence status and demand data of surplus food in real time and analyze it using AI, for example, by using a cloud platform. The collection unit supports real-time data updates, for example. The collection unit proposes food quality control and storage methods, for example. The matching unit matches the data collected by the collection unit with appropriate food banks or organizations. The matching unit matches the location of surplus food generation with the location of demand, for example, using AI. The matching unit estimates the user's emotions and adjusts the matching criteria based on the estimated emotions. The suggestion unit proposes the optimal delivery route based on the data matched by the matching unit. The suggestion unit proposes the optimal delivery route using, for example, a map information provision API. The suggestion unit enables time and cost reduction and immediate response through, for example, AI-based route optimization. The suggestion unit responds to changes in the delivery route. The suggestion unit estimates the user's emotions and adjusts the way the suggestion is presented based on the estimated emotions. As a result, the food loss reduction system according to the embodiment can reduce food loss and achieve efficient food recovery.

[0030] The integration unit integrates with logistics management software. For example, by integrating with the logistics management software using an API, the integration unit can grasp the inventory and delivery status of food products in real time. Specifically, it obtains inventory data, shipping data, and delivery route information from each warehouse and distribution center via the logistics management software's API. This allows the integration unit to centrally manage the distribution status of food products and quickly detect problems such as inventory shortages or surpluses and delivery delays. Furthermore, through integration with the logistics management software, the integration unit can obtain information on food product expiration dates and best-before dates, and optimize inventory turnover based on this data. For example, by issuing instructions to prioritize the shipment of food products nearing their expiration dates, food waste can be prevented. The integration unit can also optimize delivery routes and adjust delivery schedules through integration with the logistics management software. This improves delivery efficiency and reduces costs. Additionally, through integration with the logistics management software, the integration unit can respond to real-time data updates, enabling decision-making based on the latest information at all times. This will enable the collaborative department to efficiently manage food distribution and contribute to reducing food waste.

[0031] The data collection unit collects and analyzes data linked by the collaboration unit in real time. For example, the data collection unit uses a cloud platform to collect and analyze surplus food and demand data in real time. Specifically, it builds a database on the cloud platform and centrally manages inventory data, delivery data, and demand data obtained from the collaboration unit. This allows the data collection unit to grasp the status of surplus food and demand data in real time and respond quickly. Furthermore, the data collection unit can analyze the collected data using AI to identify the causes of surplus food and patterns of demand fluctuations. For example, based on past data, it can predict increases and decreases in demand during specific seasons or events and take measures to prevent surplus food from occurring. The data collection unit also has a function to suggest food quality control and storage methods. For example, based on collected data, it suggests optimal storage temperature, humidity, and storage location, providing advice to maintain food quality. This allows the data collection unit to suppress surplus food while maintaining food quality. Additionally, the data collection unit supports real-time data updates, enabling analysis based on the latest information at all times. This allows the data collection unit to contribute to reducing food waste through rapid and accurate data collection and analysis.

[0032] The matching unit matches surplus food with appropriate food banks and organizations based on data collected by the collection unit. Specifically, it uses AI to match the location of surplus food with the location of demand. For example, it compares data on surplus food provided by the collection unit with demand data provided by food banks and organizations to perform the optimal match. The AI ​​learns from past matching data and demand trends to achieve more accurate matching. Furthermore, the matching unit also has a function to estimate the user's emotions and adjust the matching criteria based on those emotions. For example, it can respond flexibly based on emotions, such as when a food bank or organization representative is in a hurry or has a strong desire for a specific food item. As a result, the matching unit can provide optimal matching that is tailored to the user's needs and circumstances, not just simple data matching. In addition, the matching unit updates matching results in real time, ensuring that matching is always based on the latest information. Through this, the matching unit can promote the effective use of surplus food and contribute to reducing food waste through rapid and accurate matching.

[0033] The Proposal Department suggests the optimal delivery route based on data matched by the Matching Department. Specifically, it uses a map information provision API to suggest the best delivery route. For example, the Proposal Department can reduce time and costs and respond immediately through AI-powered route optimization. The AI ​​calculates the optimal route considering traffic conditions, road congestion, and the location of the delivery destination. This allows the Proposal Department to shorten delivery times, reduce fuel costs, and support efficient delivery. Furthermore, the Proposal Department also has a function to respond to changes in delivery routes. For example, if unexpected events such as traffic congestion or road construction occur, the AI ​​immediately calculates a new route and notifies the driver. This allows the Proposal Department to always provide the optimal route and minimize delivery delays. The Proposal Department also has a function to estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, it can respond flexibly according to the driver's emotions, such as when the driver is stressed or in a hurry. This allows the Proposal Department to make appropriate suggestions to the user and support efficient delivery. Furthermore, the proposal department can collect user feedback and optimize its proposal algorithm to continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to always provide highly accurate proposals based on the latest information, contributing to the reduction of food waste.

[0034] The data collection unit can collect and analyze surplus food and demand data in real time using a cloud platform. For example, by using the cloud platform, the data collection unit can collect data on the occurrence of surplus food and demand in real time and analyze it using AI. The data collection unit can, for example, respond to real-time data updates. The data collection unit can, for example, propose food quality control and storage methods. This enables efficient collection and analysis of surplus food and demand data by utilizing the cloud platform. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data collected using the cloud platform into a generating AI and have the generating AI perform data analysis.

[0035] The proposal unit can propose the optimal delivery route using a map information provision API. For example, the proposal unit can propose the optimal delivery route by utilizing the map information provision API. For example, the proposal unit can achieve time and cost reductions and enable immediate response through AI-based route optimization. The proposal unit can respond to changes in the delivery route, for example. This allows for the proposal of the optimal delivery route by utilizing the map information provision API. Some or all of the above-described processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input map data obtained using the map information provision API into a generating AI and have the generating AI execute the proposal of the optimal delivery route.

[0036] The data collection unit can handle real-time data updates. For example, by utilizing a cloud platform, the data collection unit can collect data on surplus food occurrences and demand in real time and analyze it using AI. The data collection unit can, for example, handle real-time data updates. The data collection unit can, for example, propose food quality control and storage methods. This ensures that the latest information is always reflected by handling real-time data updates. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data collected using a cloud platform into a generating AI and have the generating AI perform data updates.

[0037] The proposal unit can accommodate changes in delivery routes. For example, by utilizing a map information provision API, the proposal unit can propose the optimal delivery route. For example, by using AI-based route optimization, the proposal unit can reduce time and costs and respond immediately. The proposal unit can accommodate changes in delivery routes. This allows for flexible delivery planning. Some or all of the above-described processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input map data obtained using a map information provision API into a generating AI and have the generating AI execute changes to the delivery route.

[0038] The data collection unit can propose food quality control and preservation methods. The data collection unit can, for example, use a cloud platform to collect real-time data on surplus food occurrences and demand, and analyze it using AI. The data collection unit can, for example, support real-time data updates. The data collection unit can, for example, propose food quality control and preservation methods. This prevents food spoilage by proposing food quality control and preservation methods. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data collected using a cloud platform into a generating AI, and have the generating AI execute proposals for food quality control and preservation methods.

[0039] The integration unit can automatically adjust the integration method according to the version and settings of the logistics management software during integration. For example, if the versions of the logistics management software are different, the integration unit will automatically select a compatible integration method. For example, the integration unit will select the optimal data format according to the settings of the logistics management software and perform the integration. For example, the integration unit will automatically adjust the integration method in conjunction with updates to the logistics management software to ensure continuity of integration. In this way, continuity of integration can be ensured by automatically adjusting the integration method according to the version and settings of the logistics management software. Some or all of the above processes in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the version and setting information of the logistics management software into a generating AI and have the generating AI perform the automatic adjustment of the integration method.

[0040] The integration unit can evaluate the reliability of data during integration and prioritize the integration of highly reliable data. For example, the integration unit can evaluate the source of the data and prioritize the integration of highly reliable data. For example, the integration unit can consider the frequency of data updates and prioritize the integration of the latest data. For example, the integration unit can verify the consistency of the data and prioritize the integration of consistent data. In this way, data consistency can be maintained by evaluating the reliability of the data and prioritizing the integration of highly reliable data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the data reliability evaluation into a generating AI and have the generating AI perform the selection of highly reliable data.

[0041] The integration unit can monitor the usage status of the logistics management software in real time during integration and determine the optimal integration timing. For example, the integration unit monitors the usage status of the logistics management software in real time and determines the optimal integration timing. For example, the integration unit adjusts the integration timing according to the usage status of the logistics management software to achieve efficient integration. For example, the integration unit analyzes the usage status of the logistics management software and predicts the optimal integration timing. This enables efficient data integration by monitoring the usage status of the logistics management software in real time. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the usage status data of the logistics management software into a generating AI and have the generating AI determine the optimal integration timing.

[0042] The integration unit can maintain data consistency by coordinating with other related systems (e.g., inventory management systems) during integration. For example, the integration unit can maintain data consistency by coordinating with the inventory management system. The integration unit can ensure data integrity by coordinating with other related systems. For example, the integration unit can perform adjustments to maintain data consistency across multiple systems during integration. This allows for data consistency to be maintained through coordination with other related systems. Some or all of the above-described processes in the integration unit may be performed using AI, or without AI. For example, the integration unit can input integration data with the inventory management system into a generating AI and have the generating AI perform the task of ensuring data consistency.

[0043] The data collection unit can adjust the collection frequency based on the importance of the data during collection. For example, the data collection unit may collect important data frequently to provide up-to-date information. For example, the data collection unit may reduce the collection frequency of less important data to ensure efficient data collection. The data collection unit may dynamically adjust the collection frequency according to the importance of the data. This enables efficient data collection by adjusting the collection frequency based on the importance of the 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 may input the data importance evaluation into a generating AI and have the generating AI perform the adjustment of the collection frequency.

[0044] The data collection unit can apply different collection algorithms depending on the data category during collection. For example, the data collection unit can select the optimal collection algorithm depending on the data category. For example, the data collection unit can apply an appropriate collection algorithm to data of different categories. For example, the data collection unit can dynamically adjust the collection algorithm for each data category. This improves the accuracy of data collection by applying the collection algorithm according to the data category. 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 data category information into a generating AI and have the generating AI execute the application of the collection algorithm.

[0045] The data collection unit can determine collection priorities by considering the geographical distribution of the data during collection. For example, the data collection unit may prioritize the collection of data from important regions by considering the geographical distribution of the data. For example, the data collection unit may dynamically adjust collection priorities based on geographical distribution. For example, the data collection unit may analyze the geographical distribution and formulate an optimal collection strategy. This enables efficient data collection by determining collection priorities while considering the geographical distribution of the data. 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 may input geographical distribution data into a generating AI and have the generating AI perform the determination of collection priorities.

[0046] The data collection unit can improve the accuracy of data collection by referring to relevant literature during the collection process. For example, the data collection unit can refer to relevant literature to improve the accuracy of data collection. For example, the data collection unit can adjust the collection algorithm based on the relevant literature. For example, the data collection unit can analyze relevant literature for the data and propose the optimal collection method. This improves the accuracy of data collection by referring to relevant literature. 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 relevant literature data into a generating AI and have the generating AI adjust the collection algorithm.

[0047] The matching unit can improve the accuracy of matching by considering the interrelationships of the data during the matching process. For example, the matching unit considers the interrelationships of the data and performs the optimal matching. For example, the matching unit analyzes the interrelationships of the data and improves the accuracy of matching. For example, the matching unit adjusts the matching algorithm based on the interrelationships of the data. This improves the accuracy of matching by considering the interrelationships of the data. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data interrelationship information into a generating AI and have the generating AI adjust the matching algorithm.

[0048] The matching unit can perform matching while considering the attribute information of the data submitter. For example, the matching unit considers the attribute information of the data submitter to perform the optimal matching. For example, the matching unit analyzes the attribute information of the submitter to improve the accuracy of the matching. For example, the matching unit adjusts the matching algorithm based on the attribute information of the submitter. This makes it possible to perform more appropriate matching by considering the attribute information of the data submitter. Some or all of the above processes in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the attribute information of the submitter into a generating AI and have the generating AI perform the adjustment of the matching algorithm.

[0049] The matching unit can perform matching while considering the geographical distribution of the data. For example, the matching unit considers the geographical distribution of the data and performs the optimal matching. For example, the matching unit improves the accuracy of matching based on the geographical distribution. For example, the matching unit analyzes the geographical distribution and proposes the optimal matching algorithm. This makes it possible to perform more appropriate matching by considering the geographical distribution of the data. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data into a generating AI and have the generating AI execute a matching algorithm proposal.

[0050] The matching unit can improve the accuracy of matching by referring to relevant literature for the data during the matching process. For example, the matching unit can refer to relevant literature for the data to improve the accuracy of matching. For example, the matching unit can adjust the matching algorithm based on the relevant literature. For example, the matching unit can analyze the relevant literature for the data and propose the optimal matching method. As a result, the accuracy of matching is improved by referring to relevant literature for the data. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the relevant literature data into a generating AI and have the generating AI perform the adjustment of the matching algorithm.

[0051] The proposal unit can adjust the level of detail of its proposals based on the importance of the data. For example, the proposal unit will provide detailed proposals based on important data, giving the user sufficient information. For example, the proposal unit will provide concise proposals based on less important data, reducing the burden on the user. The proposal unit can dynamically adjust the level of detail of its proposals according to the importance of the data. This allows for more appropriate proposals by adjusting the level of detail of proposals based on the importance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data importance evaluation into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0052] The proposal unit can apply different proposal algorithms depending on the data category during the proposal process. For example, the proposal unit can select the optimal proposal algorithm based on the data category. For example, the proposal unit can apply an appropriate proposal algorithm to data of different categories. For example, the proposal unit can dynamically adjust the proposal algorithm for each data category. This improves the accuracy of the proposal by applying the proposal algorithm according to the data category. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data category information into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0053] The proposal department can determine the priority of proposals based on the data submission timing at the time of submission. For example, the proposal department may prioritize proposals based on the latest data. For example, the proposal department may postpone proposals based on older data. For example, the proposal department may dynamically adjust the priority of proposals according to the data submission timing. This allows for more appropriate proposals by determining the priority of proposals based on the data submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department may input data submission timing information into a generating AI and have the generating AI perform the determination of proposal priority.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the data during the proposal process. For example, the proposal unit may prioritize proposals based on highly relevant data. For example, the proposal unit may postpone proposals based on less relevant data. For example, the proposal unit may dynamically adjust the order of proposals according to the relevance of the data. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit may input data relevance information into a generating AI and have the generating AI perform the adjustment of the order of proposals.

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

[0056] The data collection unit can adjust the collection frequency based on the importance of the data. For example, important data can be collected frequently to provide up-to-date information. Conversely, less important data can be collected less frequently for more efficient data collection. Furthermore, the collection frequency can be dynamically adjusted according to the importance of the data. This allows for efficient data collection by adjusting the collection frequency based on the importance of the data.

[0057] The matching unit can improve the accuracy of matching by considering the interrelationships between data during the matching process. For example, it can perform optimal matching by considering the interrelationships between data. It can also analyze the interrelationships between data to improve matching accuracy. Furthermore, it can adjust the matching algorithm based on the interrelationships between data. As a result, the accuracy of matching is improved by considering the interrelationships between data.

[0058] The data collection unit can apply different collection algorithms depending on the data category during collection. For example, it can select the optimal collection algorithm based on the data category. It can also apply appropriate collection algorithms to data of different categories. Furthermore, it can dynamically adjust the collection algorithm for each data category. This improves the accuracy of data collection by applying collection algorithms according to the data category.

[0059] The integration unit can automatically adjust the integration method according to the version and settings of the logistics management software during integration. For example, if the versions of the logistics management software are different, it can automatically select a compatible integration method. It can also select the optimal data format according to the settings of the logistics management software and perform the integration. Furthermore, it can automatically adjust the integration method in response to updates to the logistics management software, ensuring continuity of integration. In this way, continuity of integration can be ensured by automatically adjusting the integration method according to the version and settings of the logistics management software.

[0060] The proposal department can prioritize proposals based on the timing of data submission. For example, proposals based on the latest data can be given priority. Conversely, proposals based on older data can be postponed. Furthermore, the priority of proposals can be dynamically adjusted according to the timing of data submission. This allows for more appropriate proposals by prioritizing proposals based on the timing of data submission.

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

[0062] Step 1: The integration unit connects with logistics management software. For example, by using an API for integration, it becomes possible to monitor food inventory and delivery status in real time. This enables efficient management of food distribution. Step 2: The data collection unit collects and analyzes the data linked by the integration unit in real time. For example, it uses a cloud platform to collect and analyze surplus food and demand data in real time. This allows for the proposal of food quality control and storage methods. Step 3: The matching unit matches the data collected by the collection unit with appropriate food banks or organizations. For example, AI is used to match the location of surplus food with the location of demand. This enables efficient food recovery. Step 4: The proposal unit proposes the optimal delivery route based on the data matched by the matching unit. For example, it might use a map information provision API to propose the optimal delivery route. This reduces time and costs and enables immediate response.

[0063] (Example of form 2) The food loss reduction system according to an embodiment of the present invention is a system that reduces food loss and enables rapid delivery to food banks. This system integrates with existing logistics systems using APIs to optimize food distribution. Furthermore, it uses a cloud platform to collect and analyze surplus food and demand data in real time. Using AI, it matches with appropriate food banks and organizations and proposes the optimal delivery route using a map information provision API. This mechanism reduces food loss and enables efficient food recovery. For example, by integrating with logistics management software using APIs, the inventory and delivery status of food can be grasped in real time. This enables efficient management of food distribution. Next, by using a cloud platform, the occurrence of surplus food and demand data can be collected in real time and analyzed using AI. This enables efficient matching of surplus food and demand. Furthermore, using AI, the location of surplus food generation and the location of demand can be matched, and the optimal delivery route can be proposed using a map information provision API. This enables efficient food delivery. As a result, food loss can be reduced and efficient food recovery can be achieved. For example, food loss can be reduced by quickly delivering surplus food to food banks. Furthermore, AI-powered route optimization reduces time and costs, enabling immediate response. It also strengthens support for local food banks and promotes social contribution. For example, providing surplus food to local food banks contributes to the community. Thus, this invention is a system for reducing food waste and enabling rapid delivery to food banks. It integrates with existing logistics systems using APIs, collects and analyzes surplus food and demand data in real time using a cloud platform, matches appropriate food banks and organizations using AI, and proposes optimal delivery routes using a map information API, thereby reducing food waste and achieving efficient food recovery. As a result, this food waste reduction system can reduce food waste and achieve efficient food recovery.

[0064] The food loss reduction system according to this embodiment comprises a linking unit, a collection unit, a matching unit, and a proposal unit. The linking unit links with logistics management software. The linking unit can grasp the inventory status and delivery status of food in real time by linking with the logistics management software, for example, using an API. The linking unit can efficiently manage the distribution of food by linking with the logistics management software, for example. The collection unit collects and analyzes the data linked by the linking unit in real time. The collection unit collects and analyzes surplus food and demand data in real time, for example, using a cloud platform. The collection unit can collect the occurrence status and demand data of surplus food in real time and analyze it using AI, for example, by using a cloud platform. The collection unit supports real-time data updates, for example. The collection unit proposes food quality control and storage methods, for example. The matching unit matches the data collected by the collection unit with appropriate food banks or organizations. The matching unit matches the location of surplus food generation with the location of demand, for example, using AI. The matching unit estimates the user's emotions and adjusts the matching criteria based on the estimated emotions. The suggestion unit proposes the optimal delivery route based on the data matched by the matching unit. The suggestion unit proposes the optimal delivery route using, for example, a map information provision API. The suggestion unit enables time and cost reduction and immediate response through, for example, AI-based route optimization. The suggestion unit responds to changes in the delivery route. The suggestion unit estimates the user's emotions and adjusts the way the suggestion is presented based on the estimated emotions. As a result, the food loss reduction system according to the embodiment can reduce food loss and achieve efficient food recovery.

[0065] The integration unit integrates with logistics management software. For example, by integrating with the logistics management software using an API, the integration unit can grasp the inventory and delivery status of food products in real time. Specifically, it obtains inventory data, shipping data, and delivery route information from each warehouse and distribution center via the logistics management software's API. This allows the integration unit to centrally manage the distribution status of food products and quickly detect problems such as inventory shortages or surpluses and delivery delays. Furthermore, through integration with the logistics management software, the integration unit can obtain information on food product expiration dates and best-before dates, and optimize inventory turnover based on this data. For example, by issuing instructions to prioritize the shipment of food products nearing their expiration dates, food waste can be prevented. The integration unit can also optimize delivery routes and adjust delivery schedules through integration with the logistics management software. This improves delivery efficiency and reduces costs. Additionally, through integration with the logistics management software, the integration unit can respond to real-time data updates, enabling decision-making based on the latest information at all times. This will enable the collaborative department to efficiently manage food distribution and contribute to reducing food waste.

[0066] The data collection unit collects and analyzes data linked by the collaboration unit in real time. For example, the data collection unit uses a cloud platform to collect and analyze surplus food and demand data in real time. Specifically, it builds a database on the cloud platform and centrally manages inventory data, delivery data, and demand data obtained from the collaboration unit. This allows the data collection unit to grasp the status of surplus food and demand data in real time and respond quickly. Furthermore, the data collection unit can analyze the collected data using AI to identify the causes of surplus food and patterns of demand fluctuations. For example, based on past data, it can predict increases and decreases in demand during specific seasons or events and take measures to prevent surplus food from occurring. The data collection unit also has a function to suggest food quality control and storage methods. For example, based on collected data, it suggests optimal storage temperature, humidity, and storage location, providing advice to maintain food quality. This allows the data collection unit to suppress surplus food while maintaining food quality. Additionally, the data collection unit supports real-time data updates, enabling analysis based on the latest information at all times. This allows the data collection unit to contribute to reducing food waste through rapid and accurate data collection and analysis.

[0067] The matching unit matches surplus food with appropriate food banks and organizations based on data collected by the collection unit. Specifically, it uses AI to match the location of surplus food with the location of demand. For example, it compares data on surplus food provided by the collection unit with demand data provided by food banks and organizations to perform the optimal match. The AI ​​learns from past matching data and demand trends to achieve more accurate matching. Furthermore, the matching unit also has a function to estimate the user's emotions and adjust the matching criteria based on those emotions. For example, it can respond flexibly based on emotions, such as when a food bank or organization representative is in a hurry or has a strong desire for a specific food item. As a result, the matching unit can provide optimal matching that is tailored to the user's needs and circumstances, not just simple data matching. In addition, the matching unit updates matching results in real time, ensuring that matching is always based on the latest information. Through this, the matching unit can promote the effective use of surplus food and contribute to reducing food waste through rapid and accurate matching.

[0068] The Proposal Department suggests the optimal delivery route based on data matched by the Matching Department. Specifically, it uses a map information provision API to suggest the best delivery route. For example, the Proposal Department can reduce time and costs and respond immediately through AI-powered route optimization. The AI ​​calculates the optimal route considering traffic conditions, road congestion, and the location of the delivery destination. This allows the Proposal Department to shorten delivery times, reduce fuel costs, and support efficient delivery. Furthermore, the Proposal Department also has a function to respond to changes in delivery routes. For example, if unexpected events such as traffic congestion or road construction occur, the AI ​​immediately calculates a new route and notifies the driver. This allows the Proposal Department to always provide the optimal route and minimize delivery delays. The Proposal Department also has a function to estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, it can respond flexibly according to the driver's emotions, such as when the driver is stressed or in a hurry. This allows the Proposal Department to make appropriate suggestions to the user and support efficient delivery. Furthermore, the proposal department can collect user feedback and optimize its proposal algorithm to continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to always provide highly accurate proposals based on the latest information, contributing to the reduction of food waste.

[0069] The data collection unit can collect and analyze surplus food and demand data in real time using a cloud platform. For example, by using the cloud platform, the data collection unit can collect data on the occurrence of surplus food and demand in real time and analyze it using AI. The data collection unit can, for example, respond to real-time data updates. The data collection unit can, for example, propose food quality control and storage methods. This enables efficient collection and analysis of surplus food and demand data by utilizing the cloud platform. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data collected using the cloud platform into a generating AI and have the generating AI perform data analysis.

[0070] The proposal unit can propose the optimal delivery route using a map information provision API. For example, the proposal unit can propose the optimal delivery route by utilizing the map information provision API. For example, the proposal unit can achieve time and cost reductions and enable immediate response through AI-based route optimization. The proposal unit can respond to changes in the delivery route, for example. This allows for the proposal of the optimal delivery route by utilizing the map information provision API. Some or all of the above-described processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input map data obtained using the map information provision API into a generating AI and have the generating AI execute the proposal of the optimal delivery route.

[0071] The data collection unit can handle real-time data updates. For example, by utilizing a cloud platform, the data collection unit can collect data on surplus food occurrences and demand in real time and analyze it using AI. The data collection unit can, for example, handle real-time data updates. The data collection unit can, for example, propose food quality control and storage methods. This ensures that the latest information is always reflected by handling real-time data updates. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data collected using a cloud platform into a generating AI and have the generating AI perform data updates.

[0072] The proposal unit can accommodate changes in delivery routes. For example, by utilizing a map information provision API, the proposal unit can propose the optimal delivery route. For example, by using AI-based route optimization, the proposal unit can reduce time and costs and respond immediately. The proposal unit can accommodate changes in delivery routes. This allows for flexible delivery planning. Some or all of the above-described processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input map data obtained using a map information provision API into a generating AI and have the generating AI execute changes to the delivery route.

[0073] The data collection unit can propose food quality control and preservation methods. The data collection unit can, for example, use a cloud platform to collect real-time data on surplus food occurrences and demand, and analyze it using AI. The data collection unit can, for example, support real-time data updates. The data collection unit can, for example, propose food quality control and preservation methods. This prevents food spoilage by proposing food quality control and preservation methods. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data collected using a cloud platform into a generating AI, and have the generating AI execute proposals for food quality control and preservation methods.

[0074] The integration unit can estimate the user's emotions and determine the priority of data to integrate based on the estimated user emotions. For example, if the user is stressed, the integration unit will prioritize integrating important data to facilitate a quick response. For example, if the user is relaxed, the integration unit will integrate detailed data to provide comprehensive information. For example, if the user is in a hurry, the integration unit will integrate only the most necessary data to support quick decision-making. This enables more appropriate data integration by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0075] The integration unit can automatically adjust the integration method according to the version and settings of the logistics management software during integration. For example, if the versions of the logistics management software are different, the integration unit will automatically select a compatible integration method. For example, the integration unit will select the optimal data format according to the settings of the logistics management software and perform the integration. For example, the integration unit will automatically adjust the integration method in conjunction with updates to the logistics management software to ensure continuity of integration. In this way, continuity of integration can be ensured by automatically adjusting the integration method according to the version and settings of the logistics management software. Some or all of the above processes in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the version and setting information of the logistics management software into a generating AI and have the generating AI perform the automatic adjustment of the integration method.

[0076] The integration unit can evaluate the reliability of data during integration and prioritize the integration of highly reliable data. For example, the integration unit can evaluate the source of the data and prioritize the integration of highly reliable data. For example, the integration unit can consider the frequency of data updates and prioritize the integration of the latest data. For example, the integration unit can verify the consistency of the data and prioritize the integration of consistent data. In this way, data consistency can be maintained by evaluating the reliability of the data and prioritizing the integration of highly reliable data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the data reliability evaluation into a generating AI and have the generating AI perform the selection of highly reliable data.

[0077] The integration unit can estimate the user's emotions and filter the data to be integrated based on the estimated user emotions. For example, if the user is stressed, the integration unit filters and integrates only the important data. For example, if the user is relaxed, the integration unit filters and integrates detailed data. For example, if the user is in a hurry, the integration unit filters and integrates only the most necessary data. This allows for more appropriate data integration by filtering data based on 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 integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform data filtering.

[0078] The integration unit can monitor the usage status of the logistics management software in real time during integration and determine the optimal integration timing. For example, the integration unit monitors the usage status of the logistics management software in real time and determines the optimal integration timing. For example, the integration unit adjusts the integration timing according to the usage status of the logistics management software to achieve efficient integration. For example, the integration unit analyzes the usage status of the logistics management software and predicts the optimal integration timing. This enables efficient data integration by monitoring the usage status of the logistics management software in real time. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the usage status data of the logistics management software into a generating AI and have the generating AI determine the optimal integration timing.

[0079] The integration unit can maintain data consistency by coordinating with other related systems (e.g., inventory management systems) during integration. For example, the integration unit can maintain data consistency by coordinating with the inventory management system. The integration unit can ensure data integrity by coordinating with other related systems. For example, the integration unit can perform adjustments to maintain data consistency across multiple systems during integration. This allows for data consistency to be maintained through coordination with other related systems. Some or all of the above-described processes in the integration unit may be performed using AI, or without AI. For example, the integration unit can input integration data with the inventory management system into a generating AI and have the generating AI perform the task of ensuring data consistency.

[0080] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect only important data. If the user is relaxed, the data collection unit will collect detailed data. If the user is in a hurry, the data collection unit will collect only the most necessary data. This allows for more appropriate data collection by adjusting the type of data collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the type of data to be collected.

[0081] The data collection unit can adjust the collection frequency based on the importance of the data during collection. For example, the data collection unit may collect important data frequently to provide up-to-date information. For example, the data collection unit may reduce the collection frequency of less important data to ensure efficient data collection. The data collection unit may dynamically adjust the collection frequency according to the importance of the data. This enables efficient data collection by adjusting the collection frequency based on the importance of the 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 may input the data importance evaluation into a generating AI and have the generating AI perform the adjustment of the collection frequency.

[0082] The data collection unit can apply different collection algorithms depending on the data category during collection. For example, the data collection unit can select the optimal collection algorithm depending on the data category. For example, the data collection unit can apply an appropriate collection algorithm to data of different categories. For example, the data collection unit can dynamically adjust the collection algorithm for each data category. This improves the accuracy of data collection by applying the collection algorithm according to the data category. 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 data category information into a generating AI and have the generating AI execute the application of the collection algorithm.

[0083] The data collection unit can estimate the user's emotions and adjust how the collected data is displayed based on the estimated emotions. For example, if the user is stressed, the data collection unit will display only the most important data. If the user is relaxed, the data collection unit will display detailed data. If the user is in a hurry, the data collection unit will display only the most necessary data. By adjusting how the collected data is displayed based on the user's emotions, more appropriate data display becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI adjust the display method.

[0084] The data collection unit can determine collection priorities by considering the geographical distribution of the data during collection. For example, the data collection unit may prioritize the collection of data from important regions by considering the geographical distribution of the data. For example, the data collection unit may dynamically adjust collection priorities based on geographical distribution. For example, the data collection unit may analyze the geographical distribution and formulate an optimal collection strategy. This enables efficient data collection by determining collection priorities while considering the geographical distribution of the data. 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 may input geographical distribution data into a generating AI and have the generating AI perform the determination of collection priorities.

[0085] The data collection unit can improve the accuracy of data collection by referring to relevant literature during the collection process. For example, the data collection unit can refer to relevant literature to improve the accuracy of data collection. For example, the data collection unit can adjust the collection algorithm based on the relevant literature. For example, the data collection unit can analyze relevant literature for the data and propose the optimal collection method. This improves the accuracy of data collection by referring to relevant literature. 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 relevant literature data into a generating AI and have the generating AI adjust the collection algorithm.

[0086] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is stressed, the matching unit will consider only the most important criteria for matching. For example, if the user is relaxed, the matching unit will consider detailed criteria for matching. For example, if the user is in a hurry, the matching unit will consider only the most necessary criteria for matching. This allows for more appropriate matching by adjusting the matching criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not using AI. For example, the matching unit can input user emotion data into a generative AI and have the generative AI adjust the matching criteria.

[0087] The matching unit can improve the accuracy of matching by considering the interrelationships of the data during the matching process. For example, the matching unit considers the interrelationships of the data and performs the optimal matching. For example, the matching unit analyzes the interrelationships of the data and improves the accuracy of matching. For example, the matching unit adjusts the matching algorithm based on the interrelationships of the data. This improves the accuracy of matching by considering the interrelationships of the data. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data interrelationship information into a generating AI and have the generating AI adjust the matching algorithm.

[0088] The matching unit can perform matching while considering the attribute information of the data submitter. For example, the matching unit considers the attribute information of the data submitter to perform the optimal matching. For example, the matching unit analyzes the attribute information of the submitter to improve the accuracy of the matching. For example, the matching unit adjusts the matching algorithm based on the attribute information of the submitter. This makes it possible to perform more appropriate matching by considering the attribute information of the data submitter. Some or all of the above processes in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the attribute information of the submitter into a generating AI and have the generating AI perform the adjustment of the matching algorithm.

[0089] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is stressed, the matching unit will prioritize displaying important results. For example, if the user is relaxed, the matching unit will display detailed results. For example, if the user is in a hurry, the matching unit will prioritize displaying the most necessary results. This allows for more appropriate result display by adjusting the order in which matching results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 matching unit may be performed using AI or not using AI. For example, the matching unit can input user emotion data into the generative AI and have the generative AI adjust the order in which results are displayed.

[0090] The matching unit can perform matching while considering the geographical distribution of the data. For example, the matching unit considers the geographical distribution of the data and performs the optimal matching. For example, the matching unit improves the accuracy of matching based on the geographical distribution. For example, the matching unit analyzes the geographical distribution and proposes the optimal matching algorithm. This makes it possible to perform more appropriate matching by considering the geographical distribution of the data. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data into a generating AI and have the generating AI execute a matching algorithm proposal.

[0091] The matching unit can improve the accuracy of matching by referring to relevant literature for the data during the matching process. For example, the matching unit can refer to relevant literature for the data to improve the accuracy of matching. For example, the matching unit can adjust the matching algorithm based on the relevant literature. For example, the matching unit can analyze the relevant literature for the data and propose the optimal matching method. As a result, the accuracy of matching is improved by referring to relevant literature for the data. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the relevant literature data into a generating AI and have the generating AI perform the adjustment of the matching algorithm.

[0092] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit will provide concise and clear suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will prioritize presenting the most important information. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0093] The proposal unit can adjust the level of detail of its proposals based on the importance of the data. For example, the proposal unit will provide detailed proposals based on important data, giving the user sufficient information. For example, the proposal unit will provide concise proposals based on less important data, reducing the burden on the user. The proposal unit can dynamically adjust the level of detail of its proposals according to the importance of the data. This allows for more appropriate proposals by adjusting the level of detail of proposals based on the importance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data importance evaluation into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0094] The proposal unit can apply different proposal algorithms depending on the data category during the proposal process. For example, the proposal unit can select the optimal proposal algorithm based on the data category. For example, the proposal unit can apply an appropriate proposal algorithm to data of different categories. For example, the proposal unit can dynamically adjust the proposal algorithm for each data category. This improves the accuracy of the proposal by applying the proposal algorithm according to the data category. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data category information into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make concise and short suggestions. For example, if the user is relaxed, the suggestion unit will make detailed and long suggestions. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting the most important information. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0096] The proposal department can determine the priority of proposals based on the data submission timing at the time of submission. For example, the proposal department may prioritize proposals based on the latest data. For example, the proposal department may postpone proposals based on older data. For example, the proposal department may dynamically adjust the priority of proposals according to the data submission timing. This allows for more appropriate proposals by determining the priority of proposals based on the data submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department may input data submission timing information into a generating AI and have the generating AI perform the determination of proposal priority.

[0097] The proposal unit can adjust the order of proposals based on the relevance of the data during the proposal process. For example, the proposal unit may prioritize proposals based on highly relevant data. For example, the proposal unit may postpone proposals based on less relevant data. For example, the proposal unit may dynamically adjust the order of proposals according to the relevance of the data. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit may input data relevance information into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The integration unit can estimate the user's emotions and prioritize the data to be integrated based on those emotions. For example, if the user is stressed, important data can be prioritized for faster response. If the user is relaxed, detailed data can be integrated to provide comprehensive information. Furthermore, if the user is in a hurry, only the most necessary data can be integrated to support quick decision-making. In this way, prioritizing data based on the user's emotions enables more appropriate data integration.

[0100] The data collection unit can adjust the collection frequency based on the importance of the data. For example, important data can be collected frequently to provide up-to-date information. Conversely, less important data can be collected less frequently for more efficient data collection. Furthermore, the collection frequency can be dynamically adjusted according to the importance of the data. This allows for efficient data collection by adjusting the collection frequency based on the importance of the data.

[0101] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide concise and clear suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can prioritize presenting the most important information. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made.

[0102] The matching unit can improve the accuracy of matching by considering the interrelationships between data during the matching process. For example, it can perform optimal matching by considering the interrelationships between data. It can also analyze the interrelationships between data to improve matching accuracy. Furthermore, it can adjust the matching algorithm based on the interrelationships between data. As a result, the accuracy of matching is improved by considering the interrelationships between data.

[0103] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on that estimation. For example, if the user is stressed, it can offer a concise and short suggestion. Conversely, if the user is relaxed, it can offer a detailed and longer suggestion. Furthermore, if the user is in a hurry, it can prioritize suggesting the most important information. By adjusting the suggestion length based on the user's emotions, it becomes possible to offer more appropriate suggestions.

[0104] The data collection unit can apply different collection algorithms depending on the data category during collection. For example, it can select the optimal collection algorithm based on the data category. It can also apply appropriate collection algorithms to data of different categories. Furthermore, it can dynamically adjust the collection algorithm for each data category. This improves the accuracy of data collection by applying collection algorithms according to the data category.

[0105] The matching unit can estimate the user's emotions and adjust the matching criteria based on those emotions. For example, if the user is stressed, the system can consider only the most important criteria for matching. If the user is relaxed, it can consider more detailed criteria for matching. Furthermore, if the user is in a hurry, it can consider only the most essential criteria for matching. By adjusting the matching criteria based on the user's emotions, more appropriate matching becomes possible.

[0106] The integration unit can automatically adjust the integration method according to the version and settings of the logistics management software during integration. For example, if the versions of the logistics management software are different, it can automatically select a compatible integration method. It can also select the optimal data format according to the settings of the logistics management software and perform the integration. Furthermore, it can automatically adjust the integration method in response to updates to the logistics management software, ensuring continuity of integration. In this way, continuity of integration can be ensured by automatically adjusting the integration method according to the version and settings of the logistics management software.

[0107] The proposal department can prioritize proposals based on the timing of data submission. For example, proposals based on the latest data can be given priority. Conversely, proposals based on older data can be postponed. Furthermore, the priority of proposals can be dynamically adjusted according to the timing of data submission. This allows for more appropriate proposals by prioritizing proposals based on the timing of data submission.

[0108] The data collection unit can estimate the user's emotions and adjust how the collected data is displayed based on those emotions. For example, if the user is stressed, only important data can be displayed. If the user is relaxed, detailed data can be displayed. Furthermore, if the user is in a hurry, only the most necessary data can be displayed. By adjusting how the collected data is displayed based on the user's emotions, more appropriate data can be displayed.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The integration unit connects with logistics management software. For example, by using an API for integration, it becomes possible to monitor food inventory and delivery status in real time. This enables efficient management of food distribution. Step 2: The data collection unit collects and analyzes the data linked by the integration unit in real time. For example, it uses a cloud platform to collect and analyze surplus food and demand data in real time. This allows for the proposal of food quality control and storage methods. Step 3: The matching unit matches the data collected by the collection unit with appropriate food banks or organizations. For example, AI is used to match the location of surplus food with the location of demand. This enables efficient food recovery. Step 4: The proposal unit proposes the optimal delivery route based on the data matched by the matching unit. For example, it might use a map information provision API to propose the optimal delivery route. This reduces time and costs and enables immediate response.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collaboration unit, collection unit, matching unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collaboration unit is implemented by the control unit 46A of the smart device 14 and collaborates with logistics management software using an API. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects and analyzes surplus food and demand data in real time using a cloud platform. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the location of surplus food generation with the location of demand using AI. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes the optimal delivery route using a map information provision API. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collaboration unit, collection unit, matching unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collaboration unit is implemented by the control unit 46A of the smart glasses 214 and collaborates with logistics management software using an API. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects and analyzes surplus food and demand data in real time using a cloud platform. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the location of surplus food generation with the location of demand using AI. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes the optimal delivery route using a map information provision API. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collaboration unit, collection unit, matching unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collaboration unit is implemented by the control unit 46A of the headset terminal 314 and collaborates with logistics management software using an API. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects and analyzes surplus food and demand data in real time using a cloud platform. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the location of surplus food generation with the location of demand using AI. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes the optimal delivery route using a map information provision API. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collaboration unit, collection unit, matching unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collaboration unit is implemented by the control unit 46A of the robot 414 and collaborates with logistics management software using an API. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects and analyzes surplus food and demand data in real time using a cloud platform. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the location of surplus food generation with the location of demand using AI. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes the optimal delivery route using a map information provision API. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The integration department that works with logistics management software, A collection unit collects and analyzes the data linked by the aforementioned collaboration unit in real time, Based on the data collected by the aforementioned collection unit, a matching unit matches the data with appropriate food banks or organizations. A proposal unit that proposes the optimal delivery route based on the data matched by the matching unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We use a cloud platform to collect and analyze surplus food and demand data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose the optimal delivery route using a map information API. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Supports real-time data updates The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Responding to changes in delivery routes The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We propose food quality control and preservation methods. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned linkage unit is, It estimates the user's emotions and prioritizes the data to link based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned linkage unit is, During integration, the integration method is automatically adjusted according to the version and settings of the logistics management software. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned linkage unit is, During data integration, the reliability of the data is evaluated, and highly reliable data is prioritized for integration. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned linkage unit is, It estimates the user's emotions and filters the linked data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned linkage unit is, During integration, the usage status of the logistics management software is monitored in real time to determine the optimal integration timing. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned linkage unit is, During integration, the system also integrates with other related systems to maintain data consistency. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, adjust the collection frequency based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, different collection algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and adjusts how collected data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting data, prioritize collection based on the data's geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is During data collection, we refer to relevant literature to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is During matching, the accuracy of the matching process is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is During the matching process, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During matching, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature for the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section 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 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section 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 29) The aforementioned proposal section is, When submitting a proposal, we will prioritize the proposals based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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. The integration department that works with logistics management software, A collection unit collects and analyzes the data linked by the aforementioned collaboration unit in real time, Based on the data collected by the aforementioned collection unit, a matching unit matches the data with appropriate food banks or organizations. A proposal unit that proposes the optimal delivery route based on the data matched by the matching unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Using a cloud platform, we collect and analyze surplus food and demand data in real time. The system according to feature 1.

3. The aforementioned proposal section is, We propose the optimal delivery route using a map information API. The system according to feature 1.

4. The aforementioned collection unit is Supports real-time data updates The system according to feature 1.

5. The aforementioned proposal section is, Responding to changes in delivery routes The system according to feature 1.

6. The aforementioned collection unit is We propose food quality control and preservation methods. The system according to feature 1.

7. The aforementioned linkage unit is, It estimates the user's emotions and prioritizes the data to link based on the estimated user emotions. The system according to feature 1.

8. The aforementioned linkage unit is, During integration, the integration method is automatically adjusted according to the version and settings of the logistics management software. The system according to feature 1.

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