system

Abstract

The system according to this embodiment aims to streamline inventory management and expiration date management of food ingredients, and to propose optimal usage methods and procurement plans. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an alert unit. The collection unit collects the type, quantity, expiration date, and inventory status of ingredients. The analysis unit analyzes the data collected by the collection unit and proposes the optimal way to use the ingredients and a procurement plan. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The alert unit issues an alert for ingredients nearing their expiration date and provides recipe suggestions.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is a problem that inventory management and expiration date management of food ingredients are troublesome, and it is difficult to establish an optimal usage method and procurement plan.

[0005] The system according to the embodiment aims to streamline the inventory management and expiration date management of food ingredients and propose an optimal usage method and procurement plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an alert unit. The data collection unit collects the type, quantity, expiration date, and inventory status of ingredients. The analysis unit analyzes the data collected by the data collection unit and proposes the optimal way to use the ingredients and a procurement plan. The proposal unit makes specific suggestions based on the analysis results obtained by the analysis unit. The alert unit issues an alert for ingredients nearing their expiration date and provides recipe suggestions. [Effects of the Invention]

[0007] The system according to this embodiment can streamline inventory management and expiration date management of food ingredients, and can propose optimal usage methods and procurement plans. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, 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 management system according to an embodiment of the present invention is a system that uses AI to collect information on the type, quantity, expiration date, and inventory status of food ingredients, and proposes optimal ways to use and procure them. This food management system collects information on the type, quantity, expiration date, and inventory status of food ingredients and proposes optimal ways to use and procure them. Furthermore, it learns from past data and trends to support demand forecasting and appropriate inventory management. For food ingredients nearing their expiration date, it issues an alert and suggests recipes. For example, the food management system collects information on the type, quantity, expiration date, and inventory status of food ingredients. In this case, it can collect data in real time in cooperation with IoT devices. For example, a sensor in the refrigerator detects the type and quantity of food ingredients and sends the data to the AI. This makes it possible to always know the latest inventory status. Next, based on the collected data, the AI ​​proposes optimal ways to use and procure the food ingredients. For example, for food ingredients nearing their expiration date, it suggests recipes for consuming them sooner. Also, for food ingredients with low inventory, it suggests re-procurement at the appropriate time. This reduces food waste and enables efficient inventory management. Furthermore, it learns from past data and trends to support demand forecasting and appropriate inventory management. For example, it can analyze past consumption patterns to predict ingredients that will be in high demand at specific times. This enables appropriate inventory management in line with demand. It also issues alerts and suggests recipes for ingredients nearing their expiration date. For example, it will issue an alert and suggest recipes using ingredients that are within a week of their expiration date. This reduces food waste and enables efficient consumption. This system can reduce food waste by 30% and inventory costs by 20%. It can also save time by streamlining purchasing plans. In addition, by suggesting customized recipes tailored to user preferences and the season, it can help realize a healthy and sustainable diet. In summary, the ingredient management system can reduce food waste, lower inventory costs, and save time by streamlining purchasing plans.

[0029] The food management system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an alert unit. The collection unit collects the type, quantity, expiration date, and inventory status of food ingredients. The collection unit can, for example, use sensors inside a refrigerator to detect the type and quantity of food ingredients and collect data. The collection unit can also collect data in real time in cooperation with IoT devices. For example, the collection unit can collect food ingredient data using a smart refrigerator or a smart scale. The collection unit can also use AI to analyze the data and identify the type and quantity of food ingredients. The analysis unit analyzes the data collected by the collection unit and proposes the optimal way to use the food ingredients and a procurement plan. The analysis unit can, for example, learn from past data and trends to predict demand. The analysis unit can also use AI to analyze data and predict demand. The analysis unit can analyze past consumption patterns and predict food ingredients whose demand will increase at specific times. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose recipes for consuming food ingredients that are nearing their expiration date as soon as possible. The suggestion unit can also propose customized recipes tailored to the user's preferences and the season. The suggestion unit can also suggest timely re-procurement of ingredients when inventory is running low. The alert unit issues alerts for ingredients nearing their expiration date and provides recipe suggestions. For example, the alert unit can issue an alert for ingredients with an expiration date of less than a week and suggest recipes using those ingredients. The alert unit can also use AI to identify ingredients nearing their expiration date and issue alerts. This allows the ingredient management system according to the embodiment to efficiently collect, analyze, suggest, and alert on ingredients.

[0030] The data collection unit collects information on the type, quantity, expiration date, and inventory status of food items. For example, the data collection unit uses sensors inside the refrigerator to detect the type and quantity of food items and collect data. Specifically, cameras and weight sensors installed inside the refrigerator detect the type and quantity of food items in real time and transmit this data to a central database. The cameras use image recognition technology to identify the type of food item, and the weight sensors determine the quantity by measuring the weight of the food item. This allows the data collection unit to accurately collect detailed information about the food items inside the refrigerator. Furthermore, the data collection unit can also collect data in real time in conjunction with IoT devices. For example, it can collect food data using smart refrigerators and smart scales. Smart refrigerators use built-in cameras and sensors to detect the type and quantity of food items, and smart scales measure the weight of the food items and collect data. These devices transmit data to a central database via Wi-Fi or Bluetooth®, allowing the data collection unit to acquire data in real time. The data collection unit can also use AI to analyze the data and identify the type and quantity of food items. The AI ​​uses image recognition technology and machine learning algorithms to analyze the collected data and identify the type and quantity of food items. For example, the AI ​​analyzes images captured by the camera to identify the type of food ingredient and analyzes data from weight sensors to determine the quantity. This allows the collection unit to efficiently and accurately collect food ingredient data, improving the overall performance of the system.

[0031] The analysis department analyzes data collected by the data collection department and proposes optimal ways to use ingredients and procurement plans. For example, the analysis department learns from past data and trends to predict demand. Specifically, it analyzes consumption patterns of specific ingredients based on past consumption data and predicts future demand. For example, it identifies ingredients whose demand increases during specific seasons or events from past data and develops procurement plans tailored to those times. The analysis department can also use AI to analyze data and predict demand. The AI ​​uses machine learning algorithms to learn from past data and predict future demand. For example, the AI ​​builds a demand forecasting model for a specific ingredient based on past consumption data and external market data, and uses that model to predict future demand. This allows the analysis department to make accurate demand forecasts and develop optimal procurement plans. Furthermore, the analysis department can analyze past consumption patterns and predict ingredients whose demand increases at specific times. For example, it identifies ingredients whose demand increases during specific seasons or events from past data and develops procurement plans tailored to those times. This allows the analysis department to respond flexibly to fluctuations in demand and achieve efficient ingredient management.

[0032] The proposal department makes specific suggestions based on the analysis results obtained by the analysis department. For example, the proposal department suggests recipes for consuming ingredients that are nearing their expiration date as soon as possible. Specifically, it identifies ingredients that are nearing their expiration date and suggests recipes using those ingredients to the user. The proposal department can also suggest customized recipes tailored to the user's preferences and the season. For example, it suggests the most suitable recipe for the user based on the user's past recipe history and preferred ingredients. Furthermore, the proposal department can suggest recipes using seasonal ingredients. For example, it suggests cold dishes in the summer and hot dishes in the winter. The proposal department can also suggest re-procurement of ingredients at the appropriate time when inventory is running low. Specifically, it identifies ingredients that are running low and suggests re-procurement of those ingredients to the user. The proposal department suggests the optimal re-procurement timing based on the user's purchase history and consumption patterns. In this way, the proposal department can provide users with specific and practical suggestions and support efficient ingredient management.

[0033] The alert unit issues alerts for ingredients nearing their expiration date and suggests recipes. Specifically, it issues alerts for ingredients with an expiration date of one week or less and suggests recipes using those ingredients. The alert unit sends alerts to users through, for example, smartphone notifications, email, and voice assistants. This allows users to consume ingredients nearing their expiration date sooner, reducing food waste. The alert unit can also use AI to identify ingredients nearing their expiration date and issue alerts. The AI ​​analyzes collected data to identify ingredients nearing their expiration date. For example, the AI ​​analyzes expiration date data for ingredients to identify ingredients with an expiration date of one week or less and issues an alert for those ingredients. This allows the alert unit to issue alerts efficiently and accurately, providing users with appropriate information. Furthermore, the alert unit can collect user feedback and continuously improve the accuracy and effectiveness of its alerts. For example, based on user feedback, it can review the timing and content of alerts to provide more effective alerts. This allows the alerting unit to provide users with quick and accurate alerts, supporting efficient food management.

[0034] The data collection unit can collect data in real time in conjunction with IoT devices. For example, the data collection unit can use a smart refrigerator to detect the type and quantity of food items and collect data. The data collection unit can also use a smart scale to measure the weight of food items and collect data. The data collection unit can also use AI to analyze the data and identify the type and quantity of food items. This allows for real-time data collection in conjunction with IoT devices. 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 acquired from a smart refrigerator into a generating AI and have the generating AI identify the type and quantity of food items.

[0035] The analysis unit can learn from past data and trends to predict demand. For example, the analysis unit can analyze past purchase history to predict which food items will be in high demand at a particular time. The analysis unit can also analyze consumption history to predict demand for specific food items. The analysis unit can also use AI to analyze data and predict demand. This makes it possible to predict demand by learning from past data and trends. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past purchase history data into a generating AI and have the generating AI perform demand prediction.

[0036] The suggestion unit can propose customized recipes tailored to the user's preferences and the season. For example, the suggestion unit can analyze the user's past preference history and propose recipes that match the user's preferences. The suggestion unit can also propose recipes using seasonal ingredients. The suggestion unit can also use AI to analyze data and propose recipes tailored to the user's preferences and the season. This optimizes the use of ingredients by suggesting recipes tailored to the user's preferences and the season. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's preference history data into a generating AI and have the generating AI execute the process of proposing customized recipes.

[0037] The alert unit can issue alerts for ingredients with an expiration date of one week or less and suggest recipes using those ingredients. For example, the alert unit can issue alerts for ingredients with an expiration date of one week or less and suggest recipes using those ingredients. The alert unit can also use AI to identify ingredients nearing their expiration date and issue alerts. This reduces food waste by issuing alerts for ingredients nearing their expiration date and suggesting recipes. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input data on ingredients nearing their expiration date into a generating AI and have the generating AI issue alerts and suggest recipes.

[0038] The proposal unit can suggest re-procurement of ingredients when inventory levels are low at the appropriate time. For example, the proposal unit can suggest re-procurement of ingredients when inventory levels are low at the appropriate time. The proposal unit can also use AI to analyze inventory data and suggest re-procurement timing. This streamlines inventory management by suggesting re-procurement of ingredients when inventory levels are low at the appropriate time. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input inventory data into a generating AI and have the generating AI execute a suggestion for re-procurement timing.

[0039] The collection unit can analyze the user's past consumption patterns when collecting ingredients and select the optimal collection method. For example, the collection unit can prioritize collecting ingredients that the user has frequently purchased in the past. The collection unit can also adjust the collection frequency of specific ingredients based on the user's past consumption patterns. The collection unit can also analyze the user's past consumption patterns and suggest a waste-free collection method. In this way, the optimal collection method can be selected by analyzing the user's past consumption patterns. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past consumption data into a generating AI and have the generating AI select the optimal collection method.

[0040] The collection unit can filter ingredients based on the user's current diet and health status when collecting them. For example, if the user follows a healthy diet, the collection unit will prioritize collecting healthy ingredients. The collection unit can also exclude ingredients that the user avoids from collection. The collection unit can also select appropriate ingredients based on the user's health status. This allows for the collection of more appropriate ingredients by filtering ingredients based on the user's current diet and health status. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's health data into a generating AI and have the generating AI perform the ingredient filtering.

[0041] The collection unit can prioritize the collection of highly relevant ingredients by considering the user's geographical location information when collecting ingredients. For example, if the user is in a specific region, the collection unit will prioritize the collection of ingredients that are easily available in that region. The collection unit can also collect local specialty products based on the user's geographical location information. If the user is traveling, the collection unit can also prioritize the collection of local ingredients. In this way, by considering the user's geographical location information, highly relevant ingredients can be collected preferentially. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant ingredients.

[0042] The collection unit can analyze the user's social media activity when collecting ingredients and collect relevant ingredients. For example, the collection unit can prioritize collecting ingredients that the user is talking about on social media. The collection unit can also collect ingredients that the user is interested in from their social media activity. The collection unit can also collect ingredients recommended by influencers that the user follows. In this way, relevant ingredients can be collected by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI select relevant ingredients.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the ingredients during the analysis. For example, the analysis unit performs a detailed analysis for important ingredients. The analysis unit can also perform a simplified analysis for less important ingredients. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the ingredients. By adjusting the level of detail of the analysis based on the importance of the ingredients, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input ingredient importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of food ingredient during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes nutritional value to vegetables. The analysis unit can also apply an analysis algorithm that emphasizes freshness to meats. The analysis unit can also apply an analysis algorithm that emphasizes frequency of use to seasonings. By applying different analysis algorithms depending on the category of food ingredient, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input food ingredient category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the ingredients. For example, it can provide detailed suggestions for important ingredients, and concise suggestions for less important ingredients. The suggestion unit can also adjust the level of detail in its suggestions in stages according to the importance of the ingredients. This allows it to provide more appropriate suggestions by adjusting the level of detail based on the importance of the ingredients. 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 ingredient importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the suggestions.

[0046] The suggestion unit can apply different suggestion algorithms depending on the category of the food ingredient when making suggestions. For example, for vegetables, the suggestion unit can apply a suggestion algorithm that prioritizes nutritional value. For meats, the suggestion unit can also apply a suggestion algorithm that prioritizes freshness. For seasonings, the suggestion unit can also apply a suggestion algorithm that prioritizes frequency of use. By applying different suggestion algorithms depending on the category of food ingredient, more appropriate suggestions can be provided. 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 food category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0047] The alert unit can adjust the level of detail of an alert based on the importance of the ingredient when an alert occurs. For example, the alert unit can display a detailed alert for important ingredients. The alert unit can also display a concise alert for less important ingredients. The alert unit can also adjust the level of detail of the alert in stages according to the importance of the ingredient. This allows for the provision of more appropriate alerts by adjusting the level of detail of the alert based on the importance of the ingredient. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input ingredient importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the alert.

[0048] The alert unit can apply different alert algorithms depending on the food category when an alert is triggered. For example, the alert unit can apply an alert algorithm that prioritizes nutritional value to vegetables. For meats, it can also apply an alert algorithm that prioritizes freshness. For seasonings, it can also apply an alert algorithm that prioritizes frequency of use. By applying different alert algorithms depending on the food category, more appropriate alerts can be provided. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input food category data into a generating AI and have the generating AI execute the application of the alert algorithm.

[0049] The alert unit can determine the priority of alerts based on the timing of food collection when an alert occurs. For example, the alert unit can prioritize alerts for recently collected food. The alert unit can also prioritize alerts for food nearing its expiration date. The alert unit can also adjust the priority of alerts in stages based on the collection timing. This allows for more appropriate alerts to be provided by determining the priority of alerts based on the timing of food collection. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input food collection timing data into a generating AI and have the generating AI determine the priority of alerts.

[0050] The alert unit can adjust the order of alerts based on the relevance of ingredients when an alert occurs. For example, the alert unit may prioritize alerting on highly relevant ingredients. It can also postpone alerting on less relevant ingredients. The alert unit can also adjust the order of alerts in stages based on the relevance of ingredients. This allows for the provision of more appropriate alerts by adjusting the order of alerts based on the relevance of ingredients. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input ingredient relevance data into a generating AI and have the generating AI perform the adjustment of the alert order.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze the user's past consumption patterns when collecting ingredients and select the optimal collection method. For example, it can prioritize collecting ingredients that the user has frequently purchased in the past. It can also adjust the collection frequency of specific ingredients based on the user's past consumption patterns. It can also analyze the user's past consumption patterns and suggest a waste-free collection method. In this way, the optimal collection method can be selected by analyzing the user's past consumption patterns.

[0053] The collection unit can filter ingredients based on the user's current diet and health status. For example, if a user follows a healthy diet, it will prioritize collecting healthy ingredients. If a user avoids certain ingredients, those ingredients can be excluded from collection. It can also select appropriate ingredients based on the user's health status. This allows for the collection of more appropriate ingredients by filtering ingredients based on the user's current diet and health status.

[0054] The collection unit can prioritize the collection of ingredients that are highly relevant to the user's geographical location, taking this information into account. For example, if the user is in a specific region, it will prioritize the collection of ingredients that are readily available in that region. It can also collect local specialties based on the user's geographical location. If the user is traveling, it can prioritize the collection of local ingredients. In this way, by considering the user's geographical location, it can prioritize the collection of ingredients that are highly relevant to the user.

[0055] The collection unit can analyze users' social media activity when collecting ingredients and collect relevant ingredients. For example, it can prioritize collecting ingredients that users are talking about on social media. It can also collect ingredients that users are interested in based on their social media activity. It can also collect ingredients recommended by influencers that users follow. In this way, relevant ingredients can be collected by analyzing users' social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects information on the type, quantity, expiration date, and inventory status of food ingredients. For example, it uses sensors inside the refrigerator to detect the type and quantity of food ingredients and collect data. The data collection unit can also work with IoT devices to collect data in real time. For example, it can use smart refrigerators or smart scales to collect data on food ingredients. The data collection unit can also use AI to analyze the data and identify the type and quantity of food ingredients. Step 2: The analysis unit analyzes the data collected by the data collection unit and proposes optimal ways to use ingredients and procurement plans. For example, it learns from past data and trends to predict demand. The analysis unit can also use AI to analyze data and predict demand. By analyzing past consumption patterns, it can predict ingredients that will see increased demand at specific times. Step 3: The proposal department makes specific suggestions based on the analysis results obtained by the analysis department. For example, it may suggest recipes for consuming ingredients that are nearing their expiration date as soon as possible. The proposal department can also suggest customized recipes tailored to the user's preferences and the season. It can also suggest re-procurement of ingredients at the appropriate time when inventory is running low. Step 4: The alert unit issues alerts for ingredients nearing their expiration date and suggests recipes. For example, it will issue an alert for ingredients with an expiration date of less than a week and suggest recipes using those ingredients. The alert unit can also use AI to identify ingredients nearing their expiration date and issue alerts.

[0058] (Example of form 2) The food management system according to an embodiment of the present invention is a system that uses AI to collect information on the type, quantity, expiration date, and inventory status of food ingredients, and proposes optimal ways to use and procure them. This food management system collects information on the type, quantity, expiration date, and inventory status of food ingredients and proposes optimal ways to use and procure them. Furthermore, it learns from past data and trends to support demand forecasting and appropriate inventory management. For food ingredients nearing their expiration date, it issues an alert and suggests recipes. For example, the food management system collects information on the type, quantity, expiration date, and inventory status of food ingredients. In this case, it can collect data in real time in cooperation with IoT devices. For example, a sensor in the refrigerator detects the type and quantity of food ingredients and sends the data to the AI. This makes it possible to always know the latest inventory status. Next, based on the collected data, the AI ​​proposes optimal ways to use and procure the food ingredients. For example, for food ingredients nearing their expiration date, it suggests recipes for consuming them sooner. Also, for food ingredients with low inventory, it suggests re-procurement at the appropriate time. This reduces food waste and enables efficient inventory management. Furthermore, it learns from past data and trends to support demand forecasting and appropriate inventory management. For example, it can analyze past consumption patterns to predict ingredients that will be in high demand at specific times. This enables appropriate inventory management in line with demand. It also issues alerts and suggests recipes for ingredients nearing their expiration date. For example, it will issue an alert and suggest recipes using ingredients that are within a week of their expiration date. This reduces food waste and enables efficient consumption. This system can reduce food waste by 30% and inventory costs by 20%. It can also save time by streamlining purchasing plans. In addition, by suggesting customized recipes tailored to user preferences and the season, it can help realize a healthy and sustainable diet. In summary, the ingredient management system can reduce food waste, lower inventory costs, and save time by streamlining purchasing plans.

[0059] The food management system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an alert unit. The collection unit collects the type, quantity, expiration date, and inventory status of food ingredients. The collection unit can, for example, use sensors inside a refrigerator to detect the type and quantity of food ingredients and collect data. The collection unit can also collect data in real time in cooperation with IoT devices. For example, the collection unit can collect food ingredient data using a smart refrigerator or a smart scale. The collection unit can also use AI to analyze the data and identify the type and quantity of food ingredients. The analysis unit analyzes the data collected by the collection unit and proposes the optimal way to use the food ingredients and a procurement plan. The analysis unit can, for example, learn from past data and trends to predict demand. The analysis unit can also use AI to analyze data and predict demand. The analysis unit can analyze past consumption patterns and predict food ingredients whose demand will increase at specific times. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The proposal unit can, for example, propose recipes for consuming food ingredients that are nearing their expiration date as soon as possible. The suggestion unit can also propose customized recipes tailored to the user's preferences and the season. The suggestion unit can also suggest timely re-procurement of ingredients when inventory is running low. The alert unit issues alerts for ingredients nearing their expiration date and provides recipe suggestions. For example, the alert unit can issue an alert for ingredients with an expiration date of less than a week and suggest recipes using those ingredients. The alert unit can also use AI to identify ingredients nearing their expiration date and issue alerts. This allows the ingredient management system according to the embodiment to efficiently collect, analyze, suggest, and alert on ingredients.

[0060] The data collection unit collects information on the type, quantity, expiration date, and inventory status of food items. For example, the unit uses sensors inside the refrigerator to detect the type and quantity of food items and collect data. Specifically, cameras and weight sensors installed inside the refrigerator detect the type and quantity of food items in real time and transmit this data to a central database. The cameras use image recognition technology to identify the type of food item, and the weight sensors determine the quantity by measuring the weight of the food item. This allows the data collection unit to accurately collect detailed information about the food items inside the refrigerator. Furthermore, the data collection unit can also collect data in real time in conjunction with IoT devices. For example, it can collect food data using smart refrigerators and smart scales. Smart refrigerators use built-in cameras and sensors to detect the type and quantity of food items, and smart scales measure the weight of the food items and collect data. These devices transmit data to a central database via Wi-Fi or Bluetooth, allowing the data collection unit to acquire data in real time. The data collection unit can also use AI to analyze the data and identify the type and quantity of food items. The AI ​​uses image recognition technology and machine learning algorithms to analyze the collected data and identify the type and quantity of food items. For example, the AI ​​analyzes images captured by the camera to identify the type of food ingredient and analyzes data from weight sensors to determine the quantity. This allows the collection unit to efficiently and accurately collect food ingredient data, improving the overall performance of the system.

[0061] The analysis department analyzes data collected by the data collection department and proposes optimal ways to use ingredients and procurement plans. For example, the analysis department learns from past data and trends to predict demand. Specifically, it analyzes consumption patterns of specific ingredients based on past consumption data and predicts future demand. For example, it identifies ingredients whose demand increases during specific seasons or events from past data and develops procurement plans tailored to those times. The analysis department can also use AI to analyze data and predict demand. The AI ​​uses machine learning algorithms to learn from past data and predict future demand. For example, the AI ​​builds a demand forecasting model for a specific ingredient based on past consumption data and external market data, and uses that model to predict future demand. This allows the analysis department to make accurate demand forecasts and develop optimal procurement plans. Furthermore, the analysis department can analyze past consumption patterns and predict ingredients whose demand increases at specific times. For example, it identifies ingredients whose demand increases during specific seasons or events from past data and develops procurement plans tailored to those times. This allows the analysis department to respond flexibly to fluctuations in demand and achieve efficient ingredient management.

[0062] The proposal department makes specific suggestions based on the analysis results obtained by the analysis department. For example, the proposal department suggests recipes for consuming ingredients that are nearing their expiration date as soon as possible. Specifically, it identifies ingredients that are nearing their expiration date and suggests recipes using those ingredients to the user. The proposal department can also suggest customized recipes tailored to the user's preferences and the season. For example, it suggests the most suitable recipe for the user based on the user's past recipe history and preferred ingredients. Furthermore, the proposal department can suggest recipes using seasonal ingredients. For example, it suggests cold dishes in the summer and hot dishes in the winter. The proposal department can also suggest re-procurement of ingredients at the appropriate time when inventory is running low. Specifically, it identifies ingredients that are running low and suggests re-procurement of those ingredients to the user. The proposal department suggests the optimal re-procurement timing based on the user's purchase history and consumption patterns. In this way, the proposal department can provide users with specific and practical suggestions and support efficient ingredient management.

[0063] The alert unit issues alerts for ingredients nearing their expiration date and suggests recipes. Specifically, it issues alerts for ingredients with an expiration date of one week or less and suggests recipes using those ingredients. The alert unit sends alerts to users through, for example, smartphone notifications, email, and voice assistants. This allows users to consume ingredients nearing their expiration date sooner, reducing food waste. The alert unit can also use AI to identify ingredients nearing their expiration date and issue alerts. The AI ​​analyzes collected data to identify ingredients nearing their expiration date. For example, the AI ​​analyzes expiration date data for ingredients to identify ingredients with an expiration date of one week or less and issues an alert for those ingredients. This allows the alert unit to issue alerts efficiently and accurately, providing users with appropriate information. Furthermore, the alert unit can collect user feedback and continuously improve the accuracy and effectiveness of its alerts. For example, based on user feedback, it can review the timing and content of alerts to provide more effective alerts. This allows the alerting unit to provide users with quick and accurate alerts, supporting efficient food management.

[0064] The data collection unit can collect data in real time in conjunction with IoT devices. For example, the data collection unit can use a smart refrigerator to detect the type and quantity of food items and collect data. The data collection unit can also use a smart scale to measure the weight of food items and collect data. The data collection unit can also use AI to analyze the data and identify the type and quantity of food items. This allows for real-time data collection in conjunction with IoT devices. 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 acquired from a smart refrigerator into a generating AI and have the generating AI identify the type and quantity of food items.

[0065] The analysis unit can learn from past data and trends to predict demand. For example, the analysis unit can analyze past purchase history to predict which food items will be in high demand at a particular time. The analysis unit can also analyze consumption history to predict demand for specific food items. The analysis unit can also use AI to analyze data and predict demand. This makes it possible to predict demand by learning from past data and trends. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past purchase history data into a generating AI and have the generating AI perform demand prediction.

[0066] The suggestion unit can propose customized recipes tailored to the user's preferences and the season. For example, the suggestion unit can analyze the user's past preference history and propose recipes that match the user's preferences. The suggestion unit can also propose recipes using seasonal ingredients. The suggestion unit can also use AI to analyze data and propose recipes tailored to the user's preferences and the season. This optimizes the use of ingredients by suggesting recipes tailored to the user's preferences and the season. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's preference history data into a generating AI and have the generating AI execute the process of proposing customized recipes.

[0067] The alert unit can issue alerts for ingredients with an expiration date of one week or less and suggest recipes using those ingredients. For example, the alert unit can issue alerts for ingredients with an expiration date of one week or less and suggest recipes using those ingredients. The alert unit can also use AI to identify ingredients nearing their expiration date and issue alerts. This reduces food waste by issuing alerts for ingredients nearing their expiration date and suggesting recipes. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input data on ingredients nearing their expiration date into a generating AI and have the generating AI issue alerts and suggest recipes.

[0068] The proposal unit can suggest re-procurement of ingredients when inventory levels are low at the appropriate time. For example, the proposal unit can suggest re-procurement of ingredients when inventory levels are low at the appropriate time. The proposal unit can also use AI to analyze inventory data and suggest re-procurement timing. This streamlines inventory management by suggesting re-procurement of ingredients when inventory levels are low at the appropriate time. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input inventory data into a generating AI and have the generating AI execute a suggestion for re-procurement timing.

[0069] The collection unit can estimate the user's emotions and adjust the timing of ingredient collection based on the estimated emotions. For example, if the user is stressed, the collection unit can delay the timing of ingredient collection and collect it in a relaxed state. If the user is in a hurry, the collection unit can also speed up the timing of ingredient collection and complete the collection quickly. If the user is relaxed, the collection unit can collect the ingredients at the normal timing. In this way, by adjusting the timing of ingredient collection according to the user's emotions, ingredients can be collected at a more appropriate time. 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 collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the collection timing.

[0070] The collection unit can analyze the user's past consumption patterns when collecting ingredients and select the optimal collection method. For example, the collection unit can prioritize collecting ingredients that the user has frequently purchased in the past. The collection unit can also adjust the collection frequency of specific ingredients based on the user's past consumption patterns. The collection unit can also analyze the user's past consumption patterns and suggest a waste-free collection method. In this way, the optimal collection method can be selected by analyzing the user's past consumption patterns. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past consumption data into a generating AI and have the generating AI select the optimal collection method.

[0071] The collection unit can filter ingredients based on the user's current diet and health status when collecting them. For example, if the user follows a healthy diet, the collection unit will prioritize collecting healthy ingredients. The collection unit can also exclude ingredients that the user avoids from collection. The collection unit can also select appropriate ingredients based on the user's health status. This allows for the collection of more appropriate ingredients by filtering ingredients based on the user's current diet and health status. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's health data into a generating AI and have the generating AI perform the ingredient filtering.

[0072] The collection unit can estimate the user's emotions and determine the priority of ingredients to collect based on the estimated emotions. For example, if the user is stressed, the collection unit will prioritize collecting ingredients with relaxing effects. If the user is in a hurry, the collection unit may also prioritize collecting ingredients that are easy to cook. If the user is relaxed, the collection unit may collect ingredients with normal priority. This allows for the collection of more appropriate ingredients by determining the priority of ingredients to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of ingredients to collect.

[0073] The collection unit can prioritize the collection of highly relevant ingredients by considering the user's geographical location information when collecting ingredients. For example, if the user is in a specific region, the collection unit will prioritize the collection of ingredients that are easily available in that region. The collection unit can also collect local specialty products based on the user's geographical location information. If the user is traveling, the collection unit can also prioritize the collection of local ingredients. In this way, by considering the user's geographical location information, highly relevant ingredients can be collected preferentially. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant ingredients.

[0074] The collection unit can analyze the user's social media activity when collecting ingredients and collect relevant ingredients. For example, the collection unit can prioritize collecting ingredients that the user is talking about on social media. The collection unit can also collect ingredients that the user is interested in from their social media activity. The collection unit can also collect ingredients recommended by influencers that the user follows. In this way, relevant ingredients can be collected by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI select relevant ingredients.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If the user is stressed, the analysis unit can also provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the ingredients during the analysis. For example, the analysis unit performs a detailed analysis for important ingredients. The analysis unit can also perform a simplified analysis for less important ingredients. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the ingredients. By adjusting the level of detail of the analysis based on the importance of the ingredients, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input ingredient importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of food ingredient during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes nutritional value to vegetables. The analysis unit can also apply an analysis algorithm that emphasizes freshness to meats. The analysis unit can also apply an analysis algorithm that emphasizes frequency of use to seasonings. By applying different analysis algorithms depending on the category of food ingredient, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input food ingredient category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0078] 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 relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. 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.

[0079] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the ingredients. For example, it can provide detailed suggestions for important ingredients, and concise suggestions for less important ingredients. The suggestion unit can also adjust the level of detail in its suggestions in stages according to the importance of the ingredients. This allows it to provide more appropriate suggestions by adjusting the level of detail based on the importance of the ingredients. 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 ingredient importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the suggestions.

[0080] The suggestion unit can apply different suggestion algorithms depending on the category of the food ingredient when making suggestions. For example, for vegetables, the suggestion unit can apply a suggestion algorithm that prioritizes nutritional value. For meats, the suggestion unit can also apply a suggestion algorithm that prioritizes freshness. For seasonings, the suggestion unit can also apply a suggestion algorithm that prioritizes frequency of use. By applying different suggestion algorithms depending on the category of food ingredient, more appropriate suggestions can be provided. 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 food category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0081] The alert unit can estimate the user's emotions and adjust how alerts are displayed based on the estimated emotions. For example, if the user is relaxed, the alert unit can display a detailed alert. If the user is in a hurry, the alert unit can also display a concise alert that gets straight to the point. If the user is stressed, the alert unit can also display a visually easy-to-understand alert. This allows for more appropriate alerts to be provided by adjusting how alerts are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into a generative AI and have the generative AI adjust how alerts are displayed.

[0082] The alert unit can adjust the level of detail of an alert based on the importance of the ingredient when an alert occurs. For example, the alert unit can display a detailed alert for important ingredients. The alert unit can also display a concise alert for less important ingredients. The alert unit can also adjust the level of detail of the alert in stages according to the importance of the ingredient. This allows for the provision of more appropriate alerts by adjusting the level of detail of the alert based on the importance of the ingredient. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input ingredient importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the alert.

[0083] The alert unit can apply different alert algorithms depending on the food category when an alert is triggered. For example, the alert unit can apply an alert algorithm that prioritizes nutritional value to vegetables. For meats, it can also apply an alert algorithm that prioritizes freshness. For seasonings, it can also apply an alert algorithm that prioritizes frequency of use. By applying different alert algorithms depending on the food category, more appropriate alerts can be provided. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input food category data into a generating AI and have the generating AI execute the application of the alert algorithm.

[0084] The alert unit can estimate the user's emotions and adjust the length of the alert based on the estimated emotions. For example, if the user is in a hurry, the alert unit can display a short, concise alert. If the user is relaxed, the alert unit can also display a detailed alert. If the user is stressed, the alert unit can also display a visually clear alert. This allows for more appropriate alerts to be provided by adjusting the length of the alert according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into a generative AI and have the generative AI adjust the length of the alert.

[0085] The alert unit can determine the priority of alerts based on the timing of food collection when an alert occurs. For example, the alert unit can prioritize alerts for recently collected food. The alert unit can also prioritize alerts for food nearing its expiration date. The alert unit can also adjust the priority of alerts in stages based on the collection timing. This allows for more appropriate alerts to be provided by determining the priority of alerts based on the timing of food collection. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input food collection timing data into a generating AI and have the generating AI determine the priority of alerts.

[0086] The alert unit can adjust the order of alerts based on the relevance of ingredients when an alert occurs. For example, the alert unit may prioritize alerting on highly relevant ingredients. It can also postpone alerting on less relevant ingredients. The alert unit can also adjust the order of alerts in stages based on the relevance of ingredients. This allows for the provision of more appropriate alerts by adjusting the order of alerts based on the relevance of ingredients. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input ingredient relevance data into a generating AI and have the generating AI perform the adjustment of the alert order.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The collection unit can estimate the user's emotions and adjust the timing of ingredient collection based on those emotions. For example, if the user is stressed, the collection timing can be delayed to allow for a more relaxed state. If the user is in a hurry, the collection timing can be sped up to complete the collection quickly. If the user is relaxed, ingredients can be collected at the normal timing. By adjusting the timing of ingredient collection according to the user's emotions, ingredients can be collected at a more appropriate time.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. If the user is stressed, it can provide visually easy-to-understand analysis results. By adjusting the presentation of the analysis according to the user's emotions, it can provide more appropriate analysis results.

[0090] 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 relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. If the user is stressed, it can provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented according to the user's emotions, it can provide more appropriate suggestions.

[0091] The alert function can estimate the user's emotions and adjust how alerts are displayed based on that estimation. For example, if the user is relaxed, a detailed alert can be displayed. If the user is in a hurry, a concise alert focusing on the key points can be displayed. If the user is stressed, a visually easy-to-understand alert can be displayed. This allows for more appropriate alerts to be provided by adjusting how alerts are displayed according to the user's emotions.

[0092] The collection unit can estimate the user's emotions and determine the priority of ingredients to collect based on those emotions. For example, if the user is stressed, it will prioritize collecting ingredients with relaxing effects. If the user is in a hurry, it can also prioritize collecting ingredients that are easy to cook. If the user is relaxed, it can collect ingredients according to the normal priority. In this way, by determining the priority of ingredients to collect according to the user's emotions, it is possible to collect more appropriate ingredients.

[0093] The collection unit can analyze the user's past consumption patterns when collecting ingredients and select the optimal collection method. For example, it can prioritize collecting ingredients that the user has frequently purchased in the past. It can also adjust the collection frequency of specific ingredients based on the user's past consumption patterns. It can also analyze the user's past consumption patterns and suggest a waste-free collection method. In this way, the optimal collection method can be selected by analyzing the user's past consumption patterns.

[0094] The collection unit can filter ingredients based on the user's current diet and health status. For example, if a user follows a healthy diet, it will prioritize collecting healthy ingredients. If a user avoids certain ingredients, those ingredients can be excluded from collection. It can also select appropriate ingredients based on the user's health status. This allows for the collection of more appropriate ingredients by filtering ingredients based on the user's current diet and health status.

[0095] The collection unit can prioritize the collection of ingredients that are highly relevant to the user's geographical location, taking this information into account. For example, if the user is in a specific region, it will prioritize the collection of ingredients that are readily available in that region. It can also collect local specialties based on the user's geographical location. If the user is traveling, it can prioritize the collection of local ingredients. In this way, by considering the user's geographical location, it can prioritize the collection of ingredients that are highly relevant to the user.

[0096] The collection unit can analyze users' social media activity when collecting ingredients and collect relevant ingredients. For example, it can prioritize collecting ingredients that users are talking about on social media. It can also collect ingredients that users are interested in based on their social media activity. It can also collect ingredients recommended by influencers that users follow. In this way, relevant ingredients can be collected by analyzing users' social media activity.

[0097] The collection unit can estimate the user's emotions when collecting ingredients and determine the priority of ingredients to collect based on those emotions. For example, if the user is stressed, it will prioritize collecting ingredients with relaxing effects. If the user is in a hurry, it can also prioritize collecting ingredients that are easy to cook. If the user is relaxed, it can collect ingredients according to the normal priority. In this way, by determining the priority of ingredients to collect according to the user's emotions, it is possible to collect more appropriate ingredients.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects information on the type, quantity, expiration date, and inventory status of food ingredients. For example, it uses sensors inside the refrigerator to detect the type and quantity of food ingredients and collect data. The data collection unit can also work with IoT devices to collect data in real time. For example, it can use smart refrigerators or smart scales to collect data on food ingredients. The data collection unit can also use AI to analyze the data and identify the type and quantity of food ingredients. Step 2: The analysis unit analyzes the data collected by the data collection unit and proposes optimal ways to use ingredients and procurement plans. For example, it learns from past data and trends to predict demand. The analysis unit can also use AI to analyze data and predict demand. By analyzing past consumption patterns, it can predict ingredients that will see increased demand at specific times. Step 3: The proposal department makes specific suggestions based on the analysis results obtained by the analysis department. For example, it may suggest recipes for consuming ingredients that are nearing their expiration date as soon as possible. The proposal department can also suggest customized recipes tailored to the user's preferences and the season. It can also suggest re-procurement of ingredients at the appropriate time when inventory is running low. Step 4: The alert unit issues alerts for ingredients nearing their expiration date and suggests recipes. For example, it will issue an alert for ingredients with an expiration date of less than a week and suggest recipes using those ingredients. The alert unit can also use AI to identify ingredients nearing their expiration date and issue alerts.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the sensors of the smart device 14 to detect the type and quantity of ingredients and collect data. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and proposes the optimal way to use the ingredients and a procurement plan. The proposal unit makes specific suggestions based on the analysis results using the identification processing unit 290 of the data processing unit 12. The alert unit uses the control unit 46A of the smart device 14 to issue an alert for ingredients nearing their expiration date and makes recipe suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and alert unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the sensors of the smart glasses 214 to detect the type and quantity of ingredients and collect data. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and proposes the optimal way to use the ingredients and a procurement plan. The proposal unit makes specific suggestions based on the analysis results using the identification processing unit 290 of the data processing unit 12. The alert unit uses the control unit 46A of the smart glasses 214 to issue an alert for ingredients nearing their expiration date and makes recipe suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and alert unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the sensors of the headset terminal 314 to detect the type and quantity of ingredients and collect data. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and proposes the optimal way to use the ingredients and a procurement plan. The proposal unit makes specific suggestions based on the analysis results using the identification processing unit 290 of the data processing unit 12. The alert unit uses the control unit 46A of the headset terminal 314 to issue alerts for ingredients nearing their expiration date and makes recipe suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and alert unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the robot 414's sensors to detect the type and quantity of ingredients and collect data. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal way to use the ingredients and a procurement plan. The proposal unit makes specific suggestions based on the analysis results using the specific processing unit 290 of the data processing unit 12. The alert unit uses the control unit 46A of the robot 414 to issue an alert for ingredients nearing their expiration date and makes recipe suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) The collection department collects information on the type, quantity, expiration date, and inventory status of ingredients. The data collected by the aforementioned collection unit is analyzed by the analysis unit, which then proposes the optimal way to use and procure the ingredients. A proposal unit makes specific proposals based on the analysis results obtained by the aforementioned analysis unit, It includes an alert unit that issues an alert when food is nearing its expiration date and provides recipe suggestions. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data in real time by working with IoT devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It learns from past data and trends to forecast demand. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest customized recipes tailored to the user's preferences and the season. The system described in Appendix 1, characterized by the features described herein. (Note 5) The alert unit is, The app will issue an alert for ingredients with a shelf life of less than one week and suggest recipes using those ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose re-procuring ingredients at the appropriate time when inventory is running low. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of ingredient collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting ingredients, the system analyzes the user's past consumption patterns to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting ingredients, filtering is performed based on the user's current diet and health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of ingredients to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting ingredients, the system prioritizes collecting ingredients that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting ingredients, the system analyzes users' social media activity and collects relevant ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the representation of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 19) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, When an alert is issued, adjust the level of detail of the alert based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, When an alert is triggered, different alert algorithms are applied depending on the food category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, It estimates the user's sentiment and adjusts the length of the alert based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, When an alert is triggered, the alert priority is determined based on when the ingredients were collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, When an alert is issued, adjust the order of the alerts based on the relevance of the ingredients. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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 collection department collects information on the type, quantity, expiration date, and inventory status of ingredients. The data collected by the aforementioned collection unit is analyzed by the analysis unit, which then proposes the optimal way to use and procure the ingredients. A proposal unit makes specific proposals based on the analysis results obtained by the aforementioned analysis unit, It includes an alert unit that issues an alert when food is nearing its expiration date and provides recipe suggestions. A system characterized by the following features.

2. The aforementioned collection unit is It collects data in real time by working with IoT devices. The system according to feature 1.

3. The aforementioned analysis unit, It learns from past data and trends to forecast demand. The system according to feature 1.

4. The aforementioned proposal section is, We suggest customized recipes tailored to the user's preferences and the season. The system according to feature 1.

5. The alert unit is, The app will issue an alert for ingredients with a shelf life of less than one week and suggest recipes using those ingredients. The system according to feature 1.

6. The aforementioned proposal section is, We propose re-procuring ingredients at the appropriate time when inventory is running low. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of ingredient collection based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting ingredients, the system analyzes the user's past consumption patterns to select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting ingredients, filtering is performed based on the user's current diet and health status. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of ingredients to collect based on the estimated user emotions. The system according to feature 1.

Images