System

Abstract

An object of a system according to an embodiment is to automate ordering of daily necessities by a user and to realize efficient consumption management.SOLUTION: A system includes a consumption pattern analysis unit, an order generation unit, an interaction setting unit, and a food ingredient monitoring unit. The consumption pattern analysis unit analyzes a consumption pattern of a user. The order generation unit generates an order based on the data analyzed by the consumption pattern analysis unit. The dialog setting unit sets the order item and the order frequency in a dialog form with the user. The food ingredient monitoring unit monitors food ingredients in the refrigerator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology requires users to manually order daily necessities, which makes it difficult to efficiently manage consumption.

[0005] The system according to the embodiment aims to enable users to automate ordering of daily necessities and realize efficient consumption management. [Means for solving the problem]

[0006] The system according to the embodiment includes a consumption pattern analysis unit, an order generation unit, a dialogue setting unit, and an ingredient monitoring unit. The consumption pattern analysis unit analyzes a user's consumption pattern. The order generation unit generates an order based on the data analyzed by the consumption pattern analysis unit. The dialogue setting unit interactively sets the items to be ordered and the order frequency with the user. The ingredient monitoring unit monitors the ingredients in the refrigerator. [Effects of the Invention]

[0007] The system according to the embodiment allows users to automate ordering of daily necessities and realize efficient consumption management. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The automatic replenishment system according to an embodiment of the present invention is a system that automatically replenishes common daily necessities. This system uses AI to shift the trigger for automatic ordering from humans to AI, automatically ordering what is needed at the right time. This allows the automatic replenishment system to automatically replenish common daily necessities and support the user's lifestyle.

[0029] An automatic replenishment system according to an embodiment includes a consumption pattern analysis unit, an order generation unit, a dialogue setting unit, and an ingredient monitoring unit. The consumption pattern analysis unit analyzes a user's consumption pattern. For example, the consumption pattern analysis unit analyzes a user's purchase frequency and purchase amount to identify consumption trends. The consumption pattern analysis unit can also analyze a user's purchase timing to identify the optimal timing for ordering. The order generation unit generates an order based on data analyzed by the consumption pattern analysis unit. For example, the order generation unit automatically generates an order when a consumable item is low. The order generation unit can also generate an order at a specific timing based on a user's instruction. The dialogue setting unit interactively sets the order items and order frequency with the user. For example, the dialogue setting unit allows the user to input instructions via text or voice to set the order items and order frequency. The dialogue setting unit can also set the order items and order frequency using gestures. The ingredient monitoring unit monitors ingredients in a refrigerator. For example, the ingredient monitoring unit measures the remaining amount of ingredients using a camera or weight sensor in the refrigerator. The ingredient monitoring unit can also monitor the storage conditions of ingredients using temperature and humidity sensors. This allows the automatic replenishment system according to the embodiment to automatically replenish common daily necessities, supporting the user's daily life. For example, the user no longer needs to worry about stock of consumables, and can always have what they need on hand. Furthermore, the user can interactively and flexibly set the items and frequency of orders.

[0030] The consumption pattern analysis unit analyzes a user's social media posts and calendar information and can make orders that take into account events or seasonal fluctuations. For example, the consumption pattern analysis unit uses AI to analyze a user's social media posts and identify consumption patterns related to specific events or seasons. For example, if a user posts on social media that they are having a barbecue, the AI ​​will automatically order ingredients and supplies for the barbecue. The consumption pattern analysis unit also analyzes the user's calendar information and makes orders tailored to specific events or plans. For example, if a user has a "family birthday party" registered on their calendar, the AI ​​will automatically order ingredients and decorations for the party. The consumption pattern analysis unit also analyzes a user's past purchase history and social media posts to identify seasonal consumption patterns. For example, if a user tends to order more cold drinks and ice cream in the summer, the AI ​​will automatically make orders accordingly. This makes it possible to make orders that take into account events and seasonal fluctuations.

[0031] The consumption pattern analysis unit can monitor the user's health condition and automatically order vitamins or cold medicine if the user catches a cold. For example, the consumption pattern analysis unit uses AI to monitor the user's health data and automatically order vitamins or cold medicine if it detects signs of a cold. For example, it analyzes the user's body temperature and heart rate data and orders the necessary medicine if an abnormality is detected. The consumption pattern analysis unit also works with the user's health app to monitor changes in health condition in real time. For example, if the user enters "I have a cold" into the health app, the AI ​​automatically orders the necessary medicine based on that information. The consumption pattern analysis unit also monitors the user's health condition over the long term and takes preventive measures against seasonal illnesses. For example, it automatically schedules vaccinations and orders preventive medicine during influenza season. This makes it possible to order products according to the user's health condition.

[0032] The order generation unit can be expanded to handle not only daily necessities but also other categories, such as pet supplies or gardening supplies. For example, the order generation unit uses AI to analyze a user's pet consumption patterns and automatically order pet supplies such as pet food and toys. For example, the order generation unit orders necessary supplies based on the pet's food intake and frequency of play. The order generation unit also uses AI to analyze a user's gardening activities to automatically order gardening supplies. For example, the order generation unit places an order based on the seasonal growth status of plants and the necessary fertilizer and tools. The order generation unit also uses AI to automatically order supplies from specific categories tailored to the user's hobbies and lifestyle. For example, if a user enjoys outdoor activities, the order generation unit automatically orders camping and mountain climbing equipment. This diversifies the types of orders that can be ordered.

[0033] The consumption pattern analysis unit analyzes the consumption patterns of all family members of the user and can place orders based on the needs of the entire family. For example, the consumption pattern analysis unit uses AI to analyze the consumption patterns of all family members and place orders based on the needs of the entire family. For example, it automatically orders ingredients and daily necessities based on the food preferences and consumption amounts of each family member. The consumption pattern analysis unit also monitors the health data of all family members and automatically orders necessary medications and supplements. For example, if someone in the family has an allergy, it will order food and medication appropriate for that person. The consumption pattern analysis unit also automatically orders necessary items based on the lifestyles and events of each family member. For example, it can order travel supplies needed for a family trip or equipment needed for children's school events. This makes it possible to place orders based on the needs of the entire family.

[0034] The dialogue setting unit can set the order items or frequency not only by text but also by voice or gesture. The dialogue setting unit, for example, allows a user to set the order items and frequency by voice. For example, by giving a voice instruction such as "Please place my next order in two weeks," AI analyzes the instruction and sets the order. The dialogue setting unit also uses gesture recognition technology to allow a user to set the order items and frequency by hand movements or facial expressions. For example, by making a specific gesture, AI analyzes the instruction and sets the order. The dialogue setting unit also provides an interface that allows the user to set the order items and frequency by selecting text, voice, or gesture. For example, the user can select the method that is easiest for them to use to make the settings. This diversifies the user interface and improves usability.

[0035] The dialogue setting unit allows the AI ​​to make optimal suggestions based on past data when the user sets the items to be ordered and the frequency of purchases. For example, the dialogue setting unit analyzes the user's past order data and suggests optimal items and frequency of purchases. For example, it might suggest, "It's been a month since your last order. Would you like to reorder the same item?" The dialogue setting unit also presents optimal options based on past data when the user sets the items to be ordered and the frequency of purchases. For example, it might suggest, "Since detergent is usually used up in two weeks, should we set your next order for two weeks later?" The dialogue setting unit also allows the AI ​​to learn the user's consumption patterns and suggest the optimal timing for ordering. For example, it might suggest, "The best time to order toilet paper is on the first day of each month." This reduces the burden on the user and enables optimal ordering.

[0036] The dialogue setting unit can perform settings not only on a smartphone app but also on other devices such as a smartwatch or smart speaker. The dialogue setting unit, for example, enables users to set order items and frequency not only on a smartphone app but also on a smartwatch. For example, settings are performed using the voice recognition function of a smartwatch. The dialogue setting unit also enables users to set order items and frequency by voice using a smart speaker. For example, the user can say, "Alexa, set my next toilet paper order for one week later." The dialogue setting unit also synchronizes setting information between multiple devices, allowing users to set order items and frequency from any device. For example, information set on a smartphone is reflected on a smartwatch or smart speaker. This allows users to set items from multiple devices, improving convenience.

[0037] The dialogue setting unit can suggest the optimal settings by referring to the setting data of other users. For example, the dialogue setting unit uses AI to analyze the setting data of other users and suggest the optimal settings. For example, the dialogue setting unit suggests the optimal order frequency based on the setting data of users living in the same area. Furthermore, when a user makes settings, the dialogue setting unit uses AI to refer to the setting data of other users and present the optimal options. For example, it may suggest, "Many users order this product every two weeks." Furthermore, the dialogue setting unit uses AI to learn the setting data of other users and suggest the optimal setting method. For example, it may suggest, "Users with the same lifestyle choose these settings." This makes it possible to utilize the data of other users to achieve optimal settings.

[0038] The food monitoring unit can monitor food in the refrigerator using not only cameras and weight sensors, but also temperature and humidity sensors. The food monitoring unit, for example, installs temperature and humidity sensors inside the refrigerator to monitor the freshness of food in real time. For example, it detects fluctuations in temperature and humidity and issues an alert to prevent food deterioration. The food monitoring unit also combines temperature and humidity sensors in addition to cameras and weight sensors to build a system for monitoring the freshness of food. For example, it analyzes the storage conditions of food based on temperature and humidity data. The food monitoring unit also uses the temperature and humidity sensors to optimize the environment inside the refrigerator and maintain the freshness of food. For example, it automatically adjusts the refrigerator settings to keep the temperature and humidity within appropriate ranges. This allows for more accurate monitoring of food freshness.

[0039] The ingredient monitoring unit can analyze the expiration dates of ingredients in the refrigerator and suggest recipes that prioritize using ingredients that are close to their expiration date. For example, the ingredient monitoring unit uses AI to analyze the expiration dates of ingredients in the refrigerator and suggest recipes that prioritize using ingredients that are close to their expiration date. For example, it could suggest a "pasta recipe using tomatoes that are close to their expiration date." The ingredient monitoring unit can also build a system where AI suggests optimal recipes based on expiration date data of ingredients in the refrigerator. For example, it could suggest a menu that combines ingredients that are close to their expiration date. The ingredient monitoring unit can also use AI to monitor the expiration dates of ingredients in the refrigerator in real time and automatically generate recipes that prioritize using ingredients that are close to their expiration date. For example, it could suggest a "recipe that uses ingredients that have an expiration date tomorrow." This reduces food waste and allows for more efficient use of ingredients.

[0040] The food monitoring unit can extend food monitoring in the refrigerator to other storage locations, such as the freezer and pantry. For example, the food monitoring unit can extend food monitoring in the refrigerator to the freezer and monitor the inventory status of frozen foods in real time. For example, it measures the remaining amount of food using a camera or weight sensor in the freezer. The food monitoring unit also installs a camera or weight sensor to monitor food in the pantry and monitors the inventory status of dried foods and canned goods in real time. For example, it analyzes the expiration dates of food ingredients in the pantry and automatically orders the necessary ingredients. The food monitoring unit also monitors all storage locations, including the refrigerator, freezer, and pantry, in an integrated manner, building a system for centrally managing food inventory. For example, it orders the most appropriate ingredients based on data from each storage location. This allows for centralized management of food ingredients in multiple storage locations.

[0041] The ingredient monitoring unit can make nutritional balance suggestions to improve the user's diet based on the food data in the refrigerator. For example, the ingredient monitoring unit uses AI to analyze the food data in the refrigerator and make nutritional balance suggestions to improve the user's diet. For example, it might suggest, "You're lacking in vitamin C, would you like to order more oranges?" The ingredient monitoring unit also builds a system in which AI suggests nutritionally balanced recipes based on the food data in the refrigerator. For example, it might suggest "recipes using ingredients rich in protein." The ingredient monitoring unit also uses AI to analyze the user's diet data over a long period of time and identify areas for improvement in nutritional balance. For example, it might suggest, "You've been lacking in calcium in your recent meals, would you like to order more milk?" This can improve the user's diet and support their health.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The automatic replenishment system may further include an energy consumption monitoring unit. The energy consumption monitoring unit monitors household power consumption in real time and makes suggestions to optimize energy efficiency. For example, the energy consumption monitoring unit may identify peak power consumption times and make suggestions to reduce power consumption during those times. The energy consumption monitoring unit may also analyze the frequency of use of specific home appliances and suggest energy-efficient alternatives. Furthermore, the energy consumption monitoring unit may analyze household energy consumption data over a long period of time and suggest ways to reduce energy costs. For example, the energy consumption monitoring unit may identify seasonal energy consumption patterns and suggest energy-efficient usage methods. This allows for optimization of household energy consumption and cost reduction.

[0044] The automatic replenishment system may further include a recycling management unit. The recycling management unit monitors the amount of recyclable waste generated in the home and suggests appropriate recycling methods. For example, the recycling management unit may use sensors installed on trash bins in the home to identify the type and amount of recyclable waste. The recycling management unit may also suggest appropriate sorting methods based on local recycling rules. Furthermore, the recycling management unit may manage the collection schedule for recyclable waste and send reminders to the user. For example, the recycling management unit may notify the user when the collection date is approaching and encourage the user to put out the waste at the appropriate time. This improves the efficiency of recycling activities in the home and reduces the environmental impact.

[0045] The automatic replenishment system may further include a security monitoring unit. The security monitoring unit uses security cameras and sensors inside and outside the home to detect abnormal activity and notify the user. For example, if the security monitoring unit detects suspicious activity at night, it may send an alert to the user's smartphone. The security monitoring unit may also detect signs of intrusion using door and window open / close sensors. The security monitoring unit may also analyze home security data and suggest ways to strengthen security. For example, the security monitoring unit may identify security risks at specific times or locations and suggest additional security measures. This improves home security, allowing users to live with peace of mind.

[0046] The automatic replenishment system may further include an environmental monitoring unit. The environmental monitoring unit monitors environmental data inside and outside the home in real time and makes suggestions to maintain a comfortable living environment. For example, the environmental monitoring unit may monitor indoor temperature, humidity, and air quality and suggest optimal environmental settings. The environmental monitoring unit may also analyze external weather data and suggest appropriate clothing and timing for going outside. Furthermore, the environmental monitoring unit may analyze environmental data inside the home over a long period of time and make suggestions for improving the environment. For example, the environmental monitoring unit may suggest optimal ways to use air conditioners and humidifiers based on seasonal environmental data. This improves the living environment inside the home and enables a more comfortable life.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The consumption pattern analysis unit analyzes the user's consumption pattern. For example, the consumption pattern analysis unit analyzes the user's purchase frequency and purchase amount to identify consumption trends. The consumption pattern analysis unit can also analyze the user's purchase timing to identify the optimal timing for ordering. Step 2: The order generation unit generates an order based on the data analyzed by the consumption pattern analysis unit. For example, the order generation unit automatically generates an order when the consumable item is running low. The order generation unit can also generate an order at a specific time based on a user's instruction. Step 3: The dialogue setting unit interactively sets the order items and order frequency with the user. For example, the dialogue setting unit sets the order items and order frequency by the user inputting instructions by text or voice. The dialogue setting unit can also set the order items and order frequency using gestures. Step 4: The food ingredient monitoring unit monitors the food ingredients in the refrigerator. For example, the food ingredient monitoring unit measures the remaining amount of food ingredients using a camera or weight sensor inside the refrigerator. The food ingredient monitoring unit can also monitor the storage condition of the food ingredients using a temperature sensor or humidity sensor.

[0049] (Example 2) The automatic replenishment system according to an embodiment of the present invention is a system that automatically replenishes common daily necessities. This system uses AI to shift the trigger for automatic ordering from humans to AI, automatically ordering what is needed at the right time. This allows the automatic replenishment system to automatically replenish common daily necessities and support the user's lifestyle.

[0050] An automatic replenishment system according to an embodiment includes a consumption pattern analysis unit, an order generation unit, a dialogue setting unit, and an ingredient monitoring unit. The consumption pattern analysis unit analyzes a user's consumption pattern. For example, the consumption pattern analysis unit analyzes a user's purchase frequency and purchase amount to identify consumption trends. The consumption pattern analysis unit can also analyze a user's purchase timing to identify the optimal timing for ordering. The order generation unit generates an order based on data analyzed by the consumption pattern analysis unit. For example, the order generation unit automatically generates an order when a consumable item is low. The order generation unit can also generate an order at a specific timing based on a user's instruction. The dialogue setting unit interactively sets the order items and order frequency with the user. For example, the dialogue setting unit allows the user to input instructions via text or voice to set the order items and order frequency. The dialogue setting unit can also set the order items and order frequency using gestures. The ingredient monitoring unit monitors ingredients in a refrigerator. For example, the ingredient monitoring unit measures the remaining amount of ingredients using a camera or weight sensor in the refrigerator. The ingredient monitoring unit can also monitor the storage conditions of ingredients using temperature and humidity sensors. This allows the automatic replenishment system according to the embodiment to automatically replenish common daily necessities, supporting the user's daily life. For example, the user no longer needs to worry about stock of consumables, and can always have what they need on hand. Furthermore, the user can interactively and flexibly set the items and frequency of orders.

[0051] The consumption pattern analysis unit analyzes a user's social media posts and calendar information and can make orders that take into account events or seasonal fluctuations. For example, the consumption pattern analysis unit uses AI to analyze a user's social media posts and identify consumption patterns related to specific events or seasons. For example, if a user posts on social media that they are having a barbecue, the AI ​​will automatically order ingredients and supplies for the barbecue. The consumption pattern analysis unit also analyzes the user's calendar information and makes orders tailored to specific events or plans. For example, if a user has a "family birthday party" registered on their calendar, the AI ​​will automatically order ingredients and decorations for the party. The consumption pattern analysis unit also analyzes a user's past purchase history and social media posts to identify seasonal consumption patterns. For example, if a user tends to order more cold drinks and ice cream in the summer, the AI ​​will automatically make orders accordingly. This makes it possible to make orders that take into account events and seasonal fluctuations.

[0052] The consumption pattern analysis unit can monitor the user's health condition and automatically order vitamins or cold medicine if the user catches a cold. For example, the consumption pattern analysis unit uses AI to monitor the user's health data and automatically order vitamins or cold medicine if it detects signs of a cold. For example, it analyzes the user's body temperature and heart rate data and orders the necessary medicine if an abnormality is detected. The consumption pattern analysis unit also works with the user's health app to monitor changes in health condition in real time. For example, if the user enters "I have a cold" into the health app, the AI ​​automatically orders the necessary medicine based on that information. The consumption pattern analysis unit also monitors the user's health condition over the long term and takes preventive measures against seasonal illnesses. For example, it automatically schedules vaccinations and orders preventive medicine during influenza season. This makes it possible to order products according to the user's health condition.

[0053] The consumption pattern analysis unit analyzes the user's emotional state and can order relaxation goods when stress levels are high. For example, the consumption pattern analysis unit uses AI to analyze the user's emotional state and automatically order relaxation goods when stress levels are high. For example, it analyzes the user's facial expressions and voice and orders aroma candles or massagers if it determines that the user's stress level is high. The consumption pattern analysis unit also monitors the user's emotional state in real time using an emotion estimation function. For example, it automatically orders subscriptions for music or movies that will help the user relax when they are feeling stressed. The consumption pattern analysis unit also analyzes the user's emotional data over a long period of time to identify patterns of emotional fluctuation. For example, if stress levels tend to increase on certain days or during certain times of the day, it can order relaxation goods to coincide with those times. This enables ordering based on the user's emotional state.

[0054] The order generation unit can be expanded to handle not only daily necessities but also other categories, such as pet supplies or gardening supplies. For example, the order generation unit uses AI to analyze a user's pet consumption patterns and automatically order pet supplies such as pet food and toys. For example, the order generation unit orders necessary supplies based on the pet's food intake and frequency of play. The order generation unit also uses AI to analyze a user's gardening activities to automatically order gardening supplies. For example, the order generation unit places an order based on the seasonal growth status of plants and the necessary fertilizer and tools. The order generation unit also uses AI to automatically order supplies from specific categories tailored to the user's hobbies and lifestyle. For example, if a user enjoys outdoor activities, the order generation unit automatically orders camping and mountain climbing equipment. This diversifies the types of orders that can be ordered.

[0055] The consumption pattern analysis unit analyzes the consumption patterns of all family members of the user and can place orders based on the needs of the entire family. For example, the consumption pattern analysis unit uses AI to analyze the consumption patterns of all family members and place orders based on the needs of the entire family. For example, it automatically orders ingredients and daily necessities based on the food preferences and consumption amounts of each family member. The consumption pattern analysis unit also monitors the health data of all family members and automatically orders necessary medications and supplements. For example, if someone in the family has an allergy, it will order food and medication appropriate for that person. The consumption pattern analysis unit also automatically orders necessary items based on the lifestyles and events of each family member. For example, it can order travel supplies needed for a family trip or equipment needed for children's school events. This makes it possible to place orders based on the needs of the entire family.

[0056] The consumption pattern analysis unit can analyze the emotional state of each family member and order refreshing items when everyone is tired. For example, the consumption pattern analysis unit uses AI to analyze the emotional state of each family member and automatically order refreshing items when everyone is tired. For example, it analyzes the facial expressions and voices of each family member and orders refreshing bath salts or massagers. The consumption pattern analysis unit also uses emotion estimation functionality to monitor the emotional state of each family member in real time. For example, it automatically orders subscriptions for movies and games that will help the family relax when they are feeling stressed. The consumption pattern analysis unit also analyzes the emotional data of each family member over a long period of time to identify patterns of emotional fluctuation. For example, if all family members tend to get tired easily during certain events or seasons, it can order refreshing items to coincide with those times. This makes it possible to order items based on the emotional state of each family member.

[0057] The dialogue setting unit can set the order items or frequency not only by text but also by voice or gesture. The dialogue setting unit, for example, allows a user to set the order items and frequency by voice. For example, by giving a voice instruction such as "Please place my next order in two weeks," AI analyzes the instruction and sets the order. The dialogue setting unit also uses gesture recognition technology to allow a user to set the order items and frequency by hand movements or facial expressions. For example, by making a specific gesture, AI analyzes the instruction and sets the order. The dialogue setting unit also provides an interface that allows the user to set the order items and frequency by selecting text, voice, or gesture. For example, the user can select the method that is easiest for them to use to make the settings. This diversifies the user interface and improves usability.

[0058] The dialogue setting unit allows the AI ​​to make optimal suggestions based on past data when the user sets the items to be ordered and the frequency of purchases. For example, the dialogue setting unit analyzes the user's past order data and suggests optimal items and frequency of purchases. For example, it might suggest, "It's been a month since your last order. Would you like to reorder the same item?" The dialogue setting unit also presents optimal options based on past data when the user sets the items to be ordered and the frequency of purchases. For example, it might suggest, "Since detergent is usually used up in two weeks, should we set your next order for two weeks later?" The dialogue setting unit also allows the AI ​​to learn the user's consumption patterns and suggest the optimal timing for ordering. For example, it might suggest, "The best time to order toilet paper is on the first day of each month." This reduces the burden on the user and enables optimal ordering.

[0059] The dialogue setting unit can use the emotion estimation function to analyze the emotional state of the user when making settings and suggest a setting method that causes less stress. The dialogue setting unit, for example, uses the emotion estimation function to analyze the emotional state of the user when making settings and suggest a setting method that causes less stress. For example, if the user is feeling stressed, it suggests a simple setting method. The dialogue setting unit also monitors the user's emotional state in real time and suggests a setting method that causes less stress. For example, it suggests making detailed settings when the user is relaxed. The dialogue setting unit also identifies a setting method that causes the least stress to the user based on the emotion estimation data and suggests that method. For example, it avoids setting methods that the user has found stressful in the past. This reduces the user's stress and enables comfortable setting.

[0060] The dialogue setting unit can perform settings not only on a smartphone app but also on other devices such as a smartwatch or smart speaker. The dialogue setting unit, for example, enables users to set order items and frequency not only on a smartphone app but also on a smartwatch. For example, settings are performed using the voice recognition function of a smartwatch. The dialogue setting unit also enables users to set order items and frequency by voice using a smart speaker. For example, the user can say, "Alexa, set my next toilet paper order for one week later." The dialogue setting unit also synchronizes setting information between multiple devices, allowing users to set order items and frequency from any device. For example, information set on a smartphone is reflected on a smartwatch or smart speaker. This allows users to set items from multiple devices, improving convenience.

[0061] The dialogue setting unit can suggest the optimal settings by referring to the setting data of other users. For example, the dialogue setting unit uses AI to analyze the setting data of other users and suggest the optimal settings. For example, the dialogue setting unit suggests the optimal order frequency based on the setting data of users living in the same area. Furthermore, when a user makes settings, the dialogue setting unit uses AI to refer to the setting data of other users and present the optimal options. For example, it may suggest, "Many users order this product every two weeks." Furthermore, the dialogue setting unit uses AI to learn the setting data of other users and suggest the optimal setting method. For example, it may suggest, "Users with the same lifestyle choose these settings." This makes it possible to utilize the data of other users to achieve optimal settings.

[0062] The dialogue setting unit can use the emotion estimation function to analyze the emotional state of the user when making settings and propose an interface design that elicits positive emotions. For example, the dialogue setting unit uses the emotion estimation function to analyze the emotional state of the user when making settings and proposes an interface design that elicits positive emotions. For example, it uses colors and designs that relax the user. The dialogue setting unit also monitors the user's emotional state in real time and proposes an interface design that elicits positive emotions. For example, it uses music and animations that bring joy to the user. The dialogue setting unit also identifies the interface design that elicits the most positive emotions from the user based on the emotion estimation data and proposes that design. For example, it incorporates design elements that the user prefers based on past data. This elicits positive emotions from the user and enables comfortable setup.

[0063] The food monitoring unit can monitor food in the refrigerator using not only cameras and weight sensors, but also temperature and humidity sensors. The food monitoring unit, for example, installs temperature and humidity sensors inside the refrigerator to monitor the freshness of food in real time. For example, it detects fluctuations in temperature and humidity and issues an alert to prevent food deterioration. The food monitoring unit also combines temperature and humidity sensors in addition to cameras and weight sensors to build a system for monitoring the freshness of food. For example, it analyzes the storage conditions of food based on temperature and humidity data. The food monitoring unit also uses the temperature and humidity sensors to optimize the environment inside the refrigerator and maintain the freshness of food. For example, it automatically adjusts the refrigerator settings to keep the temperature and humidity within appropriate ranges. This allows for more accurate monitoring of food freshness.

[0064] The ingredient monitoring unit can analyze the expiration dates of ingredients in the refrigerator and suggest recipes that prioritize using ingredients that are close to their expiration date. For example, the ingredient monitoring unit uses AI to analyze the expiration dates of ingredients in the refrigerator and suggest recipes that prioritize using ingredients that are close to their expiration date. For example, it could suggest a "pasta recipe using tomatoes that are close to their expiration date." The ingredient monitoring unit can also build a system where AI suggests optimal recipes based on expiration date data of ingredients in the refrigerator. For example, it could suggest a menu that combines ingredients that are close to their expiration date. The ingredient monitoring unit can also use AI to monitor the expiration dates of ingredients in the refrigerator in real time and automatically generate recipes that prioritize using ingredients that are close to their expiration date. For example, it could suggest a "recipe that uses ingredients that have an expiration date tomorrow." This reduces food waste and allows for more efficient use of ingredients.

[0065] The ingredient monitoring unit uses the emotion estimation function to analyze the user's emotional state when selecting ingredients and can suggest ingredients that are easy to prepare when the user is tired. For example, the ingredient monitoring unit uses the emotion estimation function to analyze the user's emotional state when selecting ingredients and can suggest ingredients that are easy to prepare when the user is tired. For example, it can suggest "frozen foods that can be cooked in a microwave" when the user is tired. The ingredient monitoring unit also monitors the user's emotional state in real time and suggests ingredients according to the emotion. For example, it can suggest "herbal tea with a relaxing effect" when the user is feeling stressed. The ingredient monitoring unit also identifies ingredients that the user can prepare most easily based on the emotion estimation data and suggests those ingredients. For example, it can make suggestions based on ingredients that the user has been able to prepare easily in the past. This makes it possible to suggest ingredients according to the user's emotional state.

[0066] The food monitoring unit can extend food monitoring in the refrigerator to other storage locations, such as the freezer and pantry. For example, the food monitoring unit can extend food monitoring in the refrigerator to the freezer and monitor the inventory status of frozen foods in real time. For example, it measures the remaining amount of food using a camera or weight sensor in the freezer. The food monitoring unit also installs a camera or weight sensor to monitor food in the pantry and monitors the inventory status of dried foods and canned goods in real time. For example, it analyzes the expiration dates of food ingredients in the pantry and automatically orders the necessary ingredients. The food monitoring unit also monitors all storage locations, including the refrigerator, freezer, and pantry, in an integrated manner, building a system for centrally managing food inventory. For example, it orders the most appropriate ingredients based on data from each storage location. This allows for centralized management of food ingredients in multiple storage locations.

[0067] The ingredient monitoring unit can make nutritional balance suggestions to improve the user's diet based on the food data in the refrigerator. For example, the ingredient monitoring unit uses AI to analyze the food data in the refrigerator and make nutritional balance suggestions to improve the user's diet. For example, it might suggest, "You're lacking in vitamin C, would you like to order more oranges?" The ingredient monitoring unit also builds a system in which AI suggests nutritionally balanced recipes based on the food data in the refrigerator. For example, it might suggest "recipes using ingredients rich in protein." The ingredient monitoring unit also uses AI to analyze the user's diet data over a long period of time and identify areas for improvement in nutritional balance. For example, it might suggest, "You've been lacking in calcium in your recent meals, would you like to order more milk?" This can improve the user's diet and support their health.

[0068] The ingredient monitoring unit can use the emotion estimation function to analyze the user's emotional state when selecting ingredients and suggest ingredients that will elicit positive emotions. For example, the ingredient monitoring unit can use the emotion estimation function to analyze the user's emotional state when selecting ingredients and suggest ingredients that will elicit positive emotions. For example, it can suggest ingredients and recipes that will bring joy to the user. The ingredient monitoring unit can also monitor the user's emotional state in real time and suggest ingredients that will elicit positive emotions. For example, it can suggest herbal teas and smoothies that will help the user relax. The ingredient monitoring unit can also identify ingredients that will elicit the most positive emotions from the user based on the emotion estimation data and suggest those ingredients. For example, it can make suggestions based on ingredients that the user has previously enjoyed. This can elicit positive emotions from the user and enable comfortable ingredient selection.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The automatic replenishment system may further include an energy consumption monitoring unit. The energy consumption monitoring unit monitors household power consumption in real time and makes suggestions to optimize energy efficiency. For example, the energy consumption monitoring unit may identify peak power consumption times and make suggestions to reduce power consumption during those times. The energy consumption monitoring unit may also analyze the frequency of use of specific home appliances and suggest energy-efficient alternatives. Furthermore, the energy consumption monitoring unit may analyze household energy consumption data over a long period of time and suggest ways to reduce energy costs. For example, the energy consumption monitoring unit may identify seasonal energy consumption patterns and suggest energy-efficient usage methods. This allows for optimization of household energy consumption and cost reduction.

[0071] The automatic replenishment system may further include a recycling management unit. The recycling management unit monitors the amount of recyclable waste generated in the home and suggests appropriate recycling methods. For example, the recycling management unit may use sensors installed on trash bins in the home to identify the type and amount of recyclable waste. The recycling management unit may also suggest appropriate sorting methods based on local recycling rules. Furthermore, the recycling management unit may manage the collection schedule for recyclable waste and send reminders to the user. For example, the recycling management unit may notify the user when the collection date is approaching and encourage the user to put out the waste at the appropriate time. This improves the efficiency of recycling activities in the home and reduces the environmental impact.

[0072] The automatic replenishment system may further include a security monitoring unit. The security monitoring unit uses security cameras and sensors inside and outside the home to detect abnormal activity and notify the user. For example, if the security monitoring unit detects suspicious activity at night, it may send an alert to the user's smartphone. The security monitoring unit may also detect signs of intrusion using door and window open / close sensors. The security monitoring unit may also analyze home security data and suggest ways to strengthen security. For example, the security monitoring unit may identify security risks at specific times or locations and suggest additional security measures. This improves home security, allowing users to live with peace of mind.

[0073] The automatic replenishment system may further include an entertainment suggestion unit. The entertainment suggestion unit suggests optimal entertainment content based on the user's hobbies and interests. For example, the entertainment suggestion unit may analyze the user's past viewing history and music playback history to suggest movies and music that match the user's preferences. The entertainment suggestion unit may also analyze the user's emotional state to suggest content that matches the user's mood at that time. For example, if the user feels like relaxing, the entertainment suggestion unit may suggest relaxing music or movies. The entertainment suggestion unit may also analyze the user's schedule to suggest optimal entertainment timing. For example, the entertainment suggestion unit may suggest content that will help the user relax after work. This improves the user's entertainment experience and makes daily life more fulfilling.

[0074] The automatic replenishment system may further include a health management unit. The health management unit monitors the user's health data and makes suggestions based on the user's health condition. For example, the health management unit may analyze the user's data, such as weight, blood pressure, and heart rate, and provide advice for maintaining health. The health management unit may also analyze the user's diet and exercise records and suggest balanced meals and appropriate exercise plans. Furthermore, the health management unit may analyze the user's emotional state and make suggestions for stress management. For example, if the user is feeling stressed, it may suggest relaxing activities and meals. This provides comprehensive support for the user's health condition and enables them to live a healthier life.

[0075] The automatic replenishment system can further include an educational support unit. The educational support unit proposes learning materials and study plans that meet the user's learning needs. For example, the educational support unit analyzes the user's learning history and interests to propose optimal learning materials and study methods. The educational support unit can also analyze the user's schedule and propose optimal time periods for studying. Furthermore, the educational support unit analyzes the user's emotional state and makes suggestions to increase motivation to study. For example, if the user is not feeling motivated to study, it can propose interesting learning materials and study methods. This improves the user's learning effectiveness and enables them to study more efficiently.

[0076] The automatic replenishment system may further include a travel planning unit. The travel planning unit proposes travel plans that meet the user's travel needs. For example, the travel planning unit may analyze the user's past travel history and interests to propose optimal travel destinations and activities. The travel planning unit may also analyze the user's schedule to propose optimal travel timings. Furthermore, the travel planning unit may analyze the user's emotional state to propose travel plans that will refresh the user. For example, if the user is feeling stressed, the travel planning unit may propose plans for a relaxing hot spring trip or nature walk. This improves the user's travel experience and makes them feel refreshed.

[0077] The automatic replenishment system may further include a communication support unit. The communication support unit makes suggestions based on the user's communication needs. For example, the communication support unit may analyze the user's past communication history and suggest the most appropriate communication method. The communication support unit may also analyze the user's emotional state and suggest a communication method that corresponds to the emotion. For example, if the user is feeling stressed, the communication support unit may suggest conversation topics and methods that will help the user relax. Furthermore, the communication support unit may analyze the user's schedule and suggest the most appropriate timing for communication. For example, the communication support unit may suggest that the user avoid busy times and communicate at a time when they can relax. This improves the user's communication experience and enables smoother communication.

[0078] The automatic replenishment system may further include an environmental monitoring unit. The environmental monitoring unit monitors environmental data inside and outside the home in real time and makes suggestions to maintain a comfortable living environment. For example, the environmental monitoring unit may monitor indoor temperature, humidity, and air quality and suggest optimal environmental settings. The environmental monitoring unit may also analyze external weather data and suggest appropriate clothing and timing for going outside. Furthermore, the environmental monitoring unit may analyze environmental data inside the home over a long period of time and make suggestions for improving the environment. For example, the environmental monitoring unit may suggest optimal ways to use air conditioners and humidifiers based on seasonal environmental data. This improves the living environment inside the home and enables a more comfortable life.

[0079] The automatic replenishment system may further include a pet care unit. The pet care unit monitors the health and behavior of the user's pet and suggests appropriate care methods. For example, the pet care unit may analyze the pet's diet and exercise records and suggest optimal diet plans and exercise methods. The pet care unit may also monitor the pet's health data and suggest appropriate measures if an abnormality is detected. Furthermore, the pet care unit may analyze the pet's emotional state and make suggestions for stress management. For example, if the pet is feeling stressed, it may suggest a relaxing environment or activity. This provides comprehensive support for the pet's health and enables the pet to live a healthier life.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The consumption pattern analysis unit analyzes the user's consumption pattern. For example, the consumption pattern analysis unit analyzes the user's purchase frequency and purchase amount to identify consumption trends. The consumption pattern analysis unit can also analyze the user's purchase timing to identify the optimal timing for ordering. Step 2: The order generation unit generates an order based on the data analyzed by the consumption pattern analysis unit. For example, the order generation unit automatically generates an order when the consumable item is running low. The order generation unit can also generate an order at a specific time based on a user's instruction. Step 3: The dialogue setting unit interactively sets the order items and order frequency with the user. For example, the dialogue setting unit sets the order items and order frequency by the user inputting instructions by text or voice. The dialogue setting unit can also set the order items and order frequency using gestures. Step 4: The food ingredient monitoring unit monitors the food ingredients in the refrigerator. For example, the food ingredient monitoring unit measures the remaining amount of food ingredients using a camera or weight sensor inside the refrigerator. The food ingredient monitoring unit can also monitor the storage condition of the food ingredients using a temperature sensor or humidity sensor.

[0082] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a consumption pattern analysis unit that analyzes a consumption pattern of a user; an order generation unit that generates an order based on the data analyzed by the consumption pattern analysis unit; an interactive setting unit that allows a user to interactively set items to be ordered and order frequency; and a food monitoring unit that monitors food ingredients in the refrigerator. A system characterized by:

2. The consumption pattern analysis unit Analyzing the user's SNS posts and calendar information and placing the order taking into account events or seasonal fluctuations 2. The system of claim 1.

3. The consumption pattern analysis unit Monitor the user's health and automatically order vitamins or cold medicine if the user catches a cold.

2. The system of claim 1.

4. The consumption pattern analysis unit Analyzing the user's emotional state and ordering relaxation goods when stress levels are high 2. The system of claim 1.

5. The order generation unit: Expand beyond everyday items to other categories, such as pet supplies or gardening supplies 2. The system of claim 1.

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