System

Abstract

An object of a system according to an embodiment is to automate inventory management of daily necessities and prevent out-of-stock.SOLUTION: A system according to an embodiment includes a usage tracking unit, a reminding unit, and a purchase option providing unit. The usage tracking unit is configured to track usage of the daily necessities. The reminding part reminds when the stock is reduced based on the data tracked by the use state tracking part. The purchase option providing unit provides an online direct purchase option when reminded by the reminding unit.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] With conventional technology, inventory management of daily necessities was a time-consuming process, and there was a problem that it was sometimes difficult to notice when an item was out of stock.

[0005] The system according to the embodiment aims to automate inventory management of daily necessities and prevent stockouts. [Means for solving the problem]

[0006] The system according to the embodiment includes a usage tracking unit, a reminder unit, and a purchase option providing unit. The usage tracking unit tracks usage of the daily necessities. The reminder unit provides a reminder when inventory is low based on data tracked by the usage tracking unit. The purchase option providing unit provides a direct online purchase option when reminded by the reminder unit. [Effects of the Invention]

[0007] The system according to the embodiment automates inventory management of daily necessities and can prevent stockouts. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 daily necessities management system according to an embodiment of the present invention uses AI to manage the consumption of daily necessities and notify the user in advance, allowing the user to efficiently manage inventory of daily necessities and quickly replenish them when needed.

[0029] A daily necessities management system according to an embodiment includes a usage tracking unit, a reminder unit, and a purchase option providing unit. The usage tracking unit tracks the usage of daily necessities. For example, it uses sensors and cameras to monitor the amount of tissue and toilet paper used in real time. The usage tracking unit can also use voice recognition technology to track the sounds made when a user uses daily necessities and understand their usage. For example, it recognizes the sound of pulling out tissue or tearing toilet paper and counts the number of uses. The usage tracking unit can also predict the usage of daily necessities using a generative AI that learns the user's behavioral patterns. For example, it predicts the next time the user will use tissue based on past usage data. The reminder unit sends a reminder when inventory is low based on the data tracked by the usage tracking unit. For example, it sends a notification to a smartphone or tablet, such as, "Your tissue inventory is low. You need to replenish." The reminder unit can also include a personalized message in the reminder notification based on the user's past purchase history and usage patterns. For example, it sends a notification such as, "Your last purchased tissue is running low. You need to replenish." Furthermore, the reminder unit can link reminder notifications with the user's schedule and location information and send them at the optimal time. For example, the reminder can be sent when the user is at home. The purchase option providing unit provides direct online purchase options when the reminder unit reminds the user. For example, the reminder can provide a link to an online store along with the reminder notification, allowing the user to easily complete the purchase process. The purchase option providing unit can also enable the purchase process to be completed by voice command using a voice assistant. For example, the purchase process can be completed by simply entering the voice command "buy tissues." Furthermore, the purchase option providing unit can use an emotion estimation function to analyze the user's emotions when completing the purchase process and provide an interface to reduce stress. For example, if the user feels stressed, the interface design or operation method can be adjusted.As a result, the daily necessities management system according to the embodiment allows users to efficiently manage inventory of daily necessities and quickly replenish them when needed. For example, users can receive reminders before they run out of tissues or toilet paper and easily complete purchase procedures online. Furthermore, customized services based on individual usage patterns and preferences improve user convenience.

[0030] In addition to sensors or cameras, the usage tracking unit can use voice recognition technology to track the sounds made when a user uses daily necessities and grasp their usage status. For example, the usage tracking unit uses voice recognition technology to detect the sounds of using tissues or toilet paper, and analyzes the data to grasp their usage status. For example, it recognizes the sounds of pulling out tissues or tearing toilet paper and counts the number of uses. In this way, using voice recognition technology can grasp the usage status of daily necessities more accurately.

[0031] The usage tracking unit can predict the use of daily necessities using a generation AI that learns the user's behavioral patterns. The usage tracking unit, for example, uses a generation AI to learn the user's behavioral patterns and predict the use of daily necessities. For example, based on past usage data, it predicts the next time tissues will be used. This makes it possible to use a generation AI to make accurate usage predictions based on the user's behavioral patterns.

[0032] The usage tracking unit also tracks the usage of consumables other than daily necessities, enabling comprehensive inventory management. The usage tracking unit also tracks the usage of consumables other than daily necessities (e.g., food and beverages), enabling comprehensive inventory management. For example, a sensor monitors the consumption of food in a refrigerator. This allows comprehensive inventory management by tracking consumables other than daily necessities.

[0033] The usage tracking unit can promote communication within the home by sharing tracking data with all family members and visualizing each member's usage. The usage tracking unit, for example, builds a system that shares tracking data with all family members and visualizes each member's usage. For example, it allows all family members to check usage status through a smartphone app. In this way, sharing tracking data promotes communication within the home.

[0034] The reminding unit can include a personalized message based on the user's past purchase history and usage patterns in the reminder notification. For example, the reminding unit may include a personalized message based on the user's past purchase history and usage patterns in the reminder notification. For example, the reminding unit may send a notification such as, "Your last purchased tissues are running low. Refills are required." By including a personalized message, more appropriate reminders can be provided to the user.

[0035] The reminding unit can link reminder notifications with the user's schedule and location information and send them at the optimal timing. The reminding unit, for example, builds a system that links reminder notifications with the user's schedule and location information and sends them at the optimal timing. For example, reminders are sent during times when the user is at home. In this way, by linking reminder notifications with the user's schedule and location information, reminders can be sent at the optimal timing.

[0036] The reminder unit can also send reminder notifications to devices such as smart speakers or smart watches, and can provide notifications via multiple channels. For example, the reminder unit can also send reminder notifications to devices such as smart speakers and smart watches, building a system that provides notifications via multiple channels. For example, a reminder can be sent by voice from a smart speaker. This reduces the chance of a user missing a reminder by providing notifications via multiple devices.

[0037] The reminding unit can provide the user with options by including information on new products and substitutes for daily necessities that are running low in stock in the reminder notification. The reminding unit, for example, builds a system that provides the user with options by including information on new products and substitutes for daily necessities that are running low in stock in the reminder notification. For example, it sends a notification such as "Tissues are running low in stock. How about this product as a substitute?" By providing information on substitutes and new products, it is possible to provide the user with a variety of options.

[0038] The purchase option providing unit can suggest recommended products based on the user's past purchase history and preferences as purchase options. The purchase option providing unit, for example, builds a system that suggests recommended products based on the user's past purchase history and preferences as purchase options. For example, related products are suggested based on brands and products purchased in the past. This improves user convenience by suggesting recommended products based on the user's past purchase history and preferences.

[0039] The purchase option providing unit can enable the purchase procedure to be completed by voice commands using a voice assistant. The purchase option providing unit, for example, builds a system that enables the purchase procedure to be completed by voice commands using a voice assistant. For example, the purchase procedure can be completed by simply inputting the voice command "purchase tissues." This allows the purchase procedure to be completed by voice commands, improving user convenience.

[0040] The purchase option providing unit can add a subscription service to the purchase options and propose periodic automatic replenishment. The purchase option providing unit, for example, builds a system that adds a subscription service to the purchase options and proposes periodic automatic replenishment. For example, a fixed amount of tissues or toilet paper is automatically delivered every month. In this way, adding a subscription service enables periodic automatic replenishment, improving user convenience.

[0041] The purchase option providing unit can link the purchase procedure with multiple online stores and propose optimal prices and delivery options. The purchase option providing unit, for example, builds a system that links the purchase procedure with multiple online stores and proposes optimal prices and delivery options. For example, it compares prices and delivery conditions from multiple stores and proposes the optimal option. In this way, by linking with multiple online stores, it is possible to propose optimal prices and delivery options.

[0042] The system can customize reminders and purchasing options based on the user's usage patterns and preferences. For example, the system builds a system that automatically customizes reminders and purchasing options based on the user's usage patterns and preferences. For example, the system prioritizes reminders for specific brands or products. This improves user convenience by customizing reminders and purchasing options based on the user's usage patterns and preferences.

[0043] The system collects user feedback and allows the generative AI to learn and continuously improve the service. For example, the system collects user feedback and allows the generative AI to learn and continuously improve the service. For example, reminders and purchase options are improved based on user opinions and requests. In this way, the system collects user feedback and allows the generative AI to learn and continuously improve the service.

[0044] The system can be linked with other smart home devices to build an integrated home management system. For example, the system can be linked with other smart home devices to build an integrated home management system. For example, the system can be linked with smart refrigerators and smart lighting to centrally manage the usage of consumables in the home. This allows the system to be linked with other smart home devices to build an integrated home management system.

[0045] The system can provide information on new products and campaigns for daily necessities based on the user's preferences. For example, the system is constructed to provide information on new products and campaigns for daily necessities based on the user's preferences. For example, product information on brands that the user frequently purchases is provided preferentially. This improves user convenience by providing information on new products and campaigns based on the user's preferences.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The household goods management system may further include an energy consumption tracking unit. The energy consumption tracking unit monitors the amount of electricity consumed in the home in real time and makes suggestions to improve energy efficiency. For example, it may suggest a schedule to avoid peak electricity consumption hours. The energy consumption tracking unit may also monitor the power consumption of specific home appliances individually and provide advice to reduce unnecessary power consumption. Furthermore, the energy consumption tracking unit may monitor the usage of renewable energy sources and support efficient energy management.

[0048] The daily necessities management system may further include a health management unit. The health management unit monitors the user's health status and provides health advice in association with the usage of daily necessities. For example, the health management unit may periodically measure the user's weight and blood pressure and recommend the use of daily necessities according to the user's health status. The health management unit may also track the user's diet and exercise records and make suggestions to support a balanced lifestyle. Furthermore, the health management unit may analyze the user's sleep patterns and provide advice to promote quality sleep.

[0049] The household goods management system may further include an entertainment provider. The entertainment provider suggests entertainment content such as movies, music, and games based on the user's preferences. For example, it may recommend movies suitable for when the user wants to relax. The entertainment provider may also provide personalized content based on the user's past viewing history and ratings. Furthermore, the entertainment provider may suggest content that the whole family can enjoy, promoting communication within the home.

[0050] The household goods management system can further include a safety management unit. The safety management unit monitors safety within the home and issues an alert if an abnormality is detected. For example, it detects signs of a gas leak or fire and notifies the user. The safety management unit can also monitor the opening and closing status of doors and windows and issue an alarm if it detects suspicious activity. Furthermore, the safety management unit can automatically strengthen the security system when the user goes out to ensure the safety of the home.

[0051] The household goods management system can further include an environmental monitoring unit. The environmental monitoring unit monitors the air quality, temperature, and humidity in the home in real time and makes suggestions to maintain a comfortable environment. For example, if the air quality deteriorates, it can send a notification to encourage ventilation. The environmental monitoring unit can also suggest optimal room temperature and humidity levels depending on the season and weather. Furthermore, the environmental monitoring unit can provide environmental conditions suitable for plant growth, supporting a green lifestyle within the home.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The usage tracking unit tracks the usage of daily necessities. For example, sensors and cameras can be used to monitor the amount of tissue and toilet paper used in real time. Voice recognition technology can also be used to track the sounds made when the user uses daily necessities, allowing for an understanding of usage. Furthermore, generative AI can be used to learn the user's behavioral patterns and predict the use of daily necessities. Step 2: The reminder unit sends a reminder when inventory is low based on the data tracked by the usage tracking unit. For example, a notification such as "Your tissue inventory is low. Refills are needed" is sent to the smartphone or tablet. The reminder notification can also include a personalized message based on the user's past purchase history and usage patterns. Furthermore, reminder notifications can be linked to the user's schedule and location information to send them at the optimal time. Step 3: The purchase option providing unit provides a direct online purchase option when reminded by the reminding unit. For example, along with the reminder notification, a link to an online store may be provided to allow the user to easily complete the purchase procedure. The purchase procedure may also be completed by voice command using a voice assistant. Furthermore, an emotion estimation function may be used to analyze the user's emotions when completing the purchase procedure, and an interface may be provided to reduce stress.

[0054] (Example 2) The daily necessities management system according to an embodiment of the present invention uses AI to manage the consumption of daily necessities and notify the user in advance, allowing the user to efficiently manage inventory of daily necessities and quickly replenish them when needed.

[0055] A daily necessities management system according to an embodiment includes a usage tracking unit, a reminder unit, and a purchase option providing unit. The usage tracking unit tracks the usage of daily necessities. For example, it uses sensors and cameras to monitor the amount of tissue and toilet paper used in real time. The usage tracking unit can also use voice recognition technology to track the sounds made when a user uses daily necessities and understand their usage. For example, it recognizes the sound of pulling out tissue or tearing toilet paper and counts the number of uses. The usage tracking unit can also predict the usage of daily necessities using a generative AI that learns the user's behavioral patterns. For example, it predicts the next time the user will use tissue based on past usage data. The reminder unit sends a reminder when inventory is low based on the data tracked by the usage tracking unit. For example, it sends a notification to a smartphone or tablet, such as, "Your tissue inventory is low. You need to replenish." The reminder unit can also include a personalized message in the reminder notification based on the user's past purchase history and usage patterns. For example, it sends a notification such as, "Your last purchased tissue is running low. You need to replenish." Furthermore, the reminder unit can link reminder notifications with the user's schedule and location information and send them at the optimal time. For example, the reminder can be sent when the user is at home. The purchase option providing unit provides direct online purchase options when the reminder unit reminds the user. For example, the reminder can provide a link to an online store along with the reminder notification, allowing the user to easily complete the purchase process. The purchase option providing unit can also enable the purchase process to be completed by voice command using a voice assistant. For example, the purchase process can be completed by simply entering the voice command "buy tissues." Furthermore, the purchase option providing unit can use an emotion estimation function to analyze the user's emotions when completing the purchase process and provide an interface to reduce stress. For example, if the user feels stressed, the interface design or operation method can be adjusted.As a result, the daily necessities management system according to the embodiment allows users to efficiently manage inventory of daily necessities and quickly replenish them when needed. For example, users can receive reminders before they run out of tissues or toilet paper and easily complete purchase procedures online. Furthermore, customized services based on individual usage patterns and preferences improve user convenience.

[0056] In addition to sensors or cameras, the usage tracking unit can use voice recognition technology to track the sounds made when a user uses daily necessities and grasp their usage status. For example, the usage tracking unit uses voice recognition technology to detect the sounds of using tissues or toilet paper, and analyzes the data to grasp their usage status. For example, it recognizes the sounds of pulling out tissues or tearing toilet paper and counts the number of uses. In this way, using voice recognition technology can grasp the usage status of daily necessities more accurately.

[0057] The usage tracking unit can predict the use of daily necessities using a generation AI that learns the user's behavioral patterns. The usage tracking unit, for example, uses a generation AI to learn the user's behavioral patterns and predict the use of daily necessities. For example, based on past usage data, it predicts the next time tissues will be used. This makes it possible to use a generation AI to make accurate usage predictions based on the user's behavioral patterns.

[0058] The usage tracking unit uses the emotion estimation function to analyze the emotions of the user when using the daily commodity, and can reflect the stress and satisfaction in the tracking data. For example, the usage tracking unit uses the emotion estimation function to analyze the emotions of the user when using the daily commodity, and can reflect the stress and satisfaction in the tracking data. For example, the emotion estimation function calculates an emotion score by analyzing facial expressions and voices during use. In this way, by using the emotion estimation function, tracking that takes into account the user's emotional state becomes possible.

[0059] The usage tracking unit also tracks the usage of consumables other than daily necessities, enabling comprehensive inventory management. The usage tracking unit also tracks the usage of consumables other than daily necessities (e.g., food and beverages), enabling comprehensive inventory management. For example, a sensor monitors the consumption of food in a refrigerator. This allows comprehensive inventory management by tracking consumables other than daily necessities.

[0060] The usage tracking unit can promote communication within the home by sharing tracking data with all family members and visualizing each member's usage. The usage tracking unit, for example, builds a system that shares tracking data with all family members and visualizes each member's usage. For example, it allows all family members to check usage status through a smartphone app. In this way, sharing tracking data promotes communication within the home.

[0061] The usage tracking unit can use the emotion estimation function to analyze the emotions of the user when using the daily necessities in real time and make suggestions to elicit positive emotions. For example, the usage tracking unit can use the emotion estimation function to analyze the emotions of the user when using the daily necessities in real time and make suggestions to elicit positive emotions. For example, the usage tracking unit can suggest a relaxing environment based on the emotion data during use. In this way, by using the emotion estimation function, it is possible to make suggestions to elicit positive emotions from the user.

[0062] The reminding unit can include a personalized message based on the user's past purchase history and usage patterns in the reminder notification. For example, the reminding unit may include a personalized message based on the user's past purchase history and usage patterns in the reminder notification. For example, the reminding unit may send a notification such as, "Your last purchased tissues are running low. Refills are required." By including a personalized message, more appropriate reminders can be provided to the user.

[0063] The reminding unit can link reminder notifications with the user's schedule and location information and send them at the optimal timing. The reminding unit, for example, builds a system that links reminder notifications with the user's schedule and location information and sends them at the optimal timing. For example, reminders are sent during times when the user is at home. In this way, by linking reminder notifications with the user's schedule and location information, reminders can be sent at the optimal timing.

[0064] The reminding unit can use the emotion estimation function to select a reminding method according to the user's emotional state and reduce stress. The reminding unit, for example, uses the emotion estimation function to select a reminding method according to the user's emotional state and builds a system that reduces stress. For example, a reminder is sent when the user is relaxed. In this way, by using the emotion estimation function, reminders according to the user's emotional state are possible, thereby reducing stress.

[0065] The reminder unit can also send reminder notifications to devices such as smart speakers or smart watches, and can provide notifications via multiple channels. For example, the reminder unit can also send reminder notifications to devices such as smart speakers and smart watches, building a system that provides notifications via multiple channels. For example, a reminder can be sent by voice from a smart speaker. This reduces the chance of a user missing a reminder by providing notifications via multiple devices.

[0066] The reminding unit can provide the user with options by including information on new products and substitutes for daily necessities that are running low in stock in the reminder notification. The reminding unit, for example, builds a system that provides the user with options by including information on new products and substitutes for daily necessities that are running low in stock in the reminder notification. For example, it sends a notification such as "Tissues are running low in stock. How about this product as a substitute?" By providing information on substitutes and new products, it is possible to provide the user with a variety of options.

[0067] The reminder unit can use the emotion estimation function to analyze the emotional response of the user when receiving a reminder notification and optimize the notification method. For example, the reminder unit uses the emotion estimation function to analyze the emotional response of the user when receiving a reminder notification and builds a system that optimizes the notification method. For example, if the user feels stressed, the tone and timing of the notification can be adjusted. In this way, by using the emotion estimation function, it is possible to provide an optimal notification method based on the user's emotional response.

[0068] The purchase option providing unit can suggest recommended products based on the user's past purchase history and preferences as purchase options. The purchase option providing unit, for example, builds a system that suggests recommended products based on the user's past purchase history and preferences as purchase options. For example, related products are suggested based on brands and products purchased in the past. This improves user convenience by suggesting recommended products based on the user's past purchase history and preferences.

[0069] The purchase option providing unit can enable the purchase procedure to be completed by voice commands using a voice assistant. The purchase option providing unit, for example, builds a system that enables the purchase procedure to be completed by voice commands using a voice assistant. For example, the purchase procedure can be completed by simply inputting the voice command "purchase tissues." This allows the purchase procedure to be completed by voice commands, improving user convenience.

[0070] The purchase option providing unit can use the emotion estimation function to analyze the emotion of the user when performing the purchase procedure and provide an interface for reducing stress. The purchase option providing unit, for example, uses the emotion estimation function to build a system that analyzes the emotion of the user when performing the purchase procedure and provides an interface for reducing stress. For example, if the user feels stressed, the design or operation method of the interface can be adjusted. In this way, by using the emotion estimation function, an interface for reducing stress for the user can be provided.

[0071] The purchase option providing unit can add a subscription service to the purchase options and propose periodic automatic replenishment. The purchase option providing unit, for example, builds a system that adds a subscription service to the purchase options and proposes periodic automatic replenishment. For example, a fixed amount of tissues or toilet paper is automatically delivered every month. In this way, adding a subscription service enables periodic automatic replenishment, improving user convenience.

[0072] The purchase option providing unit can link the purchase procedure with multiple online stores and propose optimal prices and delivery options. The purchase option providing unit, for example, builds a system that links the purchase procedure with multiple online stores and proposes optimal prices and delivery options. For example, it compares prices and delivery conditions from multiple stores and proposes the optimal option. In this way, by linking with multiple online stores, it is possible to propose optimal prices and delivery options.

[0073] The purchase option providing unit can use the emotion estimation function to analyze the emotional response of the user when making a purchase and improve the purchasing experience. For example, the purchase option providing unit uses the emotion estimation function to analyze the emotional response of the user when making a purchase and build a system that improves the purchasing experience. For example, if the user shows positive emotions, a similar purchasing experience is provided. In this way, by using the emotion estimation function, an optimal purchasing experience can be provided based on the user's emotional response.

[0074] The system can customize reminders and purchasing options based on the user's usage patterns and preferences. For example, the system builds a system that automatically customizes reminders and purchasing options based on the user's usage patterns and preferences. For example, the system prioritizes reminders for specific brands or products. This improves user convenience by customizing reminders and purchasing options based on the user's usage patterns and preferences.

[0075] The system collects user feedback and allows the generative AI to learn and continuously improve the service. For example, the system collects user feedback and allows the generative AI to learn and continuously improve the service. For example, reminders and purchase options are improved based on user opinions and requests. In this way, the system collects user feedback and allows the generative AI to learn and continuously improve the service.

[0076] The system uses the emotion estimation function to provide services according to the emotional state of the user, thereby improving satisfaction. For example, the system uses the emotion estimation function to provide services according to the emotional state of the user, thereby improving satisfaction. For example, a system is constructed that provides a reminder when the user is relaxed. In this way, by using the emotion estimation function, it is possible to provide services according to the emotional state of the user, thereby improving satisfaction.

[0077] The system can be linked with other smart home devices to build an integrated home management system. For example, the system can be linked with other smart home devices to build an integrated home management system. For example, the system can be linked with smart refrigerators and smart lighting to centrally manage the usage of consumables in the home. This allows the system to be linked with other smart home devices to build an integrated home management system.

[0078] The system can provide information on new products and campaigns for daily necessities based on the user's preferences. For example, the system is constructed to provide information on new products and campaigns for daily necessities based on the user's preferences. For example, product information on brands that the user frequently purchases is provided preferentially. This improves user convenience by providing information on new products and campaigns based on the user's preferences.

[0079] The system can use the emotion estimation function to analyze the user's emotional response and improve the degree of personalization of the service. For example, the system uses the emotion estimation function to analyze the user's emotional response and build a system that improves the degree of personalization of the service. For example, if the user shows positive emotion, a similar service is provided. In this way, by using the emotion estimation function, a highly personalized service based on the user's emotional response can be provided.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The household goods management system may further include an energy consumption tracking unit. The energy consumption tracking unit monitors the amount of electricity consumed in the home in real time and makes suggestions to improve energy efficiency. For example, it may suggest a schedule to avoid peak electricity consumption hours. The energy consumption tracking unit may also monitor the power consumption of specific home appliances individually and provide advice to reduce unnecessary power consumption. Furthermore, the energy consumption tracking unit may monitor the usage of renewable energy sources and support efficient energy management.

[0082] The daily necessities management system may further include a health management unit. The health management unit monitors the user's health status and provides health advice in association with the usage of daily necessities. For example, the health management unit may periodically measure the user's weight and blood pressure and recommend the use of daily necessities according to the user's health status. The health management unit may also track the user's diet and exercise records and make suggestions to support a balanced lifestyle. Furthermore, the health management unit may analyze the user's sleep patterns and provide advice to promote quality sleep.

[0083] The household goods management system may further include an entertainment provider. The entertainment provider suggests entertainment content such as movies, music, and games based on the user's preferences. For example, it may recommend movies suitable for when the user wants to relax. The entertainment provider may also provide personalized content based on the user's past viewing history and ratings. Furthermore, the entertainment provider may suggest content that the whole family can enjoy, promoting communication within the home.

[0084] The household goods management system can further include a safety management unit. The safety management unit monitors safety within the home and issues an alert if an abnormality is detected. For example, it detects signs of a gas leak or fire and notifies the user. The safety management unit can also monitor the opening and closing status of doors and windows and issue an alarm if it detects suspicious activity. Furthermore, the safety management unit can automatically strengthen the security system when the user goes out to ensure the safety of the home.

[0085] The household goods management system can further include an environmental monitoring unit. The environmental monitoring unit monitors the air quality, temperature, and humidity in the home in real time and makes suggestions to maintain a comfortable environment. For example, if the air quality deteriorates, it can send a notification to encourage ventilation. The environmental monitoring unit can also suggest optimal room temperature and humidity levels depending on the season and weather. Furthermore, the environmental monitoring unit can provide environmental conditions suitable for plant growth, supporting a green lifestyle within the home.

[0086] The daily necessities management system can further use an emotion estimation function to select a reminder method based on the user's emotional state. For example, if the user is feeling stressed, the system can send less reminder notifications, and if the user is relaxed, it can send more proactive notifications. The emotion estimation function can also be used to send encouraging messages when the user shows positive emotions. Furthermore, by using the emotion estimation function to select a reminder method according to the user's emotional state, the effectiveness of notifications can be maximized.

[0087] The daily necessities management system can further use an emotion estimation function to suggest purchasing options based on the user's emotional state. For example, if the user is feeling stressed, products with a relaxing effect can be suggested. Also, if the user expresses positive emotions using the emotion estimation function, new or limited-edition products can be suggested. Furthermore, by using the emotion estimation function to suggest purchasing options according to the user's emotional state, the purchasing experience can be improved.

[0088] The daily necessities management system can further use the emotion estimation function to suggest entertainment content based on the user's emotional state. For example, when a user wants to relax, relaxing music or movies can be recommended. Also, when the user expresses positive emotions, the emotion estimation function can suggest fun games or activities. Furthermore, by using the emotion estimation function to suggest entertainment content according to the user's emotional state, user satisfaction can be improved.

[0089] The daily necessities management system can also use the emotion estimation function to provide health advice based on the user's emotional state. For example, if the user is feeling stressed, it can suggest exercise or meals that have a relaxing effect. Also, if the user shows positive emotions using the emotion estimation function, it can suggest active exercise or new health habits. Furthermore, by using the emotion estimation function to provide health advice according to the user's emotional state, it can support the user's health management.

[0090] The household goods management system can further use an emotion estimation function to adjust the home environment based on the user's emotional state. For example, when the user wants to relax, the brightness and color temperature of the lights can be adjusted. Also, when the user expresses a positive emotion using the emotion estimation function, music or fragrance can be added. Furthermore, by using the emotion estimation function to adjust the environment according to the user's emotional state, the comfort of the home can be improved.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The usage tracking unit tracks the usage of daily necessities. For example, sensors and cameras can be used to monitor the amount of tissue and toilet paper used in real time. Voice recognition technology can also be used to track the sounds made when the user uses daily necessities, allowing for an understanding of usage. Furthermore, generative AI can be used to learn the user's behavioral patterns and predict the use of daily necessities. Step 2: The reminder unit sends a reminder when inventory is low based on the data tracked by the usage tracking unit. For example, a notification such as "Your tissue inventory is low. Refills are needed" is sent to the smartphone or tablet. The reminder notification can also include a personalized message based on the user's past purchase history and usage patterns. Furthermore, reminder notifications can be linked to the user's schedule and location information to send them at the optimal time. Step 3: The purchase option providing unit provides a direct online purchase option when reminded by the reminding unit. For example, along with the reminder notification, a link to an online store may be provided to allow the user to easily complete the purchase procedure. The purchase procedure may also be completed by voice command using a voice assistant. Furthermore, an emotion estimation function may be used to analyze the user's emotions when completing the purchase procedure, and an interface may be provided to reduce stress.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 usage tracking unit that tracks usage of the daily necessities; a reminder unit that reminds users when inventory is low based on the data tracked by the usage tracking unit; a purchase option providing unit that provides an online direct purchase option when reminded by the reminding unit. A system characterized by:

2. The usage tracking unit: In addition to sensors or cameras, voice recognition technology is used to track the sounds made when a user uses the everyday item, and to understand how it is being used. The system of claim 1 .

3. The usage tracking unit: Track the usage of consumables other than the above-mentioned daily necessities and perform comprehensive inventory management. The system of claim 1 .

4. The reminding unit Reminder notifications include personalized messages based on the user's past purchases and usage patterns The system of claim 1 .

5. The purchase option providing unit The purchasing options suggest recommended products based on the user's past purchase history and preferences. The system of claim 1 .

6. The usage tracking unit: Using the emotion estimation function, the emotions felt when the user uses the daily necessities are analyzed, and stress and satisfaction are reflected in the tracking data. The system of claim 1 .

7. The reminding unit Using emotion estimation function, the system selects reminder methods according to the user's emotional state to reduce stress. The system of claim 1 .

8. The purchase option providing unit Using emotion estimation functionality, we analyze the emotions felt by users during the purchasing process and provide an interface to reduce stress. The system of claim 1 .

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