System

Abstract

An object of the system according to the embodiment is to provide appropriate care according to the growth stage of the plant and the season so that the user does not forget the timing of watering and fertilizer.SOLUTION: A system according to an embodiment includes an information collection unit, a proposal unit, and a reminder unit. The information collection unit collects information according to the growth stage of the plant and the season. The proposal unit proposes watering and fertilizer based on the information collected by the information collection unit. The reminder unit provides a reminder in accordance with the lifestyle of the user.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 technologies make it difficult to provide appropriate care according to the plant's growth stage and season, and there is a risk that users will forget to water or fertilize the plant.

[0005] The system according to the embodiment aims to provide appropriate care according to the growth stage and season of the plant, so that the user does not forget to water or fertilize the plant. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a suggestion unit, and a reminder unit. The information collection unit collects information according to the growth stage and season of the plant. The suggestion unit makes suggestions about watering and fertilizing based on the information collected by the information collection unit. The reminder unit provides reminders tailored to the user's lifestyle. [Effects of the Invention]

[0007] The system according to the embodiment provides appropriate care according to the growth stage and season of the plant, and enables the user to remember when to water and fertilize the plant. [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 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 AI-supported gardening service according to an embodiment of the present invention is a system that provides a customized cultivation plan based on information about the plants being cultivated by the user and their growing environment, thereby providing comprehensive support for the user to grow healthy plants.

[0029] An AI-supported gardening service according to an embodiment includes an information collection unit, a suggestion unit, and a reminder unit. The information collection unit collects information according to the growth stage and season of a plant. For example, the information collection unit collects information such as the type of plant, growth stage, and season provided by the user. The information collection unit can also collect environmental data such as temperature, humidity, and sunshine hours using sensors. The suggestion unit makes suggestions about watering and fertilizing based on the information collected by the information collection unit. For example, the suggestion unit can suggest specific fertilizer to promote growth for plants that have begun to sprout in spring, and suggest appropriate watering frequencies to prevent dryness in summer. The suggestion unit can also analyze the user's emotional state in real time using a generation AI (e.g., a text generation AI or a multimodal generation AI) and make more proactive gardening suggestions if the user's emotions are strong. The reminder unit provides reminders tailored to the user's lifestyle. For example, if the user tends to forget to water plants on busy weekdays, the reminder unit can set a reminder to water them all at once on the weekend. The reminder unit can also use the generation AI to analyze the user's emotional state in real time and adjust the timing and content of reminders if the user's emotions are strong. This allows the AI-supported gardening service according to the embodiment to provide comprehensive support for users to grow healthy plants. For example, users can maintain the health of their plants by receiving suggestions on appropriate watering and fertilization according to the plant's growth stage and season. Furthermore, by receiving reminders tailored to the user's lifestyle, the user can grow plants without forgetting to water them.

[0030] The suggestion unit can analyze past cultivation data, learn optimal cultivation patterns, and make suggestions. The suggestion unit, for example, analyzes past cultivation data and learns the growth patterns of the same plant species. For example, it identifies optimal watering frequencies and types of fertilizer from past data and makes suggestions based on them. The suggestion unit can also identify unsuccessful cultivation methods from past data and make suggestions to avoid them. The suggestion unit can also identify successful cultivation methods from past data and make suggestions based on them. In this way, it is possible to suggest optimal cultivation methods based on past data.

[0031] The suggestion unit can make suggestions based on weather and environmental data that is updated in real time. For example, the suggestion unit collects weather data in real time and updates suggestions for watering and fertilizing according to changes in the weather. For example, if rain is expected, the suggestion unit can notify the user to refrain from watering. The suggestion unit can also collect temperature data in real time and adjust the cultivation plan according to changes in temperature. The suggestion unit can also collect humidity data in real time and adjust the cultivation plan according to changes in humidity. This allows suggestions to be made in response to changes in the environment in real time.

[0032] The reminder unit can analyze past behavioral data and learn and provide optimal reminder patterns. The reminder unit, for example, analyzes the user's past behavioral data and learns optimal reminder patterns. For example, it sets reminders based on the timing and frequency of watering in the past. The reminder unit can also identify from past data time periods when the user is likely to respond to reminders and set reminders for those time periods. The reminder unit can also identify from past data time periods when the user is unlikely to respond to reminders and set reminders to avoid those time periods. This makes it possible to provide optimal reminders based on past behavioral data.

[0033] The reminder unit can provide reminders based on the user's schedule data that is updated in real time. The reminder unit, for example, collects the user's schedule data in real time and updates reminders according to changes in the schedule. For example, the reminder unit adjusts the timing of reminders if there is a sudden change in plans. The reminder unit can also set the optimal timing of reminders based on the user's schedule. The reminder unit can also adjust the content of reminders based on the user's schedule. This makes it possible to provide reminders that respond to changes in the schedule in real time.

[0034] The reminder unit can send notifications to different devices. For example, the reminder unit builds a system that sends reminders to a user's smartphone. For example, it notifies the user when it's time to water plants through an app. The reminder unit can also send reminders to a user's smartwatch. The reminder unit can also send reminders to a user's tablet. In this way, by sending notifications to different devices, the user can receive reminders regardless of which device they are using.

[0035] The reminder section allows users to share reminders with family and friends and work together to take care of plants. The reminder section, for example, provides a function for sharing reminders with family and friends, building a system for collaborative plant care. For example, it can be set up so that all family members can receive reminders. The reminder section also allows users to share reminders with friends and work together to take care of plants. The reminder section can also allocate tasks based on shared reminders and share progress. This allows users to work together to take care of plants by sharing reminders with family and friends.

[0036] The information collection unit can monitor the health of plants and send alerts to users if an abnormality is detected. For example, to monitor the health of plants, the information collection unit collects photos and observation data of plants provided by users. For example, if the color of leaves changes or growth slows, the generation AI analyzes the cause and proposes appropriate measures. The information collection unit can also monitor the health of plants using sensors. For example, it can detect changes in temperature and humidity and send an alert to the user if an abnormality is detected. The information collection unit can also perform emotion analysis using the generation AI and adjust the content of the alert based on the user's emotions. This allows the health of plants to be monitored and a prompt response to any abnormalities detected.

[0037] The information collection unit can send immediate alerts if an abnormality is detected based on health data updated in real time. The information collection unit, for example, builds a system that monitors the health of plants in real time and sends immediate alerts if an abnormality is detected. For example, it can immediately notify if the color of the leaves changes. The information collection unit can also monitor changes in temperature and humidity in real time and send immediate alerts if an abnormality is detected. The information collection unit can also use generative AI to analyze the cause of the abnormality and propose appropriate countermeasures. This allows health data to be monitored in real time and an immediate response to be taken if an abnormality is detected.

[0038] The information collection unit can be adapted to different plant species and cultivation environments, and can accommodate a wide range of users. The information collection unit, for example, builds a system that provides optimal health monitoring and alerts for each type of plant. For example, it makes suggestions suited to different plant species, such as ornamental plants, flowers, and vegetables. The information collection unit can also provide monitoring and alerts suited to different cultivation environments (indoors, outdoors, greenhouses, etc.). The information collection unit can also suggest optimal monitoring methods based on the user's cultivation environment. This allows it to accommodate different plant species and cultivation environments, and thus accommodate a wide range of users.

[0039] The information collection unit can work with a voice assistant to notify the user of alerts by voice. For example, the information collection unit can work with a voice assistant to build a system that notifies the user of alerts about the health status of plants by voice. For example, abnormalities can be notified through Alexa or Google Assistant. The information collection unit can also suggest countermeasures to the user through the voice assistant. The information collection unit can also notify the user of reminders through the voice assistant. This allows the user to be notified of alerts through the voice assistant.

[0040] The information collection unit can analyze past environmental data, learn, and propose optimal environmental adjustment patterns. The information collection unit, for example, analyzes past environmental data and learns patterns of the same growing environment. For example, it identifies optimal temperature and humidity adjustment methods from the past data and makes proposals based on those. The information collection unit can also identify unsuccessful environmental adjustment methods from the past data and make proposals to avoid them. The information collection unit can also identify successful environmental adjustment methods from the past data and make proposals based on those. This makes it possible to propose optimal environmental adjustment methods based on past data.

[0041] The information collection unit can make suggestions based on environmental data that is updated in real time. The information collection unit, for example, builds a system that collects environmental data in real time and updates suggestions according to changes in the environment. For example, if there is a sudden change in temperature, an adjustment method is immediately suggested. The information collection unit can also immediately suggest an adjustment method if there is a sudden change in humidity. The information collection unit can also immediately suggest an adjustment method if there is a sudden change in sunlight hours. This allows suggestions to be made in real time in response to changes in the environment.

[0042] The information collection unit can be adapted to different growing environments (indoors, outdoors, greenhouses, etc.) and can accommodate a wide range of users. The information collection unit, for example, builds a system that collects and analyzes environmental data according to different growing environments, such as indoors, outdoors, and greenhouses. For example, it suggests appropriate lighting conditions for indoor plants and suggests weather-appropriate care methods for outdoor plants. The information collection unit can also suggest optimal environmental adjustment methods according to different growing environments. The information collection unit can also suggest optimal environmental adjustment methods based on the user's growing environment. This allows the system to accommodate different growing environments and accommodate a wide range of users.

[0043] The information collection unit can work in conjunction with a smart home system to automate environmental adjustments. For example, the information collection unit can work in conjunction with a smart home system to build a system that automatically adjusts the environment based on environmental data. For example, the information collection unit can automatically adjust an air conditioner or humidifier in response to changes in temperature and humidity. The information collection unit can also work in conjunction with a smart home system to automate lighting adjustments. The information collection unit can also work in conjunction with a smart home system to automate an irrigation system. In this way, environmental adjustments can be automated by working in conjunction with a smart home system.

[0044] The suggestion unit can analyze past cultivation data, learn optimal cultivation patterns, and propose them. The suggestion unit, for example, analyzes past cultivation data and learns the growth patterns of the same plant species. For example, it identifies an optimal cultivation plan from the past data and makes a proposal based on that. The suggestion unit can also identify unsuccessful cultivation methods from the past data and make a proposal to avoid them. The suggestion unit can also identify successful cultivation methods from the past data and make a proposal based on that. In this way, it is possible to propose optimal cultivation methods based on past data.

[0045] The suggestion unit can adjust the cultivation plan in response to changes in the user's preferences and goals, which are updated in real time. The suggestion unit, for example, builds a system that updates the cultivation plan in real time in response to changes in the user's preferences and goals. For example, when a user starts growing a new plant, the cultivation plan is immediately adjusted. The suggestion unit can also suggest an optimal cultivation plan based on changes in the user's preferences and goals. The suggestion unit can also adjust the content of the cultivation plan based on changes in the user's preferences and goals. This allows the cultivation plan to be adjusted in response to changes in the user's preferences and goals.

[0046] The suggestion unit can be adapted to different plant species and growing environments, and can accommodate a wide range of users. The suggestion unit, for example, builds a system that provides an optimal growing plan for each type of plant. For example, it makes suggestions for different plant species, such as ornamental plants, flowers, and vegetables. The suggestion unit can also provide optimal growing plans according to different growing environments. The suggestion unit can also propose an optimal growing plan based on the user's growing environment. This allows it to accommodate different plant species and growing environments, and can accommodate a wide range of users.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The suggestion unit can analyze past cultivation data, learn optimal cultivation patterns, and make suggestions. For example, it analyzes past cultivation data and learns the growth patterns of the same plant species. For example, it identifies the optimal watering frequency and type of fertilizer from the past data and makes suggestions based on that. The suggestion unit can also identify unsuccessful cultivation methods from the past data and make suggestions to avoid them. The suggestion unit can also identify successful cultivation methods from the past data and make suggestions based on that. In this way, it is possible to suggest optimal cultivation methods based on past data.

[0049] The suggestion unit can make suggestions based on weather and environmental data that is updated in real time. For example, it can collect weather data in real time and update suggestions for watering and fertilizing according to changes in the weather. For example, if rain is expected, it can notify the user to refrain from watering. The suggestion unit can also collect temperature data in real time and adjust the cultivation plan according to changes in temperature. The suggestion unit can also collect humidity data in real time and adjust the cultivation plan according to changes in humidity. This allows suggestions to be made in response to changes in the environment in real time.

[0050] The reminder unit can analyze past behavioral data and learn and provide optimal reminder patterns. For example, it analyzes the user's past behavioral data and learns optimal reminder patterns. For example, it sets reminders based on the timing and frequency of watering in the past. The reminder unit can also identify from past data time periods when the user is likely to respond to reminders and set reminders for those time periods. The reminder unit can also identify from past data time periods when the user is unlikely to respond to reminders and set reminders to avoid those time periods. This makes it possible to provide optimal reminders based on past behavioral data.

[0051] The reminder unit can provide reminders based on the user's schedule data that is updated in real time. For example, the reminder unit collects the user's schedule data in real time and updates reminders according to changes in the schedule. For example, the reminder unit adjusts the timing of reminders if there is a sudden change in plans. The reminder unit can also set the optimal reminder timing based on the user's schedule. The reminder unit can also adjust the content of reminders based on the user's schedule. This makes it possible to provide reminders that respond to changes in the schedule in real time.

[0052] The Reminders section allows users to share reminders with family and friends and work together to take care of plants. For example, the system provides a function for sharing reminders with family and friends, building a system for collaborative plant care. For example, you can set it so that all family members receive reminders. The Reminders section also allows users to share reminders with friends and work together to take care of plants. The Reminders section also allows users to divide tasks based on shared reminders and share progress. This allows users to work together to take care of plants by sharing reminders with family and friends.

[0053] The information collection unit can work with a voice assistant to notify the user of alerts by voice. For example, a system can be built that works with a voice assistant to notify the user of alerts about the health of plants by voice. For example, an abnormality can be notified through Alexa or Google Assistant. The information collection unit can also suggest countermeasures to the user through the voice assistant. The information collection unit can also notify the user of reminders through the voice assistant. This allows alerts to be notified to the user through the voice assistant.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The information collection unit collects information according to the growth stage and season of the plant. For example, the information collection unit collects information such as the type of plant, growth stage, and season provided by the user. The information collection unit can also collect environmental data such as temperature, humidity, and hours of sunlight using sensors. Step 2: The suggestion unit makes suggestions about watering and fertilizing based on the information collected by the information collection unit. For example, the suggestion unit may suggest specific fertilizer to promote growth for plants that have begun to sprout in spring, and the appropriate frequency of watering to prevent dryness in summer. The suggestion unit can also use the generation AI to analyze the user's emotional state in real time, and make more proactive suggestions about plant care if the user's emotions are strong. Step 3: The reminder section provides reminders tailored to the user's lifestyle. For example, if a user tends to forget to water their plants during busy weekdays, the reminder section can set a reminder to water them all at once on the weekend. The reminder section can also use generative AI to analyze the user's emotional state in real time, and adjust the timing and content of reminders if the user's emotions are strong.

[0056] (Example 2) The AI-supported gardening service according to an embodiment of the present invention is a system that provides a customized cultivation plan based on information about the plants being cultivated by the user and their growing environment, thereby providing comprehensive support for the user to grow healthy plants.

[0057] An AI-supported gardening service according to an embodiment includes an information collection unit, a suggestion unit, and a reminder unit. The information collection unit collects information according to the growth stage and season of a plant. For example, the information collection unit collects information such as the type of plant, growth stage, and season provided by the user. The information collection unit can also collect environmental data such as temperature, humidity, and sunshine hours using sensors. The suggestion unit makes suggestions about watering and fertilizing based on the information collected by the information collection unit. For example, the suggestion unit can suggest specific fertilizer to promote growth for plants that have begun to sprout in spring, and suggest appropriate watering frequencies to prevent dryness in summer. The suggestion unit can also analyze the user's emotional state in real time using a generation AI (e.g., a text generation AI or a multimodal generation AI) and make more proactive gardening suggestions if the user's emotions are strong. The reminder unit provides reminders tailored to the user's lifestyle. For example, if the user tends to forget to water plants on busy weekdays, the reminder unit can set a reminder to water them all at once on the weekend. The reminder unit can also use the generation AI to analyze the user's emotional state in real time and adjust the timing and content of reminders if the user's emotions are strong. This allows the AI-supported gardening service according to the embodiment to provide comprehensive support for users to grow healthy plants. For example, users can maintain the health of their plants by receiving suggestions on appropriate watering and fertilization according to the plant's growth stage and season. Furthermore, by receiving reminders tailored to the user's lifestyle, the user can grow plants without forgetting to water them.

[0058] The suggestion unit can analyze the user's emotional state in real time, and if the user's emotional state is strong, make more proactive cultivation suggestions. The suggestion unit can, for example, use a generative AI to analyze the user's emotional state in real time, and if the user's emotional state is strong, make more proactive cultivation suggestions. For example, if the user is expressing joy, the suggestion unit can suggest a specific fertilizer to promote growth. The suggestion unit can also suggest more frequent watering if the user is expressing satisfaction. The suggestion unit can also suggest introducing a new plant if the user is excited. This allows the suggestion content to be adjusted based on the user's emotions.

[0059] The suggestion unit can analyze past cultivation data, learn optimal cultivation patterns, and make suggestions. The suggestion unit, for example, analyzes past cultivation data and learns the growth patterns of the same plant species. For example, it identifies optimal watering frequencies and types of fertilizer from past data and makes suggestions based on them. The suggestion unit can also identify unsuccessful cultivation methods from past data and make suggestions to avoid them. The suggestion unit can also identify successful cultivation methods from past data and make suggestions based on them. In this way, it is possible to suggest optimal cultivation methods based on past data.

[0060] The suggestion unit can make suggestions based on weather and environmental data that is updated in real time. For example, the suggestion unit collects weather data in real time and updates suggestions for watering and fertilizing according to changes in the weather. For example, if rain is expected, the suggestion unit can notify the user to refrain from watering. The suggestion unit can also collect temperature data in real time and adjust the cultivation plan according to changes in temperature. The suggestion unit can also collect humidity data in real time and adjust the cultivation plan according to changes in humidity. This allows suggestions to be made in response to changes in the environment in real time.

[0061] The reminder unit analyzes the user's emotional state in real time, and if the user's positive emotions are strong, it can adjust the timing and content of the reminder. For example, the reminder unit uses a generative AI to analyze the user's emotional state in real time, and if the user's positive emotions are strong, it can adjust the timing and content of the reminder. For example, it can set a watering reminder when the user is relaxed. The reminder unit can also provide a detailed reminder when the user is feeling satisfied. The reminder unit can also provide a reminder on new plant care methods when the user is excited. This makes it possible to adjust the timing and content of reminders based on the user's emotions.

[0062] The reminder unit can analyze past behavioral data and learn and provide optimal reminder patterns. The reminder unit, for example, analyzes the user's past behavioral data and learns optimal reminder patterns. For example, it sets reminders based on the timing and frequency of watering in the past. The reminder unit can also identify from past data time periods when the user is likely to respond to reminders and set reminders for those time periods. The reminder unit can also identify from past data time periods when the user is unlikely to respond to reminders and set reminders to avoid those time periods. This makes it possible to provide optimal reminders based on past behavioral data.

[0063] The reminder unit can provide reminders based on the user's schedule data that is updated in real time. The reminder unit, for example, collects the user's schedule data in real time and updates reminders according to changes in the schedule. For example, the reminder unit adjusts the timing of reminders if there is a sudden change in plans. The reminder unit can also set the optimal timing of reminders based on the user's schedule. The reminder unit can also adjust the content of reminders based on the user's schedule. This makes it possible to provide reminders that respond to changes in the schedule in real time.

[0064] The reminder unit can send notifications to different devices. For example, the reminder unit builds a system that sends reminders to a user's smartphone. For example, it notifies the user when it's time to water plants through an app. The reminder unit can also send reminders to a user's smartwatch. The reminder unit can also send reminders to a user's tablet. In this way, by sending notifications to different devices, the user can receive reminders regardless of which device they are using.

[0065] The reminder section allows users to share reminders with family and friends and work together to take care of plants. The reminder section, for example, provides a function for sharing reminders with family and friends, building a system for collaborative plant care. For example, it can be set up so that all family members can receive reminders. The reminder section also allows users to share reminders with friends and work together to take care of plants. The reminder section can also allocate tasks based on shared reminders and share progress. This allows users to work together to take care of plants by sharing reminders with family and friends.

[0066] The reminder unit can use the emotion estimation function to prioritize the timing and content of reminders that will make the user feel the most positive. For example, the reminder unit can use the emotion estimation function to identify the timing of a reminder that will make the user feel the most positive and set a reminder at that timing. For example, the reminder unit can send a reminder when the user is relaxed. The reminder unit can also use the emotion estimation function to identify the content of a reminder that will make the user feel the most positive and set a reminder based on that content. The reminder unit can also use the emotion estimation function to identify the frequency of reminders that will make the user feel the most positive and set a reminder based on that frequency. This makes it possible to optimize the timing and content of reminders based on the user's emotions.

[0067] The information collection unit can monitor the health of plants and send alerts to users if an abnormality is detected. For example, to monitor the health of plants, the information collection unit collects photos and observation data of plants provided by users. For example, if the color of leaves changes or growth slows, the generation AI analyzes the cause and proposes appropriate measures. The information collection unit can also monitor the health of plants using sensors. For example, it can detect changes in temperature and humidity and send an alert to the user if an abnormality is detected. The information collection unit can also perform emotion analysis using the generation AI and adjust the content of the alert based on the user's emotions. This allows the health of plants to be monitored and a prompt response to any abnormalities detected.

[0068] The information collection unit can send immediate alerts if an abnormality is detected based on health data updated in real time. The information collection unit, for example, builds a system that monitors the health of plants in real time and sends immediate alerts if an abnormality is detected. For example, it can immediately notify if the color of the leaves changes. The information collection unit can also monitor changes in temperature and humidity in real time and send immediate alerts if an abnormality is detected. The information collection unit can also use generative AI to analyze the cause of the abnormality and propose appropriate countermeasures. This allows health data to be monitored in real time and an immediate response to be taken if an abnormality is detected.

[0069] The information collection unit can be adapted to different plant species and cultivation environments, and can accommodate a wide range of users. The information collection unit, for example, builds a system that provides optimal health monitoring and alerts for each type of plant. For example, it makes suggestions suited to different plant species, such as ornamental plants, flowers, and vegetables. The information collection unit can also provide monitoring and alerts suited to different cultivation environments (indoors, outdoors, greenhouses, etc.). The information collection unit can also suggest optimal monitoring methods based on the user's cultivation environment. This allows it to accommodate different plant species and cultivation environments, and thus accommodate a wide range of users.

[0070] The information collection unit can work with a voice assistant to notify the user of alerts by voice. For example, the information collection unit can work with a voice assistant to build a system that notifies the user of alerts about the health status of plants by voice. For example, abnormalities can be notified through Alexa or Google Assistant. The information collection unit can also suggest countermeasures to the user through the voice assistant. The information collection unit can also notify the user of reminders through the voice assistant. This allows the user to be notified of alerts through the voice assistant.

[0071] The information collection unit can use the emotion estimation function to prioritize the content of alerts that evoke the most positive emotions in the user. For example, the information collection unit can use the emotion estimation function to identify the content of alerts that evoke the most positive emotions in the user and set alerts based on the content. For example, a detailed health report can be sent when the user is relaxed. The information collection unit can also use the emotion estimation function to identify the timing of alerts that evoke the most positive emotions in the user and set alerts at those timings. The information collection unit can also use the emotion estimation function to identify the frequency of alerts that evoke the most positive emotions in the user and set alerts based on the frequency. This allows the content of alerts to be optimized based on the user's emotions.

[0072] The information collection unit collects environmental data, analyzes the user's emotional state in real time, and, if the user's positive emotions are strong, makes detailed environmental adjustment suggestions. For example, when collecting environmental data, the information collection unit analyzes the user's emotional state in real time, and, if the user's positive emotions are strong, makes detailed environmental adjustment suggestions. For example, when the user is relaxing, the information collection unit suggests detailed adjustment methods for indoor temperature and humidity. The information collection unit can also use the emotion estimation function to identify environmental adjustment suggestions that evoke the user's most positive emotions and prioritize those suggestions. The information collection unit can also use the emotion estimation function to identify the frequency of environmental adjustments that evoke the user's most positive emotions and make suggestions based on that frequency. This allows the environmental adjustment suggestions to be optimized based on the user's emotions.

[0073] The information collection unit can analyze past environmental data, learn, and propose optimal environmental adjustment patterns. The information collection unit, for example, analyzes past environmental data and learns patterns of the same growing environment. For example, it identifies optimal temperature and humidity adjustment methods from the past data and makes proposals based on those. The information collection unit can also identify unsuccessful environmental adjustment methods from the past data and make proposals to avoid them. The information collection unit can also identify successful environmental adjustment methods from the past data and make proposals based on those. This makes it possible to propose optimal environmental adjustment methods based on past data.

[0074] The information collection unit can make suggestions based on environmental data that is updated in real time. The information collection unit, for example, builds a system that collects environmental data in real time and updates suggestions according to changes in the environment. For example, if there is a sudden change in temperature, an adjustment method is immediately suggested. The information collection unit can also immediately suggest an adjustment method if there is a sudden change in humidity. The information collection unit can also immediately suggest an adjustment method if there is a sudden change in sunlight hours. This allows suggestions to be made in real time in response to changes in the environment.

[0075] The information collection unit can be adapted to different growing environments (indoors, outdoors, greenhouses, etc.) and can accommodate a wide range of users. The information collection unit, for example, builds a system that collects and analyzes environmental data according to different growing environments, such as indoors, outdoors, and greenhouses. For example, it suggests appropriate lighting conditions for indoor plants and suggests weather-appropriate care methods for outdoor plants. The information collection unit can also suggest optimal environmental adjustment methods according to different growing environments. The information collection unit can also suggest optimal environmental adjustment methods based on the user's growing environment. This allows the system to accommodate different growing environments and accommodate a wide range of users.

[0076] The information collection unit can work in conjunction with a smart home system to automate environmental adjustments. For example, the information collection unit can work in conjunction with a smart home system to build a system that automatically adjusts the environment based on environmental data. For example, the information collection unit can automatically adjust an air conditioner or humidifier in response to changes in temperature and humidity. The information collection unit can also work in conjunction with a smart home system to automate lighting adjustments. The information collection unit can also work in conjunction with a smart home system to automate an irrigation system. In this way, environmental adjustments can be automated by working in conjunction with a smart home system.

[0077] The information collection unit can use the emotion estimation function to prioritize suggestions for environmental adjustments that will evoke the most positive emotions in the user. For example, the information collection unit can use the emotion estimation function to identify suggestions for environmental adjustments that will evoke the most positive emotions in the user and build a system that prioritizes those suggestions. For example, the information collection unit can suggest methods for adjusting temperature and humidity that are optimal for when the user is relaxing. The information collection unit can also use the emotion estimation function to identify the frequency of environmental adjustments that evoke the most positive emotions in the user and make suggestions based on that frequency. The information collection unit can also use the emotion estimation function to identify the timing of environmental adjustments that evoke the most positive emotions in the user and make suggestions based on that timing. This allows the suggestions for environmental adjustments to be optimized based on the user's emotions.

[0078] The suggestion unit can analyze past cultivation data, learn optimal cultivation patterns, and propose them. The suggestion unit, for example, analyzes past cultivation data and learns the growth patterns of the same plant species. For example, it identifies an optimal cultivation plan from the past data and makes a proposal based on that. The suggestion unit can also identify unsuccessful cultivation methods from the past data and make a proposal to avoid them. The suggestion unit can also identify successful cultivation methods from the past data and make a proposal based on that. In this way, it is possible to propose optimal cultivation methods based on past data.

[0079] The suggestion unit can adjust the cultivation plan in response to changes in the user's preferences and goals, which are updated in real time. The suggestion unit, for example, builds a system that updates the cultivation plan in real time in response to changes in the user's preferences and goals. For example, when a user starts growing a new plant, the cultivation plan is immediately adjusted. The suggestion unit can also suggest an optimal cultivation plan based on changes in the user's preferences and goals. The suggestion unit can also adjust the content of the cultivation plan based on changes in the user's preferences and goals. This allows the cultivation plan to be adjusted in response to changes in the user's preferences and goals.

[0080] The suggestion unit can be adapted to different plant species and growing environments, and can accommodate a wide range of users. The suggestion unit, for example, builds a system that provides an optimal growing plan for each type of plant. For example, it makes suggestions for different plant species, such as ornamental plants, flowers, and vegetables. The suggestion unit can also provide optimal growing plans according to different growing environments. The suggestion unit can also propose an optimal growing plan based on the user's growing environment. This allows it to accommodate different plant species and growing environments, and can accommodate a wide range of users.

[0081] The suggestion unit can use the emotion estimation function to prioritize the development plan that evokes the most positive emotion in the user. The suggestion unit, for example, uses the emotion estimation function to identify the development plan that evokes the most positive emotion in the user and builds a system that prioritizes setting that plan. For example, the suggestion unit can provide a development plan that is optimal for a period when the user is relaxed. The suggestion unit can also use the emotion estimation function to identify the content of the development plan that evokes the most positive emotion in the user and set the development plan based on that content. The suggestion unit can also use the emotion estimation function to identify the frequency of the development plan that evokes the most positive emotion in the user and set the development plan based on that frequency. This makes it possible to optimize the development plan based on the user's emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The suggestion unit analyzes the user's emotional state in real time, and if the user's emotional state is strong, the suggestion unit can make more proactive cultivation suggestions. For example, if the user is happy, the suggestion unit can suggest a specific fertilizer to promote growth. If the user is satisfied, the suggestion unit can also suggest more frequent watering. If the user is excited, the suggestion unit can also suggest introducing a new plant. In this way, the suggestion content can be adjusted based on the user's emotional state.

[0084] The suggestion unit can analyze past cultivation data, learn optimal cultivation patterns, and make suggestions. For example, it analyzes past cultivation data and learns the growth patterns of the same plant species. For example, it identifies the optimal watering frequency and type of fertilizer from the past data and makes suggestions based on that. The suggestion unit can also identify unsuccessful cultivation methods from the past data and make suggestions to avoid them. The suggestion unit can also identify successful cultivation methods from the past data and make suggestions based on that. In this way, it is possible to suggest optimal cultivation methods based on past data.

[0085] The suggestion unit can make suggestions based on weather and environmental data that is updated in real time. For example, it can collect weather data in real time and update suggestions for watering and fertilizing according to changes in the weather. For example, if rain is expected, it can notify the user to refrain from watering. The suggestion unit can also collect temperature data in real time and adjust the cultivation plan according to changes in temperature. The suggestion unit can also collect humidity data in real time and adjust the cultivation plan according to changes in humidity. This allows suggestions to be made in response to changes in the environment in real time.

[0086] The reminder unit analyzes the user's emotional state in real time, and if the user's emotional state is strong, it can adjust the timing and content of the reminder. For example, the system uses generative AI to analyze the user's emotional state in real time, and if the user's emotional state is strong, it can adjust the timing and content of the reminder. For example, it can set a watering reminder when the user is relaxed. The reminder unit can also provide detailed reminders when the user is feeling satisfied. The reminder unit can also provide a reminder on new plant care methods when the user is excited. This allows the system to adjust the timing and content of reminders based on the user's emotions.

[0087] The reminder unit can analyze past behavioral data and learn and provide optimal reminder patterns. For example, it analyzes the user's past behavioral data and learns optimal reminder patterns. For example, it sets reminders based on the timing and frequency of watering in the past. The reminder unit can also identify from past data time periods when the user is likely to respond to reminders and set reminders for those time periods. The reminder unit can also identify from past data time periods when the user is unlikely to respond to reminders and set reminders to avoid those time periods. This makes it possible to provide optimal reminders based on past behavioral data.

[0088] The reminder unit can provide reminders based on the user's schedule data that is updated in real time. For example, the reminder unit collects the user's schedule data in real time and updates reminders according to changes in the schedule. For example, the reminder unit adjusts the timing of reminders if there is a sudden change in plans. The reminder unit can also set the optimal reminder timing based on the user's schedule. The reminder unit can also adjust the content of reminders based on the user's schedule. This makes it possible to provide reminders that respond to changes in the schedule in real time.

[0089] The Reminders section allows users to share reminders with family and friends and work together to take care of plants. For example, the system provides a function for sharing reminders with family and friends, building a system for collaborative plant care. For example, you can set it so that all family members receive reminders. The Reminders section also allows users to share reminders with friends and work together to take care of plants. The Reminders section also allows users to divide tasks based on shared reminders and share progress. This allows users to work together to take care of plants by sharing reminders with family and friends.

[0090] The information collection unit can monitor the health of plants and send alerts to users if an abnormality is detected. For example, to monitor the health of plants, it collects photos and observation data of plants provided by users. For example, if the color of leaves changes or growth slows, the generation AI analyzes the cause and proposes appropriate measures. The information collection unit can also monitor the health of plants using sensors. For example, it can detect changes in temperature and humidity and send an alert to the user if an abnormality is detected. The information collection unit can also perform emotion analysis using the generation AI and adjust the content of the alert based on the user's emotions. This allows the system to monitor the health of plants and respond quickly if an abnormality is detected.

[0091] The information collection unit can work with a voice assistant to notify the user of alerts by voice. For example, a system can be built that works with a voice assistant to notify the user of alerts about the health of plants by voice. For example, an abnormality can be notified through Alexa or Google Assistant. The information collection unit can also suggest countermeasures to the user through the voice assistant. The information collection unit can also notify the user of reminders through the voice assistant. This allows alerts to be notified to the user through the voice assistant.

[0092] The information collection unit can use the emotion estimation function to prioritize the content of alerts that evoke the most positive emotions in the user. For example, the emotion estimation function can be used to identify the content of alerts that evoke the most positive emotions in the user and set alerts based on that content. For example, a detailed health report can be sent when the user is relaxed. The information collection unit can also use the emotion estimation function to identify the timing of alerts that evoke the most positive emotions in the user and set alerts at that timing. The information collection unit can also use the emotion estimation function to identify the frequency of alerts that evoke the most positive emotions in the user and set alerts based on that frequency. This allows the content of alerts to be optimized based on the user's emotions.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The information collection unit collects information according to the growth stage and season of the plant. For example, the information collection unit collects information such as the type of plant, growth stage, and season provided by the user. The information collection unit can also collect environmental data such as temperature, humidity, and hours of sunlight using sensors. Step 2: The suggestion unit makes suggestions about watering and fertilizing based on the information collected by the information collection unit. For example, the suggestion unit may suggest specific fertilizer to promote growth for plants that have begun to sprout in spring, and the appropriate frequency of watering to prevent dryness in summer. The suggestion unit can also use the generation AI to analyze the user's emotional state in real time, and make more proactive suggestions about plant care if the user's emotions are strong. Step 3: The reminder section provides reminders tailored to the user's lifestyle. For example, if a user tends to forget to water their plants during busy weekdays, the reminder section can set a reminder to water them all at once on the weekend. The reminder section can also use generative AI to analyze the user's emotional state in real time, and adjust the timing and content of reminders if the user's emotions are strong.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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. an information gathering unit that collects information according to the growth stage and season of the plant; a suggestion unit that suggests watering and fertilizing based on the information collected by the information collection unit; A reminder unit that provides reminders tailored to the user's lifestyle. A system characterized by:

2. The proposal unit Analyzes the user's emotional state in real time, and if the user has strong positive emotions, makes more proactive training suggestions.

2. The system of claim 1.

3. The proposal unit Make suggestions based on real-time weather and environmental data 2. The system of claim 1.

4. The reminder unit Analyzes the user's emotional state in real time and adjusts the timing and content of reminders if the user's emotional state is strong and positive.

2. The system of claim 1.

5. The information collecting unit Monitors plant health and alerts users if anomalies are detected 2. The system of claim 1.

6. The information collecting unit Collects environmental data, analyzes the user's emotional state in real time, and if the user's emotions are strong, makes detailed suggestions for adjusting the environment.

2. The system of claim 1.

7. The proposal unit Integrate with the community feature to share your development plans with other users and get feedback 2. The system of claim 1.

8. The proposal unit Using emotion estimation, the system prioritizes the development plan that evokes the most positive emotions from the user.

2. The system of claim 1.

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