system

Abstract

The system according to the embodiment aims to visualize the frequency of use of pet supplies and to efficiently declutter unnecessary items. [Solution] A system according to an embodiment includes a recognition unit, a measurement unit, an identification unit, and a suggestion unit. The recognition unit recognizes items in a room. The measurement unit measures the frequency of use of the items recognized by the recognition unit. The identification unit identifies unnecessary items based on the frequency of use measured by the measurement unit. The suggestion unit suggests decluttering based on the unnecessary items identified by the identification unit.

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 techniques have had the problem of making it difficult to grasp the frequency of use of pet supplies and to efficiently discard unnecessary items.

[0005] The system according to the embodiment aims to visualize the frequency of use of pet supplies and to efficiently declutter unnecessary items. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a measurement unit, an identification unit, and a suggestion unit. The recognition unit recognizes items in a room. The measurement unit measures the frequency of use of the items recognized by the recognition unit. The identification unit identifies unnecessary items based on the frequency of use measured by the measurement unit. The suggestion unit suggests decluttering based on the unnecessary items identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment visualizes the frequency of use of pet supplies, enabling efficient decluttering of unnecessary items. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The decluttering camera AI system according to an embodiment of the present invention visualizes the frequency of pet item use and suggests decluttering. This decluttering camera AI system recognizes items in a room, visualizes the frequency of item use based on the pet's daily life, and identifies unnecessary items. Furthermore, a decluttering generation AI mentor consults with the pet owner on personalized decluttering solutions, such as automatically listing the items at a flea market, linking to a donation destination, requesting appraisals at a recycle shop, or applying for local government collection services, based on the AI ​​camera's view of the unwanted items and the owner's requests. For example, the AI ​​camera recognizes items in a room. The AI ​​camera then uses image language generation AI to identify pet items in the room. For example, it can recognize pet beds, toys, and dishes. This allows it to create a list of pet items in the room. Next, the AI ​​camera monitors the pet's daily life and records the frequency of use of each item. For example, it records which bed the pet sleeps on, which toys it plays with, and which dishes it uses. This allows the frequency of use of each item to be visualized. Furthermore, the decluttering generation AI mentor suggests decluttering solutions based on the AI ​​camera's view of the unwanted items and the owner's requests. For example, it can identify rarely used items and suggest automatically listing them at a flea market. It can also provide consultations on decluttering tailored to individual needs, such as linking to donation destinations, requesting appraisals from recycling shops, and applying for local government collection. This system can support a satisfying lifestyle for both humans and pets. For example, by decluttering unused toys and beds, a room can be tidy and pets can live comfortably. It also contributes to society through reducing, reusing, and recycling. For example, donating unused pet supplies can help other pets and their owners. The Danshari Camera AI system visualizes the frequency of pet supplies use and suggests decluttering unnecessary items, supporting a satisfying lifestyle for both humans and pets.

[0029] The decluttering camera AI system according to the embodiment includes a recognition unit, a measurement unit, an identification unit, and a suggestion unit. The recognition unit recognizes items in a room. The recognition unit identifies pet supplies in the room using, for example, image recognition technology. For example, the recognition unit can recognize pet beds, toys, dishes, etc. This allows a list of pet supplies in the room to be created. The measurement unit measures the frequency of use of the items recognized by the recognition unit. For example, the measurement unit monitors the pet's daily life and records the frequency of use of each item. For example, the measurement unit records which bed the pet sleeps on, which toys it plays with, which dishes it uses, etc. This allows the frequency of use of each item to be visualized. The identification unit identifies unnecessary items based on the frequency of use measured by the measurement unit. For example, the identification unit can identify items that are used infrequently. For example, the identification unit can identify items that have not been used for a certain period of time or items that are damaged as unnecessary items. The suggestion unit suggests decluttering based on the unnecessary items identified by the identification unit. For example, the suggestion unit can suggest automatic listing of items at a flea market. In addition, the suggestion unit can provide advice on decluttering tailored to individual needs, such as linking with donation destinations, requesting appraisals from recycling shops, applying for local government collection of unwanted items, etc. As a result, the decluttering camera AI system according to the embodiment can visualize the frequency of use of pet products and suggest decluttering of unwanted items, thereby supporting a life in which both humans and pets are satisfied.

[0030] The suggestion unit can suggest automatic listing at a flea market. The suggestion unit can, for example, automatically list unwanted items at a flea market. For example, the suggestion unit can identify items that are used infrequently and automatically list them through a flea market app. This makes decluttering more efficient. For example, the suggestion unit can register account information for a flea market app in advance and automatically perform listing procedures when unwanted items are identified. The suggestion unit can also automatically enter information required for listing (such as product name, description, and price), simplifying the listing process. This allows owners to list unwanted items at a flea market without hassle. Furthermore, the suggestion unit can also manage the items after listing. For example, the suggestion unit can monitor the sales status of listed items and automatically notify owners when they are sold. The suggestion unit can also suggest price revisions or relisting of unsold items. This allows the suggestion unit to support efficient decluttering through automatic listing at a flea market.

[0031] The suggestion unit can suggest linking to donation destinations. The suggestion unit can, for example, link unwanted items to donation destinations. For example, the suggestion unit can identify infrequently used items and suggest appropriate donation destinations. This allows decluttering to contribute to society. The suggestion unit can, for example, pre-register a list of donation destinations and automatically carry out donation procedures when unwanted items are identified. The suggestion unit can also automatically input information required for the donation destination (such as the type, condition, and quantity of the item) to simplify the donation process. This allows owners to donate unwanted items without hassle. Furthermore, the suggestion unit can also manage donations after they are made. For example, the suggestion unit can monitor the receipt status of donated items and automatically notify owners when receipt is confirmed. The suggestion unit can also collect feedback from donation destinations and use it to help with the next donation. This allows the suggestion unit to support efficient decluttering and social contribution through linking to donation destinations.

[0032] The suggestion unit can suggest requesting an appraisal from a recycle shop. For example, the suggestion unit can request appraisal of unwanted items from a recycle shop. For example, the suggestion unit can identify infrequently used items and suggest appropriate recycle shops. This leads to decluttering and recycling. For example, the suggestion unit can register a list of recycle shops in advance and automatically initiate an appraisal request procedure when unwanted items are identified. The suggestion unit can also automatically input information required for the appraisal request (item type, condition, quantity, etc.), simplifying the appraisal request process. This allows owners to easily request appraisals of unwanted items from recycle shops. Furthermore, the suggestion unit can also manage the appraisal process. For example, the suggestion unit can monitor the appraisal results and automatically notify when an appraisal price is presented. The suggestion unit can also make a sale proposal based on the appraisal price. This allows the suggestion unit to support efficient decluttering and recycling through appraisal requests from recycle shops.

[0033] The suggestion unit can suggest applying for local government collection of unwanted items. For example, the suggestion unit can apply for local government collection of unwanted items. For example, the suggestion unit can identify infrequently used items and suggest an appropriate local government collection service. This makes decluttering more efficient. For example, the suggestion unit can pre-register a list of local government collection services and automatically initiate collection application procedures when unwanted items are identified. The suggestion unit can also automatically input information required for collection applications (such as the type, condition, and quantity of the item) to simplify the collection application process. This allows owners to apply for local government collection of unwanted items without hassle. Furthermore, the suggestion unit can also manage post-collection activities. For example, the suggestion unit can monitor the collection status and automatically notify owners when collection is complete. The suggestion unit can also collect feedback on the collection service and use it to plan the next collection. This allows the suggestion unit to support efficient decluttering through applications for local government collection of unwanted items.

[0034] The recognition unit can recognize items in a room using an image language generation AI. The recognition unit can recognize items in a room using, for example, an image language generation AI. For example, the recognition unit can identify pet supplies in the room using the image language generation AI. This makes it possible to create a list of pet supplies in the room. The recognition unit can recognize pet beds, toys, dishes, etc. using the image language generation AI. This makes it possible to create a list of pet supplies in the room. Furthermore, the recognition unit can recognize the type and condition of items in the room in detail using the image language generation AI. For example, the recognition unit can recognize the material, color, shape, etc. of items using the image language generation AI. This makes it possible to obtain detailed information about the items in the room. This makes it possible for the recognition unit to improve recognition accuracy by recognizing items in the room using the image language generation AI.

[0035] The measurement unit can monitor the pet's daily life and record the frequency of use of each item. The measurement unit can, for example, monitor the pet's daily life and record the frequency of use of each item. For example, the measurement unit can record which bed the pet sleeps on, which toys it plays with, which dishes it uses, etc. This makes it possible to visualize the frequency of use of each item. The measurement unit can, for example, monitor the pet's behavior using a camera or sensor and record the usage status of each item in real time. For example, the measurement unit can record the number of times and duration of use each time the pet uses a particular item. This makes it possible to accurately grasp the frequency of use of each item. Furthermore, the measurement unit can analyze the pet's behavioral patterns and detect changes in the frequency of use. For example, if the pet stops using a particular item, the measurement unit can detect this change and record the decrease in frequency of use. This makes it possible to monitor the pet's daily life and accurately record the frequency of use of each item, thereby providing data for decluttering.

[0036] The recognition unit can optimize the recognition algorithm according to the lighting conditions of the room and the time of day during recognition. For example, the recognition unit can optimize the recognition algorithm according to the lighting conditions of the room and the time of day during recognition. For example, the recognition unit can improve recognition accuracy by using an infrared camera when the room is dark. Furthermore, the recognition unit can perform recognition with normal camera settings during bright daytime hours. Furthermore, the recognition unit can automatically adjust the exposure settings of the camera during times when the lighting in the room changes. In this way, the recognition unit can improve recognition accuracy by optimizing the recognition algorithm according to the lighting conditions of the room and the time of day. To take the lighting conditions and the time of day into consideration, the recognition unit can use, for example, an illuminance sensor. For example, the recognition unit can measure the brightness of the room with the illuminance sensor and adjust the camera settings based on the data. Furthermore, the recognition unit can use different recognition algorithms for different time periods. For example, the recognition unit can use different algorithms for daytime and nighttime to perform recognition that is optimal for each condition. In this way, the recognition unit can improve recognition accuracy by optimizing the recognition algorithm according to the lighting conditions of the room and the time of day.

[0037] The recognition unit can improve recognition accuracy based on the pet's movements and behavioral patterns during recognition. For example, the recognition unit can improve recognition accuracy based on the pet's movements and behavioral patterns during recognition. For example, if the pet moves at high speed, the recognition unit can increase the frame rate to improve recognition accuracy. Furthermore, if the pet often plays in a particular place, the recognition unit can focus on monitoring that place. Furthermore, the recognition unit can learn the pet's behavioral patterns and improve recognition accuracy based on the predictions. Thus, the recognition unit can improve recognition accuracy by taking the pet's movements and behavioral patterns into account. To take the pet's movements and behavioral patterns into account, the recognition unit can use, for example, movement tracking technology. For example, the recognition unit can track the pet's movements with a camera and adjust the recognition algorithm based on the data. Furthermore, the recognition unit can use a behavioral pattern analysis algorithm to analyze the pet's behavioral patterns. For example, the recognition unit can predict future behavior based on the pet's past behavioral data and improve recognition accuracy based on the predictions. Thus, the recognition unit can improve recognition accuracy by taking the pet's movements and behavioral patterns into account.

[0038] The recognition unit can improve the recognition accuracy by taking into account room layout information during recognition. The recognition unit can improve the recognition accuracy by taking into account room layout information during recognition. For example, the recognition unit can consider the furniture arrangement in the room and arrange the cameras to reduce blind spots. Furthermore, the recognition unit can focus on monitoring a specific area based on the room layout information. Furthermore, the recognition unit can automatically adjust the position of the camera when the room layout is changed. In this way, the recognition unit can improve the recognition accuracy by taking into account the room layout information. To consider the room layout information, the recognition unit can use, for example, a floor plan or furniture arrangement information of the room. For example, the recognition unit can optimize the camera arrangement based on the room floor plan and reduce blind spots. Furthermore, the recognition unit can focus on monitoring a specific area based on the furniture arrangement information. In this way, the recognition unit can improve the recognition accuracy by taking into account the room layout information.

[0039] The recognition unit can customize the recognition algorithm according to the type and size of the pet during recognition. For example, the recognition unit can customize the recognition algorithm according to the type and size of the pet during recognition. For example, the recognition unit can use different recognition algorithms for small dogs and large dogs. Furthermore, the recognition unit can use a recognition algorithm that takes into account different behavioral patterns for cats and dogs. Furthermore, the recognition unit can update the recognition algorithm according to the growth of the pet. As a result, the recognition unit can customize the recognition algorithm according to the type and size of the pet, thereby improving recognition accuracy. To take the type and size of the pet into consideration, the recognition unit can use, for example, data related to the type and size of the pet. For example, the recognition unit can adjust the recognition algorithm based on behavioral pattern data according to the type and size of the pet. Furthermore, the recognition unit can periodically update the algorithm according to the growth of the pet to maintain recognition accuracy. As a result, the recognition unit can customize the recognition algorithm according to the type and size of the pet, thereby improving recognition accuracy.

[0040] The measurement unit can improve measurement accuracy by referring to the pet's behavioral history during measurement. The measurement unit can improve measurement accuracy by referring to the pet's behavioral history, for example. For example, if the pet behaves in a specific time period, the measurement unit can focus on measuring that time period. The measurement unit can also optimize the timing of measurement based on the pet's past behavioral history. Furthermore, the measurement unit can learn the pet's behavioral patterns and improve measurement accuracy based on predictions. In this way, the measurement unit can improve measurement accuracy by referring to the pet's behavioral history. To refer to the pet's behavioral history, the measurement unit can use, for example, past behavioral data. For example, the measurement unit can store the pet's past behavioral data in a database and adjust the measurement algorithm based on the data. In addition, the measurement unit can analyze the pet's behavioral patterns using a behavior pattern analysis algorithm and improve measurement accuracy based on the analysis results. In this way, the measurement unit can improve measurement accuracy by referring to the pet's behavioral history.

[0041] The measurement unit can monitor the item usage status in real time and update the measurement data during measurement. The measurement unit can monitor the item usage status in real time and update the measurement data during measurement, for example. For example, the measurement unit can update the data in real time every time a pet uses a specific item. The measurement unit can also constantly monitor the item usage status and issue an alert if an abnormality occurs. Furthermore, the measurement unit can collect data in real time and immediately reflect changes in usage frequency. This allows the measurement unit to provide the latest data by monitoring the item usage status in real time. For real-time monitoring, the measurement unit can use, for example, a sensor. For example, the measurement unit can detect the usage status using a sensor attached to the item and collect the data in real time. The measurement unit can also adjust the data update frequency and immediately update the data as necessary. This allows the measurement unit to provide the latest data by monitoring the item usage status in real time and updating the measurement data.

[0042] The measurement unit can correct the measurement data during measurement, taking into account the pet's health condition. For example, the measurement unit can correct the measurement data during measurement, taking into account the pet's health condition. For example, if the pet is healthy, the measurement unit can provide normal measurement data. Furthermore, if the pet is sick, the measurement unit can correct the measurement data to detect abnormalities. Furthermore, the measurement unit can adjust the accuracy of the measurement data according to the pet's health condition. As a result, the measurement unit can improve the accuracy of the measurement data by taking into account the pet's health condition. To take the pet's health condition into account, the measurement unit can use, for example, health checkup results or changes in behavioral patterns. For example, the measurement unit can correct the measurement data based on the pet's health checkup results and detect abnormalities. Furthermore, the measurement unit can analyze changes in the pet's behavioral patterns and correct the measurement data based on those changes. As a result, the measurement unit can improve the accuracy of the measurement data by taking into account the pet's health condition.

[0043] The measurement unit can customize the measurement algorithm according to the type and material of the item when performing measurement. For example, the measurement unit can customize the measurement algorithm according to the type and material of the item when performing measurement. For example, the measurement unit can use different measurement algorithms for a plastic item and a cloth item. The measurement unit can also adjust the measurement accuracy according to the type of item. Furthermore, the measurement unit can correct the measurement data based on the material of the item. As a result, the measurement unit can improve the measurement accuracy by customizing the measurement algorithm according to the type and material of the item. In order to take the type and material of the item into consideration, the measurement unit can use, for example, data related to the type and material of the item. For example, the measurement unit can correct the measurement data based on the measurement algorithm according to the type and material of the item. Furthermore, the measurement unit can adjust the accuracy of the measurement data based on the material of the item. As a result, the measurement unit can improve the measurement accuracy by customizing the measurement algorithm according to the type and material of the item.

[0044] The identification unit can improve the identification accuracy by referring to past use frequency data during identification. The identification unit can improve the identification accuracy by referring to past use frequency data during identification, for example. For example, the identification unit can identify unused items based on past use frequency data. Furthermore, the identification unit can preferentially identify less frequently used items as unnecessary items. Furthermore, the identification unit can analyze past data and improve the identification accuracy. As a result, the identification unit can improve the identification accuracy of unnecessary items by referring to past use frequency data. To refer to past use frequency data, the identification unit can use, for example, a database. For example, the identification unit can store past use frequency data in a database and adjust the identification algorithm based on the data. Furthermore, the identification unit can adjust the data update frequency and improve the identification accuracy based on the latest data. As a result, the identification unit can improve the identification accuracy of unnecessary items by referring to past use frequency data.

[0045] The identification unit can identify unnecessary items by taking into account the behavioral patterns of the pet during identification. For example, the identification unit can identify unnecessary items by taking into account the behavioral patterns of the pet during identification. For example, if the pet does not use a specific item, the identification unit can identify the item as unnecessary. The identification unit can also analyze the behavioral patterns of the pet and identify unused items. Furthermore, the identification unit can improve the accuracy of identifying unnecessary items based on the behavioral patterns of the pet. As a result, the identification unit can improve the accuracy of identifying unnecessary items by taking into account the behavioral patterns of the pet. To take into account the behavioral patterns of the pet, the identification unit can use, for example, movement tracking technology. For example, the identification unit can track the movements of the pet with a camera and adjust the identification algorithm based on the data. As a result, the identification unit can analyze the behavioral patterns of the pet using a behavioral pattern analysis algorithm and improve the accuracy of identifying items based on the analysis results. As a result, the identification unit can improve the accuracy of identifying unnecessary items by taking into account the behavioral patterns of the pet.

[0046] The identification unit can identify unnecessary items by taking into account the aging deterioration information of the items during identification. For example, the identification unit can identify unnecessary items by taking into account the aging deterioration information of the items during identification. For example, the identification unit can prioritize identifying items that have deteriorated over time as unnecessary items. The identification unit can also identify unnecessary items based on the number of years the items have been in use. Furthermore, the identification unit can analyze the aging deterioration information and improve the identification accuracy. As a result, the identification unit can improve the identification accuracy of unnecessary items by taking into account the aging deterioration information of the items. To take into account the aging deterioration information of the items, the identification unit can use, for example, data regarding the number of years the items have been in use and the degree of deterioration. For example, the identification unit can store the number of years the items have been in use in a database and adjust the identification algorithm based on that data. As a result, the identification unit can periodically check the condition of the items to evaluate the degree of deterioration and improve the identification accuracy based on that data. As a result, the identification unit can improve the identification accuracy of unnecessary items by taking into account the aging deterioration information of the items.

[0047] The identification unit can identify unnecessary items by taking into account the health condition of the pet during identification. For example, the identification unit can identify unnecessary items by taking into account the health condition of the pet during identification. For example, if the pet is healthy, the identification unit can identify unnecessary items using normal standards. Furthermore, if the pet is sick, the identification unit can identify unnecessary items by using stricter identification standards. Furthermore, the identification unit can adjust the accuracy of identifying unnecessary items according to the pet's health condition. As a result, the identification unit can improve the accuracy of identifying unnecessary items by taking into account the pet's health condition. To take the pet's health condition into account, the identification unit can use, for example, health checkup results or changes in behavioral patterns. For example, the identification unit can adjust the identification algorithm based on the pet's health checkup results to detect abnormalities. Furthermore, the identification unit can analyze changes in the pet's behavioral patterns and improve the accuracy of identification based on the changes. As a result, the identification unit can improve the accuracy of identifying unnecessary items by taking into account the pet's health condition.

[0048] The suggestion unit can improve the accuracy of the suggestion by referring to the past decluttering history when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to the past decluttering history when making a suggestion, for example. For example, the suggestion unit can make optimal suggestions based on the past decluttering history. Furthermore, the suggestion unit can make suggestions by referring to items that the owner has decluttered in the past. Furthermore, the suggestion unit can analyze the past history and improve the accuracy of the suggestion. As a result, the suggestion unit can improve the accuracy of the suggestion by referring to the past decluttering history. To refer to the past decluttering history, the suggestion unit can use, for example, a database. For example, the suggestion unit can store the past decluttering history in the database and adjust the suggestion algorithm based on the data. Furthermore, the suggestion unit can adjust the update frequency of the data and improve the accuracy of the suggestion based on the latest data. As a result, the suggestion unit can improve the accuracy of the suggestion by referring to the past decluttering history.

[0049] The suggestion unit can propose an optimal decluttering method by taking into account the market value of the item when making the suggestion. For example, the suggestion unit can propose an optimal decluttering method by taking into account the market value of the item when making the suggestion. For example, the suggestion unit can suggest selling items with high market value at a flea market. The suggestion unit can also suggest donating or recycling items with low market value. Furthermore, the suggestion unit can reflect fluctuations in market value in real time and propose an optimal decluttering method. This allows the suggestion unit to propose an optimal decluttering method by taking into account the market value of the item. To consider the market value of the item, the suggestion unit can use, for example, data regarding the current market price or the balance between supply and demand. For example, the suggestion unit can store the market price of the item in a database and adjust the suggestion algorithm based on that data. The suggestion unit can also monitor the balance between supply and demand in real time and propose an optimal decluttering method based on that data. This allows the suggestion unit to propose an optimal decluttering method by taking into account the market value of the item.

[0050] The suggestion unit can determine the priorities for decluttering by taking into account the health condition of the pet when making the suggestion. For example, the suggestion unit can determine the priorities for decluttering by taking into account the health condition of the pet when making the suggestion. For example, if the pet is healthy, the suggestion unit can suggest decluttering with normal priority. Furthermore, if the pet is sick, the suggestion unit can prioritize decluttering of items that affect the pet's health. Furthermore, the suggestion unit can adjust the priorities for decluttering according to the pet's health condition. This allows the suggestion unit to appropriately determine the priorities for decluttering by taking into account the pet's health condition. To take the pet's health condition into account, the suggestion unit can use, for example, health checkup results or changes in behavioral patterns. For example, the suggestion unit can adjust the suggestion algorithm based on the pet's health checkup results to prioritize decluttering of items that affect health. Furthermore, the suggestion unit can analyze changes in the pet's behavioral patterns and adjust the priorities for decluttering based on the changes. This allows the suggestion unit to appropriately determine the priorities for decluttering by taking into account the pet's health condition.

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

[0052] The recognition unit can monitor the room temperature and humidity and evaluate the deterioration status of items based on these environmental conditions. For example, the recognition unit can identify items that are prone to deterioration in hot and humid environments and suggest early decluttering. The recognition unit can also identify items that are best stored in low-temperature, dry environments and suggest storage methods for these items. Furthermore, the recognition unit can evaluate the deterioration risk of items according to seasonal environmental changes and suggest appropriate timing for decluttering. This allows the recognition unit to make decluttering suggestions that take environmental conditions into account.

[0053] The measurement unit can monitor the pet's health condition and adjust the frequency of item use based on the health condition. For example, if the pet is sick, the measurement unit can lower the frequency of item use during that period and emphasize the frequency of use after the pet recovers. The measurement unit can also recommend or restrict the use of specific items depending on the pet's health condition. Furthermore, the measurement unit can reflect changes in the pet's health condition in real time and reflect them in decluttering suggestions. This allows the measurement unit to make decluttering suggestions that take the pet's health condition into account.

[0054] The identification unit can learn the behavioral patterns of pets and identify unnecessary items based on the behavioral patterns. For example, the identification unit can analyze the tendency of pets to use certain items at certain times of the day and identify unnecessary items based on the frequency of use. The identification unit can also detect changes in the pet's behavioral patterns and identify items that are no longer used as unnecessary items. Furthermore, the identification unit can predict the pet's behavioral patterns and identify items that are unlikely to be used in the future as unnecessary items. This allows the identification unit to make highly accurate decluttering suggestions that take the pet's behavioral patterns into consideration.

[0055] The recognition unit can optimize camera placement based on room layout information and reduce blind spots. For example, the recognition unit can analyze a room's floor plan and suggest optimal camera placement. The recognition unit can also focus on monitoring specific areas based on furniture placement information. Furthermore, the recognition unit can automatically adjust camera positions if the room layout changes. This allows the recognition unit to perform highly accurate item recognition that takes room layout information into account.

[0056] The measurement unit can refer to the pet's behavioral history and improve measurement accuracy based on the behavioral history. For example, if the pet behaves in a specific way during a specific time period, the measurement unit can focus on measuring during that time period. The measurement unit can also optimize the timing of measurements based on the pet's past behavioral history. Furthermore, the measurement unit can learn the pet's behavioral patterns and improve measurement accuracy based on predictions. This allows the measurement unit to perform highly accurate measurements that take the pet's behavioral history into account.

[0057] The suggestion unit can refer to past decluttering history and improve the accuracy of suggestions based on the history. For example, the suggestion unit can make optimal suggestions based on past decluttering history. The suggestion unit can also make suggestions by referring to items that the owner has decluttered in the past. Furthermore, the suggestion unit can analyze past history and improve the accuracy of suggestions. This allows the suggestion unit to make highly accurate suggestions that take into account past decluttering history.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The recognition unit recognizes items in the room. The recognition unit identifies pet supplies in the room using, for example, image recognition technology. For example, the recognition unit can recognize pet beds, toys, dishes, etc. This allows a list of pet supplies in the room to be created. Step 2: The measurement unit measures the frequency of use of the items recognized by the recognition unit. The measurement unit, for example, monitors the pet's daily life and records the frequency of use of each item. For example, the measurement unit records which bed the pet sleeps on, which toys it plays with, which dishes it uses, etc. This makes it possible to visualize the frequency of use of each item. Step 3: The identifying unit identifies unnecessary items based on the frequency of use measured by the measuring unit. The identifying unit can, for example, identify items that are used infrequently. For example, the identifying unit identifies items that have not been used for a certain period of time or items that are damaged as unnecessary items. Step 4: The suggestion unit makes suggestions for decluttering based on the unwanted items identified by the identification unit. For example, the suggestion unit can suggest automatically listing the items at a flea market. The suggestion unit can also provide advice on decluttering tailored to individual needs, such as linking with donation recipients, requesting appraisals at recycling shops, or applying for local government collection of unwanted items.

[0060] (Example 2) The decluttering camera AI system according to an embodiment of the present invention visualizes the frequency of pet item use and suggests decluttering. This decluttering camera AI system recognizes items in a room, visualizes the frequency of item use based on the pet's daily life, and identifies unnecessary items. Furthermore, a decluttering generation AI mentor consults with the pet owner on personalized decluttering solutions, such as automatically listing the items at a flea market, linking to a donation destination, requesting appraisals at a recycle shop, or applying for local government collection services, based on the AI ​​camera's view of the unwanted items and the owner's requests. For example, the AI ​​camera recognizes items in a room. The AI ​​camera then uses image language generation AI to identify pet items in the room. For example, it can recognize pet beds, toys, and dishes. This allows it to create a list of pet items in the room. Next, the AI ​​camera monitors the pet's daily life and records the frequency of use of each item. For example, it records which bed the pet sleeps on, which toys it plays with, and which dishes it uses. This allows the frequency of use of each item to be visualized. Furthermore, the decluttering generation AI mentor suggests decluttering solutions based on the AI ​​camera's view of the unwanted items and the owner's requests. For example, it can identify rarely used items and suggest automatically listing them at a flea market. It can also provide consultations on decluttering tailored to individual needs, such as linking to donation destinations, requesting appraisals from recycling shops, and applying for local government collection. This system can support a satisfying lifestyle for both humans and pets. For example, by decluttering unused toys and beds, a room can be tidy and pets can live comfortably. It also contributes to society through reducing, reusing, and recycling. For example, donating unused pet supplies can help other pets and their owners. The Danshari Camera AI system visualizes the frequency of pet supplies use and suggests decluttering unnecessary items, supporting a satisfying lifestyle for both humans and pets.

[0061] The decluttering camera AI system according to the embodiment includes a recognition unit, a measurement unit, an identification unit, and a suggestion unit. The recognition unit recognizes items in a room. The recognition unit identifies pet supplies in the room using, for example, image recognition technology. For example, the recognition unit can recognize pet beds, toys, dishes, etc. This allows a list of pet supplies in the room to be created. The measurement unit measures the frequency of use of the items recognized by the recognition unit. For example, the measurement unit monitors the pet's daily life and records the frequency of use of each item. For example, the measurement unit records which bed the pet sleeps on, which toys it plays with, which dishes it uses, etc. This allows the frequency of use of each item to be visualized. The identification unit identifies unnecessary items based on the frequency of use measured by the measurement unit. For example, the identification unit can identify items that are used infrequently. For example, the identification unit can identify items that have not been used for a certain period of time or items that are damaged as unnecessary items. The suggestion unit suggests decluttering based on the unnecessary items identified by the identification unit. For example, the suggestion unit can suggest automatic listing of items at a flea market. In addition, the suggestion unit can provide advice on decluttering tailored to individual needs, such as linking with donation destinations, requesting appraisals from recycling shops, applying for local government collection of unwanted items, etc. As a result, the decluttering camera AI system according to the embodiment can visualize the frequency of use of pet products and suggest decluttering of unwanted items, thereby supporting a life in which both humans and pets are satisfied.

[0062] The suggestion unit can suggest automatic listing at a flea market. The suggestion unit can, for example, automatically list unwanted items at a flea market. For example, the suggestion unit can identify items that are used infrequently and automatically list them through a flea market app. This makes decluttering more efficient. For example, the suggestion unit can register account information for a flea market app in advance and automatically perform listing procedures when unwanted items are identified. The suggestion unit can also automatically enter information required for listing (such as product name, description, and price), simplifying the listing process. This allows owners to list unwanted items at a flea market without hassle. Furthermore, the suggestion unit can also manage the items after listing. For example, the suggestion unit can monitor the sales status of listed items and automatically notify owners when they are sold. The suggestion unit can also suggest price revisions or relisting of unsold items. This allows the suggestion unit to support efficient decluttering through automatic listing at a flea market.

[0063] The suggestion unit can suggest linking to donation destinations. The suggestion unit can, for example, link unwanted items to donation destinations. For example, the suggestion unit can identify infrequently used items and suggest appropriate donation destinations. This allows decluttering to contribute to society. The suggestion unit can, for example, pre-register a list of donation destinations and automatically carry out donation procedures when unwanted items are identified. The suggestion unit can also automatically input information required for the donation destination (such as the type, condition, and quantity of the item) to simplify the donation process. This allows owners to donate unwanted items without hassle. Furthermore, the suggestion unit can also manage donations after they are made. For example, the suggestion unit can monitor the receipt status of donated items and automatically notify owners when receipt is confirmed. The suggestion unit can also collect feedback from donation destinations and use it to help with the next donation. This allows the suggestion unit to support efficient decluttering and social contribution through linking to donation destinations.

[0064] The suggestion unit can suggest requesting an appraisal from a recycle shop. For example, the suggestion unit can request appraisal of unwanted items from a recycle shop. For example, the suggestion unit can identify infrequently used items and suggest appropriate recycle shops. This leads to decluttering and recycling. For example, the suggestion unit can register a list of recycle shops in advance and automatically initiate an appraisal request procedure when unwanted items are identified. The suggestion unit can also automatically input information required for the appraisal request (item type, condition, quantity, etc.), simplifying the appraisal request process. This allows owners to easily request appraisals of unwanted items from recycle shops. Furthermore, the suggestion unit can also manage the appraisal process. For example, the suggestion unit can monitor the appraisal results and automatically notify when an appraisal price is presented. The suggestion unit can also make a sale proposal based on the appraisal price. This allows the suggestion unit to support efficient decluttering and recycling through appraisal requests from recycle shops.

[0065] The suggestion unit can suggest applying for local government collection of unwanted items. For example, the suggestion unit can apply for local government collection of unwanted items. For example, the suggestion unit can identify infrequently used items and suggest an appropriate local government collection service. This makes decluttering more efficient. For example, the suggestion unit can pre-register a list of local government collection services and automatically initiate collection application procedures when unwanted items are identified. The suggestion unit can also automatically input information required for collection applications (such as the type, condition, and quantity of the item) to simplify the collection application process. This allows owners to apply for local government collection of unwanted items without hassle. Furthermore, the suggestion unit can also manage post-collection activities. For example, the suggestion unit can monitor the collection status and automatically notify owners when collection is complete. The suggestion unit can also collect feedback on the collection service and use it to plan the next collection. This allows the suggestion unit to support efficient decluttering through applications for local government collection of unwanted items.

[0066] The recognition unit can recognize items in a room using an image language generation AI. The recognition unit can recognize items in a room using, for example, an image language generation AI. For example, the recognition unit can identify pet supplies in the room using the image language generation AI. This makes it possible to create a list of pet supplies in the room. The recognition unit can recognize pet beds, toys, dishes, etc. using the image language generation AI. This makes it possible to create a list of pet supplies in the room. Furthermore, the recognition unit can recognize the type and condition of items in the room in detail using the image language generation AI. For example, the recognition unit can recognize the material, color, shape, etc. of items using the image language generation AI. This makes it possible to obtain detailed information about the items in the room. This makes it possible for the recognition unit to improve recognition accuracy by recognizing items in the room using the image language generation AI.

[0067] The measurement unit can monitor the pet's daily life and record the frequency of use of each item. The measurement unit can, for example, monitor the pet's daily life and record the frequency of use of each item. For example, the measurement unit can record which bed the pet sleeps on, which toys it plays with, which dishes it uses, etc. This makes it possible to visualize the frequency of use of each item. The measurement unit can, for example, monitor the pet's behavior using a camera or sensor and record the usage status of each item in real time. For example, the measurement unit can record the number of times and duration of use each time the pet uses a particular item. This makes it possible to accurately grasp the frequency of use of each item. Furthermore, the measurement unit can analyze the pet's behavioral patterns and detect changes in the frequency of use. For example, if the pet stops using a particular item, the measurement unit can detect this change and record the decrease in frequency of use. This makes it possible to monitor the pet's daily life and accurately record the frequency of use of each item, thereby providing data for decluttering.

[0068] The recognition unit can estimate the owner's emotions and adjust the recognition accuracy based on the estimated owner's emotions. For example, the recognition unit can estimate the owner's emotions and adjust the recognition accuracy based on the estimated owner's emotions. For example, if the owner is stressed, the recognition unit can increase the recognition accuracy to reduce erroneous recognition. Furthermore, if the owner is relaxed, the recognition unit can maintain normal recognition accuracy. Furthermore, if the owner is in a hurry, the recognition unit can prioritize recognition speed and tolerate some erroneous recognition. This allows the recognition unit to adjust the recognition accuracy according to the owner's emotions, thereby reducing erroneous recognition and improving usability. To estimate the owner's emotions, the recognition unit can use, for example, facial expression recognition technology or voice analysis technology. For example, the recognition unit can capture the owner's facial expressions with a camera and estimate the emotion using a facial expression recognition algorithm. Furthermore, the recognition unit can record the owner's voice and estimate the emotion using a voice analysis algorithm. This allows the recognition unit to accurately estimate the owner's emotions and adjust the recognition accuracy based on those emotions.

[0069] The recognition unit can optimize the recognition algorithm according to the lighting conditions of the room and the time of day during recognition. For example, the recognition unit can optimize the recognition algorithm according to the lighting conditions of the room and the time of day during recognition. For example, the recognition unit can improve recognition accuracy by using an infrared camera when the room is dark. Furthermore, the recognition unit can perform recognition with normal camera settings during bright daytime hours. Furthermore, the recognition unit can automatically adjust the exposure settings of the camera during times when the lighting in the room changes. In this way, the recognition unit can improve recognition accuracy by optimizing the recognition algorithm according to the lighting conditions of the room and the time of day. To take the lighting conditions and the time of day into consideration, the recognition unit can use, for example, an illuminance sensor. For example, the recognition unit can measure the brightness of the room with the illuminance sensor and adjust the camera settings based on the data. Furthermore, the recognition unit can use different recognition algorithms for different time periods. For example, the recognition unit can use different algorithms for daytime and nighttime to perform recognition that is optimal for each condition. In this way, the recognition unit can improve recognition accuracy by optimizing the recognition algorithm according to the lighting conditions of the room and the time of day.

[0070] The recognition unit can improve recognition accuracy based on the pet's movements and behavioral patterns during recognition. For example, the recognition unit can improve recognition accuracy based on the pet's movements and behavioral patterns during recognition. For example, if the pet moves at high speed, the recognition unit can increase the frame rate to improve recognition accuracy. Furthermore, if the pet often plays in a particular place, the recognition unit can focus on monitoring that place. Furthermore, the recognition unit can learn the pet's behavioral patterns and improve recognition accuracy based on the predictions. Thus, the recognition unit can improve recognition accuracy by taking the pet's movements and behavioral patterns into account. To take the pet's movements and behavioral patterns into account, the recognition unit can use, for example, movement tracking technology. For example, the recognition unit can track the pet's movements with a camera and adjust the recognition algorithm based on the data. Furthermore, the recognition unit can use a behavioral pattern analysis algorithm to analyze the pet's behavioral patterns. For example, the recognition unit can predict future behavior based on the pet's past behavioral data and improve recognition accuracy based on the predictions. Thus, the recognition unit can improve recognition accuracy by taking the pet's movements and behavioral patterns into account.

[0071] The recognition unit can estimate the owner's emotions and adjust the display method of the recognition results based on the estimated owner's emotions. For example, the recognition unit can estimate the owner's emotions and adjust the display method of the recognition results based on the estimated owner's emotions. For example, the recognition unit can provide a simple display method when the owner is stressed. Furthermore, the recognition unit can display detailed recognition results when the owner is relaxed. Furthermore, the recognition unit can provide a display method that focuses on the main points when the owner is in a hurry. This allows the recognition unit to improve usability by adjusting the display method of the recognition results according to the owner's emotions. To estimate the owner's emotions, the recognition unit can use, for example, facial expression recognition technology or voice analysis technology. For example, the recognition unit can capture the owner's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Furthermore, the recognition unit can record the owner's voice and estimate the emotions using a voice analysis algorithm. This allows the recognition unit to accurately estimate the owner's emotions and adjust the display method of the recognition results based on those emotions.

[0072] The recognition unit can improve the recognition accuracy by taking into account room layout information during recognition. The recognition unit can improve the recognition accuracy by taking into account room layout information during recognition. For example, the recognition unit can consider the furniture arrangement in the room and arrange the cameras to reduce blind spots. Furthermore, the recognition unit can focus on monitoring a specific area based on the room layout information. Furthermore, the recognition unit can automatically adjust the position of the camera when the room layout is changed. In this way, the recognition unit can improve the recognition accuracy by taking into account the room layout information. To consider the room layout information, the recognition unit can use, for example, a floor plan or furniture arrangement information of the room. For example, the recognition unit can optimize the camera arrangement based on the room floor plan and reduce blind spots. Furthermore, the recognition unit can focus on monitoring a specific area based on the furniture arrangement information. In this way, the recognition unit can improve the recognition accuracy by taking into account the room layout information.

[0073] The recognition unit can customize the recognition algorithm according to the type and size of the pet during recognition. For example, the recognition unit can customize the recognition algorithm according to the type and size of the pet during recognition. For example, the recognition unit can use different recognition algorithms for small dogs and large dogs. Furthermore, the recognition unit can use a recognition algorithm that takes into account different behavioral patterns for cats and dogs. Furthermore, the recognition unit can update the recognition algorithm according to the growth of the pet. As a result, the recognition unit can customize the recognition algorithm according to the type and size of the pet, thereby improving recognition accuracy. To take the type and size of the pet into consideration, the recognition unit can use, for example, data related to the type and size of the pet. For example, the recognition unit can adjust the recognition algorithm based on behavioral pattern data according to the type and size of the pet. Furthermore, the recognition unit can periodically update the algorithm according to the growth of the pet to maintain recognition accuracy. As a result, the recognition unit can customize the recognition algorithm according to the type and size of the pet, thereby improving recognition accuracy.

[0074] The measurement unit can estimate the owner's emotions and adjust the measurement frequency based on the estimated owner's emotions. For example, the measurement unit can estimate the owner's emotions and adjust the measurement frequency based on the estimated owner's emotions. For example, if the owner is stressed, the measurement unit can increase the measurement frequency to provide detailed data. Furthermore, if the owner is relaxed, the measurement unit can maintain a normal measurement frequency. Furthermore, if the owner is in a hurry, the measurement unit can decrease the measurement frequency to provide concise data. In this way, the measurement unit can provide detailed data by adjusting the measurement frequency according to the owner's emotions. To estimate the owner's emotions, the measurement unit can use, for example, facial expression recognition technology or voice analysis technology. For example, the measurement unit can capture the owner's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Furthermore, the measurement unit can record the owner's voice and estimate the emotions using a voice analysis algorithm. In this way, the measurement unit can accurately estimate the owner's emotions and adjust the measurement frequency based on the emotions.

[0075] The measurement unit can improve measurement accuracy by referring to the pet's behavioral history during measurement. The measurement unit can improve measurement accuracy by referring to the pet's behavioral history, for example. For example, if the pet behaves in a specific time period, the measurement unit can focus on measuring that time period. The measurement unit can also optimize the timing of measurement based on the pet's past behavioral history. Furthermore, the measurement unit can learn the pet's behavioral patterns and improve measurement accuracy based on predictions. In this way, the measurement unit can improve measurement accuracy by referring to the pet's behavioral history. To refer to the pet's behavioral history, the measurement unit can use, for example, past behavioral data. For example, the measurement unit can store the pet's past behavioral data in a database and adjust the measurement algorithm based on the data. In addition, the measurement unit can analyze the pet's behavioral patterns using a behavior pattern analysis algorithm and improve measurement accuracy based on the analysis results. In this way, the measurement unit can improve measurement accuracy by referring to the pet's behavioral history.

[0076] The measurement unit can monitor the item usage status in real time and update the measurement data during measurement. The measurement unit can monitor the item usage status in real time and update the measurement data during measurement, for example. For example, the measurement unit can update the data in real time every time a pet uses a specific item. The measurement unit can also constantly monitor the item usage status and issue an alert if an abnormality occurs. Furthermore, the measurement unit can collect data in real time and immediately reflect changes in usage frequency. This allows the measurement unit to provide the latest data by monitoring the item usage status in real time. For real-time monitoring, the measurement unit can use, for example, a sensor. For example, the measurement unit can detect the usage status using a sensor attached to the item and collect the data in real time. The measurement unit can also adjust the data update frequency and immediately update the data as necessary. This allows the measurement unit to provide the latest data by monitoring the item usage status in real time and updating the measurement data.

[0077] The measurement unit can estimate the owner's emotions and adjust the display method of the measurement results based on the estimated owner's emotions. The measurement unit can, for example, estimate the owner's emotions and adjust the display method of the measurement results based on the estimated owner's emotions. For example, the measurement unit can provide a simple display method when the owner is stressed. Furthermore, the measurement unit can display detailed measurement results when the owner is relaxed. Furthermore, the measurement unit can provide a display method that focuses on the main points when the owner is in a hurry. This allows the measurement unit to improve usability by adjusting the display method of the measurement results according to the owner's emotions. To estimate the owner's emotions, the measurement unit can use, for example, facial expression recognition technology or voice analysis technology. For example, the measurement unit can capture the owner's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Furthermore, the measurement unit can record the owner's voice and estimate the emotions using a voice analysis algorithm. This allows the measurement unit to accurately estimate the owner's emotions and adjust the display method of the measurement results based on those emotions.

[0078] The measurement unit can correct the measurement data during measurement, taking into account the pet's health condition. For example, the measurement unit can correct the measurement data during measurement, taking into account the pet's health condition. For example, if the pet is healthy, the measurement unit can provide normal measurement data. Furthermore, if the pet is sick, the measurement unit can correct the measurement data to detect abnormalities. Furthermore, the measurement unit can adjust the accuracy of the measurement data according to the pet's health condition. As a result, the measurement unit can improve the accuracy of the measurement data by taking into account the pet's health condition. To take the pet's health condition into account, the measurement unit can use, for example, health checkup results or changes in behavioral patterns. For example, the measurement unit can correct the measurement data based on the pet's health checkup results and detect abnormalities. Furthermore, the measurement unit can analyze changes in the pet's behavioral patterns and correct the measurement data based on those changes. As a result, the measurement unit can improve the accuracy of the measurement data by taking into account the pet's health condition.

[0079] The measurement unit can customize the measurement algorithm according to the type and material of the item when performing measurement. For example, the measurement unit can customize the measurement algorithm according to the type and material of the item when performing measurement. For example, the measurement unit can use different measurement algorithms for a plastic item and a cloth item. The measurement unit can also adjust the measurement accuracy according to the type of item. Furthermore, the measurement unit can correct the measurement data based on the material of the item. As a result, the measurement unit can improve the measurement accuracy by customizing the measurement algorithm according to the type and material of the item. In order to take the type and material of the item into consideration, the measurement unit can use, for example, data related to the type and material of the item. For example, the measurement unit can correct the measurement data based on the measurement algorithm according to the type and material of the item. Furthermore, the measurement unit can adjust the accuracy of the measurement data based on the material of the item. As a result, the measurement unit can improve the measurement accuracy by customizing the measurement algorithm according to the type and material of the item.

[0080] The identification unit can estimate the owner's emotions and adjust the criteria for identifying unnecessary items based on the estimated owner's emotions. The identification unit can, for example, estimate the owner's emotions and adjust the criteria for identifying unnecessary items based on the estimated owner's emotions. For example, if the owner is stressed, the identification unit can identify unnecessary items using strict criteria. Furthermore, if the owner is relaxed, the identification unit can identify unnecessary items using normal criteria. Furthermore, if the owner is in a hurry, the identification unit can identify unnecessary items using simple criteria. This allows the identification unit to adjust the criteria for identifying unnecessary items according to the owner's emotions, thereby enabling more appropriate identification of unnecessary items. To estimate the owner's emotions, the identification unit can use, for example, facial expression recognition technology or voice analysis technology. For example, the identification unit can capture the owner's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Furthermore, the identification unit can record the owner's voice and estimate the emotions using a voice analysis algorithm. This allows the identification unit to accurately estimate the owner's emotions and adjust the criteria for identifying unwanted items based on those emotions.

[0081] The identification unit can improve the identification accuracy by referring to past use frequency data during identification. The identification unit can improve the identification accuracy by referring to past use frequency data during identification, for example. For example, the identification unit can identify unused items based on past use frequency data. Furthermore, the identification unit can preferentially identify less frequently used items as unnecessary items. Furthermore, the identification unit can analyze past data and improve the identification accuracy. As a result, the identification unit can improve the identification accuracy of unnecessary items by referring to past use frequency data. To refer to past use frequency data, the identification unit can use, for example, a database. For example, the identification unit can store past use frequency data in a database and adjust the identification algorithm based on the data. Furthermore, the identification unit can adjust the data update frequency and improve the identification accuracy based on the latest data. As a result, the identification unit can improve the identification accuracy of unnecessary items by referring to past use frequency data.

[0082] The identification unit can identify unnecessary items by taking into account the behavioral patterns of the pet during identification. For example, the identification unit can identify unnecessary items by taking into account the behavioral patterns of the pet during identification. For example, if the pet does not use a specific item, the identification unit can identify the item as unnecessary. The identification unit can also analyze the behavioral patterns of the pet and identify unused items. Furthermore, the identification unit can improve the accuracy of identifying unnecessary items based on the behavioral patterns of the pet. As a result, the identification unit can improve the accuracy of identifying unnecessary items by taking into account the behavioral patterns of the pet. To take into account the behavioral patterns of the pet, the identification unit can use, for example, movement tracking technology. For example, the identification unit can track the movements of the pet with a camera and adjust the identification algorithm based on the data. As a result, the identification unit can analyze the behavioral patterns of the pet using a behavioral pattern analysis algorithm and improve the accuracy of identifying items based on the analysis results. As a result, the identification unit can improve the accuracy of identifying unnecessary items by taking into account the behavioral patterns of the pet.

[0083] The identification unit can estimate the owner's emotions and adjust the display method of the identification results based on the estimated owner's emotions. The identification unit can, for example, estimate the owner's emotions and adjust the display method of the identification results based on the estimated owner's emotions. For example, the identification unit can provide a simple display method when the owner is stressed. Furthermore, the identification unit can display detailed identification results when the owner is relaxed. Furthermore, the identification unit can provide a display method that focuses on the main points when the owner is in a hurry. This allows the identification unit to improve usability by adjusting the display method of the identification results according to the owner's emotions. To estimate the owner's emotions, the identification unit can use, for example, facial expression recognition technology or voice analysis technology. For example, the identification unit can capture the owner's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Furthermore, the identification unit can record the owner's voice and estimate the emotions using a voice analysis algorithm. This allows the identification unit to accurately estimate the owner's emotions and adjust the display method of the identification result based on those emotions.

[0084] The identification unit can identify unnecessary items by taking into account the aging deterioration information of the items during identification. For example, the identification unit can identify unnecessary items by taking into account the aging deterioration information of the items during identification. For example, the identification unit can prioritize identifying items that have deteriorated over time as unnecessary items. The identification unit can also identify unnecessary items based on the number of years the items have been in use. Furthermore, the identification unit can analyze the aging deterioration information and improve the identification accuracy. As a result, the identification unit can improve the identification accuracy of unnecessary items by taking into account the aging deterioration information of the items. To take into account the aging deterioration information of the items, the identification unit can use, for example, data regarding the number of years the items have been in use and the degree of deterioration. For example, the identification unit can store the number of years the items have been in use in a database and adjust the identification algorithm based on that data. As a result, the identification unit can periodically check the condition of the items to evaluate the degree of deterioration and improve the identification accuracy based on that data. As a result, the identification unit can improve the identification accuracy of unnecessary items by taking into account the aging deterioration information of the items.

[0085] The identification unit can identify unnecessary items by taking into account the health condition of the pet during identification. For example, the identification unit can identify unnecessary items by taking into account the health condition of the pet during identification. For example, if the pet is healthy, the identification unit can identify unnecessary items using normal standards. Furthermore, if the pet is sick, the identification unit can identify unnecessary items by using stricter identification standards. Furthermore, the identification unit can adjust the accuracy of identifying unnecessary items according to the pet's health condition. As a result, the identification unit can improve the accuracy of identifying unnecessary items by taking into account the pet's health condition. To take the pet's health condition into account, the identification unit can use, for example, health checkup results or changes in behavioral patterns. For example, the identification unit can adjust the identification algorithm based on the pet's health checkup results to detect abnormalities. Furthermore, the identification unit can analyze changes in the pet's behavioral patterns and improve the accuracy of identification based on the changes. As a result, the identification unit can improve the accuracy of identifying unnecessary items by taking into account the pet's health condition.

[0086] The suggestion unit can estimate the owner's emotions and adjust the proposed method for decluttering based on the estimated owner's emotions. The suggestion unit can, for example, estimate the owner's emotions and adjust the proposed method for decluttering based on the estimated owner's emotions. For example, the suggestion unit can provide a simple proposed method if the owner is stressed. Furthermore, the suggestion unit can provide a detailed proposed method if the owner is relaxed. Furthermore, the suggestion unit can provide a basic proposed method if the owner is in a hurry. In this way, the suggestion unit can improve usability by adjusting the proposed method for decluttering according to the owner's emotions. To estimate the owner's emotions, the suggestion unit can use, for example, facial expression recognition technology or voice analysis technology. For example, the suggestion unit can capture the owner's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Furthermore, the suggestion unit can record the owner's voice and estimate the emotions using a voice analysis algorithm. This allows the suggestion unit to accurately estimate the owner's emotions and adjust the decluttering suggestion method based on those emotions.

[0087] The suggestion unit can improve the accuracy of the suggestion by referring to the past decluttering history when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to the past decluttering history when making a suggestion, for example. For example, the suggestion unit can make optimal suggestions based on the past decluttering history. Furthermore, the suggestion unit can make suggestions by referring to items that the owner has decluttered in the past. Furthermore, the suggestion unit can analyze the past history and improve the accuracy of the suggestion. As a result, the suggestion unit can improve the accuracy of the suggestion by referring to the past decluttering history. To refer to the past decluttering history, the suggestion unit can use, for example, a database. For example, the suggestion unit can store the past decluttering history in the database and adjust the suggestion algorithm based on the data. Furthermore, the suggestion unit can adjust the update frequency of the data and improve the accuracy of the suggestion based on the latest data. As a result, the suggestion unit can improve the accuracy of the suggestion by referring to the past decluttering history.

[0088] The suggestion unit can estimate the owner's emotions and adjust the display method of the suggestions based on the estimated owner's emotions. The suggestion unit can, for example, estimate the owner's emotions and adjust the display method of the suggestions based on the estimated owner's emotions. For example, the suggestion unit can provide a simple display method when the owner is stressed. Furthermore, the suggestion unit can display detailed suggestions when the owner is relaxed. Furthermore, the suggestion unit can provide a display method that focuses on the main points when the owner is in a hurry. This allows the suggestion unit to improve usability by adjusting the display method of the suggestions according to the owner's emotions. To estimate the owner's emotions, the suggestion unit can use, for example, facial expression recognition technology or voice analysis technology. For example, the suggestion unit can capture the owner's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Furthermore, the suggestion unit can record the owner's voice and estimate the emotions using a voice analysis algorithm. This allows the suggestion unit to accurately estimate the owner's emotions and adjust the display method of the suggestion content based on those emotions.

[0089] The suggestion unit can propose an optimal decluttering method by taking into account the market value of the item when making the suggestion. For example, the suggestion unit can propose an optimal decluttering method by taking into account the market value of the item when making the suggestion. For example, the suggestion unit can suggest selling items with high market value at a flea market. The suggestion unit can also suggest donating or recycling items with low market value. Furthermore, the suggestion unit can reflect fluctuations in market value in real time and propose an optimal decluttering method. This allows the suggestion unit to propose an optimal decluttering method by taking into account the market value of the item. To consider the market value of the item, the suggestion unit can use, for example, data regarding the current market price or the balance between supply and demand. For example, the suggestion unit can store the market price of the item in a database and adjust the suggestion algorithm based on that data. The suggestion unit can also monitor the balance between supply and demand in real time and propose an optimal decluttering method based on that data. This allows the suggestion unit to propose an optimal decluttering method by taking into account the market value of the item.

[0090] The suggestion unit can determine the priorities for decluttering by taking into account the health condition of the pet when making the suggestion. For example, the suggestion unit can determine the priorities for decluttering by taking into account the health condition of the pet when making the suggestion. For example, if the pet is healthy, the suggestion unit can suggest decluttering with normal priority. Furthermore, if the pet is sick, the suggestion unit can prioritize decluttering of items that affect the pet's health. Furthermore, the suggestion unit can adjust the priorities for decluttering according to the pet's health condition. This allows the suggestion unit to appropriately determine the priorities for decluttering by taking into account the pet's health condition. To take the pet's health condition into account, the suggestion unit can use, for example, health checkup results or changes in behavioral patterns. For example, the suggestion unit can adjust the suggestion algorithm based on the pet's health checkup results to prioritize decluttering of items that affect health. Furthermore, the suggestion unit can analyze changes in the pet's behavioral patterns and adjust the priorities for decluttering based on the changes. This allows the suggestion unit to appropriately determine the priorities for decluttering by taking into account the pet's health condition. === Hard Collateral 1-1 === Each of the multiple elements including the recognition unit, measurement unit, identification unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit recognizes items in a room using the camera 42 of the smart device 14 and identifies pet supplies using the control unit 46A. The measurement unit monitors the pet's daily life using the control unit 46A of the smart device 14 and records the frequency of use of each item. The identification unit identifies unnecessary items based on their frequency of use using the identification processing unit 290 of the data processing device 12. The suggestion unit suggests decluttering using the identification processing unit 290 of the data processing device 12 and presents the suggestions to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the recognition unit, measurement unit, identification unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit recognizes items in a room using the camera 42 of the smart glasses 214 and identifies pet supplies using the control unit 46A. For example, the measurement unit monitors the pet's daily life using the control unit 46A of the smart glasses 214 and records the frequency of use of each item. For example, the identification unit identifies unnecessary items based on their frequency of use using the identification processing unit 290 of the data processing device 12. For example, the suggestion unit suggests decluttering using the identification processing unit 290 of the data processing device 12 and presents the suggestion to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described recognition unit, measurement unit, identification unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the recognition unit recognizes items in a room using the camera 42 of the headset-type terminal 314 and identifies pet supplies using the control unit 46A. The measurement unit monitors the pet's daily life using the control unit 46A of the headset-type terminal 314 and records the frequency of use of each item. The identification unit identifies unnecessary items based on their frequency of use using the identification processing unit 290 of the data processing device 12. The suggestion unit suggests decluttering using the identification processing unit 290 of the data processing device 12, and presents the suggestions to the user via the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the recognition unit, measurement unit, identification unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit recognizes items in a room using the camera 42 of the robot 414 and identifies pet supplies using the control unit 46A. For example, the measurement unit monitors the pet's daily life using the control unit 46A of the robot 414 and records the frequency of use of each item. For example, the identification unit identifies unnecessary items based on the frequency of use using the identification processing unit 290 of the data processing device 12. For example, the suggestion unit suggests decluttering using the identification processing unit 290 of the data processing device 12 and presents the suggestion to the user via the speaker 240 of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The suggestion unit can estimate the owner's emotions and personalize the decluttering suggestions based on the estimated emotions. For example, if the owner is feeling stressed, the suggestion unit can suggest a simple and quick decluttering method. If the owner is relaxed, the suggestion unit can provide detailed decluttering methods and background information about items. Furthermore, if the owner is emotionally attached to a particular item, the suggestion unit can temporarily remove that item from the decluttering list. This allows the suggestion unit to make flexible decluttering suggestions that correspond to the owner's emotions.

[0093] The recognition unit can monitor the room temperature and humidity and evaluate the deterioration status of items based on these environmental conditions. For example, the recognition unit can identify items that are prone to deterioration in hot and humid environments and suggest early decluttering. The recognition unit can also identify items that are best stored in low-temperature, dry environments and suggest storage methods for these items. Furthermore, the recognition unit can evaluate the deterioration risk of items according to seasonal environmental changes and suggest appropriate timing for decluttering. This allows the recognition unit to make decluttering suggestions that take environmental conditions into account.

[0094] The measurement unit can monitor the pet's health condition and adjust the frequency of item use based on the health condition. For example, if the pet is sick, the measurement unit can lower the frequency of item use during that period and emphasize the frequency of use after the pet recovers. The measurement unit can also recommend or restrict the use of specific items depending on the pet's health condition. Furthermore, the measurement unit can reflect changes in the pet's health condition in real time and reflect them in decluttering suggestions. This allows the measurement unit to make decluttering suggestions that take the pet's health condition into account.

[0095] The identification unit can learn the behavioral patterns of pets and identify unnecessary items based on the behavioral patterns. For example, the identification unit can analyze the tendency of pets to use certain items at certain times of the day and identify unnecessary items based on the frequency of use. The identification unit can also detect changes in the pet's behavioral patterns and identify items that are no longer used as unnecessary items. Furthermore, the identification unit can predict the pet's behavioral patterns and identify items that are unlikely to be used in the future as unnecessary items. This allows the identification unit to make highly accurate decluttering suggestions that take the pet's behavioral patterns into consideration.

[0096] The suggestion unit can estimate the owner's emotions and adjust the priorities for decluttering based on the estimated emotions. For example, if the owner is feeling stressed, the suggestion unit can prioritize and suggest items that are easy to declutter. On the other hand, if the owner is relaxed, the suggestion unit can suggest more complicated decluttering tasks. Furthermore, if the owner is emotionally attached to a particular item, the suggestion unit can postpone decluttering of that item. In this way, the suggestion unit can adjust the priorities for decluttering according to the owner's emotions.

[0097] The recognition unit can optimize camera placement based on room layout information and reduce blind spots. For example, the recognition unit can analyze a room's floor plan and suggest optimal camera placement. The recognition unit can also focus on monitoring specific areas based on furniture placement information. Furthermore, the recognition unit can automatically adjust camera positions if the room layout changes. This allows the recognition unit to perform highly accurate item recognition that takes room layout information into account.

[0098] The measurement unit can refer to the pet's behavioral history and improve measurement accuracy based on the behavioral history. For example, if the pet behaves in a specific way during a specific time period, the measurement unit can focus on measuring during that time period. The measurement unit can also optimize the timing of measurements based on the pet's past behavioral history. Furthermore, the measurement unit can learn the pet's behavioral patterns and improve measurement accuracy based on predictions. This allows the measurement unit to perform highly accurate measurements that take the pet's behavioral history into account.

[0099] The identification unit can estimate the owner's emotions and adjust the criteria for identifying unnecessary items based on the estimated emotions. For example, if the owner is feeling stressed, the identification unit can identify unnecessary items using strict criteria. If the owner is relaxed, the identification unit can identify unnecessary items using normal criteria. Furthermore, if the owner is in a hurry, the identification unit can identify unnecessary items using simple criteria. This allows the identification unit to flexibly identify unnecessary items according to the owner's emotions.

[0100] The suggestion unit can refer to past decluttering history and improve the accuracy of suggestions based on the history. For example, the suggestion unit can make optimal suggestions based on past decluttering history. The suggestion unit can also make suggestions by referring to items that the owner has decluttered in the past. Furthermore, the suggestion unit can analyze past history and improve the accuracy of suggestions. This allows the suggestion unit to make highly accurate suggestions that take into account past decluttering history.

[0101] The suggestion unit can estimate the owner's emotions and adjust the display method of the suggestions based on the estimated emotions. For example, if the owner is feeling stressed, the suggestion unit can provide a simple display method. If the owner is relaxed, the suggestion unit can display detailed suggestions. If the owner is in a hurry, the suggestion unit can provide a display method that focuses on the main points. This allows the suggestion unit to flexibly display suggestions according to the owner's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The recognition unit recognizes items in the room. The recognition unit identifies pet supplies in the room using, for example, image recognition technology. For example, the recognition unit can recognize pet beds, toys, dishes, etc. This allows a list of pet supplies in the room to be created. Step 2: The measurement unit measures the frequency of use of the items recognized by the recognition unit. The measurement unit, for example, monitors the pet's daily life and records the frequency of use of each item. For example, the measurement unit records which bed the pet sleeps on, which toys it plays with, which dishes it uses, etc. This makes it possible to visualize the frequency of use of each item. Step 3: The identifying unit identifies unnecessary items based on the frequency of use measured by the measuring unit. The identifying unit can, for example, identify items that are used infrequently. For example, the identifying unit identifies items that have not been used for a certain period of time or items that are damaged as unnecessary items. Step 4: The suggestion unit makes suggestions for decluttering based on the unwanted items identified by the identification unit. For example, the suggestion unit can suggest automatically listing the items at a flea market. The suggestion unit can also provide advice on decluttering tailored to individual needs, such as linking with donation recipients, requesting appraisals at recycling shops, or applying for local government collection of unwanted items.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recognition unit that recognizes items in a room; a measurement unit that measures the frequency of use of the item recognized by the recognition unit; an identification unit that identifies unnecessary items based on the frequency of use measured by the measurement unit; a suggestion unit that suggests decluttering based on the unnecessary items identified by the identification unit; A system characterized by:

2. The proposal unit Suggest automatic listings on flea markets 2. The system of claim 1.

3. The proposal unit Propose a donation link 2. The system of claim 1.

4. The proposal unit Suggest requesting an appraisal from a recycle shop 2. The system of claim 1.

5. The proposal unit Suggest applying for local government waste collection 2. The system of claim 1.

6. The recognition unit Recognizing items in a room using image language generation AI 2. The system of claim 1.

7. The measurement unit Monitor your pet's daily life and record how often each item is used 2. The system of claim 1.

8. The recognition unit Estimate the owner's emotions and adjust the recognition accuracy based on the estimated emotions.

2. The system of claim 1.

9. The proposal unit When proposing a solution, consider the market value of the items and suggest ways to declutter.

2. The system of claim 1.

10. The recognition unit During recognition, the recognition accuracy is improved based on the pet's movements and behavior patterns.

2. The system of claim 1.

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