System

Abstract

An object of a system according to an embodiment is to identify a location where a user has left an item behind.SOLUTION: A system according to an embodiment includes a forgotten item inputter, a behavior analyzer, an estimator, and a notifier. The forgotten item input component is used by the user to input a forgotten item. The behavior analysis unit analyzes a behavior pattern or a habit of the user on the basis of the information input by the forgotten item input unit. The estimation part estimates a place where the article is most likely to be left behind on the basis of the information analyzed by the action analysis part. The notification unit notifies the user of the place estimated by the estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users to locate items they have left behind.

[0005] The system according to the embodiment aims to identify the location of an item when the user leaves something behind. [Means for solving the problem]

[0006] The system according to the embodiment includes a lost item input unit, a behavior analysis unit, an estimation unit, and a notification unit. The lost item input unit allows a user to input a lost item. The behavior analysis unit analyzes the user's behavioral patterns and habits based on the information input by the lost item input unit. The estimation unit estimates the most likely location of the lost item based on the information analyzed by the behavior analysis unit. The notification unit notifies the user of the location estimated by the estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the location of an item left behind by a user. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The lost item prediction system according to an embodiment of the present invention is a system in which a user inputs the lost item, and a generation AI analyzes the user's behavioral patterns and habits, predicts the most likely location, and notifies the user. This allows the lost item prediction system to efficiently find the user's lost item.

[0029] A lost item estimation system according to an embodiment includes a lost item input unit, a behavior analysis unit, an estimation unit, and a notification unit. The lost item input unit allows a user to input a lost item. For example, the user may input, "I don't know where I put my keys." The behavior analysis unit analyzes the user's behavioral patterns and habits based on the information input by the lost item input unit. For example, it analyzes the user's usual route and the actions they take in different locations. The estimation unit estimates the most likely location of the lost item based on the information analyzed by the behavior analysis unit. For example, it estimates, "The keys are likely on the table in the living room." The notification unit notifies the user of the location estimated by the estimation unit. For example, it notifies, "Please check the table in the living room." This allows the lost item estimation system to efficiently find the user's lost item.

[0030] The lost item input unit can automatically identify an item using voice input or image recognition. For example, when a user inputs a lost item, the lost item input unit uses voice input to identify the item. For example, when a user says, "I'm looking for my keys," voice recognition technology analyzes the voice and automatically identifies the item. The lost item input unit also uses image recognition to identify the item. For example, when a user takes an image of their keys with a camera, image recognition technology analyzes the image and automatically identifies the item. This allows the user to easily input a lost item.

[0031] The lost item input unit can automatically suggest items that are frequently forgotten based on the user's past input history. For example, when the user inputs an item that they have forgotten, the lost item input unit automatically suggests items that they frequently forget by referring to the past input history. For example, if the user has forgotten "keys" many times in the past, the app automatically suggests "keys." This allows the user to easily input items that they frequently forget.

[0032] The lost item input unit can create a common lost item list based on data from when other users have forgotten the same item. For example, when a user inputs a lost item, the lost item input unit references data from when other users have forgotten the same item and creates a common lost item list. For example, if multiple users forget their "keys," a common list is created based on that data. By creating a common lost item list, it becomes easier to identify the location of the lost item.

[0033] The lost item input unit can automatically categorize items, making it easier to search. For example, when a user inputs a lost item, the lost item input unit automatically categorizes the item into a category, making it easier to search. For example, if "key" is input, the item is automatically categorized into the "key" category. Also, if "smartphone" is input, the item is automatically categorized into the "electronic device" category. This makes it easier to search for items.

[0034] The behavior analysis unit can perform a detailed analysis of the user's daily rhythm and behavioral patterns for specific days of the week. For example, the generation AI analyzes the user's daily rhythm and performs a detailed analysis of the behavioral patterns for specific days of the week. For example, if the user has the habit of going to a specific place every Monday, the analysis is based on that information. Also, if the user has different behavioral patterns on weekdays and weekends, the differences are analyzed. This allows for a detailed analysis of the user's behavioral patterns, making it possible to more accurately predict the location of lost items.

[0035] The behavior analysis unit can perform more accurate analysis by referring to the user's social media posts or calendar schedule. In the behavior analysis unit, for example, the generation AI analyzes the user's social media posts and identifies behavioral patterns and habits. For example, the analysis is performed based on information about the user's check-in at a specific location. The behavior analysis unit also refers to the user's calendar schedule and analyzes behavioral patterns. For example, if the user plans to go to a specific location on a specific day, the analysis is performed based on that information. In this way, by referring to social media posts and calendar schedules, the accuracy of behavioral pattern analysis is improved.

[0036] The behavior analysis unit can also take into account the behavior patterns of family members or housemates to predict the location of lost items in a shared living environment. For example, the generation AI analyzes the behavior patterns of family members and housemates along with the user's behavior patterns to predict the location of lost items in a shared living environment. For example, it takes into account places frequently used by family members and places frequently used by housemates. This allows for more accurate prediction of the location of lost items in a shared living environment.

[0037] The behavior analysis unit can analyze the user's health data and identify the relationship between the health condition and behavioral patterns. For example, the behavior analysis unit uses a generating AI to analyze the user's health data and identify the relationship between the health condition and behavioral patterns. For example, the behavior analysis unit analyzes the user's heart rate and step count data to identify the relationship between the health condition and behavioral patterns. The behavior analysis unit also analyzes the user's sleep data to identify the relationship between the health condition and behavioral patterns. By identifying the relationship between the health condition and behavioral patterns, the location of the lost item can be more accurately estimated.

[0038] The prediction unit can refer to data on past lost items and improve prediction accuracy from similar cases. For example, the generation AI can refer to data on past lost items and improve prediction accuracy from similar cases. For example, based on data that a user has previously left their "keys" in the living room, it can increase the likelihood that they will be in the living room this time as well. Also, based on data that a user has previously left their "wallet" in the kitchen, it can increase the likelihood that they will be in the kitchen this time as well. In this way, by referring to past data, prediction accuracy can be improved.

[0039] The estimation unit can refer to the user's current location or nearby Wi-Fi network information and update the estimation results in real time. For example, the generation AI in the estimation unit refers to the user's current location and updates the estimation results of the most likely location in real time. For example, if the user is currently in the living room, it increases the likelihood that the item is in the living room. The estimation unit also refers to nearby Wi-Fi network information and updates the estimation results in real time. For example, if the user is connected to a specific Wi-Fi network, it increases the likelihood that the item is in that location. This allows the location of the lost item to be more accurately estimated by updating the estimation results in real time.

[0040] The estimation unit can identify common patterns of lost items by referencing anonymized data of other users. For example, the generation AI can identify common patterns of lost items by referencing anonymized data of other users. For example, it can identify a pattern in which multiple users leave their "keys" in the living room. It can also identify a pattern in which multiple users leave their "wallets" in the kitchen. By identifying common patterns of lost items, it can more accurately estimate the location of lost items.

[0041] The prediction unit can work in conjunction with the user's smart home devices to notify the user of the location of the lost item by voice. For example, the generation AI in the prediction unit can work in conjunction with the user's smart home devices to notify the user of the location of the lost item by voice. For example, it can notify the user through a smart speaker, saying, "The keys are on the table in the living room." It can also indicate the location of the lost item by flashing a smart light. In this way, by working in conjunction with smart home devices, the location of the lost item can be notified by voice.

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

[0043] The lost item prediction system can further include a health analysis unit that analyzes the user's health data. The health analysis unit, for example, analyzes the user's heart rate and sleep data to identify the correlation between the user's health condition and behavioral patterns. This makes it possible to identify situations in which the user is likely to forget things based on their health condition and suggest preventative measures. For example, if the user is prone to forgetting things when they are sleep-deprived, advice on how to improve their sleep can be provided. Furthermore, if the user's heart rate is high, relaxation methods can be suggested. This makes it possible to provide measures to prevent forgetting things that take the user's health condition into consideration.

[0044] The lost item prediction system may further include a smart home linking unit that links with the user's smart home devices. The smart home linking unit can, for example, notify the user of the location of the lost item by voice via a smart speaker. This allows the user to confirm the location of the lost item by voice. For example, it may notify the user that "The keys are on the table in the living room." It can also indicate the location of the lost item by flashing a smart light. In this way, by linking with the smart home devices, the location of the lost item can also be visually notified.

[0045] The lost item prediction system can further include a detailed analysis unit that analyzes the user's behavioral patterns in detail. The detailed analysis unit analyzes, for example, the user's daily rhythm and behavioral patterns for specific days of the week. This allows the user to identify habits of going to specific places on specific days of the week, improving the accuracy of predicting the location of lost items. For example, if a user has a habit of going to a specific place every Monday, analysis can be performed based on that information. In addition, if a user has different behavioral patterns on weekdays and weekends, the differences can also be analyzed. This allows for a more accurate prediction of the location of lost items by analyzing the user's behavioral patterns in detail.

[0046] The lost item prediction system can further include a data reference unit that references data on items previously lost by the user. The data reference unit, for example, uses data on items previously lost by the user to improve prediction accuracy from similar cases. This makes it possible to more accurately predict the location of a lost item by utilizing past data. For example, based on data that a user previously left their "keys" in the living room, it can increase the likelihood that they will be in the living room this time. Also, based on data that a user previously left their "wallet" in the kitchen, it can increase the likelihood that they will be in the kitchen this time. In this way, by referencing past data, prediction accuracy is improved.

[0047] The lost item estimation system can further include a current location reference unit that references the user's current location. The current location reference unit, for example, updates the most likely location estimation result based on the user's current location in real time. This improves the accuracy of estimating the location of the lost item based on the user's current location. For example, if the user is currently in the living room, the likelihood that the item is in the living room increases. The system can also reference nearby Wi-Fi network information and update the estimation result in real time. This allows the location of the lost item to be more accurately estimated by updating the estimation result in real time.

[0048] The lost item prediction system can further include a common pattern analysis unit that anonymizes and references data from other users. The common pattern analysis unit, for example, identifies common patterns of lost items based on the data of other users. This makes it possible to improve the accuracy of predicting the location of lost items by utilizing data from when multiple users have forgotten the same item. For example, it can identify a pattern in which multiple users leave their "keys" in the living room. It can also identify a pattern in which multiple users leave their "wallets" in the kitchen. By identifying common patterns of lost items, it is possible to more accurately predict the location of lost items.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: In the lost item input section, the user inputs the item that he or she has lost. For example, the user may input "I don't know where I put my keys." Step 2: The behavior analysis unit analyzes the user's behavioral patterns and habits based on the information input by the lost item input unit. For example, it analyzes the user's usual route, the location, and the behavior they perform. Step 3: The prediction unit predicts the most likely location of the forgotten item based on the information analyzed by the behavior analysis unit. For example, it predicts that "the key is most likely on the table in the living room." Step 4: The notification unit notifies the user of the location estimated by the estimation unit, for example, by saying, "Check the table in the living room."

[0051] (Example 2) The lost item prediction system according to an embodiment of the present invention is a system in which a user inputs the lost item, and a generation AI analyzes the user's behavioral patterns and habits, predicts the most likely location, and notifies the user. This allows the lost item prediction system to efficiently find the user's lost item.

[0052] A lost item estimation system according to an embodiment includes a lost item input unit, a behavior analysis unit, an estimation unit, and a notification unit. The lost item input unit allows a user to input a lost item. For example, the user may input, "I don't know where I put my keys." The behavior analysis unit analyzes the user's behavioral patterns and habits based on the information input by the lost item input unit. For example, it analyzes the user's usual route and the actions they take in different locations. The estimation unit estimates the most likely location of the lost item based on the information analyzed by the behavior analysis unit. For example, it estimates, "The keys are likely on the table in the living room." The notification unit notifies the user of the location estimated by the estimation unit. For example, it notifies, "Please check the table in the living room." This allows the lost item estimation system to efficiently find the user's lost item.

[0053] The lost item input unit can automatically identify an item using voice input or image recognition. For example, when a user inputs a lost item, the lost item input unit uses voice input to identify the item. For example, when a user says, "I'm looking for my keys," voice recognition technology analyzes the voice and automatically identifies the item. The lost item input unit also uses image recognition to identify the item. For example, when a user takes an image of their keys with a camera, image recognition technology analyzes the image and automatically identifies the item. This allows the user to easily input a lost item.

[0054] The lost item input unit can automatically suggest items that are frequently forgotten based on the user's past input history. For example, when the user inputs an item that they have forgotten, the lost item input unit automatically suggests items that they frequently forget by referring to the past input history. For example, if the user has forgotten "keys" many times in the past, the app automatically suggests "keys." This allows the user to easily input items that they frequently forget.

[0055] The lost item input unit can use the emotion estimation function to analyze the user's emotion when entering information and suggest relaxation methods to reduce stress or impatience. For example, when the user enters the item they have left behind, the lost item input unit can use the emotion estimation function to analyze the user's emotion and suggest relaxation methods to reduce stress or impatience. For example, if the user is impatient, a message encouraging the user to take a deep breath is displayed. Also, if the user is feeling stressed, relaxing music is played. This can reduce the user's stress and impatience.

[0056] The lost item input unit can create a common lost item list based on data from when other users have forgotten the same item. For example, when a user inputs a lost item, the lost item input unit references data from when other users have forgotten the same item and creates a common lost item list. For example, if multiple users forget their "keys," a common list is created based on that data. By creating a common lost item list, it becomes easier to identify the location of the lost item.

[0057] The lost item input unit can automatically categorize items, making it easier to search. For example, when a user inputs a lost item, the lost item input unit automatically categorizes the item into a category, making it easier to search. For example, if "key" is input, the item is automatically categorized into the "key" category. Also, if "smartphone" is input, the item is automatically categorized into the "electronic device" category. This makes it easier to search for items.

[0058] The lost item input unit can use the emotion estimation function to analyze the user's emotion when entering information and provide an interface design that elicits positive emotions. For example, when a user enters a lost item, the lost item input unit can use the emotion estimation function to analyze the user's emotion and provide an interface design that elicits positive emotions. For example, bright colors and positive messages can be displayed. Relaxing music can also be played while the user is entering information. This can elicit positive emotions from the user.

[0059] The behavior analysis unit can perform a detailed analysis of the user's daily rhythm and behavioral patterns for specific days of the week. For example, the generation AI analyzes the user's daily rhythm and performs a detailed analysis of the behavioral patterns for specific days of the week. For example, if the user has the habit of going to a specific place every Monday, the analysis is based on that information. Also, if the user has different behavioral patterns on weekdays and weekends, the differences are analyzed. This allows for a detailed analysis of the user's behavioral patterns, making it possible to more accurately predict the location of lost items.

[0060] The behavior analysis unit can perform more accurate analysis by referring to the user's social media posts or calendar schedule. In the behavior analysis unit, for example, the generation AI analyzes the user's social media posts and identifies behavioral patterns and habits. For example, the analysis is performed based on information about the user's check-in at a specific location. The behavior analysis unit also refers to the user's calendar schedule and analyzes behavioral patterns. For example, if the user plans to go to a specific location on a specific day, the analysis is performed based on that information. In this way, by referring to social media posts and calendar schedules, the accuracy of behavioral pattern analysis is improved.

[0061] The behavior analysis unit can use the emotion estimation function to analyze emotional fluctuations in the user's behavior patterns and identify emotionally significant locations. For example, the behavior analysis unit uses the emotion estimation function to analyze emotional fluctuations in the user's behavior patterns and identify emotionally significant locations. For example, if the user has positive emotions at a specific location, the system identifies that location. Also, if the user has negative emotions at a specific location, the system identifies that location. In this way, by identifying emotionally significant locations, the location of the lost item can be more accurately estimated.

[0062] The behavior analysis unit can also take into account the behavior patterns of family members or housemates to predict the location of lost items in a shared living environment. For example, the generation AI analyzes the behavior patterns of family members and housemates along with the user's behavior patterns to predict the location of lost items in a shared living environment. For example, it takes into account places frequently used by family members and places frequently used by housemates. This allows for more accurate prediction of the location of lost items in a shared living environment.

[0063] The behavior analysis unit can analyze the user's health data and identify the relationship between the health condition and behavioral patterns. For example, the behavior analysis unit uses a generating AI to analyze the user's health data and identify the relationship between the health condition and behavioral patterns. For example, the behavior analysis unit analyzes the user's heart rate and step count data to identify the relationship between the health condition and behavioral patterns. The behavior analysis unit also analyzes the user's sleep data to identify the relationship between the health condition and behavioral patterns. By identifying the relationship between the health condition and behavioral patterns, the location of the lost item can be more accurately estimated.

[0064] The behavior analysis unit can use the emotion estimation function to analyze emotional fluctuations in the user's behavior patterns and make suggestions to promote emotionally positive behavior. For example, the behavior analysis unit uses the emotion estimation function to analyze emotional fluctuations in the user's behavior patterns and make suggestions to promote emotionally positive behavior. For example, the behavior analysis unit can suggest places where the user can relax or activities that the user can enjoy. This promotes emotionally positive behavior, thereby improving the user's quality of life.

[0065] The prediction unit can refer to data on past lost items and improve prediction accuracy from similar cases. For example, the generation AI can refer to data on past lost items and improve prediction accuracy from similar cases. For example, based on data that a user has previously left their "keys" in the living room, it can increase the likelihood that they will be in the living room this time as well. Also, based on data that a user has previously left their "wallet" in the kitchen, it can increase the likelihood that they will be in the kitchen this time as well. In this way, by referring to past data, prediction accuracy can be improved.

[0066] The estimation unit can refer to the user's current location or nearby Wi-Fi network information and update the estimation results in real time. For example, the generation AI in the estimation unit refers to the user's current location and updates the estimation results of the most likely location in real time. For example, if the user is currently in the living room, it increases the likelihood that the item is in the living room. The estimation unit also refers to nearby Wi-Fi network information and updates the estimation results in real time. For example, if the user is connected to a specific Wi-Fi network, it increases the likelihood that the item is in that location. This allows the location of the lost item to be more accurately estimated by updating the estimation results in real time.

[0067] The estimation unit can identify common patterns of lost items by referencing anonymized data of other users. For example, the generation AI can identify common patterns of lost items by referencing anonymized data of other users. For example, it can identify a pattern in which multiple users leave their "keys" in the living room. It can also identify a pattern in which multiple users leave their "wallets" in the kitchen. By identifying common patterns of lost items, it can more accurately estimate the location of lost items.

[0068] The prediction unit can work in conjunction with the user's smart home devices to notify the user of the location of the lost item by voice. For example, the generation AI in the prediction unit can work in conjunction with the user's smart home devices to notify the user of the location of the lost item by voice. For example, it can notify the user through a smart speaker, saying, "The keys are on the table in the living room." It can also indicate the location of the lost item by flashing a smart light. In this way, by working in conjunction with smart home devices, the location of the lost item can be notified by voice.

[0069] The estimation unit uses the emotion estimation function to analyze the emotions a user feels when they forget something, and provides emotionally positive feedback, thereby reducing stress. For example, the generation AI uses the emotion estimation function to analyze the emotions a user feels when they forget something, and provides emotionally positive feedback. For example, it displays a message such as "Don't worry, you'll find it soon." It also displays a message such as "Relax and try to look for it." This provides emotionally positive feedback, thereby reducing stress for the user.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The lost item prediction system can further include a health analysis unit that analyzes the user's health data. The health analysis unit, for example, analyzes the user's heart rate and sleep data to identify the correlation between the user's health condition and behavioral patterns. This makes it possible to identify situations in which the user is likely to forget things based on their health condition and suggest preventative measures. For example, if the user is prone to forgetting things when they are sleep-deprived, advice on how to improve their sleep can be provided. Furthermore, if the user's heart rate is high, relaxation methods can be suggested. This makes it possible to provide measures to prevent forgetting things that take the user's health condition into consideration.

[0072] The lost item prediction system can further include a social analysis unit that analyzes the user's social media posts. The social analysis unit, for example, analyzes behavioral patterns based on information about the user's check-in at specific locations. This allows the system to identify locations frequently visited by the user and improve the accuracy of predicting the location of lost items. For example, based on information about the user's check-in at a cafe, the system can increase the likelihood that the item was left at the cafe. It can also infer emotions from the content of the user's posts and identify emotionally significant locations. This allows the system to more accurately predict the location of lost items by utilizing social media information.

[0073] The lost item prediction system may further include a smart home linking unit that links with the user's smart home devices. The smart home linking unit can, for example, notify the user of the location of the lost item by voice via a smart speaker. This allows the user to confirm the location of the lost item by voice. For example, it may notify the user that "The keys are on the table in the living room." It can also indicate the location of the lost item by flashing a smart light. In this way, by linking with the smart home devices, the location of the lost item can also be visually notified.

[0074] The lost item prediction system can further include a feedback unit that predicts the user's emotions and provides emotionally positive feedback. For example, the feedback unit analyzes the user's emotions when they lose something and displays a positive message. This can reduce the user's stress. For example, the feedback unit can display a message such as "Don't worry, you'll find it soon." It can also display a message such as "Relax and try to look for it." This provides emotionally positive feedback, thereby reducing the user's stress.

[0075] The lost item prediction system can further include a detailed analysis unit that analyzes the user's behavioral patterns in detail. The detailed analysis unit analyzes, for example, the user's daily rhythm and behavioral patterns for specific days of the week. This allows the user to identify habits of going to specific places on specific days of the week, improving the accuracy of predicting the location of lost items. For example, if a user has a habit of going to a specific place every Monday, analysis can be performed based on that information. In addition, if a user has different behavioral patterns on weekdays and weekends, the differences can also be analyzed. This allows for a more accurate prediction of the location of lost items by analyzing the user's behavioral patterns in detail.

[0076] The lost item prediction system may further include a suggestion unit that estimates the user's emotions and promotes emotionally positive behavior. The suggestion unit, for example, analyzes emotional fluctuations in the user's behavioral patterns and makes suggestions to promote emotionally positive behavior. This can improve the user's quality of life. For example, the suggestion unit can suggest places where the user can relax. It can also suggest activities that the user can enjoy. This can improve the user's quality of life by promoting emotionally positive behavior.

[0077] The lost item prediction system can further include a data reference unit that references data on items previously lost by the user. The data reference unit, for example, uses data on items previously lost by the user to improve prediction accuracy from similar cases. This makes it possible to more accurately predict the location of a lost item by utilizing past data. For example, based on data that a user previously left their "keys" in the living room, it can increase the likelihood that they will be in the living room this time. Also, based on data that a user previously left their "wallet" in the kitchen, it can increase the likelihood that they will be in the kitchen this time. In this way, by referencing past data, prediction accuracy is improved.

[0078] The lost item estimation system can further include a current location reference unit that references the user's current location. The current location reference unit, for example, updates the most likely location estimation result based on the user's current location in real time. This improves the accuracy of estimating the location of the lost item based on the user's current location. For example, if the user is currently in the living room, the likelihood that the item is in the living room increases. The system can also reference nearby Wi-Fi network information and update the estimation result in real time. This allows the location of the lost item to be more accurately estimated by updating the estimation result in real time.

[0079] The lost item prediction system can further include a common pattern analysis unit that anonymizes and references data from other users. The common pattern analysis unit, for example, identifies common patterns of lost items based on the data of other users. This makes it possible to improve the accuracy of predicting the location of lost items by utilizing data from when multiple users have forgotten the same item. For example, it can identify a pattern in which multiple users leave their "keys" in the living room. It can also identify a pattern in which multiple users leave their "wallets" in the kitchen. By identifying common patterns of lost items, it is possible to more accurately predict the location of lost items.

[0080] The lost item prediction system can further include an emotion analysis unit that predicts the user's emotions and identifies emotionally significant locations. The emotion analysis unit, for example, analyzes emotional fluctuations in the user's behavioral patterns to identify emotionally significant locations. This makes it possible to identify situations in which the user is likely to leave something in emotionally significant locations and propose preventative measures. For example, if the user has positive emotions in a particular location, the system can identify that location. Also, if the user has negative emotions in a particular location, the system can identify that location. In this way, by identifying emotionally significant locations, the system can more accurately predict the location of the lost item.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: In the lost item input section, the user inputs the item that he or she has lost. For example, the user may input "I don't know where I put my keys." Step 2: The behavior analysis unit analyzes the user's behavioral patterns and habits based on the information input by the lost item input unit. For example, it analyzes the user's usual route, the location, and the behavior they perform. Step 3: The prediction unit predicts the most likely location of the forgotten item based on the information analyzed by the behavior analysis unit. For example, it predicts that "the key is most likely on the table in the living room." Step 4: The notification unit notifies the user of the location estimated by the estimation unit, for example, by saying, "Check the table in the living room."

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 lost item input unit for inputting a lost item by a user; a behavior analysis unit that analyzes the behavioral patterns and habits of the user based on the information input by the lost item input unit; an estimation unit that estimates the most likely location of the left-behind item based on the information analyzed by the behavior analysis unit; a notification unit that notifies a user of the location estimated by the estimation unit. A system characterized by:

2. The lost item input unit Automatically identify items using voice input or image recognition 2. The system of claim 1.

3. The behavior analysis unit Detailed analysis of users' daily rhythms and behavioral patterns on specific days of the week 2. The system of claim 1.

4. The estimation unit Refer to data on past lost items and improve prediction accuracy based on similar cases 2. The system of claim 1.

5. The lost item input unit Analyzes the user's emotions as they type and suggests relaxation techniques to reduce stress or impatience 2. The system of claim 1.

6. The behavior analysis unit Analyzing emotional fluctuations in user behavior patterns and identifying emotionally significant locations 2. The system of claim 1.

7. The estimation unit Analyzing the emotions of users when they lose something and prioritizing locations that are emotionally reassuring 2. The system of claim 1.

8. The estimation unit Analyzing the user's emotions when they forget something and providing emotionally positive feedback to reduce stress 2. The system of claim 1.

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