System

Abstract

An object of a system according to an embodiment is to efficiently match information of a lost article and a found article and improve a rate of returning the found article to an owner.SOLUTION: A system according to an embodiment includes a platform providing unit, an information matching unit, and an additional question unit. The platform providing unit inputs found article information and searches for a lost article. The information matching unit matches the found article information and the lost article information input by the platform providing unit. The additional questioning unit makes an additional question to the item having a high matching rate by the information matching 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 made it difficult to efficiently match information on lost and found items, resulting in a low rate of lost items being returned to their owners.

[0005] The system according to the embodiment aims to efficiently match information on lost and found items and improve the rate at which found items are returned to their owners. [Means for solving the problem]

[0006] The system according to the embodiment includes a platform providing unit, an information matching unit, and an additional questioning unit. The platform providing unit inputs lost item information and searches for lost items. The information matching unit matches the lost item information input by the platform providing unit with the lost item information. The additional questioning unit asks additional questions for cases with a high match rate determined by the information matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match information on lost and found items, thereby improving the rate at which lost items are returned to their owners. [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 matching system according to an embodiment of the present invention allows the person who found the lost or forgotten item and the person who dropped it to register and search for information using a smartphone, and then matches that information using AI, thereby significantly improving the rate at which lost items are returned and reducing the amount of items that are disposed of.

[0029] A lost item matching system according to an embodiment includes a platform provider, an information matching unit, and an additional questioning unit. The platform provider inputs lost item information and searches for lost items. For example, the finder can input the lost item information using a smartphone, and the loser can search for the lost item information using the smartphone. The information matching unit matches the lost item information input by the platform provider with the lost item information. For example, if the found item information is input as "black wallet, brand name, location where found, date and time," and the lost item information is input as "black wallet, brand name, location where dropped, date and time," the information matching unit performs matching based on this information. The additional questioning unit asks additional questions for cases with a high match rate determined by the information matching unit. For example, the AI ​​generation system asks a question such as "What types of cards are in your wallet?" and performs further verification based on the answer. This allows the lost item matching system to significantly improve the rate at which lost items are returned and reduce the number of items discarded.

[0030] The platform provider can automatically analyze photos of lost items, extract features, and register them along with text information. For example, when a photo of a lost item is uploaded, the platform provider automatically extracts features using image analysis technology and registers them as text information. For example, the platform provider can analyze the color, shape, brand logo, etc. of the wallet and store them in a database. This allows the features of the lost item to be automatically analyzed and registered as text information.

[0031] The platform provider supports voice input when entering information about found items and can convert it into text using voice recognition technology. The platform provider adds a function to support voice input when entering information about found items, for example. For example, if a user says, "I found a black wallet," the voice recognition technology converts it into text and registers it in a database. This allows voice input to be supported and converted into text using voice recognition technology.

[0032] The platform provider may make the platform multilingual, allowing users to register and search for found-item information in different languages. For example, the platform provider may make the platform multilingual, allowing users to register found-item information in different languages. For example, the platform may support input in multiple languages, such as Japanese, English, Chinese, and Spanish. This allows users to register and search for found-item information in different languages ​​through multilingual support.

[0033] The platform providing unit can automatically obtain location information when registering lost-item information and display it on a map. For example, when registering lost-item information, the platform providing unit can automatically obtain location information using the GPS function of a smartphone and display it on a map. For example, the location of the lost item can be indicated with a pin. This allows the location information to be automatically obtained and displayed on a map.

[0034] The information matching unit can add a function to the matching algorithm that takes into account the usage history of the found item and the behavioral patterns of the owner. For example, the information matching unit adds a function to the matching algorithm that takes into account the usage history of the found item. For example, the owner is identified based on the usage history of the cards in the wallet. This improves the matching accuracy by taking into account the usage history of the found item and the behavioral patterns of the owner.

[0035] The information matching unit can use visual infographics to make the matching results easier to understand when presenting the matching results to the user. For example, the information matching unit uses visual infographics to make the matching results easier to understand when presenting the matching results to the user. For example, the matching probability and matching items are displayed in graphs or charts. This makes the matching results easier to understand using visual infographics.

[0036] The information matching unit can add a function to the matching algorithm that integrates information from different data sources. For example, the information matching unit adds a function to the matching algorithm that integrates information from social media. For example, the information matching unit matches lost items based on information obtained from a user's social media account. This improves matching accuracy by integrating information from different data sources.

[0037] The information matching unit can personalize the matching results based on the user's past search history and behavior history. The information matching unit, for example, personalizes the matching results based on the user's past search history. For example, the matching results are customized based on keywords and categories that the user has searched for in the past. This allows the matching results to be personalized based on the user's past search history and behavior history.

[0038] The additional question section can customize the content of the additional question based on the user's past answer history and behavioral patterns. The additional question section customizes the content of the additional question based on the user's past answer history, for example. For example, if a user has lost their wallet in the past, detailed questions about the contents of the wallet are asked. This allows the additional question section to customize the content of the additional question based on the user's past answer history and behavioral patterns.

[0039] The additional questioning unit can perform visual confirmation using images or videos when asking additional questions. For example, the additional questioning unit adds a function for visual confirmation using images when asking additional questions. For example, a photo of the lost item can be uploaded and the user can check it. This improves the accuracy of the additional questions by performing visual confirmation using images or videos.

[0040] The additional question unit can dynamically change the content of the additional question based on the user's location information and time of day. The additional question unit dynamically changes the content of the additional question based on the user's location information, for example. For example, if the user is in a specific location, a question related to that location is asked. This allows the content of the additional question to be dynamically changed based on the user's location information and time of day.

[0041] When providing information about storage locations, a navigation function on a map can be added to enable users to easily access the storage location. When providing information about storage locations, a navigation function on a map can be added. For example, Google Maps can be used to display the route to the storage location. This adds a navigation function on a map to enable users to easily access the storage location.

[0042] It is possible to support multiple payment methods when collecting fees. Add a function to support credit card payments when collecting fees. For example, it will be possible to use credit cards such as VISA, MasterCard, and American Express. This will support multiple payment methods and improve user convenience.

[0043] When providing information about a storage location, reviews and ratings of the storage location can be displayed, allowing users to use the service with peace of mind. When providing information about a storage location, add a function that allows users to check reviews and ratings of the storage location. For example, reviews and ratings posted by other users can be displayed. This allows reviews and ratings of the storage location to be displayed, allowing users to use the service with peace of mind.

[0044] It is possible to improve user satisfaction by offering point rewards or discount coupons when fees are collected. Add a function to offer point rewards when fees are collected. For example, part of the fee can be returned as points that can be used the next time the service is used. This allows point rewards or discount coupons to be offered when fees are collected, improving user satisfaction.

[0045] The reward for the finder can be customized based on the user's past information about found items and their behavioral history. The reward for the finder can be customized based on the user's past information about found items. For example, a higher reward can be offered to a user who has turned in many found items in the past. This allows the reward for the finder to be customized based on the user's past information about found items and their behavioral history.

[0046] The types of rewards can be diversified, and in addition to electronic payment points, gift cards, gift certificates, etc. can be offered. In addition to electronic payment points, gift cards can be offered as a reward to the finder. For example, Amazon gift cards, iTunes gift cards, etc. can be selected. This diversifies the types of rewards and increases the user's options.

[0047] By linking the reward to finders with local specialties and services, it is possible to revitalize the local economy. As a reward to finders, local specialties can be offered. For example, local agricultural products or crafts can be chosen as the reward. This will link the reward to finders with local specialties and services, and it will be possible to revitalize the local economy.

[0048] When providing rewards, the user's social contribution level can be visualized and shared with other users. A function will be added to visualize the user's social contribution level when providing rewards. For example, the number of times a lost item has been returned and the level of contribution can be displayed as a score. This will make it possible to visualize the user's social contribution level when providing rewards and share it with other users.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] When a user enters information about a found item, the platform provider can automatically translate the information entered by the user and provide the same information to users who speak other languages. For example, found item information entered in Japanese can be automatically translated into English or Chinese, allowing users who speak different languages ​​to use the same information. This makes it easier to share found item information across language barriers.

[0051] The platform provider can add a function that automatically suggests information previously entered by the user when entering information about a lost item. For example, if a user who has previously found a wallet finds the wallet again, suggestions can be made automatically based on the information previously entered, eliminating the need for input. This can make the user's input work more efficient.

[0052] When a user inputs information about a found item, the platform provider can display related news and articles based on the information entered by the user. For example, if a user finds a wallet from a specific brand, the platform provider can display the latest news and articles about that brand and provide related information to the user. This allows the user to obtain background information about the found item.

[0053] When a user inputs information about a lost item, the platform provider can automatically provide related insurance information based on the information entered by the user. For example, if a user finds an expensive watch, insurance information related to the watch can be displayed to help the user decide whether to purchase insurance. This helps users understand the importance of insurance for lost items.

[0054] The information matching unit can add a function to the matching algorithm that takes into account information about places that the owner of the lost item has visited in the past. For example, the lost item is matched based on information about places that the owner has frequently visited in the past. This allows for improved matching accuracy by taking into account the owner's behavioral patterns.

[0055] When presenting the matching results to the user, the information matching unit can provide explanations using audio guidance. For example, in addition to visually displaying the matching results, detailed audio explanations can be provided to make it easier for the user to understand. This makes it possible to effectively communicate the matching results using both visual and auditory senses.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The platform provider inputs information about the lost item and searches for the lost item. For example, the person who found the item can input information about the lost item using a smartphone, and the person who lost the item can search for information about the lost item using a smartphone. Step 2: The information matching unit matches the found item information entered by the platform provider with the lost item information. For example, if the found item information is entered as "black wallet, brand name, location where found, date and time," and the lost item information is entered as "black wallet, brand name, location where dropped, date and time," the information matching unit will perform matching based on this information. Step 3: The additional questioning section asks additional questions for cases with a high match rate from the information matching section. For example, the generation AI asks a question such as, "Please tell me the types of cards in your wallet," and performs further confirmation based on the answer.

[0058] (Example 2) The lost item matching system according to an embodiment of the present invention allows the person who found the lost or forgotten item and the person who dropped it to register and search for information using a smartphone, and then matches that information using AI, thereby significantly improving the rate at which lost items are returned and reducing the amount of items that are disposed of.

[0059] A lost item matching system according to an embodiment includes a platform provider, an information matching unit, and an additional questioning unit. The platform provider inputs lost item information and searches for lost items. For example, the finder can input the lost item information using a smartphone, and the loser can search for the lost item information using the smartphone. The information matching unit matches the lost item information input by the platform provider with the lost item information. For example, if the found item information is input as "black wallet, brand name, location where found, date and time," and the lost item information is input as "black wallet, brand name, location where dropped, date and time," the information matching unit performs matching based on this information. The additional questioning unit asks additional questions for cases with a high match rate determined by the information matching unit. For example, the AI ​​generation system asks a question such as "What types of cards are in your wallet?" and performs further verification based on the answer. This allows the lost item matching system to significantly improve the rate at which lost items are returned and reduce the number of items discarded.

[0060] The platform provider can automatically analyze photos of lost items, extract features, and register them along with text information. For example, when a photo of a lost item is uploaded, the platform provider automatically extracts features using image analysis technology and registers them as text information. For example, the platform provider can analyze the color, shape, brand logo, etc. of the wallet and store them in a database. This allows the features of the lost item to be automatically analyzed and registered as text information.

[0061] The platform provider supports voice input when entering information about found items and can convert it into text using voice recognition technology. The platform provider adds a function to support voice input when entering information about found items, for example. For example, if a user says, "I found a black wallet," the voice recognition technology converts it into text and registers it in a database. This allows voice input to be supported and converted into text using voice recognition technology.

[0062] The platform providing unit can use the emotion estimation function to analyze the user's emotion when registering a found item and provide an interface that elicits positive emotions. For example, the platform providing unit adds a function to analyze the user's facial expression and voice and estimate the emotion when registering a found item. For example, the platform providing unit can provide an interface that analyzes the user's emotion using a camera or microphone and elicits positive emotions. This makes it possible to provide an interface that analyzes the user's emotion and elicits positive emotions.

[0063] The platform provider may make the platform multilingual, allowing users to register and search for found-item information in different languages. For example, the platform provider may make the platform multilingual, allowing users to register found-item information in different languages. For example, the platform may support input in multiple languages, such as Japanese, English, Chinese, and Spanish. This allows users to register and search for found-item information in different languages ​​through multilingual support.

[0064] The platform providing unit can automatically obtain location information when registering lost-item information and display it on a map. For example, when registering lost-item information, the platform providing unit can automatically obtain location information using the GPS function of a smartphone and display it on a map. For example, the location of the lost item can be indicated with a pin. This allows the location information to be automatically obtained and displayed on a map.

[0065] The platform providing unit can use the emotion estimation function to analyze the user's emotions in real time when registering found-item information and provide appropriate feedback. For example, when registering found-item information, the platform providing unit adds a function to analyze the user's facial expressions and voice and analyze emotions in real time. For example, the platform providing unit analyzes the user's emotions using a camera or microphone and provides appropriate feedback. This makes it possible to analyze the user's emotions in real time and provide appropriate feedback.

[0066] The information matching unit can add a function to the matching algorithm that takes into account the usage history of the found item and the behavioral patterns of the owner. For example, the information matching unit adds a function to the matching algorithm that takes into account the usage history of the found item. For example, the owner is identified based on the usage history of the cards in the wallet. This improves the matching accuracy by taking into account the usage history of the found item and the behavioral patterns of the owner.

[0067] The information matching unit can use visual infographics to make the matching results easier to understand when presenting the matching results to the user. For example, the information matching unit uses visual infographics to make the matching results easier to understand when presenting the matching results to the user. For example, the matching probability and matching items are displayed in graphs or charts. This makes the matching results easier to understand using visual infographics.

[0068] The information matching unit can use the emotion estimation function to analyze the user's emotional response to the matching result and make suggestions that will elicit a positive response. The information matching unit, for example, analyzes the user's emotional response to the matching result in real time and makes suggestions that will elicit a positive response. For example, it analyzes the user's facial expressions and voice and displays positive messages. This analyzes the user's emotional response and makes suggestions that will elicit a positive response.

[0069] The information matching unit can add a function to the matching algorithm that integrates information from different data sources. For example, the information matching unit adds a function to the matching algorithm that integrates information from social media. For example, the information matching unit matches lost items based on information obtained from a user's social media account. This improves matching accuracy by integrating information from different data sources.

[0070] The information matching unit can personalize the matching results based on the user's past search history and behavior history. The information matching unit, for example, personalizes the matching results based on the user's past search history. For example, the matching results are customized based on keywords and categories that the user has searched for in the past. This allows the matching results to be personalized based on the user's past search history and behavior history.

[0071] The information matching unit can use the emotion estimation function to analyze the user's emotion regarding the matching result in real time and provide the optimal matching result. The information matching unit can, for example, analyze the user's emotion regarding the matching result in real time and provide the optimal matching result. For example, it can analyze the user's facial expression and voice and adjust the matching result based on the emotion score. This allows the user's emotion to be analyzed in real time and the optimal matching result to be provided.

[0072] The additional question section can customize the content of the additional question based on the user's past answer history and behavioral patterns. The additional question section customizes the content of the additional question based on the user's past answer history, for example. For example, if a user has lost their wallet in the past, detailed questions about the contents of the wallet are asked. This allows the additional question section to customize the content of the additional question based on the user's past answer history and behavioral patterns.

[0073] The additional questioning unit can perform visual confirmation using images or videos when asking additional questions. For example, the additional questioning unit adds a function for visual confirmation using images when asking additional questions. For example, a photo of the lost item can be uploaded and the user can check it. This improves the accuracy of the additional questions by performing visual confirmation using images or videos.

[0074] The follow-up question unit can use the emotion estimation function to analyze the user's emotional response to the follow-up question and generate a question that elicits a positive response. The follow-up question unit, for example, analyzes the user's emotional response to the follow-up question in real time and generates a question that elicits a positive response. For example, the follow-up question unit analyzes the user's facial expression and voice and adjusts the question based on the emotion score. This makes it possible to analyze the user's emotional response and generate a question that elicits a positive response.

[0075] The additional question unit can dynamically change the content of the additional question based on the user's location information and time of day. The additional question unit dynamically changes the content of the additional question based on the user's location information, for example. For example, if the user is in a specific location, a question related to that location is asked. This allows the content of the additional question to be dynamically changed based on the user's location information and time of day.

[0076] The additional question unit can use the emotion estimation function to analyze the user's emotion regarding the additional question in real time and provide the most appropriate question. The additional question unit can, for example, analyze the user's emotion regarding the additional question in real time and provide the most appropriate question. For example, the additional question unit can analyze the user's facial expression and voice and adjust the question based on the emotion score. This allows the user's emotion to be analyzed in real time and the most appropriate question to be provided.

[0077] When providing information about storage locations, a navigation function on a map can be added to enable users to easily access the storage location. When providing information about storage locations, a navigation function on a map can be added. For example, Google Maps can be used to display the route to the storage location. This adds a navigation function on a map to enable users to easily access the storage location.

[0078] It is possible to support multiple payment methods when collecting fees. Add a function to support credit card payments when collecting fees. For example, it will be possible to use credit cards such as VISA, MasterCard, and American Express. This will support multiple payment methods and improve user convenience.

[0079] Using the emotion estimation function, it is possible to analyze the user's emotions when collecting fees and provide an interface that elicits positive emotions. When collecting fees, a function is added that analyzes the user's facial expressions and voice to analyze emotions in real time. For example, an interface is provided that uses a camera or microphone to analyze the user's emotions and elicits positive emotions. This makes it possible to provide an interface that analyzes the user's emotions when collecting fees and elicits positive emotions.

[0080] When providing information about a storage location, reviews and ratings of the storage location can be displayed, allowing users to use the service with peace of mind. When providing information about a storage location, add a function that allows users to check reviews and ratings of the storage location. For example, reviews and ratings posted by other users can be displayed. This allows reviews and ratings of the storage location to be displayed, allowing users to use the service with peace of mind.

[0081] It is possible to improve user satisfaction by offering point rewards or discount coupons when fees are collected. Add a function to offer point rewards when fees are collected. For example, part of the fee can be returned as points that can be used the next time the service is used. This allows point rewards or discount coupons to be offered when fees are collected, improving user satisfaction.

[0082] Using the emotion estimation function, it is possible to analyze the user's emotions in real time when collecting fees and provide optimal feedback. When collecting fees, it is possible to add a function that analyzes the user's facial expressions and voice to analyze emotions in real time. For example, it is possible to analyze the user's emotions using a camera or microphone and provide optimal feedback. This allows it to analyze the user's emotions in real time when collecting fees and provide optimal feedback.

[0083] The reward for the finder can be customized based on the user's past information about found items and their behavioral history. The reward for the finder can be customized based on the user's past information about found items. For example, a higher reward can be offered to a user who has turned in many found items in the past. This allows the reward for the finder to be customized based on the user's past information about found items and their behavioral history.

[0084] The types of rewards can be diversified, and in addition to electronic payment points, gift cards, gift certificates, etc. can be offered. In addition to electronic payment points, gift cards can be offered as a reward to the finder. For example, Amazon gift cards, iTunes gift cards, etc. can be selected. This diversifies the types of rewards and increases the user's options.

[0085] The emotion estimation function can be used to analyze the user's emotions when providing a reward, and provide a reward that elicits positive emotions. When providing a reward, a function can be added to analyze the user's facial expressions and voice to analyze emotions in real time. For example, a camera or microphone can be used to analyze the user's emotions and provide a reward that elicits positive emotions. This makes it possible to analyze the user's emotions when providing a reward, and provide a reward that elicits positive emotions.

[0086] By linking the reward to finders with local specialties and services, it is possible to revitalize the local economy. As a reward to finders, local specialties can be offered. For example, local agricultural products or crafts can be chosen as the reward. This will link the reward to finders with local specialties and services, and it will be possible to revitalize the local economy.

[0087] When providing rewards, the user's social contribution level can be visualized and shared with other users. A function will be added to visualize the user's social contribution level when providing rewards. For example, the number of times a lost item has been returned and the level of contribution can be displayed as a score. This will make it possible to visualize the user's social contribution level when providing rewards and share it with other users.

[0088] The emotion estimation function can be used to analyze the user's emotions in real time when providing a reward, and provide the most appropriate reward. When providing a reward, a function can be added to analyze the user's facial expressions and voice to analyze emotions in real time. For example, a camera or microphone can be used to analyze the user's emotions and provide the most appropriate reward. This allows the user's emotions to be analyzed in real time when providing a reward, and provide the most appropriate reward.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] When a user enters information about a found item, the platform provider can automatically translate the information entered by the user and provide the same information to users who speak other languages. For example, found item information entered in Japanese can be automatically translated into English or Chinese, allowing users who speak different languages ​​to use the same information. This makes it easier to share found item information across language barriers.

[0091] The platform provider can add a function that automatically suggests information previously entered by the user when entering information about a lost item. For example, if a user who has previously found a wallet finds the wallet again, suggestions can be made automatically based on the information previously entered, eliminating the need for input. This can make the user's input work more efficient.

[0092] When a user inputs information about a found item, the platform provider can display related news and articles based on the information entered by the user. For example, if a user finds a wallet from a specific brand, the platform provider can display the latest news and articles about that brand and provide related information to the user. This allows the user to obtain background information about the found item.

[0093] The platform provider can use the emotion estimation function to analyze the user's emotions when registering a lost item and provide relaxation music and messages to reduce stress. For example, if the user is feeling nervous, the platform provider can play relaxing music and display a message that gives a sense of security. This can reduce the user's stress and provide a comfortable user experience.

[0094] The platform provider can use the emotion estimation function to analyze the user's emotions when registering a found item and display a message that makes the user feel grateful. For example, when a user registers a found item, a message of gratitude or words of encouragement can be displayed to elicit positive emotions. This can increase the user's motivation and encourage them to register found items.

[0095] When a user inputs information about a lost item, the platform provider can automatically provide related insurance information based on the information entered by the user. For example, if a user finds an expensive watch, insurance information related to the watch can be displayed to help the user decide whether to purchase insurance. This helps users understand the importance of insurance for lost items.

[0096] The platform provider uses the emotion estimation function to analyze the user's emotions when registering a found item and can provide an interface that gives the user a sense of security. For example, if the user is feeling anxious, the platform provider can display an interface with a design and color that gives the user a sense of security. This reduces the user's anxiety and allows them to register a found item with peace of mind.

[0097] The information matching unit can add a function to the matching algorithm that takes into account information about places that the owner of the lost item has visited in the past. For example, the lost item is matched based on information about places that the owner has frequently visited in the past. This allows for improved matching accuracy by taking into account the owner's behavioral patterns.

[0098] When presenting the matching results to the user, the information matching unit can provide explanations using audio guidance. For example, in addition to visually displaying the matching results, detailed audio explanations can be provided to make it easier for the user to understand. This makes it possible to effectively communicate the matching results using both visual and auditory senses.

[0099] The information matching unit uses the emotion estimation function to analyze the user's emotional response to the matching results and can make customized suggestions that will satisfy the user. For example, if the user expresses joy, additional suggestions will be made to further enhance that emotion. This makes it possible to make optimal suggestions based on the user's emotions and improve satisfaction.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The platform provider inputs information about the lost item and searches for the lost item. For example, the person who found the item can input information about the lost item using a smartphone, and the person who lost the item can search for information about the lost item using a smartphone. Step 2: The information matching unit matches the found item information entered by the platform provider with the lost item information. For example, if the found item information is entered as "black wallet, brand name, location where found, date and time," and the lost item information is entered as "black wallet, brand name, location where dropped, date and time," the information matching unit will perform matching based on this information. Step 3: The additional questioning section asks additional questions for cases with a high match rate from the information matching section. For example, the generation AI asks a question such as, "Please tell me the types of cards in your wallet," and performs further confirmation based on the answer.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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, in order to avoid confusion and to 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.

[0168] 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]

[0169] 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 platform provider that inputs lost item information and searches for lost items; an information matching unit that matches found item information and lost item information input by the platform providing unit; an additional questioning unit that asks additional questions for cases where the information matching unit has a high matching rate; A system characterized by:

2. The platform providing unit When the lost item information is registered, the location information is automatically acquired and displayed on a map.

2. The system of claim 1.

3. The information matching unit Add functionality to the matching algorithm that takes into account the usage history of lost items and the behavioral patterns of the owner.

2. The system of claim 1.

4. The additional question section The content of the follow-up questions is customized based on the user's past answer history and behavioral patterns.

2. The system of claim 1.

5. Add a map navigation function when providing storage location information to allow users to easily access it.

2. The system of claim 1.

6. The platform providing unit Analyze the user's feelings when registering a lost item and provide an interface that makes the user's feelings positive.

2. The system of claim 1.

7. The information matching unit Analyze the user's emotional response to the matching results and make suggestions to make the user's emotions more positive.

2. The system of claim 1.

8. The additional question section Analyzing the user's emotional response to the additional question and generating a question that makes the user feel positive 2. The system of claim 1.

Images