System

Abstract

An object of a system according to an embodiment is to propose a method for disposing of an article and quickly make a purchase assessment offer.SOLUTION: A system includes a reception unit, an analysis unit, a proposal unit, and an offer unit. The reception unit transmits the photograph of the article photographed by the user with the smartphone. The analysis unit analyzes the photograph transmitted by the reception unit and determines a type and a state of the article. The proposal unit proposes a disposal method based on the type and state of the article determined by the analysis unit. The offer unit transmits a purchase evaluation offer based on the disposal method proposed by the proposal 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 techniques have had the problem of making it difficult to quickly and accurately determine how to dispose of items and assess their purchase value.

[0005] The system according to the embodiment aims to propose methods for disposing of items and to quickly make purchase appraisal offers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and an offer unit. The reception unit transmits a photo of an item taken by a user with a smartphone. The analysis unit analyzes the photo transmitted by the reception unit and determines the type and condition of the item. The proposal unit proposes a disposal method based on the type and condition of the item determined by the analysis unit. The offer unit transmits a purchase appraisal offer based on the disposal method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose methods for disposing of items and quickly make purchase appraisal offers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, a system automatically determines the appropriate disposal method for an item by sending a photo taken with a smartphone to the system, and then issues a purchase appraisal offer. In this system, a user takes a photo of the item with their smartphone and sends it to the system. The AI ​​then analyzes the sent photo to determine the type and condition of the item. For example, the AI ​​classifies the item into categories such as furniture, home appliances, and clothing, and further determines its condition (new, used, damaged, etc.). This allows the system to suggest an appropriate disposal method. For example, it suggests recyclable items to recycling companies, and items that need to be disposed of to waste disposal companies. Furthermore, for items that can be purchased, it sends an offer to a purchasing company and conducts a purchase appraisal. This allows the system to easily learn how to dispose of an item and receive a purchase appraisal. For example, a user can simply send a photo of an item taken with their smartphone to receive a proposal for an appropriate disposal method and a purchase appraisal offer. This eliminates the hassle of disposing of items and allows users to dispose of items efficiently.

[0029] A disposal suggestion system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and an offer unit. The reception unit transmits photos of items taken by a user with a smartphone. For example, a user can transmit photos of items taken with a smartphone to the system. The analysis unit analyzes the transmitted photos and determines the type and condition of the items. For example, the analysis unit uses an image analysis algorithm to determine the category and condition of the items. The analysis unit can classify items into categories such as furniture, home appliances, and clothing, and further determine their condition (new, used, damaged, etc.). The analysis unit can also use AI to analyze the transmitted photos and determine the type and condition of the items. The proposal unit proposes an appropriate disposal method based on the determined type and condition of the items. For example, the proposal unit can propose recyclable items to a recycling company and discard items that need to be disposed of to a waste disposal company. The proposal unit can also use AI to propose an appropriate disposal method based on the determined type and condition of the items. The offer unit transmits a purchase appraisal offer based on the proposed disposal method. For example, the offer unit can transmit an offer to a purchasing company for items that can be purchased, and perform a purchase appraisal. The offer unit can also use AI to send a purchase appraisal offer based on the proposed disposal method. As a result, the disposal proposal system according to the embodiment allows a user to receive a proposal for an appropriate disposal method and a purchase appraisal offer simply by sending a photo of an item taken with a smartphone.

[0030] The reception unit analyzes the user's past sending history and selects a sending method. For example, the reception unit preferentially suggests sending methods (email, in-app sending, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal sending method for a specific time period based on the user's past sending history. The reception unit can also select the optimal sending method by taking into account the resolution and format of photos the user has sent in the past. In this way, the optimal sending method can be selected by analyzing the user's past sending history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0031] The reception unit filters photos based on the user's current project and areas of interest when sending the photos. For example, the reception unit filters the photos so that only photos related to the user's ongoing project are sent. The reception unit can also prioritize sending related photos based on the user's areas of interest (e.g., furniture, home appliances, etc.). The reception unit can also filter highly relevant photos by referring to the user's past project history. In this way, highly relevant photos can be sent by filtering based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0032] When sending a photo, the reception unit selects a transmission means according to the user's input method. For example, if the user uses voice input, the reception unit transmits the photo using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also transmit the photo using text analysis technology. Furthermore, if the user uses image input, the reception unit can also transmit the photo using image analysis technology. This improves the convenience of transmission by selecting the optimal transmission means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0033] When transmitting photos, the reception unit prioritizes transmitting highly relevant photos in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes transmitting photos related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize transmitting photos related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize transmitting photos related to the home. In this way, highly relevant photos can be transmitted preferentially by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0034] When sending a photo, the reception unit analyzes the user's social media activity and sends related photos. The reception unit, for example, sends photos related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and send related photos. The reception unit can also send related photos by referring to the activity of the user's friends on social media. In this way, related photos can be sent by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0035] The reception unit customizes the transmission method by reflecting the user's past feedback when sending a photo. The reception unit, for example, suggests the optimal transmission method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific transmission method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the transmission method. In this way, the transmission method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0036] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the item. For example, the analysis unit performs a detailed analysis for items with high importance. The analysis unit can also perform a brief analysis for items with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the item. This makes it possible to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the item. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0037] During analysis, the analysis unit applies different analysis algorithms depending on the category of the item. For example, in the case of furniture, the analysis unit applies an analysis algorithm specifically for furniture. Furthermore, in the case of home appliances, the analysis unit can also apply an analysis algorithm specifically for home appliances. Furthermore, in the case of clothing, the analysis unit can also apply an analysis algorithm specifically for clothing. In this way, by applying different analysis algorithms depending on the category of the item, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0038] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis by referring, for example, to data on items that the user has previously analyzed. The analysis unit can also improve the accuracy of the analysis by learning specific patterns from the user's past analysis results. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0039] During analysis, the analysis unit determines the priority of analysis based on the time of submission of the item. For example, if the item is submitted early, the analysis unit will prioritize analysis. Furthermore, if the item is submitted late, the analysis unit can postpone analysis. Furthermore, the analysis unit can dynamically adjust the priority of analysis depending on the time of submission of the item. This enables efficient analysis by determining the priority of analysis based on the time of submission of the item. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0040] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the items. For example, the analysis unit prioritizes analysis of highly relevant items. The analysis unit can also postpone analysis of less relevant items. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the items. This enables efficient analysis by adjusting the order of analysis based on the relevance of the items. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0041] During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0042] The suggestion unit adjusts the level of detail of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an item with a high level of importance. The suggestion unit can also make a concise suggestion for an item with a low level of importance. The suggestion unit can also dynamically adjust the level of detail of the suggestion according to the importance of the item. In this way, by adjusting the level of detail of the suggestion based on the importance of the item, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0043] When making a suggestion, the suggestion unit applies different suggestion algorithms depending on the category of the item. For example, in the case of furniture, the suggestion unit applies a suggestion algorithm dedicated to furniture. Furthermore, in the case of home appliances, the suggestion unit can also apply a suggestion algorithm dedicated to home appliances. Furthermore, in the case of clothing, the suggestion unit can also apply a suggestion algorithm dedicated to clothing. In this way, by applying different suggestion algorithms depending on the category of the item, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0044] When making a suggestion, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, by referring to data on suggestions the user has received in the past. The suggestion unit can also improve the accuracy of the suggestion by learning specific patterns from the user's past suggestion results. The suggestion unit can also optimize the suggestion algorithm based on the user's past suggestion results. This improves the accuracy of the suggestion by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0045] The suggestion unit determines the priority of the suggestion based on the submission time of the item when making the suggestion. For example, if the item is submitted early, the suggestion unit will make the suggestion with priority. Also, if the item is submitted late, the suggestion unit can make the suggestion later. Also, the suggestion unit can dynamically adjust the priority of the suggestion depending on the submission time of the item. This enables efficient suggestions by determining the priority of the suggestion based on the submission time of the item. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The suggestion unit adjusts the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant items. The suggestion unit can also suggest less relevant items later. The suggestion unit can also dynamically adjust the order of suggestions according to the relevance of the items. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the items. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0047] When making a proposal, the suggestion unit adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide a concise and easy-to-understand proposal. Furthermore, the suggestion unit can dynamically adjust the use of technical terminology in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0048] When making an offer, the offer unit adjusts the level of detail of the offer based on the importance of the item. For example, the offer unit makes a detailed offer for an item with a high level of importance. The offer unit can also make a brief offer for an item with a low level of importance. The offer unit can also dynamically adjust the level of detail of the offer according to the importance of the item. In this way, by adjusting the level of detail of the offer based on the importance of the item, a more appropriate offer can be provided. Some or all of the above-described processing in the offer unit may be performed, for example, using AI or without using AI.

[0049] When making an offer, the offer unit applies different offer algorithms depending on the category of the item. For example, in the case of furniture, the offer unit applies an offer algorithm dedicated to furniture. In addition, in the case of home appliances, the offer unit can also apply an offer algorithm dedicated to home appliances. In addition, in the case of clothing, the offer unit can also apply an offer algorithm dedicated to clothing. In this way, by applying different offer algorithms depending on the category of the item, the accuracy of the offer is improved. Some or all of the above-mentioned processing in the offer unit may be performed, for example, using AI, or may be performed without using AI.

[0050] When making an offer, the offer unit improves the accuracy of the offer by referring to the user's past offer results. The offer unit improves the accuracy of the offer by referring, for example, to data on offers the user has received in the past. The offer unit can also improve the accuracy of the offer by learning specific patterns from the user's past offer results. The offer unit can also optimize the offer algorithm based on the user's past offer results. This improves the accuracy of the offer by referring to the user's past offer results. Some or all of the above-described processing in the offer unit may be performed, for example, using AI, or may be performed without using AI.

[0051] When making an offer, the offer unit determines the priority of the offer based on the time of submission of the item. For example, if the item is submitted early, the offer unit will make the offer preferentially. Furthermore, if the item is submitted late, the offer unit can also make the offer later. Furthermore, the offer unit can dynamically adjust the priority of the offer depending on the time of submission of the item. This enables efficient offers by determining the priority of the offer based on the time of submission of the item. Some or all of the above-mentioned processing in the offer unit may be performed, for example, using AI or without using AI.

[0052] The offering unit adjusts the order of offers based on the relevance of the items when making offers. For example, the offering unit prioritizes offering highly relevant items. The offering unit can also postpone offering less relevant items. The offering unit can also dynamically adjust the order of offers according to the relevance of the items. This enables efficient offers by adjusting the order of offers based on the relevance of the items. Some or all of the above-described processing in the offering unit may be performed, for example, using AI or without using AI.

[0053] When making an offer, the offer unit adjusts the use of technical terminology in the offer according to the user's level of expertise. For example, if the user has technical expertise, the offer unit provides an offer that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the offer unit can also provide a concise and easy-to-understand offer. Furthermore, the offer unit can dynamically adjust the use of technical terminology in the offer according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the offer according to the user's level of expertise, it is possible to provide an offer that is easier to understand. Some or all of the above-mentioned processing in the offer unit may be performed, for example, using AI or without using AI.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can also analyze the user's past purchase history and prioritize sending photos of related items. For example, photos of furniture that the user has purchased in the past can be sent with priority. The reception unit can also prioritize sending photos of home appliances that the user has purchased in the past. Furthermore, the reception unit can also prioritize sending photos of clothing that the user has purchased in the past. In this way, by taking the user's past purchase history into consideration, photos of highly related items can be sent with priority.

[0056] The suggestion unit can analyze the user's past suggestion history and prioritize relevant suggestions. For example, the suggestion unit can suggest new furniture by referring to furniture suggestions received by the user in the past. The suggestion unit can also suggest new home appliances by referring to home appliance suggestions received by the user in the past. Furthermore, the suggestion unit can suggest new clothing by referring to clothing suggestions received by the user in the past. In this way, highly relevant suggestions can be made by taking into account the user's past suggestion history.

[0057] The reception unit can also analyze the user's current activity status and suggest the optimal transmission method. For example, if the user is exercising, the reception unit can send photos using voice input. If the user is at work, the reception unit can send photos using text input. If the user is on vacation, the reception unit can send photos using image input. This improves the convenience of transmission by suggesting the optimal transmission method according to the user's current activity status.

[0058] The suggestion unit can also analyze the user's current lifestyle and make optimal suggestions. For example, if the user is health-conscious, it can make health-related suggestions. If the user is interested in environmental protection, it can make eco-friendly suggestions. Furthermore, if the user is interested in fashion, it can make suggestions based on the latest fashion trends. This improves the accuracy of suggestions by making optimal suggestions according to the user's current lifestyle.

[0059] The reception unit can also analyze the user's past sending history and suggest the optimal sending timing. For example, if the user sent photos at a specific time period in the past, the reception unit can prompt the user to send photos at that time period. Also, if the user sent photos on a specific day of the week in the past, the reception unit can prompt the user to send photos on that day. Furthermore, if the user sent photos at a specific event in the past, the reception unit can prompt the user to send photos at the time of that event. In this way, the optimal sending timing can be suggested by taking the user's past sending history into consideration.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit transmits a photo of an item taken by a user with a smartphone. For example, the user can transmit a photo of an item taken by a smartphone to the system. Step 2: The analysis unit analyzes the submitted photo and determines the type and condition of the item. For example, the analysis unit uses an image analysis algorithm to determine the category and condition of the item. For example, the analysis unit can classify items into categories such as furniture, home appliances, and clothing, and further determine their condition (new, used, damaged, etc.). The analysis unit can also use AI to analyze the submitted photo and determine the type and condition of the item. Step 3: The proposal unit proposes an appropriate disposal method based on the determined type and condition of the item. For example, the proposal unit can propose to a recycling company for recyclable items and to a waste disposal company for items that need to be disposed of. The proposal unit can also use AI to propose an appropriate disposal method based on the determined type and condition of the item. Step 4: The offer unit sends a purchase appraisal offer based on the proposed disposal method. For example, the offer unit can send an offer to a purchasing company for items that can be purchased and perform a purchase appraisal. The offer unit can also use AI to send a purchase appraisal offer based on the proposed disposal method.

[0062] (Example 2) In an embodiment of the present invention, a system automatically determines the appropriate disposal method for an item by sending a photo taken with a smartphone to the system, and then issues a purchase appraisal offer. In this system, a user takes a photo of the item with their smartphone and sends it to the system. The AI ​​then analyzes the sent photo to determine the type and condition of the item. For example, the AI ​​classifies the item into categories such as furniture, home appliances, and clothing, and further determines its condition (new, used, damaged, etc.). This allows the system to suggest an appropriate disposal method. For example, it suggests recyclable items to recycling companies, and items that need to be disposed of to waste disposal companies. Furthermore, for items that can be purchased, it sends an offer to a purchasing company and conducts a purchase appraisal. This allows the system to easily learn how to dispose of an item and receive a purchase appraisal. For example, a user can simply send a photo of an item taken with their smartphone to receive a proposal for an appropriate disposal method and a purchase appraisal offer. This eliminates the hassle of disposing of items and allows users to dispose of items efficiently.

[0063] A disposal suggestion system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and an offer unit. The reception unit transmits photos of items taken by a user with a smartphone. For example, a user can transmit photos of items taken with a smartphone to the system. The analysis unit analyzes the transmitted photos and determines the type and condition of the items. For example, the analysis unit uses an image analysis algorithm to determine the category and condition of the items. The analysis unit can classify items into categories such as furniture, home appliances, and clothing, and further determine their condition (new, used, damaged, etc.). The analysis unit can also use AI to analyze the transmitted photos and determine the type and condition of the items. The proposal unit proposes an appropriate disposal method based on the determined type and condition of the items. For example, the proposal unit can propose recyclable items to a recycling company and discard items that need to be disposed of to a waste disposal company. The proposal unit can also use AI to propose an appropriate disposal method based on the determined type and condition of the items. The offer unit transmits a purchase appraisal offer based on the proposed disposal method. For example, the offer unit can transmit an offer to a purchasing company for items that can be purchased, and perform a purchase appraisal. The offer unit can also use AI to send a purchase appraisal offer based on the proposed disposal method. As a result, the disposal proposal system according to the embodiment allows a user to receive a proposal for an appropriate disposal method and a purchase appraisal offer simply by sending a photo of an item taken with a smartphone.

[0064] The reception unit estimates the user's emotions and adjusts the timing of photo transmission based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit delays the transmission of the photo so that the user can send it in a relaxed state. Furthermore, if the user is in a hurry, the reception unit can send the photo immediately for quick processing. Furthermore, if the user is enjoying themselves, the reception unit can encourage the user to send the photo, providing a fun experience. This allows the photo to be sent at a more appropriate time by adjusting the timing of photo transmission according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The reception unit analyzes the user's past sending history and selects a sending method. For example, the reception unit preferentially suggests sending methods (email, in-app sending, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal sending method for a specific time period based on the user's past sending history. The reception unit can also select the optimal sending method by taking into account the resolution and format of photos the user has sent in the past. In this way, the optimal sending method can be selected by analyzing the user's past sending history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0066] The reception unit filters photos based on the user's current project and areas of interest when sending the photos. For example, the reception unit filters the photos so that only photos related to the user's ongoing project are sent. The reception unit can also prioritize sending related photos based on the user's areas of interest (e.g., furniture, home appliances, etc.). The reception unit can also filter highly relevant photos by referring to the user's past project history. In this way, highly relevant photos can be sent by filtering based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0067] When sending a photo, the reception unit selects a transmission means according to the user's input method. For example, if the user uses voice input, the reception unit transmits the photo using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also transmit the photo using text analysis technology. Furthermore, if the user uses image input, the reception unit can also transmit the photo using image analysis technology. This improves the convenience of transmission by selecting the optimal transmission means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0068] The reception unit estimates the user's emotions and determines the priority of photos to be sent based on the estimated user emotions. For example, if the user is nervous, the reception unit prioritizes sending photos with a lower importance. Furthermore, if the user is relaxed, the reception unit can also prioritize sending photos with a higher importance. Furthermore, if the user is in a hurry, the reception unit can also prioritize sending photos that require quick processing. In this way, by determining the priority of photos to be sent according to the user's emotions, more appropriate photos can be sent. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] When transmitting photos, the reception unit prioritizes transmitting highly relevant photos in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes transmitting photos related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize transmitting photos related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize transmitting photos related to the home. In this way, highly relevant photos can be transmitted preferentially by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0070] When sending a photo, the reception unit analyzes the user's social media activity and sends related photos. The reception unit, for example, sends photos related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and send related photos. The reception unit can also send related photos by referring to the activity of the user's friends on social media. In this way, related photos can be sent by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0071] The reception unit customizes the transmission method by reflecting the user's past feedback when sending a photo. The reception unit, for example, suggests the optimal transmission method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific transmission method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the transmission method. In this way, the transmission method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0072] The analysis unit estimates the user's emotion and adjusts the way the analysis is presented based on the estimated user emotion. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is excited, the analysis unit can also provide visually stimulating analysis results. This allows for adjusting the way the analysis is presented according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the item. For example, the analysis unit performs a detailed analysis for items with high importance. The analysis unit can also perform a brief analysis for items with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the item. This makes it possible to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the item. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0074] During analysis, the analysis unit applies different analysis algorithms depending on the category of the item. For example, in the case of furniture, the analysis unit applies an analysis algorithm specifically for furniture. Furthermore, in the case of home appliances, the analysis unit can also apply an analysis algorithm specifically for home appliances. Furthermore, in the case of clothing, the analysis unit can also apply an analysis algorithm specifically for clothing. In this way, by applying different analysis algorithms depending on the category of the item, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0075] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis by referring, for example, to data on items that the user has previously analyzed. The analysis unit can also improve the accuracy of the analysis by learning specific patterns from the user's past analysis results. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0076] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] During analysis, the analysis unit determines the priority of analysis based on the time of submission of the item. For example, if the item is submitted early, the analysis unit will prioritize analysis. Furthermore, if the item is submitted late, the analysis unit can postpone analysis. Furthermore, the analysis unit can dynamically adjust the priority of analysis depending on the time of submission of the item. This enables efficient analysis by determining the priority of analysis based on the time of submission of the item. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0078] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the items. For example, the analysis unit prioritizes analysis of highly relevant items. The analysis unit can also postpone analysis of less relevant items. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the items. This enables efficient analysis by adjusting the order of analysis based on the relevance of the items. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0079] During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0080] The suggestion unit estimates the user's emotion and adjusts the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way the suggestion is expressed depending on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] The suggestion unit adjusts the level of detail of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an item with a high level of importance. The suggestion unit can also make a concise suggestion for an item with a low level of importance. The suggestion unit can also dynamically adjust the level of detail of the suggestion according to the importance of the item. In this way, by adjusting the level of detail of the suggestion based on the importance of the item, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0082] When making a suggestion, the suggestion unit applies different suggestion algorithms depending on the category of the item. For example, in the case of furniture, the suggestion unit applies a suggestion algorithm dedicated to furniture. Furthermore, in the case of home appliances, the suggestion unit can also apply a suggestion algorithm dedicated to home appliances. Furthermore, in the case of clothing, the suggestion unit can also apply a suggestion algorithm dedicated to clothing. In this way, by applying different suggestion algorithms depending on the category of the item, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0083] When making a suggestion, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, by referring to data on suggestions the user has received in the past. The suggestion unit can also improve the accuracy of the suggestion by learning specific patterns from the user's past suggestion results. The suggestion unit can also optimize the suggestion algorithm based on the user's past suggestion results. This improves the accuracy of the suggestion by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0084] The suggestion unit estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit provides short and to-the-point suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. The suggestion unit can also provide visually stimulating suggestions if the user is excited. This allows for adjusting the length of the suggestion according to the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The suggestion unit determines the priority of the suggestion based on the submission time of the item when making the suggestion. For example, if the item is submitted early, the suggestion unit will make the suggestion with priority. Also, if the item is submitted late, the suggestion unit can make the suggestion later. Also, the suggestion unit can dynamically adjust the priority of the suggestion depending on the submission time of the item. This enables efficient suggestions by determining the priority of the suggestion based on the submission time of the item. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The suggestion unit adjusts the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant items. The suggestion unit can also suggest less relevant items later. The suggestion unit can also dynamically adjust the order of suggestions according to the relevance of the items. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the items. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0087] When making a proposal, the suggestion unit adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide a concise and easy-to-understand proposal. Furthermore, the suggestion unit can dynamically adjust the use of technical terminology in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0088] The offer unit estimates the user's emotions and adjusts the way the offer is presented based on the estimated user emotions. For example, if the user is relaxed, the offer unit provides a detailed offer. If the user is in a hurry, the offer unit can also provide a concise offer. If the user is excited, the offer unit can also provide a visually stimulating offer. This allows the system to provide a more appropriate offer by adjusting the way the offer is presented according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] When making an offer, the offer unit adjusts the level of detail of the offer based on the importance of the item. For example, the offer unit makes a detailed offer for an item with a high level of importance. The offer unit can also make a brief offer for an item with a low level of importance. The offer unit can also dynamically adjust the level of detail of the offer according to the importance of the item. In this way, by adjusting the level of detail of the offer based on the importance of the item, a more appropriate offer can be provided. Some or all of the above-described processing in the offer unit may be performed, for example, using AI or without using AI.

[0090] When making an offer, the offer unit applies different offer algorithms depending on the category of the item. For example, in the case of furniture, the offer unit applies an offer algorithm dedicated to furniture. In addition, in the case of home appliances, the offer unit can also apply an offer algorithm dedicated to home appliances. In addition, in the case of clothing, the offer unit can also apply an offer algorithm dedicated to clothing. In this way, by applying different offer algorithms depending on the category of the item, the accuracy of the offer is improved. Some or all of the above-mentioned processing in the offer unit may be performed, for example, using AI, or may be performed without using AI.

[0091] When making an offer, the offer unit improves the accuracy of the offer by referring to the user's past offer results. The offer unit improves the accuracy of the offer by referring, for example, to data on offers the user has received in the past. The offer unit can also improve the accuracy of the offer by learning specific patterns from the user's past offer results. The offer unit can also optimize the offer algorithm based on the user's past offer results. This improves the accuracy of the offer by referring to the user's past offer results. Some or all of the above-described processing in the offer unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The offer unit estimates the user's emotions and adjusts the length of the offer based on the estimated user emotions. For example, if the user is in a hurry, the offer unit provides a short and to-the-point offer. The offer unit can also provide a detailed offer if the user is relaxed. The offer unit can also provide a visually stimulating offer if the user is excited. This allows for adjusting the length of the offer according to the user's emotions, thereby providing a more appropriate offer. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] When making an offer, the offer unit determines the priority of the offer based on the time of submission of the item. For example, if the item is submitted early, the offer unit will make the offer preferentially. Furthermore, if the item is submitted late, the offer unit can also make the offer later. Furthermore, the offer unit can dynamically adjust the priority of the offer depending on the time of submission of the item. This enables efficient offers by determining the priority of the offer based on the time of submission of the item. Some or all of the above-mentioned processing in the offer unit may be performed, for example, using AI or without using AI.

[0094] The offering unit adjusts the order of offers based on the relevance of the items when making offers. For example, the offering unit prioritizes offering highly relevant items. The offering unit can also postpone offering less relevant items. The offering unit can also dynamically adjust the order of offers according to the relevance of the items. This enables efficient offers by adjusting the order of offers based on the relevance of the items. Some or all of the above-described processing in the offering unit may be performed, for example, using AI or without using AI.

[0095] When making an offer, the offer unit adjusts the use of technical terminology in the offer according to the user's level of expertise. For example, if the user has technical expertise, the offer unit provides an offer that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the offer unit can also provide a concise and easy-to-understand offer. Furthermore, the offer unit can dynamically adjust the use of technical terminology in the offer according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the offer according to the user's level of expertise, it is possible to provide an offer that is easier to understand. Some or all of the above-mentioned processing in the offer unit may be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and offer unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can transmit a photo of an item taken by a user using the reception device 38 of the smart device 14. The analysis unit analyzes the photo transmitted by the specific processing unit 290 of the data processing device 12 and determines the type and condition of the item. The proposal unit proposes an appropriate disposal method based on the type and condition of the item determined by the specific processing unit 290 of the data processing device 12. The offer unit transmits a purchase appraisal offer based on the disposal method proposed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, and offer unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can transmit a photo of an item taken by a user using the microphone 238 of the smart glasses 214. The analysis unit analyzes the transmitted photo by the specific processing unit 290 of the data processing device 12 and determines the type and condition of the item. The proposal unit proposes an appropriate disposal method based on the type and condition of the item determined by the specific processing unit 290 of the data processing device 12. The offer unit transmits a purchase appraisal offer based on the disposal method proposed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and offer unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can transmit a photo of an item taken by the user using the microphone 238 of the headset-type terminal 314. The analysis unit analyzes the photo transmitted by the specific processing unit 290 of the data processing device 12 and determines the type and condition of the item. The proposal unit proposes an appropriate disposal method based on the type and condition of the item determined by the specific processing unit 290 of the data processing device 12. The offer unit transmits a purchase appraisal offer based on the disposal method proposed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and offer unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can transmit a photo of an item taken by a user using the microphone 238 of the robot 414. The analysis unit analyzes the photo transmitted by the specific processing unit 290 of the data processing device 12 and determines the type and condition of the item. The proposal unit proposes an appropriate disposal method based on the type and condition of the item determined by the specific processing unit 290 of the data processing device 12. The offer unit transmits a purchase appraisal offer based on the disposal method proposed by the specific processing unit 290 of the data processing device 12.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can also analyze the user's past purchase history and prioritize sending photos of related items. For example, photos of furniture that the user has purchased in the past can be sent with priority. The reception unit can also prioritize sending photos of home appliances that the user has purchased in the past. Furthermore, the reception unit can also prioritize sending photos of clothing that the user has purchased in the past. In this way, by taking the user's past purchase history into consideration, photos of highly related items can be sent with priority.

[0098] The analysis unit can also estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a brief analysis can be performed. Furthermore, if the user is excited, a visually stimulating analysis can be performed. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0099] The suggestion unit can analyze the user's past suggestion history and prioritize relevant suggestions. For example, the suggestion unit can suggest new furniture by referring to furniture suggestions received by the user in the past. The suggestion unit can also suggest new home appliances by referring to home appliance suggestions received by the user in the past. Furthermore, the suggestion unit can suggest new clothing by referring to clothing suggestions received by the user in the past. In this way, highly relevant suggestions can be made by taking into account the user's past suggestion history.

[0100] The offer unit can also estimate the user's emotions and adjust the timing of the offer based on the estimated user's emotions. For example, if the user is relaxed, a detailed offer can be provided. If the user is in a hurry, a concise offer can be provided. Furthermore, if the user is excited, a visually stimulating offer can be provided. In this way, by adjusting the timing of the offer according to the user's emotions, more appropriate offers can be provided.

[0101] The reception unit can also analyze the user's current activity status and suggest the optimal transmission method. For example, if the user is exercising, the reception unit can send photos using voice input. If the user is at work, the reception unit can send photos using text input. If the user is on vacation, the reception unit can send photos using image input. This improves the convenience of transmission by suggesting the optimal transmission method according to the user's current activity status.

[0102] The analysis unit can also estimate the user's emotions and adjust the order of analysis based on the estimated user's emotions. For example, if the user is relaxed, it can prioritize analyzing items of high importance. Also, if the user is in a hurry, it can prioritize analyzing items of low importance. Furthermore, if the user is excited, it can prioritize analyzing items that are visually stimulating. In this way, by adjusting the order of analysis according to the user's emotions, it is possible to provide more appropriate analysis results.

[0103] The suggestion unit can also analyze the user's current lifestyle and make optimal suggestions. For example, if the user is health-conscious, it can make health-related suggestions. If the user is interested in environmental protection, it can make eco-friendly suggestions. Furthermore, if the user is interested in fashion, it can make suggestions based on the latest fashion trends. This improves the accuracy of suggestions by making optimal suggestions according to the user's current lifestyle.

[0104] The offer unit can also estimate the user's emotions and adjust the content of the offer based on the estimated user's emotions. For example, if the user is relaxed, a detailed offer can be provided. If the user is in a hurry, a concise offer can be provided. Furthermore, if the user is excited, a visually stimulating offer can be provided. In this way, by adjusting the content of the offer according to the user's emotions, more appropriate offers can be provided.

[0105] The reception unit can also analyze the user's past sending history and suggest the optimal sending timing. For example, if the user sent photos at a specific time period in the past, the reception unit can prompt the user to send photos at that time period. Also, if the user sent photos on a specific day of the week in the past, the reception unit can prompt the user to send photos on that day. Furthermore, if the user sent photos at a specific event in the past, the reception unit can prompt the user to send photos at the time of that event. In this way, the optimal sending timing can be suggested by taking the user's past sending history into consideration.

[0106] The analysis unit can also estimate the user's emotions and adjust the analysis feedback based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, brief feedback can be provided. Furthermore, if the user is excited, visually stimulating feedback can be provided. In this way, by adjusting the analysis feedback according to the user's emotions, more appropriate feedback can be provided.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit transmits a photo of an item taken by a user with a smartphone. For example, the user can transmit a photo of an item taken by a smartphone to the system. Step 2: The analysis unit analyzes the submitted photo and determines the type and condition of the item. For example, the analysis unit uses an image analysis algorithm to determine the category and condition of the item. For example, the analysis unit can classify items into categories such as furniture, home appliances, and clothing, and further determine their condition (new, used, damaged, etc.). The analysis unit can also use AI to analyze the submitted photo and determine the type and condition of the item. Step 3: The proposal unit proposes an appropriate disposal method based on the determined type and condition of the item. For example, the proposal unit can propose to a recycling company for recyclable items and to a waste disposal company for items that need to be disposed of. The proposal unit can also use AI to propose an appropriate disposal method based on the determined type and condition of the item. Step 4: The offer unit sends a purchase appraisal offer based on the proposed disposal method. For example, the offer unit can send an offer to a purchasing company for items that can be purchased and perform a purchase appraisal. The offer unit can also use AI to send a purchase appraisal offer based on the proposed disposal method.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 reception unit for transmitting a photo of an item taken by a user with a smartphone; an analysis unit that analyzes the photo sent by the reception unit and determines the type and condition of the item; a suggestion unit that suggests a disposal method based on the type and condition of the item determined by the analysis unit; an offer unit that transmits a purchase appraisal offer based on the disposal method proposed by the proposal unit; A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of sending photos based on the estimated user emotions. The system of claim 1 .

3. The reception unit Analyze the user's past sending history and select the sending method The system of claim 1 .

4. The reception unit Filtering photo submissions based on a user's current projects or interests The system of claim 1 .

5. The reception unit When sending a photo, select the sending method according to the user's input method. The system of claim 1 .

6. The reception unit Estimate the user's emotions and prioritize photos to send based on the estimated user emotions. The system of claim 1 .

7. The reception unit When sending photos, the app takes into account the user's geographic location to prioritize sending more relevant photos. The system of claim 1 .

8. The reception unit When you send a photo, we analyze your social media activity and send you relevant photos. The system of claim 1 .

Images