system

Abstract

The system according to the embodiment aims to identify the type and quantity of luggage from a photo of a room and provide an estimate efficiently. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, an estimate unit, a presentation unit, and a comparison unit. The analysis unit analyzes a photo of the room provided by the user and identifies the type and amount of luggage. The generation unit generates a layout diagram of the room based on the information identified by the analysis unit. The estimate unit provides an estimate based on the layout diagram generated by the generation unit. The presentation unit presents the reason for the estimate provided by the estimate unit. The comparison unit centralizes comparative estimates from multiple contractors.

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] With conventional technology, the process of identifying the type and quantity of luggage from photos of the room and providing an appropriate estimate was laborious, time-consuming, and inefficient.

[0005] The system according to the embodiment aims to identify the type and quantity of luggage from a photo of a room and provide an estimate efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, an estimate unit, a presentation unit, and a comparison unit. The analysis unit analyzes a photo of the room provided by the user and identifies the type and amount of luggage. The generation unit generates a layout diagram of the room based on the information identified by the analysis unit. The estimate unit provides an estimate based on the layout diagram generated by the generation unit. The presentation unit presents the reason for the estimate provided by the estimate unit. The comparison unit unifies comparative estimates from multiple contractors. [Effects of the Invention]

[0007] The system according to the embodiment can identify the type and quantity of luggage from a photo of the room and provide an estimate efficiently. [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) A system according to an embodiment of the present invention analyzes photos of a room provided by a user and automatically identifies the type and quantity of luggage. The system begins by having the user provide a photo of the room and input it into the system. Next, the system uses AI to analyze the photo and identify the type and quantity of luggage. For example, the system identifies the types of furniture, appliances, clothing, and the quantities of each. Next, the system generates a room layout diagram based on the identified information. The layout diagram includes the layout of the luggage and the dimensions of the room. This allows users to grasp the overall picture of the room and facilitates work planning. The system then automatically provides an estimate. The estimate takes into account factors such as waste disposal costs and work difficulty. For example, if large furniture needs to be dismantled and transported, the estimate reflects the difficulty of the work. Furthermore, the system presents the reasons for the estimate, making it easier for users to understand the breakdown of the estimate. For example, it explains how the amount of waste and work difficulty affect the estimate. Finally, the system centralizes comparative estimates from multiple contractors. This allows users to easily compare estimates from multiple contractors. For example, users can see each contractor's estimated price and service details at a glance. This system allows users to easily obtain estimates for room cleaning and moving, improving transparency and convenience. The system analyzes photos of the room provided by the user, identifies the type and amount of luggage, generates a layout diagram, provides an estimate, explains the reasons for the estimate, and centralizes comparative estimates from multiple companies, improving transparency and convenience.

[0029] The system according to the embodiment includes an analysis unit, a generation unit, an estimation unit, a presentation unit, and a comparison unit. The analysis unit analyzes a photo of a room provided by a user and identifies the type and amount of luggage. For example, the analysis unit uses an image recognition algorithm to identify objects in the photo and determine the type and amount of each object. The analysis unit can also use a machine learning model to classify objects in the photo and identify the type and amount of luggage. For example, the analysis unit identifies types of furniture, home appliances, clothing, etc., and calculates the amount of each. The analysis unit can also improve the accuracy of identifying the type and amount of luggage by referring to past analysis data. The generation unit generates a room layout diagram based on the information identified by the analysis unit. The generation unit automatically generates the layout diagram based on, for example, the dimensions of the room and the arrangement of the luggage. The generation unit can also estimate the user's emotions and adjust the display method of the layout diagram based on the estimated user's emotions. For example, if the user is stressed, a simple layout diagram is displayed, and if the user is relaxed, a detailed layout diagram is displayed. The estimation unit provides an estimate based on the layout diagram generated by the generation unit. The estimating unit, for example, applies different estimation algorithms for each type of luggage to provide an accurate estimate. The estimating unit can also refer to past estimation data to improve the accuracy of the estimate. The presentation unit presents the reasons for the estimate provided by the estimating unit. For example, the presentation unit can provide a convincing reason for the estimate by explaining in detail the different reasons for each type of luggage. The presentation unit can also estimate the user's emotions and adjust the display method of the reasons for the estimate based on the estimated user emotions. The comparison unit unifies comparative estimates from multiple providers. For example, the comparison unit selects an optimal comparison method taking into account the characteristics of each provider to provide comparison results that are easy for the user to understand. The comparison unit can also refer to past comparison data to improve the accuracy of the comparison results. As a result, the system according to the embodiment analyzes photos of the room provided by the user, identifies the type and quantity of luggage, generates a layout diagram, provides an estimate, presents the reasons for the estimate, and unifies comparative estimates from multiple providers, thereby improving transparency and convenience.

[0030] When analyzing a photo of a room, the analysis unit can apply filtering technology to remove the effects of reflected light and shadows. For example, the analysis unit can apply a filter to remove reflected light to the photo of the room to accurately identify the type and quantity of luggage. Furthermore, if a shadow is cast on the photo of the room, the analysis unit can apply a filter to remove the shadow to accurately identify the shape of the luggage. Furthermore, the analysis unit can apply a composite filter to the photo of the room to remove both reflected light and shadows to improve the accuracy of the analysis. This allows the effects of reflected light and shadows to be removed, thereby accurately identifying the type and quantity of luggage. The filtering technology is implemented, for example, using an image processing algorithm. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input photo data of the room into the generation AI and have the generation AI perform a filtering process to remove the effects of reflected light and shadows.

[0031] The analysis unit can improve accuracy when identifying the type and quantity of luggage by referring to past analysis data. For example, the analysis unit can refer to data analyzed in the past to identify the type and quantity of similar luggage. The analysis unit can also learn the characteristics of specific luggage based on the past analysis data and improve analysis accuracy. Furthermore, the analysis unit can statistically analyze past analysis data to help identify the type and quantity of luggage. In this way, by referring to the past analysis data, the accuracy of identifying the type and quantity of luggage is improved. The past analysis data is stored, for example, in a database, and the analysis unit utilizes the past data by referring to this database. 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. For example, the analysis unit can input past analysis data into a generation AI and have the generation AI identify the type and quantity of luggage.

[0032] When analyzing the room photo, the analysis unit can identify region-specific luggage based on the user's geographical location information. For example, the analysis unit can identify region-specific furniture and home appliances based on the user's geographical location information. The analysis unit can also identify region-specific decorations and daily necessities based on the user's geographical location information. Furthermore, the analysis unit can identify region-specific seasonal items based on the user's geographical location information. This allows region-specific luggage to be accurately identified by taking the user's geographical location information into consideration. The geographical location information is acquired using, for example, GPS data. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to identify region-specific luggage.

[0033] When analyzing photos of a room, the analysis unit can analyze the user's social media activity and identify related luggage. For example, the analysis unit can analyze the user's social media posts and identify luggage featured in the posts. The analysis unit can also identify related luggage based on the user's social media interests. Furthermore, the analysis unit can analyze posts from the user's social media followers and friends to identify related luggage. This allows for accurate identification of related luggage by analyzing the user's social media activity. The analysis of social media activity is performed using, for example, natural language processing technology or image recognition technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's social media data into a generation AI and cause the generation AI to identify related luggage.

[0034] The generation unit can incorporate technology for automatically measuring room dimensions when generating a layout drawing. For example, the generation unit can automatically measure dimensions from a photo of the room and reflect them in the layout drawing. The generation unit can also automatically set reference points for measuring room dimensions to generate an accurate layout drawing. Furthermore, the generation unit can analyze multiple photos used to measure room dimensions to improve accuracy. This allows for automatic measurement of room dimensions to generate an accurate layout drawing. The technology for automatically measuring room dimensions can be realized using, for example, laser measurement or image analysis technology. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input photo data of the room into the generation AI and have the generation AI measure the dimensions.

[0035] When generating a layout drawing, the generation unit can learn and optimize furniture arrangement patterns from past data. The generation unit, for example, proposes an optimal furniture arrangement pattern based on past layout data. The generation unit can also learn past data and generate a furniture arrangement pattern that matches the user's preferences. The generation unit can also analyze past data and propose an efficient furniture arrangement pattern. In this way, by learning from past data, it is possible to propose an optimal furniture arrangement pattern. The optimization of the furniture arrangement pattern is realized, for example, using a machine learning algorithm. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input past layout data into the generation AI and cause the generation AI to optimize the furniture arrangement pattern.

[0036] When generating a layout diagram, the generation unit can propose an optimal layout based on the user's lifestyle. For example, the generation unit generates a layout diagram that centers around furniture frequently used by the user. The generation unit can also generate a layout diagram that takes into account traffic lines to suit the user's lifestyle. Furthermore, the generation unit can generate a layout diagram that optimizes storage space based on the user's lifestyle. This allows for proposing an optimal layout based on the user's lifestyle, thereby providing a layout diagram that is easy for the user to use. The lifestyle is taken into consideration based on, for example, family composition and living habits. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle data into the generation AI and have the generation AI execute the optimal layout proposal.

[0037] When generating a layout diagram, the generation unit can select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the generation unit displays a layout diagram that matches the screen size. Furthermore, if the user is using a tablet, the generation unit can display a layout diagram optimized for a large screen. Furthermore, if the user is using a personal computer, the generation unit can display a high-resolution layout diagram. This allows the optimal display method to be selected by taking the user's device information into consideration. The device information is taken into consideration based on, for example, the device's screen size and resolution. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0038] When providing an estimate, the estimation unit can improve the accuracy by referring to past estimation data. For example, the estimation unit provides an estimate under similar conditions based on past estimation data. The estimation unit can also analyze past estimation data to improve the accuracy of the estimate. Furthermore, the estimation unit can also refer to past estimation data to optimize an estimate for specific conditions. In this way, the accuracy of the estimate is improved by referring to past estimation data. The past estimation data is stored, for example, in a database, and the estimation unit utilizes the past data by referring to this database. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input past estimation data into a generation AI and have the generation AI improve the accuracy of the estimate.

[0039] When providing an estimate, the estimating unit can apply a different estimating algorithm to each type of luggage. For example, the estimating unit can apply an algorithm that takes into account the difficulty of dismantling and transportation to estimate furniture. The estimating unit can also apply an algorithm that takes into account the difficulty of installation and removal to estimate home appliances. Furthermore, the estimating unit can also apply an algorithm that takes into account the difficulty of packing and transportation to estimate clothing. In this way, by applying a different estimating algorithm to each type of luggage, accurate estimates can be provided. The estimating algorithm is realized, for example, using a machine learning algorithm. Some or all of the above-mentioned processing in the estimating unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimating unit can input a different estimating algorithm for each type of luggage into the generating AI and have the generating AI perform the calculation of the estimate.

[0040] When providing an estimate, the estimating unit can reflect region-specific costs by taking into account the user's geographical location information. For example, the estimating unit can reflect region-specific waste disposal costs based on the user's geographical location information. The estimating unit can also reflect region-specific work costs based on the user's geographical location information. Furthermore, the estimating unit can reflect region-specific transportation costs based on the user's geographical location information. This allows region-specific costs to be accurately reflected by taking the user's geographical location information into account. Region-specific costs are reflected based on, for example, local prices and transportation costs. Some or all of the above-described processing in the estimating unit may be performed using AI, or may be performed without AI. For example, the estimating unit can input the user's geographical location information into the generating AI and cause the generating AI to calculate region-specific costs.

[0041] The estimation unit may analyze the user's social media activity and reflect related expenses when providing an estimate. For example, the estimation unit may analyze the user's social media posts and reflect related expenses in the estimate. The estimation unit may also reflect related expenses based on the user's social media interests. Furthermore, the estimation unit may analyze posts from the user's social media followers and friends and reflect related expenses in the estimate. This allows the analysis of the user's social media activity to accurately reflect related expenses. The analysis of social media activity may be performed using, for example, natural language processing technology or image recognition technology. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without AI. For example, the estimation unit may input the user's social media data into a generation AI and cause the generation AI to calculate related expenses.

[0042] When presenting the reason for estimation, the presentation unit can improve accuracy by referring to past presentation data. For example, the presentation unit provides a reason for estimation under similar conditions based on past presentation data. The presentation unit can also analyze past presentation data to improve the accuracy of the reason for estimation. Furthermore, the presentation unit can also refer to past presentation data to optimize the reason for estimation for specific conditions. In this way, the accuracy of the reason for estimation is improved by referring to past presentation data. The past presentation data is stored, for example, in a database, and the presentation unit utilizes the past data by referring to this database. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input past presentation data into a generation AI and cause the generation AI to improve the accuracy of the reason for estimation.

[0043] When presenting the reason for the estimate, the presentation unit can explain in detail the reasons that differ for each type of luggage. For example, the presentation unit can explain in detail the difficulty of dismantling and transportation when providing an estimate for furniture. Furthermore, the presentation unit can also explain in detail the difficulty of installation and removal when providing an estimate for home appliances. Furthermore, the presentation unit can also explain in detail the difficulty of packing and transportation when providing an estimate for clothing. In this way, by explaining the reasons that differ for each type of luggage in detail, it is possible to provide a reason for the estimate that is convincing to the user. The explanation of the reasons that differ for each type of luggage is based on, for example, the characteristics of the luggage and a breakdown of the costs. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the reasons that differ for each type of luggage into a generation AI and cause the generation AI to explain the reasons for the estimate.

[0044] When presenting the estimate reason, the presentation unit can reflect region-specific reasons by taking into account the user's geographical location information. The presentation unit, for example, reflects region-specific waste disposal reasons based on the user's geographical location information. The presentation unit can also reflect region-specific work reasons based on the user's geographical location information. Furthermore, the presentation unit can also reflect region-specific transportation reasons based on the user's geographical location information. In this way, region-specific reasons can be accurately reflected by taking the user's geographical location information into consideration. Region-specific reasons are reflected based on, for example, the characteristics of each region and a breakdown of costs. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit can input the user's geographical location information to the generation AI and cause the generation AI to explain region-specific reasons.

[0045] The presentation unit may analyze the user's social media activity and reflect the relevant reasons when presenting the estimate reasons. The presentation unit may, for example, analyze the user's social media posts and reflect the relevant reasons in the estimate. The presentation unit may also reflect the relevant reasons in the estimate based on the user's social media interests. Furthermore, the presentation unit may analyze the posts of the user's social media followers and friends and reflect the relevant reasons in the estimate. In this way, by analyzing the user's social media activity, the relevant reasons can be accurately reflected. The analysis of social media activity is performed using, for example, natural language processing technology or image recognition technology. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit may input the user's social media data into a generation AI and cause the generation AI to explain the relevant reasons.

[0046] When centralizing comparative estimates, the comparison unit can improve accuracy by referring to past comparison data. For example, the comparison unit provides comparison results under similar conditions based on past comparison data. The comparison unit can also analyze past comparison data to improve the accuracy of the comparison results. Furthermore, the comparison unit can also refer to past comparison data to optimize comparison results for specific conditions. In this way, the accuracy of the comparison results is improved by referring to past comparison data. The past comparison data is stored, for example, in a database, and the comparison unit utilizes the past data by referring to this database. Some or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input past comparison data into a generation AI and have the generation AI improve the accuracy of the comparison results.

[0047] When centralizing comparative estimates, the comparison unit can select the optimal comparison method based on the characteristics of each provider. The comparison unit selects the optimal comparison method, for example, based on the service content of each provider. The comparison unit can also select the optimal comparison method based on the pricing of each provider. Furthermore, the comparison unit can select the optimal comparison method based on the evaluation of each provider. In this way, the optimal comparison method can be selected by taking into account the characteristics of each provider. The characteristics of each provider can be taken into account based on, for example, the reputation of the provider and the type of service provided. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input characteristic data of each provider into the generation AI and have the generation AI select the optimal comparison method.

[0048] When centralizing comparative estimates, the comparison unit can prioritize region-specific vendors by taking into account the user's geographical location information. The comparison unit, for example, prioritizes and displays region-specific vendors based on the user's geographical location information. The comparison unit can also prioritize vendors that provide region-specific services based on the user's geographical location information. The comparison unit can also prioritize vendors that offer region-specific pricing based on the user's geographical location information. This allows region-specific vendors to be prioritized and displayed by taking into account the user's geographical location information. The priority of region-specific vendors is based on, for example, a list of vendors for each region or their reputations. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the user's geographical location information into the generation AI and cause the generation AI to prioritize and display region-specific vendors.

[0049] When centralizing comparative estimates, the comparison unit can analyze the user's social media activity and prioritize relevant vendors. For example, the comparison unit can analyze the user's social media posts and prioritize relevant vendors. The comparison unit can also prioritize relevant vendors based on the user's social media interests. Furthermore, the comparison unit can analyze the posts of the user's social media followers and friends and prioritize relevant vendors. In this way, by analyzing the user's social media activity, relevant vendors can be prioritized and displayed. The analysis of social media activity can be performed using, for example, natural language processing technology or image recognition technology. Some or all of the above-mentioned processing in the comparison unit can be performed using, for example, AI, or without AI. For example, the comparison unit can input the user's social media data into the generation AI and cause the generation AI to prioritize and display relevant vendors.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can adjust the analysis algorithm based on the resolution of the photo provided by the user. For example, for low-resolution photos, the analysis unit can apply a simplified algorithm to quickly provide analysis results. For high-resolution photos, the analysis unit can apply a detailed analysis algorithm to provide highly accurate analysis results. Furthermore, for photos with medium resolution, a balanced analysis algorithm can be applied. This allows the analysis algorithm to be adjusted according to the resolution of the photo, thereby providing optimal analysis results. The adjustment of the analysis algorithm is achieved using, for example, image processing technology or a machine learning algorithm. 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. For example, the analysis unit can input photo resolution data into the generation AI and have the generation AI adjust the analysis algorithm.

[0052] The generation unit can adjust the generation of the layout diagram based on the shooting date and time of the photo provided by the user. For example, for photos taken during the day, a layout diagram taking natural light into consideration can be generated. Also, for photos taken at night, a layout diagram taking artificial lighting into consideration can be generated. Furthermore, a layout diagram that reflects a seasonal feel can be generated based on the shooting date and time of each season. In this way, by adjusting the generation of the layout diagram according to the shooting date and time of the photo, a more realistic layout diagram can be provided. The shooting date and time can be taken into consideration using, for example, photo metadata. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI. For example, the generation unit can input the shooting date and time data of the photo to the generation AI and cause the generation AI to generate the layout diagram.

[0053] The estimating unit can adjust the range of the estimate based on the location of the photo provided by the user. For example, for a photo taken in an urban area, an estimate that takes city-specific costs into account can be provided. For a photo taken in a suburban area, an estimate that takes suburban-specific costs into account can be provided. Furthermore, for a photo taken in a resort area, an estimate that takes resort-specific costs into account can be provided. This allows for adjusting the range of the estimate depending on the location of the photo, making it possible to provide a more accurate estimate. The consideration of the shooting location is achieved, for example, using metadata or GPS data of the photo. Some or all of the above-mentioned processing in the estimating unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimating unit can input photo shooting location data into the generating AI and cause the generating AI to adjust the range of the estimate.

[0054] The presentation unit can adjust the level of detail of the estimate reason based on the content of the photo provided by the user. For example, if a photo shows a lot of furniture, a detailed estimate reason can be provided. Furthermore, if a photo shows a small amount of luggage, a simplified estimate reason can be provided. Furthermore, if a photo shows a special type of luggage, an estimate reason specific to that luggage can be provided. By adjusting the level of detail of the estimate reason according to the content of the photo, it is possible to provide an estimate reason that is easy for the user to understand. Consideration of the content of the photo can be achieved, for example, using image recognition technology or a machine learning algorithm. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input content data of the photo into a generation AI and cause the generation AI to adjust the level of detail of the estimate reason.

[0055] The comparison unit can adjust the display method of the comparison results based on the shooting angle of the photo provided by the user. For example, for a photo taken with a wide angle, the comparison results can be displayed with an emphasis on the overall image. For a photo taken with a close-up, the comparison results can be displayed with an emphasis on the details. Furthermore, for a photo taken from an oblique angle, the comparison results can be displayed with a consideration of the three-dimensional effect. By adjusting the display method of the comparison results according to the shooting angle of the photo, it is possible to provide comparison results that are easy for the user to view. The shooting angle can be taken into consideration using, for example, photo metadata or image analysis technology. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the shooting angle data of the photo to the generation AI and cause the generation AI to adjust the display method of the comparison results.

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

[0057] Step 1: The analysis unit analyzes photos of the room provided by the user and identifies the type and quantity of luggage. Using image recognition algorithms and machine learning models, the analysis unit identifies objects in the photos and determines the type and quantity of each object. For example, it identifies the type of furniture, home appliances, clothing, etc., and calculates the quantity of each. It can also refer to past analysis data to improve the accuracy of identifying the type and quantity of luggage. Step 2: The generation unit generates a room layout diagram based on the information identified by the analysis unit. The generation unit automatically generates a layout diagram based on the room dimensions and the placement of luggage. It can also estimate the user's emotions and adjust the way the layout diagram is displayed based on the estimated emotions. For example, if the user is feeling stressed, a simple layout diagram is displayed, and if the user is relaxed, a detailed layout diagram is displayed. Step 3: The Estimation Unit provides an estimate based on the layout diagram generated by the Generation Unit. The Estimation Unit applies different estimation algorithms to each type of cargo to provide an accurate estimate. It can also refer to past estimation data to improve the accuracy of the estimate. Step 4: The presentation unit presents the estimate reasons provided by the estimation unit. The presentation unit provides detailed explanations of the different reasons for each type of package, and provides estimate reasons that are convincing to the user. The presentation unit can also estimate the user's emotions and adjust the display method of the estimate reasons based on the estimated emotions. Step 5: The comparison unit unifies comparative quotes from multiple vendors. The comparison unit selects the optimal comparison method taking into account the characteristics of each vendor and provides comparison results that are easy for users to understand. It is also possible to improve the accuracy of the comparison results by referring to past comparison data.

[0058] (Example 2) A system according to an embodiment of the present invention analyzes photos of a room provided by a user and automatically identifies the type and quantity of luggage. The system begins by having the user provide a photo of the room and input it into the system. Next, the system uses AI to analyze the photo and identify the type and quantity of luggage. For example, the system identifies the types of furniture, appliances, clothing, and the quantities of each. Next, the system generates a room layout diagram based on the identified information. The layout diagram includes the layout of the luggage and the dimensions of the room. This allows users to grasp the overall picture of the room and facilitates work planning. The system then automatically provides an estimate. The estimate takes into account factors such as waste disposal costs and work difficulty. For example, if large furniture needs to be dismantled and transported, the estimate reflects the difficulty of the work. Furthermore, the system presents the reasons for the estimate, making it easier for users to understand the breakdown of the estimate. For example, it explains how the amount of waste and work difficulty affect the estimate. Finally, the system centralizes comparative estimates from multiple contractors. This allows users to easily compare estimates from multiple contractors. For example, users can see each contractor's estimated price and service details at a glance. This system allows users to easily obtain estimates for room cleaning and moving, improving transparency and convenience. The system analyzes photos of the room provided by the user, identifies the type and amount of luggage, generates a layout diagram, provides an estimate, explains the reasons for the estimate, and centralizes comparative estimates from multiple companies, improving transparency and convenience.

[0059] The system according to the embodiment includes an analysis unit, a generation unit, an estimation unit, a presentation unit, and a comparison unit. The analysis unit analyzes a photo of a room provided by a user and identifies the type and amount of luggage. For example, the analysis unit uses an image recognition algorithm to identify objects in the photo and determine the type and amount of each object. The analysis unit can also use a machine learning model to classify objects in the photo and identify the type and amount of luggage. For example, the analysis unit identifies types of furniture, home appliances, clothing, etc., and calculates the amount of each. The analysis unit can also improve the accuracy of identifying the type and amount of luggage by referring to past analysis data. The generation unit generates a room layout diagram based on the information identified by the analysis unit. The generation unit automatically generates the layout diagram based on, for example, the dimensions of the room and the arrangement of the luggage. The generation unit can also estimate the user's emotions and adjust the display method of the layout diagram based on the estimated user's emotions. For example, if the user is stressed, a simple layout diagram is displayed, and if the user is relaxed, a detailed layout diagram is displayed. The estimation unit provides an estimate based on the layout diagram generated by the generation unit. The estimating unit, for example, applies different estimation algorithms for each type of luggage to provide an accurate estimate. The estimating unit can also refer to past estimation data to improve the accuracy of the estimate. The presentation unit presents the reasons for the estimate provided by the estimating unit. For example, the presentation unit can provide a convincing reason for the estimate by explaining in detail the different reasons for each type of luggage. The presentation unit can also estimate the user's emotions and adjust the display method of the reasons for the estimate based on the estimated user emotions. The comparison unit unifies comparative estimates from multiple providers. For example, the comparison unit selects an optimal comparison method taking into account the characteristics of each provider to provide comparison results that are easy for the user to understand. The comparison unit can also refer to past comparison data to improve the accuracy of the comparison results. As a result, the system according to the embodiment analyzes photos of the room provided by the user, identifies the type and quantity of luggage, generates a layout diagram, provides an estimate, presents the reasons for the estimate, and unifies comparative estimates from multiple providers, thereby improving transparency and convenience.

[0060] The analysis unit can estimate the user's emotions and adjust the timing of photo analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can quickly perform photo analysis and provide results quickly. Alternatively, if the user is relaxed, the analysis unit can slowly perform photo analysis and provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can immediately start photo analysis and provide results in the shortest time possible. This allows the timing of photo analysis to be adjusted according to the user's emotions, reducing the user's stress and enabling analysis at an appropriate time. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0061] When analyzing a photo of a room, the analysis unit can apply filtering technology to remove the effects of reflected light and shadows. For example, the analysis unit can apply a filter to remove reflected light to the photo of the room to accurately identify the type and quantity of luggage. Furthermore, if a shadow is cast on the photo of the room, the analysis unit can apply a filter to remove the shadow to accurately identify the shape of the luggage. Furthermore, the analysis unit can apply a composite filter to the photo of the room to remove both reflected light and shadows to improve the accuracy of the analysis. This allows the effects of reflected light and shadows to be removed, thereby accurately identifying the type and quantity of luggage. The filtering technology is implemented, for example, using an image processing algorithm. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input photo data of the room into the generation AI and have the generation AI perform a filtering process to remove the effects of reflected light and shadows.

[0062] The analysis unit can improve accuracy when identifying the type and quantity of luggage by referring to past analysis data. For example, the analysis unit can refer to data analyzed in the past to identify the type and quantity of similar luggage. The analysis unit can also learn the characteristics of specific luggage based on the past analysis data and improve analysis accuracy. Furthermore, the analysis unit can statistically analyze past analysis data to help identify the type and quantity of luggage. In this way, by referring to the past analysis data, the accuracy of identifying the type and quantity of luggage is improved. The past analysis data is stored, for example, in a database, and the analysis unit utilizes the past data by referring to this database. 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. For example, the analysis unit can input past analysis data into a generation AI and have the generation AI identify the type and quantity of luggage.

[0063] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying important analysis results. Furthermore, if the user is relaxed, the analysis unit can sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display the most important analysis results first. By prioritizing the analysis results according to the user's emotions, information important to the user can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0064] When analyzing the room photo, the analysis unit can identify region-specific luggage based on the user's geographical location information. For example, the analysis unit can identify region-specific furniture and home appliances based on the user's geographical location information. The analysis unit can also identify region-specific decorations and daily necessities based on the user's geographical location information. Furthermore, the analysis unit can identify region-specific seasonal items based on the user's geographical location information. This allows region-specific luggage to be accurately identified by taking the user's geographical location information into consideration. The geographical location information is acquired using, for example, GPS data. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to identify region-specific luggage.

[0065] When analyzing photos of a room, the analysis unit can analyze the user's social media activity and identify related luggage. For example, the analysis unit can analyze the user's social media posts and identify luggage featured in the posts. The analysis unit can also identify related luggage based on the user's social media interests. Furthermore, the analysis unit can analyze posts from the user's social media followers and friends to identify related luggage. This allows for accurate identification of related luggage by analyzing the user's social media activity. The analysis of social media activity is performed using, for example, natural language processing technology or image recognition technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's social media data into a generation AI and cause the generation AI to identify related luggage.

[0066] The generation unit can estimate the user's emotions and adjust the display method of the layout diagram based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can display a simple layout diagram. Furthermore, if the user is relaxed, the generation unit can display a detailed layout diagram. Furthermore, if the user is in a hurry, the generation unit can display a layout diagram with important parts highlighted. By adjusting the display method of the layout diagram according to the user's emotions, it is possible to provide a layout diagram that is easy for the user to view. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0067] The generation unit can incorporate technology for automatically measuring room dimensions when generating a layout drawing. For example, the generation unit can automatically measure dimensions from a photo of the room and reflect them in the layout drawing. The generation unit can also automatically set reference points for measuring room dimensions to generate an accurate layout drawing. Furthermore, the generation unit can analyze multiple photos used to measure room dimensions to improve accuracy. This allows for automatic measurement of room dimensions to generate an accurate layout drawing. The technology for automatically measuring room dimensions can be realized using, for example, laser measurement or image analysis technology. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input photo data of the room into the generation AI and have the generation AI measure the dimensions.

[0068] When generating a layout drawing, the generation unit can learn and optimize furniture arrangement patterns from past data. The generation unit, for example, proposes an optimal furniture arrangement pattern based on past layout data. The generation unit can also learn past data and generate a furniture arrangement pattern that matches the user's preferences. The generation unit can also analyze past data and propose an efficient furniture arrangement pattern. In this way, by learning from past data, it is possible to propose an optimal furniture arrangement pattern. The optimization of the furniture arrangement pattern is realized, for example, using a machine learning algorithm. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input past layout data into the generation AI and cause the generation AI to optimize the furniture arrangement pattern.

[0069] The generation unit can estimate the user's emotions and adjust the level of detail of the layout diagram based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can display a simple layout diagram. Furthermore, if the user is relaxed, the generation unit can display a detailed layout diagram. Furthermore, if the user is in a hurry, the generation unit can display a layout diagram that highlights important parts. By adjusting the level of detail of the layout diagram according to the user's emotions, it is possible to provide a layout diagram that is appropriate for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0070] When generating a layout diagram, the generation unit can propose an optimal layout based on the user's lifestyle. For example, the generation unit generates a layout diagram that centers around furniture frequently used by the user. The generation unit can also generate a layout diagram that takes into account traffic lines to suit the user's lifestyle. Furthermore, the generation unit can generate a layout diagram that optimizes storage space based on the user's lifestyle. This allows for proposing an optimal layout based on the user's lifestyle, thereby providing a layout diagram that is easy for the user to use. The lifestyle is taken into consideration based on, for example, family composition and living habits. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle data into the generation AI and have the generation AI execute the optimal layout proposal.

[0071] When generating a layout diagram, the generation unit can select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the generation unit displays a layout diagram that matches the screen size. Furthermore, if the user is using a tablet, the generation unit can display a layout diagram optimized for a large screen. Furthermore, if the user is using a personal computer, the generation unit can display a high-resolution layout diagram. This allows the optimal display method to be selected by taking the user's device information into consideration. The device information is taken into consideration based on, for example, the device's screen size and resolution. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0072] The estimation unit can estimate the user's emotions and adjust the way the estimate is presented based on the estimated user's emotions. For example, if the user is stressed, the estimation unit can present a simple, easy-to-understand estimate. Furthermore, if the user is relaxed, the estimation unit can present a detailed estimate. Furthermore, if the user is in a hurry, the estimation unit can present an estimate that focuses on the main points. By adjusting the way the estimate is presented according to the user's emotions, an estimate that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, 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 such examples. Some or all of the above-described processing in the estimation unit can be performed using, for example, AI, or without AI. For example, the estimation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] When providing an estimate, the estimation unit can improve the accuracy by referring to past estimation data. For example, the estimation unit provides an estimate under similar conditions based on past estimation data. The estimation unit can also analyze past estimation data to improve the accuracy of the estimate. Furthermore, the estimation unit can also refer to past estimation data to optimize an estimate for specific conditions. In this way, the accuracy of the estimate is improved by referring to past estimation data. The past estimation data is stored, for example, in a database, and the estimation unit utilizes the past data by referring to this database. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input past estimation data into a generation AI and have the generation AI improve the accuracy of the estimate.

[0074] When providing an estimate, the estimating unit can apply a different estimating algorithm to each type of luggage. For example, the estimating unit can apply an algorithm that takes into account the difficulty of dismantling and transportation to estimate furniture. The estimating unit can also apply an algorithm that takes into account the difficulty of installation and removal to estimate home appliances. Furthermore, the estimating unit can also apply an algorithm that takes into account the difficulty of packing and transportation to estimate clothing. In this way, by applying a different estimating algorithm to each type of luggage, accurate estimates can be provided. The estimating algorithm is realized, for example, using a machine learning algorithm. Some or all of the above-mentioned processing in the estimating unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimating unit can input a different estimating algorithm for each type of luggage into the generating AI and have the generating AI perform the calculation of the estimate.

[0075] The estimation unit can estimate the user's emotions and determine the priority of estimates based on the estimated user emotions. For example, if the user is feeling stressed, the estimation unit can prioritize displaying important estimate items. Furthermore, if the user is relaxed, the estimation unit can sequentially display detailed estimate items. Furthermore, if the user is in a hurry, the estimation unit can display the most important estimate items first. Thus, by determining the priority of estimates according to the user's emotions, it is possible to provide information that is important to the user preferentially. The estimation of emotions is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the estimation unit can be performed using, for example, an AI, or without an AI. For example, the estimation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] When providing an estimate, the estimating unit can reflect region-specific costs by taking into account the user's geographical location information. For example, the estimating unit can reflect region-specific waste disposal costs based on the user's geographical location information. The estimating unit can also reflect region-specific work costs based on the user's geographical location information. Furthermore, the estimating unit can reflect region-specific transportation costs based on the user's geographical location information. This allows region-specific costs to be accurately reflected by taking the user's geographical location information into account. Region-specific costs are reflected based on, for example, local prices and transportation costs. Some or all of the above-described processing in the estimating unit may be performed using AI, or may be performed without AI. For example, the estimating unit can input the user's geographical location information into the generating AI and cause the generating AI to calculate region-specific costs.

[0077] The estimation unit may analyze the user's social media activity and reflect related expenses when providing an estimate. For example, the estimation unit may analyze the user's social media posts and reflect related expenses in the estimate. The estimation unit may also reflect related expenses based on the user's social media interests. Furthermore, the estimation unit may analyze posts from the user's social media followers and friends and reflect related expenses in the estimate. This allows the analysis of the user's social media activity to accurately reflect related expenses. The analysis of social media activity may be performed using, for example, natural language processing technology or image recognition technology. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without AI. For example, the estimation unit may input the user's social media data into a generation AI and cause the generation AI to calculate related expenses.

[0078] The presentation unit can estimate the user's emotions and adjust the display method of the estimate reason based on the estimated user emotions. For example, if the user is stressed, the presentation unit can display a simple and easy-to-understand estimate reason. Furthermore, if the user is relaxed, the presentation unit can also display a detailed estimate reason. Furthermore, if the user is in a hurry, the presentation unit can display a summary of the estimate reason. By adjusting the display method of the estimate reason according to the user's emotions, it is possible to provide an estimate reason that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] When presenting the reason for estimation, the presentation unit can improve accuracy by referring to past presentation data. For example, the presentation unit provides a reason for estimation under similar conditions based on past presentation data. The presentation unit can also analyze past presentation data to improve the accuracy of the reason for estimation. Furthermore, the presentation unit can also refer to past presentation data to optimize the reason for estimation for specific conditions. In this way, the accuracy of the reason for estimation is improved by referring to past presentation data. The past presentation data is stored, for example, in a database, and the presentation unit utilizes the past data by referring to this database. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input past presentation data into a generation AI and cause the generation AI to improve the accuracy of the reason for estimation.

[0080] When presenting the reason for the estimate, the presentation unit can explain in detail the reasons that differ for each type of luggage. For example, the presentation unit can explain in detail the difficulty of dismantling and transportation when providing an estimate for furniture. Furthermore, the presentation unit can also explain in detail the difficulty of installation and removal when providing an estimate for home appliances. Furthermore, the presentation unit can also explain in detail the difficulty of packing and transportation when providing an estimate for clothing. In this way, by explaining the reasons that differ for each type of luggage in detail, it is possible to provide a reason for the estimate that is convincing to the user. The explanation of the reasons that differ for each type of luggage is based on, for example, the characteristics of the luggage and a breakdown of the costs. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the reasons that differ for each type of luggage into a generation AI and cause the generation AI to explain the reasons for the estimate.

[0081] The presentation unit can estimate the user's emotions and prioritize the estimation reasons based on the estimated user emotions. For example, when the user is stressed, the presentation unit can prioritize displaying important estimation reasons. Furthermore, when the user is relaxed, the presentation unit can sequentially display detailed estimation reasons. Furthermore, when the user is in a hurry, the presentation unit can display the most important estimation reasons first. This prioritizes the estimation reasons according to the user's emotions, allowing for prioritized provision of information important to the user. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the presentation unit can be performed using, for example, an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0082] When presenting the estimate reason, the presentation unit can reflect region-specific reasons by taking into account the user's geographical location information. The presentation unit, for example, reflects region-specific waste disposal reasons based on the user's geographical location information. The presentation unit can also reflect region-specific work reasons based on the user's geographical location information. Furthermore, the presentation unit can also reflect region-specific transportation reasons based on the user's geographical location information. In this way, region-specific reasons can be accurately reflected by taking the user's geographical location information into consideration. Region-specific reasons are reflected based on, for example, the characteristics of each region and a breakdown of costs. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit can input the user's geographical location information to the generation AI and cause the generation AI to explain region-specific reasons.

[0083] The presentation unit may analyze the user's social media activity and reflect the relevant reasons when presenting the estimate reasons. The presentation unit may, for example, analyze the user's social media posts and reflect the relevant reasons in the estimate. The presentation unit may also reflect the relevant reasons in the estimate based on the user's social media interests. Furthermore, the presentation unit may analyze the posts of the user's social media followers and friends and reflect the relevant reasons in the estimate. In this way, by analyzing the user's social media activity, the relevant reasons can be accurately reflected. The analysis of social media activity is performed using, for example, natural language processing technology or image recognition technology. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit may input the user's social media data into a generation AI and cause the generation AI to explain the relevant reasons.

[0084] The comparison unit can estimate the user's emotion and adjust the display method of the comparison result based on the estimated user emotion. For example, if the user is stressed, the comparison unit can display a simple and easy-to-understand comparison result. Furthermore, if the user is relaxed, the comparison unit can display a detailed comparison result. Furthermore, if the user is in a hurry, the comparison unit can display a comparison result that focuses on the main points. By adjusting the display method of the comparison result according to the user's emotion, it is possible to provide a comparison result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the comparison unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the comparison unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] When centralizing comparative estimates, the comparison unit can improve accuracy by referring to past comparison data. For example, the comparison unit provides comparison results under similar conditions based on past comparison data. The comparison unit can also analyze past comparison data to improve the accuracy of the comparison results. Furthermore, the comparison unit can also refer to past comparison data to optimize comparison results for specific conditions. In this way, the accuracy of the comparison results is improved by referring to past comparison data. The past comparison data is stored, for example, in a database, and the comparison unit utilizes the past data by referring to this database. Some or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input past comparison data into a generation AI and have the generation AI improve the accuracy of the comparison results.

[0086] When centralizing comparative estimates, the comparison unit can select the optimal comparison method based on the characteristics of each provider. The comparison unit selects the optimal comparison method, for example, based on the service content of each provider. The comparison unit can also select the optimal comparison method based on the pricing of each provider. Furthermore, the comparison unit can select the optimal comparison method based on the evaluation of each provider. In this way, the optimal comparison method can be selected by taking into account the characteristics of each provider. The characteristics of each provider can be taken into account based on, for example, the reputation of the provider and the type of service provided. Some or all of the above-mentioned processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input characteristic data of each provider into the generation AI and have the generation AI select the optimal comparison method.

[0087] The comparison unit can estimate the user's emotions and prioritize the comparison results based on the estimated user emotions. For example, when the user is stressed, the comparison unit can prioritize displaying important comparison results. Furthermore, when the user is relaxed, the comparison unit can sequentially display detailed comparison results. Furthermore, when the user is in a hurry, the comparison unit can display the most important comparison results first. By prioritizing the comparison results according to the user's emotions, information important to the user can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the comparison unit can be performed using, for example, an AI. For example, the comparison unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0088] When centralizing comparative estimates, the comparison unit can prioritize region-specific vendors by taking into account the user's geographical location information. The comparison unit, for example, prioritizes and displays region-specific vendors based on the user's geographical location information. The comparison unit can also prioritize vendors that provide region-specific services based on the user's geographical location information. The comparison unit can also prioritize vendors that offer region-specific pricing based on the user's geographical location information. This allows region-specific vendors to be prioritized and displayed by taking into account the user's geographical location information. The priority of region-specific vendors is based on, for example, a list of vendors for each region or their reputations. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the user's geographical location information into the generation AI and cause the generation AI to prioritize and display region-specific vendors.

[0089] When centralizing comparative estimates, the comparison unit can analyze the user's social media activity and prioritize relevant vendors. For example, the comparison unit can analyze the user's social media posts and prioritize relevant vendors. The comparison unit can also prioritize relevant vendors based on the user's social media interests. Furthermore, the comparison unit can analyze the posts of the user's social media followers and friends and prioritize relevant vendors. In this way, by analyzing the user's social media activity, relevant vendors can be prioritized and displayed. The analysis of social media activity can be performed using, for example, natural language processing technology or image recognition technology. Some or all of the above-mentioned processing in the comparison unit can be performed using, for example, AI, or without AI. For example, the comparison unit can input the user's social media data into the generation AI and cause the generation AI to prioritize and display relevant vendors. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, estimation unit, presentation unit, and comparison unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by acquiring a photo of the room using the camera 42 of the smart device 14 and executing an image recognition algorithm by the specific processing unit 290 of the data processing device 12. The generation unit generates a room layout diagram by the specific processing unit 290 of the data processing device 12. The estimation unit provides an estimate by the specific processing unit 290 of the data processing device 12. The presentation unit displays the reason for the estimate using the display 40A of the smart device 14. The comparison unit unifies estimates from multiple contractors 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 analysis unit, generation unit, estimation unit, presentation unit, and comparison unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by acquiring a photo of the room using the camera 42 of the smart glasses 214 and executing an image recognition algorithm by the specific processing unit 290 of the data processing device 12. The generation unit generates a room layout diagram by the specific processing unit 290 of the data processing device 12. The estimation unit provides an estimate by the specific processing unit 290 of the data processing device 12. The presentation unit displays the reason for the estimate using the display of the smart glasses 214. The comparison unit unifies estimates from multiple vendors 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 analysis unit, generation unit, estimation unit, presentation unit, and comparison unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by taking a photo of the room using the camera 42 of the headset type terminal 314 and executing an image recognition algorithm by the specific processing unit 290 of the data processing device 12. The generation unit generates a room layout diagram by the specific processing unit 290 of the data processing device 12. The estimation unit provides an estimate by the specific processing unit 290 of the data processing device 12. The presentation unit displays the reason for the estimate using the display 343 of the headset type terminal 314. The comparison unit unifies estimates from multiple contractors 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 analysis unit, generation unit, estimation unit, presentation unit, and comparison unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by acquiring a photo of the room using the camera 42 of the robot 414 and executing an image recognition algorithm by the specific processing unit 290 of the data processing device 12. The generation unit generates a room layout diagram by the specific processing unit 290 of the data processing device 12. The estimation unit provides an estimate by the specific processing unit 290 of the data processing device 12. The presentation unit displays the reason for the estimate using the display of the robot 414. The comparison unit unifies estimates from multiple contractors by the specific processing unit 290 of the data processing device 12.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can estimate the user's health condition and adjust the accuracy of the photo analysis based on the estimated health condition. For example, if the user is tired, the analysis unit can quickly perform photo analysis and provide results quickly. Also, if the user is healthy, detailed analysis results can be provided. Furthermore, if the user is ill, the analysis unit can immediately start photo analysis and provide results in the shortest time possible. This reduces the burden on the user by adjusting the accuracy of photo analysis according to the user's health condition, and allows analysis to be performed at an appropriate time. The health condition estimation is realized, for example, using health data or data from a wearable device. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's health data into the generation AI and have the generation AI perform a health condition estimation.

[0092] The generation unit can estimate the user's hobbies and preferences and adjust the design of the layout chart based on the estimated hobbies and preferences. For example, if the user is a minimalist, a simple layout chart can be displayed. If the user is an art lover, a layout chart emphasizing artwork can be displayed. Furthermore, if the user is a technology lover, a layout chart featuring the latest gadgets can be displayed. By adjusting the design of the layout chart according to the user's hobbies and preferences, it is possible to provide a layout chart that is attractive to the user. Hobbies and preferences can be estimated using, for example, social media data or past purchase history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's hobbies and preferences data into the generation AI and have the generation AI adjust the design.

[0093] The estimation unit can estimate the user's budget and adjust the range of the estimate based on the estimated budget. For example, if the user's budget is limited, the estimation unit can provide a cost-effective estimate. Alternatively, if the user has a sufficient budget, the estimation unit can provide a detailed estimate. Furthermore, if the user desires expensive services, the estimation unit can provide an estimate that includes high-quality services. By adjusting the range of the estimate according to the user's budget, the optimal estimate can be provided for the user. The budget estimation is realized, for example, using past expenditure data and income data. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without AI. For example, the estimation unit can input the user's budget data into a generation AI and have the generation AI adjust the range of the estimate.

[0094] The presentation unit can estimate the user's learning style and adjust the method of explaining the reason for the estimate based on the estimated learning style. For example, if the user is a visual learner, the presentation unit can display the reason for the estimate using graphs or diagrams. If the user is an auditory learner, the presentation unit can provide an audio explanation. If the user is an experiential learner, the presentation unit can provide an interactive explanation. By adjusting the method of explaining the reason for the estimate according to the user's learning style, the presentation unit can provide the reason for the estimate that is easy for the user to understand. The learning style can be estimated using, for example, past learning data or survey results. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input the user's learning style data into a generation AI and have the generation AI adjust the explanation method.

[0095] The comparison unit can refer to the user's past selection history and adjust the display method of the comparison results based on the estimated selection tendency. For example, if the user has previously prioritized price in their selections, the comparison unit can display comparison results that emphasize price. Furthermore, if the user has previously prioritized quality in their selections, the comparison unit can display comparison results that emphasize quality. Furthermore, if the user has previously prioritized balance in their selections, the comparison unit can display comparison results that take into account the balance between price and quality. By adjusting the display method of the comparison results according to the user's past selection history, it is possible to provide the user with optimal comparison results. The selection history can be referenced using, for example, past purchase data or selection data. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without AI. For example, the comparison unit can input the user's selection history data into a generation AI and have the generation AI adjust the display method.

[0096] The analysis unit can adjust the analysis algorithm based on the resolution of the photo provided by the user. For example, for low-resolution photos, the analysis unit can apply a simplified algorithm to quickly provide analysis results. For high-resolution photos, the analysis unit can apply a detailed analysis algorithm to provide highly accurate analysis results. Furthermore, for photos with medium resolution, a balanced analysis algorithm can be applied. This allows the analysis algorithm to be adjusted according to the resolution of the photo, thereby providing optimal analysis results. The adjustment of the analysis algorithm is achieved using, for example, image processing technology or a machine learning algorithm. 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. For example, the analysis unit can input photo resolution data into the generation AI and have the generation AI adjust the analysis algorithm.

[0097] The generation unit can adjust the generation of the layout diagram based on the shooting date and time of the photo provided by the user. For example, for photos taken during the day, a layout diagram taking natural light into consideration can be generated. Also, for photos taken at night, a layout diagram taking artificial lighting into consideration can be generated. Furthermore, a layout diagram that reflects a seasonal feel can be generated based on the shooting date and time of each season. In this way, by adjusting the generation of the layout diagram according to the shooting date and time of the photo, a more realistic layout diagram can be provided. The shooting date and time can be taken into consideration using, for example, photo metadata. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI. For example, the generation unit can input the shooting date and time data of the photo to the generation AI and cause the generation AI to generate the layout diagram.

[0098] The estimating unit can adjust the range of the estimate based on the location of the photo provided by the user. For example, for a photo taken in an urban area, an estimate that takes city-specific costs into account can be provided. For a photo taken in a suburban area, an estimate that takes suburban-specific costs into account can be provided. Furthermore, for a photo taken in a resort area, an estimate that takes resort-specific costs into account can be provided. This allows for adjusting the range of the estimate depending on the location of the photo, making it possible to provide a more accurate estimate. The consideration of the shooting location is achieved, for example, using metadata or GPS data of the photo. Some or all of the above-mentioned processing in the estimating unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimating unit can input photo shooting location data into the generating AI and cause the generating AI to adjust the range of the estimate.

[0099] The presentation unit can adjust the level of detail of the estimate reason based on the content of the photo provided by the user. For example, if a photo shows a lot of furniture, a detailed estimate reason can be provided. Furthermore, if a photo shows a small amount of luggage, a simplified estimate reason can be provided. Furthermore, if a photo shows a special type of luggage, an estimate reason specific to that luggage can be provided. By adjusting the level of detail of the estimate reason according to the content of the photo, it is possible to provide an estimate reason that is easy for the user to understand. Consideration of the content of the photo can be achieved, for example, using image recognition technology or a machine learning algorithm. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input content data of the photo into a generation AI and cause the generation AI to adjust the level of detail of the estimate reason.

[0100] The comparison unit can adjust the display method of the comparison results based on the shooting angle of the photo provided by the user. For example, for a photo taken with a wide angle, the comparison results can be displayed with an emphasis on the overall image. For a photo taken with a close-up, the comparison results can be displayed with an emphasis on the details. Furthermore, for a photo taken from an oblique angle, the comparison results can be displayed with a consideration of the three-dimensional effect. By adjusting the display method of the comparison results according to the shooting angle of the photo, it is possible to provide comparison results that are easy for the user to view. The shooting angle can be taken into consideration using, for example, photo metadata or image analysis technology. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI. For example, the comparison unit can input the shooting angle data of the photo to the generation AI and cause the generation AI to adjust the display method of the comparison results.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The analysis unit analyzes photos of the room provided by the user and identifies the type and quantity of luggage. Using image recognition algorithms and machine learning models, the analysis unit identifies objects in the photos and determines the type and quantity of each object. For example, it identifies the type of furniture, home appliances, clothing, etc., and calculates the quantity of each. It can also refer to past analysis data to improve the accuracy of identifying the type and quantity of luggage. Step 2: The generation unit generates a room layout diagram based on the information identified by the analysis unit. The generation unit automatically generates a layout diagram based on the room dimensions and the placement of luggage. It can also estimate the user's emotions and adjust the way the layout diagram is displayed based on the estimated emotions. For example, if the user is feeling stressed, a simple layout diagram is displayed, and if the user is relaxed, a detailed layout diagram is displayed. Step 3: The Estimation Unit provides an estimate based on the layout diagram generated by the Generation Unit. The Estimation Unit applies different estimation algorithms to each type of cargo to provide an accurate estimate. It can also refer to past estimation data to improve the accuracy of the estimate. Step 4: The presentation unit presents the estimate reasons provided by the estimation unit. The presentation unit provides detailed explanations of the different reasons for each type of package, and provides estimate reasons that are convincing to the user. The presentation unit can also estimate the user's emotions and adjust the display method of the estimate reasons based on the estimated emotions. Step 5: The comparison unit unifies comparative quotes from multiple vendors. The comparison unit selects the optimal comparison method taking into account the characteristics of each vendor and provides comparison results that are easy for users to understand. It is also possible to improve the accuracy of the comparison results by referring to past comparison data.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes a photo of the room provided by the user and identifies the type and amount of luggage; a generation unit that generates a room layout diagram based on the information identified by the analysis unit; an estimation unit that provides an estimate based on the layout drawing generated by the generation unit; a presentation unit that presents the estimate reason provided by the estimation unit; A comparison unit that unifies comparative estimates from multiple vendors. A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the timing of photo analysis based on the estimated user emotions. The system of claim 1 .

3. The analysis unit Applying filtering techniques to remove the effects of light reflections and shadows when analyzing photos of rooms The system of claim 1 .

4. The analysis unit Referencing past analysis data to improve accuracy when identifying package types and quantities The system of claim 1 .

5. The analysis unit Estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The system of claim 1 .

6. The analysis unit Identifying region-specific luggage based on the user's geographic location when analyzing photos of the room The system of claim 1 .

7. The analysis unit When analyzing photos of a room, the system analyzes the user's social media activity to identify related luggage. The system of claim 1 .

8. The generation unit Estimates user emotions and adjusts the layout diagram display method based on the estimated user emotions. The system of claim 1 .

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