system

Abstract

The system according to the embodiment aims to optimally suggest clothes for a user to wear every day, taking into consideration information about the weather and destination. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an acquisition unit, a linkage unit, a suggestion unit, and another linkage unit. The reception unit inputs a photo of clothes owned by the user. The analysis unit analyzes the photo of the clothes input by the reception unit. The acquisition unit automatically acquires weather information. The linkage unit automatically acquires destination information. The suggestion unit suggests clothes to wear today based on the information obtained by the analysis unit, acquisition unit, and linkage unit. The linkage unit makes the clothes suggested by the suggestion unit available for ordering on an e-commerce site.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to provide optimal suggestions that take into account weather and destination information when users choose clothes to wear every day.

[0005] The system according to the embodiment aims to optimally suggest clothes for a user to wear every day, taking into consideration information about the weather and destination. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an acquisition unit, a collaboration unit, a suggestion unit, and another collaboration unit. The reception unit inputs a photo of clothes owned by the user. The analysis unit analyzes the photo of the clothes input by the reception unit. The acquisition unit automatically acquires weather information. The collaboration unit automatically acquires destination information. The suggestion unit suggests clothes to wear today based on the information obtained by the analysis unit, acquisition unit, and collaboration unit. The collaboration unit makes the clothes suggested by the suggestion unit available for ordering on an e-commerce site. [Effects of the Invention]

[0007] The system according to the embodiment can optimally suggest clothes for the user to wear every day, taking into account information about the weather and destination. [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 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) An embodiment of the clothing recommendation system of the present invention uses a generative AI to provide advice on what to wear every day. This clothing recommendation system inputs photos of the clothes a user owns, information from a weather information website, and a destination (if necessary), and then suggests the best outfits to wear that day. Furthermore, the generative AI determines the next shopping timing based on information such as current trends and the user's favorite clothing, enabling the user to place an order on an e-commerce website. Payment is made using an electronic payment system. For example, a user inputs photos of the clothes they own. Detailed information about each item (e.g., color, design, material) is also input. For example, a user takes photos of their shirts, pants, jackets, etc. and uploads them to the system. Next, information from a weather information website is input. For example, today's weather forecast (e.g., temperature, probability of precipitation, wind speed) is input into the system. This allows the system to suggest outfits appropriate for the weather. Furthermore, destination information is input. For example, if a user is going to work, the system can suggest outfits appropriate for the office dress code. Similarly, if a user is attending a casual event, the system can suggest outfits appropriate for the occasion. The generative AI analyzes this information and suggests the best outfits to wear that day. For example, if the weather is cold, it will suggest clothes made of warm materials, and if it's raining, it will suggest waterproof clothes. It also suggests stylish outfits based on the user's preferences and trends. Furthermore, the generative AI determines the next shopping timing based on the user's favorite clothes and current trends. For example, if a user often wears clothes from a particular brand, it will notify the user when new items from that brand are released. It can also suggest new clothes as the seasons change. Finally, the generative AI makes the suggested clothes available for ordering on e-commerce sites. For example, if a user likes the suggested clothes, they can order them directly on the e-commerce site. Payment can be easily made using an electronic payment system. This system makes it easy for users to choose their daily clothes and enjoy stylish outfits that match the trends. Knowing when to go shopping also allows users to purchase new clothes efficiently.This allows the clothing suggestion system to make clothing selection more efficient for users and provide stylish outfits that are in line with the latest trends.

[0029] The clothing suggestion system according to the embodiment includes a reception unit, an analysis unit, an acquisition unit, a linking unit, and a suggestion unit. The reception unit inputs a photo of the user's clothing. The photo of the user's clothing includes, but is not limited to, a shirt, pants, and jacket. The reception unit, for example, takes a photo of the user's clothing and uploads it to the system. The reception unit can also input detailed information about the clothing (such as color, design, and material). For example, the reception unit inputs the color, design, and material of a shirt the user owns. The analysis unit analyzes the photo of the clothing input by the reception unit. The analysis unit can automatically analyze detailed information about the clothing, such as color, design, and material. The analysis unit can also use AI to extract clothing features and store them in a database. For example, the analysis unit can analyze the clothing color as RGB values, the design using pattern recognition technology, and the material using text analysis technology. The acquisition unit automatically acquires weather information. For example, the acquisition unit can automatically acquire today's weather forecast (such as temperature, probability of precipitation, and wind speed) from a weather information website. The acquisition unit can also analyze weather information using AI and store it in a database. For example, the acquisition unit can acquire weather forecasts from weather information sites using an API and analyze information such as temperature, precipitation probability, and wind speed. The linking unit can automatically acquire destination information. For example, the linking unit can link with a calendar app or a schedule management app to automatically acquire destination information. The linking unit can also analyze destination information using AI and store it in a database. For example, the linking unit can acquire destination information from a calendar app using an API and analyze information such as addresses, facility names, and event information. The suggestion unit can suggest outfits to wear that day based on the information obtained by the analysis unit, acquisition unit, and linking unit. For example, if the weather is cold, the suggestion unit can suggest clothes made of warm materials, and if it is raining, it can suggest waterproof clothes. The suggestion unit can also suggest stylish outfits taking into account the user's preferences and trends. For example, the suggestion unit can learn from the user's past choices and feedback to make more personalized suggestions. Furthermore, the suggestion unit can determine the next shopping timing based on the user's purchasing history and trend information.For example, if a user often wears clothes from a particular brand, the suggestion unit may notify the user when new items from that brand are released. The suggestion unit may also suggest new clothes to coincide with the change of seasons. The linking unit enables the clothes suggested by the suggestion unit to be ordered on an e-commerce site. For example, the linking unit may link with multiple e-commerce sites to present optimal prices and stock status. The linking unit may also use AI to analyze information from e-commerce sites and store the information in a database. For example, the linking unit may obtain prices and stock status from e-commerce sites using an API and present optimal prices and stock status. This allows the clothing suggestion system according to the embodiment to streamline user clothing selection and provide stylish outfits that match the latest trends. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input the user's past selections and feedback into the generation AI and cause the generation AI to suggest optimal clothes.

[0030] The analysis unit can automatically analyze specific information about the clothing. The analysis unit automatically analyzes detailed information about the clothing, such as the color, design, and material of the clothing. For example, the analysis unit analyzes the color of the clothing as RGB values, the design using pattern recognition technology, and the material using text analysis technology. The analysis unit can also use AI to extract clothing features and store them in a database. For example, the analysis unit analyzes the color of the clothing as RGB values, the design using pattern recognition technology, and the material using text analysis technology. This automatically analyzes detailed information about the clothing, enabling more accurate suggestions. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input a photo of the clothing into the generation AI and have the generation AI analyze the detailed information about the clothing.

[0031] The acquisition unit can automatically acquire a weather forecast from a weather information site. The acquisition unit, for example, automatically acquires today's weather forecast (temperature, probability of precipitation, wind speed, etc.) from the weather information site. For example, the acquisition unit acquires the weather forecast from the weather information site using an API and analyzes information such as the temperature, probability of precipitation, and wind speed. The acquisition unit can also analyze the weather information using AI and store it in a database. For example, the acquisition unit acquires the weather forecast from the weather information site using an API and analyzes information such as the temperature, probability of precipitation, and wind speed. In this way, by automatically acquiring weather information, it is possible to suggest clothing appropriate for the weather. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the weather forecast acquired from the weather information site into the generation AI and cause the generation AI to analyze the weather information.

[0032] The linking unit can automatically acquire destination information by linking with a calendar app or a schedule management app. The linking unit, for example, automatically acquires destination information by linking with a calendar app or a schedule management app. For example, the linking unit acquires destination information from a calendar app using an API and analyzes information such as addresses, facility names, and event information. The linking unit can also analyze the destination information using AI and store it in a database. For example, the linking unit acquires destination information from a calendar app using an API and analyzes information such as addresses, facility names, and event information. By automatically acquiring destination information, it is possible to suggest clothing appropriate for the destination. Some or all of the above-described processing in the linking unit may be performed using, or without, a generation AI. For example, the linking unit may input destination information acquired from a calendar app into the generation AI and cause the generation AI to analyze the destination information.

[0033] The suggestion unit can learn the user's past selections and feedback to make more personalized suggestions. The suggestion unit, for example, learns the user's past selections and feedback to make more personalized suggestions. For example, the suggestion unit stores the user's past selections and feedback in a database and learns using AI. The suggestion unit can also suggest stylish outfits taking into account the user's preferences and trends. For example, the suggestion unit learns the user's past selections and feedback to make more personalized suggestions. This makes it possible to make more personalized suggestions by learning the user's past selections and feedback. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI, for example. For example, the suggestion unit can input the user's past selections and feedback into the generation AI and have the generation AI execute personalized suggestions.

[0034] The suggestion unit can determine the timing of the next shopping trip based on the user's purchase history and trend information. The suggestion unit determines the timing of the next shopping trip based on, for example, the user's purchase history and trend information. For example, the suggestion unit stores the user's purchase history and trend information in a database and analyzes them using AI. If the user often wears clothes from a specific brand, the suggestion unit can notify the user when new items from that brand are released. The suggestion unit can also suggest new clothes in line with the change of seasons. For example, the suggestion unit can suggest new spring items in line with the arrival of spring. This enables efficient shopping by determining the timing of the next shopping trip based on the user's purchase history and trend information. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's purchase history and trend information into the generation AI and cause the generation AI to determine the timing of the next shopping trip.

[0035] The collaboration unit can collaborate with multiple e-commerce sites to present optimal prices and stock availability. The collaboration unit, for example, collaborates with multiple e-commerce sites to present optimal prices and stock availability. For example, the collaboration unit obtains prices and stock availability from the e-commerce sites using an API and presents optimal prices and stock availability. The collaboration unit can also use AI to analyze information from the e-commerce sites and store it in a database. For example, the collaboration unit obtains prices and stock availability from the e-commerce sites using an API and presents optimal prices and stock availability. In this way, by collaborating with multiple e-commerce sites, optimal prices and stock availability can be presented. Some or all of the above-described processing in the collaboration unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collaboration unit can input prices and stock availability obtained from the e-commerce sites into the generation AI and cause the generation AI to present optimal prices and stock availability.

[0036] The reception unit can analyze the user's past clothing photo input history and select the optimal input method. The reception unit, for example, prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit analyzes the time periods in which the user has previously input photos and suggests the optimal input timing. The reception unit can also analyze the types of clothes the user has previously input and prompt the user to input similar clothes. For example, the reception unit analyzes the types of clothes the user has previously input and prompts the user to input similar clothes. In this way, the optimal input method can be selected by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into the generation AI and have the generation AI select the optimal input method.

[0037] When a photo of clothing is input, the reception unit can perform filtering based on the user's current fashion trends and preferences. For example, the reception unit can prioritize displaying colors and designs preferred by the user. For example, the reception unit can prioritize displaying clothing from brands that the user has previously selected. The reception unit can also suggest related accessories and shoes based on the user's preferences. For example, the reception unit can also suggest related accessories and shoes based on the user's preferences. This allows filtering based on the user's fashion trends and preferences to input a more appropriate photo of clothing. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data on the user's fashion trends and preferences into the generation AI and have the generation AI perform filtering.

[0038] When inputting photos of clothes, the reception unit can prioritize inputting photos of highly relevant clothes taking into account the user's geographical location information. For example, if the user is in a cold region, the reception unit prioritizes inputting photos of winter clothes. For example, if the user is in a warm region, the reception unit prioritizes inputting photos of summer clothes. Furthermore, if the user is traveling, the reception unit can also prioritize inputting photos of clothes suitable for the climate of the travel destination. For example, if the user is traveling, the reception unit prioritizes inputting photos of clothes suitable for the climate of the travel destination. In this way, by taking the user's geographical location information into account, photos of highly relevant clothes can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize inputting photos of highly relevant clothes.

[0039] When a photo of clothing is input, the reception unit can analyze the user's social media activity and input photos of related clothing. The reception unit, for example, automatically inputs photos of clothing shared by the user on social media. For example, the reception unit prioritizes inputting photos of clothing that the user has "liked" on social media. The reception unit can also prioritize inputting photos of clothing from brands the user follows on social media. For example, the reception unit prioritizes inputting photos of clothing from brands the user follows on social media. In this way, photos of related clothing can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input data on the user's social media activity into the generation AI and cause the generation AI to input photos of related clothing.

[0040] When analyzing detailed clothing information, the analysis unit can adjust the level of detail of the analysis based on the importance of the clothing. For example, the analysis unit performs a detailed analysis for clothing worn for an important event. For example, the analysis unit performs a brief analysis for clothing worn daily. Furthermore, for clothing worn only in a specific season, the analysis unit can perform an analysis tailored to that season. For example, for clothing worn only in a specific season, the analysis unit performs an analysis tailored to that season. In this way, by adjusting the level of detail of the analysis based on the importance of the clothing, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input clothing importance data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] When analyzing detailed clothing information, the analysis unit can apply different analysis algorithms depending on the clothing category. For example, in the case of formal clothing, the analysis unit performs a detailed analysis of the design and material. For example, in the case of casual clothing, the analysis unit performs an analysis of the color and style. The analysis unit can also analyze the functionality and material in the case of sportswear. For example, in the case of sportswear, the analysis unit analyzes the functionality and material. In this way, by applying different analysis algorithms depending on the clothing category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input clothing category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] When analyzing detailed clothing information, the analysis unit can determine the priority of analysis based on when the clothing was purchased. For example, the analysis unit prioritizes analysis of recently purchased clothing. For example, the analysis unit postpones analysis of clothing that has been used for a long time. Furthermore, the analysis unit can also analyze clothing purchased seasonally according to the season. For example, the analysis unit analyzes clothing purchased seasonally according to the season. In this way, by determining the priority of analysis based on when the clothing was purchased, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on when the clothing was purchased into the generation AI and have the generation AI determine the analysis priority.

[0043] When analyzing detailed clothing information, the analysis unit can adjust the order of analysis based on the relevance of the clothing. For example, the analysis unit prioritizes analysis of clothing from the same brand. For example, the analysis unit prioritizes analysis of clothing of the same color or design. The analysis unit can also prioritize analysis of clothing for the same purpose. For example, the analysis unit prioritizes analysis of clothing for the same purpose. By adjusting the order of analysis based on the relevance of the clothing, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input clothing relevance data into the generation AI and cause the generation AI to adjust the order of analysis.

[0044] When acquiring weather information, the acquisition unit can predict current weather information by referring to past weather data. The acquisition unit, for example, predicts current weather based on past weather data. For example, the acquisition unit analyzes past weather patterns and predicts future weather. The acquisition unit can also improve the accuracy of current weather information by referring to past weather data. For example, the acquisition unit improves the accuracy of current weather information by referring to past weather data. In this way, the accuracy of current weather information can be improved by referring to past weather data. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input past weather data into the generation AI and cause the generation AI to predict current weather information.

[0045] When acquiring weather information, the acquisition unit can apply different acquisition methods to each weather category. For example, when it is raining, the acquisition unit prioritizes acquiring precipitation amount and precipitation probability. For example, when it is sunny, the acquisition unit prioritizes acquiring temperature and UV information. The acquisition unit can also prioritize acquiring snowfall amount and road surface conditions on snowy days. For example, when it is snowy, the acquisition unit prioritizes acquiring snowfall amount and road surface conditions. In this way, by applying different acquisition methods to each weather category, more appropriate weather information can be acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using, or without, the generation AI. For example, the acquisition unit can input weather category data into the generation AI and cause the generation AI to apply different acquisition methods.

[0046] When acquiring weather information, the acquisition unit can adjust the frequency of acquisition based on changes in the weather. For example, if the weather changes suddenly, the acquisition unit acquires weather information frequently. For example, if the weather is stable, the acquisition unit acquires weather information regularly. The acquisition unit can also acquire weather information in real time when the weather is difficult to predict. For example, if the weather is difficult to predict, the acquisition unit acquires weather information in real time. In this way, by adjusting the acquisition frequency based on changes in the weather, weather information can be acquired at more appropriate times. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input weather change data into the generation AI and have the generation AI adjust the acquisition frequency.

[0047] When acquiring weather information, the acquisition unit can improve the accuracy of the acquisition by referring to related weather data. The acquisition unit, for example, improves the accuracy of the weather information by referring to weather satellite data. For example, the acquisition unit improves the accuracy of the weather information by referring to weather radar data. The acquisition unit can also improve the accuracy of the weather information by referring to weather observation station data. For example, the acquisition unit improves the accuracy of the weather information by referring to weather observation station data. In this way, the accuracy of the weather information can be improved by referring to the related weather data. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input related weather data to the generation AI and cause the generation AI to improve the accuracy of the weather information.

[0048] When acquiring destination information, the collaboration unit can select the optimal acquisition method by referring to the user's past schedule history. The collaboration unit acquires optimal destination information, for example, based on places the user has visited in the past. For example, the collaboration unit acquires related destination information from the user's past schedule history. The collaboration unit can also analyze the user's past schedule history to acquire the most efficient destination information. For example, the collaboration unit analyzes the user's past schedule history to acquire the most efficient destination information. In this way, optimal destination information can be acquired by referring to the user's past schedule history. Some or all of the above-described processing in the collaboration unit may be performed using, or without, the generation AI. For example, the collaboration unit can input the user's past schedule history into the generation AI and cause the generation AI to select the optimal acquisition method.

[0049] When acquiring destination information, the collaboration unit can apply different acquisition methods depending on the category of the destination. For example, in the case of a business destination, the collaboration unit acquires detailed address and contact information. For example, in the case of a leisure destination, the collaboration unit acquires tourist spot and event information. Furthermore, in the case of a shopping destination, the collaboration unit can also acquire store opening hours and sale information. For example, in the case of a shopping destination, the collaboration unit acquires store opening hours and sale information. In this way, by applying different acquisition methods depending on the destination category, more appropriate destination information can be acquired. Some or all of the above-described processing in the collaboration unit may be performed using, or without, the generation AI, for example. For example, the collaboration unit can input destination category data into the generation AI and cause the generation AI to apply different acquisition methods.

[0050] When acquiring destination information, the collaboration unit can prioritize acquiring highly relevant destination information by taking into account the user's geographical location information. The collaboration unit, for example, prioritizes acquiring destination information close to the user's current location. For example, when the user is traveling, the collaboration unit prioritizes acquiring destination information for the travel destination. The collaboration unit can also prioritize acquiring destination information for a specific area when the user is in that area. For example, when the user is in a specific area, the collaboration unit prioritizes acquiring destination information for that area. In this way, highly relevant destination information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collaboration unit may be performed using, or without, the generation AI. For example, the collaboration unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize acquiring highly relevant destination information.

[0051] When acquiring destination information, the linking unit can analyze the user's social media activity and acquire related destination information. The linking unit, for example, automatically acquires destination information shared by the user on social media. For example, the linking unit prioritizes acquiring destination information that the user has "liked" on social media. The linking unit can also prioritize acquiring destination information of places the user follows on social media. For example, the linking unit prioritizes acquiring destination information of places the user follows on social media. In this way, related destination information can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the linking unit may be performed using, or without, the generation AI. For example, the linking unit may input data on the user's social media activity into the generation AI and cause the generation AI to acquire related destination information.

[0052] When suggesting clothes, the suggestion unit can learn the user's past selections and feedback and make optimal suggestions. The suggestion unit, for example, makes suggestions based on the style of clothing the user has previously chosen. For example, the suggestion unit suggests clothes that match the user's preferences based on the user's feedback. The suggestion unit can also analyze the user's past selections and suggest the most suitable clothes. For example, the suggestion unit analyzes the user's past selections and suggests the most suitable clothes. This makes it possible to suggest more appropriate clothes by learning the user's past selections and feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past selections and feedback into the generation AI and cause the generation AI to make optimal suggestions.

[0053] When suggesting clothes, the suggestion unit can adjust the level of detail of the suggestion based on the user's purchasing history and trend information. The suggestion unit, for example, makes suggestions based on brands of clothes purchased by the user in the past. For example, the suggestion unit makes suggestions taking current fashion trends into consideration. The suggestion unit can also analyze the user's purchasing history and suggest the most suitable clothes. For example, the suggestion unit analyzes the user's purchasing history and suggests the most suitable clothes. This makes it possible to suggest more suitable clothes by adjusting the level of detail of the suggestion based on the user's purchasing history and trend information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's purchasing history and trend information into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0054] When suggesting clothes, the suggestion unit can make optimal suggestions by taking into account the user's geographical location information. For example, if the user is in a cold region, the suggestion unit suggests winter clothes. For example, if the user is in a warm region, the suggestion unit suggests summer clothes. Furthermore, if the user is traveling, the suggestion unit can also suggest clothes suitable for the climate of the travel destination. For example, if the user is traveling, the suggestion unit suggests clothes suitable for the climate of the travel destination. This makes it possible to suggest more appropriate clothes by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's geographical location information into the generation AI and cause the generation AI to make optimal suggestions.

[0055] When suggesting clothes, the suggestion unit can analyze the user's social media activity and make relevant suggestions. The suggestion unit, for example, makes suggestions based on clothing styles shared by the user on social media. For example, the suggestion unit makes suggestions based on clothing styles that the user has "liked" on social media. The suggestion unit can also make suggestions based on clothing from brands the user follows on social media. For example, the suggestion unit makes suggestions based on clothing from brands the user follows on social media. This makes it possible to suggest more appropriate clothing by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's social media activity into the generation AI and cause the generation AI to make relevant suggestions.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When analyzing detailed information about clothing, the analysis unit can improve the accuracy of the analysis by taking into account the user's past selections and feedback. For example, based on the color and design of clothing previously selected by the user, it can prioritize the analysis of clothing with similar characteristics. It can also provide analysis results that match the user's preferences based on the user's feedback. Furthermore, it can analyze the user's past selections and apply the most appropriate analysis method. This allows for more accurate analysis by taking into account the user's past selections and feedback.

[0058] When acquiring destination information, the collaboration unit can analyze the user's past schedule history and suggest optimal destination information. For example, it can prioritize acquiring related destination information based on places the user has visited in the past. It can also analyze the user's past schedule history and acquire the most efficient destination information. Furthermore, it can adjust the method for acquiring destination information based on the user's past schedule history. This allows more appropriate destination information to be acquired by taking the user's past schedule history into consideration.

[0059] The linking unit links with multiple e-commerce sites and can make optimal suggestions by taking into account the user's past purchasing history when presenting optimal prices and stock availability. For example, it can present the optimal price for a similar product based on the brand or product price that the user has previously purchased. It can also analyze the user's purchasing history and present the most appropriate stock availability. Furthermore, it can select the optimal e-commerce site based on the user's past purchasing history. This makes it possible to present more appropriate prices and stock availability by taking into account the user's past purchasing history.

[0060] When analyzing detailed information about clothing, the analysis unit can apply different analysis algorithms depending on the clothing category. For example, for formal clothing, detailed analysis of design and materials can be performed. For casual clothing, color and style can be analyzed. Furthermore, for sportswear, functionality and materials can be analyzed. This allows for more appropriate analysis results to be provided by applying different analysis algorithms depending on the clothing category.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit inputs a photo of the clothes the user owns. The photo of the clothes the user owns may include, but is not limited to, a shirt, pants, jacket, etc. The reception unit, for example, takes a photo of the clothes the user owns and uploads it to the system. The reception unit can also input detailed information about the clothes (color, design, material, etc.). For example, the color, design, and material of a shirt the user owns may be input. Step 2: The analysis unit analyzes the photo of the clothes entered by the reception unit. The analysis unit automatically analyzes detailed information such as the color, design, and material of the clothes. The analysis unit can also use AI to extract the characteristics of the clothes and store them in a database. For example, the analysis unit analyzes the color of the clothes as RGB values, the design using pattern recognition technology, and the material using text analysis technology. Step 3: The acquisition unit automatically acquires weather information. For example, the acquisition unit automatically acquires today's weather forecast (temperature, probability of precipitation, wind speed, etc.) from a weather information site. The acquisition unit can also analyze the weather information using AI and store it in a database. For example, the acquisition unit acquires the weather forecast from the weather information site using an API and analyzes information such as temperature, probability of precipitation, and wind speed. Step 4: The linking unit automatically acquires destination information. For example, the linking unit may link with a calendar app or a schedule management app to automatically acquire destination information. The linking unit may also use AI to analyze the destination information and store it in a database. For example, the linking unit may acquire destination information from a calendar app using an API, and analyze information such as addresses, facility names, and event information. Step 5: The suggestion unit suggests clothes to wear today based on the information obtained by the analysis unit, acquisition unit, and linking unit. For example, if the weather is cold, the suggestion unit suggests clothes made of warm materials, and if it is raining, it suggests waterproof clothes. The suggestion unit can also suggest stylish outfits taking into account the user's preferences and trends. For example, the suggestion unit can learn the user's past choices and feedback to make more personalized suggestions. Furthermore, the suggestion unit can determine the timing of the next shopping trip based on the user's purchasing history and trend information. For example, if the user often wears clothes from a particular brand, the suggestion unit can notify the user when new items from that brand are released. The suggestion unit can also suggest new clothes to coincide with the change of seasons. Step 6: The linking unit makes the clothes suggested by the suggestion unit available for ordering on the e-commerce site. For example, the linking unit links with multiple e-commerce sites to present optimal prices and stock status. The linking unit can also use AI to analyze information from e-commerce sites and store it in a database. For example, the linking unit obtains prices and stock status from the e-commerce sites using an API, and presents optimal prices and stock status.

[0063] (Example 2) An embodiment of the clothing recommendation system of the present invention uses a generative AI to provide advice on what to wear every day. This clothing recommendation system inputs photos of the clothes a user owns, information from a weather information website, and a destination (if necessary), and then suggests the best outfits to wear that day. Furthermore, the generative AI determines the next shopping timing based on information such as current trends and the user's favorite clothing, enabling the user to place an order on an e-commerce website. Payment is made using an electronic payment system. For example, a user inputs photos of the clothes they own. Detailed information about each item (e.g., color, design, material) is also input. For example, a user takes photos of their shirts, pants, jackets, etc. and uploads them to the system. Next, information from a weather information website is input. For example, today's weather forecast (e.g., temperature, probability of precipitation, wind speed) is input into the system. This allows the system to suggest outfits appropriate for the weather. Furthermore, destination information is input. For example, if a user is going to work, the system can suggest outfits appropriate for the office dress code. Similarly, if a user is attending a casual event, the system can suggest outfits appropriate for the occasion. The generative AI analyzes this information and suggests the best outfits to wear that day. For example, if the weather is cold, it will suggest clothes made of warm materials, and if it's raining, it will suggest waterproof clothes. It also suggests stylish outfits based on the user's preferences and trends. Furthermore, the generative AI determines the next shopping timing based on the user's favorite clothes and current trends. For example, if a user often wears clothes from a particular brand, it will notify the user when new items from that brand are released. It can also suggest new clothes as the seasons change. Finally, the generative AI makes the suggested clothes available for ordering on e-commerce sites. For example, if a user likes the suggested clothes, they can order them directly on the e-commerce site. Payment can be easily made using an electronic payment system. This system makes it easy for users to choose their daily clothes and enjoy stylish outfits that match the trends. Knowing when to go shopping also allows users to purchase new clothes efficiently.This allows the clothing suggestion system to make clothing selection more efficient for users and provide stylish outfits that are in line with the latest trends.

[0064] The clothing suggestion system according to the embodiment includes a reception unit, an analysis unit, an acquisition unit, a linking unit, and a suggestion unit. The reception unit inputs a photo of the user's clothing. The photo of the user's clothing includes, but is not limited to, a shirt, pants, and jacket. The reception unit, for example, takes a photo of the user's clothing and uploads it to the system. The reception unit can also input detailed information about the clothing (such as color, design, and material). For example, the reception unit inputs the color, design, and material of a shirt the user owns. The analysis unit analyzes the photo of the clothing input by the reception unit. The analysis unit can automatically analyze detailed information about the clothing, such as color, design, and material. The analysis unit can also use AI to extract clothing features and store them in a database. For example, the analysis unit can analyze the clothing color as RGB values, the design using pattern recognition technology, and the material using text analysis technology. The acquisition unit automatically acquires weather information. For example, the acquisition unit can automatically acquire today's weather forecast (such as temperature, probability of precipitation, and wind speed) from a weather information website. The acquisition unit can also analyze weather information using AI and store it in a database. For example, the acquisition unit can acquire weather forecasts from weather information sites using an API and analyze information such as temperature, precipitation probability, and wind speed. The linking unit can automatically acquire destination information. For example, the linking unit can link with a calendar app or a schedule management app to automatically acquire destination information. The linking unit can also analyze destination information using AI and store it in a database. For example, the linking unit can acquire destination information from a calendar app using an API and analyze information such as addresses, facility names, and event information. The suggestion unit can suggest outfits to wear that day based on the information obtained by the analysis unit, acquisition unit, and linking unit. For example, if the weather is cold, the suggestion unit can suggest clothes made of warm materials, and if it is raining, it can suggest waterproof clothes. The suggestion unit can also suggest stylish outfits taking into account the user's preferences and trends. For example, the suggestion unit can learn from the user's past choices and feedback to make more personalized suggestions. Furthermore, the suggestion unit can determine the next shopping timing based on the user's purchasing history and trend information.For example, if a user often wears clothes from a particular brand, the suggestion unit may notify the user when new items from that brand are released. The suggestion unit may also suggest new clothes to coincide with the change of seasons. The linking unit enables the clothes suggested by the suggestion unit to be ordered on an e-commerce site. For example, the linking unit may link with multiple e-commerce sites to present optimal prices and stock status. The linking unit may also use AI to analyze information from e-commerce sites and store the information in a database. For example, the linking unit may obtain prices and stock status from e-commerce sites using an API and present optimal prices and stock status. This allows the clothing suggestion system according to the embodiment to streamline user clothing selection and provide stylish outfits that match the latest trends. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input the user's past selections and feedback into the generation AI and cause the generation AI to suggest optimal clothes.

[0065] The analysis unit can automatically analyze specific information about the clothing. The analysis unit automatically analyzes detailed information about the clothing, such as the color, design, and material of the clothing. For example, the analysis unit analyzes the color of the clothing as RGB values, the design using pattern recognition technology, and the material using text analysis technology. The analysis unit can also use AI to extract clothing features and store them in a database. For example, the analysis unit analyzes the color of the clothing as RGB values, the design using pattern recognition technology, and the material using text analysis technology. This automatically analyzes detailed information about the clothing, enabling more accurate suggestions. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input a photo of the clothing into the generation AI and have the generation AI analyze the detailed information about the clothing.

[0066] The acquisition unit can automatically acquire a weather forecast from a weather information site. The acquisition unit, for example, automatically acquires today's weather forecast (temperature, probability of precipitation, wind speed, etc.) from the weather information site. For example, the acquisition unit acquires the weather forecast from the weather information site using an API and analyzes information such as the temperature, probability of precipitation, and wind speed. The acquisition unit can also analyze the weather information using AI and store it in a database. For example, the acquisition unit acquires the weather forecast from the weather information site using an API and analyzes information such as the temperature, probability of precipitation, and wind speed. In this way, by automatically acquiring weather information, it is possible to suggest clothing appropriate for the weather. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the weather forecast acquired from the weather information site into the generation AI and cause the generation AI to analyze the weather information.

[0067] The linking unit can automatically acquire destination information by linking with a calendar app or a schedule management app. The linking unit, for example, automatically acquires destination information by linking with a calendar app or a schedule management app. For example, the linking unit acquires destination information from a calendar app using an API and analyzes information such as addresses, facility names, and event information. The linking unit can also analyze the destination information using AI and store it in a database. For example, the linking unit acquires destination information from a calendar app using an API and analyzes information such as addresses, facility names, and event information. By automatically acquiring destination information, it is possible to suggest clothing appropriate for the destination. Some or all of the above-described processing in the linking unit may be performed using, or without, a generation AI. For example, the linking unit may input destination information acquired from a calendar app into the generation AI and cause the generation AI to analyze the destination information.

[0068] The suggestion unit can learn the user's past selections and feedback to make more personalized suggestions. The suggestion unit, for example, learns the user's past selections and feedback to make more personalized suggestions. For example, the suggestion unit stores the user's past selections and feedback in a database and learns using AI. The suggestion unit can also suggest stylish outfits taking into account the user's preferences and trends. For example, the suggestion unit learns the user's past selections and feedback to make more personalized suggestions. This makes it possible to make more personalized suggestions by learning the user's past selections and feedback. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI, for example. For example, the suggestion unit can input the user's past selections and feedback into the generation AI and have the generation AI execute personalized suggestions.

[0069] The suggestion unit can determine the timing of the next shopping trip based on the user's purchase history and trend information. The suggestion unit determines the timing of the next shopping trip based on, for example, the user's purchase history and trend information. For example, the suggestion unit stores the user's purchase history and trend information in a database and analyzes them using AI. If the user often wears clothes from a specific brand, the suggestion unit can notify the user when new items from that brand are released. The suggestion unit can also suggest new clothes in line with the change of seasons. For example, the suggestion unit can suggest new spring items in line with the arrival of spring. This enables efficient shopping by determining the timing of the next shopping trip based on the user's purchase history and trend information. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's purchase history and trend information into the generation AI and cause the generation AI to determine the timing of the next shopping trip.

[0070] The collaboration unit can collaborate with multiple e-commerce sites to present optimal prices and stock availability. The collaboration unit, for example, collaborates with multiple e-commerce sites to present optimal prices and stock availability. For example, the collaboration unit obtains prices and stock availability from the e-commerce sites using an API and presents optimal prices and stock availability. The collaboration unit can also use AI to analyze information from the e-commerce sites and store it in a database. For example, the collaboration unit obtains prices and stock availability from the e-commerce sites using an API and presents optimal prices and stock availability. In this way, by collaborating with multiple e-commerce sites, optimal prices and stock availability can be presented. Some or all of the above-described processing in the collaboration unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collaboration unit can input prices and stock availability obtained from the e-commerce sites into the generation AI and cause the generation AI to present optimal prices and stock availability.

[0071] The reception unit can estimate the user's emotions and adjust the timing of inputting a photo of clothes based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input a photo of clothes during a time when the user is able to relax. For example, if the user is relaxed, the reception unit prompts the user to input a photo of clothes immediately. The reception unit can also set a reminder to input the photo of clothes later if the user is busy. For example, if the user is busy, the reception unit sets a reminder to input the photo later. This allows the input timing to be adjusted based on the user's emotions, allowing the photo of clothes to be input at a more appropriate time. 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 reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the input timing.

[0072] The reception unit can analyze the user's past clothing photo input history and select the optimal input method. The reception unit, for example, prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit analyzes the time periods in which the user has previously input photos and suggests the optimal input timing. The reception unit can also analyze the types of clothes the user has previously input and prompt the user to input similar clothes. For example, the reception unit analyzes the types of clothes the user has previously input and prompts the user to input similar clothes. In this way, the optimal input method can be selected by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into the generation AI and have the generation AI select the optimal input method.

[0073] When a photo of clothing is input, the reception unit can perform filtering based on the user's current fashion trends and preferences. For example, the reception unit can prioritize displaying colors and designs preferred by the user. For example, the reception unit can prioritize displaying clothing from brands that the user has previously selected. The reception unit can also suggest related accessories and shoes based on the user's preferences. For example, the reception unit can also suggest related accessories and shoes based on the user's preferences. This allows filtering based on the user's fashion trends and preferences to input a more appropriate photo of clothing. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data on the user's fashion trends and preferences into the generation AI and have the generation AI perform filtering.

[0074] The reception unit can estimate the user's emotions and determine the priority of the photos of clothes to be input based on the estimated user's emotions. For example, when the user is in a hurry, the reception unit prioritizes inputting photos of the most important clothes. For example, when the user is relaxed, the reception unit inputs photos of all clothes in order. Furthermore, when the user is feeling stressed, the reception unit can prioritize inputting photos of clothes that are easy to input. For example, when the user is feeling stressed, the reception unit prioritizes inputting photos of clothes that are easy to input. This allows the photos of clothes to be input in a more appropriate order by determining the priority of the photos of clothes to be input based on the user's emotions. 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 reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the photos of clothes to be input.

[0075] When inputting photos of clothes, the reception unit can prioritize inputting photos of highly relevant clothes taking into account the user's geographical location information. For example, if the user is in a cold region, the reception unit prioritizes inputting photos of winter clothes. For example, if the user is in a warm region, the reception unit prioritizes inputting photos of summer clothes. Furthermore, if the user is traveling, the reception unit can also prioritize inputting photos of clothes suitable for the climate of the travel destination. For example, if the user is traveling, the reception unit prioritizes inputting photos of clothes suitable for the climate of the travel destination. In this way, by taking the user's geographical location information into account, photos of highly relevant clothes can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize inputting photos of highly relevant clothes.

[0076] When a photo of clothing is input, the reception unit can analyze the user's social media activity and input photos of related clothing. The reception unit, for example, automatically inputs photos of clothing shared by the user on social media. For example, the reception unit prioritizes inputting photos of clothing that the user has "liked" on social media. The reception unit can also prioritize inputting photos of clothing from brands the user follows on social media. For example, the reception unit prioritizes inputting photos of clothing from brands the user follows on social media. In this way, photos of related clothing can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input data on the user's social media activity into the generation AI and cause the generation AI to input photos of related clothing.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis method for the detailed clothing information based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and provides detailed information. For example, if the user is in a hurry, the analysis unit performs a concise analysis and provides only the main points. The analysis unit can also provide a visually easy-to-understand analysis result if the user is feeling stressed. For example, if the user is feeling stressed, the analysis unit provides a visually easy-to-understand analysis result. This allows for adjusting the analysis method based on the user's emotions to provide more appropriate analysis results. 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, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method.

[0078] When analyzing detailed clothing information, the analysis unit can adjust the level of detail of the analysis based on the importance of the clothing. For example, the analysis unit performs a detailed analysis for clothing worn for an important event. For example, the analysis unit performs a brief analysis for clothing worn daily. Furthermore, for clothing worn only in a specific season, the analysis unit can perform an analysis tailored to that season. For example, for clothing worn only in a specific season, the analysis unit performs an analysis tailored to that season. In this way, by adjusting the level of detail of the analysis based on the importance of the clothing, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input clothing importance data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0079] When analyzing detailed clothing information, the analysis unit can apply different analysis algorithms depending on the clothing category. For example, in the case of formal clothing, the analysis unit performs a detailed analysis of the design and material. For example, in the case of casual clothing, the analysis unit performs an analysis of the color and style. The analysis unit can also analyze the functionality and material in the case of sportswear. For example, in the case of sportswear, the analysis unit analyzes the functionality and material. In this way, by applying different analysis algorithms depending on the clothing category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input clothing category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows for adjusting the display method based on the user's emotions to provide more appropriate analysis results. 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, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0081] When analyzing detailed clothing information, the analysis unit can determine the priority of analysis based on when the clothing was purchased. For example, the analysis unit prioritizes analysis of recently purchased clothing. For example, the analysis unit postpones analysis of clothing that has been used for a long time. Furthermore, the analysis unit can also analyze clothing purchased seasonally according to the season. For example, the analysis unit analyzes clothing purchased seasonally according to the season. In this way, by determining the priority of analysis based on when the clothing was purchased, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on when the clothing was purchased into the generation AI and have the generation AI determine the analysis priority.

[0082] When analyzing detailed clothing information, the analysis unit can adjust the order of analysis based on the relevance of the clothing. For example, the analysis unit prioritizes analysis of clothing from the same brand. For example, the analysis unit prioritizes analysis of clothing of the same color or design. The analysis unit can also prioritize analysis of clothing for the same purpose. For example, the analysis unit prioritizes analysis of clothing for the same purpose. By adjusting the order of analysis based on the relevance of the clothing, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input clothing relevance data into the generation AI and cause the generation AI to adjust the order of analysis.

[0083] The acquisition unit can estimate the user's emotions and adjust the timing of weather information acquisition based on the estimated user's emotions. For example, when the user is relaxed, the acquisition unit acquires weather information in real time. For example, when the user is in a hurry, the acquisition unit acquires weather information in advance. The acquisition unit can also periodically acquire weather information when the user is stressed. For example, when the user is stressed, the acquisition unit periodically acquires weather information. This allows the timing of weather information acquisition to be adjusted based on the user's emotions, thereby enabling weather information to be acquired at a more appropriate time. Emotion estimation is achieved 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-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of weather information acquisition.

[0084] When acquiring weather information, the acquisition unit can predict current weather information by referring to past weather data. The acquisition unit, for example, predicts current weather based on past weather data. For example, the acquisition unit analyzes past weather patterns and predicts future weather. The acquisition unit can also improve the accuracy of current weather information by referring to past weather data. For example, the acquisition unit improves the accuracy of current weather information by referring to past weather data. In this way, the accuracy of current weather information can be improved by referring to past weather data. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input past weather data into the generation AI and cause the generation AI to predict current weather information.

[0085] When acquiring weather information, the acquisition unit can apply different acquisition methods to each weather category. For example, when it is raining, the acquisition unit prioritizes acquiring precipitation amount and precipitation probability. For example, when it is sunny, the acquisition unit prioritizes acquiring temperature and UV information. The acquisition unit can also prioritize acquiring snowfall amount and road surface conditions on snowy days. For example, when it is snowy, the acquisition unit prioritizes acquiring snowfall amount and road surface conditions. In this way, by applying different acquisition methods to each weather category, more appropriate weather information can be acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using, or without, the generation AI. For example, the acquisition unit can input weather category data into the generation AI and cause the generation AI to apply different acquisition methods.

[0086] The acquisition unit can estimate the user's emotions and adjust the importance of weather information based on the estimated user's emotions. For example, the acquisition unit provides detailed weather information when the user is relaxed. For example, the acquisition unit provides weather information that focuses on the main points when the user is in a hurry. The acquisition unit can also provide visually easy-to-understand weather information when the user is stressed. For example, the acquisition unit provides visually easy-to-understand weather information when the user is stressed. This allows the importance of weather information to be adjusted based on the user's emotions, thereby providing more appropriate weather information. Emotion estimation is achieved 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-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of the weather information.

[0087] When acquiring weather information, the acquisition unit can adjust the frequency of acquisition based on changes in the weather. For example, if the weather changes suddenly, the acquisition unit acquires weather information frequently. For example, if the weather is stable, the acquisition unit acquires weather information regularly. The acquisition unit can also acquire weather information in real time when the weather is difficult to predict. For example, if the weather is difficult to predict, the acquisition unit acquires weather information in real time. In this way, by adjusting the acquisition frequency based on changes in the weather, weather information can be acquired at more appropriate times. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input weather change data into the generation AI and have the generation AI adjust the acquisition frequency.

[0088] When acquiring weather information, the acquisition unit can improve the accuracy of the acquisition by referring to related weather data. The acquisition unit, for example, improves the accuracy of the weather information by referring to weather satellite data. For example, the acquisition unit improves the accuracy of the weather information by referring to weather radar data. The acquisition unit can also improve the accuracy of the weather information by referring to weather observation station data. For example, the acquisition unit improves the accuracy of the weather information by referring to weather observation station data. In this way, the accuracy of the weather information can be improved by referring to the related weather data. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input related weather data to the generation AI and cause the generation AI to improve the accuracy of the weather information.

[0089] The linking unit can estimate the user's emotions and adjust the destination information acquisition method based on the estimated user's emotions. For example, when the user is relaxed, the linking unit acquires detailed destination information. For example, when the user is in a hurry, the linking unit acquires destination information that focuses on the main points. Furthermore, when the user is stressed, the linking unit can acquire visually easy-to-understand destination information. For example, when the user is stressed, the linking unit acquires visually easy-to-understand destination information. This allows the destination information acquisition method to be adjusted based on the user's emotions, thereby acquiring more appropriate destination information. Emotion estimation is achieved 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 linking unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the linking unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the destination information acquisition method.

[0090] When acquiring destination information, the collaboration unit can select the optimal acquisition method by referring to the user's past schedule history. The collaboration unit acquires optimal destination information, for example, based on places the user has visited in the past. For example, the collaboration unit acquires related destination information from the user's past schedule history. The collaboration unit can also analyze the user's past schedule history to acquire the most efficient destination information. For example, the collaboration unit analyzes the user's past schedule history to acquire the most efficient destination information. In this way, optimal destination information can be acquired by referring to the user's past schedule history. Some or all of the above-described processing in the collaboration unit may be performed using, or without, the generation AI. For example, the collaboration unit can input the user's past schedule history into the generation AI and cause the generation AI to select the optimal acquisition method.

[0091] When acquiring destination information, the collaboration unit can apply different acquisition methods depending on the category of the destination. For example, in the case of a business destination, the collaboration unit acquires detailed address and contact information. For example, in the case of a leisure destination, the collaboration unit acquires tourist spot and event information. Furthermore, in the case of a shopping destination, the collaboration unit can also acquire store opening hours and sale information. For example, in the case of a shopping destination, the collaboration unit acquires store opening hours and sale information. In this way, by applying different acquisition methods depending on the destination category, more appropriate destination information can be acquired. Some or all of the above-described processing in the collaboration unit may be performed using, or without, the generation AI, for example. For example, the collaboration unit can input destination category data into the generation AI and cause the generation AI to apply different acquisition methods.

[0092] The linking unit can estimate the user's emotions and determine the priority of destination information based on the estimated user emotions. For example, when the user is in a hurry, the linking unit prioritizes acquiring the most important destination information. For example, when the user is relaxed, the linking unit sequentially acquires all destination information. Furthermore, when the user is feeling stressed, the linking unit can prioritize acquiring destination information that is easily acquired. For example, when the user is feeling stressed, the linking unit prioritizes acquiring destination information that is easily acquired. This allows the destination information to be acquired in a more appropriate order by determining the priority of destination information based on the user's emotions. 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 linking unit may be performed using, for example, the generation AI. For example, the linking unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the destination information.

[0093] When acquiring destination information, the collaboration unit can prioritize acquiring highly relevant destination information by taking into account the user's geographical location information. The collaboration unit, for example, prioritizes acquiring destination information close to the user's current location. For example, when the user is traveling, the collaboration unit prioritizes acquiring destination information for the travel destination. The collaboration unit can also prioritize acquiring destination information for a specific area when the user is in that area. For example, when the user is in a specific area, the collaboration unit prioritizes acquiring destination information for that area. In this way, highly relevant destination information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collaboration unit may be performed using, or without, the generation AI. For example, the collaboration unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize acquiring highly relevant destination information.

[0094] When acquiring destination information, the linking unit can analyze the user's social media activity and acquire related destination information. The linking unit, for example, automatically acquires destination information shared by the user on social media. For example, the linking unit prioritizes acquiring destination information that the user has "liked" on social media. The linking unit can also prioritize acquiring destination information of places the user follows on social media. For example, the linking unit prioritizes acquiring destination information of places the user follows on social media. In this way, related destination information can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the linking unit may be performed using, or without, the generation AI. For example, the linking unit may input data on the user's social media activity into the generation AI and cause the generation AI to acquire related destination information.

[0095] The suggestion unit can estimate the user's emotions and adjust the clothing suggestion method based on the estimated user's emotions. For example, the suggestion unit makes detailed suggestions when the user is relaxed. For example, the suggestion unit makes concise suggestions when the user is in a hurry. The suggestion unit can also make visually easy-to-understand suggestions when the user is stressed. For example, the suggestion unit makes visually easy-to-understand suggestions when the user is stressed. This enables more appropriate clothing suggestions by adjusting the suggestion method based on the user's emotions. 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-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the suggestion method.

[0096] When suggesting clothes, the suggestion unit can learn the user's past selections and feedback and make optimal suggestions. The suggestion unit, for example, makes suggestions based on the style of clothing the user has previously chosen. For example, the suggestion unit suggests clothes that match the user's preferences based on the user's feedback. The suggestion unit can also analyze the user's past selections and suggest the most suitable clothes. For example, the suggestion unit analyzes the user's past selections and suggests the most suitable clothes. This makes it possible to suggest more appropriate clothes by learning the user's past selections and feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past selections and feedback into the generation AI and cause the generation AI to make optimal suggestions.

[0097] When suggesting clothes, the suggestion unit can adjust the level of detail of the suggestion based on the user's purchasing history and trend information. The suggestion unit, for example, makes suggestions based on brands of clothes purchased by the user in the past. For example, the suggestion unit makes suggestions taking current fashion trends into consideration. The suggestion unit can also analyze the user's purchasing history and suggest the most suitable clothes. For example, the suggestion unit analyzes the user's purchasing history and suggests the most suitable clothes. This makes it possible to suggest more suitable clothes by adjusting the level of detail of the suggestion based on the user's purchasing history and trend information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's purchasing history and trend information into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0098] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit prioritizes the most important suggestions. For example, if the user is relaxed, the suggestion unit prioritizes all suggestions in order. Furthermore, if the user is stressed, the suggestion unit can prioritize suggestions that are easy to understand. For example, if the user is stressed, the suggestion unit prioritizes suggestions that are easy to understand. This enables clothing suggestions to be suggested in a more appropriate order by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved 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 suggestion unit may be performed using, for example, the generation AI. For example, the suggestion unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of the suggestions.

[0099] When suggesting clothes, the suggestion unit can make optimal suggestions by taking into account the user's geographical location information. For example, if the user is in a cold region, the suggestion unit suggests winter clothes. For example, if the user is in a warm region, the suggestion unit suggests summer clothes. Furthermore, if the user is traveling, the suggestion unit can also suggest clothes suitable for the climate of the travel destination. For example, if the user is traveling, the suggestion unit suggests clothes suitable for the climate of the travel destination. This makes it possible to suggest more appropriate clothes by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's geographical location information into the generation AI and cause the generation AI to make optimal suggestions.

[0100] When suggesting clothes, the suggestion unit can analyze the user's social media activity and make relevant suggestions. The suggestion unit, for example, makes suggestions based on clothing styles shared by the user on social media. For example, the suggestion unit makes suggestions based on clothing styles that the user has "liked" on social media. The suggestion unit can also make suggestions based on clothing from brands the user follows on social media. For example, the suggestion unit makes suggestions based on clothing from brands the user follows on social media. This makes it possible to suggest more appropriate clothing by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's social media activity into the generation AI and cause the generation AI to make relevant suggestions. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, collaboration unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs a photo of the user's clothes. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input photo of the clothes. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and automatically acquires weather information. The collaboration unit is realized by the control unit 46A of the smart device 14 and automatically acquires destination information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests clothes to wear today based on the information obtained by the analysis unit, acquisition unit, and collaboration unit. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of inputting the photo of the clothes based on the estimated user's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, collaboration unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs a photo of the user's clothes. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input photo of the clothes. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and automatically acquires weather information. The collaboration unit is realized by the control unit 46A of the smart glasses 214 and automatically acquires destination information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests clothes to wear today based on the information obtained by the analysis unit, acquisition unit, and collaboration unit. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of inputting the photo of the clothes based on the estimated user's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, collaboration unit, and suggestion unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and inputs a photo of the user's clothes. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input photo of the clothes. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and automatically acquires weather information. The collaboration unit is realized by the control unit 46A of the headset-type terminal 314 and automatically acquires destination information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests clothes to wear today based on the information obtained by the analysis unit, acquisition unit, and collaboration unit. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of inputting the photo of the clothes based on the estimated user's emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, collaboration unit, and suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs a photo of the clothes the user is wearing. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input photo of the clothes. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically acquires weather information. The collaboration unit is realized, for example, by the control unit 46A of the robot 414 and automatically acquires destination information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothes to wear today based on the information obtained by the analysis unit, acquisition unit, and collaboration unit. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of inputting the photo of the clothes based on the estimated user's emotions.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The suggestion unit can estimate the user's emotions and adjust the style of clothing to be suggested based on the estimated user's emotions. For example, if the user is relaxed, casual clothing can be suggested. If the user is nervous, formal clothing can be suggested. Furthermore, if the user is feeling stressed, comfortable and relaxing clothing can be suggested. In this way, by adjusting the style of clothing to be suggested based on the user's emotions, more appropriate clothing can be suggested.

[0103] When analyzing detailed information about clothing, the analysis unit can improve the accuracy of the analysis by taking into account the user's past selections and feedback. For example, based on the color and design of clothing previously selected by the user, it can prioritize the analysis of clothing with similar characteristics. It can also provide analysis results that match the user's preferences based on the user's feedback. Furthermore, it can analyze the user's past selections and apply the most appropriate analysis method. This allows for more accurate analysis by taking into account the user's past selections and feedback.

[0104] When acquiring weather information, the acquisition unit can estimate the user's emotions and adjust the type of weather information to be acquired based on the estimated emotions. For example, if the user is relaxed, detailed weather information can be provided. If the user is in a hurry, weather information that focuses on the main points can be provided. Furthermore, if the user is feeling stressed, visually easy-to-understand weather information can be provided. In this way, by adjusting the type of weather information based on the user's emotions, more appropriate weather information can be provided.

[0105] When acquiring destination information, the collaboration unit can analyze the user's past schedule history and suggest optimal destination information. For example, it can prioritize acquiring related destination information based on places the user has visited in the past. It can also analyze the user's past schedule history and acquire the most efficient destination information. Furthermore, it can adjust the method for acquiring destination information based on the user's past schedule history. This allows more appropriate destination information to be acquired by taking the user's past schedule history into consideration.

[0106] The suggestion unit can estimate the user's emotions and adjust the color of the suggested clothes based on the estimated emotions. For example, if the user is relaxed, it can suggest clothes with subdued colors. If the user is nervous, it can suggest clothes with bright colors. Furthermore, if the user is stressed, it can suggest clothes with relaxing colors. In this way, by adjusting the color of the suggested clothes based on the user's emotions, it is possible to suggest more appropriate clothing.

[0107] When determining the next shopping timing based on the user's purchasing history and trend information, the suggestion unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification can be sent immediately. Also, if the user is busy, a reminder can be set to notify later. Furthermore, if the user is feeling stressed, the notification can be sent during a time when the user is able to relax. In this way, by adjusting the timing of notifications based on the user's emotions, notifications can be sent at more appropriate times.

[0108] The linking unit links with multiple e-commerce sites and can make optimal suggestions by taking into account the user's past purchasing history when presenting optimal prices and stock availability. For example, it can present the optimal price for a similar product based on the brand or product price that the user has previously purchased. It can also analyze the user's purchasing history and present the most appropriate stock availability. Furthermore, it can select the optimal e-commerce site based on the user's past purchasing history. This makes it possible to present more appropriate prices and stock availability by taking into account the user's past purchasing history.

[0109] The reception unit can estimate the user's emotions and adjust the method for inputting photos of clothes based on the estimated emotions. For example, if the user is relaxed, the reception unit can encourage voice input. If the user is in a hurry, the reception unit can encourage simple text input. Furthermore, if the user is feeling stressed, the reception unit can suggest a visually easy-to-understand input method. In this way, by adjusting the input method based on the user's emotions, the user can input photos of clothes in a more appropriate way.

[0110] When analyzing detailed information about clothing, the analysis unit can apply different analysis algorithms depending on the clothing category. For example, for formal clothing, detailed analysis of design and materials can be performed. For casual clothing, color and style can be analyzed. Furthermore, for sportswear, functionality and materials can be analyzed. This allows for more appropriate analysis results to be provided by applying different analysis algorithms depending on the clothing category.

[0111] The suggestion unit can estimate the user's emotions and adjust the suggested clothing accessories and shoes based on the estimated emotions. For example, if the user is relaxed, casual accessories and shoes can be suggested. If the user is nervous, formal accessories and shoes can be suggested. Furthermore, if the user is stressed, comfortable and relaxing accessories and shoes can be suggested. In this way, by adjusting the suggested accessories and shoes based on the user's emotions, more appropriate coordination can be suggested.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit inputs a photo of the clothes the user owns. The photo of the clothes the user owns may include, but is not limited to, a shirt, pants, jacket, etc. The reception unit, for example, takes a photo of the clothes the user owns and uploads it to the system. The reception unit can also input detailed information about the clothes (color, design, material, etc.). For example, the color, design, and material of a shirt the user owns may be input. Step 2: The analysis unit analyzes the photo of the clothes entered by the reception unit. The analysis unit automatically analyzes detailed information such as the color, design, and material of the clothes. The analysis unit can also use AI to extract the characteristics of the clothes and store them in a database. For example, the analysis unit analyzes the color of the clothes as RGB values, the design using pattern recognition technology, and the material using text analysis technology. Step 3: The acquisition unit automatically acquires weather information. For example, the acquisition unit automatically acquires today's weather forecast (temperature, probability of precipitation, wind speed, etc.) from a weather information site. The acquisition unit can also analyze the weather information using AI and store it in a database. For example, the acquisition unit acquires the weather forecast from the weather information site using an API and analyzes information such as temperature, probability of precipitation, and wind speed. Step 4: The linking unit automatically acquires destination information. For example, the linking unit may link with a calendar app or a schedule management app to automatically acquire destination information. The linking unit may also use AI to analyze the destination information and store it in a database. For example, the linking unit may acquire destination information from a calendar app using an API, and analyze information such as addresses, facility names, and event information. Step 5: The suggestion unit suggests clothes to wear today based on the information obtained by the analysis unit, acquisition unit, and linking unit. For example, if the weather is cold, the suggestion unit suggests clothes made of warm materials, and if it is raining, it suggests waterproof clothes. The suggestion unit can also suggest stylish outfits taking into account the user's preferences and trends. For example, the suggestion unit can learn the user's past choices and feedback to make more personalized suggestions. Furthermore, the suggestion unit can determine the timing of the next shopping trip based on the user's purchasing history and trend information. For example, if the user often wears clothes from a particular brand, the suggestion unit can notify the user when new items from that brand are released. The suggestion unit can also suggest new clothes to coincide with the change of seasons. Step 6: The linking unit makes the clothes suggested by the suggestion unit available for ordering on the e-commerce site. For example, the linking unit links with multiple e-commerce sites to present optimal prices and stock status. The linking unit can also use AI to analyze information from e-commerce sites and store it in a database. For example, the linking unit obtains prices and stock status from the e-commerce sites using an API, and presents optimal prices and stock status.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a photo of clothes owned by the user; an analysis unit that analyzes the photo of the clothes input by the reception unit; an acquisition unit that automatically acquires weather information; A linking unit that automatically acquires destination information, a suggestion unit that suggests clothes to wear today based on information obtained by the analysis unit, the acquisition unit, and the linking unit; a linking unit that enables the clothes suggested by the suggestion unit to be ordered on an e-commerce site; Equipped with A system characterized by:

2. The analysis unit Automatically analyze specific clothing information 2. The system of claim 1.

3. The acquisition unit Automatically get weather forecasts from weather information sites 2. The system of claim 1.

4. The linking unit is Link with a calendar or schedule management app to automatically obtain destination information 2. The system of claim 1.

5. The proposal unit Learn from your past choices and feedback to make more personalized suggestions 2. The system of claim 1.

6. The proposal unit Determine the next shopping timing based on the user's purchasing history and trend information 2. The system of claim 1.

7. The linking unit is Connect with multiple e-commerce sites to find the best prices and availability 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the timing of clothing photo input based on the estimated user emotions.

2. The system of claim 1.

Images