System

Abstract

An object of a system according to an embodiment is to assist a user in easily determining daily clothes.SOLUTION: A system includes a history management part, a learning part, a weather acquisition part, a trend acquisition part, and a proposal part. The history management unit manages a clothing history of the user. The learning unit learns the user's preference based on the information managed by the history management unit. The weather acquisition unit acquires weather information. The trend acquisition unit acquires trend information. The proposal unit proposes coordination on the basis of information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that users tend to be confused when deciding on their daily outfits, making it difficult to choose an appropriate outfit.

[0005] The system according to the embodiment aims to support a user in easily deciding what to wear each day. [Means for solving the problem]

[0006] The system according to the embodiment includes a history management unit, a learning unit, a weather acquisition unit, a trend acquisition unit, and a suggestion unit. The history management unit manages the user's clothing history. The learning unit learns the user's preferences based on the information managed by the history management unit. The weather acquisition unit acquires weather information. The trend acquisition unit acquires trend information. The suggestion unit suggests outfits based on the information obtained by the learning unit, weather acquisition unit, and trend acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can assist the user in easily deciding what to wear each day. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An outfit selection support system according to an embodiment of the present invention manages a user's outfit history and suggests optimal outfits based on weather and trend information. The outfit selection support system provides interactive advice to users when deciding on outfits, manages the user's past outfit history, and suggests optimal outfits based on weather and trend information. For example, if a user inputs, "I want to wear casual clothes today," the outfit selection support system suggests optimal outfits based on the user's past casual outfit history and the weather information for that day. The outfit selection support system also provides advice such as, "You've only worn this outfit recently, so you should choose different clothes." Furthermore, the outfit selection support system can learn recent fashion trends and suggest outfits incorporating the latest trends to the user. This allows the outfit selection support system to efficiently support users in choosing outfits and provide personalized suggestions. For example, users can reduce the effort of choosing outfits every day and enjoy wearing clothes that suit them better. Furthermore, by managing the outfit history, the outfit selection support system can avoid wearing the same clothes frequently and enjoy a wide variety of outfits.

[0029] The clothing selection support system according to the embodiment includes a history management unit, a learning unit, a weather acquisition unit, a trend acquisition unit, and a suggestion unit. The history management unit manages a user's clothing history. The user's clothing history includes, but is not limited to, for example, dates, clothing types, colors, and brands. The history management unit, for example, stores clothing information entered by the user in a database and manages past clothing history. The history management unit can also store images of clothing worn by the user so that the user can visually confirm the clothing history. The learning unit learns the user's preferences. For example, the learning unit uses a machine learning algorithm to learn the user's preferences based on the user's selection history. For example, the learning unit analyzes patterns of clothing selected by the user in the past to identify the user's preferences. The learning unit can also adjust the learning algorithm based on user feedback to provide more personalized suggestions. The weather acquisition unit acquires weather information. For example, the weather acquisition unit acquires weather and temperature information for the day using data from the Japan Meteorological Agency or a weather forecast API. For example, the weather acquisition unit acquires information such as temperature, humidity, precipitation, and wind speed and provides it to the suggestion unit. The trend acquisition unit acquires trend information. The trend acquisition unit acquires recent fashion trend information, for example, using data from fashion magazines, social media, and online shops. For example, the trend acquisition unit collects the latest fashion items and styles and provides them to the suggestion unit. The suggestion unit suggests outfits based on information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit. The suggestion unit suggests optimal outfits, for example, taking into account the user's preferences, the weather of the day, and recent trends. For example, if a user inputs, "I want to go out in casual clothes today," the suggestion unit suggests optimal outfits based on the user's past casual clothing history and the weather information of the day. The suggestion unit also provides advice such as, "You've only worn this outfit recently, so you should choose different clothes." This allows the clothing selection support system according to the embodiment to efficiently support users in selecting outfits and make personalized suggestions.

[0030] The history management unit can record and manage a user's past clothing history. For example, the history management unit stores clothing information entered by the user in a database and manages the user's past clothing history. For example, the history management unit records information such as the date, type of clothing, color, and brand. The history management unit can also store images of clothing worn by the user so that the user can visually check the clothing. For example, the history management unit can store images of clothing taken by the user with a smartphone in a database and manage them as past clothing history. The history management unit can also record clothing combinations selected by the user and manage them as coordination history. This allows the user's past clothing history to be managed efficiently.

[0031] The weather acquisition unit can acquire weather or temperature information for that day. The weather acquisition unit acquires weather and temperature information for that day, for example, using data from the Japan Meteorological Agency or a weather forecast API. For example, the weather acquisition unit acquires information such as temperature, humidity, precipitation, and wind speed, and provides it to the suggestion unit. The weather acquisition unit can also acquire optimal weather information based on the user's current location. For example, the weather acquisition unit identifies the user's current location using GPS data and acquires weather information for that area. In this way, by acquiring weather and temperature information for that day, it is possible to suggest appropriate clothing.

[0032] The trend acquisition unit can acquire information on recent fashion trends. The trend acquisition unit acquires information on recent fashion trends, for example, by using data from fashion magazines, social media, and online shops. For example, the trend acquisition unit collects the latest fashion items and styles and provides them to the suggestion unit. The trend acquisition unit can also preferentially acquire highly relevant trend information based on the user's preferences. For example, the trend acquisition unit analyzes fashion items and styles selected by the user in the past and acquires highly relevant trend information based on that. In this way, by acquiring information on recent fashion trends, it becomes possible to make suggestions that incorporate the latest trends.

[0033] The learning unit performs machine learning based on the user's selection history, allowing it to provide more personalized suggestions. The learning unit, for example, uses a machine learning algorithm to learn the user's preferences based on the user's selection history. For example, the learning unit analyzes patterns of clothing selected by the user in the past to identify the user's preferences. The learning unit can also adjust the learning algorithm based on user feedback to provide more personalized suggestions. For example, the learning unit analyzes ratings and comments made by the user on suggestions and improves the learning algorithm based on the results. This enables more personalized suggestions to be provided by performing machine learning based on the user's selection history.

[0034] The suggestion unit can suggest outfits based on information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit. The suggestion unit suggests optimal outfits, taking into account, for example, the user's preferences, the weather of the day, and recent trends. For example, if the user inputs, "I want to go out in casual clothes today," the suggestion unit suggests optimal outfits based on the user's past casual clothing history and the weather information of the day. The suggestion unit also provides advice such as, "I only wore this outfit recently, so you should choose different clothes." Furthermore, the suggestion unit can learn recent fashion trends and suggest outfits that incorporate the latest trends to the user. For example, the suggestion unit suggests outfits that incorporate the latest fashion items and styles. As a result, optimal outfits are suggested based on the information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit, enabling the user to select the best outfits.

[0035] When recording clothing history, the history management unit can analyze the user's past clothing selection patterns and select the optimal recording method. For example, the history management unit can automatically display clothing that the user has frequently selected in the past as candidates. The history management unit can also preferentially suggest recording methods (audio, text, etc.) that the user has used in the past. Furthermore, the history management unit can predict the clothing that will be used during a specific time period based on the user's past clothing selection patterns and select the recording method. In this way, the optimal recording method can be selected by analyzing the user's past clothing selection patterns.

[0036] When recording clothing history, the history management unit can perform filtering taking into account the user's current lifestyle and event information. For example, if the user is participating in a specific event, the history management unit will prioritize recording clothing history that is appropriate for that event. The history management unit can also filter appropriate clothing history based on the user's lifestyle rhythm. Furthermore, the history management unit can also reference the user's calendar information and record clothing history based on plans. This allows appropriate clothing history to be recorded by taking into account the user's lifestyle and event information.

[0037] When recording clothing history, the history management unit can select the optimal recording means depending on the user's input method. For example, if the user records clothing by voice, the history management unit can prioritize support for voice input. Also, if the user records clothing by text, the history management unit can prioritize support for text input. Furthermore, if the user records clothing by image, the history management unit can automatically record clothing using image recognition technology. This allows for efficient recording by selecting the optimal recording means depending on the user's input method.

[0038] When recording clothing history, the history management unit can prioritize recording highly relevant history by taking into account the user's geographical location information. For example, if the user is in a specific area, the history management unit prioritizes recording clothing history that is appropriate for that area. The history management unit can also filter highly relevant clothing history based on the user's current location. Furthermore, the history management unit can refer to the user's movement history and prioritize recording highly relevant clothing history. This allows highly relevant history to be prioritized by taking into account the user's geographical location information.

[0039] The history management unit can analyze the user's social media activity and record related history when recording the clothing history. For example, the history management unit records clothing history related to places where the user has checked in on social media. The history management unit can also analyze the content of the user's posts on social media and record related clothing history. Furthermore, the history management unit can also record related clothing history by referring to the activities of the user's friends on social media. In this way, related history can be recorded by analyzing the user's social media activity.

[0040] The history management unit can customize the recording method by reflecting the user's past feedback when recording the clothing history. The history management unit customizes the recording method based on, for example, feedback provided by the user in the past. The history management unit can also select the optimal recording method from the user's past feedback. Furthermore, the history management unit can also improve the recording method by reflecting the user's feedback. In this way, the recording method can be customized by reflecting the user's past feedback.

[0041] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm from past learning data. Furthermore, the learning unit can analyze past learning data and improve the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data.

[0042] During learning, the learning unit can analyze variations in the user's clothing history and adjust the update frequency of the learning data. For example, if there is a large variation in the user's clothing history, the learning unit can increase the update frequency of the learning data. Also, if there is a small variation in the user's clothing history, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can analyze variations in the user's clothing history and set an optimal update frequency. In this way, the update frequency of the learning data can be adjusted by analyzing variations in the user's clothing history.

[0043] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. For example, the learning unit adjusts parameters of the learning algorithm based on user feedback. The learning unit can also identify areas for improvement in the learning algorithm from user feedback. Furthermore, the learning unit can also optimize the learning algorithm by reflecting user feedback. In this way, the learning algorithm can be adjusted by reflecting user feedback.

[0044] During learning, the learning unit can weight the learning data based on the time when the clothing history was submitted. The learning unit weights the learning data based on, for example, the most recent clothing history. The learning unit can also weight the learning data based on clothing history submitted in a specific season. Furthermore, the learning unit can weight the learning data based on important events for the user. Thus, weighting the learning data based on the time when the clothing history was submitted enables more effective learning.

[0045] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit can integrate information from social media to enrich the learning data. The learning unit can also integrate information from fashion magazines to enrich the learning data. Furthermore, the learning unit can also integrate information from online shopping sites to enrich the learning data. In this way, the learning data can be enriched by integrating information from different data sources.

[0046] The learning unit can customize the learning data during learning, taking into account the user's lifestyle and event information. The learning unit customizes the learning data, for example, based on the user's lifestyle. The learning unit can also refer to the user's calendar information and customize the learning data based on the user's schedule. Furthermore, the learning unit can also customize the learning data taking into account the user's event information. This allows the learning data to be customized by taking into account the user's lifestyle and event information.

[0047] When acquiring weather information, the weather acquisition unit can predict the current weather by referring to past weather data. The weather acquisition unit predicts the current weather, for example, based on past weather data. The weather acquisition unit can also predict the current weather by extracting specific patterns from past weather data. Furthermore, the weather acquisition unit can also predict the current weather by analyzing past weather data. In this way, the current weather can be predicted by referring to past weather data.

[0048] When acquiring weather information, the weather acquisition unit can acquire optimal weather information by taking into account the user's geographical location information. The weather acquisition unit acquires optimal weather information based on the user's current location, for example. The weather acquisition unit can also acquire highly relevant weather information by referring to the user's movement history. Furthermore, the weather acquisition unit can also acquire optimal weather information by taking into account the user's geographical location information. In this way, optimal weather information can be acquired by taking into account the user's geographical location information.

[0049] When acquiring weather information, the weather acquisition unit can customize the acquisition method by taking into account the user's lifestyle and event information. The weather acquisition unit customizes the weather information acquisition method based on the user's lifestyle, for example. The weather acquisition unit can also refer to the user's calendar information and acquire weather information based on the user's schedule. Furthermore, the weather acquisition unit can also customize the weather information acquisition method by taking into account the user's event information. This allows the acquisition method to be customized by taking into account the user's lifestyle and event information.

[0050] When acquiring weather information, the weather acquisition unit can optimize the acquisition method by referring to the user's past weather history. The weather acquisition unit selects the optimal acquisition method based on the user's past weather history, for example. The weather acquisition unit can also optimize the acquisition method by extracting specific patterns from the user's past weather history. Furthermore, the weather acquisition unit can analyze the user's past weather history and improve the acquisition method. In this way, the acquisition method can be optimized by referring to the user's past weather history.

[0051] When acquiring weather information, the weather acquisition unit can improve the acquisition accuracy by integrating information from different weather data sources. For example, the weather acquisition unit can improve the acquisition accuracy by integrating information from multiple weather data sources. The weather acquisition unit can also compare information from different weather data sources and select the most appropriate information. Furthermore, the weather acquisition unit can evaluate the reliability of the weather data sources and improve the acquisition accuracy. In this way, the acquisition accuracy is improved by integrating information from different weather data sources.

[0052] When acquiring weather information, the weather acquisition unit can customize the acquired data taking into account the user's lifestyle and event information. The weather acquisition unit customizes the acquired data based on, for example, the user's lifestyle. The weather acquisition unit can also refer to the user's calendar information and customize the acquired data based on the user's schedule. Furthermore, the weather acquisition unit can also customize the acquired data taking into account the user's event information. This allows the acquired data to be customized by taking into account the user's lifestyle and event information.

[0053] When acquiring trend information, the trend acquisition unit can predict a current trend by referring to past trend data. The trend acquisition unit predicts a current trend, for example, based on past trend data. The trend acquisition unit can also predict a current trend by extracting a specific pattern from past trend data. Furthermore, the trend acquisition unit can also predict a current trend by analyzing past trend data. In this way, a current trend can be predicted by referring to past trend data.

[0054] When acquiring trend information, the trend acquisition unit can acquire optimal trend information by taking into account the user's geographical location information. The trend acquisition unit acquires optimal trend information based on the user's current location, for example. The trend acquisition unit can also acquire highly relevant trend information by referring to the user's movement history. Furthermore, the trend acquisition unit can also acquire optimal trend information by taking into account the user's geographical location information. In this way, optimal trend information can be acquired by taking into account the user's geographical location information.

[0055] When acquiring trend information, the trend acquisition unit can customize the acquisition method by taking into account the user's lifestyle and event information. The trend acquisition unit customizes the trend information acquisition method based on, for example, the user's lifestyle. The trend acquisition unit can also refer to the user's calendar information and acquire trend information based on the user's schedule. Furthermore, the trend acquisition unit can also customize the trend information acquisition method by taking into account the user's event information. This allows the acquisition method to be customized by taking into account the user's lifestyle and event information.

[0056] When acquiring trend information, the trend acquisition unit can optimize the acquisition method by referring to the user's past trend history. The trend acquisition unit selects the optimal acquisition method based on, for example, the user's past trend history. The trend acquisition unit can also optimize the acquisition method by extracting specific patterns from the user's past trend history. Furthermore, the trend acquisition unit can analyze the user's past trend history and improve the acquisition method. In this way, the acquisition method can be optimized by referring to the user's past trend history.

[0057] When acquiring trend information, the trend acquisition unit can improve acquisition accuracy by integrating information from different trend data sources. For example, the trend acquisition unit can improve acquisition accuracy by integrating information from multiple trend data sources. The trend acquisition unit can also compare information from different trend data sources and select optimal information. Furthermore, the trend acquisition unit can evaluate the reliability of the trend data sources and improve acquisition accuracy. In this way, acquisition accuracy is improved by integrating information from different trend data sources.

[0058] When acquiring trend information, the trend acquisition unit can customize the acquired data taking into account the user's lifestyle and event information. The trend acquisition unit customizes the acquired data based on, for example, the user's lifestyle. The trend acquisition unit can also refer to the user's calendar information and customize the acquired data based on the user's schedule. Furthermore, the trend acquisition unit can also customize the acquired data taking into account the user's event information. This allows the acquired data to be customized by taking into account the user's lifestyle and event information.

[0059] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the clothing when making a suggestion. For example, the suggestion unit may include detailed information when suggesting clothing for an important event. The suggestion unit may also provide simplified information when suggesting clothing for everyday use. Furthermore, the suggestion unit can adjust the level of detail of the suggestion depending on a specific situation. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the clothing.

[0060] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of clothing. For example, the suggestion unit applies a suggestion algorithm for casual clothing to casual clothing. The suggestion unit can also apply a suggestion algorithm for formal clothing to formal clothing. Furthermore, the suggestion unit can also apply a suggestion algorithm for sports to sportswear. This allows for more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of clothing.

[0061] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by extracting specific patterns from the user's past suggestion results. Furthermore, the suggestion unit can analyze the user's past suggestion results and improve the suggestion algorithm. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results.

[0062] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of submission of the outfit. For example, if an important event is coming up, the suggestion unit can prioritize suggestions for that event. The suggestion unit can also lower the priority of suggestions for everyday outfits. Furthermore, the suggestion unit can adjust the priority of suggestions depending on a specific situation. This allows for more appropriate suggestions by determining the priority of suggestions based on the time of submission of the outfit.

[0063] The suggestion unit can adjust the order of suggestions based on the relevance of the outfits when making suggestions. For example, the suggestion unit prioritizes outfit suggestions related to important events. The suggestion unit can also postpone the order of outfit suggestions for everyday wear. Furthermore, the suggestion unit can also adjust the order of suggestions depending on a specific situation. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the outfits.

[0064] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, the suggestion unit can make suggestions that use a lot of technical terms to a user who is knowledgeable about fashion. The suggestion unit can also make suggestions that explain things in simpler terms to a user who is not knowledgeable about fashion. Furthermore, the suggestion unit can adjust the use of technical terms based on the user's past feedback. This allows for more appropriate suggestions by adjusting the use of technical terms in the suggestion according to the user's level of expertise.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The history management unit can record and manage a user's past clothing history. For example, it can store clothing information entered by the user in a database and manage the user's past clothing history. For example, the history management unit can record information such as the date, type of clothing, color, and brand. The history management unit can also store images of clothing worn by the user so that the user can visually check the clothing. For example, the history management unit can store images of clothing taken by the user with a smartphone in a database and manage them as a past clothing history. The history management unit can also record clothing combinations selected by the user and manage them as a coordination history. This allows the user's past clothing history to be managed efficiently.

[0067] The weather acquisition unit can acquire weather or temperature information for that day. For example, it acquires weather and temperature information for that day using data from the Japan Meteorological Agency or a weather forecast API. For example, the weather acquisition unit acquires information such as temperature, humidity, precipitation, and wind speed, and provides it to the suggestion unit. The weather acquisition unit can also acquire optimal weather information based on the user's current location. For example, the weather acquisition unit identifies the user's current location using GPS data and acquires weather information for that area. In this way, by acquiring weather and temperature information for that day, it becomes possible to suggest appropriate clothing.

[0068] The trend acquisition unit can acquire information on recent fashion trends. For example, the trend acquisition unit acquires information on recent fashion trends using data from fashion magazines, social media, and online shops. For example, the trend acquisition unit collects the latest fashion items and styles and provides them to the suggestion unit. The trend acquisition unit can also preferentially acquire highly relevant trend information based on the user's preferences. For example, the trend acquisition unit analyzes fashion items and styles selected by the user in the past and acquires highly relevant trend information based on that. In this way, by acquiring information on recent fashion trends, it becomes possible to make suggestions that incorporate the latest trends.

[0069] The learning unit performs machine learning based on the user's selection history, allowing it to provide more personalized suggestions. For example, a machine learning algorithm is used to learn the user's preferences based on the user's selection history. For example, the learning unit analyzes the patterns of clothing the user has selected in the past to identify the user's preferences. The learning unit can also adjust the learning algorithm based on user feedback to provide more personalized suggestions. For example, the learning unit analyzes the ratings and comments the user has made on suggestions, and improves the learning algorithm based on that. This makes it possible to provide more personalized suggestions by performing machine learning based on the user's selection history.

[0070] The suggestion unit can suggest outfits based on information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit. For example, it suggests optimal outfits taking into consideration the user's preferences, the weather of the day, and recent trends. For example, if the user inputs, "I want to go out in casual clothes today," the suggestion unit suggests optimal outfits based on the user's past casual clothing history and the weather information of the day. The suggestion unit also provides advice such as, "You've only worn this outfit recently, so you should choose different clothes." Furthermore, the suggestion unit can learn recent fashion trends and suggest outfits that incorporate the latest trends to the user. For example, the suggestion unit suggests outfits that incorporate the latest fashion items and styles. As a result, optimal outfits are suggested based on the information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit, enabling the user to select the best outfits for themselves.

[0071] When recording clothing history, the history management unit can analyze the user's past clothing selection patterns and select the optimal recording method. For example, clothing that the user has frequently selected in the past can be automatically displayed as candidates. The history management unit can also prioritize and suggest recording methods (audio, text, etc.) that the user has used in the past. Furthermore, the history management unit can predict the clothing that will be used during a specific time period based on the user's past clothing selection patterns and select the recording method. In this way, the optimal recording method can be selected by analyzing the user's past clothing selection patterns.

[0072] When recording clothing history, the history management unit can filter the clothing history by taking into account the user's current lifestyle and event information. For example, if a user is participating in a specific event, the history management unit will prioritize recording clothing history that is appropriate for that event. The history management unit can also filter appropriate clothing history based on the user's lifestyle rhythm. Furthermore, the history management unit can also reference the user's calendar information and record clothing history based on plans. This allows appropriate clothing history to be recorded by taking into account the user's lifestyle and event information.

[0073] When recording clothing history, the history management unit can select the optimal recording means depending on the user's input method. For example, if the user records clothing by voice, the history management unit can prioritize support for voice input. Also, if the user records clothing by text, the history management unit can prioritize support for text input. Furthermore, if the user records clothing by image, the history management unit can automatically record clothing using image recognition technology. This allows for efficient recording by selecting the optimal recording means depending on the user's input method.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The history management unit manages the user's clothing history. The user's clothing history includes the date, type of clothing, color, brand, etc. The history management unit saves the clothing information entered by the user in a database and manages the user's past clothing history. It can also save images of the clothing worn by the user so that they can be visually checked. Step 2: The learning unit learns the user's preferences. The learning unit uses a machine learning algorithm to learn the user's preferences based on the user's selection history. For example, it analyzes the patterns of clothing the user has chosen in the past to identify the user's preferences. It can also adjust the learning algorithm based on user feedback to make more personalized suggestions. Step 3: The weather acquisition unit acquires weather information. The weather acquisition unit acquires weather and temperature information for the day using data from the Japan Meteorological Agency and weather forecast APIs. For example, it acquires information such as temperature, humidity, precipitation, and wind speed, and provides it to the proposal unit. Step 4: The trend acquisition unit acquires trend information. The trend acquisition unit acquires information on the latest fashion trends using data from fashion magazines, social media, and online shops. For example, it collects the latest fashion items and styles and provides them to the suggestion unit. Step 5: The suggestion unit suggests outfits based on the information obtained by the learning unit, weather acquisition unit, and trend acquisition unit. The suggestion unit suggests optimal outfits taking into consideration the user's preferences, the weather of the day, and recent trends. For example, if the user inputs, "I want to go out in casual clothes today," the suggestion unit will suggest optimal outfits based on the user's past casual clothing history and the weather information for the day. It may also provide advice such as, "You've only recently worn this outfit, so you should choose something different."

[0076] (Example 2) An outfit selection support system according to an embodiment of the present invention manages a user's outfit history and suggests optimal outfits based on weather and trend information. The outfit selection support system provides interactive advice to users when deciding on outfits, manages the user's past outfit history, and suggests optimal outfits based on weather and trend information. For example, if a user inputs, "I want to wear casual clothes today," the outfit selection support system suggests optimal outfits based on the user's past casual outfit history and the weather information for that day. The outfit selection support system also provides advice such as, "You've only worn this outfit recently, so you should choose different clothes." Furthermore, the outfit selection support system can learn recent fashion trends and suggest outfits incorporating the latest trends to the user. This allows the outfit selection support system to efficiently support users in choosing outfits and provide personalized suggestions. For example, users can reduce the effort of choosing outfits every day and enjoy wearing clothes that suit them better. Furthermore, by managing the outfit history, the outfit selection support system can avoid wearing the same clothes frequently and enjoy a wide variety of outfits.

[0077] The clothing selection support system according to the embodiment includes a history management unit, a learning unit, a weather acquisition unit, a trend acquisition unit, and a suggestion unit. The history management unit manages a user's clothing history. The user's clothing history includes, but is not limited to, for example, dates, clothing types, colors, and brands. The history management unit, for example, stores clothing information entered by the user in a database and manages past clothing history. The history management unit can also store images of clothing worn by the user so that the user can visually confirm the clothing history. The learning unit learns the user's preferences. For example, the learning unit uses a machine learning algorithm to learn the user's preferences based on the user's selection history. For example, the learning unit analyzes patterns of clothing selected by the user in the past to identify the user's preferences. The learning unit can also adjust the learning algorithm based on user feedback to provide more personalized suggestions. The weather acquisition unit acquires weather information. For example, the weather acquisition unit acquires weather and temperature information for the day using data from the Japan Meteorological Agency or a weather forecast API. For example, the weather acquisition unit acquires information such as temperature, humidity, precipitation, and wind speed and provides it to the suggestion unit. The trend acquisition unit acquires trend information. The trend acquisition unit acquires recent fashion trend information, for example, using data from fashion magazines, social media, and online shops. For example, the trend acquisition unit collects the latest fashion items and styles and provides them to the suggestion unit. The suggestion unit suggests outfits based on information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit. The suggestion unit suggests optimal outfits, for example, taking into account the user's preferences, the weather of the day, and recent trends. For example, if a user inputs, "I want to go out in casual clothes today," the suggestion unit suggests optimal outfits based on the user's past casual clothing history and the weather information of the day. The suggestion unit also provides advice such as, "You've only worn this outfit recently, so you should choose different clothes." This allows the clothing selection support system according to the embodiment to efficiently support users in selecting outfits and make personalized suggestions.

[0078] The history management unit can record and manage a user's past clothing history. For example, the history management unit stores clothing information entered by the user in a database and manages the user's past clothing history. For example, the history management unit records information such as the date, type of clothing, color, and brand. The history management unit can also store images of clothing worn by the user so that the user can visually check the clothing. For example, the history management unit can store images of clothing taken by the user with a smartphone in a database and manage them as past clothing history. The history management unit can also record clothing combinations selected by the user and manage them as coordination history. This allows the user's past clothing history to be managed efficiently.

[0079] The weather acquisition unit can acquire weather or temperature information for that day. The weather acquisition unit acquires weather and temperature information for that day, for example, using data from the Japan Meteorological Agency or a weather forecast API. For example, the weather acquisition unit acquires information such as temperature, humidity, precipitation, and wind speed, and provides it to the suggestion unit. The weather acquisition unit can also acquire optimal weather information based on the user's current location. For example, the weather acquisition unit identifies the user's current location using GPS data and acquires weather information for that area. In this way, by acquiring weather and temperature information for that day, it is possible to suggest appropriate clothing.

[0080] The trend acquisition unit can acquire information on recent fashion trends. The trend acquisition unit acquires information on recent fashion trends, for example, by using data from fashion magazines, social media, and online shops. For example, the trend acquisition unit collects the latest fashion items and styles and provides them to the suggestion unit. The trend acquisition unit can also preferentially acquire highly relevant trend information based on the user's preferences. For example, the trend acquisition unit analyzes fashion items and styles selected by the user in the past and acquires highly relevant trend information based on that. In this way, by acquiring information on recent fashion trends, it becomes possible to make suggestions that incorporate the latest trends.

[0081] The learning unit performs machine learning based on the user's selection history, allowing it to provide more personalized suggestions. The learning unit, for example, uses a machine learning algorithm to learn the user's preferences based on the user's selection history. For example, the learning unit analyzes patterns of clothing selected by the user in the past to identify the user's preferences. The learning unit can also adjust the learning algorithm based on user feedback to provide more personalized suggestions. For example, the learning unit analyzes ratings and comments made by the user on suggestions and improves the learning algorithm based on the results. This enables more personalized suggestions to be provided by performing machine learning based on the user's selection history.

[0082] The suggestion unit can suggest outfits based on information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit. The suggestion unit suggests optimal outfits, taking into account, for example, the user's preferences, the weather of the day, and recent trends. For example, if the user inputs, "I want to go out in casual clothes today," the suggestion unit suggests optimal outfits based on the user's past casual clothing history and the weather information of the day. The suggestion unit also provides advice such as, "I only wore this outfit recently, so you should choose different clothes." Furthermore, the suggestion unit can learn recent fashion trends and suggest outfits that incorporate the latest trends to the user. For example, the suggestion unit suggests outfits that incorporate the latest fashion items and styles. As a result, optimal outfits are suggested based on the information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit, enabling the user to select the best outfits.

[0083] The history management unit can estimate the user's emotions and adjust the clothing history recording method based on the estimated user emotions. For example, if the user is feeling stressed, the history management unit can provide a simple interface to quickly record the clothing history. Furthermore, if the user is relaxed, the history management unit can provide detailed input options and suggest a customizable recording method. Furthermore, if the user is in a hurry, the history management unit can prioritize voice input to quickly record the clothing history. This allows for more appropriate recording by adjusting the clothing history recording method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When recording clothing history, the history management unit can analyze the user's past clothing selection patterns and select the optimal recording method. For example, the history management unit can automatically display clothing that the user has frequently selected in the past as candidates. The history management unit can also preferentially suggest recording methods (audio, text, etc.) that the user has used in the past. Furthermore, the history management unit can predict the clothing that will be used during a specific time period based on the user's past clothing selection patterns and select the recording method. In this way, the optimal recording method can be selected by analyzing the user's past clothing selection patterns.

[0085] When recording clothing history, the history management unit can perform filtering taking into account the user's current lifestyle and event information. For example, if the user is participating in a specific event, the history management unit will prioritize recording clothing history that is appropriate for that event. The history management unit can also filter appropriate clothing history based on the user's lifestyle rhythm. Furthermore, the history management unit can also reference the user's calendar information and record clothing history based on plans. This allows appropriate clothing history to be recorded by taking into account the user's lifestyle and event information.

[0086] When recording clothing history, the history management unit can select the optimal recording means depending on the user's input method. For example, if the user records clothing by voice, the history management unit can prioritize support for voice input. Also, if the user records clothing by text, the history management unit can prioritize support for text input. Furthermore, if the user records clothing by image, the history management unit can automatically record clothing using image recognition technology. This allows for efficient recording by selecting the optimal recording means depending on the user's input method.

[0087] The history management unit can estimate the user's emotions and determine the priority of the clothing history to be recorded based on the estimated user emotions. For example, when the user is feeling stressed, the history management unit can prioritize recording important clothing history. Furthermore, when the user is relaxed, the history management unit can also prioritize recording detailed clothing history. Furthermore, when the user is in a hurry, the history management unit can also prioritize recording simplified clothing history. In this way, by determining the priority of the clothing history to be recorded according to the user's emotions, important history can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] When recording clothing history, the history management unit can prioritize recording highly relevant history by taking into account the user's geographical location information. For example, if the user is in a specific area, the history management unit prioritizes recording clothing history that is appropriate for that area. The history management unit can also filter highly relevant clothing history based on the user's current location. Furthermore, the history management unit can refer to the user's movement history and prioritize recording highly relevant clothing history. This allows highly relevant history to be prioritized by taking into account the user's geographical location information.

[0089] The history management unit can analyze the user's social media activity and record related history when recording the clothing history. For example, the history management unit records clothing history related to places where the user has checked in on social media. The history management unit can also analyze the content of the user's posts on social media and record related clothing history. Furthermore, the history management unit can also record related clothing history by referring to the activities of the user's friends on social media. In this way, related history can be recorded by analyzing the user's social media activity.

[0090] The history management unit can customize the recording method by reflecting the user's past feedback when recording the clothing history. The history management unit customizes the recording method based on, for example, feedback provided by the user in the past. The history management unit can also select the optimal recording method from the user's past feedback. Furthermore, the history management unit can also improve the recording method by reflecting the user's feedback. In this way, the recording method can be customized by reflecting the user's past feedback.

[0091] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects detailed training data. Furthermore, if the user is in a hurry, the learning unit can also select simplified training data. Furthermore, if the user is feeling stressed, the learning unit can prioritize the selection of important training data. This enables more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm from past learning data. Furthermore, the learning unit can analyze past learning data and improve the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data.

[0093] During learning, the learning unit can analyze variations in the user's clothing history and adjust the update frequency of the learning data. For example, if there is a large variation in the user's clothing history, the learning unit can increase the update frequency of the learning data. Also, if there is a small variation in the user's clothing history, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can analyze variations in the user's clothing history and set an optimal update frequency. In this way, the update frequency of the learning data can be adjusted by analyzing variations in the user's clothing history.

[0094] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. For example, the learning unit adjusts parameters of the learning algorithm based on user feedback. The learning unit can also identify areas for improvement in the learning algorithm from user feedback. Furthermore, the learning unit can also optimize the learning algorithm by reflecting user feedback. In this way, the learning algorithm can be adjusted by reflecting user feedback.

[0095] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is feeling stressed. This allows for more appropriate learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] During learning, the learning unit can weight the learning data based on the time when the clothing history was submitted. The learning unit weights the learning data based on, for example, the most recent clothing history. The learning unit can also weight the learning data based on clothing history submitted in a specific season. Furthermore, the learning unit can weight the learning data based on important events for the user. Thus, weighting the learning data based on the time when the clothing history was submitted enables more effective learning.

[0097] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit can integrate information from social media to enrich the learning data. The learning unit can also integrate information from fashion magazines to enrich the learning data. Furthermore, the learning unit can also integrate information from online shopping sites to enrich the learning data. In this way, the learning data can be enriched by integrating information from different data sources.

[0098] The learning unit can customize the learning data during learning, taking into account the user's lifestyle and event information. The learning unit customizes the learning data, for example, based on the user's lifestyle. The learning unit can also refer to the user's calendar information and customize the learning data based on the user's schedule. Furthermore, the learning unit can also customize the learning data taking into account the user's event information. This allows the learning data to be customized by taking into account the user's lifestyle and event information.

[0099] The weather acquisition unit can estimate the user's emotions and adjust the method of acquiring weather information based on the estimated user emotions. For example, when the user is relaxed, the weather acquisition unit acquires detailed weather information. Furthermore, when the user is in a hurry, the weather acquisition unit can also acquire simplified weather information. Furthermore, when the user is feeling stressed, the weather acquisition unit can prioritize acquiring important weather information. In this way, by adjusting the method of acquiring weather information according to the user's emotions, more appropriate weather information can be acquired. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] When acquiring weather information, the weather acquisition unit can predict the current weather by referring to past weather data. The weather acquisition unit predicts the current weather, for example, based on past weather data. The weather acquisition unit can also predict the current weather by extracting specific patterns from past weather data. Furthermore, the weather acquisition unit can also predict the current weather by analyzing past weather data. In this way, the current weather can be predicted by referring to past weather data.

[0101] When acquiring weather information, the weather acquisition unit can acquire optimal weather information by taking into account the user's geographical location information. The weather acquisition unit acquires optimal weather information based on the user's current location, for example. The weather acquisition unit can also acquire highly relevant weather information by referring to the user's movement history. Furthermore, the weather acquisition unit can also acquire optimal weather information by taking into account the user's geographical location information. In this way, optimal weather information can be acquired by taking into account the user's geographical location information.

[0102] When acquiring weather information, the weather acquisition unit can customize the acquisition method by taking into account the user's lifestyle and event information. The weather acquisition unit customizes the weather information acquisition method based on the user's lifestyle, for example. The weather acquisition unit can also refer to the user's calendar information and acquire weather information based on the user's schedule. Furthermore, the weather acquisition unit can also customize the weather information acquisition method by taking into account the user's event information. This allows the acquisition method to be customized by taking into account the user's lifestyle and event information.

[0103] The weather acquisition unit can estimate the user's emotions and adjust the display method of weather information based on the estimated user emotions. For example, the weather acquisition unit can display detailed weather information when the user is relaxed. The weather acquisition unit can also display simplified weather information when the user is in a hurry. Furthermore, the weather acquisition unit can prioritize displaying important weather information when the user is feeling stressed. This allows for more appropriate information display by adjusting the display method of weather information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] When acquiring weather information, the weather acquisition unit can optimize the acquisition method by referring to the user's past weather history. The weather acquisition unit selects the optimal acquisition method based on the user's past weather history, for example. The weather acquisition unit can also optimize the acquisition method by extracting specific patterns from the user's past weather history. Furthermore, the weather acquisition unit can analyze the user's past weather history and improve the acquisition method. In this way, the acquisition method can be optimized by referring to the user's past weather history.

[0105] When acquiring weather information, the weather acquisition unit can improve the acquisition accuracy by integrating information from different weather data sources. For example, the weather acquisition unit can improve the acquisition accuracy by integrating information from multiple weather data sources. The weather acquisition unit can also compare information from different weather data sources and select the most appropriate information. Furthermore, the weather acquisition unit can evaluate the reliability of the weather data sources and improve the acquisition accuracy. In this way, the acquisition accuracy is improved by integrating information from different weather data sources.

[0106] When acquiring weather information, the weather acquisition unit can customize the acquired data taking into account the user's lifestyle and event information. The weather acquisition unit customizes the acquired data based on, for example, the user's lifestyle. The weather acquisition unit can also refer to the user's calendar information and customize the acquired data based on the user's schedule. Furthermore, the weather acquisition unit can also customize the acquired data taking into account the user's event information. This allows the acquired data to be customized by taking into account the user's lifestyle and event information.

[0107] The trend acquisition unit can estimate the user's emotions and adjust the trend information acquisition method based on the estimated user emotions. For example, when the user is relaxed, the trend acquisition unit acquires detailed trend information. Furthermore, when the user is in a hurry, the trend acquisition unit can also acquire simplified trend information. Furthermore, when the user is feeling stressed, the trend acquisition unit can prioritize acquiring important trend information. In this way, by adjusting the trend information acquisition method according to the user's emotions, more appropriate trend information can be acquired. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] When acquiring trend information, the trend acquisition unit can predict a current trend by referring to past trend data. The trend acquisition unit predicts a current trend, for example, based on past trend data. The trend acquisition unit can also predict a current trend by extracting a specific pattern from past trend data. Furthermore, the trend acquisition unit can also predict a current trend by analyzing past trend data. In this way, a current trend can be predicted by referring to past trend data.

[0109] When acquiring trend information, the trend acquisition unit can acquire optimal trend information by taking into account the user's geographical location information. The trend acquisition unit acquires optimal trend information based on the user's current location, for example. The trend acquisition unit can also acquire highly relevant trend information by referring to the user's movement history. Furthermore, the trend acquisition unit can also acquire optimal trend information by taking into account the user's geographical location information. In this way, optimal trend information can be acquired by taking into account the user's geographical location information.

[0110] When acquiring trend information, the trend acquisition unit can customize the acquisition method by taking into account the user's lifestyle and event information. The trend acquisition unit customizes the trend information acquisition method based on, for example, the user's lifestyle. The trend acquisition unit can also refer to the user's calendar information and acquire trend information based on the user's schedule. Furthermore, the trend acquisition unit can also customize the trend information acquisition method by taking into account the user's event information. This allows the acquisition method to be customized by taking into account the user's lifestyle and event information.

[0111] The trend acquisition unit can estimate the user's emotions and adjust the display method of trend information based on the estimated user emotions. For example, when the user is relaxed, the trend acquisition unit can display detailed trend information. Furthermore, when the user is in a hurry, the trend acquisition unit can also display simplified trend information. Furthermore, when the user is feeling stressed, the trend acquisition unit can prioritize displaying important trend information. This allows for more appropriate information display by adjusting the display method of trend information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] When acquiring trend information, the trend acquisition unit can optimize the acquisition method by referring to the user's past trend history. The trend acquisition unit selects the optimal acquisition method based on, for example, the user's past trend history. The trend acquisition unit can also optimize the acquisition method by extracting specific patterns from the user's past trend history. Furthermore, the trend acquisition unit can analyze the user's past trend history and improve the acquisition method. In this way, the acquisition method can be optimized by referring to the user's past trend history.

[0113] When acquiring trend information, the trend acquisition unit can improve acquisition accuracy by integrating information from different trend data sources. For example, the trend acquisition unit can improve acquisition accuracy by integrating information from multiple trend data sources. The trend acquisition unit can also compare information from different trend data sources and select optimal information. Furthermore, the trend acquisition unit can evaluate the reliability of the trend data sources and improve acquisition accuracy. In this way, acquisition accuracy is improved by integrating information from different trend data sources.

[0114] When acquiring trend information, the trend acquisition unit can customize the acquired data taking into account the user's lifestyle and event information. The trend acquisition unit customizes the acquired data based on, for example, the user's lifestyle. The trend acquisition unit can also refer to the user's calendar information and customize the acquired data based on the user's schedule. Furthermore, the trend acquisition unit can also customize the acquired data taking into account the user's event information. This allows the acquired data to be customized by taking into account the user's lifestyle and event information.

[0115] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide simplified suggestions. Furthermore, if the user is feeling stressed, the suggestion unit can prioritize important suggestions. This allows for more appropriate suggestions by adjusting the way suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0116] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the clothing when making a suggestion. For example, the suggestion unit may include detailed information when suggesting clothing for an important event. The suggestion unit may also provide simplified information when suggesting clothing for everyday use. Furthermore, the suggestion unit can adjust the level of detail of the suggestion depending on a specific situation. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the clothing.

[0117] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of clothing. For example, the suggestion unit applies a suggestion algorithm for casual clothing to casual clothing. The suggestion unit can also apply a suggestion algorithm for formal clothing to formal clothing. Furthermore, the suggestion unit can also apply a suggestion algorithm for sports to sportswear. This allows for more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of clothing.

[0118] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by extracting specific patterns from the user's past suggestion results. Furthermore, the suggestion unit can analyze the user's past suggestion results and improve the suggestion algorithm. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results.

[0119] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide simplified suggestions when the user is in a hurry. Furthermore, the suggestion unit can prioritize important suggestions when the user is stressed. This allows for more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0120] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of submission of the outfit. For example, if an important event is coming up, the suggestion unit can prioritize suggestions for that event. The suggestion unit can also lower the priority of suggestions for everyday outfits. Furthermore, the suggestion unit can adjust the priority of suggestions depending on a specific situation. This allows for more appropriate suggestions by determining the priority of suggestions based on the time of submission of the outfit.

[0121] The suggestion unit can adjust the order of suggestions based on the relevance of the outfits when making suggestions. For example, the suggestion unit prioritizes outfit suggestions related to important events. The suggestion unit can also postpone the order of outfit suggestions for everyday wear. Furthermore, the suggestion unit can also adjust the order of suggestions depending on a specific situation. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the outfits.

[0122] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, the suggestion unit can make suggestions that use a lot of technical terms to a user who is knowledgeable about fashion. The suggestion unit can also make suggestions that explain things in simpler terms to a user who is not knowledgeable about fashion. Furthermore, the suggestion unit can adjust the use of technical terms based on the user's past feedback. This allows for more appropriate suggestions by adjusting the use of technical terms in the suggestion according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the history management unit, learning unit, weather acquisition unit, trend acquisition unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the history management unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the weather acquisition unit acquires information from a weather forecast API via the communication I / F 44 of the smart device 14 and processes the information by the specific processing unit 290 of the data processing device 12. For example, the trend acquisition unit acquires information from fashion magazines or SNS via the communication I / F 44 of the smart device 14 and processes the information by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described history management unit, learning unit, weather acquisition unit, trend acquisition unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the history management unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the weather acquisition unit acquires information from a weather forecast API via the communication I / F 44 of the smart glasses 214, and is processed by the specific processing unit 290 of the data processing device 12. For example, the trend acquisition unit acquires information from fashion magazines or SNS via the communication I / F 44 of the smart glasses 214, and is processed by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-described history management unit, learning unit, weather acquisition unit, trend acquisition 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 history management unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the weather acquisition unit acquires information from a weather forecast API via the communication I / F 44 of the headset type terminal 314, and the acquired information is processed by the specific processing unit 290 of the data processing device 12. For example, the trend acquisition unit acquires information from fashion magazines or SNS via the communication I / F 44 of the headset type terminal 314, and the acquired information is processed by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the history management unit, learning unit, weather acquisition unit, trend acquisition unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the history management unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12. For example, the weather acquisition unit acquires information from a weather forecast API via the communication I / F 44 of the robot 414, and the acquired information is processed by the specific processing unit 290 of the data processing device 12. For example, the trend acquisition unit acquires information from fashion magazines or SNS via the communication I / F 44 of the robot 414, and the acquired information is processed by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0123] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0124] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, detailed suggestions can be made. If the user is in a hurry, simplified suggestions can be made. Furthermore, if the user is stressed, important suggestions can be given priority. This allows for more appropriate suggestions to be made by adjusting the way suggestions are expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0125] The history management unit can record and manage a user's past clothing history. For example, it can store clothing information entered by the user in a database and manage the user's past clothing history. For example, the history management unit can record information such as the date, type of clothing, color, and brand. The history management unit can also store images of clothing worn by the user so that the user can visually check the clothing. For example, the history management unit can store images of clothing taken by the user with a smartphone in a database and manage them as a past clothing history. The history management unit can also record clothing combinations selected by the user and manage them as a coordination history. This allows the user's past clothing history to be managed efficiently.

[0126] The weather acquisition unit can acquire weather or temperature information for that day. For example, it acquires weather and temperature information for that day using data from the Japan Meteorological Agency or a weather forecast API. For example, the weather acquisition unit acquires information such as temperature, humidity, precipitation, and wind speed, and provides it to the suggestion unit. The weather acquisition unit can also acquire optimal weather information based on the user's current location. For example, the weather acquisition unit identifies the user's current location using GPS data and acquires weather information for that area. In this way, by acquiring weather and temperature information for that day, it becomes possible to suggest appropriate clothing.

[0127] The trend acquisition unit can acquire information on recent fashion trends. For example, the trend acquisition unit acquires information on recent fashion trends using data from fashion magazines, social media, and online shops. For example, the trend acquisition unit collects the latest fashion items and styles and provides them to the suggestion unit. The trend acquisition unit can also preferentially acquire highly relevant trend information based on the user's preferences. For example, the trend acquisition unit analyzes fashion items and styles selected by the user in the past and acquires highly relevant trend information based on that. In this way, by acquiring information on recent fashion trends, it becomes possible to make suggestions that incorporate the latest trends.

[0128] The learning unit performs machine learning based on the user's selection history, allowing it to provide more personalized suggestions. For example, a machine learning algorithm is used to learn the user's preferences based on the user's selection history. For example, the learning unit analyzes the patterns of clothing the user has selected in the past to identify the user's preferences. The learning unit can also adjust the learning algorithm based on user feedback to provide more personalized suggestions. For example, the learning unit analyzes the ratings and comments the user has made on suggestions, and improves the learning algorithm based on that. This makes it possible to provide more personalized suggestions by performing machine learning based on the user's selection history.

[0129] The suggestion unit can suggest outfits based on information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit. For example, it suggests optimal outfits taking into consideration the user's preferences, the weather of the day, and recent trends. For example, if the user inputs, "I want to go out in casual clothes today," the suggestion unit suggests optimal outfits based on the user's past casual clothing history and the weather information of the day. The suggestion unit also provides advice such as, "You've only worn this outfit recently, so you should choose different clothes." Furthermore, the suggestion unit can learn recent fashion trends and suggest outfits that incorporate the latest trends to the user. For example, the suggestion unit suggests outfits that incorporate the latest fashion items and styles. As a result, optimal outfits are suggested based on the information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit, enabling the user to select the best outfits for themselves.

[0130] The history management unit can estimate the user's emotions and adjust the clothing history recording method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to quickly record the clothing history. The history management unit can also provide detailed input options and suggest a customizable recording method if the user is relaxed. Furthermore, if the user is in a hurry, the history management unit can prioritize voice input to quickly record the clothing history. This allows for more appropriate recording by adjusting the clothing history recording method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0131] When recording clothing history, the history management unit can analyze the user's past clothing selection patterns and select the optimal recording method. For example, clothing that the user has frequently selected in the past can be automatically displayed as candidates. The history management unit can also prioritize and suggest recording methods (audio, text, etc.) that the user has used in the past. Furthermore, the history management unit can predict the clothing that will be used during a specific time period based on the user's past clothing selection patterns and select the recording method. In this way, the optimal recording method can be selected by analyzing the user's past clothing selection patterns.

[0132] When recording clothing history, the history management unit can filter the clothing history by taking into account the user's current lifestyle and event information. For example, if a user is participating in a specific event, the history management unit will prioritize recording clothing history that is appropriate for that event. The history management unit can also filter appropriate clothing history based on the user's lifestyle rhythm. Furthermore, the history management unit can also reference the user's calendar information and record clothing history based on plans. This allows appropriate clothing history to be recorded by taking into account the user's lifestyle and event information.

[0133] When recording clothing history, the history management unit can select the optimal recording means depending on the user's input method. For example, if the user records clothing by voice, the history management unit can prioritize support for voice input. Also, if the user records clothing by text, the history management unit can prioritize support for text input. Furthermore, if the user records clothing by image, the history management unit can automatically record clothing using image recognition technology. This allows for efficient recording by selecting the optimal recording means depending on the user's input method.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The history management unit manages the user's clothing history. The user's clothing history includes the date, type of clothing, color, brand, etc. The history management unit saves the clothing information entered by the user in a database and manages the user's past clothing history. It can also save images of the clothing worn by the user so that they can be visually checked. Step 2: The learning unit learns the user's preferences. The learning unit uses a machine learning algorithm to learn the user's preferences based on the user's selection history. For example, it analyzes the patterns of clothing the user has chosen in the past to identify the user's preferences. It can also adjust the learning algorithm based on user feedback to make more personalized suggestions. Step 3: The weather acquisition unit acquires weather information. The weather acquisition unit uses data from the Japan Meteorological Agency and weather forecast APIs to acquire weather and temperature information for the day. For example, information such as temperature, humidity, precipitation, and wind speed is acquired and provided to the proposal unit. Step 4: The trend acquisition unit acquires trend information. The trend acquisition unit acquires information on the latest fashion trends using data from fashion magazines, social media, and online shops. For example, it collects the latest fashion items and styles and provides them to the suggestion unit. Step 5: The suggestion unit suggests outfits based on the information obtained by the learning unit, weather acquisition unit, and trend acquisition unit. The suggestion unit suggests optimal outfits taking into consideration the user's preferences, the weather of the day, and recent trends. For example, if the user inputs, "I want to go out in casual clothes today," the suggestion unit will suggest optimal outfits based on the user's past casual clothing history and the weather information for the day. It may also provide advice such as, "You've only recently worn this outfit, so you should choose something different."

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the 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.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

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

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0206] 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.

[0207] [Explanation of symbols]

[0208] 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 history management unit that manages the user's clothing history; a learning unit that learns user preferences based on the information managed by the history management unit; a weather acquisition unit that acquires weather information; a trend acquisition unit that acquires trend information; a suggestion unit that suggests outfits based on the information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit; A system characterized by:

2. The history management unit Record and manage the user's clothing history 2. The system of claim 1.

3. The weather acquisition unit Get weather or temperature information for the day 2. The system of claim 1.

4. The trend acquisition unit Get the latest fashion trends 2. The system of claim 1.

5. The learning unit Use machine learning based on user selection history to make more personalized suggestions 2. The system of claim 1.

6. The proposal unit Proposing coordination based on information obtained by the learning unit, the weather acquisition unit, and the trend acquisition unit 2. The system of claim 1.

7. The history management unit Estimate the user's emotions and adjust the method of recording clothing history based on the estimated user emotions.

2. The system of claim 1.

8. The history management unit When recording clothing history, the system analyzes the user's past clothing selection patterns and selects the optimal recording method.

2. The system of claim 1.

Images