system

Abstract

The system according to the embodiment aims to allow users to easily select cosmetics that suit them and try them on virtually. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, and a simulation unit. The input unit inputs the characteristics of a user's skin type or preferred cosmetics. The analysis unit analyzes the information input by the input unit. The suggestion unit suggests cosmetics based on the information analyzed by the analysis unit. The simulation unit virtually tries on the cosmetics suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for users to choose cosmetics that suit them, and there is a risk of failure if they are unable to actually try them out.

[0005] The system according to the embodiment aims to allow users to easily select cosmetics that suit them and try them on virtually. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, and a simulation unit. The input unit inputs the characteristics of the user's skin type or preferred cosmetics. The analysis unit analyzes the information input by the input unit. The suggestion unit suggests cosmetics based on the information analyzed by the analysis unit. The simulation unit virtually tries on the cosmetics suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily select cosmetics that suit them and try them on virtually. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A cosmetics selection support system according to an embodiment of the present invention uses a generation AI to suggest optimal cosmetics based on the user's skin type and preferences, allowing them to virtually try them on. The system allows users to input their skin type and preferred cosmetic characteristics, and the generation AI analyzes that information to suggest optimal cosmetics. Furthermore, even if the user is unable to try them on in person, the generation AI performs a virtual try-on simulation and provides the results to the user. For example, if a user inputs information such as "I have dry, sensitive skin and I like pink lipsticks," the generation AI analyzes that information and suggests a moisturizing, hypoallergenic pink lipstick. The generation AI also suggests skincare series suited to the user's skin type. Furthermore, the generation AI virtually tests whether the lipstick color selected by the user suits them and provides the results via images and videos. This allows users to confirm the effectiveness of the cosmetics before purchasing, preventing disappointment. This system also contributes to improving the cosmetics industry's sluggish e-commerce adoption rate. Users can easily find the cosmetics that suit them, which is expected to increase online purchases. This will improve the e-commerce adoption rate in the cosmetics industry and revitalize the industry as a whole. The cosmetics selection support system then suggests optimal cosmetics based on the user's skin type and preferences, allowing them to virtually try them on.

[0029] A cosmetics selection support system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, and a simulation unit. The input unit inputs the user's skin type or preferred cosmetic characteristics. Examples of the user's skin type include, but are not limited to, dry skin, oily skin, and combination skin. Examples of the preferred cosmetic characteristics include, but are not limited to, color, texture, and ingredients. The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, but is not limited to, a data analysis algorithm. The suggestion unit suggests cosmetics based on the information analyzed by the analysis unit. The suggestions are made based on, for example, but are not limited to, the priority and content of the suggestions. The simulation unit virtually tries on the cosmetics suggested by the suggestion unit and provides the results as images or videos. The virtual try-on method uses, for example, but is not limited to, simulation technology. As a result, the cosmetics selection support system according to an embodiment suggests optimal cosmetics based on the user's skin type and preferences, allowing the user to virtually try them on.

[0030] The suggestion unit can suggest a series of cosmetics suitable for the user's skin type. For example, the suggestion unit suggests a series of cosmetics suitable for the user's skin type. A series of cosmetics includes, but is not limited to, a group of products from the same brand or a group of products with a specific effect. This allows the user to select the most suitable cosmetics all at once.

[0031] The simulation unit can virtually test the effects of cosmetics selected by the user and provide the results in the form of images or videos. For example, the simulation unit can virtually test the effects of cosmetics selected by the user and provide the results in the form of images or videos. Methods for virtually testing the effects include, but are not limited to, simulation technology. Methods for providing the images or videos include, but are not limited to, resolution, format, and display method. This allows the user to check the effects of cosmetics before purchasing, thereby preventing mistakes.

[0032] The suggestion unit can suggest cosmetics with moisturizing effects based on the user's skin type or preferences. The suggestion unit suggests cosmetics with moisturizing effects based on the user's skin type or preferences, for example. Moisturizing cosmetics include, but are not limited to, specific ingredients and duration of effects. This allows the user to easily find cosmetics that suit them.

[0033] The suggestion unit can suggest optimal cosmetics based on the user's preferred colors and textures. The suggestion unit suggests optimal cosmetics based on, for example, the user's preferred colors and textures. Preferred colors and textures include, but are not limited to, types of colors and types of textures. This allows the user to easily find cosmetics that suit them.

[0034] The simulation unit can virtually test whether the lip color selected by the user suits the user and provide the results in the form of images or videos. The simulation unit can virtually test whether the lip color selected by the user suits the user and provide the results in the form of images or videos. Lip color includes, but is not limited to, hue, saturation, and brightness, for example. This allows the user to check whether the lip color suits them before purchasing, thereby preventing mistakes.

[0035] The input unit can analyze the user's past input history and suggest the optimal input method. The input unit, for example, analyzes the user's past input history and suggests the optimal input method. The past input history includes, for example, but is not limited to, the type and frequency of input data. The optimal input method includes, for example, but is not limited to, input efficiency and accuracy. This can make input work more efficient.

[0036] The input unit can scan the user's current skin condition in real time when inputting information and automatically input that information. For example, when the user opens the app, the input unit uses a camera to scan the user's current skin condition and reflects the information as input information. Methods for scanning the skin condition in real time include, but are not limited to, the sensor used and the accuracy of the scan. This allows cosmetics to be recommended based on more accurate information.

[0037] The input unit can automatically input region-specific cosmetic information taking into account the user's geographical location information when inputting information. For example, if the user is in a specific region, the input unit automatically inputs information about cosmetics popular in that region. Examples of geographical location information include, but are not limited to, GPS data and IP address. Examples of region-specific cosmetic information include, but are not limited to, the climate and culture of the region. This allows the user to receive more appropriate cosmetic information.

[0038] The input unit can analyze the user's social media activity at the time of input and automatically input related cosmetic information. For example, the input unit automatically inputs cosmetic information that the user has "liked" or commented on on social media. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related cosmetic information includes, for example, but is not limited to, trends and popularity. This allows the user to be provided with more relevant cosmetic information.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past cosmetic use history. The analysis unit adjusts the current analysis results, for example, based on the effects of cosmetics used by the user in the past. Past cosmetic use history includes, but is not limited to, the products used and frequency of use. This improves the accuracy of the analysis and provides more appropriate analysis results.

[0040] The analysis unit can dynamically update the analysis results during analysis, taking into account changes in the user's skin condition. For example, when the user's skin condition changes, the analysis unit updates the analysis results in real time. Changes in skin condition include, but are not limited to, dryness and oil content. Methods for dynamically updating the analysis results include, but are not limited to, the use of real-time data and update frequency. This allows for more accurate analysis results to be provided.

[0041] During analysis, the analysis unit can provide region-specific analysis results by taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit can provide analysis results appropriate for the climate and environment of that region. Examples of geographical location information include, but are not limited to, GPS data and IP address. Examples of region-specific analysis results include, but are not limited to, the climate and culture of the region. This allows for more appropriate analysis results to be provided.

[0042] During the analysis, the analysis unit can analyze the user's social media activity and provide relevant analysis results. The analysis unit can provide, for example, analysis results for cosmetics that the user has "liked" or commented on on social media. Social media activity can include, for example, but is not limited to, the content of posts and the number of likes. Related analysis results can include, for example, but are not limited to, trends and popularity. This can provide more relevant analysis results to the user.

[0043] When making a suggestion, the suggestion unit can make optimal suggestions by referring to the user's past cosmetic use history. The suggestion unit adjusts the current suggestion, for example, based on the effects of cosmetics used by the user in the past. The past cosmetic use history includes, for example, but is not limited to, the products used and the frequency of use. This allows the suggestion unit to make optimal suggestions and recommend cosmetics that are more suitable for the user.

[0044] The suggestion unit may dynamically update the suggestion content in consideration of changes in the user's skin condition when making a suggestion. For example, the suggestion unit updates the suggestion content in real time when the user's skin condition changes. Changes in skin condition include, but are not limited to, dryness and oil content. Methods for dynamically updating the suggestion content include, but are not limited to, the use of real-time data and update frequency. This allows for more accurate suggestions to be provided.

[0045] The suggestion unit can suggest cosmetics specific to a region by taking into account the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can suggest cosmetics that are popular in that region. Examples of geographical location information include, but are not limited to, GPS data and IP address. Examples of region-specific cosmetics include, but are not limited to, the climate and culture of the region. This allows for more appropriate cosmetics to be provided.

[0046] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related cosmetics. For example, the suggestion unit can suggest cosmetics that the user has "liked" or commented on on social media. Social media activity includes, but is not limited to, the content of posts and the number of likes. Related cosmetics include, but are not limited to, trends and popularity. This allows cosmetics that are more relevant to the user to be provided.

[0047] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past cosmetic use history. The simulation unit adjusts the simulation results, for example, based on the effects of cosmetics used by the user in the past. The past cosmetic use history includes, for example, but is not limited to, the products used and the frequency of use. This improves the accuracy of the simulation and provides more accurate simulation results.

[0048] The simulation unit can dynamically update the simulation results during the simulation, taking into account changes in the user's skin condition. For example, the simulation unit updates the simulation results in real time when the user's skin condition changes. Changes in skin condition include, but are not limited to, dryness and oil content. Methods for dynamically updating the simulation results include, but are not limited to, the use of real-time data and update frequency. This allows for more accurate simulation results to be provided.

[0049] During the simulation, the simulation unit can provide region-specific simulation results by taking into account the user's geographical location information. For example, if the user is in a specific region, the simulation unit can provide simulation results appropriate for the climate and environment of that region. Examples of geographical location information include, but are not limited to, GPS data and IP address. Examples of region-specific simulation results include, but are not limited to, the climate and culture of the region. This allows for more appropriate simulation results to be provided.

[0050] The simulation unit can analyze the user's social media activity during the simulation and provide related simulation results. For example, the simulation unit can provide simulation results for cosmetics that the user has "liked" or commented on on social media. Social media activity can include, but is not limited to, the content of posts and the number of likes. Related simulation results can include, but are not limited to, trends and popularity. This can provide simulation results that are more relevant to the user.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The suggestion unit can suggest the best cosmetics for each season based on the user's skin type and preferences. For example, it can suggest cosmetics with high moisturizing effects in winter and cosmetics with low oil content in summer. It can also suggest skin care taking into account seasonal changes in skin. It can also suggest cosmetics incorporating seasonal trend colors. This allows the user to choose the best cosmetics for each season.

[0053] The analysis unit can incorporate information about the user's lifestyle in addition to their skin type and preferences. For example, if a user enjoys outdoor activities, it can suggest cosmetics with sunscreen effects. Or, if a user works night shifts, it can suggest cosmetics that refresh tired skin. Furthermore, if a user has a specific diet, it can also suggest cosmetics that take that influence into account. This makes it possible to provide cosmetics that suit the user's lifestyle.

[0054] The simulation unit can simulate how cosmetics look under different lighting conditions when users virtually test the effects of their selected cosmetics. For example, it provides information on how cosmetics look under different lighting conditions, such as natural light, fluorescent light, and LED light. It can also simulate how cosmetics look at different times of the day (morning, afternoon, night). It can also provide information on how cosmetics look on different background colors (white, black, colorful). This allows users to check the effects of cosmetics under conditions similar to the actual usage environment.

[0055] The suggestion unit can suggest cosmetics suitable for specific events or occasions based on the user's skin type and preferences. For example, it can suggest glamorous cosmetics suitable for special events such as weddings and parties. It can also suggest understated cosmetics suitable for business settings. It can also suggest natural cosmetics suitable for casual everyday use. This allows the user to choose the best cosmetics for each occasion.

[0056] The input unit can input allergy information in addition to the user's skin type and preferences. For example, if a user is allergic to a specific ingredient, cosmetics that do not contain that ingredient can be suggested. For users with sensitive skin, hypoallergenic cosmetics can also be suggested. Furthermore, based on the allergy information, skin care products suitable for the user can also be suggested. This allows users to select cosmetics that they can use with confidence.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The input unit inputs the user's skin type or preferred cosmetic characteristics. Examples of the user's skin type include, but are not limited to, dry skin, oily skin, and combination skin. Examples of the preferred cosmetic characteristics include, but are not limited to, color, texture, and ingredients. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, but not limited to, a data analysis algorithm. Step 3: The suggestion unit suggests cosmetics based on the information analyzed by the analysis unit. The suggestions are made based on, for example, but not limited to, the priority and content of the suggestions. Step 4: The simulation unit virtually tries on the cosmetics suggested by the suggestion unit and provides the results in the form of images and videos. The virtual try-on method may use, for example, simulation technology, but is not limited to, this example.

[0059] (Example 2) A cosmetics selection support system according to an embodiment of the present invention uses a generation AI to suggest optimal cosmetics based on the user's skin type and preferences, allowing them to virtually try them on. The system allows users to input their skin type and preferred cosmetic characteristics, and the generation AI analyzes that information to suggest optimal cosmetics. Furthermore, even if the user is unable to try them on in person, the generation AI performs a virtual try-on simulation and provides the results to the user. For example, if a user inputs information such as "I have dry, sensitive skin and I like pink lipsticks," the generation AI analyzes that information and suggests a moisturizing, hypoallergenic pink lipstick. The generation AI also suggests skincare series suited to the user's skin type. Furthermore, the generation AI virtually tests whether the lipstick color selected by the user suits them and provides the results via images and videos. This allows users to confirm the effectiveness of the cosmetics before purchasing, preventing disappointment. This system also contributes to improving the cosmetics industry's sluggish e-commerce adoption rate. Users can easily find the cosmetics that suit them, which is expected to increase online purchases. This will improve the e-commerce adoption rate in the cosmetics industry and revitalize the industry as a whole. The cosmetics selection support system then suggests optimal cosmetics based on the user's skin type and preferences, allowing them to virtually try them on.

[0060] A cosmetics selection support system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, and a simulation unit. The input unit inputs the user's skin type or preferred cosmetic characteristics. Examples of the user's skin type include, but are not limited to, dry skin, oily skin, and combination skin. Examples of the preferred cosmetic characteristics include, but are not limited to, color, texture, and ingredients. The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, but is not limited to, a data analysis algorithm. The suggestion unit suggests cosmetics based on the information analyzed by the analysis unit. The suggestions are made based on, for example, but are not limited to, the priority and content of the suggestions. The simulation unit virtually tries on the cosmetics suggested by the suggestion unit and provides the results as images or videos. The virtual try-on method uses, for example, but is not limited to, simulation technology. As a result, the cosmetics selection support system according to an embodiment suggests optimal cosmetics based on the user's skin type and preferences, allowing the user to virtually try them on.

[0061] The suggestion unit can suggest a series of cosmetics suitable for the user's skin type. For example, the suggestion unit suggests a series of cosmetics suitable for the user's skin type. A series of cosmetics includes, but is not limited to, a group of products from the same brand or a group of products with a specific effect. This allows the user to select the most suitable cosmetics all at once.

[0062] The simulation unit can virtually test the effects of cosmetics selected by the user and provide the results in the form of images or videos. For example, the simulation unit can virtually test the effects of cosmetics selected by the user and provide the results in the form of images or videos. Methods for virtually testing the effects include, but are not limited to, simulation technology. Methods for providing the images or videos include, but are not limited to, resolution, format, and display method. This allows the user to check the effects of cosmetics before purchasing, thereby preventing mistakes.

[0063] The suggestion unit can suggest cosmetics with moisturizing effects based on the user's skin type or preferences. The suggestion unit suggests cosmetics with moisturizing effects based on the user's skin type or preferences, for example. Moisturizing cosmetics include, but are not limited to, specific ingredients and duration of effects. This allows the user to easily find cosmetics that suit them.

[0064] The suggestion unit can suggest optimal cosmetics based on the user's preferred colors and textures. The suggestion unit suggests optimal cosmetics based on, for example, the user's preferred colors and textures. Preferred colors and textures include, but are not limited to, types of colors and types of textures. This allows the user to easily find cosmetics that suit them.

[0065] The simulation unit can virtually test whether the lip color selected by the user suits the user and provide the results in the form of images or videos. The simulation unit can virtually test whether the lip color selected by the user suits the user and provide the results in the form of images or videos. Lip color includes, but is not limited to, hue, saturation, and brightness, for example. This allows the user to check whether the lip color suits them before purchasing, thereby preventing mistakes.

[0066] The input unit can estimate a user's emotion and adjust the design of the input interface based on the estimated user's emotion. The input unit, for example, estimates a user's emotion and adjusts the design of the input interface based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. Methods for adjusting the design of the input interface include, but are not limited to, changing the color or layout. This reduces stress for the user and makes input work more enjoyable.

[0067] The input unit can analyze the user's past input history and suggest the optimal input method. The input unit, for example, analyzes the user's past input history and suggests the optimal input method. The past input history includes, for example, but is not limited to, the type and frequency of input data. The optimal input method includes, for example, but is not limited to, input efficiency and accuracy. This can make input work more efficient.

[0068] The input unit can scan the user's current skin condition in real time when inputting information and automatically input that information. For example, when the user opens the app, the input unit uses a camera to scan the user's current skin condition and reflects the information as input information. Methods for scanning the skin condition in real time include, but are not limited to, the sensor used and the accuracy of the scan. This allows cosmetics to be recommended based on more accurate information.

[0069] The input unit can estimate the user's emotion and determine the priority of input items based on the estimated user's emotion. The input unit, for example, estimates the user's emotion and determines the priority of input items based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. Methods for determining the priority of input items include, but are not limited to, importance and urgency. This can reduce user stress and improve the efficiency of input work.

[0070] The input unit can automatically input region-specific cosmetic information taking into account the user's geographical location information when inputting information. For example, if the user is in a specific region, the input unit automatically inputs information about cosmetics popular in that region. Examples of geographical location information include, but are not limited to, GPS data and IP address. Examples of region-specific cosmetic information include, but are not limited to, the climate and culture of the region. This allows the user to receive more appropriate cosmetic information.

[0071] The input unit can analyze the user's social media activity at the time of input and automatically input related cosmetic information. For example, the input unit automatically inputs cosmetic information that the user has "liked" or commented on on social media. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related cosmetic information includes, for example, but is not limited to, trends and popularity. This allows the user to be provided with more relevant cosmetic information.

[0072] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, if the user is relaxed, the analysis unit performs a detailed analysis and provides more information. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. Methods for adjusting the analysis algorithm include, but are not limited to, parameter changes and model selection. This can provide more appropriate analysis results.

[0073] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past cosmetic use history. The analysis unit adjusts the current analysis results, for example, based on the effects of cosmetics used by the user in the past. Past cosmetic use history includes, but is not limited to, the products used and frequency of use. This improves the accuracy of the analysis and provides more appropriate analysis results.

[0074] The analysis unit can dynamically update the analysis results during analysis, taking into account changes in the user's skin condition. For example, when the user's skin condition changes, the analysis unit updates the analysis results in real time. Changes in skin condition include, but are not limited to, dryness and oil content. Methods for dynamically updating the analysis results include, but are not limited to, the use of real-time data and update frequency. This allows for more accurate analysis results to be provided.

[0075] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. Methods for adjusting the display method of the analysis results include, but are not limited to, changing the color or layout. This makes it possible to provide analysis results that are easier for the user to see.

[0076] During analysis, the analysis unit can provide region-specific analysis results by taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit can provide analysis results appropriate for the climate and environment of that region. Examples of geographical location information include, but are not limited to, GPS data and IP address. Examples of region-specific analysis results include, but are not limited to, the climate and culture of the region. This allows for more appropriate analysis results to be provided.

[0077] During the analysis, the analysis unit can analyze the user's social media activity and provide relevant analysis results. The analysis unit can provide, for example, analysis results for cosmetics that the user has "liked" or commented on on social media. Social media activity can include, for example, but is not limited to, the content of posts and the number of likes. Related analysis results can include, for example, but are not limited to, trends and popularity. This can provide more relevant analysis results to the user.

[0078] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. For example, if the user is nervous, the suggestion unit provides a simple and highly visible suggestion method. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. Methods for adjusting the way the suggestion is presented include, but are not limited to, changing the wording or visuals. This makes it possible to provide suggestions that are easier for the user to see.

[0079] When making a suggestion, the suggestion unit can make optimal suggestions by referring to the user's past cosmetic use history. The suggestion unit adjusts the current suggestion, for example, based on the effects of cosmetics used by the user in the past. The past cosmetic use history includes, for example, but is not limited to, the products used and the frequency of use. This allows the suggestion unit to make optimal suggestions and recommend cosmetics that are more suitable for the user.

[0080] The suggestion unit may dynamically update the suggestion content in consideration of changes in the user's skin condition when making a suggestion. For example, the suggestion unit updates the suggestion content in real time when the user's skin condition changes. Changes in skin condition include, but are not limited to, dryness and oil content. Methods for dynamically updating the suggestion content include, but are not limited to, the use of real-time data and update frequency. This allows for more accurate suggestions to be provided.

[0081] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. For example, when the user is feeling stressed, the suggestion unit prioritizes displaying the most important suggestions and simplifies the suggestion procedure. Methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. Methods for determining the priority of suggestions include, but are not limited to, importance and urgency. This allows suggestions that are more important to the user to be provided preferentially.

[0082] The suggestion unit can suggest cosmetics specific to a region by taking into account the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can suggest cosmetics that are popular in that region. Examples of geographical location information include, but are not limited to, GPS data and IP address. Examples of region-specific cosmetics include, but are not limited to, the climate and culture of the region. This allows for more appropriate cosmetics to be provided.

[0083] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related cosmetics. For example, the suggestion unit can suggest cosmetics that the user has "liked" or commented on on social media. Social media activity includes, but is not limited to, the content of posts and the number of likes. Related cosmetics include, but are not limited to, trends and popularity. This allows cosmetics that are more relevant to the user to be provided.

[0084] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, when the user is nervous, the simulation unit provides a simple and highly visible display method. Methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. Methods for adjusting the display method of the simulation results include, but are not limited to, changing the color or layout. This makes it possible to provide simulation results that are easier for the user to see.

[0085] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past cosmetic use history. The simulation unit adjusts the simulation results, for example, based on the effects of cosmetics used by the user in the past. The past cosmetic use history includes, for example, but is not limited to, the products used and the frequency of use. This improves the accuracy of the simulation and provides more accurate simulation results.

[0086] The simulation unit can dynamically update the simulation results during the simulation, taking into account changes in the user's skin condition. For example, the simulation unit updates the simulation results in real time when the user's skin condition changes. Changes in skin condition include, but are not limited to, dryness and oil content. Methods for dynamically updating the simulation results include, but are not limited to, the use of real-time data and update frequency. This allows for more accurate simulation results to be provided.

[0087] The simulation unit can estimate the user's emotions and prioritize the simulation results based on the estimated user emotions. For example, when the user is feeling stressed, the simulation unit prioritizes displaying the most important simulation results and simplifies the simulation procedure. Methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. Methods for determining the priority of the simulation results include, but are not limited to, importance and urgency. This allows simulation results that are more important to the user to be provided preferentially.

[0088] During the simulation, the simulation unit can provide region-specific simulation results by taking into account the user's geographical location information. For example, if the user is in a specific region, the simulation unit can provide simulation results appropriate for the climate and environment of that region. Examples of geographical location information include, but are not limited to, GPS data and IP address. Examples of region-specific simulation results include, but are not limited to, the climate and culture of the region. This allows for more appropriate simulation results to be provided.

[0089] The simulation unit can analyze the user's social media activity during the simulation and provide related simulation results. For example, the simulation unit can provide simulation results for cosmetics that the user has "liked" or commented on on social media. Social media activity can include, but is not limited to, the content of posts and the number of likes. Related simulation results can include, but are not limited to, trends and popularity. This can provide simulation results that are more relevant to the user. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, and simulation unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is implemented by the reception device 38 of the smart device 14 and inputs the user's skin type and preferred cosmetic characteristics. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal cosmetics based on the analysis results. The simulation unit is implemented, for example, by the control unit 46A of the smart device 14 and virtually tries on the suggested cosmetics and provides the results as images or videos. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, and simulation unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is implemented by the microphone 238 of the smart glasses 214 and inputs the user's skin type and preferred cosmetic characteristics. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal cosmetics based on the analysis results. The simulation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and virtually tries on the suggested cosmetics and provides the results as images or videos. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, and simulation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314 and inputs the user's skin type and preferred cosmetic characteristics. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal cosmetics based on the analysis results. The simulation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and virtually tries on the suggested cosmetics and provides the results in the form of images or videos. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, and simulation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414 and inputs the user's skin type and preferred cosmetic characteristics. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal cosmetics based on the analysis results. The simulation unit is realized, for example, by the control unit 46A of the robot 414 and virtually tries on the suggested cosmetics and provides the results in the form of images or videos.

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

[0091] The suggestion unit can suggest the best cosmetics for each season based on the user's skin type and preferences. For example, it can suggest cosmetics with high moisturizing effects in winter and cosmetics with low oil content in summer. It can also suggest skin care taking into account seasonal changes in skin. It can also suggest cosmetics incorporating seasonal trend colors. This allows the user to choose the best cosmetics for each season.

[0092] The analysis unit can incorporate information about the user's lifestyle in addition to their skin type and preferences. For example, if a user enjoys outdoor activities, it can suggest cosmetics with sunscreen effects. Or, if a user works night shifts, it can suggest cosmetics that refresh tired skin. Furthermore, if a user has a specific diet, it can also suggest cosmetics that take that influence into account. This makes it possible to provide cosmetics that suit the user's lifestyle.

[0093] The simulation unit can simulate how cosmetics look under different lighting conditions when users virtually test the effects of their selected cosmetics. For example, it provides information on how cosmetics look under different lighting conditions, such as natural light, fluorescent light, and LED light. It can also simulate how cosmetics look at different times of the day (morning, afternoon, night). It can also provide information on how cosmetics look on different background colors (white, black, colorful). This allows users to check the effects of cosmetics under conditions similar to the actual usage environment.

[0094] The suggestion unit can suggest cosmetics suitable for specific events or occasions based on the user's skin type and preferences. For example, it can suggest glamorous cosmetics suitable for special events such as weddings and parties. It can also suggest understated cosmetics suitable for business settings. It can also suggest natural cosmetics suitable for casual everyday use. This allows the user to choose the best cosmetics for each occasion.

[0095] The input unit can input allergy information in addition to the user's skin type and preferences. For example, if a user is allergic to a specific ingredient, cosmetics that do not contain that ingredient can be suggested. For users with sensitive skin, hypoallergenic cosmetics can also be suggested. Furthermore, based on the allergy information, skin care products suitable for the user can also be suggested. This allows users to select cosmetics that they can use with confidence.

[0096] The suggestion unit can estimate the user's emotions and suggest cosmetics with a relaxing effect based on the estimated user emotions. For example, if the user is feeling stressed, cosmetics with an aroma effect can be suggested. Also, if the user is relaxed, cosmetics with a refreshing effect can be suggested. Furthermore, skin care products with a relaxing effect can also be suggested according to the user's emotions. This allows the user to select the optimal cosmetics according to their emotions.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. On the other hand, if the user is relaxed, detailed analysis results can be provided. Furthermore, it is possible to adjust the color and font size of the analysis results depending on the user's emotions. This makes it possible to provide analysis results that are easy for the user to see.

[0098] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. On the other hand, if the user is relaxed, detailed simulation results can be provided. Furthermore, it is also possible to adjust the color and font size of the simulation results according to the user's emotions. This makes it possible to provide simulation results that are easy for the user to see.

[0099] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the most important suggestions can be displayed preferentially and the suggestion procedure can be simplified. Also, if the user is relaxed, detailed suggestions can be provided. Furthermore, it is possible to adjust the content and presentation of the suggestions according to the user's emotions. This makes it possible to provide the most suitable suggestions for the user.

[0100] The simulation unit can estimate the user's emotions and prioritize the simulation results based on the estimated user emotions. For example, if the user is feeling stressed, the most important simulation results can be displayed preferentially and the simulation procedure can be simplified. Also, if the user is relaxed, detailed simulation results can be provided. Furthermore, it is possible to adjust the content and presentation of the simulation results according to the user's emotions. This makes it possible to provide the user with the optimal simulation results.

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

[0102] Step 1: The input unit inputs the user's skin type or preferred cosmetic characteristics. Examples of the user's skin type include, but are not limited to, dry skin, oily skin, and combination skin. Examples of the preferred cosmetic characteristics include, but are not limited to, color, texture, and ingredients. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, but not limited to, a data analysis algorithm. Step 3: The suggestion unit suggests cosmetics based on the information analyzed by the analysis unit. The suggestions are made based on, for example, but not limited to, the priority and content of the suggestions. Step 4: The simulation unit virtually tries on the cosmetics suggested by the suggestion unit and provides the results in the form of images and videos. The virtual try-on method may use, for example, simulation technology, but is not limited to, this example.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

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

Claims

1. A system comprising an input unit for inputting the characteristics of a user's skin type or preferred cosmetics, an analysis unit for analyzing the information input by the input unit, a suggestion unit for suggesting cosmetics based on the information analyzed by the analysis unit, and a simulation unit for virtually trying out the cosmetics suggested by the suggestion unit.

2. The proposal unit Propose a cosmetic series suited to the user's skin type The system of claim 1 .

3. The simulation unit Users can virtually try out the effects of their chosen cosmetics and see the results in images and videos. The system of claim 1 .

4. The system according to claim 1 , wherein the suggestion unit suggests cosmetics with moisturizing effects based on the user's skin type or preferences.

5. The proposal unit Recommends the best cosmetics based on the user's preferred colors and textures The system of claim 1 .

6. The simulation unit Users can virtually try on the lip color they have chosen to see if it suits them, and the results are provided as images and videos. The system of claim 1 .

7. The input unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions. The system of claim 1 .

8. The input unit Analyzes the user's past input history and suggests the optimal input method The system of claim 1 .

Images