System

Abstract

An object of a system according to an embodiment is to propose optimal coordination in accordance with a schedule of a user or weather.SOLUTION: A system includes a schedule acquisition unit, a weather information acquisition unit, a clothing storage unit, and a coordination proposal unit. The schedule acquisition unit acquires a schedule of a user. The weather information acquisition unit acquires weather information. The clothes storage unit stores clothes of a user. The coordination proposing section proposes coordination based on the information acquired by the schedule acquiring section and the weather information acquiring section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to choose the best outfit for the day based on their plans and the weather.

[0005] The system according to the embodiment aims to propose optimal outfits that match the user's schedule and the weather. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule acquisition unit, a weather information acquisition unit, an outfit storage unit, and a coordination suggestion unit. The schedule acquisition unit acquires a user's schedule. The weather information acquisition unit acquires weather information. The outfit storage unit stores the user's outfit. The coordination suggestion unit suggests outfits based on the information acquired by the schedule acquisition unit and the weather information acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the best outfits to suit the user's schedule and weather. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​chat consultation system according to the embodiment of the present invention is a system that proposes optimal outfits based on the user's schedule and weather for the day. This allows the AI ​​chat consultation system to propose optimal outfits based on the user's schedule and weather.

[0029] The AI ​​chat consultation system according to the embodiment includes a schedule acquisition unit, a weather information acquisition unit, an outfit storage unit, and an outfit suggestion unit. The schedule acquisition unit acquires a user's schedule. For example, it acquires schedules entered by the user in a calendar app. It can also acquire schedules entered directly by the user. Furthermore, the schedule acquisition unit can refer to the user's past activity history and automatically suggest similar schedules. For example, it can refer to schedules made when the user visited the same location in the past. The weather information acquisition unit acquires weather information. For example, it works with a weather forecast API to acquire weather information in real time. It can also update the weather forecast for the current location in real time based on the user's location information. Furthermore, the weather information acquisition unit can respond to sudden weather changes. For example, it can suggest bringing an umbrella if it looks like it's going to rain. The outfit storage unit stores the outfits the user owns. For example, the outfit storage unit stores the information when the user inputs photos and information about the outfit. The outfit storage unit also records detailed information about the outfit, such as the material, color, and design, allowing it to suggest outfits with greater accuracy. For example, it can make suggestions that take into account the characteristics of the material. The outfit suggestion unit suggests outfits based on information acquired by the schedule acquisition unit and the weather information acquisition unit. For example, if a user inputs, "I'm planning to go to a cafe with a friend today. The weather is sunny," the outfit suggestion unit suggests outfits suitable for the cafe from among the clothes the user already owns. Furthermore, the outfit suggestion unit can also consider the user's emotional state and suggest outfits that match the user's mood for the day. For example, it can suggest casual clothes when the user feels like relaxing. This allows the AI ​​chat consultation system according to the embodiment to suggest optimal outfits based on the user's schedule and weather. For example, by utilizing the clothes the user already owns, there is no need to purchase new clothes, which is economical. Furthermore, by suggesting clothes from an online shopping site, the user can try out new styles.

[0030] The schedule acquisition unit can refer to the user's past activity history and automatically suggest similar schedules. In the schedule acquisition unit, for example, the generation AI works with the user's calendar app to analyze past schedules and automatically suggest similar schedules. For example, it refers to schedules made when the user went to the same place in the past. In addition, the schedule acquisition unit has the generation AI suggest similar schedules based on the user's past activity history. For example, it suggests a similar schedule based on a schedule of a past trip to a cafe with friends. In addition, the schedule acquisition unit has the generation AI analyze the user's past activity history and suggest similar schedules based on frequently visited places and activities. For example, it suggests places and activities to go to on the weekend. In this way, it is possible to suggest similar schedules based on the user's past activity history.

[0031] The weather information acquisition unit updates the weather forecast in real time, allowing it to respond to sudden changes in weather. For example, the generation AI of the weather information acquisition unit works with a weather forecast API to acquire weather information in real time, allowing it to respond to sudden changes in weather. For example, it may suggest carrying an umbrella if it looks like it might rain. The generation AI of the weather information acquisition unit also updates the weather forecast for the current location in real time based on the user's location information. For example, it can also respond to changes in the weather while traveling. The generation AI of the weather information acquisition unit also analyzes weather forecast data and suggests outfits to respond to sudden changes in weather. For example, it suggests clothing to respond to sudden changes in temperature. This allows it to respond to sudden changes in weather.

[0032] The schedule acquisition unit can work with the user's calendar app and automatically import schedules. In the schedule acquisition unit, for example, the generation AI works with the user's calendar app and automatically imports schedules. For example, schedules are acquired from Google Calendar or Outlook Calendar. The schedule acquisition unit also works with the user's calendar app, and the generation AI automatically imports schedules and combines them with weather information to suggest outfits. For example, it suggests the best outfit based on the schedule and weather. The schedule acquisition unit also works with the user's calendar app, and the generation AI imports schedules in real time and combines them with weather information to suggest outfits. For example, it can also handle schedule changes. This allows the schedule to be automatically imported in cooperation with the user's calendar app.

[0033] The weather information acquisition unit can suggest appropriate clothing based on the user's health condition. In the weather information acquisition unit, for example, the generation AI takes the user's health condition into consideration and suggests appropriate clothing based on allergy information. For example, it suggests clothing to protect against pollen during hay fever season. The weather information acquisition unit also takes the user's health condition into consideration and the generation AI suggests appropriate clothing. For example, it suggests warm clothing for a user who is sensitive to the cold. The weather information acquisition unit also suggests appropriate clothing based on the user's health information. For example, it suggests clothing made of skin-friendly materials for a user with sensitive skin. This makes it possible to suggest appropriate clothing based on the user's health condition.

[0034] The generation AI can analyze the user's schedule, evaluate its importance and urgency, and suggest an outfit based on that. For example, the generation AI can analyze the user's schedule, evaluate its importance and urgency, and suggest an outfit based on that. For example, it can suggest formal attire for an important business meeting. The generation AI can also analyze the user's schedule, evaluate its importance and urgency, and suggest an appropriate outfit based on that. For example, it can suggest easy-to-move-in outfits for urgent schedules. The generation AI can also analyze the user's schedule, and suggest an outfit based on its importance and urgency. For example, it can suggest eye-catching outfits for important events. This makes it possible to suggest outfits based on the importance and urgency of the schedule.

[0035] The generation AI can refer to weather data and suggest outfits that reflect seasonal trends. The generation AI, for example, refers to past weather data and suggests outfits that reflect seasonal trends. For example, it would suggest light clothing in spring. In addition, in analyzing weather information, the generation AI suggests outfits that reflect seasonal trends based on past weather data. For example, it would suggest cool clothing in summer. In addition, the generation AI analyzes past weather data and suggests outfits that reflect seasonal trends. For example, it would suggest warm clothing in autumn. This makes it possible to suggest outfits that reflect seasonal trends.

[0036] The generation AI can compare the schedule with similar plans of other users and suggest the optimal outfit. The generation AI, for example, compares the schedule with similar plans of other users and suggests the optimal outfit. For example, it takes into account the outfits of other users attending the same event. The generation AI can also analyze the user's schedule and suggest the optimal outfit by comparing it with similar plans of other users. For example, it takes into account the outfits of other users going to the same place. The generation AI can also compare the schedule with similar plans of other users and suggest the optimal outfit. For example, it takes into account the outfits of other users doing the same activity. This allows it to suggest the optimal outfit by comparing it with similar plans of other users.

[0037] The generation AI can take into account regional fashion trends and suggest outfits that are appropriate for the region. The generation AI, for example, can take into account regional fashion trends and suggest outfits that are appropriate for the region. For example, in urban areas, it can suggest outfits that are in line with the trends. In addition to analyzing weather information, the generation AI can also take into account regional fashion trends and suggest outfits that are appropriate for the region. For example, it can suggest resort-style outfits in resort areas. The generation AI can also take into account regional fashion trends and suggest outfits that are appropriate for the region. For example, it can suggest outfits that protect against the cold in cold regions. This makes it possible to take into account regional fashion trends and suggest outfits that are appropriate for the region.

[0038] When the generation AI memorizes the user's clothing, it also records detailed information about the material, color, and design, allowing it to suggest more accurate coordinations. For example, the generation AI records detailed information about the material, color, design, etc. of the clothing the user owns, and suggests more accurate coordinations. For example, it makes suggestions that take into account the characteristics of the material. The generation AI also records detailed information about the clothing the user owns, and suggests more accurate coordinations. For example, it makes suggestions that take into account color combinations. The generation AI also records detailed information about the clothing the user owns, and suggests more accurate coordinations. For example, it makes suggestions that take into account the characteristics of the design. This allows it to record detailed information about the clothing, and suggests more accurate coordinations.

[0039] When managing a user's clothing information, the generation AI can track the frequency of use and condition of the clothing, and suggest appropriate maintenance and replacement timing. For example, the generation AI can track the frequency of use of the user's clothing and suggest appropriate maintenance and replacement timing. For example, it can suggest maintenance for frequently worn clothing. The generation AI can also track the condition of the user's clothing and suggest appropriate maintenance and replacement timing. For example, it can detect fading or tears and make suggestions. The generation AI can also track the frequency of use and condition of the user's clothing and suggest appropriate maintenance and replacement timing. For example, it can suggest maintenance at the change of seasons. This allows the generation AI to track the frequency of use and condition of clothing, and suggest appropriate maintenance and replacement timing.

[0040] The generation AI can compare the user's clothing with that of other users and suggest outfits that reflect the latest trends. For example, the generation AI can compare the user's clothing with that of other users and suggest outfits that reflect the latest trends. For example, it can refer to trends among users of the same age. The generation AI can also memorize the clothing the user owns and suggest outfits that reflect the latest trends by comparing it with the clothing of other users. For example, it can suggest popular designs. The generation AI can also compare the user's clothing with that of other users and suggest outfits that reflect the latest trends. For example, it can refer to seasonal trends. This allows it to suggest outfits that reflect the latest trends by comparing it with the clothing of other users.

[0041] When managing a user's clothing information, the generative AI can suggest recycling and donation options and promote environmentally conscious coordination. For example, the generative AI manages a user's clothing information and suggests recycling and donation options. For example, it may suggest taking unwanted clothes to a recycle shop. The generative AI also manages a user's clothing information and suggests recycling and donation options. For example, it may suggest donating unwanted clothes. The generative AI also manages a user's clothing information and suggests recycling and donation options. For example, it may suggest environmentally conscious coordination. This allows the generative AI to suggest options for recycling and donating clothes and promote environmentally conscious coordination.

[0042] The generation AI records the user's clothing purchase history and brand information and can suggest outfits for each brand. For example, the generation AI records the user's clothing purchase history and brand information and suggests outfits for each brand. For example, it makes suggestions to combine clothes from specific brands. The generation AI also memorizes the user's clothing and suggests outfits for each brand based on the purchase history and brand information. For example, it makes suggestions to combine items from the same brand. The generation AI also records the user's clothing purchase history and brand information and suggests outfits for each brand. For example, it makes suggestions that make use of the characteristics of the brand. This makes it possible to suggest outfits for each brand based on the user's clothing purchase history and brand information.

[0043] When managing a user's clothing information, the generation AI can evaluate its applicability for each season and suggest coordination according to the season. For example, the generation AI manages a user's clothing information, evaluates its applicability for each season, and suggests coordination. For example, it suggests warm clothing in winter. The generation AI also manages a user's clothing information, evaluates its applicability for each season, and suggests coordination. For example, it suggests cool clothing in summer. The generation AI also manages a user's clothing information, evaluates its applicability for each season, and suggests coordination. For example, it suggests light clothing in spring. In this way, it can evaluate its applicability for each season and suggest coordination according to the season.

[0044] The generation AI can work in conjunction with other users' clothing data to make community-based coordination suggestions. The generation AI, for example, works in conjunction with other users' clothing data to make community-based coordination suggestions. For example, it can suggest coordination that is popular within the same community. The generation AI also memorizes the user's clothing and works in conjunction with other users' clothing data to make community-based coordination suggestions. For example, it can use friends' coordination as reference. The generation AI also works in conjunction with other users' clothing data to make community-based coordination suggestions. For example, it can suggest coordination from users who have the same hobbies. This makes it possible to work in conjunction with other users' clothing data to make community-based coordination suggestions.

[0045] The generative AI can work with clothing rental services to suggest clothing suitable for special events. The generative AI, for example, works with clothing rental services to suggest clothing suitable for special events. For example, it suggests clothing suitable for weddings and parties. The generative AI also manages the user's clothing information and works with clothing rental services to suggest clothing suitable for special events. For example, it suggests clothing suitable for formal events. The generative AI also works with clothing rental services to suggest clothing suitable for special events. For example, it suggests clothing suitable for theme parties. This allows the generative AI to work with clothing rental services to suggest clothing suitable for special events.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The AI ​​chat consultation system can also be equipped with an outfit suggestion unit that takes into account the user's health condition. For example, if a user has allergies, it can suggest clothes that protect against pollen during hay fever season. For users who are sensitive to the cold, it can also suggest clothes made from warm materials. Furthermore, it can suggest clothes made from materials that are gentle on the skin to users with sensitive skin. This makes it possible to suggest more appropriate outfits based on the user's health condition.

[0048] When managing a user's clothing information, the AI ​​chat consultation system can also track the frequency of use and condition of the clothing, and suggest appropriate maintenance and replacement timing. For example, it can suggest maintenance for frequently worn clothing. It can also detect fading and tears and suggest replacement. It can also suggest maintenance at the change of seasons. This allows it to track the frequency of use and condition of clothing, and suggest appropriate maintenance and replacement timing.

[0049] The AI ​​chat consultation system can also link with other users' clothing data to make community-based outfit suggestions. For example, it can suggest outfits that are popular within the same community. It can also use friends' outfits as reference. It can also suggest outfits from users with the same hobbies. This allows it to link with other users' clothing data and suggest a wider variety of outfits.

[0050] The AI ​​chat consultation system can also work with clothing rental services to suggest outfits suitable for special events. For example, it can suggest outfits suitable for weddings and parties. It can also suggest outfits suitable for formal events. It can even suggest outfits suitable for theme parties. This allows it to work with clothing rental services to suggest outfits suitable for special events.

[0051] The AI ​​chat consultation system can also manage the user's clothing information and suggest recycling or donation options. For example, it can suggest taking unwanted clothes to a recycle shop. It can also suggest donating unwanted clothes. It can also suggest environmentally friendly outfits. This allows it to suggest options for recycling or donating clothes and promote environmentally friendly outfits.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The schedule acquisition unit acquires the user's schedule. For example, it acquires schedules that the user has entered into a calendar app or entered manually. It can also refer to the user's past activity history and automatically suggest similar schedules. Step 2: The weather information acquisition unit acquires weather information. For example, it can connect to a weather forecast API to acquire weather information in real time. It can also update the weather forecast for the user's current location in real time based on the user's location information. It can also respond to sudden changes in weather. Step 3: The clothing memory unit memorizes the clothing the user owns. For example, the user can input photos and information about the clothing, and the clothing memory unit memorizes that information. It also records detailed information such as the material, color, and design of the clothing, allowing it to suggest more accurate outfits. Step 4: The outfit suggestion unit suggests outfits based on the information acquired by the schedule acquisition unit and the weather information acquisition unit. For example, if a user inputs, "I'm planning to go to a cafe with a friend today. The weather is sunny," the outfit suggestion unit will suggest outfits suitable for the cafe from among the clothes the user already owns. Furthermore, it can also take into account the user's emotional state and suggest outfits that match the user's mood that day.

[0054] (Example 2) The AI ​​chat consultation system according to the embodiment of the present invention is a system that proposes optimal outfits based on the user's schedule and weather for the day. This allows the AI ​​chat consultation system to propose optimal outfits based on the user's schedule and weather.

[0055] The AI ​​chat consultation system according to the embodiment includes a schedule acquisition unit, a weather information acquisition unit, an outfit storage unit, and an outfit suggestion unit. The schedule acquisition unit acquires a user's schedule. For example, it acquires schedules entered by the user in a calendar app. It can also acquire schedules entered directly by the user. Furthermore, the schedule acquisition unit can refer to the user's past activity history and automatically suggest similar schedules. For example, it can refer to schedules made when the user visited the same location in the past. The weather information acquisition unit acquires weather information. For example, it works with a weather forecast API to acquire weather information in real time. It can also update the weather forecast for the current location in real time based on the user's location information. Furthermore, the weather information acquisition unit can respond to sudden weather changes. For example, it can suggest bringing an umbrella if it looks like it's going to rain. The outfit storage unit stores the outfits the user owns. For example, the outfit storage unit stores the information when the user inputs photos and information about the outfit. The outfit storage unit also records detailed information about the outfit, such as the material, color, and design, allowing it to suggest outfits with greater accuracy. For example, it can make suggestions that take into account the characteristics of the material. The outfit suggestion unit suggests outfits based on information acquired by the schedule acquisition unit and the weather information acquisition unit. For example, if a user inputs, "I'm planning to go to a cafe with a friend today. The weather is sunny," the outfit suggestion unit suggests outfits suitable for the cafe from among the clothes the user already owns. Furthermore, the outfit suggestion unit can also consider the user's emotional state and suggest outfits that match the user's mood for the day. For example, it can suggest casual clothes when the user feels like relaxing. This allows the AI ​​chat consultation system according to the embodiment to suggest optimal outfits based on the user's schedule and weather. For example, by utilizing the clothes the user already owns, there is no need to purchase new clothes, which is economical. Furthermore, by suggesting clothes from an online shopping site, the user can try out new styles.

[0056] The schedule acquisition unit can refer to the user's past activity history and automatically suggest similar schedules. In the schedule acquisition unit, for example, the generation AI works with the user's calendar app to analyze past schedules and automatically suggest similar schedules. For example, it refers to schedules made when the user went to the same place in the past. In addition, the schedule acquisition unit has the generation AI suggest similar schedules based on the user's past activity history. For example, it suggests a similar schedule based on a schedule of a past trip to a cafe with friends. In addition, the schedule acquisition unit has the generation AI analyze the user's past activity history and suggest similar schedules based on frequently visited places and activities. For example, it suggests places and activities to go to on the weekend. In this way, it is possible to suggest similar schedules based on the user's past activity history.

[0057] The weather information acquisition unit updates the weather forecast in real time, allowing it to respond to sudden changes in weather. For example, the generation AI of the weather information acquisition unit works with a weather forecast API to acquire weather information in real time, allowing it to respond to sudden changes in weather. For example, it may suggest carrying an umbrella if it looks like it might rain. The generation AI of the weather information acquisition unit also updates the weather forecast for the current location in real time based on the user's location information. For example, it can also respond to changes in the weather while traveling. The generation AI of the weather information acquisition unit also analyzes weather forecast data and suggests outfits to respond to sudden changes in weather. For example, it suggests clothing to respond to sudden changes in temperature. This allows it to respond to sudden changes in weather.

[0058] The coordination suggestion unit can suggest coordination that matches the mood of the user based on the user's emotional state. For example, the generation AI in the coordination suggestion unit analyzes the user's emotional state in real time and suggests coordination that matches the mood of the day. For example, it suggests casual clothing when the user is in a relaxed mood. The coordination suggestion unit also uses an emotion estimation function to consider the user's emotional state and suggest clothing with colors and designs that match the mood. For example, it suggests bright-colored clothing when the user is in a positive mood. The coordination suggestion unit also analyzes the user's emotional state using the generation AI and suggests a style that matches the mood of the day. For example, it suggests clothing that will help you relax when you are feeling stressed. This makes it possible to suggest coordination that matches the user's mood.

[0059] The schedule acquisition unit can work with the user's calendar app and automatically import schedules. In the schedule acquisition unit, for example, the generation AI works with the user's calendar app and automatically imports schedules. For example, schedules are acquired from Google Calendar or Outlook Calendar. The schedule acquisition unit also works with the user's calendar app, and the generation AI automatically imports schedules and combines them with weather information to suggest outfits. For example, it suggests the best outfit based on the schedule and weather. The schedule acquisition unit also works with the user's calendar app, and the generation AI imports schedules in real time and combines them with weather information to suggest outfits. For example, it can also handle schedule changes. This allows the schedule to be automatically imported in cooperation with the user's calendar app.

[0060] The weather information acquisition unit can suggest appropriate clothing based on the user's health condition. In the weather information acquisition unit, for example, the generation AI takes the user's health condition into consideration and suggests appropriate clothing based on allergy information. For example, it suggests clothing to protect against pollen during hay fever season. The weather information acquisition unit also takes the user's health condition into consideration and the generation AI suggests appropriate clothing. For example, it suggests warm clothing for a user who is sensitive to the cold. The weather information acquisition unit also suggests appropriate clothing based on the user's health information. For example, it suggests clothing made of skin-friendly materials for a user with sensitive skin. This makes it possible to suggest appropriate clothing based on the user's health condition.

[0061] The coordination suggestion unit can analyze the user's emotional response and suggest plans that will elicit positive emotions. For example, the coordination suggestion unit uses a generation AI to analyze the user's emotional response and suggest plans that will elicit positive emotions. For example, it prioritizes suggesting plans that the user is looking forward to. The coordination suggestion unit also uses an emotion estimation function to analyze the emotional response to plans entered by the user and suggest plans that will elicit positive emotions. For example, it suggests plans that will allow the user to relax. The coordination suggestion unit also uses a generation AI to analyze the user's emotional response and suggest plans that will elicit positive emotions. For example, it suggests activities that the user can enjoy. In this way, it is possible to suggest plans that will elicit positive emotions based on the user's emotional response.

[0062] The generation AI can analyze the user's schedule, evaluate its importance and urgency, and suggest an outfit based on that. For example, the generation AI can analyze the user's schedule, evaluate its importance and urgency, and suggest an outfit based on that. For example, it can suggest formal attire for an important business meeting. The generation AI can also analyze the user's schedule, evaluate its importance and urgency, and suggest an appropriate outfit based on that. For example, it can suggest easy-to-move-in outfits for urgent schedules. The generation AI can also analyze the user's schedule, and suggest an outfit based on its importance and urgency. For example, it can suggest eye-catching outfits for important events. This makes it possible to suggest outfits based on the importance and urgency of the schedule.

[0063] The generation AI can refer to weather data and suggest outfits that reflect seasonal trends. The generation AI, for example, refers to past weather data and suggests outfits that reflect seasonal trends. For example, it would suggest light clothing in spring. In addition, in analyzing weather information, the generation AI suggests outfits that reflect seasonal trends based on past weather data. For example, it would suggest cool clothing in summer. In addition, the generation AI analyzes past weather data and suggests outfits that reflect seasonal trends. For example, it would suggest warm clothing in autumn. This makes it possible to suggest outfits that reflect seasonal trends.

[0064] The generation AI can analyze the user's emotions and suggest outfits to reduce stress. The generation AI can, for example, analyze the user's emotions and suggest outfits to reduce stress. For example, it can suggest relaxing clothing. The generation AI can also use its emotion estimation function to analyze the user's emotions regarding their plans and suggest outfits to reduce stress. For example, it can suggest comfortable clothing. The generation AI can also analyze the user's emotions and suggest outfits to reduce stress. For example, it can suggest outfits made of soft materials. This makes it possible to suggest outfits to reduce the user's stress.

[0065] The generation AI can compare the schedule with similar plans of other users and suggest the optimal outfit. The generation AI, for example, compares the schedule with similar plans of other users and suggests the optimal outfit. For example, it takes into account the outfits of other users attending the same event. The generation AI can also analyze the user's schedule and suggest the optimal outfit by comparing it with similar plans of other users. For example, it takes into account the outfits of other users going to the same place. The generation AI can also compare the schedule with similar plans of other users and suggest the optimal outfit. For example, it takes into account the outfits of other users doing the same activity. This allows it to suggest the optimal outfit by comparing it with similar plans of other users.

[0066] The generation AI can take into account regional fashion trends and suggest outfits that are appropriate for the region. The generation AI, for example, can take into account regional fashion trends and suggest outfits that are appropriate for the region. For example, in urban areas, it can suggest outfits that are in line with the trends. In addition to analyzing weather information, the generation AI can also take into account regional fashion trends and suggest outfits that are appropriate for the region. For example, it can suggest resort-style outfits in resort areas. The generation AI can also take into account regional fashion trends and suggest outfits that are appropriate for the region. For example, it can suggest outfits that protect against the cold in cold regions. This makes it possible to take into account regional fashion trends and suggest outfits that are appropriate for the region.

[0067] The generative AI can analyze the user's emotions and suggest activities that will elicit positive emotions. For example, the generative AI can analyze the user's emotions and suggest activities that will elicit positive emotions. For example, it can suggest events that the user can enjoy. The generative AI can also use its emotion estimation function to analyze the user's emotions regarding their plans and suggest activities that will elicit positive emotions. For example, it can suggest relaxing activities. The generative AI can also analyze the user's emotions and suggest activities that will elicit positive emotions. For example, it can suggest spending time with friends. This makes it possible to suggest activities that will elicit positive emotions in the user.

[0068] When the generation AI memorizes the user's clothing, it also records detailed information about the material, color, and design, allowing it to suggest more accurate coordinations. For example, the generation AI records detailed information about the material, color, design, etc. of the clothing the user owns, and suggests more accurate coordinations. For example, it makes suggestions that take into account the characteristics of the material. The generation AI also records detailed information about the clothing the user owns, and suggests more accurate coordinations. For example, it makes suggestions that take into account color combinations. The generation AI also records detailed information about the clothing the user owns, and suggests more accurate coordinations. For example, it makes suggestions that take into account the characteristics of the design. This allows it to record detailed information about the clothing, and suggests more accurate coordinations.

[0069] When managing a user's clothing information, the generation AI can track the frequency of use and condition of the clothing, and suggest appropriate maintenance and replacement timing. For example, the generation AI can track the frequency of use of the user's clothing and suggest appropriate maintenance and replacement timing. For example, it can suggest maintenance for frequently worn clothing. The generation AI can also track the condition of the user's clothing and suggest appropriate maintenance and replacement timing. For example, it can detect fading or tears and make suggestions. The generation AI can also track the frequency of use and condition of the user's clothing and suggest appropriate maintenance and replacement timing. For example, it can suggest maintenance at the change of seasons. This allows the generation AI to track the frequency of use and condition of clothing, and suggest appropriate maintenance and replacement timing.

[0070] The generation AI can record the user's emotions toward specific clothing and suggest outfits based on those emotions. The generation AI, for example, can record the user's emotions toward specific clothing and suggest outfits based on those emotions. For example, it can prioritize suggestions for clothing that the user likes. The generation AI can also use an emotion estimation function to record the user's emotions toward specific clothing and suggest outfits based on those emotions. For example, it can suggest clothing that makes the user feel relaxed. The generation AI can also record the user's emotions toward specific clothing and suggest outfits based on those emotions. For example, it can suggest clothing that makes the user feel confident. This makes it possible to suggest outfits based on the user's emotions toward specific clothing.

[0071] The generation AI can compare the user's clothing with that of other users and suggest outfits that reflect the latest trends. For example, the generation AI can compare the user's clothing with that of other users and suggest outfits that reflect the latest trends. For example, it can refer to trends among users of the same age. The generation AI can also memorize the clothing the user owns and suggest outfits that reflect the latest trends by comparing it with the clothing of other users. For example, it can suggest popular designs. The generation AI can also compare the user's clothing with that of other users and suggest outfits that reflect the latest trends. For example, it can refer to seasonal trends. This allows it to suggest outfits that reflect the latest trends by comparing it with the clothing of other users.

[0072] When managing a user's clothing information, the generative AI can suggest recycling and donation options and promote environmentally conscious coordination. For example, the generative AI manages a user's clothing information and suggests recycling and donation options. For example, it may suggest taking unwanted clothes to a recycle shop. The generative AI also manages a user's clothing information and suggests recycling and donation options. For example, it may suggest donating unwanted clothes. The generative AI also manages a user's clothing information and suggests recycling and donation options. For example, it may suggest environmentally conscious coordination. This allows the generative AI to suggest options for recycling and donating clothes and promote environmentally conscious coordination.

[0073] The generation AI can record the user's emotions toward specific clothing and suggest clothing that will elicit positive emotions. For example, the generation AI can record the user's emotions toward specific clothing and suggest clothing that will elicit positive emotions. For example, it can suggest clothing in the user's favorite color. The generation AI can also use its emotion estimation function to record the user's emotions toward specific clothing and suggest clothing that will elicit positive emotions. For example, it can suggest clothing that will make the user feel happy. The generation AI can also record the user's emotions toward specific clothing and suggest clothing that will elicit positive emotions. For example, it can suggest clothing that will make the user feel confident. This makes it possible to suggest clothing that will elicit positive emotions based on the user's emotions toward specific clothing.

[0074] The generation AI records the user's clothing purchase history and brand information and can suggest outfits for each brand. For example, the generation AI records the user's clothing purchase history and brand information and suggests outfits for each brand. For example, it makes suggestions to combine clothes from specific brands. The generation AI also memorizes the user's clothing and suggests outfits for each brand based on the purchase history and brand information. For example, it makes suggestions to combine items from the same brand. The generation AI also records the user's clothing purchase history and brand information and suggests outfits for each brand. For example, it makes suggestions that make use of the characteristics of the brand. This makes it possible to suggest outfits for each brand based on the user's clothing purchase history and brand information.

[0075] When managing a user's clothing information, the generation AI can evaluate its applicability for each season and suggest coordination according to the season. For example, the generation AI manages a user's clothing information, evaluates its applicability for each season, and suggests coordination. For example, it suggests warm clothing in winter. The generation AI also manages a user's clothing information, evaluates its applicability for each season, and suggests coordination. For example, it suggests cool clothing in summer. The generation AI also manages a user's clothing information, evaluates its applicability for each season, and suggests coordination. For example, it suggests light clothing in spring. In this way, it can evaluate its applicability for each season and suggest coordination according to the season.

[0076] The generative AI can record a user's emotions toward a particular brand or design and make brand suggestions based on those emotions. For example, the generative AI can record a user's emotions toward a particular brand or design and make brand suggestions based on those emotions. For example, it can suggest new products from a brand that the user likes. The generative AI can also use an emotion estimation function to record a user's emotions toward a particular brand or design and make brand suggestions based on those emotions. For example, it can suggest brands with designs that the user likes. The generative AI can also record a user's emotions toward a particular brand or design and make brand suggestions based on those emotions. For example, it can suggest brands that make the user feel relaxed. This makes it possible to make brand suggestions based on the user's emotions toward a particular brand or design.

[0077] The generation AI can work in conjunction with other users' clothing data to make community-based coordination suggestions. The generation AI, for example, works in conjunction with other users' clothing data to make community-based coordination suggestions. For example, it can suggest coordination that is popular within the same community. The generation AI also memorizes the user's clothing and works in conjunction with other users' clothing data to make community-based coordination suggestions. For example, it can use friends' coordination as reference. The generation AI also works in conjunction with other users' clothing data to make community-based coordination suggestions. For example, it can suggest coordination from users who have the same hobbies. This makes it possible to work in conjunction with other users' clothing data to make community-based coordination suggestions.

[0078] The generative AI can work with clothing rental services to suggest clothing suitable for special events. The generative AI, for example, works with clothing rental services to suggest clothing suitable for special events. For example, it suggests clothing suitable for weddings and parties. The generative AI also manages the user's clothing information and works with clothing rental services to suggest clothing suitable for special events. For example, it suggests clothing suitable for formal events. The generative AI also works with clothing rental services to suggest clothing suitable for special events. For example, it suggests clothing suitable for theme parties. This allows the generative AI to work with clothing rental services to suggest clothing suitable for special events.

[0079] The generative AI can record a user's emotions toward a particular brand or design, and make brand suggestions to elicit positive emotions. For example, the generative AI can record a user's emotions toward a particular brand or design, and make brand suggestions to elicit positive emotions. For example, it can suggest new products from a brand that the user likes. The generative AI can also use its emotion estimation function to record a user's emotions toward a particular brand or design, and make brand suggestions to elicit positive emotions. For example, it can suggest brands with designs that the user likes. The generative AI can also record a user's emotions toward a particular brand or design, and make brand suggestions to elicit positive emotions. For example, it can suggest brands that make the user feel relaxed. This makes it possible to make brand suggestions to elicit positive emotions based on emotions toward a particular brand or design.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The AI ​​chat consultation system can also be equipped with an outfit suggestion unit that takes into account the user's health condition. For example, if a user has allergies, it can suggest clothes that protect against pollen during hay fever season. For users who are sensitive to the cold, it can also suggest clothes made from warm materials. Furthermore, it can suggest clothes made from materials that are gentle on the skin to users with sensitive skin. This makes it possible to suggest more appropriate outfits based on the user's health condition.

[0082] The AI ​​chat consultation system can also analyze the user's emotions and suggest outfits to reduce stress. For example, it can suggest relaxing clothing. It can also suggest clothing made from comfortable materials. It can also suggest clothing designed to make the user feel confident. This makes it possible to suggest outfits to reduce stress based on the user's emotions.

[0083] When managing a user's clothing information, the AI ​​chat consultation system can also track the frequency of use and condition of the clothing, and suggest appropriate maintenance and replacement timing. For example, it can suggest maintenance for frequently worn clothing. It can also detect fading and tears and suggest replacement. It can also suggest maintenance at the change of seasons. This allows it to track the frequency of use and condition of clothing, and suggest appropriate maintenance and replacement timing.

[0084] The AI ​​chat consultation system can also record the user's feelings toward specific clothing and suggest outfits based on those feelings. For example, it can prioritize suggestions for clothes that the user likes. It can also suggest clothes that make the user feel relaxed. It can also suggest clothes that make the user feel confident. This allows it to suggest more appropriate outfits based on the user's feelings toward specific clothing.

[0085] The AI ​​chat consultation system can also link with other users' clothing data to make community-based outfit suggestions. For example, it can suggest outfits that are popular within the same community. It can also use friends' outfits as reference. It can also suggest outfits from users with the same hobbies. This allows it to link with other users' clothing data and suggest a wider variety of outfits.

[0086] The AI ​​chat consultation system can further analyze the user's emotional response and suggest plans that will elicit positive emotions. For example, it can prioritize plans that the user is looking forward to. It can also suggest plans that will help the user relax. It can also suggest activities that the user can enjoy. This makes it possible to suggest plans that will elicit positive emotions based on the user's emotional response.

[0087] The AI ​​chat consultation system can also work with clothing rental services to suggest outfits suitable for special events. For example, it can suggest outfits suitable for weddings and parties. It can also suggest outfits suitable for formal events. It can even suggest outfits suitable for theme parties. This allows it to work with clothing rental services to suggest outfits suitable for special events.

[0088] The AI ​​chat consultation system can also record the user's feelings toward specific brands and designs and make brand suggestions based on those feelings. For example, it can suggest new products from a brand the user likes. It can also suggest brands with designs the user likes. It can even suggest brands that make the user feel relaxed. This allows for more appropriate brand suggestions based on the user's feelings toward specific brands and designs.

[0089] The AI ​​chat consultation system can also manage the user's clothing information and suggest recycling or donation options. For example, it can suggest taking unwanted clothes to a recycle shop. It can also suggest donating unwanted clothes. It can also suggest environmentally friendly outfits. This allows it to suggest options for recycling or donating clothes and promote environmentally friendly outfits.

[0090] The AI ​​chat consultation system can also analyze the user's emotions and suggest activities to elicit positive emotions. For example, it can suggest events the user can enjoy. It can also suggest activities to relax in. It can even suggest time to spend with friends. In this way, it can suggest activities to elicit positive emotions based on the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The schedule acquisition unit acquires the user's schedule. For example, it acquires schedules that the user has entered into a calendar app or entered manually. It can also refer to the user's past activity history and automatically suggest similar schedules. Step 2: The weather information acquisition unit acquires weather information. For example, it can connect to a weather forecast API to acquire weather information in real time. It can also update the weather forecast for the user's current location in real time based on the user's location information. It can also respond to sudden changes in weather. Step 3: The clothing memory unit memorizes the clothing the user owns. For example, the user can input photos and information about the clothing, and the clothing memory unit memorizes that information. It also records detailed information such as the material, color, and design of the clothing, allowing it to suggest more accurate outfits. Step 4: The outfit suggestion unit suggests outfits based on the information acquired by the schedule acquisition unit and the weather information acquisition unit. For example, if a user inputs, "I'm planning to go to a cafe with a friend today. The weather is sunny," the outfit suggestion unit will suggest outfits suitable for the cafe from among the clothes the user already owns. Furthermore, it can also take into account the user's emotional state and suggest outfits that match the user's mood that day.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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. [Explanation of symbols]

[0160] 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 schedule acquisition unit that acquires a schedule of a user; a weather information acquisition unit that acquires weather information; a clothing storage unit that stores the user's clothing; a coordination suggestion unit that suggests coordination based on the information acquired by the schedule acquisition unit and the weather information acquisition unit. A system characterized by:

2. The weather information acquisition unit Weather forecasts are updated in real time, allowing for the sudden changes in weather.

2. The system of claim 1.

3. The schedule acquisition unit Link with the user's calendar app and automatically import the schedule.

2. The system of claim 1.

4. The generating AI is Refer to weather data to suggest outfits that reflect seasonal trends 2. The system of claim 1.

5. The generating AI is When storing the user's clothing, detailed information on materials, colors, and designs is also recorded, and more accurate coordination suggestions are made.

2. The system of claim 1.

6. The coordination suggestion unit Suggesting outfits that match the user's mood based on the user's emotional state 2. The system of claim 1.

7. The generating AI is Analyzing the user's emotions and proposing the coordination to reduce stress 2. The system of claim 1.

8. The generating AI is Recording the user's feelings about a particular brand or design and making brand suggestions based on the feelings 2. The system of claim 1.

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