System

Abstract

An object of the system according to the embodiment is to improve the efficiency of the specification adjustment of the custom-made product and to reduce the burden on the orderer and the seller.SOLUTION: A system according to an embodiment includes a chatbot reception unit and an image generation unit. The chat bot reception unit responds to a question or a request of the orderer using the sentence generation AI. The image generation unit generates a completed image drawing on the basis of the answer generated by the chatbot reception unit and the specification selected by the orderer.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] With conventional technology, adjusting the specifications of custom-made products required complicated communication between the orderer and the seller, which was a significant burden.

[0005] The system according to the embodiment aims to improve the efficiency of specification adjustments for made-to-order products and reduce the burden on both the orderer and the seller. [Means for solving the problem]

[0006] The system according to the embodiment includes a chatbot reception unit and an image generation unit. The chatbot reception unit responds to questions and requests from customers using a text generation AI. The image generation unit generates an image of the finished product based on the responses generated by the chatbot reception unit and the specifications selected by the customer. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of adjusting the specifications of made-to-order products and reduce the burden on both the orderer and the seller. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The custom-made sales system according to an embodiment of the present invention utilizes text generation AI and image generation AI to reduce the burden of detailed specification adjustments between the orderer and the seller. When the orderer places a custom-made order through a web form, a chatbot utilizing text generation AI responds, generating appropriate answers to the orderer's questions and requests in real time. Furthermore, based on the specifications selected by the orderer, the image generation AI generates an image of the finished product and presents it to the orderer. This reduces the burden of detailed specification adjustments between the orderer and the seller, enabling the system to provide a purchasing experience of a quality comparable to face-to-face customer service.

[0029] A custom-made sales system according to an embodiment includes a chatbot reception unit and an image generation unit. When a customer places a custom-made order through a web form, the chatbot reception unit uses a text generation AI to generate appropriate answers to the customer's questions and requests, responding in real time. For example, when a customer asks, "What are the characteristics of this material?", the chatbot reception unit generates an answer such as, "This material is lightweight and breathable, making it perfect for summer." When a customer asks, "What is the delivery time?", the chatbot reception unit generates an answer such as, "Usually, delivery takes place within two weeks of ordering." When a customer asks, "What are the characteristics of this design?", the chatbot reception unit generates an answer such as, "This design has a classic style and is suitable for formal occasions." The image generation unit uses the image generation AI to generate an image of the finished product based on the specifications selected by the customer and presents it to the customer. For example, when a customer selects a specific design, color, or material, the image generation AI uses that information to create an image of the finished product in real time and presents it to the customer. The image generation unit also generates finished product images from different angles and backgrounds based on the specifications selected by the orderer, providing the orderer with multiple perspectives. Furthermore, the image generation unit realistically reproduces the texture and gloss of the selected material, providing a more specific image. This allows the custom-made sales system according to the embodiment to reduce the burden of detailed specification adjustments between the orderer and the seller, and to provide a purchasing experience of a quality comparable to face-to-face customer service.

[0030] The chatbot reception unit can refer to the orderer's past purchase history and make individually customized suggestions. For example, the chatbot reception unit can refer to the orderer's past purchase history and suggest new items that go well with previously purchased items. For example, it can suggest accessories that go well with a previously purchased dress. The chatbot reception unit can also analyze the orderer's preferences and style based on the past purchase history and make customized suggestions based on that. For example, it can suggest new products with casual designs to an orderer who prefers a casual style. Furthermore, the chatbot reception unit can refer to the orderer's past purchase history and suggest special offers and discounts to encourage repeat purchases. For example, it can offer a discount when repurchasing a previously purchased item. This allows for more personalized suggestions to be made based on the orderer's past purchase history.

[0031] The chatbot reception unit can analyze the orderer's input content in real time, predict latent needs, and make suggestions. For example, the chatbot reception unit analyzes the orderer's input content in real time, predicts needs that the orderer has not explicitly stated, and makes suggestions. For example, in response to an input such as "I'm looking for clothes to wear in the summer," the chatbot reception unit suggests clothes made of breathable materials. The chatbot reception unit also analyzes the orderer's input content and suggests related products and services. For example, in response to an input such as "I'm looking for clothes to wear in the summer," the chatbot reception unit suggests accessories and shoes that go well with a wedding. Furthermore, the chatbot reception unit predicts needs that the orderer has not realized based on the orderer's input content and makes suggestions accordingly. For example, in response to an input such as "I'm looking for a travel bag," the chatbot reception unit suggests small items and accessories that are convenient for travel. In this way, the chatbot can predict the orderer's latent needs and make more appropriate suggestions.

[0032] The chatbot reception unit also supports voice input and can respond to the orderer's questions and requests using voice recognition technology. The chatbot reception unit allows the orderer to input questions and requests by voice, for example, by voice input such as "Please tell me the characteristics of this material." The chatbot reception unit also uses voice recognition technology to convert the orderer's voice input into text and generate an appropriate response. For example, a text response is generated in response to the voice input such as "What are the characteristics of this design?" Furthermore, by supporting voice input, the chatbot reception unit allows the orderer to input questions and requests without using their hands. For example, the order details can be input by voice using a smartphone. By supporting voice input, the orderer can input questions and requests without using their hands.

[0033] The chatbot reception unit can support different languages ​​and can accommodate international orderers. The chatbot reception unit, for example, can support multiple languages ​​and allow orderers to input questions and requests in the language of their choice. For example, it can support English, French, Chinese, etc. Furthermore, by supporting different languages, the chatbot reception unit can accommodate international orderers. For example, it can smoothly handle orders from overseas. Furthermore, the chatbot reception unit has an automatic translation function that translates the language input by the orderer in real time and generates an appropriate response. For example, it can translate a question input in Japanese into English and generate a response in English. This allows it to support different languages ​​and accommodate international orderers.

[0034] The image generation unit generates image drawings of the completed product from different angles and backgrounds, providing the customer with a variety of perspectives. For example, the image generation unit uses image generation AI to generate image drawings of the completed product from different angles, providing the customer with a variety of perspectives. For example, it generates image drawings from the front, back, and side. The image generation unit also generates image drawings of the completed product against different backgrounds, providing the customer with images in a variety of situations. For example, it uses backgrounds such as indoors, outdoors, and specific event scenes. Furthermore, the image generation unit generates image drawings of the completed product under different lighting conditions, providing the customer with a variety of perspectives. For example, it generates image drawings under different lighting conditions such as daytime, nighttime, and indoor lighting. This allows the customer to receive a variety of perspectives by generating image drawings of the completed product from different angles and backgrounds.

[0035] The image generation unit can realistically reproduce the texture and luster of the selected material, providing a more specific image. For example, the image generation unit uses image generation AI to realistically reproduce the texture of the selected material, providing a specific image to the orderer. For example, it expresses the texture of materials such as silk, leather, and denim in detail. The image generation unit also realistically reproduces the luster of the selected material, providing a specific image to the orderer. For example, it realistically expresses the texture of shiny satin or matte cotton. Furthermore, the image generation unit uses high-resolution textures so that the image generation AI can realistically reproduce the texture and luster of the selected material. For example, it can express the fine details of the material in detail, providing a more specific image. This allows the texture and luster of the selected material to be realistically reproduced, providing a more specific image to the orderer.

[0036] The image generation unit generates animations to dynamically display changes in the specifications selected by the orderer. For example, the image generation unit uses an image generation AI to generate animations to dynamically display changes in the specifications selected by the orderer. For example, it displays changes in color or design using animations. The image generation unit also uses animations to visually display changes in the materials or designs selected by the orderer. For example, it displays combinations of different materials and designs using animations. Furthermore, the image generation unit uses an image generation AI to generate animations to display changes in the specifications selected by the orderer in real time. For example, the animations are automatically updated depending on the options selected by the orderer. This makes it possible to dynamically display changes in the specifications selected by the orderer by generating animations.

[0037] The image generation unit generates image drawings of the finished product in different seasons and situations, allowing the customer to propose a variety of usage scenarios. For example, the image generation AI generates image drawings of the finished product in different seasons, allowing the customer to propose a variety of usage scenarios. For example, image drawings are generated for each season: summer, winter, spring, and autumn. The image generation unit also generates image drawings of the finished product in different situations, allowing the customer to propose a variety of usage scenarios. For example, image drawings are generated for business, casual, and formal occasions. Furthermore, the image generation unit generates image drawings by changing the background and lighting conditions so that the image generation AI can generate image drawings of the finished product in different seasons and situations. For example, backgrounds such as indoors, outdoors, and specific event scenes are used. This allows the customer to propose a variety of usage scenarios by generating image drawings of the finished product in different seasons and situations.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The custom-made sales system may further include a voice assistant unit. The voice assistant unit allows the customer to input questions and requests by voice, converts the customer's voice input into text using voice recognition technology, and generates an appropriate response. For example, if the customer inputs, "Please tell me the characteristics of this material," the voice assistant unit generates a response such as, "This material is lightweight and breathable, making it perfect for summer." The voice assistant unit may also input order details by voice via a smartphone or smart speaker, allowing the customer to input questions and requests without using their hands. Furthermore, the voice assistant unit may suggest related products and services based on the customer's voice input. For example, in response to a voice input such as, "I'm looking for a wedding dress," the voice assistant unit may suggest accessories and shoes that would suit a wedding. This allows the system to support voice input, providing the customer with the convenience of being able to input questions and requests without using their hands.

[0040] The made-to-order sales system may further include a recommendation unit. The recommendation unit may refer to the orderer's past purchase history and browsing history to make individually customized suggestions. For example, it may suggest new items that go well with items previously purchased by the orderer. For example, it may suggest accessories that go well with a previously purchased dress. The recommendation unit may also analyze the orderer's preferences and style based on the past purchase history and make customized suggestions based thereon. For example, it may suggest new products with casual designs to an orderer who prefers a casual style. Furthermore, the recommendation unit may refer to the orderer's past purchase history to suggest special offers and discounts to encourage repeat purchases. For example, it may offer a discount when a previously purchased item is repurchased. This allows for more personalized suggestions to be made based on the orderer's past purchase history.

[0041] The made-to-order sales system may further include a multilingual support unit that supports different languages. The multilingual support unit may support multiple languages ​​and allow orderers to input questions or requests in the language of their choice. For example, it may support English, French, Chinese, etc. Furthermore, by supporting different languages, the multilingual support unit may be able to accommodate orderers from around the world. For example, it may be able to smoothly handle orders from overseas. Furthermore, the multilingual support unit may have an automatic translation function that can translate the language input by the orderer in real time and generate an appropriate response. For example, it may be able to translate a question input in Japanese into English and generate a response in English. This allows it to support different languages ​​and accommodate orderers from around the world.

[0042] The made-to-order sales system may further include an animation generation unit. The animation generation unit uses image generation AI to generate animations and dynamically display changes in the specifications selected by the orderer. For example, changes in color or design may be displayed using animation. The animation generation unit may also use animations to visually display changes in materials or designs selected by the orderer. For example, combinations of different materials or designs may be displayed using animation. The animation generation unit may also use image generation AI to generate animations and display changes in the specifications selected by the orderer in real time. For example, the animation is automatically updated depending on the options selected by the orderer. This allows changes in the specifications selected by the orderer to be dynamically displayed by generating animations.

[0043] The custom-made sales system can further include a season / situation response unit that generates completed image drawings for different seasons and situations. The season / situation response unit uses the image generation AI to generate completed image drawings for different seasons, thereby proposing a variety of usage scenarios to the customer. For example, it can generate image drawings for each season: summer, winter, spring, and autumn. The season / situation response unit can also generate completed image drawings for different situations, thereby proposing a variety of usage scenarios to the customer. For example, it can generate image drawings for business, casual, and formal occasions. Furthermore, the season / situation response unit can generate image drawings with different backgrounds and lighting conditions, allowing the image generation AI to generate completed image drawings for different seasons and situations. For example, it can use backgrounds such as indoors, outdoors, and specific event scenes. This allows the generation of completed image drawings for different seasons and situations, thereby proposing a variety of usage scenarios to the customer.

[0044] The custom-made sales system can further include a multi-perspective providing unit that generates completed image drawings from different angles and backgrounds. The multi-perspective providing unit uses image generation AI to generate completed image drawings from different angles, providing the orderer with multiple perspectives. For example, image drawings from the front, back, and side can be generated. The multi-perspective providing unit can also generate completed image drawings with different backgrounds, providing the orderer with images in a variety of situations. For example, backgrounds such as indoors, outdoors, and specific event scenes can be used. Furthermore, the multi-perspective providing unit can generate completed image drawings under different lighting conditions, providing the orderer with multiple perspectives. For example, image drawings can be generated under different lighting conditions, such as daytime, nighttime, and indoor lighting. This allows the orderer to be provided with multiple perspectives by generating completed image drawings from different angles and backgrounds.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: When a customer places a custom-made order through a web form, the chatbot reception unit uses text generation AI to generate appropriate answers to the customer's questions and requests, responding in real time. For example, if a customer asks, "What are the characteristics of this material?", the chatbot generates a response such as, "This material is lightweight and breathable, making it ideal for summer." If a customer asks, "What is the delivery time?", the chatbot generates a response such as, "We usually deliver within two weeks of ordering." If a customer asks, "What are the characteristics of this design?", the chatbot generates a response such as, "This design has a classic style and is suitable for formal occasions." Step 2: The image generation unit uses image generation AI to generate an image of the finished product based on the specifications selected by the customer and presents it to the customer. For example, if the customer selects a specific design, color, or material, the image generation AI creates an image of the finished product in real time based on that information and presents it to the customer. The image generation unit also generates image images of the finished product from different angles and backgrounds based on the specifications selected by the customer, providing the customer with multiple perspectives. Furthermore, the image generation unit realistically reproduces the texture and gloss of the selected material, providing a more concrete image.

[0047] (Example 2) The custom-made sales system according to an embodiment of the present invention utilizes text generation AI and image generation AI to reduce the burden of detailed specification adjustments between the orderer and the seller. When the orderer places a custom-made order through a web form, a chatbot utilizing text generation AI responds, generating appropriate answers to the orderer's questions and requests in real time. Furthermore, based on the specifications selected by the orderer, the image generation AI generates an image of the finished product and presents it to the orderer. This reduces the burden of detailed specification adjustments between the orderer and the seller, enabling the system to provide a purchasing experience of a quality comparable to face-to-face customer service.

[0048] A custom-made sales system according to an embodiment includes a chatbot reception unit and an image generation unit. When a customer places a custom-made order through a web form, the chatbot reception unit uses a text generation AI to generate appropriate answers to the customer's questions and requests, responding in real time. For example, when a customer asks, "What are the characteristics of this material?", the chatbot reception unit generates an answer such as, "This material is lightweight and breathable, making it perfect for summer." When a customer asks, "What is the delivery time?", the chatbot reception unit generates an answer such as, "Usually, delivery takes place within two weeks of ordering." When a customer asks, "What are the characteristics of this design?", the chatbot reception unit generates an answer such as, "This design has a classic style and is suitable for formal occasions." The image generation unit uses the image generation AI to generate an image of the finished product based on the specifications selected by the customer and presents it to the customer. For example, when a customer selects a specific design, color, or material, the image generation AI uses that information to create an image of the finished product in real time and presents it to the customer. The image generation unit also generates finished product images from different angles and backgrounds based on the specifications selected by the orderer, providing the orderer with multiple perspectives. Furthermore, the image generation unit realistically reproduces the texture and gloss of the selected material, providing a more specific image. This allows the custom-made sales system according to the embodiment to reduce the burden of detailed specification adjustments between the orderer and the seller, and to provide a purchasing experience of a quality comparable to face-to-face customer service.

[0049] The chatbot reception unit can refer to the orderer's past purchase history and make individually customized suggestions. For example, the chatbot reception unit can refer to the orderer's past purchase history and suggest new items that go well with previously purchased items. For example, it can suggest accessories that go well with a previously purchased dress. The chatbot reception unit can also analyze the orderer's preferences and style based on the past purchase history and make customized suggestions based on that. For example, it can suggest new products with casual designs to an orderer who prefers a casual style. Furthermore, the chatbot reception unit can refer to the orderer's past purchase history and suggest special offers and discounts to encourage repeat purchases. For example, it can offer a discount when repurchasing a previously purchased item. This allows for more personalized suggestions to be made based on the orderer's past purchase history.

[0050] The chatbot reception unit can analyze the orderer's input content in real time, predict latent needs, and make suggestions. For example, the chatbot reception unit analyzes the orderer's input content in real time, predicts needs that the orderer has not explicitly stated, and makes suggestions. For example, in response to an input such as "I'm looking for clothes to wear in the summer," the chatbot reception unit suggests clothes made of breathable materials. The chatbot reception unit also analyzes the orderer's input content and suggests related products and services. For example, in response to an input such as "I'm looking for clothes to wear in the summer," the chatbot reception unit suggests accessories and shoes that go well with a wedding. Furthermore, the chatbot reception unit predicts needs that the orderer has not realized based on the orderer's input content and makes suggestions accordingly. For example, in response to an input such as "I'm looking for a travel bag," the chatbot reception unit suggests small items and accessories that are convenient for travel. In this way, the chatbot can predict the orderer's latent needs and make more appropriate suggestions.

[0051] The chatbot reception unit uses the emotion estimation function to generate a response that corresponds to the emotional state of the orderer, thereby realizing more personalized customer service. For example, the chatbot reception unit uses the emotion estimation function to analyze the emotional state of the orderer when entering information and generate a response that elicits positive emotions. For example, if the orderer is feeling anxious, the chatbot reception unit provides a response that gives a sense of security. The chatbot reception unit also adjusts the tone and content of the response according to the orderer's emotional state. For example, if the orderer is excited, the chatbot reception unit provides a response that shares the orderer's excitement. Furthermore, the chatbot reception unit uses the emotion estimation function to analyze the orderer's emotional state in real time and generate a response that is sensitive to the orderer's emotions. For example, if the orderer is confused, the chatbot reception unit provides a response that provides a thorough explanation. In this way, by generating a response that corresponds to the orderer's emotional state, more personalized customer service can be realized.

[0052] The chatbot reception unit also supports voice input and can respond to the orderer's questions and requests using voice recognition technology. The chatbot reception unit allows the orderer to input questions and requests by voice, for example, by voice input such as "Please tell me the characteristics of this material." The chatbot reception unit also uses voice recognition technology to convert the orderer's voice input into text and generate an appropriate response. For example, a text response is generated in response to the voice input such as "What are the characteristics of this design?" Furthermore, by supporting voice input, the chatbot reception unit allows the orderer to input questions and requests without using their hands. For example, the order details can be input by voice using a smartphone. By supporting voice input, the orderer can input questions and requests without using their hands.

[0053] The chatbot reception unit can support different languages ​​and can accommodate international orderers. The chatbot reception unit, for example, can support multiple languages ​​and allow orderers to input questions and requests in the language of their choice. For example, it can support English, French, Chinese, etc. Furthermore, by supporting different languages, the chatbot reception unit can accommodate international orderers. For example, it can smoothly handle orders from overseas. Furthermore, the chatbot reception unit has an automatic translation function that translates the language input by the orderer in real time and generates an appropriate response. For example, it can translate a question input in Japanese into English and generate a response in English. This allows it to support different languages ​​and accommodate international orderers.

[0054] The chatbot reception unit uses the emotion estimation function to analyze the emotions of the orderer when he / she enters information in real time, and can make suggestions that elicit positive emotions. The chatbot reception unit, for example, uses the emotion estimation function to analyze the emotions of the orderer when he / she enters information in real time, and can make suggestions that elicit positive emotions. For example, if the orderer is feeling anxious, the chatbot reception unit makes suggestions that give the orderer a sense of security. The chatbot reception unit also adjusts the content and tone of the suggestions depending on the orderer's emotional state. For example, if the orderer is excited, the chatbot reception unit makes suggestions that share the orderer's excitement. Furthermore, the chatbot reception unit uses the emotion estimation function to analyze the orderer's emotional state in real time, and can make suggestions that are sensitive to the orderer's emotions. For example, if the orderer is confused, the chatbot reception unit makes suggestions that carefully explain the situation. In this way, the orderer's emotions can be analyzed in real time, and suggestions that elicit positive emotions can be made.

[0055] The image generation unit generates image drawings of the completed product from different angles and backgrounds, providing the customer with a variety of perspectives. For example, the image generation unit uses image generation AI to generate image drawings of the completed product from different angles, providing the customer with a variety of perspectives. For example, it generates image drawings from the front, back, and side. The image generation unit also generates image drawings of the completed product against different backgrounds, providing the customer with images in a variety of situations. For example, it uses backgrounds such as indoors, outdoors, and specific event scenes. Furthermore, the image generation unit generates image drawings of the completed product under different lighting conditions, providing the customer with a variety of perspectives. For example, it generates image drawings under different lighting conditions such as daytime, nighttime, and indoor lighting. This allows the customer to receive a variety of perspectives by generating image drawings of the completed product from different angles and backgrounds.

[0056] The image generation unit can realistically reproduce the texture and luster of the selected material, providing a more specific image. For example, the image generation unit uses image generation AI to realistically reproduce the texture of the selected material, providing a specific image to the orderer. For example, it expresses the texture of materials such as silk, leather, and denim in detail. The image generation unit also realistically reproduces the luster of the selected material, providing a specific image to the orderer. For example, it realistically expresses the texture of shiny satin or matte cotton. Furthermore, the image generation unit uses high-resolution textures so that the image generation AI can realistically reproduce the texture and luster of the selected material. For example, it can express the fine details of the material in detail, providing a more specific image. This allows the texture and luster of the selected material to be realistically reproduced, providing a more specific image to the orderer.

[0057] The image generation unit uses the emotion estimation function to generate an image that evokes the most positive emotion in the orderer, thereby increasing the desire to purchase. The image generation unit, for example, uses the emotion estimation function to generate an image that evokes the most positive emotion in the orderer. For example, an image is generated based on the orderer's preferred colors and designs. The image generation unit also analyzes the orderer's emotional state in real time to generate an image that elicits positive emotions. For example, if the orderer is excited, it generates an image that shares that excitement. Furthermore, the image generation unit uses the emotion estimation function to analyze the orderer's emotional state in real time to generate an image that is in tune with that emotion. For example, if the orderer is confused, it generates an image that gives the orderer a sense of security. In this way, by generating an image that evokes the most positive emotion in the orderer, it is possible to increase the desire to purchase.

[0058] The image generation unit generates animations to dynamically display changes in the specifications selected by the orderer. For example, the image generation unit uses an image generation AI to generate animations to dynamically display changes in the specifications selected by the orderer. For example, it displays changes in color or design using animations. The image generation unit also uses animations to visually display changes in the materials or designs selected by the orderer. For example, it displays combinations of different materials and designs using animations. Furthermore, the image generation unit uses an image generation AI to generate animations to display changes in the specifications selected by the orderer in real time. For example, the animations are automatically updated depending on the options selected by the orderer. This makes it possible to dynamically display changes in the specifications selected by the orderer by generating animations.

[0059] The image generation unit generates image drawings of the finished product in different seasons and situations, allowing the customer to propose a variety of usage scenarios. For example, the image generation AI generates image drawings of the finished product in different seasons, allowing the customer to propose a variety of usage scenarios. For example, image drawings are generated for each season: summer, winter, spring, and autumn. The image generation unit also generates image drawings of the finished product in different situations, allowing the customer to propose a variety of usage scenarios. For example, image drawings are generated for business, casual, and formal occasions. Furthermore, the image generation unit generates image drawings by changing the background and lighting conditions so that the image generation AI can generate image drawings of the finished product in different seasons and situations. For example, backgrounds such as indoors, outdoors, and specific event scenes are used. This allows the customer to propose a variety of usage scenarios by generating image drawings of the finished product in different seasons and situations.

[0060] The image generation unit can use the emotion estimation function to identify the design and color that the orderer is most interested in and generate an image that emphasizes those elements. The image generation unit, for example, uses the emotion estimation function to identify the design and color that the orderer is most interested in and generate an image that emphasizes those elements. For example, an image diagram is generated based on the colors and designs that the orderer prefers. The image generation unit also analyzes the emotional state of the orderer in real time and generates an image that emphasizes the design and color that attracts the orderer's attention. For example, if the orderer is excited, it generates an image that shares the orderer's excitement. Furthermore, the image generation unit uses the emotion estimation function to analyze the emotional state of the orderer in real time and generate an image that is in tune with that emotion. For example, if the orderer is confused, it generates an image that gives the orderer a sense of security. In this way, by identifying the design and color that the orderer is most interested in and generating an image that emphasizes those elements, it is possible to increase the orderer's desire to purchase.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The custom-made sales system may further include a voice assistant unit. The voice assistant unit allows the customer to input questions and requests by voice, converts the customer's voice input into text using voice recognition technology, and generates an appropriate response. For example, if the customer inputs, "Please tell me the characteristics of this material," the voice assistant unit generates a response such as, "This material is lightweight and breathable, making it perfect for summer." The voice assistant unit may also input order details by voice via a smartphone or smart speaker, allowing the customer to input questions and requests without using their hands. Furthermore, the voice assistant unit may suggest related products and services based on the customer's voice input. For example, in response to a voice input such as, "I'm looking for a wedding dress," the voice assistant unit may suggest accessories and shoes that would suit a wedding. This allows the system to support voice input, providing the customer with the convenience of being able to input questions and requests without using their hands.

[0063] The made-to-order sales system may further include a recommendation unit. The recommendation unit may refer to the orderer's past purchase history and browsing history to make individually customized suggestions. For example, it may suggest new items that go well with items previously purchased by the orderer. For example, it may suggest accessories that go well with a previously purchased dress. The recommendation unit may also analyze the orderer's preferences and style based on the past purchase history and make customized suggestions based thereon. For example, it may suggest new products with casual designs to an orderer who prefers a casual style. Furthermore, the recommendation unit may refer to the orderer's past purchase history to suggest special offers and discounts to encourage repeat purchases. For example, it may offer a discount when a previously purchased item is repurchased. This allows for more personalized suggestions to be made based on the orderer's past purchase history.

[0064] The custom-made sales system can further use an emotion estimation function to generate a response that corresponds to the emotional state of the orderer. For example, the emotion estimation function can be used to analyze the emotional state of the orderer when entering information and generate a response that elicits positive emotions. For example, if the orderer is feeling anxious, a response that gives a sense of security can be generated. The emotion estimation function can also be used to adjust the tone and content of the response according to the orderer's emotional state. For example, if the orderer is excited, a response that shares the orderer's excitement can be generated. The emotion estimation function can also be used to analyze the orderer's emotional state in real time and generate a response that is sensitive to the orderer's emotions. For example, if the orderer is confused, a response that provides a thorough explanation can be generated. This allows for more personalized customer service by generating a response that corresponds to the orderer's emotional state.

[0065] The made-to-order sales system may further include a multilingual support unit that supports different languages. The multilingual support unit may support multiple languages ​​and allow orderers to input questions or requests in the language of their choice. For example, it may support English, French, Chinese, etc. Furthermore, by supporting different languages, the multilingual support unit may be able to accommodate orderers from around the world. For example, it may be able to smoothly handle orders from overseas. Furthermore, the multilingual support unit may have an automatic translation function that can translate the language input by the orderer in real time and generate an appropriate response. For example, it may be able to translate a question input in Japanese into English and generate a response in English. This allows it to support different languages ​​and accommodate orderers from around the world.

[0066] The custom-made sales system can further use an emotion estimation function to generate an image that evokes the most positive emotions in the orderer. For example, the emotion estimation function can be used to generate an image that evokes the most positive emotions in the orderer. For example, an image can be generated based on the orderer's preferred colors and designs. The emotion estimation function can also be used to analyze the orderer's emotional state in real time and generate an image that elicits positive emotions. For example, if the orderer is excited, an image that shares the orderer's excitement can be generated. The emotion estimation function can also be used to analyze the orderer's emotional state in real time and generate an image that is in tune with the orderer's emotions. For example, if the orderer is confused, an image that gives the orderer a sense of security can be generated. In this way, by generating an image that evokes the most positive emotions in the orderer, the orderer's desire to purchase can be increased.

[0067] The made-to-order sales system may further include an animation generation unit. The animation generation unit uses image generation AI to generate animations and dynamically display changes in the specifications selected by the orderer. For example, changes in color or design may be displayed using animation. The animation generation unit may also use animations to visually display changes in materials or designs selected by the orderer. For example, combinations of different materials or designs may be displayed using animation. The animation generation unit may also use image generation AI to generate animations and display changes in the specifications selected by the orderer in real time. For example, the animation is automatically updated depending on the options selected by the orderer. This allows changes in the specifications selected by the orderer to be dynamically displayed by generating animations.

[0068] The custom-made sales system can further use an emotion estimation function to identify the design and color that the orderer is most interested in and generate an image that emphasizes those elements. For example, the emotion estimation function can be used to identify the design and color that the orderer is most interested in and generate an image that emphasizes those elements. For example, an image diagram can be generated based on the orderer's preferred colors and designs. The emotion estimation function can also be used to analyze the orderer's emotional state in real time and generate an image that emphasizes designs and colors that interest the orderer. For example, if the orderer is excited, an image that shares the orderer's excitement can be generated. The emotion estimation function can also be used to analyze the orderer's emotional state in real time and generate an image that is in tune with the orderer's emotions. For example, if the orderer is confused, an image that gives the orderer a sense of security can be generated. In this way, by identifying the design and color that the orderer is most interested in and generating an image that emphasizes those elements, the orderer's desire to purchase can be increased.

[0069] The custom-made sales system can further include a season / situation response unit that generates completed image drawings for different seasons and situations. The season / situation response unit uses the image generation AI to generate completed image drawings for different seasons, thereby proposing a variety of usage scenarios to the customer. For example, it can generate image drawings for each season: summer, winter, spring, and autumn. The season / situation response unit can also generate completed image drawings for different situations, thereby proposing a variety of usage scenarios to the customer. For example, it can generate image drawings for business, casual, and formal occasions. Furthermore, the season / situation response unit can generate image drawings with different backgrounds and lighting conditions, allowing the image generation AI to generate completed image drawings for different seasons and situations. For example, it can use backgrounds such as indoors, outdoors, and specific event scenes. This allows the generation of completed image drawings for different seasons and situations, thereby proposing a variety of usage scenarios to the customer.

[0070] The custom-made sales system can further use an emotion estimation function to generate an image that evokes the most positive emotions in the orderer, thereby increasing the desire to purchase. For example, the emotion estimation function can be used to generate an image that evokes the most positive emotions in the orderer. For example, an image can be generated based on the orderer's preferred colors and designs. The emotion estimation function can also be used to analyze the orderer's emotional state in real time and generate an image that elicits positive emotions. For example, if the orderer is excited, an image that shares the orderer's excitement can be generated. The emotion estimation function can also be used to analyze the orderer's emotional state in real time and generate an image that is in tune with the orderer's emotions. For example, if the orderer is confused, an image that gives the orderer a sense of security can be generated. In this way, the generation of an image that evokes the most positive emotions in the orderer can increase the desire to purchase.

[0071] The custom-made sales system can further include a multi-perspective providing unit that generates completed image drawings from different angles and backgrounds. The multi-perspective providing unit uses image generation AI to generate completed image drawings from different angles, providing the orderer with multiple perspectives. For example, image drawings from the front, back, and side can be generated. The multi-perspective providing unit can also generate completed image drawings with different backgrounds, providing the orderer with images in a variety of situations. For example, backgrounds such as indoors, outdoors, and specific event scenes can be used. Furthermore, the multi-perspective providing unit can generate completed image drawings under different lighting conditions, providing the orderer with multiple perspectives. For example, image drawings can be generated under different lighting conditions, such as daytime, nighttime, and indoor lighting. This allows the orderer to be provided with multiple perspectives by generating completed image drawings from different angles and backgrounds.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: When a customer places a custom-made order through a web form, the chatbot reception unit uses text generation AI to generate appropriate answers to the customer's questions and requests, responding in real time. For example, if a customer asks, "What are the characteristics of this material?", the chatbot generates a response such as, "This material is lightweight and breathable, making it ideal for summer." If a customer asks, "What is the delivery time?", the chatbot generates a response such as, "We usually deliver within two weeks of ordering." If a customer asks, "What are the characteristics of this design?", the chatbot generates a response such as, "This design has a classic style and is suitable for formal occasions." Step 2: The image generation unit uses image generation AI to generate an image of the finished product based on the specifications selected by the customer and presents it to the customer. For example, if the customer selects a specific design, color, or material, the image generation AI creates an image of the finished product in real time based on that information and presents it to the customer. The image generation unit also generates image images of the finished product from different angles and backgrounds based on the specifications selected by the customer, providing the customer with multiple perspectives. Furthermore, the image generation unit realistically reproduces the texture and gloss of the selected material, providing a more concrete image.

[0074] 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0088] 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.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0110] The 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.

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

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] 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]

[0141] 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 chatbot reception unit that uses sentence generation AI, an image generation unit that responds to questions and requests of the orderer based on the answers generated by the chatbot reception unit; an image generating unit that generates a completed image drawing based on the specifications selected by the orderer; A system characterized by:

2. The chatbot reception unit Refer to the customer's past purchase history and provide personalized suggestions 2. The system of claim 1.

3. The chatbot reception unit Analyze the customer's input in real time, predict potential needs, and make suggestions 2. The system of claim 1.

4. The chatbot reception unit Generate responses according to the customer's emotional state to provide more personalized customer service 2. The system of claim 1.

5. The chatbot reception unit It also supports voice input and uses voice recognition technology to respond to the customer's questions and requests.

2. The system of claim 1.

6. The chatbot reception unit Support different languages ​​and cater to international customers 2. The system of claim 1.

7. The chatbot reception unit Analyze the emotions of the customer in real time when they input their order, and make suggestions that elicit positive emotions.

2. The system of claim 1.

8. The image generation unit Generates completed image drawings from different angles and backgrounds to provide the customer with multiple perspectives 2. The system of claim 1.

Images