System

Abstract

To provide a system that estimates the emotions of a customer doing online shopping and provides a response accordingly.SOLUTION: A system according to an embodiment comprises an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotions of an online shopping customer. The estimation unit estimates the emotions of the customer on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines a response according to the emotions of the customer as estimated by the estimation unit. The response unit gives the response determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to accurately grasp customer emotions when shopping online and respond accordingly.

[0005] The system according to the embodiment aims to estimate the emotions of customers during online shopping and respond based on the estimated emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotion of an online shopping customer. The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The determination unit determines a response according to the emotion of the customer estimated by the estimation unit. The response unit executes the response determined by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can estimate the customer's emotions during online shopping and respond based on the estimation. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A purchase support system according to an embodiment of the present invention is a system that reads the emotions of a customer and provides appropriate product suggestions and purchase support. This system acquires information for estimating the emotions of a customer, estimates the emotions of the customer based on the information, determines an appropriate response, and executes the response. For example, the system acquires information such as the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. Next, based on the acquired information, the system estimates the emotions of the customer. For example, AI analyzes the facial expression and voice of the customer and estimates whether the customer is happy, sad, angry, etc. The system determines an appropriate response according to the estimated emotions of the customer. For example, if the customer is happy, a related product can be suggested. Also, if the customer is anxious, detailed product explanations and support can be provided. The determined response is executed by the system. For example, the system can suggest a product that suits the customer, provide detailed product information, and process the payment. This mechanism allows the system to respond according to the emotions of the customer, making online shopping smoother. For example, if a customer is unsure about a purchase, the system can encourage the purchase by suggesting a product at the appropriate time. Furthermore, if a customer feels uneasy about the purchasing procedure, the system can provide support, allowing the customer to proceed with the purchase with peace of mind. Furthermore, the system can determine how to respond based on the customer's emotions and attributes. For example, a more personalized response can be provided by taking into account attribute information such as the customer's age, gender, and purchasing history. In this way, the use of a system with an emotion engine can improve the online shopping experience. Customer satisfaction can be increased by suggesting products and providing purchasing support according to the customer's emotions. As a result, the purchasing support system can make online shopping smoother by responding according to the customer's emotions.

[0029] The purchase support system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information for estimating the emotion of a customer includes, for example, the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content, but is not limited to such examples. The acquisition unit, for example, captures the customer's facial expression using a camera and acquires it as emotion estimation information. The acquisition unit can also capture the customer's voice using a microphone and acquire it as emotion estimation information. Furthermore, the acquisition unit can acquire the customer's heart rate using a heart rate sensor and use it as emotion estimation information. For example, the acquisition unit acquires the heart rate using a heart rate sensor while the customer is browsing a product. The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the customer's facial expression and estimates the emotion. For example, AI analyzes the customer's smile and estimates the emotion of joy. The estimation unit can also use AI to analyze the customer's voice and estimate the emotion. For example, the AI ​​analyzes the tone of the customer's voice and estimates the emotion of joy. The estimation unit can also use AI to analyze the customer's heart rate and estimate the emotion. For example, the AI ​​analyzes the increase in the customer's heart rate and estimates the emotion of excitement. The determination unit determines a response according to the emotion of the customer estimated by the estimation unit. For example, if the customer is happy, the determination unit determines a related product suggestion. In addition, if the customer is anxious, the determination unit can also determine a detailed product description. In addition, the determination unit can determine a response based on the emotion and attributes of the customer. For example, a more personalized response is determined by taking into account attribute information such as the customer's age, gender, and purchase history. The response unit executes the response determined by the determination unit. For example, the response unit suggests a product that suits the customer. In addition, the response unit can provide detailed information about the product. In addition, the response unit can perform payment procedures. For example, if the customer is unsure about a purchase, the response unit suggests a product at an appropriate time. As a result, the purchase support system according to the embodiment can make online shopping smoother by responding in accordance with the customer's emotions.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. The emotion estimation information for estimating the emotion of the customer includes, for example, the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content, but is not limited to such examples. The acquisition unit, for example, captures the customer's facial expression using a camera and acquires it as emotion estimation information. The camera has high resolution and can capture even subtle changes in facial expression. The acquisition unit can also capture the customer's voice using a microphone and acquire it as emotion estimation information. The microphone has a noise canceling function and eliminates surrounding noise to clearly collect the customer's voice. Furthermore, the acquisition unit can also acquire the customer's heart rate using a heart rate sensor and use it as emotion estimation information. The heart rate sensor monitors the fluctuation of the heart rate in real time while the customer is browsing the product and acquires the data. As a result, the acquisition unit can collect various data for capturing the customer's emotion from various angles. Furthermore, the acquisition unit also has a motion sensor for capturing the customer's movement and an eye tracking device for detecting the pupil response. These devices capture the subtle movements and gaze of customers and acquire this information for emotion estimation. For example, if a customer focuses their gaze on a particular product, it can be determined that the customer is interested in that product. They are also equipped with a text analysis engine for analyzing content posted by customers, and acquire information for emotion estimation from the content posted by customers on social media and review sites. This allows the acquisition unit to collect a variety of data to capture customer emotions from multiple angles, improving the accuracy of emotion estimation.

[0031] The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the customer's facial expression and estimate the emotion. The AI ​​learns a large amount of facial expression data using deep learning technology, and can estimate emotions such as joy, sadness, surprise, and anger with high accuracy from subtle changes in facial expression. The estimation unit can also use AI to analyze the customer's voice and estimate the emotion. The AI ​​analyzes features such as tone, pitch, speed, and strength of the voice to estimate the emotional state of the customer. For example, the AI ​​analyzes the tone of the customer's voice and estimates the emotion of joy. Furthermore, the estimation unit can also use AI to analyze the customer's heart rate and estimate the emotion. The AI ​​analyzes the heart rate fluctuation pattern and estimates the emotional state such as excitement, tension, and relaxation. For example, the AI ​​analyzes the increase in the customer's heart rate and estimates the emotion of excitement. The estimation unit can comprehensively analyze these emotion estimation information and estimate the customer's emotion from various angles. Furthermore, the estimation unit also analyzes information such as the customer's movements, pupil reactions, and posted content to improve the accuracy of emotion estimation. For example, if a customer focuses their gaze on a particular product, it can be determined that the customer is interested in that product. Furthermore, by analyzing the customer's posted content, it is possible to gain a deeper understanding of the customer's emotions and intentions. This allows the estimation unit to estimate the customer's emotions with high accuracy, and use this information to help determine the next step, which is how to respond.

[0032] The determination unit determines a response according to the customer's emotion estimated by the estimation unit. For example, if the customer is happy, the determination unit determines a suggestion of a related product. Specifically, if the customer shows an emotion of joy when looking at a specific product, the determination unit suggests an accessory or optional product related to the product. In addition, the determination unit can determine a detailed product description if the customer is anxious. For example, if the customer is anxious about the function or usage of the product, a detailed explanation or a demonstration video is provided. Furthermore, the determination unit can determine a response based on the customer's emotion and attributes. For example, a more personalized response is determined by taking into account attribute information such as the customer's age, gender, and purchase history. For example, the latest trend products are suggested to younger customers, and products that emphasize ease of use are suggested to elderly customers. In addition, a new product related to a product previously purchased by the customer can be suggested based on the past purchase history. In this way, the determination unit can determine an optimal response based on the customer's emotion and attributes and improve customer satisfaction. Furthermore, the determination unit can continuously review and optimize the response based on the emotion estimation result updated in real time. For example, if the customer's emotion changes, a new response is immediately determined and provided to the customer. This allows the decision unit to respond flexibly based on the latest information and quickly meet customer needs.

[0033] The response unit executes the response determined by the determination unit. For example, the response unit proposes a product that suits the customer. Specifically, if the customer is interested in a particular product, the response unit proposes accessories and optional products related to the product. The response unit can also provide detailed information about the product. For example, if the customer is unsure about the function or usage of the product, the response unit provides a detailed explanation or a demonstration video. Furthermore, the response unit can also process the payment. For example, if the customer is unsure about the purchase, the response unit proposes the product at an appropriate time. As a result, the purchase support system according to the embodiment can make online shopping smoother by responding according to the customer's emotions. Furthermore, the response unit has various means for facilitating communication with the customer. For example, the response unit can use a chatbot to answer the customer's questions in real time or provide important information via email or SMS. The response unit can also collect customer feedback and use it to improve services. For example, the response unit records how the customer responded to the proposed product and reflects it in the next proposal. As a result, the response unit can more accurately meet the needs of the customer and improve customer satisfaction. In addition, the customer support department can analyze the customer's purchase history and behavioral patterns to predict future purchasing behavior, which allows the customer support department to provide more personalized service and build long-term customer relationships.

[0034] The determination unit determines a product suggestion according to the customer's emotions as a response according to the customer's emotions, and the response unit can make a product suggestion according to the customer's emotions. For example, when the customer is happy, the determination unit determines a related product suggestion. Furthermore, when the customer is sad, the determination unit can also determine a product suggestion that will lift the customer's spirits. Furthermore, when the customer is angry, the determination unit can also determine a product suggestion that will relax the customer. The response unit executes the product suggestion determined by the determination unit. In this way, customer satisfaction can be increased by making product suggestions according to the customer's emotions.

[0035] The determination unit determines a proposal in a manner corresponding to the customer's emotions as a response corresponding to the customer's emotions, and the response unit can make a proposal in a manner corresponding to the customer's emotions. For example, when the customer is relaxed, the determination unit determines a proposal including a detailed product description. Also, when the customer is nervous, the determination unit can determine a proposal including a concise product description. Furthermore, when the customer is excited, the determination unit can determine a visually attractive proposal. The response unit executes the proposal in the manner determined by the determination unit. This makes it possible to provide a more personalized response by making a proposal in a manner corresponding to the customer's emotions.

[0036] The determination unit determines product purchasing support in a manner corresponding to the customer's emotions as a response corresponding to the customer's emotions, and the response unit can provide purchasing support in a manner corresponding to the customer's emotions. For example, the determination unit determines a detailed explanation of the purchasing procedure when the customer is feeling anxious. The determination unit can also determine a concise explanation of the purchasing procedure when the customer is relaxed. Furthermore, the determination unit can also determine a visually appealing explanation of the purchasing procedure when the customer is excited. The response unit executes purchasing support in the manner determined by the determination unit. As a result, the purchasing procedure can be smoothly progressed by providing purchasing support in a manner corresponding to the customer's emotions.

[0037] The determination unit can determine a response according to the customer's emotions based on the customer's emotions and the customer's attributes. The determination unit determines a more personalized response by taking into account attribute information such as the customer's age, gender, and purchase history. For example, if the customer is a young person, the determination unit can suggest the latest trending products. Also, if the customer is an elderly person, the determination unit can decide to suggest products that are easy to use. Furthermore, the determination unit can decide to suggest related products based on the customer's purchase history. For example, a new product related to a product that the customer has purchased in the past is suggested. This makes it possible to provide a more personalized response by determining a response based on the customer's emotions and attributes.

[0038] The acquisition unit can acquire the facial expression of the customer and use it as emotion estimation information. For example, the acquisition unit captures the facial expression in real time when the customer faces the camera and acquires it as emotion estimation information. The acquisition unit can also periodically capture the facial expression while the customer is browsing products and acquire it as emotion estimation information. Furthermore, the acquisition unit can capture the facial expression while the customer is performing a purchase procedure and acquire it as emotion estimation information. In this way, the accuracy of emotion estimation is improved by acquiring the customer's facial expression. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI or may be performed without using AI. For example, the acquisition unit can input the facial expression data of the customer acquired by the camera to the generation AI and cause the generation AI to execute emotion estimation from the facial expression data.

[0039] The acquisition unit can acquire the customer's actions and use them as emotion estimation information. For example, the acquisition unit captures hand movements when the customer is selecting a product and acquires them as emotion estimation information. The acquisition unit can also capture the customer's actions of putting a product into a cart and acquire them as emotion estimation information. Furthermore, the acquisition unit can capture the customer's actions while performing a purchase procedure and acquire them as emotion estimation information. In this way, the accuracy of emotion estimation is improved by acquiring the customer's actions. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the customer's action data acquired by a camera to the generation AI and cause the generation AI to estimate emotions from the action data.

[0040] The acquisition unit can acquire the voice of the customer and use it as emotion estimation information. For example, the acquisition unit captures the voice of the customer talking about a product and acquires it as emotion estimation information. The acquisition unit can also capture the voice of the customer while the customer is making a purchase procedure and acquire it as emotion estimation information. Furthermore, the acquisition unit can capture the voice of the customer when the customer is requesting support and acquire it as emotion estimation information. By acquiring the voice of the customer, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the voice data of the customer acquired by a microphone to the generation AI and cause the generation AI to execute emotion estimation from the voice data.

[0041] The acquisition unit can acquire the heart rate of the customer and use it as emotion estimation information. For example, the acquisition unit can acquire the heart rate using a heart rate sensor while the customer is browsing products. The acquisition unit can also acquire the heart rate using the heart rate sensor while the customer is making a purchase. Furthermore, the acquisition unit can also acquire the heart rate using the heart rate sensor when the customer is seeking support. In this way, the accuracy of emotion estimation is improved by acquiring the customer's heart rate. A part or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI or can be performed without using an AI. For example, the acquisition unit can input the customer's heart rate data acquired by the heart rate sensor to the generation AI and cause the generation AI to estimate emotions from the heart rate data.

[0042] The acquisition unit can acquire the customer's blood pressure and use it as emotion estimation information. The acquisition unit can acquire the customer's blood pressure using a blood pressure sensor, for example, while the customer is browsing products. The acquisition unit can also acquire the customer's blood pressure using the blood pressure sensor while the customer is making a purchase. The acquisition unit can also acquire the customer's blood pressure using the blood pressure sensor when the customer is seeking support. In this way, acquiring the customer's blood pressure improves the accuracy of emotion estimation. A part or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the acquisition unit can input the customer's blood pressure data acquired by the blood pressure sensor to the generation AI and cause the generation AI to estimate emotions from the blood pressure data.

[0043] The acquisition unit can acquire the pupillary response of the customer and use it as emotion estimation information. For example, the acquisition unit captures the pupillary response while the customer is browsing a product and acquires it as emotion estimation information. The acquisition unit can also capture the pupillary response while the customer is making a purchase and acquire it as emotion estimation information. Furthermore, the acquisition unit can capture the pupillary response when the customer is seeking support and acquire it as emotion estimation information. Thereby, by acquiring the pupillary response of the customer, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the customer's pupil data acquired by the pupil measurement device to the generation AI and cause the generation AI to execute emotion estimation from the pupillary data.

[0044] The estimation unit can analyze the customer's facial expression and estimate the emotion. For example, the estimation unit uses an AI to analyze the customer's smile and estimate the emotion of joy. The estimation unit can also use an AI to analyze the customer's brow wrinkles and estimate the emotion of anger. The estimation unit can also use an AI to analyze the customer's tears and estimate the emotion of sadness. This improves the accuracy of emotion estimation by analyzing the customer's facial expression. Some or all of the above-mentioned processing in the estimation unit may be performed using an AI, for example, or may be performed without using an AI. For example, the estimation unit can input facial expression data of the customer acquired by a camera to the generation AI and cause the generation AI to estimate the emotion from the facial expression data.

[0045] The estimation unit can analyze the customer's voice and estimate the emotion. For example, the estimation unit can use an AI to analyze the tone of the customer's voice and estimate the emotion of joy. The estimation unit can also use an AI to analyze the trembling of the customer's voice and estimate the emotion of tension. Furthermore, the estimation unit can also use an AI to analyze the volume of the customer's voice and estimate the emotion of anger. This improves the accuracy of emotion estimation by analyzing the customer's voice. A part or all of the above-mentioned processing in the estimation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the estimation unit can input the customer's voice data acquired by a microphone to the generation AI and cause the generation AI to estimate the emotion from the voice data.

[0046] The estimation unit can analyze the customer's movements and estimate the emotion. For example, the estimation unit can use an AI to analyze the customer's hand movements and estimate the emotion of excitement. The estimation unit can also use an AI to analyze the customer's walking speed and estimate the emotion of impatience. Furthermore, the estimation unit can also use an AI to analyze the customer's posture and estimate the emotion of relaxation. This improves the accuracy of emotion estimation by analyzing the customer's movements. A part or all of the above-mentioned processing in the estimation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the estimation unit can input the customer's movement data acquired by a camera to the generation AI and cause the generation AI to estimate the emotion from the movement data.

[0047] The estimation unit can analyze the customer's heart rate and estimate the emotion. For example, the estimation unit can use an AI to analyze an increase in the customer's heart rate and estimate an emotion of excitement. The estimation unit can also use an AI to analyze a decrease in the customer's heart rate and estimate an emotion of relaxation. The estimation unit can also use an AI to analyze the fluctuation in the customer's heart rate and estimate an emotion of tension. This improves the accuracy of emotion estimation by analyzing the customer's heart rate. A part or all of the above-mentioned processing in the estimation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the estimation unit can input the customer's heart rate data acquired by a heart rate sensor to the generation AI and cause the generation AI to estimate the emotion from the heart rate data.

[0048] The estimation unit can analyze the customer's blood pressure and estimate the emotion. For example, the estimation unit can use an AI to analyze an increase in the customer's blood pressure and estimate an emotion of excitement. The estimation unit can also use an AI to analyze a decrease in the customer's blood pressure and estimate an emotion of relaxation. The estimation unit can also use an AI to analyze fluctuations in the customer's blood pressure and estimate an emotion of tension. This improves the accuracy of emotion estimation by analyzing the customer's blood pressure. Some or all of the above-mentioned processing in the estimation unit can be performed using an AI, for example, or can be performed without using an AI. For example, the estimation unit can input the customer's blood pressure data acquired by a blood pressure sensor to the generation AI and cause the generation AI to estimate the emotion from the blood pressure data.

[0049] The estimation unit can analyze the customer's pupil response and estimate the emotion. For example, the estimation unit can use an AI to analyze the dilation of the customer's pupil and estimate the emotion of excitement. The estimation unit can also use an AI to analyze the contraction of the customer's pupil and estimate the emotion of relaxation. The estimation unit can also use an AI to analyze the fluctuation of the customer's pupil and estimate the emotion of tension. This improves the accuracy of emotion estimation by analyzing the customer's pupil response. Some or all of the above-mentioned processing in the estimation unit can be performed using an AI, for example, or can be performed without using an AI. For example, the estimation unit can input the customer's pupil data acquired by a pupil measurement device to the generation AI and cause the generation AI to estimate the emotion from the pupil data.

[0050] The determination unit can determine a product suggestion according to the customer's emotions. For example, when the customer is happy, the determination unit can determine a related product suggestion. Furthermore, when the customer is sad, the determination unit can also determine a product suggestion that will lift the customer's mood. Furthermore, when the customer is angry, the determination unit can also determine a product suggestion that will relax the customer. This makes it possible to increase customer satisfaction by suggesting products according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to suggest products based on the emotion data.

[0051] The determination unit can determine the timing of product proposal according to the customer's emotions. For example, when the customer is relaxed, the determination unit immediately proposes the product. Also, when the customer is nervous, the determination unit can wait a short time before proposing the product. Furthermore, when the customer is excited, the determination unit can propose the product at an appropriate timing. This makes it possible to promote purchases by proposing the product at a timing according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to execute the proposal timing based on the emotion data.

[0052] The determination unit can determine a method of proposing a product according to the customer's emotions. For example, when the customer is relaxed, the determination unit can determine a proposal including a detailed product description. Furthermore, when the customer is nervous, the determination unit can also determine a proposal including a concise product description. Furthermore, when the customer is excited, the determination unit can also determine a visually attractive proposal. This makes it possible to increase customer satisfaction by proposing a product in a manner according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to execute the proposal method based on the emotion data.

[0053] The determination unit can determine the content of the purchase support according to the customer's emotions. For example, when the customer feels anxious, the determination unit determines a detailed explanation of the purchase procedure. In addition, when the customer is relaxed, the determination unit can also determine a brief explanation of the purchase procedure. Furthermore, when the customer is excited, the determination unit can also determine a visually attractive explanation of the purchase procedure. In this way, the purchase support is provided according to the customer's emotions, and the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the customer's emotion data to the generation AI and cause the generation AI to execute the content of the purchase support based on the emotion data.

[0054] The determination unit can determine the timing of purchase support according to the customer's emotions. For example, if the customer feels anxious, the determination unit immediately provides purchase support. Also, if the customer is relaxed, the determination unit can provide purchase support after a short time. Furthermore, if the customer is excited, the determination unit can provide purchase support at an appropriate timing. As a result, purchase support is provided at a timing according to the customer's emotions, so that the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to execute the timing of purchase support based on the emotion data.

[0055] The determination unit can determine a method of purchase support according to the customer's emotions. For example, when the customer feels anxious, the determination unit determines purchase support including detailed explanations. Furthermore, when the customer is relaxed, the determination unit can also determine purchase support including concise explanations. Furthermore, when the customer is excited, the determination unit can also determine visually attractive purchase support. As a result, by providing purchase support in a manner according to the customer's emotions, the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to execute a method of purchase support based on the emotion data.

[0056] The response unit can execute product suggestions according to the customer's emotions. For example, when the customer is happy, the response unit executes suggestions of related products. Furthermore, when the customer is sad, the response unit can also execute suggestions of products that will lift the customer's mood. Furthermore, when the customer is angry, the response unit can also execute suggestions of products that will relax the customer. In this way, by making product suggestions according to the customer's emotions, customer satisfaction can be increased. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input customer emotion data to the generation AI and cause the generation AI to execute product suggestions based on the emotion data.

[0057] The response unit can execute the timing of product proposal according to the customer's emotions. For example, when the customer is relaxed, the response unit immediately executes the product proposal. Also, when the customer is nervous, the response unit can execute the product proposal after a short time. Furthermore, when the customer is excited, the response unit can execute the product proposal at an appropriate timing. In this way, it is possible to promote purchases by proposing products at a timing according to the customer's emotions. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input customer emotion data to the generation AI and cause the generation AI to execute the proposal timing based on the emotion data.

[0058] The corresponding unit can execute a method of suggesting a product according to the customer's emotions. For example, when the customer is relaxed, the corresponding unit executes a proposal including a detailed product description. Furthermore, when the customer is nervous, the corresponding unit can execute a proposal including a concise product description. Furthermore, when the customer is excited, the corresponding unit can execute a visually attractive proposal. This makes it possible to increase customer satisfaction by suggesting a product in a manner according to the customer's emotions. A part or all of the above-mentioned processing in the corresponding unit may be performed, for example, using AI or may be performed without using AI. For example, the corresponding unit can input customer emotion data to the generation AI and cause the generation AI to execute the suggestion method based on the emotion data.

[0059] The response unit can execute the content of the purchase support according to the customer's emotions. For example, when the customer feels anxious, the response unit executes a detailed explanation of the purchase procedure. In addition, when the customer feels relaxed, the response unit can execute a concise explanation of the purchase procedure. Furthermore, when the customer feels excited, the response unit can execute a visually attractive explanation of the purchase procedure. In this way, the purchase procedure can be smoothly carried out by providing purchase support according to the customer's emotions. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the customer's emotion data to the generation AI and cause the generation AI to execute the content of the purchase support based on the emotion data.

[0060] The response unit can execute the timing of purchase support according to the customer's emotions. For example, if the customer feels anxious, the response unit executes purchase support immediately. Also, if the customer is relaxed, the response unit can execute purchase support after a short time. Furthermore, if the customer is excited, the response unit can execute purchase support at an appropriate timing. In this way, by executing purchase support at a timing according to the customer's emotions, the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input customer emotion data to the generation AI and cause the generation AI to execute the timing of purchase support based on the emotion data.

[0061] The response unit can execute a method of purchase support according to the customer's emotions. For example, when the customer feels anxious, the response unit executes purchase support including detailed explanations. Furthermore, when the customer is relaxed, the response unit can execute purchase support including concise explanations. Furthermore, when the customer is excited, the response unit can execute visually attractive purchase support. In this way, by performing purchase support in a manner according to the customer's emotions, the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input customer emotion data to the generation AI and cause the generation AI to execute a method of purchase support based on the emotion data.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The acquisition unit can also acquire the purchase history of the customer and use it as emotion estimation information. For example, the acquisition unit acquires data on products purchased by the customer in the past and uses it as emotion estimation information. The acquisition unit can also acquire data on products viewed by the customer in the past and uses it as emotion estimation information. Furthermore, the acquisition unit can also acquire data on products added to the cart by the customer in the past but not purchased and uses it as emotion estimation information. In this way, by acquiring the purchase history of the customer, the accuracy of emotion estimation is improved.

[0064] The determination unit can also take into account the customer's purchasing history when making product suggestions according to the customer's emotions. For example, the determination unit can determine a suggestion of a product related to a product that the customer has previously purchased. The determination unit can also determine a suggestion of a product related to a product that the customer has previously viewed. The determination unit can also determine a suggestion of a product related to a product that the customer has previously added to a cart but not purchased. In this way, by making product suggestions that take into account the customer's purchasing history, customer satisfaction can be increased.

[0065] The determination unit can also take into account the customer's current location information when making product suggestions according to the customer's emotions. For example, if the customer is in a specific area in the store, the determination unit can determine a suggestion of a product related to that area. Also, if the customer is near a specific product, the determination unit can determine a suggestion of that product. Furthermore, if the customer is at the entrance of the store, the determination unit can determine a suggestion of recommended products for the entire store. In this way, by making product suggestions that take into account the customer's current location information, customer satisfaction can be increased.

[0066] The determination unit may also take into account the social media activity of the customer when making product suggestions according to the customer's emotions. For example, the determination unit may determine a product suggestion related to a product that the customer has "liked" on social media. The determination unit may also determine a product suggestion related to a product that the customer has shared on social media. The determination unit may also determine a product suggestion related to a product that the customer has commented on on social media. In this way, by making product suggestions that take into account the social media activity of the customer, customer satisfaction can be increased.

[0067] The determination unit can also take into account the customer's past purchase frequency when making product suggestions according to the customer's emotions. For example, the determination unit can determine suggestions for products that the customer frequently purchases. The determination unit can also determine suggestions for products that the customer has purchased multiple times within a certain period of time. The determination unit can also determine suggestions for products that the customer purchases in a specific season. In this way, by making product suggestions that take into account the customer's past purchase frequency, customer satisfaction can be increased.

[0068] The process flow of the first embodiment will be briefly described below.

[0069] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information includes the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. For example, the customer's facial expression is captured using a camera, the customer's voice is captured using a microphone, and the heart rate is acquired using a heart rate sensor. Step 2: The estimation unit estimates the customer's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the estimation unit uses AI to analyze the customer's facial expressions, voice, and heart rate to estimate emotions such as joy and excitement. Step 3: The determination unit determines a response according to the customer's emotions estimated by the estimation unit. For example, if the customer is happy, the determination unit determines a suggestion of a related product, and if the customer is anxious, the determination unit determines a detailed product explanation. The determination unit also determines a personalized response taking into account the customer's attribute information. Step 4: The response unit executes the response decided by the decision unit. For example, it suggests a product suitable for the customer, provides detailed information about the product, and processes the payment.

[0070] (Example 2) A purchase support system according to an embodiment of the present invention is a system that reads the emotions of a customer and provides appropriate product suggestions and purchase support. This system acquires information for estimating the emotions of a customer, estimates the emotions of the customer based on the information, determines an appropriate response, and executes the response. For example, the system acquires information such as the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. Next, based on the acquired information, the system estimates the emotions of the customer. For example, AI analyzes the facial expression and voice of the customer and estimates whether the customer is happy, sad, angry, etc. The system determines an appropriate response according to the estimated emotions of the customer. For example, if the customer is happy, a related product can be suggested. Also, if the customer is anxious, detailed product explanations and support can be provided. The determined response is executed by the system. For example, the system can suggest a product that suits the customer, provide detailed product information, and process the payment. This mechanism allows the system to respond according to the emotions of the customer, making online shopping smoother. For example, if a customer is unsure about a purchase, the system can encourage the purchase by suggesting a product at the appropriate time. Furthermore, if a customer feels uneasy about the purchasing procedure, the system can provide support, allowing the customer to proceed with the purchase with peace of mind. Furthermore, the system can determine how to respond based on the customer's emotions and attributes. For example, a more personalized response can be provided by taking into account attribute information such as the customer's age, gender, and purchasing history. In this way, the use of a system with an emotion engine can improve the online shopping experience. Customer satisfaction can be increased by suggesting products and providing purchasing support according to the customer's emotions. As a result, the purchasing support system can make online shopping smoother by responding according to the customer's emotions.

[0071] The purchase support system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information for estimating the emotion of a customer includes, for example, the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content, but is not limited to such examples. The acquisition unit, for example, captures the customer's facial expression using a camera and acquires it as emotion estimation information. The acquisition unit can also capture the customer's voice using a microphone and acquire it as emotion estimation information. Furthermore, the acquisition unit can acquire the customer's heart rate using a heart rate sensor and use it as emotion estimation information. For example, the acquisition unit acquires the heart rate using a heart rate sensor while the customer is browsing a product. The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the customer's facial expression and estimates the emotion. For example, AI analyzes the customer's smile and estimates the emotion of joy. The estimation unit can also use AI to analyze the customer's voice and estimate the emotion. For example, the AI ​​analyzes the tone of the customer's voice and estimates the emotion of joy. The estimation unit can also use AI to analyze the customer's heart rate and estimate the emotion. For example, the AI ​​analyzes the increase in the customer's heart rate and estimates the emotion of excitement. The determination unit determines a response according to the emotion of the customer estimated by the estimation unit. For example, if the customer is happy, the determination unit determines a related product suggestion. In addition, if the customer is anxious, the determination unit can also determine a detailed product description. In addition, the determination unit can determine a response based on the emotion and attributes of the customer. For example, a more personalized response is determined by taking into account attribute information such as the customer's age, gender, and purchase history. The response unit executes the response determined by the determination unit. For example, the response unit suggests a product that suits the customer. In addition, the response unit can provide detailed information about the product. In addition, the response unit can perform payment procedures. For example, if the customer is unsure about a purchase, the response unit suggests a product at an appropriate time. As a result, the purchase support system according to the embodiment can make online shopping smoother by responding in accordance with the customer's emotions.

[0072] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. The emotion estimation information for estimating the emotion of the customer includes, for example, the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content, but is not limited to such examples. The acquisition unit, for example, captures the customer's facial expression using a camera and acquires it as emotion estimation information. The camera has high resolution and can capture even subtle changes in facial expression. The acquisition unit can also capture the customer's voice using a microphone and acquire it as emotion estimation information. The microphone has a noise canceling function and eliminates surrounding noise to clearly collect the customer's voice. Furthermore, the acquisition unit can also acquire the customer's heart rate using a heart rate sensor and use it as emotion estimation information. The heart rate sensor monitors the fluctuation of the heart rate in real time while the customer is browsing the product and acquires the data. As a result, the acquisition unit can collect various data for capturing the customer's emotion from various angles. Furthermore, the acquisition unit also has a motion sensor for capturing the customer's movement and an eye tracking device for detecting the pupil response. These devices capture the subtle movements and gaze of customers and acquire this information for emotion estimation. For example, if a customer focuses their gaze on a particular product, it can be determined that the customer is interested in that product. They are also equipped with a text analysis engine for analyzing content posted by customers, and acquire information for emotion estimation from the content posted by customers on social media and review sites. This allows the acquisition unit to collect a variety of data to capture customer emotions from multiple angles, improving the accuracy of emotion estimation.

[0073] The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the customer's facial expression and estimate the emotion. The AI ​​learns a large amount of facial expression data using deep learning technology, and can estimate emotions such as joy, sadness, surprise, and anger with high accuracy from subtle changes in facial expression. The estimation unit can also use AI to analyze the customer's voice and estimate the emotion. The AI ​​analyzes features such as tone, pitch, speed, and strength of the voice to estimate the emotional state of the customer. For example, the AI ​​analyzes the tone of the customer's voice and estimates the emotion of joy. Furthermore, the estimation unit can also use AI to analyze the customer's heart rate and estimate the emotion. The AI ​​analyzes the heart rate fluctuation pattern and estimates the emotional state such as excitement, tension, and relaxation. For example, the AI ​​analyzes the increase in the customer's heart rate and estimates the emotion of excitement. The estimation unit can comprehensively analyze these emotion estimation information and estimate the customer's emotion from various angles. Furthermore, the estimation unit also analyzes information such as the customer's movements, pupil reactions, and posted content to improve the accuracy of emotion estimation. For example, if a customer focuses their gaze on a particular product, it can be determined that the customer is interested in that product. Furthermore, by analyzing the customer's posted content, it is possible to gain a deeper understanding of the customer's emotions and intentions. This allows the estimation unit to estimate the customer's emotions with high accuracy, and use this information to help determine the next step, which is how to respond.

[0074] The determination unit determines a response according to the customer's emotion estimated by the estimation unit. For example, if the customer is happy, the determination unit determines a suggestion of a related product. Specifically, if the customer shows an emotion of joy when looking at a specific product, the determination unit suggests an accessory or optional product related to the product. In addition, the determination unit can determine a detailed product description if the customer is anxious. For example, if the customer is anxious about the function or usage of the product, a detailed explanation or a demonstration video is provided. Furthermore, the determination unit can determine a response based on the customer's emotion and attributes. For example, a more personalized response is determined by taking into account attribute information such as the customer's age, gender, and purchase history. For example, the latest trend products are suggested to younger customers, and products that emphasize ease of use are suggested to elderly customers. In addition, a new product related to a product previously purchased by the customer can be suggested based on the past purchase history. In this way, the determination unit can determine an optimal response based on the customer's emotion and attributes and improve customer satisfaction. Furthermore, the determination unit can continuously review and optimize the response based on the emotion estimation result updated in real time. For example, if the customer's emotion changes, a new response is immediately determined and provided to the customer. This allows the decision unit to respond flexibly based on the latest information and quickly meet customer needs.

[0075] The response unit executes the response determined by the determination unit. For example, the response unit proposes a product that suits the customer. Specifically, if the customer is interested in a particular product, the response unit proposes accessories and optional products related to the product. The response unit can also provide detailed information about the product. For example, if the customer is unsure about the function or usage of the product, the response unit provides a detailed explanation or a demonstration video. Furthermore, the response unit can also process the payment. For example, if the customer is unsure about the purchase, the response unit proposes the product at an appropriate time. As a result, the purchase support system according to the embodiment can make online shopping smoother by responding according to the customer's emotions. Furthermore, the response unit has various means for facilitating communication with the customer. For example, the response unit can use a chatbot to answer the customer's questions in real time or provide important information via email or SMS. The response unit can also collect customer feedback and use it to improve services. For example, the response unit records how the customer responded to the proposed product and reflects it in the next proposal. As a result, the response unit can more accurately meet the needs of the customer and improve customer satisfaction. In addition, the customer support department can analyze the customer's purchase history and behavioral patterns to predict future purchasing behavior, which allows the customer support department to provide more personalized service and build long-term customer relationships.

[0076] The determination unit determines a product suggestion according to the customer's emotions as a response according to the customer's emotions, and the response unit can make a product suggestion according to the customer's emotions. For example, when the customer is happy, the determination unit determines a related product suggestion. Furthermore, when the customer is sad, the determination unit can also determine a product suggestion that will lift the customer's spirits. Furthermore, when the customer is angry, the determination unit can also determine a product suggestion that will relax the customer. The response unit executes the product suggestion determined by the determination unit. In this way, customer satisfaction can be increased by making product suggestions according to the customer's emotions.

[0077] The determination unit determines a proposal in a manner corresponding to the customer's emotions as a response corresponding to the customer's emotions, and the response unit can make a proposal in a manner corresponding to the customer's emotions. For example, when the customer is relaxed, the determination unit determines a proposal including a detailed product description. Also, when the customer is nervous, the determination unit can determine a proposal including a concise product description. Furthermore, when the customer is excited, the determination unit can determine a visually attractive proposal. The response unit executes the proposal in the manner determined by the determination unit. This makes it possible to provide a more personalized response by making a proposal in a manner corresponding to the customer's emotions.

[0078] The determination unit determines product purchasing support in a manner corresponding to the customer's emotions as a response corresponding to the customer's emotions, and the response unit can provide purchasing support in a manner corresponding to the customer's emotions. For example, the determination unit determines a detailed explanation of the purchasing procedure when the customer is feeling anxious. The determination unit can also determine a concise explanation of the purchasing procedure when the customer is relaxed. Furthermore, the determination unit can also determine a visually appealing explanation of the purchasing procedure when the customer is excited. The response unit executes purchasing support in the manner determined by the determination unit. As a result, the purchasing procedure can be smoothly progressed by providing purchasing support in a manner corresponding to the customer's emotions.

[0079] The determination unit can determine a response according to the customer's emotions based on the customer's emotions and the customer's attributes. The determination unit determines a more personalized response by taking into account attribute information such as the customer's age, gender, and purchase history. For example, if the customer is a young person, the determination unit can suggest the latest trending products. Also, if the customer is an elderly person, the determination unit can decide to suggest products that are easy to use. Furthermore, the determination unit can decide to suggest related products based on the customer's purchase history. For example, a new product related to a product that the customer has purchased in the past is suggested. This makes it possible to provide a more personalized response by determining a response based on the customer's emotions and attributes.

[0080] The acquisition unit can acquire the facial expression of the customer and use it as emotion estimation information. For example, the acquisition unit captures the facial expression in real time when the customer faces the camera and acquires it as emotion estimation information. The acquisition unit can also periodically capture the facial expression while the customer is browsing products and acquire it as emotion estimation information. Furthermore, the acquisition unit can capture the facial expression while the customer is performing a purchase procedure and acquire it as emotion estimation information. In this way, the accuracy of emotion estimation is improved by acquiring the customer's facial expression. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI or may be performed without using AI. For example, the acquisition unit can input the facial expression data of the customer acquired by the camera to the generation AI and cause the generation AI to execute emotion estimation from the facial expression data.

[0081] The acquisition unit can acquire the customer's actions and use them as emotion estimation information. For example, the acquisition unit captures hand movements when the customer is selecting a product and acquires them as emotion estimation information. The acquisition unit can also capture the customer's actions of putting a product into a cart and acquire them as emotion estimation information. Furthermore, the acquisition unit can capture the customer's actions while performing a purchase procedure and acquire them as emotion estimation information. In this way, the accuracy of emotion estimation is improved by acquiring the customer's actions. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the customer's action data acquired by a camera to the generation AI and cause the generation AI to estimate emotions from the action data.

[0082] The acquisition unit can acquire the voice of the customer and use it as emotion estimation information. For example, the acquisition unit captures the voice of the customer talking about a product and acquires it as emotion estimation information. The acquisition unit can also capture the voice of the customer while the customer is making a purchase procedure and acquire it as emotion estimation information. Furthermore, the acquisition unit can capture the voice of the customer when the customer is requesting support and acquire it as emotion estimation information. By acquiring the voice of the customer, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the voice data of the customer acquired by a microphone to the generation AI and cause the generation AI to execute emotion estimation from the voice data.

[0083] The acquisition unit can acquire the heart rate of the customer and use it as emotion estimation information. For example, the acquisition unit can acquire the heart rate using a heart rate sensor while the customer is browsing products. The acquisition unit can also acquire the heart rate using the heart rate sensor while the customer is making a purchase. Furthermore, the acquisition unit can also acquire the heart rate using the heart rate sensor when the customer is seeking support. In this way, the accuracy of emotion estimation is improved by acquiring the customer's heart rate. A part or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI or can be performed without using an AI. For example, the acquisition unit can input the customer's heart rate data acquired by the heart rate sensor to the generation AI and cause the generation AI to estimate emotions from the heart rate data.

[0084] The acquisition unit can acquire the customer's blood pressure and use it as emotion estimation information. The acquisition unit can acquire the customer's blood pressure using a blood pressure sensor, for example, while the customer is browsing products. The acquisition unit can also acquire the customer's blood pressure using the blood pressure sensor while the customer is making a purchase. The acquisition unit can also acquire the customer's blood pressure using the blood pressure sensor when the customer is seeking support. In this way, acquiring the customer's blood pressure improves the accuracy of emotion estimation. A part or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the acquisition unit can input the customer's blood pressure data acquired by the blood pressure sensor to the generation AI and cause the generation AI to estimate emotions from the blood pressure data.

[0085] The acquisition unit can acquire the pupillary response of the customer and use it as emotion estimation information. For example, the acquisition unit captures the pupillary response while the customer is browsing a product and acquires it as emotion estimation information. The acquisition unit can also capture the pupillary response while the customer is making a purchase and acquire it as emotion estimation information. Furthermore, the acquisition unit can capture the pupillary response when the customer is seeking support and acquire it as emotion estimation information. Thereby, by acquiring the pupillary response of the customer, the accuracy of emotion estimation is improved. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the customer's pupil data acquired by the pupil measurement device to the generation AI and cause the generation AI to execute emotion estimation from the pupillary data.

[0086] The estimation unit can analyze the customer's facial expression and estimate the emotion. For example, the estimation unit uses an AI to analyze the customer's smile and estimate the emotion of joy. The estimation unit can also use an AI to analyze the customer's brow wrinkles and estimate the emotion of anger. The estimation unit can also use an AI to analyze the customer's tears and estimate the emotion of sadness. This improves the accuracy of emotion estimation by analyzing the customer's facial expression. Some or all of the above-mentioned processing in the estimation unit may be performed using an AI, for example, or may be performed without using an AI. For example, the estimation unit can input facial expression data of the customer acquired by a camera to the generation AI and cause the generation AI to estimate the emotion from the facial expression data.

[0087] The estimation unit can analyze the customer's voice and estimate the emotion. For example, the estimation unit can use an AI to analyze the tone of the customer's voice and estimate the emotion of joy. The estimation unit can also use an AI to analyze the trembling of the customer's voice and estimate the emotion of tension. Furthermore, the estimation unit can also use an AI to analyze the volume of the customer's voice and estimate the emotion of anger. This improves the accuracy of emotion estimation by analyzing the customer's voice. A part or all of the above-mentioned processing in the estimation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the estimation unit can input the customer's voice data acquired by a microphone to the generation AI and cause the generation AI to estimate the emotion from the voice data.

[0088] The estimation unit can analyze the customer's movements and estimate the emotion. For example, the estimation unit can use an AI to analyze the customer's hand movements and estimate the emotion of excitement. The estimation unit can also use an AI to analyze the customer's walking speed and estimate the emotion of impatience. Furthermore, the estimation unit can also use an AI to analyze the customer's posture and estimate the emotion of relaxation. This improves the accuracy of emotion estimation by analyzing the customer's movements. A part or all of the above-mentioned processing in the estimation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the estimation unit can input the customer's movement data acquired by a camera to the generation AI and cause the generation AI to estimate the emotion from the movement data.

[0089] The estimation unit can analyze the customer's heart rate and estimate the emotion. For example, the estimation unit can use an AI to analyze an increase in the customer's heart rate and estimate an emotion of excitement. The estimation unit can also use an AI to analyze a decrease in the customer's heart rate and estimate an emotion of relaxation. The estimation unit can also use an AI to analyze the fluctuation in the customer's heart rate and estimate an emotion of tension. This improves the accuracy of emotion estimation by analyzing the customer's heart rate. A part or all of the above-mentioned processing in the estimation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the estimation unit can input the customer's heart rate data acquired by a heart rate sensor to the generation AI and cause the generation AI to estimate the emotion from the heart rate data.

[0090] The estimation unit can analyze the customer's blood pressure and estimate the emotion. For example, the estimation unit can use an AI to analyze an increase in the customer's blood pressure and estimate an emotion of excitement. The estimation unit can also use an AI to analyze a decrease in the customer's blood pressure and estimate an emotion of relaxation. The estimation unit can also use an AI to analyze fluctuations in the customer's blood pressure and estimate an emotion of tension. This improves the accuracy of emotion estimation by analyzing the customer's blood pressure. Some or all of the above-mentioned processing in the estimation unit can be performed using an AI, for example, or can be performed without using an AI. For example, the estimation unit can input the customer's blood pressure data acquired by a blood pressure sensor to the generation AI and cause the generation AI to estimate the emotion from the blood pressure data.

[0091] The estimation unit can analyze the customer's pupil response and estimate the emotion. For example, the estimation unit can use an AI to analyze the dilation of the customer's pupil and estimate the emotion of excitement. The estimation unit can also use an AI to analyze the contraction of the customer's pupil and estimate the emotion of relaxation. The estimation unit can also use an AI to analyze the fluctuation of the customer's pupil and estimate the emotion of tension. This improves the accuracy of emotion estimation by analyzing the customer's pupil response. Some or all of the above-mentioned processing in the estimation unit can be performed using an AI, for example, or can be performed without using an AI. For example, the estimation unit can input the customer's pupil data acquired by a pupil measurement device to the generation AI and cause the generation AI to estimate the emotion from the pupil data.

[0092] The determination unit can determine a product suggestion according to the customer's emotions. For example, when the customer is happy, the determination unit can determine a related product suggestion. Furthermore, when the customer is sad, the determination unit can also determine a product suggestion that will lift the customer's mood. Furthermore, when the customer is angry, the determination unit can also determine a product suggestion that will relax the customer. This makes it possible to increase customer satisfaction by suggesting products according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to suggest products based on the emotion data.

[0093] The determination unit can determine the timing of product proposal according to the customer's emotions. For example, when the customer is relaxed, the determination unit immediately proposes the product. Also, when the customer is nervous, the determination unit can wait a short time before proposing the product. Furthermore, when the customer is excited, the determination unit can propose the product at an appropriate timing. This makes it possible to promote purchases by proposing the product at a timing according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to execute the proposal timing based on the emotion data.

[0094] The determination unit can determine a method of proposing a product according to the customer's emotions. For example, when the customer is relaxed, the determination unit can determine a proposal including a detailed product description. Furthermore, when the customer is nervous, the determination unit can also determine a proposal including a concise product description. Furthermore, when the customer is excited, the determination unit can also determine a visually attractive proposal. This makes it possible to increase customer satisfaction by proposing a product in a manner according to the customer's emotions. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to execute the proposal method based on the emotion data.

[0095] The determination unit can determine the content of the purchase support according to the customer's emotions. For example, when the customer feels anxious, the determination unit determines a detailed explanation of the purchase procedure. In addition, when the customer is relaxed, the determination unit can also determine a brief explanation of the purchase procedure. Furthermore, when the customer is excited, the determination unit can also determine a visually attractive explanation of the purchase procedure. In this way, the purchase support is provided according to the customer's emotions, and the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the customer's emotion data to the generation AI and cause the generation AI to execute the content of the purchase support based on the emotion data.

[0096] The determination unit can determine the timing of purchase support according to the customer's emotions. For example, if the customer feels anxious, the determination unit immediately provides purchase support. Also, if the customer is relaxed, the determination unit can provide purchase support after a short time. Furthermore, if the customer is excited, the determination unit can provide purchase support at an appropriate timing. As a result, purchase support is provided at a timing according to the customer's emotions, so that the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to execute the timing of purchase support based on the emotion data.

[0097] The determination unit can determine a method of purchase support according to the customer's emotions. For example, when the customer feels anxious, the determination unit determines purchase support including detailed explanations. Furthermore, when the customer is relaxed, the determination unit can also determine purchase support including concise explanations. Furthermore, when the customer is excited, the determination unit can also determine visually attractive purchase support. As a result, by providing purchase support in a manner according to the customer's emotions, the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input customer emotion data to the generation AI and cause the generation AI to execute a method of purchase support based on the emotion data.

[0098] The response unit can execute product suggestions according to the customer's emotions. For example, when the customer is happy, the response unit executes suggestions of related products. Furthermore, when the customer is sad, the response unit can also execute suggestions of products that will lift the customer's mood. Furthermore, when the customer is angry, the response unit can also execute suggestions of products that will relax the customer. In this way, by making product suggestions according to the customer's emotions, customer satisfaction can be increased. A part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input customer emotion data to the generation AI and cause the generation AI to execute product suggestions based on the emotion data.

[0099] The response unit can execute the timing of product proposal according to the customer's emotions. For example, when the customer is relaxed, the response unit immediately executes the product proposal. Also, when the customer is nervous, the response unit can execute the product proposal after a short time. Furthermore, when the customer is excited, the response unit can execute the product proposal at an appropriate timing. In this way, it is possible to promote purchases by proposing products at a timing according to the customer's emotions. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input customer emotion data to the generation AI and cause the generation AI to execute the proposal timing based on the emotion data.

[0100] The corresponding unit can execute a method of suggesting a product according to the customer's emotions. For example, when the customer is relaxed, the corresponding unit executes a proposal including a detailed product description. Furthermore, when the customer is nervous, the corresponding unit can execute a proposal including a concise product description. Furthermore, when the customer is excited, the corresponding unit can execute a visually attractive proposal. This makes it possible to increase customer satisfaction by suggesting a product in a manner according to the customer's emotions. A part or all of the above-mentioned processing in the corresponding unit may be performed, for example, using AI or may be performed without using AI. For example, the corresponding unit can input customer emotion data to the generation AI and cause the generation AI to execute the suggestion method based on the emotion data.

[0101] The response unit can execute the content of the purchase support according to the customer's emotions. For example, when the customer feels anxious, the response unit executes a detailed explanation of the purchase procedure. In addition, when the customer feels relaxed, the response unit can execute a concise explanation of the purchase procedure. Furthermore, when the customer feels excited, the response unit can execute a visually attractive explanation of the purchase procedure. In this way, the purchase procedure can be smoothly carried out by providing purchase support according to the customer's emotions. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the customer's emotion data to the generation AI and cause the generation AI to execute the content of the purchase support based on the emotion data.

[0102] The response unit can execute the timing of purchase support according to the customer's emotions. For example, if the customer feels anxious, the response unit executes purchase support immediately. Also, if the customer is relaxed, the response unit can execute purchase support after a short time. Furthermore, if the customer is excited, the response unit can execute purchase support at an appropriate timing. In this way, by executing purchase support at a timing according to the customer's emotions, the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input customer emotion data to the generation AI and cause the generation AI to execute the timing of purchase support based on the emotion data.

[0103] The response unit can execute a method of purchase support according to the customer's emotions. For example, when the customer feels anxious, the response unit executes purchase support including detailed explanations. Furthermore, when the customer is relaxed, the response unit can execute purchase support including concise explanations. Furthermore, when the customer is excited, the response unit can execute visually attractive purchase support. In this way, by performing purchase support in a manner according to the customer's emotions, the purchase procedure proceeds smoothly. A part or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input customer emotion data to the generation AI and cause the generation AI to execute a method of purchase support based on the emotion data.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The acquisition unit can also acquire the purchase history of the customer and use it as emotion estimation information. For example, the acquisition unit acquires data on products purchased by the customer in the past and uses it as emotion estimation information. The acquisition unit can also acquire data on products viewed by the customer in the past and uses it as emotion estimation information. Furthermore, the acquisition unit can also acquire data on products added to the cart by the customer in the past but not purchased and uses it as emotion estimation information. In this way, by acquiring the purchase history of the customer, the accuracy of emotion estimation is improved.

[0106] The determination unit can also take into account the customer's purchasing history when making product suggestions according to the customer's emotions. For example, the determination unit can determine a suggestion of a product related to a product that the customer has previously purchased. The determination unit can also determine a suggestion of a product related to a product that the customer has previously viewed. The determination unit can also determine a suggestion of a product related to a product that the customer has previously added to a cart but not purchased. In this way, by making product suggestions that take into account the customer's purchasing history, customer satisfaction can be increased.

[0107] The determination unit can also take into account the customer's current location information when making product suggestions according to the customer's emotions. For example, if the customer is in a specific area in the store, the determination unit can determine a suggestion of a product related to that area. Also, if the customer is near a specific product, the determination unit can determine a suggestion of that product. Furthermore, if the customer is at the entrance of the store, the determination unit can determine a suggestion of recommended products for the entire store. In this way, by making product suggestions that take into account the customer's current location information, customer satisfaction can be increased.

[0108] The determination unit may also take into account the social media activity of the customer when making product suggestions according to the customer's emotions. For example, the determination unit may determine a product suggestion related to a product that the customer has "liked" on social media. The determination unit may also determine a product suggestion related to a product that the customer has shared on social media. The determination unit may also determine a product suggestion related to a product that the customer has commented on on social media. In this way, by making product suggestions that take into account the social media activity of the customer, customer satisfaction can be increased.

[0109] The determination unit can also take into account the customer's past purchase frequency when making product suggestions according to the customer's emotions. For example, the determination unit can determine suggestions for products that the customer frequently purchases. The determination unit can also determine suggestions for products that the customer has purchased multiple times within a certain period of time. The determination unit can also determine suggestions for products that the customer purchases in a specific season. In this way, by making product suggestions that take into account the customer's past purchase frequency, customer satisfaction can be increased.

[0110] The acquisition unit can also acquire biometric information of the customer to estimate the emotion of the customer. For example, the acquisition unit acquires the electrical resistance of the customer's skin and uses it as emotion estimation information. The acquisition unit can also acquire an electromyogram of the customer and use it as emotion estimation information. Furthermore, the acquisition unit can acquire an electroencephalogram of the customer and use it as emotion estimation information. In this way, the accuracy of emotion estimation is improved by acquiring the biometric information of the customer.

[0111] The estimation unit can also analyze the customer's biometric information when estimating the customer's emotion. For example, the estimation unit can analyze the electrical resistance of the customer's skin to estimate the emotion. The estimation unit can also analyze the customer's electromyogram to estimate the emotion. Furthermore, the estimation unit can analyze the customer's brain waves to estimate the emotion. In this way, the accuracy of emotion estimation is improved by analyzing the customer's biometric information.

[0112] The decision unit can also take the customer's biometric information into consideration when making product suggestions according to the customer's emotions. For example, if the customer's skin has high electrical resistance, the decision unit can decide to suggest products that are relaxing. Also, if the customer's electromyogram is high, the decision unit can decide to suggest products that consume energy. Furthermore, if the customer's brain waves show a specific pattern, the decision unit can decide to suggest products that correspond to that pattern. In this way, by making product suggestions that take the customer's biometric information into consideration, customer satisfaction can be increased.

[0113] The determination unit can also take into account changes in the customer's emotions when making product suggestions in accordance with the customer's emotions. For example, if the customer's emotions change suddenly, the determination unit can determine product suggestions in accordance with the changes. Also, if the customer's emotions change gradually, the determination unit can determine product suggestions in accordance with the changes. Furthermore, if the customer's emotions show a certain pattern, the determination unit can determine product suggestions in accordance with the pattern. In this way, by making product suggestions that take into account changes in the customer's emotions, customer satisfaction can be increased.

[0114] The decision unit can also take into account the intensity of the customer's emotion when making product suggestions according to the customer's emotion. For example, if the customer's emotion is strong, the decision unit can decide to suggest products that are strongly related to that emotion. Also, if the customer's emotion is weak, the decision unit can decide to suggest products that are only slightly related to that emotion. Furthermore, if the customer's emotion is moderate, the decision unit can decide to suggest products that are moderately related to that emotion. In this way, by making product suggestions that take into account the intensity of the customer's emotion, customer satisfaction can be increased.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information includes the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwaves, body temperature, breathing, and posted content. For example, the customer's facial expression is captured using a camera, the customer's voice is captured using a microphone, and the heart rate is acquired using a heart rate sensor. Step 2: The estimation unit estimates the customer's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the estimation unit uses AI to analyze the customer's facial expressions, voice, and heart rate to estimate emotions such as joy and excitement. Step 3: The determination unit determines a response according to the customer's emotions estimated by the estimation unit. For example, if the customer is happy, the determination unit determines a suggestion of a related product, and if the customer is anxious, the determination unit determines a detailed product explanation. The determination unit also determines a personalized response taking into account the customer's attribute information. Step 4: The response unit executes the response decided by the decision unit. For example, it suggests a product suitable for the customer, provides detailed information about the product, and processes the payment.

[0117] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0118] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0119] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] For example, the acquisition unit can acquire the facial expression and voice of the customer using the camera 42 and microphone 38B of the smart device 14. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the customer's emotion by analyzing the information acquired from the acquisition unit. The decision unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The response unit is realized by the control unit 46A of the smart device 14, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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, the RAM 30, and the 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.

[0124] 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.

[0125] 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 the voice according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0127] 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 performed in a secure state.

[0128] Fig. 4 shows an example of 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0133] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0134] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0135] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] For example, the acquisition unit can acquire the facial expression and voice of the customer using the camera 42 and microphone 238 of the smart glasses 214. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the customer's emotion by analyzing the information acquired from the acquisition unit. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The response unit is realized by the control unit 46A of the smart glasses 214, and executes the determined response. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] 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, the RAM 30, and the 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.

[0140] 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.

[0141] 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 the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] 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 performed in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] 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 a voice indicating a user input for 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.

[0150] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] For example, the acquisition unit can acquire the facial expression and voice of the customer using the camera 42 and microphone 238 of the headset type terminal 314. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the customer's emotions by analyzing information acquired from the acquisition unit. The decision unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotion. The response unit is realized by the control unit 46A of the headset type terminal 314, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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, the RAM 30, and the 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.

[0156] 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.

[0157] 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 the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0159] 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 performed in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0165] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0166] 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 a voice indicating a user input for 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.

[0167] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0168] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0169] For example, the acquisition unit can acquire the facial expression and voice of the customer using the camera 42 and microphone 238 of the robot 414. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the customer's emotions by analyzing information acquired from the acquisition unit. The decision unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate response based on the estimated emotions. The response unit is realized by the control unit 46A of the robot 414, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0171] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0173] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0175] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0177] 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.

[0178] 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 Universal Serial Bus (USB) 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.

[0179] In addition, 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 upon request from the data processing device 12.

[0180] 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.

[0181] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0182] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0183] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0184] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0186] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0188] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of an online shopping customer; an estimation unit that estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response according to the emotion of the customer estimated by the estimation unit; a response unit that executes the response determined by the determination unit. A system characterized by: (Appendix 2) The determination unit is determining a product proposal corresponding to the customer's emotion as a response corresponding to the customer's emotion; The corresponding portion is Propose products according to the customer's emotions 2. The system of claim 1, (Appendix 3) The determination unit is determining the proposal in a manner corresponding to the customer's emotion as a response corresponding to the customer's emotion; The corresponding portion is The proposal is made in a manner corresponding to the customer's feelings. 2. The system of claim 1, (Appendix 4) The determination unit is determining, as a response to the customer's emotions, support for purchasing a product in a manner corresponding to the customer's emotions; The corresponding portion is Providing the purchase support in a manner corresponding to the customer's feelings 2. The system of claim 1, (Appendix 5) The determination unit is Deciding a response according to the customer's emotion based on the customer's emotion and the customer's attributes 2. The system of claim 1, (Appendix 6) The acquisition unit is Acquire customer facial expressions and use them as emotion estimation information 2. The system of claim 1, (Appendix 7) The acquisition unit is Obtaining customer behavior and using it as emotion estimation information 2. The system of claim 1, (Appendix 8) The acquisition unit is Acquire customer voice and use it as information for estimating emotions 2. The system of claim 1, (Appendix 9) The acquisition unit is Obtaining the customer's heart rate and using it as information for estimating emotions 2. The system of claim 1, (Appendix 10) The acquisition unit is Obtaining the customer's blood pressure and using it as information for estimating emotions 2. The system of claim 1, (Appendix 11) The acquisition unit is Obtaining the customer's pupil response and using it as information for estimating emotions 2. The system of claim 1, (Appendix 12) The estimation unit is Analyzing customer facial expressions to estimate emotions 2. The system of claim 1, (Appendix 13) The estimation unit is Analyze customer voice and infer emotions 2. The system of claim 1, (Appendix 14) The estimation unit is Analyzing customer behavior and inferring emotions 2. The system of claim 1, (Appendix 15) The estimation unit is Analyzing customer heartbeats and inferring emotions 2. The system of claim 1, (Appendix 16) The estimation unit is Analyzing customer blood pressure and inferring emotions 2. The system of claim 1, (Appendix 17) The estimation unit is Analyzing customer pupil responses to estimate emotions 2. The system of claim 1, (Appendix 18) The determination unit is Determine product suggestions based on customer sentiment 2. The system of claim 1, (Appendix 19) The determination unit is Decide the timing of product proposals based on customer emotions 2. The system of claim 1, (Appendix 20) The determination unit is Determine how to recommend products based on customer emotions 2. The system of claim 1, (Appendix 21) The determination unit is Decide on the content of purchase support according to customer emotions 2. The system of claim 1, (Appendix 22) The determination unit is Decide the timing of purchase support based on customer sentiment 2. The system of claim 1, (Appendix 23) The determination unit is Determine how to help customers buy based on their emotions 2. The system of claim 1, (Appendix 24) The corresponding portion is Implement product suggestions based on customer sentiment 2. The system of claim 1, (Appendix 25) The corresponding portion is Implement product suggestions based on customer sentiment 2. The system of claim 1, (Appendix 26) The corresponding portion is Implement product suggestions based on customer emotions 2. The system of claim 1, (Appendix 27) The corresponding portion is Implement purchasing support content that responds to customer emotions 2. The system of claim 1, (Appendix 28) The corresponding portion is Implement purchasing support timing based on customer sentiment 2. The system of claim 1, (Appendix 29) The corresponding portion is Implement purchasing support methods that respond to customer emotions 2. The system of claim 1, [Explanation of symbols]

[0189] 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. An acquisition unit that acquires emotion estimation information for estimating an emotion of an online shopping customer; an estimation unit that estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines a response in accordance with the emotion of the customer estimated by the estimation unit; a response unit that executes the response determined by the determination unit. A system characterized by:

2. The determination unit is determining a product proposal corresponding to the customer's emotion as a response corresponding to the customer's emotion; The corresponding portion is Propose products according to the customer's emotions 2. The system of claim 1 .

3. The determination unit is determining the proposal in a manner corresponding to the customer's emotion as a response corresponding to the customer's emotion; The corresponding portion is The proposal is made in a manner corresponding to the customer's feelings.

3. The system of claim 2.

4. The determination unit is determining, as a response to the customer's emotions, support for purchasing a product in a manner corresponding to the customer's emotions; The corresponding portion is Providing the purchase support in a manner corresponding to the customer's feelings 2. The system of claim 1 .

5. The determination unit is Deciding a response according to the customer's emotion based on the customer's emotion and the customer's attributes 2. The system of claim 1 .

6. The acquisition unit is The facial expression of the customer is acquired and used as the emotion estimation information.

2. The system of claim 1 .

7. The acquisition unit is The customer's actions are acquired and used as emotion estimation information.

2. The system of claim 1 .

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