System

Abstract

To provide a system that estimates the emotions of a customer and proposes an appropriate product or service on the basis of the estimation.SOLUTION: A system according to an embodiment comprises an acquisition unit, an estimation unit, a determination unit, and a proposal unit. The acquisition unit acquires emotion estimation information for estimating the emotions of a 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 product or service according to the emotions of the customer as estimated by the estimation unit. The proposal unit proposes the product or service determined by the determination unit to the customer.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 estimate a customer's emotions and suggest appropriate products or services based on that.

[0005] The system according to the embodiment aims to estimate the emotions of a customer and propose appropriate products and services 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 proposal unit. The acquisition unit acquires emotion estimation information for estimating an emotion of a customer. The estimation unit estimates an emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The determination unit determines a product or service according to the emotion of the customer estimated by the estimation unit. The proposal unit proposes the product or service determined by the determination unit to the customer. Effect of the Invention

[0007] The system according to the embodiment can estimate the customer's emotions and suggest appropriate products and services based on the estimated emotions. [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, aperture, and 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 marketing support system according to an embodiment of the present invention is a system that reads the emotions of a customer and proposes appropriate products and services. The system acquires emotion estimation information for estimating the emotions of a customer, and estimates the emotions of the customer based on the acquired information. According to the estimated emotions, the system determines appropriate products and services and proposes them to the customer. The system can also collect customer feedback and use it to improve products and services. 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. This information is input to the system as emotion estimation information. Next, based on the acquired information, the system estimates the emotions of the customer. The system analyzes the emotion estimation information and estimates the emotions of the customer. For example, if the customer is smiling, the system can estimate that the customer is happy. According to the estimated emotions, the system determines appropriate products and services. The system determines the most suitable products and services for the customer based on the estimated emotions and the customer's attributes and behavior history. For example, if the customer is happy, the system can propose fun products and services to the customer. The determined products and services are proposed to the customer by the system. The system can suggest products and services in a manner according to the customer's emotions. For example, if the customer is relaxed, the system can suggest products and services while playing relaxing music. Furthermore, the system can collect customer feedback and use it to improve the products and services. The system collects customer feedback on the proposed products and services and identifies areas to improve the products and services based on the feedback. For example, if a customer is dissatisfied with a product, the system can identify the areas of dissatisfaction and provide information to improve the product. In this way, the system can read the customer's emotions and suggest appropriate products and services, thereby enabling more efficient marketing. In addition, by collecting customer feedback and using it to improve the products and services, customer satisfaction can be improved. In this way, the marketing support system can read the customer's emotions and suggest appropriate products and services.

[0029] The marketing support system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a proposal unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information 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. For example, the acquisition unit photographs the customer's facial expression with a camera and acquires facial expression data using facial expression recognition technology. The acquisition unit can also record the customer's voice with a microphone and acquire voice data using voice analysis technology. Furthermore, the acquisition unit can measure the customer's heart rate with a sensor and acquire heart rate data. For example, the acquisition unit photographs the customer's facial expression with a camera and acquires facial expression data such as smiling and angry using facial expression recognition technology. The voice data is used as information for analyzing the tone of voice and speaking style of the customer and estimating the emotion. The heart rate data is used as information for analyzing the fluctuation of the customer's heart rate and estimating the stress or relaxation state. The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses a machine learning algorithm to analyze the emotion estimation information and estimate the emotion of the customer. The estimation unit, for example, estimates the emotion of the customer using a machine learning model that takes facial expression data as an input and outputs emotions such as smiling and anger. The estimation unit can also estimate emotions based on tone of voice and speaking style using voice data as an input. Furthermore, the estimation unit can also estimate stress and relaxation states based on fluctuations in heart rate using heart rate data as an input. For example, the estimation unit estimates the emotion of the customer using a machine learning model that takes facial expression data as an input and outputs emotions such as smiling and anger. The voice data is used as information for estimating emotions based on tone of voice and speaking style. The heart rate data is used as information for estimating stress and relaxation states based on fluctuations in heart rate. The determination unit determines a product or service according to the emotion of the customer estimated by the estimation unit. The determination unit determines a product or service that is optimal for the customer based on, for example, the estimated emotion and the attributes and behavioral history of the customer. For example, if the customer is happy, the decision unit can suggest a pleasant product or service.In addition, the determination unit can also suggest a product or service that can relax the customer when the customer is relaxed. Furthermore, the determination unit can also suggest a product or service that helps reduce stress when the customer is stressed. For example, the determination unit determines a product or service that is optimal for the customer based on the estimated emotion and the attributes and behavior history of the customer. The suggestion unit suggests the product or service determined by the determination unit to the customer. The suggestion unit, for example, suggests a product or service in a manner according to the emotion of the customer. For example, the suggestion unit can suggest a product or service while playing relaxing music when the customer is relaxed. Furthermore, the suggestion unit can suggest a product or service in a calm tone when the customer is stressed. Furthermore, the suggestion unit can suggest a product or service in an energetic tone when the customer is excited. For example, the suggestion unit can suggest a product or service while playing relaxing music when the customer is relaxed. If the customer is stressed, it can also suggest a product or service in a calm tone. If the customer is excited, it can also suggest a product or service in an energetic tone. In this way, the marketing support system according to the embodiment can read the emotion of the customer and suggest appropriate products and services. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may make suggestions using an AI model that inputs customer emotion data and suggests appropriate products and services.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. The emotion estimation information 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. For example, the acquisition unit photographs the customer's facial expression with a camera and acquires facial expression data using facial expression recognition technology. Specifically, the camera photographs the customer's face with high resolution and identifies emotions such as smile, anger, and surprise using an algorithm that extracts facial feature points. The acquisition unit can also record the customer's voice with a microphone and acquire voice data using voice analysis technology. The voice data is analyzed for features such as tone, pitch, speed, and intensity of the voice to detect changes in emotion. Furthermore, the acquisition unit can measure the customer's heart rate with a sensor and acquire heart rate data. The heart rate sensor is attached to the customer's wrist or chest and monitors the fluctuation of the heart rate in real time. For example, the acquisition unit photographs the customer's facial expression with a camera and acquires facial expression data such as smile and anger using facial expression recognition technology. The voice data is used as information for analyzing the tone of the customer's voice and the way of speaking and estimating emotions. The heart rate data is used as information for analyzing fluctuations in the customer's heart rate and estimating stress and relaxation states. This allows the acquisition unit to collect a wide range of data from various devices and grasp the situation in real time. Furthermore, the acquisition unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the estimation unit and the determination unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the acquisition unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses a machine learning algorithm to analyze the emotion estimation information and estimate the emotion of the customer. Specifically, the emotion of the customer is estimated using a machine learning model that receives facial expression data as an input and outputs emotions such as smiling and anger. The facial expression recognition algorithm analyzes facial feature points and detects changes in emotion. The estimation unit can also receive voice data as an input and estimate emotions based on the tone of voice and speaking style. The voice analysis algorithm analyzes features such as the tone, pitch, speed, and intensity of the voice and detects changes in emotion. Furthermore, the estimation unit can also receive heart rate data as an input and estimate stress or relaxation state based on heart rate fluctuations. The heart rate analysis algorithm analyzes heart rate fluctuations and estimates stress or relaxation state. For example, the estimation unit receives facial expression data as an input and estimates the emotion of the customer using a machine learning model that outputs emotions such as smiling and anger. The voice data is used as information for estimating emotions based on the tone of voice and speaking style. The heart rate data is used as information for estimating stress and relaxation states based on heart rate fluctuations. This allows the estimation unit to quickly and accurately analyze the collected data and grasp the customer's emotions in real time. Furthermore, the estimation unit can also analyze long-term emotional trends using past data and statistical information. For example, based on past emotional data, it predicts emotional fluctuations in specific time periods and situations and plans future marketing strategies. In addition, the estimation unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the estimation unit to not only grasp emotions in real time, but also handle long-term emotion management and anomaly detection, improving the reliability and effectiveness of the entire system.

[0032] The determination unit determines a product or service according to the customer's emotion estimated by the estimation unit. The determination unit determines the optimal product or service for the customer based on, for example, the estimated emotion and the customer's attributes and behavior history. Specifically, when the customer is happy, it is possible to propose a fun product or service. For example, when the customer is smiling, it is possible to propose entertainment-related products or services. In addition, when the customer is relaxed, the determination unit can also propose products or services that can relax the customer. For example, when the customer shows a relaxed expression or a decrease in heart rate, it is possible to propose spa or relaxation-related products or services. Furthermore, when the customer is stressed, the determination unit can also propose products or services that are useful for stress reduction. For example, when the customer shows a stressed expression or an increase in heart rate, it is possible to propose stress relief goods or relaxation-related products or services. The determination unit determines the optimal product or service for the customer based on the estimated emotion and the customer's attributes and behavior history. Thereby, the determination unit can quickly determine the optimal product or service according to the customer's emotion and improve customer satisfaction. Furthermore, the determination unit can collect customer feedback and continuously improve the accuracy and effectiveness of the proposal content. For example, the customer's reaction to the proposed product or service is analyzed and reflected in the next proposal. The decision unit can also integrate multiple data sources to make more accurate proposals. This allows the decision unit to provide the most suitable product or service to the customer and improve customer satisfaction.

[0033] The suggestion unit suggests the product or service determined by the decision unit to the customer. For example, the suggestion unit suggests the product or service in a manner according to the customer's emotions. Specifically, when the customer is relaxed, the suggestion unit can suggest the product or service while playing relaxing music. For example, when the customer shows a relaxed expression or a slow heart rate, the suggestion unit suggests a spa or relaxation-related product or service while playing relaxing music in the background. In addition, when the customer is stressed, the suggestion unit can suggest the product or service in a calm tone. For example, when the customer shows a stressed expression or an increased heart rate, the suggestion unit suggests a stress relief item or a relaxation-related product or service in a calm tone. Furthermore, when the customer is excited, the suggestion unit can suggest the product or service in an energetic tone. For example, when the customer shows an excited expression or an increased heart rate, the suggestion unit suggests an entertainment-related product or service in an energetic tone. When the customer is relaxed, the suggestion unit can suggest the product or service while playing relaxing music. In this way, the suggestion unit can suggest the product or service in an optimal manner according to the customer's emotions, thereby improving customer satisfaction. Furthermore, the proposal department can collect customer feedback and continuously improve the accuracy and effectiveness of proposals. For example, the customer's reaction to proposed products and services is analyzed and reflected in the next proposal. The proposal department can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, email, etc. in combination. This allows the proposal department to quickly and reliably propose products and services to customers and improve customer satisfaction.

[0034] The suggestion unit can suggest a product or service in a manner according to the customer's emotions. The suggestion unit, for example, suggests a product or service in a manner according to the customer's emotions. For example, when the customer is relaxed, the suggestion unit can suggest a product or service while playing relaxing music. In addition, when the customer is stressed, the suggestion unit can also suggest a product or service in a calm tone. Furthermore, when the customer is excited, the suggestion unit can also suggest a product or service in an energetic tone. This makes it possible to improve customer satisfaction by suggesting a product or service in a manner according to the customer's emotions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs customer emotion data and suggests appropriate products or services.

[0035] The suggestion unit can suggest a product or service while playing music according to the customer's emotions. The suggestion unit, for example, suggests a product or service while playing music according to the customer's emotions. For example, when the customer is relaxed, the suggestion unit can suggest a product or service while playing relaxing music. In addition, when the customer is stressed, the suggestion unit can also suggest a product or service while playing calming music. Furthermore, when the customer is excited, the suggestion unit can also suggest a product or service while playing energetic music. In this way, the effect of the suggestion can be enhanced by suggesting a product or service while playing music according to the customer's emotions. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion while playing music using an AI model that inputs customer emotion data and selects appropriate music.

[0036] The determination unit can determine a product or service based on the customer's emotions and attributes. The determination unit determines the optimal product or service for the customer based on, for example, the customer's emotions and attributes estimated by the estimation unit. For example, the determination unit can suggest a fun product or service when the customer is happy. In addition, the determination unit can suggest a relaxing product or service when the customer is relaxed. Furthermore, the determination unit can suggest a product or service that helps reduce stress when the customer is stressed. For example, the determination unit selects the optimal product or service for the customer by taking into account attribute information such as the customer's age, gender, and purchase history. This makes it possible to make a more appropriate proposal by determining a product or service based on the customer's emotions and attributes. 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 make a proposal using an AI model that inputs the customer's emotion data and attribute data and determines an appropriate product or service.

[0037] The determination unit can determine a product or service based on the customer's emotions and behavioral history. The determination unit determines the optimal product or service for the customer based on, for example, the customer's emotions estimated by the estimation unit and the customer's behavioral history. For example, the determination unit can suggest a fun product or service when the customer is happy. In addition, the determination unit can suggest a relaxing product or service when the customer is relaxed. Furthermore, the determination unit can suggest a product or service that helps reduce stress when the customer is stressed. For example, the determination unit selects the optimal product or service for the customer by taking into account the customer's behavioral history such as website browsing history and purchase history. This enables more personalized suggestions by determining products and services based on the customer's emotions and behavioral history. 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 make suggestions using an AI model that inputs the customer's emotion data and behavioral history data and determines appropriate products and services.

[0038] The suggestion unit includes a collection unit that collects customer feedback on the proposed product or service. The collection unit, for example, collects customer feedback on the proposed product or service. The collection unit can collect feedback such as questionnaire results and review comments. The collection unit can also collect non-verbal feedback such as customer facial expressions and voices. For example, when a customer is dissatisfied with a product, the collection unit can identify the dissatisfaction and provide information for improving the product. In this way, collecting customer feedback can be useful for improving products and services. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect customer feedback data using an AI model that collects feedback.

[0039] The collection unit includes an identification unit that identifies improvements to the product or service based on the collected feedback. The identification unit identifies improvements to the product or service based on the collected feedback, for example. The identification unit can, for example, analyze customer dissatisfaction and requests and identify improvements to the product or service. For example, when a customer is dissatisfied with a product, the identification unit can identify the dissatisfaction and provide information for improving the product. This makes it possible to improve customer satisfaction by identifying improvements to the product or service based on the collected feedback. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can perform identification using an AI model that inputs customer feedback data and identifies improvements.

[0040] The acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit captures the customer's facial expression with a camera and acquires facial expression data using facial expression recognition technology. The acquisition unit can also record the customer's voice with a microphone and acquire voice data using voice analysis technology. Furthermore, the acquisition unit can measure the customer's heart rate with a sensor and acquire heart rate data. For example, the acquisition unit captures the customer's facial expression with a camera and acquires facial expression data such as smile and anger using facial expression recognition technology. The voice data is used as information for analyzing the tone of the customer's voice and speaking style and estimating the emotion. The heart rate data is used as information for analyzing the fluctuation of the customer's heart rate and estimating the stress or relaxation state. In this way, by acquiring various information as emotion estimation information, the emotion of the customer can be more accurately estimated. 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 facial expression data of a customer and acquire the facial expression data using an AI model that estimates emotions.

[0041] The decision unit can select the optimal product or service by analyzing the customer's past purchase history at the time of decision. For example, the decision unit selects the optimal product or service by analyzing the customer's past purchase history at the time of decision. The decision unit can, for example, propose similar products or services based on products or services purchased by the customer in the past. The decision unit can also select highly relevant products or services from the customer's purchase history. Furthermore, the decision unit can analyze the customer's purchase history and select products or services that match the customer's preferences. For example, the decision unit can propose similar products or services based on products or services purchased by the customer in the past. Highly relevant products or services can also be selected from the customer's purchase history. The customer's purchase history can also be analyzed to select products or services that match the customer's preferences. As a result, a more appropriate product or service can be selected by analyzing the customer's past purchase history. Part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can make the selection using an AI model that inputs the customer's purchase history data and selects the optimal product or service.

[0042] The determination unit can customize the selection of products or services based on the customer's current living situation at the time of the determination. The determination unit, for example, customizes the selection of products or services based on the customer's current living situation at the time of the determination. For example, when the customer moves to a new house, the determination unit can suggest products or services related to the new house. In addition, when the customer starts a new job, the determination unit can also suggest products or services related to the job. In addition, when the customer starts a new hobby, the determination unit can also suggest products or services related to the hobby. For example, when the customer moves to a new house, the determination unit can suggest products or services related to the new house. When the customer starts a new job, the determination unit can also suggest products or services related to the job. When the customer starts a new hobby, the determination unit can also suggest products or services related to the hobby. This enables more personalized suggestions by customizing products and services based on the customer's current living situation. 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 make a selection using an AI model that uses the customer's living situation data as an input and customizes the selection of products and services.

[0043] The decision unit can select the optimal product or service in consideration of the geographical location information of the customer at the time of decision. For example, the decision unit selects the optimal product or service in consideration of the geographical location information of the customer at the time of decision. For example, when the customer is traveling, the decision unit can propose products and services related to the travel destination. Furthermore, when the customer is at home, the decision unit can also propose products and services related to activities at home. Furthermore, when the customer is at work, the decision unit can also propose products and services related to activities at work. For example, when the customer is traveling, the decision unit can propose products and services related to the travel destination. When the customer is at home, the decision unit can also propose products and services related to activities at home. When the customer is at work, the decision unit can also propose products and services related to activities at work. In this way, by considering the geographical location information of the customer, more appropriate products and services can be selected. Part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can make the selection using an AI model that inputs the geographical location information data of the customer and selects the optimal product or service.

[0044] The determination unit can select a product or service by analyzing the social media activity of the customer at the time of determination. The determination unit can select a product or service by analyzing the social media activity of the customer at the time of determination. For example, the determination unit can select a product or service based on photos and posted content shared by the customer on social media. The determination unit can also select a product or service based on the activity of an account followed by the customer on social media. Furthermore, the determination unit can select a product or service based on a group or event in which the customer participates on social media. For example, the determination unit can select a product or service based on photos and posted content shared by the customer on social media. The product or service can also be selected based on the activity of an account followed by the customer on social media. The product or service can also be selected based on a group or event in which the customer participates on social media. In this way, by analyzing the social media activity of the customer, a more appropriate product or service can be selected. 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 select a product or service using an AI model that inputs social media activity data of the customer and selects a product or service.

[0045] The suggestion unit can adjust the level of detail of the proposal based on the importance of the product at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the product at the time of suggestion. For example, for a product with high importance, the suggestion unit can make a proposal including a detailed description. Furthermore, for a product with low importance, the suggestion unit can make a proposal including a brief description. Furthermore, for a product with medium importance, the suggestion unit can make a proposal including an explanation with a moderate level of detail. For example, the suggestion unit can make a proposal including a detailed description for a product with high importance. For a product with low importance, the suggestion unit can make a proposal including a brief description. For a product with medium importance, the suggestion unit can make a proposal including an explanation with a moderate level of detail. As a result, by adjusting the level of detail of the proposal based on the importance of the product, a more appropriate proposal can be made. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can adjust the level of detail using an AI model that inputs the importance data of the product and adjusts the level of detail of the proposal.

[0046] The suggestion unit can apply different suggestion algorithms depending on the category of the product when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the product when making a suggestion. For example, the suggestion unit can make visually attractive suggestions for entertainment products. Furthermore, the suggestion unit can make suggestions based on scientific data for health-related products. Furthermore, the suggestion unit can make suggestions based on trend information for fashion products. For example, the suggestion unit can make visually attractive suggestions for entertainment products. For health-related products, the suggestion unit can make suggestions based on scientific data. For fashion products, the suggestion unit can make suggestions based on trend information. This makes it possible to make more appropriate suggestions by applying the suggestion algorithm depending on the category of the product. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can apply an algorithm using an AI model that inputs product category data and applies different suggestion algorithms.

[0047] The suggestion unit can determine the priority of the proposal based on the submission time of the product at the time of proposal. The suggestion unit, for example, determines the priority of the proposal based on the submission time of the product at the time of proposal. The suggestion unit can, for example, give priority to new products. Furthermore, the suggestion unit can also give proposals at an appropriate time for seasonal products. Furthermore, the suggestion unit can give priority to proposals during a sale period for sale products. For example, the suggestion unit can give priority to proposals for new products. For seasonal products, it can also give proposals at an appropriate time. For sale products, it can also give proposals at a preferential time during a sale period. As a result, by determining the priority of the proposal based on the submission time of the product, more appropriate proposals can be made. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can determine the priority using an AI model that inputs product submission time data and determines the priority of the proposal.

[0048] The suggestion unit can adjust the order of suggestions based on the relevance of the products at the time of suggestion. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the products at the time of suggestion. For example, the suggestion unit can give priority to suggestions for products with high relevance. In addition, the suggestion unit can also make complementary suggestions for products with low relevance. Furthermore, the suggestion unit can give suggestions in a moderate order for products with medium relevance. For example, the suggestion unit can give priority to suggestions for products with high relevance. In addition, the suggestion unit can also make complementary suggestions for products with low relevance. In addition, the suggestion unit can give suggestions in a moderate order for products with medium relevance. As a result, more appropriate suggestions can be made by adjusting the order of suggestions based on the relevance of the products. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can adjust the order using an AI model that uses product relevance data as input and adjusts the order of suggestions.

[0049] The collection unit can select the optimal collection method by analyzing the past feedback history of the customer at the time of collection. For example, the collection unit selects the optimal collection method by analyzing the past feedback history of the customer at the time of collection. For example, if the customer has provided detailed feedback in the past, the collection unit can request similar detailed feedback. Furthermore, if the customer has provided brief feedback in the past, the collection unit can request similar brief feedback. Furthermore, the collection unit can analyze the past feedback history of the customer and select the optimal collection method. For example, if the customer has provided detailed feedback in the past, the collection unit can request similar detailed feedback. If the customer has provided brief feedback in the past, the collection unit can request similar brief feedback. The past feedback history of the customer can be analyzed and the optimal collection method can be selected. As a result, a more appropriate collection method can be selected by analyzing the past feedback history of the customer. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can make the selection using an AI model that inputs the feedback history data of the customer and selects the optimal collection method.

[0050] The collection unit can preferentially collect highly relevant feedback in consideration of the geographical location information of the customer at the time of collection. For example, the collection unit preferentially collects highly relevant feedback in consideration of the geographical location information of the customer at the time of collection. For example, when the customer is traveling, the collection unit can preferentially collect feedback related to the travel destination. Furthermore, when the customer is at home, the collection unit can preferentially collect feedback related to activities at home. Furthermore, when the customer is at work, the collection unit can preferentially collect feedback related to activities at work. For example, when the customer is traveling, the collection unit can preferentially collect feedback related to the travel destination. When the customer is at home, the collection unit can preferentially collect feedback related to activities at home. When the customer is at work, the collection unit can preferentially collect feedback related to activities at work. In this way, by considering the geographical location information of the customer, more relevant feedback can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can perform collection using an AI model that inputs geographical location information data of the customer and preferentially collects highly relevant feedback.

[0051] The identification unit can identify the optimal improvement point by analyzing the past feedback history of the customer at the time of identification. For example, the identification unit can identify the optimal improvement point by analyzing the past feedback history of the customer at the time of identification. The identification unit can identify a similar improvement point based on, for example, feedback provided by the customer in the past. The identification unit can also analyze the past feedback history of the customer and identify the optimal improvement point. Furthermore, the identification unit can identify a highly relevant improvement point based on the past feedback history of the customer. For example, the identification unit can identify a similar improvement point based on feedback provided by the customer in the past. The identification unit can also analyze the past feedback history of the customer and identify the optimal improvement point. The identification unit can also identify a highly relevant improvement point based on the past feedback history of the customer. As a result, a more appropriate improvement point can be identified by analyzing the past feedback history of the customer. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can perform identification using an AI model that inputs feedback history data of the customer and identifies the optimal improvement point.

[0052] The identification unit can identify the optimal improvement point by considering the geographical location information of the customer at the time of identification. For example, the identification unit can identify the optimal improvement point by considering the geographical location information of the customer at the time of identification. For example, when the customer is traveling, the identification unit can identify the improvement point related to the travel destination. Furthermore, when the customer is at home, the identification unit can also identify the improvement point related to the activity at home. Furthermore, when the customer is at work, the identification unit can also identify the improvement point related to the activity at work. For example, when the customer is traveling, the identification unit can identify the improvement point related to the travel destination. When the customer is at home, the identification unit can also identify the improvement point related to the activity at home. When the customer is at work, the identification unit can also identify the improvement point related to the activity at work. In this way, by considering the geographical location information of the customer, a more appropriate improvement point can be identified. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can perform identification using an AI model that inputs the geographical location information data of the customer and identifies the optimal improvement point.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The marketing support system may further include a behavioral analysis unit that analyzes customer behavioral patterns. The behavioral analysis unit may, for example, analyze the customer's website browsing history and purchase history to identify the customer's interests and concerns. It may identify product categories frequently viewed by the customer and prioritize products in those categories. It may also suggest products similar to products the customer has previously purchased. Furthermore, if the customer tends to browse websites during a specific time period, it may also suggest products that match that time period. This allows for more personalized suggestions based on the customer's behavioral patterns.

[0055] The marketing support system may further include a history suggestion unit that suggests products and services based on the customer's purchasing history. The history suggestion unit may, for example, suggest products similar to products the customer has previously purchased. It may also suggest accessories and additional services related to products the customer has previously purchased. It may also analyze the usage status of products the customer has previously purchased and suggest repeat purchases at appropriate times. This makes it possible to suggest more appropriate products and services based on the customer's purchasing history.

[0056] The marketing support system may further include a location suggestion unit that suggests products and services based on the geographical location information of the customer. For example, when the customer is traveling, the location suggestion unit may suggest products and services related to the travel destination. When the customer is at home, the location suggestion unit may suggest products and services related to activities at home. When the customer is at work, the location suggestion unit may suggest products and services related to activities at work. This allows more appropriate products and services to be suggested based on the geographical location information of the customer.

[0057] The marketing support system may further include a social suggestion unit that suggests products and services based on the social media activities of the customer. The social suggestion unit may suggest products and services based on, for example, photos and posts shared by the customer on social media. It may also suggest products and services based on the activities of accounts the customer follows on social media. It may also suggest products and services based on groups and events the customer participates in on social media. This makes it possible to suggest more appropriate products and services based on the customer's social media activities.

[0058] The marketing support system may further include a feedback analysis unit that analyzes customer feedback to identify areas for improvement in products and services. The feedback analysis unit may, for example, analyze customer survey results and review comments to identify areas for improvement in products and services. It may also analyze non-verbal feedback such as customer facial expressions and voice. It may also analyze customer feedback history and identify areas for improvement by comparing with past feedback. In this way, by analyzing customer feedback, it is possible to identify areas for improvement in products and services and improve customer satisfaction.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] 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 acquisition unit may capture the customer's facial expression with a camera and acquire the facial expression data using facial expression recognition technology. It may also record the customer's voice with a microphone and acquire the voice data using voice analysis technology. It may also measure the customer's heart rate with a sensor and acquire the heart rate data. Step 2: The estimation unit estimates the customer's emotions based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the emotion estimation information using a machine learning algorithm to estimate the customer's emotions. For example, a machine learning model is used that takes facial expression data as input and outputs emotions such as smiling or anger. It is also possible to use voice data as input and estimate emotions based on the tone of voice and speaking style. Furthermore, it is also possible to use heart rate data as input and estimate stress or relaxation state based on heart rate fluctuations. Step 3: The determination unit determines a product or service according to the customer's emotion estimated by the estimation unit. The determination unit determines the most suitable product or service for the customer based on the estimated emotion and the customer's attributes and behavioral history. For example, if the customer is happy, the determination unit suggests fun products or services, and if the customer is relaxed, the determination unit suggests relaxing products or services. If the customer is stressed, the determination unit suggests products or services that help reduce stress. Step 4: The suggestion unit suggests the product or service determined by the decision unit to the customer. The suggestion unit suggests the product or service in a manner according to the customer's emotions. For example, if the customer is relaxed, the suggestion unit suggests the product or service while playing relaxing music, if the customer is stressed, the suggestion unit suggests the product or service in a calm tone, if the customer is excited, the suggestion unit suggests the product or service in an energetic tone.

[0061] (Example 2) A marketing support system according to an embodiment of the present invention is a system that reads the emotions of a customer and proposes appropriate products and services. The system acquires emotion estimation information for estimating the emotions of a customer, and estimates the emotions of the customer based on the acquired information. According to the estimated emotions, the system determines appropriate products and services and proposes them to the customer. The system can also collect customer feedback and use it to improve products and services. 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. This information is input to the system as emotion estimation information. Next, based on the acquired information, the system estimates the emotions of the customer. The system analyzes the emotion estimation information and estimates the emotions of the customer. For example, if the customer is smiling, the system can estimate that the customer is happy. According to the estimated emotions, the system determines appropriate products and services. The system determines the most suitable products and services for the customer based on the estimated emotions and the customer's attributes and behavior history. For example, if the customer is happy, the system can propose fun products and services to the customer. The determined products and services are proposed to the customer by the system. The system can suggest products and services in a manner according to the customer's emotions. For example, if the customer is relaxed, the system can suggest products and services while playing relaxing music. Furthermore, the system can collect customer feedback and use it to improve the products and services. The system collects customer feedback on the proposed products and services and identifies areas to improve the products and services based on the feedback. For example, if a customer is dissatisfied with a product, the system can identify the areas of dissatisfaction and provide information to improve the product. In this way, the system can read the customer's emotions and suggest appropriate products and services, thereby enabling more efficient marketing. In addition, by collecting customer feedback and using it to improve the products and services, customer satisfaction can be improved. In this way, the marketing support system can read the customer's emotions and suggest appropriate products and services.

[0062] The marketing support system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a proposal unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a customer. The emotion estimation information 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. For example, the acquisition unit photographs the customer's facial expression with a camera and acquires facial expression data using facial expression recognition technology. The acquisition unit can also record the customer's voice with a microphone and acquire voice data using voice analysis technology. Furthermore, the acquisition unit can measure the customer's heart rate with a sensor and acquire heart rate data. For example, the acquisition unit photographs the customer's facial expression with a camera and acquires facial expression data such as smiling and angry using facial expression recognition technology. The voice data is used as information for analyzing the tone of voice and speaking style of the customer and estimating the emotion. The heart rate data is used as information for analyzing the fluctuation of the customer's heart rate and estimating the stress or relaxation state. The estimation unit estimates the emotion of the customer based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses a machine learning algorithm to analyze the emotion estimation information and estimate the emotion of the customer. The estimation unit, for example, estimates the emotion of the customer using a machine learning model that takes facial expression data as an input and outputs emotions such as smiling and anger. The estimation unit can also estimate emotions based on tone of voice and speaking style using voice data as an input. Furthermore, the estimation unit can also estimate stress and relaxation states based on fluctuations in heart rate using heart rate data as an input. For example, the estimation unit estimates the emotion of the customer using a machine learning model that takes facial expression data as an input and outputs emotions such as smiling and anger. The voice data is used as information for estimating emotions based on tone of voice and speaking style. The heart rate data is used as information for estimating stress and relaxation states based on fluctuations in heart rate. The determination unit determines a product or service according to the emotion of the customer estimated by the estimation unit. The determination unit determines a product or service that is optimal for the customer based on, for example, the estimated emotion and the attributes and behavioral history of the customer. For example, if the customer is happy, the decision unit can suggest a pleasant product or service.In addition, the determination unit can also suggest a product or service that can relax the customer when the customer is relaxed. Furthermore, the determination unit can also suggest a product or service that helps reduce stress when the customer is stressed. For example, the determination unit determines the most suitable product or service for the customer based on the estimated emotion and the attributes and behavior history of the customer. When the customer is happy, a fun product or service can be suggested. When the customer is relaxed, a relaxing product or service can be suggested. When the customer is stressed, a product or service that helps reduce stress can be suggested. The suggestion unit suggests the product or service determined by the determination unit to the customer. The suggestion unit, for example, suggests a product or service in a manner according to the emotion of the customer. For example, when the customer is relaxed, the suggestion unit can suggest a product or service while playing relaxing music. Furthermore, when the customer is stressed, the suggestion unit can suggest a product or service in a calm tone. Furthermore, when the customer is excited, the suggestion unit can suggest a product or service in an energetic tone. In this way, the marketing support system according to the embodiment can read the emotion of the customer and suggest an appropriate product or service. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may make suggestions using an AI model that inputs customer emotion data and suggests appropriate products and services.

[0063] The acquisition unit acquires emotion estimation information for estimating the emotion of the customer. The emotion estimation information 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. For example, the acquisition unit photographs the customer's facial expression with a camera and acquires facial expression data using facial expression recognition technology. Specifically, the camera photographs the customer's face with high resolution and identifies emotions such as smile, anger, and surprise using an algorithm that extracts facial feature points. The acquisition unit can also record the customer's voice with a microphone and acquire voice data using voice analysis technology. The voice data is analyzed for features such as tone, pitch, speed, and intensity of the voice to detect changes in emotion. Furthermore, the acquisition unit can measure the customer's heart rate with a sensor and acquire heart rate data. The heart rate sensor is attached to the customer's wrist or chest and monitors the fluctuation of the heart rate in real time. For example, the acquisition unit photographs the customer's facial expression with a camera and acquires facial expression data such as smile and anger using facial expression recognition technology. The voice data is used as information for analyzing the tone of the customer's voice and the way of speaking and estimating emotions. The heart rate data is used as information for analyzing fluctuations in the customer's heart rate and estimating stress and relaxation states. This allows the acquisition unit to collect a wide range of data from various devices and grasp the situation in real time. Furthermore, the acquisition unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the estimation unit and the determination unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the acquisition unit to efficiently and effectively collect data and improve the performance of the entire system.

[0064] The estimation unit estimates the customer's emotion based on the emotion estimation information acquired by the acquisition unit. Specifically, the customer's emotion is estimated using a machine learning model that takes facial expression data as input and outputs emotions such as smiling and anger. The facial expression recognition algorithm analyzes facial feature points and detects changes in emotion. The estimation unit can also estimate emotions based on tone of voice and speaking style using voice data as input. The voice analysis algorithm analyzes features such as tone, pitch, speed, and intensity of voice and detects changes in emotion. The estimation unit can also estimate stress and relaxation states based on heart rate data as input. The heart rate analysis algorithm analyzes heart rate fluctuations and estimates stress and relaxation states. For example, the estimation unit estimates the customer's emotion using a machine learning model that takes facial expression data as input and outputs emotions such as smiling and anger. The voice data is used as information for estimating emotions based on tone of voice and speaking style. The heart rate data is used as information for estimating stress and relaxation states based on heart rate fluctuations. This allows the estimation unit to quickly and accurately analyze collected data and grasp customer emotions in real time. Furthermore, the estimation unit can also analyze long-term emotional trends using past data and statistical information. For example, based on past emotional data, it predicts emotional fluctuations in specific time periods and situations and plans future marketing strategies. In addition, the estimation unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the estimation unit to handle not only real-time emotion understanding, but also long-term emotion management and anomaly detection, improving the reliability and effectiveness of the entire system.

[0065] The determination unit determines a product or service according to the customer's emotion estimated by the estimation unit. Specifically, when the customer is happy, a fun product or service can be proposed. For example, when the customer is smiling, entertainment-related products or services can be proposed. In addition, when the customer is relaxed, the determination unit can also propose products or services that can relax the customer. For example, when the customer shows a relaxed expression or a decrease in heart rate, spa or relaxation-related products or services can be proposed. Furthermore, when the customer is stressed, the determination unit can also propose products or services that help relieve stress. For example, when the customer shows a stressed expression or an increase in heart rate, stress relief goods or relaxation-related products or services can be proposed. The determination unit determines the most suitable product or service for the customer based on the estimated emotion and the customer's attributes and behavior history. When the customer is happy, a fun product or service can be proposed. When the customer is relaxed, relaxing products or services can be proposed. When the customer is stressed, products or services that help relieve stress can be proposed. In this way, the determination unit can quickly determine the most suitable product or service according to the customer's emotion and improve customer satisfaction. Furthermore, the decision unit can collect customer feedback and continuously improve the accuracy and effectiveness of the proposals. For example, the customer's reaction to the proposed product or service is analyzed and reflected in the next proposal. The decision unit can also integrate multiple data sources to make more accurate proposals. This allows the decision unit to provide optimal products and services to customers and improve customer satisfaction.

[0066] The suggestion unit suggests the product or service determined by the decision unit to the customer. The suggestion unit suggests the product or service in a manner according to the customer's emotions, for example. Specifically, when the customer is relaxed, the suggestion unit can suggest the product or service while playing relaxing music. For example, when the customer shows a relaxed expression or a slow heart rate, the suggestion unit suggests a spa or relaxation-related product or service while playing relaxing music in the background. In addition, when the customer is stressed, the suggestion unit can suggest the product or service in a calm tone. For example, when the customer shows a stressed expression or an increased heart rate, the suggestion unit suggests a stress relief item or a relaxation-related product or service in a calm tone. In addition, when the customer is excited, the suggestion unit can suggest the product or service in an energetic tone. For example, when the customer shows an excited expression or an increased heart rate, the suggestion unit suggests an entertainment-related product or service in an energetic tone. When the customer is relaxed, the suggestion unit can suggest the product or service while playing relaxing music. When the customer is stressed, the suggestion unit can suggest the product or service in a calm tone. When the customer is excited, the suggestion unit can suggest the product or service in an energetic tone. This allows the proposal unit to propose products and services in an optimal manner according to the customer's emotions, thereby improving customer satisfaction. Furthermore, the proposal unit can collect customer feedback and continuously improve the accuracy and effectiveness of the proposal content. For example, the customer's reaction to the proposed product or service is analyzed and reflected in the next proposal. Furthermore, the proposal unit can reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications, but also voice calls, SMS, email, etc. in combination. This allows the proposal unit to quickly and reliably propose products and services to customers, thereby improving customer satisfaction.

[0067] The suggestion unit can suggest a product or service in a manner according to the customer's emotions. The suggestion unit, for example, suggests a product or service in a manner according to the customer's emotions. For example, when the customer is relaxed, the suggestion unit can suggest a product or service while playing relaxing music. In addition, when the customer is stressed, the suggestion unit can also suggest a product or service in a calm tone. Furthermore, when the customer is excited, the suggestion unit can also suggest a product or service in an energetic tone. This makes it possible to improve customer satisfaction by suggesting a product or service in a manner according to the customer's emotions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs customer emotion data and suggests appropriate products or services.

[0068] The suggestion unit can suggest a product or service while playing music according to the customer's emotions. The suggestion unit, for example, suggests a product or service while playing music according to the customer's emotions. For example, when the customer is relaxed, the suggestion unit can suggest a product or service while playing relaxing music. In addition, when the customer is stressed, the suggestion unit can also suggest a product or service while playing calming music. Furthermore, when the customer is excited, the suggestion unit can also suggest a product or service while playing energetic music. In this way, the effect of the suggestion can be enhanced by suggesting a product or service while playing music according to the customer's emotions. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion while playing music using an AI model that inputs customer emotion data and selects appropriate music.

[0069] The determination unit can determine a product or service based on the customer's emotions and attributes. The determination unit determines the optimal product or service for the customer based on, for example, the customer's emotions and attributes estimated by the estimation unit. For example, the determination unit can suggest a fun product or service when the customer is happy. In addition, the determination unit can suggest a relaxing product or service when the customer is relaxed. Furthermore, the determination unit can suggest a product or service that helps reduce stress when the customer is stressed. For example, the determination unit selects the optimal product or service for the customer by taking into account attribute information such as the customer's age, gender, and purchase history. This makes it possible to make a more appropriate proposal by determining a product or service based on the customer's emotions and attributes. 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 make a proposal using an AI model that inputs the customer's emotion data and attribute data and determines an appropriate product or service.

[0070] The determination unit can determine a product or service based on the customer's emotions and behavioral history. The determination unit determines the optimal product or service for the customer based on, for example, the customer's emotions estimated by the estimation unit and the customer's behavioral history. For example, the determination unit can suggest a fun product or service when the customer is happy. In addition, the determination unit can suggest a relaxing product or service when the customer is relaxed. Furthermore, the determination unit can suggest a product or service that helps reduce stress when the customer is stressed. For example, the determination unit selects the optimal product or service for the customer by taking into account the customer's behavioral history such as website browsing history and purchase history. This enables more personalized suggestions by determining products and services based on the customer's emotions and behavioral history. 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 make suggestions using an AI model that inputs the customer's emotion data and behavioral history data and determines appropriate products and services.

[0071] The suggestion unit includes a collection unit that collects customer feedback on the proposed product or service. The collection unit, for example, collects customer feedback on the proposed product or service. The collection unit can collect feedback such as questionnaire results and review comments. The collection unit can also collect non-verbal feedback such as customer facial expressions and voices. For example, when a customer is dissatisfied with a product, the collection unit can identify the dissatisfaction and provide information for improving the product. In this way, collecting customer feedback can be useful for improving products and services. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect customer feedback data using an AI model that collects feedback.

[0072] The collection unit includes an identification unit that identifies improvements to the product or service based on the collected feedback. The identification unit identifies improvements to the product or service based on the collected feedback, for example. The identification unit can, for example, analyze customer dissatisfaction and requests and identify improvements to the product or service. For example, when a customer is dissatisfied with a product, the identification unit can identify the dissatisfaction and provide information for improving the product. This makes it possible to improve customer satisfaction by identifying improvements to the product or service based on the collected feedback. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can perform identification using an AI model that inputs customer feedback data and identifies improvements.

[0073] The acquisition unit can acquire one or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit captures the customer's facial expression with a camera and acquires facial expression data using facial expression recognition technology. The acquisition unit can also record the customer's voice with a microphone and acquire voice data using voice analysis technology. Furthermore, the acquisition unit can measure the customer's heart rate with a sensor and acquire heart rate data. For example, the acquisition unit captures the customer's facial expression with a camera and acquires facial expression data such as smile and anger using facial expression recognition technology. The voice data is used as information for analyzing the tone of the customer's voice and speaking style and estimating emotions. The heart rate data is used as information for analyzing fluctuations in the customer's heart rate and estimating stress and relaxation states. In this way, by acquiring various information as emotion estimation information, the customer's emotions can be more accurately estimated. 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 facial expression data of a customer and acquire the facial expression data using an AI model that estimates emotions.

[0074] The acquisition unit can estimate the emotion of the customer and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the customer. For example, when the customer is stressed, the acquisition unit can delay acquiring the emotion estimation information until the customer is in a relaxed state. Also, when the customer is excited, the acquisition unit can instantly acquire the emotion estimation information and analyze the emotion in real time. Furthermore, when the customer is tired, the acquisition unit can adjust the timing to acquire the emotion estimation information after a break. For example, when the customer is stressed, the acquisition unit can delay acquiring the emotion estimation information until the customer is in a relaxed state. When the customer is excited, the emotion estimation information can instantly acquire the emotion estimation information and analyze the emotion in real time. When the customer is tired, the acquisition unit can adjust the timing to acquire the emotion estimation information after a break. In this way, by adjusting the timing of acquiring information according to the emotion of the customer, more appropriate information can be acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. 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 adjust the timing by using an AI model that receives customer emotion data as an input and adjusts the timing of acquiring emotion estimation information.

[0075] The acquisition unit can analyze the customer's past emotion data and select the optimal acquisition method. For example, the acquisition unit can acquire emotion estimation information in a similar situation based on data acquired when the customer was relaxed in the past. The acquisition unit can also acquire emotion estimation information in a manner to reduce stress based on data acquired when the customer was stressed in the past. The acquisition unit can also acquire emotion estimation information in a manner to suppress excitement based on data acquired when the customer was excited in the past. For example, the acquisition unit can acquire emotion estimation information in a similar situation based on data acquired when the customer was relaxed in the past. Emotion estimation information can also be acquired in a manner to reduce stress based on data acquired when the customer was stressed in the past. Emotion estimation information can also be acquired in a manner to suppress excitement based on data acquired when the customer was excited in the past. In this way, the optimal information acquisition method can be selected by analyzing the past emotion data. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. 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 may select an acquisition method using an AI model that inputs past emotion data of a customer and selects an optimal acquisition method.

[0076] The acquisition unit can perform filtering based on the customer's current activity status and field of interest when acquiring the emotion estimation information. For example, when the customer is playing sports, the acquisition unit can preferentially acquire emotion estimation information related to the activity. In addition, when the customer is reading, the acquisition unit can preferentially acquire emotion estimation information related to the activity. In addition, when the customer is working, the acquisition unit can preferentially acquire emotion estimation information related to the activity. For example, when the customer is playing sports, the acquisition unit can preferentially acquire emotion estimation information related to the activity. In addition, when the customer is reading, the acquisition unit can preferentially acquire emotion estimation information related to the activity. In addition, when the customer is working, the acquisition unit can preferentially acquire emotion estimation information related to the activity. In this way, by filtering information based on the customer's activity status and field of interest, more relevant information can be acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. 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 may perform filtering using an AI model that inputs customer activity status data and filters emotion estimation information.

[0077] The acquisition unit can estimate the emotion of the customer and determine the priority of information to be acquired based on the estimated emotion of the customer. For example, when the customer is relaxed, the acquisition unit can preferentially acquire information related to relaxation. In addition, when the customer is stressed, the acquisition unit can preferentially acquire information related to stress reduction. In addition, when the customer is excited, the acquisition unit can preferentially acquire information for suppressing excitement. For example, when the customer is relaxed, the acquisition unit can preferentially acquire information related to relaxation. In addition, when the customer is stressed, the acquisition unit can preferentially acquire information related to stress reduction. In addition, when the customer is excited, the acquisition unit can preferentially acquire information for suppressing excitement. In this way, important information can be preferentially acquired by determining the priority of information based on the emotion of the customer. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the acquisition unit can determine priorities using an AI model that uses customer emotion data as input and determines the priority of information to be acquired.

[0078] When acquiring emotion estimating information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the customer. For example, when the customer is traveling, the acquisition unit can preferentially acquire emotion estimating information related to the travel destination. Furthermore, when the customer is at home, the acquisition unit can preferentially acquire emotion estimating information related to activities at home. Furthermore, when the customer is at work, the acquisition unit can preferentially acquire emotion estimating information related to activities at work. For example, when the customer is traveling, the acquisition unit can preferentially acquire emotion estimating information related to the travel destination. When the customer is at home, emotion estimating information related to activities at home can also be preferentially acquired. When the customer is at work, emotion estimating information related to activities at work can also be preferentially acquired. In this way, by considering the geographical location information of the customer, highly relevant information can be preferentially acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. 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 may acquire information using an AI model that receives the geographical location information of the customer as an input and preferentially acquires highly relevant information.

[0079] The acquisition unit can analyze the social media activity of the customer when acquiring the emotion estimation information and acquire related information. The acquisition unit can acquire the emotion estimation information based on, for example, photos and posted content shared by the customer on social media. The acquisition unit can also acquire the emotion estimation information based on the activity of an account followed by the customer on social media. The acquisition unit can also acquire the emotion estimation information based on a group or event in which the customer participates on social media. For example, the acquisition unit can acquire the emotion estimation information based on photos and posted content shared by the customer on social media. The emotion estimation information can also be acquired based on the activity of an account followed by the customer on social media. The emotion estimation information can also be acquired based on a group or event in which the customer participates on social media. In this way, related information can be acquired by analyzing the social media activity of the customer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. 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 acquire information using an AI model that takes customer social media activity data as input and acquires related information.

[0080] The estimation unit can estimate the emotion of the customer and adjust the emotion estimation algorithm based on the estimated emotion of the customer. For example, when the customer is relaxed, the estimation unit can apply an emotion estimation algorithm related to relaxation. Furthermore, when the customer is stressed, the estimation unit can also apply an emotion estimation algorithm related to stress reduction. Furthermore, when the customer is excited, the estimation unit can apply an emotion estimation algorithm for suppressing the excitement. For example, when the customer is relaxed, the estimation unit can apply an emotion estimation algorithm related to relaxation. When the customer is stressed, the estimation unit can also apply an emotion estimation algorithm related to stress reduction. When the customer is excited, the estimation unit can also apply an emotion estimation algorithm for suppressing the excitement. Thereby, by adjusting the algorithm based on the emotion of the customer, the accuracy of the emotion estimation is improved. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can adjust the emotion estimation algorithm using an AI model that takes customer emotion data as input and adjusts the emotion estimation algorithm.

[0081] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. The estimation unit can perform detailed emotion estimation based on information of high importance, for example. The estimation unit can also perform simplified emotion estimation based on information of low importance. Furthermore, the estimation unit can perform emotion estimation with a moderate level of detail based on information of medium importance. For example, the estimation unit can perform detailed emotion estimation based on information of high importance. The estimation unit can also perform simplified emotion estimation based on information of low importance. The estimation unit can also perform emotion estimation with a moderate level of detail based on information of medium importance. Thereby, by adjusting the level of detail of the estimation based on the importance of the emotion estimation information, efficient emotion estimation is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can adjust the level of detail using an AI model that receives importance data of the emotion estimation information and adjusts the level of detail of the estimation.

[0082] The estimation unit can apply different estimation algorithms according to the category of the emotion estimation information at the time of estimation. The estimation unit can apply, for example, an algorithm for estimating an emotion based on facial expression data. The estimation unit can also apply an algorithm for estimating an emotion based on voice data. The estimation unit can also apply an algorithm for estimating an emotion based on motion data. For example, the estimation unit can apply an algorithm for estimating an emotion based on facial expression data. The estimation unit can also apply an algorithm for estimating an emotion based on voice data. The estimation unit can also apply an algorithm for estimating an emotion based on motion data. This enables more accurate emotion estimation by applying an algorithm according to the category of the emotion estimation information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can apply an algorithm using an AI model that inputs category data of the emotion estimation information and applies different estimation algorithms.

[0083] The estimation unit can estimate the customer's emotion and adjust the display method of the estimation result based on the estimated emotion of the customer. For example, when the customer is relaxed, the estimation unit can provide a display method related to relaxation. Furthermore, when the customer is stressed, the estimation unit can also provide a display method related to stress reduction. Furthermore, when the customer is excited, the estimation unit can also provide a display method for suppressing the excitement. For example, when the customer is relaxed, the estimation unit can provide a display method related to relaxation. When the customer is stressed, the estimation unit can also provide a display method related to stress reduction. When the customer is excited, the estimation unit can also provide a display method for suppressing the excitement. This makes it possible to provide more appropriate information by adjusting the display method based on the customer's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the estimation unit can adjust the display method using an AI model that receives customer emotion data as input and adjusts the display method of the estimation result.

[0084] The estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information at the time of estimation. The estimation unit can, for example, preferentially estimate the emotion based on the latest emotion estimation information. The estimation unit can also supplementarily estimate the emotion based on past emotion estimation information. The estimation unit can also estimate the emotion with moderate priority based on emotion estimation information acquired at a medium time. For example, the estimation unit can preferentially estimate the emotion based on the latest emotion estimation information. The estimation unit can also supplementarily estimate the emotion based on past emotion estimation information. The estimation unit can also supplementarily estimate the emotion based on emotion estimation information acquired at a medium time. Thereby, by determining the priority of estimation based on the acquisition time of the emotion estimation information, the latest information can be preferentially used. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can determine the priority order using an AI model that receives as input data on the acquisition time of emotion estimation information and determines the priority order.

[0085] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. The estimation unit can, for example, preferentially estimate emotions based on emotion estimation information having high relevance. The estimation unit can also supplementarily estimate emotions based on emotion estimation information having low relevance. The estimation unit can also estimate emotions in a moderate order based on emotion estimation information having a medium relevance. For example, the estimation unit can preferentially estimate emotions based on emotion estimation information having high relevance. Emotions can also be supplementarily estimated based on emotion estimation information having low relevance. Emotions can also be supplementarily estimated in a moderate order based on emotion estimation information having a medium relevance. Thereby, by adjusting the order of estimation based on the relevance of the emotion estimation information, more relevant information can be preferentially used. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can adjust the order using an AI model that receives as input the association data of the emotion estimation information and adjusts the order of estimation.

[0086] The determination unit can estimate the customer's emotion and adjust the product or service decision method based on the estimated customer's emotion. For example, when the customer is relaxed, the determination unit can prioritize products and services related to relaxation. In addition, when the customer is stressed, the determination unit can also prioritize products and services related to stress reduction. In addition, when the customer is excited, the determination unit can also prioritize products and services for suppressing excitement. For example, when the customer is relaxed, the determination unit can prioritize products and services related to relaxation. In addition, when the customer is stressed, the determination unit can also prioritize products and services related to stress reduction. In addition, when the customer is excited, the determination unit can also prioritize products and services for suppressing excitement. In this way, by adjusting the product or service decision method based on the customer's emotion, more appropriate suggestions can be made. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the decision unit can adjust the decision method using an AI model that takes customer sentiment data as input and adjusts the product or service decision method.

[0087] At the time of decision, the decision unit can select the optimal product or service by analyzing the customer's past purchase history. The decision unit can, for example, suggest similar products or services based on products or services purchased by the customer in the past. The decision unit can also select highly relevant products or services from the customer's purchase history. Furthermore, the decision unit can analyze the customer's purchase history and select products or services that match the customer's preferences. In this way, by analyzing the customer's past purchase history, more appropriate products or services can be selected. Part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can make the selection using an AI model that inputs the customer's purchase history data and selects the optimal product or service.

[0088] The decision unit can customize the selection of the product or service based on the customer's current living situation at the time of decision. For example, if the customer moves to a new house, the decision unit can suggest products and services related to the new house. In addition, if the customer starts a new job, the decision unit can also suggest products and services related to the job. In addition, if the customer starts a new hobby, the decision unit can also suggest products and services related to the hobby. This allows for more personalized suggestions by customizing products and services based on the customer's current living situation. Some or all of the above-mentioned processing in the decision unit may be performed, for example, using AI or may be performed without using AI. For example, the decision unit can make the selection using an AI model that uses the customer's living situation data as an input and customizes the selection of products and services.

[0089] The determination unit can estimate the customer's emotions and determine the priority order of products or services based on the estimated customer's emotions. For example, when the customer is relaxed, the determination unit can preferentially suggest products or services related to relaxation. In addition, when the customer is stressed, the determination unit can preferentially suggest products or services related to stress reduction. Furthermore, when the customer is excited, the determination unit can preferentially suggest products or services for suppressing excitement. This allows more appropriate suggestions to be made by determining the priority order of products and services based on the customer's emotions. 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 determine the priority order using an AI model that inputs customer emotion data and determines the priority order of products and services.

[0090] The decision unit can select the optimal product or service by taking into consideration the geographical location information of the customer at the time of decision. For example, when the customer is traveling, the decision unit can suggest products and services related to the travel destination. Furthermore, when the customer is at home, the decision unit can also suggest products and services related to activities at home. Furthermore, when the customer is at work, the decision unit can also suggest products and services related to activities at work. In this way, by taking into consideration the geographical location information of the customer, more appropriate products and services can be selected. Part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can make the selection using an AI model that inputs the geographical location information data of the customer and selects the optimal product or service.

[0091] At the time of decision, the decision unit can select a product or service by analyzing the social media activity of the customer. The decision unit can select a product or service based on, for example, photos or posts shared by the customer on social media. The decision unit can also select a product or service based on the activity of an account that the customer follows on social media. Furthermore, the decision unit can select a product or service based on a group or event that the customer participates in on social media. In this way, by analyzing the social media activity of the customer, more appropriate products and services can be selected. A part or all of the above-mentioned processing in the decision unit may be performed, for example, using AI, or may be performed without using AI. For example, the decision unit can make the selection using an AI model that inputs the social media activity data of the customer and selects products and services.

[0092] The suggestion unit can estimate the customer's emotions and adjust the way of expressing the proposal based on the estimated customer's emotions. For example, when the customer is relaxed, the suggestion unit can suggest products or services while playing relaxing music. In addition, when the customer is stressed, the suggestion unit can also suggest products or services in a calm tone. In addition, when the customer is excited, the suggestion unit can also suggest products or services in an energetic tone. This enables more effective proposals by adjusting the way of expressing the proposal based on the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can adjust the way of expressing the proposal using an AI model that inputs customer emotion data and adjusts the way of expressing the proposal.

[0093] The suggestion unit can adjust the level of detail of the proposal based on the importance of the product when making a proposal. For example, the suggestion unit can make a proposal including a detailed description for a product with high importance. Furthermore, the suggestion unit can make a proposal including a brief description for a product with low importance. Furthermore, the suggestion unit can make a proposal including an explanation with a moderate level of detail for a product with medium importance. This allows for a more appropriate proposal by adjusting the level of detail of the proposal based on the importance of the product. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can adjust the level of detail using an AI model that inputs product importance data and adjusts the level of detail of the proposal.

[0094] The suggestion unit can apply different suggestion algorithms depending on the category of the product when making a suggestion. For example, for entertainment products, the suggestion unit can make visually attractive suggestions. For health-related products, the suggestion unit can also make suggestions based on scientific data. For fashion products, the suggestion unit can also make suggestions based on trend information. This allows for more appropriate suggestions by applying the suggestion algorithm depending on the category of the product. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can apply an algorithm using an AI model that inputs product category data and applies different suggestion algorithms.

[0095] The suggestion unit can estimate the customer's emotion and adjust the length of the suggestion based on the estimated customer's emotion. For example, the suggestion unit can make a detailed suggestion when the customer is relaxed. In addition, the suggestion unit can make a concise suggestion when the customer is stressed. In addition, the suggestion unit can make an energetic suggestion when the customer is excited. For example, the suggestion unit can make a detailed suggestion when the customer is relaxed. In addition, the suggestion unit can make a concise suggestion when the customer is stressed. In addition, the suggestion unit can make an energetic suggestion when the customer is excited. This allows for a more appropriate suggestion by adjusting the length of the suggestion based on the customer's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can adjust the length using an AI model that inputs customer emotion data and adjusts the length of the suggestion.

[0096] The suggestion unit can determine the priority of the suggestion based on the submission time of the product at the time of suggestion. For example, the suggestion unit can give priority to new products. The suggestion unit can also give priority to seasonal products at an appropriate time. Furthermore, the suggestion unit can also give priority to sale products during a sale period. This enables more appropriate suggestions by determining the priority of the suggestion based on the submission time of the product. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can determine the priority using an AI model that inputs product submission time data and determines the priority of the suggestion.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit can give priority to suggestions for products with high relevance. In addition, the suggestion unit can also make complementary suggestions for products with low relevance. Furthermore, the suggestion unit can also make suggestions in a moderate order for products with moderate relevance. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the products. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can adjust the order using an AI model that uses product relevance data as input and adjusts the order of suggestions.

[0098] The collection unit can estimate the customer's emotion and adjust the feedback collection method based on the estimated customer's emotion. For example, the collection unit can request detailed feedback when the customer is relaxed. Also, the collection unit can request brief feedback when the customer is stressed. Furthermore, the collection unit can request energetic feedback when the customer is excited. For example, the collection unit can request detailed feedback when the customer is relaxed. Also, the collection unit can request brief feedback when the customer is stressed. Also, the collection unit can request energetic feedback when the customer is excited. This allows more appropriate feedback to be collected by adjusting the feedback collection method based on the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can adjust the collection method using an AI model that inputs customer emotion data and adjusts the feedback collection method.

[0099] At the time of collection, the collection unit can select the optimal collection method by analyzing the past feedback history of the customer. For example, if the customer provided detailed feedback in the past, the collection unit can request similar detailed feedback. In addition, if the customer provided brief feedback in the past, the collection unit can request similar brief feedback. In addition, the collection unit can analyze the past feedback history of the customer and select the optimal collection method. For example, if the customer provided detailed feedback in the past, the collection unit can request similar detailed feedback. In addition, if the customer provided brief feedback in the past, the collection unit can request similar brief feedback. The past feedback history of the customer can be analyzed and the optimal collection method can be selected. In this way, a more appropriate collection method can be selected by analyzing the past feedback history of the customer. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can make the selection using an AI model that inputs the feedback history data of the customer and selects the optimal collection method.

[0100] The collection unit can estimate the customer's emotions and determine the priority of the feedback to be collected based on the estimated customer's emotions. For example, when the customer is relaxed, the collection unit can preferentially collect detailed feedback. In addition, when the customer is stressed, the collection unit can preferentially collect brief feedback. In addition, when the customer is excited, the collection unit can preferentially collect energetic feedback. For example, when the customer is relaxed, the collection unit can preferentially collect detailed feedback. In addition, when the customer is stressed, the collection unit can preferentially collect brief feedback. In addition, when the customer is excited, the collection unit can preferentially collect energetic feedback. In this way, by determining the priority of the feedback based on the customer's emotions, more appropriate feedback can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can determine the priority using an AI model that inputs customer emotion data and determines the priority of feedback.

[0101] The collection unit can preferentially collect highly relevant feedback in consideration of the geographical location information of the customer at the time of collection. For example, when the customer is traveling, the collection unit can preferentially collect feedback related to the travel destination. In addition, when the customer is at home, the collection unit can preferentially collect feedback related to activities at home. In addition, when the customer is at work, the collection unit can preferentially collect feedback related to activities at work. For example, when the customer is traveling, the collection unit can preferentially collect feedback related to the travel destination. When the customer is at home, the collection unit can preferentially collect feedback related to activities at home. When the customer is at work, the collection unit can preferentially collect feedback related to activities at work. In this way, by considering the geographical location information of the customer, more relevant feedback can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can perform collection using an AI model that inputs geographical location information data of the customer and preferentially collects highly relevant feedback.

[0102] The identification unit can estimate the customer's emotion and adjust the method of identifying the improvement point based on the estimated emotion of the customer. For example, when the customer is relaxed, the identification unit can identify a detailed improvement point. Furthermore, when the customer is stressed, the identification unit can also identify a concise improvement point. Furthermore, when the customer is excited, the identification unit can also identify an energetic improvement point. For example, when the customer is relaxed, the identification unit can identify a detailed improvement point. When the customer is stressed, the identification unit can also identify a concise improvement point. When the customer is excited, the identification unit can also identify an energetic improvement point. Thereby, by adjusting the method of identifying the improvement point based on the emotion of the customer, a more appropriate improvement point can be identified. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can adjust the identification method using an AI model that takes customer emotion data as input and adjusts the identification method for improvements.

[0103] At the time of identification, the identification unit can identify an optimal improvement point by analyzing the past feedback history of the customer. The identification unit can identify a similar improvement point based on, for example, feedback provided by the customer in the past. The identification unit can also analyze the past feedback history of the customer and identify an optimal improvement point. The identification unit can also identify a highly relevant improvement point based on the past feedback history of the customer. For example, the identification unit can identify a similar improvement point based on feedback provided by the customer in the past. The identification unit can also analyze the past feedback history of the customer and identify an optimal improvement point. The identification unit can also identify a highly relevant improvement point based on the past feedback history of the customer. In this way, a more appropriate improvement point can be identified by analyzing the past feedback history of the customer. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can perform identification using an AI model that inputs feedback history data of the customer and identifies an optimal improvement point.

[0104] The identification unit can estimate the emotion of the customer and determine the priority of the improvement points to be identified based on the estimated emotion of the customer. For example, when the customer is relaxed, the identification unit can preferentially identify detailed improvement points. Furthermore, when the customer is stressed, the identification unit can preferentially identify concise improvement points. Furthermore, when the customer is excited, the identification unit can preferentially identify energetic improvement points. For example, when the customer is relaxed, the identification unit can preferentially identify detailed improvement points. When the customer is stressed, the identification unit can preferentially identify concise improvement points. When the customer is excited, the identification unit can preferentially identify energetic improvement points. In this way, by determining the priority of the improvement points based on the emotion of the customer, more appropriate improvement points can be identified. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the identification unit can determine priorities using an AI model that uses customer emotion data as input and determines the priorities of improvement points.

[0105] The identification unit can identify the optimal improvement point by considering the geographical location information of the customer at the time of identification. For example, when the customer is traveling, the identification unit can identify the improvement point related to the travel destination. Furthermore, when the customer is at home, the identification unit can also identify the improvement point related to the activity at home. Furthermore, when the customer is at work, the identification unit can also identify the improvement point related to the activity at work. For example, when the customer is traveling, the identification unit can identify the improvement point related to the travel destination. When the customer is at home, the identification unit can also identify the improvement point related to the activity at home. When the customer is at work, the identification unit can also identify the improvement point related to the activity at work. In this way, by considering the geographical location information of the customer, a more appropriate improvement point can be identified. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can perform identification using an AI model that inputs the geographical location information data of the customer and identifies the optimal improvement point.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The marketing support system may further include an incentive providing unit for increasing the customer's willingness to purchase. The incentive providing unit may, for example, provide discount coupons or points based on the customer's emotions. If the customer is happy, a discount coupon usable for the next purchase may be provided. If the customer is relaxed, points may be provided for relaxing products. Furthermore, if the customer is stressed, rewards may be provided for products that help reduce stress. In this way, by providing incentives according to the customer's emotions, it is possible to increase the customer's willingness to purchase.

[0108] The marketing support system may further include a behavioral analysis unit that analyzes customer behavioral patterns. The behavioral analysis unit may, for example, analyze the customer's website browsing history and purchase history to identify the customer's interests and concerns. It may identify product categories frequently viewed by the customer and prioritize products in those categories. It may also suggest products similar to products the customer has previously purchased. Furthermore, if the customer tends to browse websites during a specific time period, it may also suggest products that match that time period. This allows for more personalized suggestions based on the customer's behavioral patterns.

[0109] The marketing support system may further include an advertisement display unit that adjusts the advertisement display method based on the customer's emotions. For example, when the customer is relaxed, the advertisement display unit may display the advertisement while playing relaxing music. When the customer is stressed, the advertisement may be displayed in a calm tone. When the customer is excited, the advertisement may be displayed in an energetic tone. In this way, the advertisement display method can be adjusted based on the customer's emotions, thereby enhancing the effectiveness of the advertisement.

[0110] The marketing support system can further include a support adjustment unit that adjusts the way in which customer support is provided based on the emotions of the customer. For example, when the customer is relaxed, the support adjustment unit can provide support in a relaxing tone. When the customer is stressed, the support adjustment unit can also provide a quick and polite response. Furthermore, when the customer is excited, the support adjustment unit can also provide support in an energetic tone. In this way, customer satisfaction can be improved by adjusting the way in which customer support is provided based on the emotions of the customer.

[0111] The marketing support system may further include a review display unit that adjusts the display method of the product review based on the customer's emotions. For example, when the customer is relaxed, the review display unit may display the product review in a relaxing tone. When the customer is stressed, the review display unit may display a brief review that is to the point. When the customer is excited, the review may be displayed in an energetic tone. In this way, the effectiveness of the review can be increased by adjusting the display method of the product review based on the customer's emotions.

[0112] The marketing support system may further include a history suggestion unit that suggests products and services based on the customer's purchasing history. The history suggestion unit may, for example, suggest products similar to products the customer has previously purchased. It may also suggest accessories and additional services related to products the customer has previously purchased. It may also analyze the usage status of products the customer has previously purchased and suggest repeat purchases at appropriate times. This makes it possible to suggest more appropriate products and services based on the customer's purchasing history.

[0113] The marketing support system may further include a location suggestion unit that suggests products and services based on the geographical location information of the customer. For example, when the customer is traveling, the location suggestion unit may suggest products and services related to the travel destination. When the customer is at home, the location suggestion unit may suggest products and services related to activities at home. When the customer is at work, the location suggestion unit may suggest products and services related to activities at work. This allows more appropriate products and services to be suggested based on the geographical location information of the customer.

[0114] The marketing support system may further include a social suggestion unit that suggests products and services based on the social media activities of the customer. The social suggestion unit may suggest products and services based on, for example, photos and posts shared by the customer on social media. It may also suggest products and services based on the activities of accounts the customer follows on social media. It may also suggest products and services based on groups and events the customer participates in on social media. This makes it possible to suggest more appropriate products and services based on the customer's social media activities.

[0115] The marketing support system may further include a feedback analysis unit that analyzes customer feedback to identify areas for improvement in products and services. The feedback analysis unit may, for example, analyze customer survey results and review comments to identify areas for improvement in products and services. It may also analyze non-verbal feedback such as customer facial expressions and voice. It may also analyze customer feedback history and identify areas for improvement by comparing with past feedback. In this way, by analyzing customer feedback, it is possible to identify areas for improvement in products and services and improve customer satisfaction.

[0116] The marketing support system may further include a feedback adjustment unit that adjusts the feedback collection method based on the customer's emotions. For example, the feedback adjustment unit may request detailed feedback when the customer is relaxed. Also, the feedback adjustment unit may request brief feedback when the customer is stressed. Furthermore, the feedback adjustment unit may request energetic feedback when the customer is excited. In this way, by adjusting the feedback collection method based on the customer's emotions, more appropriate feedback can be collected.

[0117] The process flow of the second embodiment will be briefly described below.

[0118] 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 acquisition unit may capture the customer's facial expression with a camera and acquire the facial expression data using facial expression recognition technology. It may also record the customer's voice with a microphone and acquire the voice data using voice analysis technology. It may also measure the customer's heart rate with a sensor and acquire the heart rate data. Step 2: The estimation unit estimates the customer's emotions based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the emotion estimation information using a machine learning algorithm to estimate the customer's emotions. For example, a machine learning model is used that takes facial expression data as input and outputs emotions such as smiling or anger. It is also possible to use voice data as input and estimate emotions based on the tone of voice and speaking style. Furthermore, it is also possible to use heart rate data as input and estimate stress or relaxation state based on heart rate fluctuations. Step 3: The determination unit determines a product or service according to the customer's emotion estimated by the estimation unit. The determination unit determines the most suitable product or service for the customer based on the estimated emotion and the customer's attributes and behavioral history. For example, if the customer is happy, the determination unit suggests fun products or services, and if the customer is relaxed, the determination unit suggests relaxing products or services. If the customer is stressed, the determination unit suggests products or services that help reduce stress. Step 4: The suggestion unit suggests the product or service determined by the decision unit to the customer. The suggestion unit suggests the product or service in a manner according to the customer's emotions. For example, if the customer is relaxed, the suggestion unit suggests the product or service while playing relaxing music, if the customer is stressed, the suggestion unit suggests the product or service in a calm tone, if the customer is excited, the suggestion unit suggests the product or service in an energetic tone.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 analyzes the acquired emotion estimation information to estimate the emotion of the customer. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate product or service based on the estimated emotion. The suggestion unit is realized by the control unit 46A of the smart device 14, and proposes the determined product or service to the customer. The collection unit collects customer feedback using the camera 42 and microphone 38B of the smart device 14. The identification unit is realized by the specific processing unit 290 of the data processing device 12, and identifies improvements to the product or service based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0126] 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.

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

[0128] 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).

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

[0130] 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.

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

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0133] 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0137] 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.

[0138] 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 analyzes the acquired emotion estimation information to estimate the emotion of the customer. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate product or service based on the estimated emotion. The suggestion unit is realized by the control unit 46A of the smart glasses 214, and suggests the determined product or service to the customer. The collection unit collects customer feedback using the camera 42 and microphone 238 of the smart glasses 214. The identification unit is realized by the specific processing unit 290 of the data processing device 12, and identifies improvements to the product or service based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0142] 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.

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

[0144] 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).

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 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.

[0152] 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.

[0153] 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.

[0154] 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 analyzes the acquired emotion estimation information to estimate the emotion of the customer. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate product or service based on the estimated emotion. The suggestion unit is realized by the control unit 46A of the headset type terminal 314, and proposes the determined product or service to the customer. The collection unit collects customer feedback using the camera 42 and microphone 238 of the headset type terminal 314. The identification unit is realized by the specific processing unit 290 of the data processing device 12, and identifies improvements to the product or service based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0166] 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.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0170] 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.

[0171] 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 analyzes the acquired emotion estimation information to estimate the emotion of the customer. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate product or service based on the estimated emotion. The suggestion unit is realized by the control unit 46A of the robot 414, and proposes the determined product or service to the customer. The collection unit collects customer feedback using the camera 42 and microphone 238 of the robot 414. The identification unit is realized by the specific processing unit 290 of the data processing device 12, and identifies improvements to the product or service based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of a 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 product or service according to the customer's emotion estimated by the estimation unit; A proposal unit that proposes the product or service determined by the decision unit to the customer. A system characterized by: (Appendix 2) The suggestion unit, To propose the product or the service to the customer in a manner corresponding to the emotion of the customer 2. The system of claim 1, (Appendix 3) The suggestion unit, The product or service is proposed to the customer while playing music corresponding to the emotion of the customer. 3. The system of claim 2, (Appendix 4) The determination unit is A product or service according to the customer's emotion is determined based on the customer's emotion and the customer's attributes estimated by the estimation unit. 2. The system of claim 1, (Appendix 5) The determination unit is A product or service according to the customer's emotion is determined based on the emotion of the customer estimated by the estimation unit and a behavior history of the customer. 2. The system of claim 1, (Appendix 6) The suggestion unit, A collection unit is provided for collecting customer feedback regarding the proposed products and services. 2. The system of claim 1, (Appendix 7) The collecting unit includes: and an identification unit that identifies improvements to the product or service based on the collected feedback. 7. The system of claim 6, (Appendix 8) The acquisition unit is One or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, respiration, and posted content are acquired as the emotion estimation information. 2. The system of claim 1, (Appendix 9) The acquisition unit is To estimate a customer's emotion and adjust timing of acquiring emotion estimation information based on the estimated emotion of the customer. 2. The system of claim 1, (Appendix 10) The acquisition unit is Analyze past customer sentiment data and select a method to acquire it 2. The system of claim 1, (Appendix 11) The acquisition unit is When acquiring information for sentiment estimation, filtering is performed based on the customer's current activities and areas of interest. 2. The system of claim 1, (Appendix 12) The acquisition unit is Estimate customer sentiment and prioritize information to be retrieved based on the estimated customer sentiment 2. The system of claim 1, (Appendix 13) The acquisition unit is When acquiring information for emotion estimation, the system prioritizes acquiring information that is highly relevant by taking into account the geographic location information of the customer. 2. The system of claim 1, (Appendix 14) The acquisition unit is When obtaining sentiment estimation information, analyze the customer's social media activity to obtain related information. 2. The system of claim 1, (Appendix 15) The estimation unit is Estimate customer sentiment and adjust sentiment estimation algorithms based on estimated customer sentiment 2. The system of claim 1, (Appendix 16) The estimation unit is During estimation, the level of detail of the estimation is adjusted based on the importance of the information for emotion estimation. 2. The system of claim 1, (Appendix 17) The estimation unit is During estimation, different estimation algorithms are applied depending on the category of emotion estimation information. 2. The system of claim 1, (Appendix 18) The estimation unit is Estimate customer sentiment and adjust how the results are displayed based on the estimated customer sentiment 2. The system of claim 1, (Appendix 19) The estimation unit is During estimation, the priority of estimation is determined based on the time when emotion estimation information was acquired. 2. The system of claim 1, (Appendix 20) The estimation unit is During estimation, the order of estimation is adjusted based on the relevance of emotion estimation information. 2. The system of claim 1, (Appendix 21) The determination unit is Infer customer sentiment and adjust product or service decisions based on the inferred customer sentiment 2. The system of claim 1, (Appendix 22) The determination unit is At the time of decision making, analyze the customer's past purchase history to select the most suitable product or service 2. The system of claim 1, (Appendix 23) The determination unit is At the point of decision, customize product or service selection based on the customer's current life situation 2. The system of claim 1, (Appendix 24) The determination unit is Estimating customer sentiment and prioritizing products or services based on estimated customer sentiment 2. The system of claim 1, (Appendix 25) The determination unit is Considering the customer's geographic location when making decisions to select the most appropriate product or service 2. The system of claim 1, (Appendix 26) The determination unit is At decision time, analyzing customers' social media activity to help guide product or service selection 2. The system of claim 1, (Appendix 27) The suggestion unit, Infer customer sentiment and tailor the wording of offers based on the inferred customer sentiment 2. The system of claim 1, (Appendix 28) The suggestion unit, When making a recommendation, adjust the detail of the recommendation based on the importance of the product 2. The system of claim 1, (Appendix 29) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the product category 2. The system of claim 1, (Appendix 30) The suggestion unit, Estimate customer sentiment and adjust the length of the offer based on the estimated customer sentiment 2. The system of claim 1, (Appendix 31) The suggestion unit, At the time of proposal, prioritize proposals based on when the product was submitted 2. The system of claim 1, (Appendix 32) The suggestion unit, When making suggestions, adjust the order of suggestions based on product relevance 2. The system of claim 1, (Appendix 33) The collecting unit includes: Estimate customer sentiment and adjust your feedback collection methods based on estimated customer sentiment 2. The system of claim 1, (Appendix 34) The collecting unit includes: When collecting data, analyze the customer's past feedback history to select the optimal collection method. 2. The system of claim 1, (Appendix 35) The collecting unit includes: Estimate customer sentiment and prioritize feedback to collect based on estimated customer sentiment 2. The system of claim 1, (Appendix 36) The collecting unit includes: Prioritize relevant feedback by taking into account the customer's geographic location at the time of collection 2. The system of claim 1, (Appendix 37) The identification unit is Estimate customer sentiment and tailor how you identify improvements based on that sentiment 2. The system of claim 1, (Appendix 38) The identification unit is Upon identification, analyze past customer feedback history to identify optimal areas for improvement 2. The system of claim 1, (Appendix 39) The identification unit is Estimate customer sentiment and prioritize identified improvements based on estimated customer sentiment 2. The system of claim 1, (Appendix 40) The identification unit is During identification, consider the geographic location of your customers to identify the best areas for improvement 2. The system of claim 1, [Explanation of symbols]

[0191] 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 a 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 product or service according to the customer's emotion estimated by the estimation unit; A proposal unit that proposes the product or service determined by the decision unit to the customer. A system characterized by:

2. The suggestion unit, To propose the product or the service to the customer in a manner corresponding to the emotion of the customer 2. The system of claim 1 .

3. The suggestion unit, The product or service is proposed to the customer while playing music corresponding to the emotion of the customer.

3. The system of claim 2.

4. The determination unit is A product or service according to the customer's emotion is determined based on the customer's emotion and the customer's attributes estimated by the estimation unit.

2. The system of claim 1 .

5. The determination unit is A product or service according to the customer's emotion is determined based on the emotion of the customer estimated by the estimation unit and a behavior history of the customer.

2. The system of claim 1 .

6. The suggestion unit, A collection unit is provided for collecting customer feedback regarding the proposed products and services.

2. The system of claim 1 .

7. The collecting unit includes: and an identification unit that identifies improvements to the product or service based on the collected feedback.

7. The system of claim 6.

8. The acquisition unit is One or more pieces of information among the customer's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, respiration, and posted content are acquired as the emotion estimation information.

2. The system of claim 1 .

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