system

Abstract

The system according to this embodiment aims to analyze customer information and provide optimal counseling results and suggestions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects customer information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides counseling results based on the results analyzed by the analysis unit. The proposal unit makes optimal proposals based on the counseling results provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, counseling based on customer information has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze customer information and provide optimal counseling results and proposals.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects customer information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides a counseling result based on the result analyzed by the analysis unit. The proposal unit makes an optimal proposal based on the counseling result provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze customer information and provide optimal counseling results and suggestions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The counseling tool according to an embodiment of the present invention is a system that uses an AI agent to engage in natural dialogue with customers, eliciting their preferences, lifestyle, and concerns about their hairstyles. This counseling tool begins with the customer receiving counseling via a tablet or smart device. The AI ​​agent uses natural language processing and machine learning to accurately understand the customer's words and emotions, and provides the counseling results to the stylist. This allows the stylist to gain a deeper understanding of the customer's needs and propose the most suitable hairstyle. Furthermore, the AI ​​agent can continuously support both the customer and the hair salon (stylist) from the first visit, increasing engagement. For example, a customer can express a desire such as "I want to increase the volume of my hair." The AI ​​agent then elicits the customer's preferences, lifestyle, and concerns about their hairstyle. For example, if a customer expresses a concern such as "My hair has been damaged recently," the AI ​​agent accurately understands this concern and provides appropriate counseling. The AI ​​agent then provides the counseling results to the stylist. This allows the stylist to gain a deeper understanding of the customer's needs and propose the most suitable hairstyle. For example, the stylist can understand the condition of the customer's hair and propose an appropriate treatment. Furthermore, the AI ​​agent provides continuous support to both the customer and the hair salon (stylist) from the first visit. This enhances customer engagement. For example, when a customer returns, the AI ​​agent can make suggestions based on the results of their previous consultation. This system improves customer satisfaction and repeat business, and enhances the service level of the hair salon. For instance, even if a customer has difficulty expressing their wishes, the AI ​​agent can elicit those needs and provide them to the stylist, ensuring that the service meets the customer's expectations. In addition, the quality of consultations becomes more consistent and less dependent on the stylist's experience, improving the overall service level. As a result, the consultation tool can accurately understand customer needs and propose the optimal hairstyle.

[0029] The counseling tool according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects customer information. Customer information includes, but is not limited to, personal information, purchase history, and preferences. The collection unit collects, for example, basic customer information and preferences. The analysis unit analyzes the information collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, and machine learning, but is not limited to, such methods. The analysis unit analyzes the collected information to understand the customer's needs. The provision unit provides counseling results based on the results analyzed by the analysis unit. The counseling results are provided in, for example, the form of a report, advice, or recommendation, but is not limited to, such forms. The provision unit provides, for example, the analysis results to a stylist. The proposal unit makes the best proposal based on the counseling results provided by the provision unit. The best proposal is made by, for example, a proposal based on the customer's needs, a proposal based on past data, or similar methods, but is not limited to, such methods. The proposal unit makes the best proposal based on the provided counseling results. As a result, the counseling tool according to the embodiment can collect, analyze, and provide customer information and make optimal suggestions. Some or all of the above-described processes in the collection unit, analysis unit, provision unit, and suggestion unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the customer's basic information and preferences as input to the AI ​​in order to collect customer information, the AI ​​can analyze the information, the provision unit can provide the analysis results to the stylist, and the suggestion unit can make optimal suggestions.

[0030] The data collection department collects customer information. This information includes, but is not limited to, personal information, purchase history, and preferences. Specifically, the data collection department collects basic information provided by customers (such as name, age, gender, and contact information), past purchase history (such as purchased products and services, purchase frequency, and purchase amount), and customer preferences (such as favorite colors, styles, brands, and hobbies). This information is either entered by the customer into an online form or automatically retrieved from a database of past transactions. The data collection department also collects customer wishes and requests. For example, it interviews customers about their purpose for seeking counseling, specific problems or concerns, and goals they wish to achieve. This allows the data collection department to understand the customer's detailed profile and needs. The collected information is stored in a secure database and managed so that the analytics department can access it. It is important for the data collection department to regularly update customer information to keep it up-to-date. For example, if a customer purchases a new product or their preferences change, quickly reflecting this information enables more accurate counseling. The data collection department adheres to strict information handling policies to protect customer privacy and collects information only with the customer's consent. This allows the data collection department to efficiently collect necessary information while gaining the trust of customers.

[0031] The analysis unit analyzes the information collected by the data collection unit. Analysis is performed using methods such as data mining, statistical analysis, and machine learning, but is not limited to these examples. Specifically, the analysis unit retrieves collected customer information from the database and performs data preprocessing. Preprocessing includes data cleaning (imputing missing values, removing outliers, etc.) and data normalization (scaling, encoding, etc.). Next, the analysis unit uses data mining techniques to extract customer purchasing patterns and preference trends. For example, it identifies frequently purchased items and items popular in specific seasons. It also uses statistical analysis to analyze the relationship between customer attributes (age, gender, region, etc.) and purchasing behavior. Furthermore, it uses machine learning algorithms to build models that predict customer needs. For example, it creates a model that predicts the next item a customer is likely to purchase based on their past purchase history and preferences. Based on these analysis results, the analysis unit understands customer needs and preferences and uses this information for individualized counseling. It is crucial for the analysis unit to analyze data in real time and reflect the latest information. For example, when a customer purchases a new product, this information can be immediately reflected in the analysis, enabling more accurate counseling. The analysis unit visualizes the analysis results and provides them to the service unit in an easy-to-understand format. This allows the analysis unit to effectively analyze the collected information and accurately grasp customer needs.

[0032] The service department provides counseling results based on the analysis conducted by the analysis department. These results may be provided in the form of reports, advice, or recommendations, but are not limited to these examples. Specifically, the service department receives the analysis results from the analysis department and creates reports to communicate them clearly to the customer. These reports include advice and recommendations based on the customer's needs and preferences. For example, they may include suggestions for products and services that match the customer's preferences, or advice tailored to their lifestyle. The service department effectively communicates the counseling results through communication with the customer. For example, they may provide counseling results through face-to-face counseling, online video calls, or email. The service department collects customer feedback to continuously improve the accuracy and effectiveness of the counseling results. For example, they collect information on how the customer reacted to the advice provided, whether they purchased the suggested products, and provide this feedback to the analysis department. This allows the service department to provide appropriate counseling results to customers and improve customer satisfaction. To protect customer privacy, the service department adheres to strict policies regarding the handling of counseling results and provides information only with the customer's consent. This allows the service department to gain customer trust and provide effective counseling.

[0033] The Proposal Department makes optimal suggestions based on the counseling results provided by the Service Department. These optimal suggestions may be based on customer needs, past data, or other methods, but are not limited to these. Specifically, the Proposal Department analyzes the counseling results received from the Service Department in detail and makes suggestions that best suit the customer's needs and preferences. For example, if a customer prefers a particular style or brand, the Proposal Department will suggest new products or services that match those preferences. It can also suggest products that are compatible with items the customer has previously purchased, based on past data. The Proposal Department utilizes AI to make optimal suggestions based on customer needs. For example, it uses machine learning algorithms to analyze customer purchase history and preferences and predict products they are most likely to purchase next. Furthermore, the Proposal Department collects customer feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, it collects information such as whether the customer purchased the suggested product and how they reacted to the suggestion, and provides this feedback to the Analysis Department. This allows the Proposal Department to make optimal suggestions to customers and improve customer satisfaction. To protect customer privacy, the Proposal Department adheres to strict policies regarding the handling of suggestion content and provides information only with the customer's consent. This allows the proposal department to make effective proposals while gaining the trust of customers.

[0034] The simulation unit can perform simulations to alleviate customers' anxieties before treatment. For example, the simulation unit provides simulations to alleviate anxieties regarding pain during treatment, uncertainty of results, and costs. For example, the simulation unit can visually simulate the treatment process to help customers understand the flow of the treatment. The simulation unit can also simulate the results after treatment and provide customers with an image of the post-treatment state. For example, the simulation unit can display the post-treatment hairstyle as a 3D model to provide customers with an image of the post-treatment state. This can alleviate customers' anxieties before treatment. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, to alleviate customers' anxieties before treatment, the simulation unit can input the treatment process and results into a generative AI, which can then perform the simulation.

[0035] The sharing function can share the customer's treatment history. For example, the sharing function can share the treatment history, such as the type of treatment, date and time, and results. For example, the sharing function can share the customer's treatment history with stylists, allowing stylists to suggest the most suitable treatment based on the customer's past treatment history. The sharing function can also share the customer's treatment history with other staff members, allowing staff to provide support based on the customer's treatment history. For example, the sharing function can save the customer's treatment history as digital data and share it as needed. This allows the customer's treatment history to be shared. Some or all of the above processing in the sharing function may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing function can input the customer's treatment history into a generative AI, which can then analyze and share the treatment history.

[0036] The support unit can provide support during the treatment. For example, the support unit can provide technical support and psychological support. For example, the support unit can answer the customer's questions during the treatment and explain the progress of the treatment. The support unit can also provide music or videos to help the customer relax during the treatment. For example, the support unit can play relaxing music to help the customer relax. The support unit can also provide counseling to provide psychological support to the customer during the treatment. For example, the support unit can provide counseling to alleviate the customer's anxiety. In this way, support can be provided during the treatment. Some or all of the above processes in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the customer's support during the treatment into a generative AI, and the generative AI can provide the support.

[0037] The data collection unit can collect basic customer information and preferences. For example, the data collection unit collects basic information such as the customer's name, age, gender, and contact information. The data collection unit can also collect preferences such as the desired treatment content and expected results. For example, the data collection unit collects the customer's desired hairstyle and treatment content. This allows the data collection unit to collect basic customer information and preferences. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input the customer's basic information and preferences into a generating AI, and the generating AI can collect the information.

[0038] The analysis unit can analyze the collected information and understand customer needs. The analysis unit analyzes the collected information using methods such as data mining, statistical analysis, and machine learning. The analysis unit understands customer needs such as the purpose of the treatment and the expected effects. This allows the analysis unit to understand customer needs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected information into a generative AI, which will analyze the information and understand customer needs.

[0039] The service provider can provide the analysis results to the stylist. The service provider can provide the analysis results in the form of reports, advice, recommendations, etc. The service provider can provide information to help the stylist understand the customer's needs and propose the most suitable treatment. This allows the service provider to provide the analysis results to the stylist. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without a generating AI. For example, the service provider can input the analysis results into a generating AI, and the generating AI can provide the information.

[0040] The proposal department can make optimal suggestions based on the provided counseling results. For example, the proposal department can make suggestions based on customer needs or based on past data. For example, the proposal department can suggest hairstyles and treatment methods that suit customer needs. This enables the proposal department to make optimal suggestions. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input the provided counseling results into a generative AI, which can then make optimal suggestions.

[0041] The data collection unit can analyze a customer's past counseling history and select the optimal information collection method. For example, the data collection unit can automatically generate relevant questions based on information the customer has previously provided. The data collection unit can also analyze the customer's past response patterns and determine the optimal order of questions. Furthermore, the data collection unit can prioritize questions related to specific topics based on the customer's past counseling history. This allows for the analysis of the customer's past counseling history and the selection of the optimal information collection method. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's past counseling history into a generative AI, which can analyze the information and select the optimal information collection method.

[0042] The data collection unit can filter information based on the customer's current lifestyle and areas of interest during the information gathering process. For example, the data collection unit can ask questions tailored to the customer's lifestyle and collect relevant information. The data collection unit can also prioritize questions on specific topics based on the customer's areas of interest. Furthermore, the data collection unit can omit unnecessary questions based on the customer's lifestyle and areas of interest. This allows for filtering based on the customer's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's lifestyle and areas of interest into a generative AI, which can then filter the information.

[0043] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location during data collection. For example, the data collection unit can collect region-specific information based on the customer's place of residence. It can also collect information about nearby services and facilities based on the customer's current location. Furthermore, it can collect information related to the customer's travel destination based on their travel destination. This allows for the priority collection of highly relevant information by considering the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input the customer's geographical location information into a generating AI, which can then collect the information.

[0044] The data collection unit can analyze the customer's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the content of the customer's social media posts and collect information on topics of interest. The data collection unit can also analyze the customer's followers and followed accounts and collect relevant information. Furthermore, the data collection unit can analyze the customer's social media activity history and collect information based on past interests. This allows for the analysis of the customer's social media activity and the collection of relevant information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's social media activity into a generative AI, which can then collect the information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important information. It can also perform a concise analysis on less important information. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows the level of detail of the analysis to be adjusted based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the importance of the information into the generating AI, which can then analyze the information and adjust the level of detail.

[0046] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an image analysis algorithm to information about hairstyles. It can also apply a text analysis algorithm to information about lifestyles. Furthermore, it can apply an emotion analysis algorithm to information about customer emotions. This allows for the application of different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category of information into a generative AI, which can analyze the information and apply an appropriate algorithm.

[0047] The analysis unit can determine the priority of analysis based on the information submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. It can also postpone the analysis of older information. Furthermore, the analysis unit can determine the order of analysis based on the submission date. This allows the analysis priority to be determined based on the information submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the information submission date into a generating AI, which can then analyze the information and determine the priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the information into a generative AI, which can then analyze the information and adjust the order.

[0049] The data delivery unit can adjust the level of detail provided based on the importance of the analysis results at the time of delivery. For example, the data delivery unit can provide detailed information for important analysis results. It can also provide concise information for less important analysis results. Furthermore, the data delivery unit can determine the priority of delivery according to the importance of the analysis results. This allows for adjustment of the level of detail provided based on the importance of the analysis results. Some or all of the above processing in the data delivery unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data delivery unit can input the importance of the analysis results into a generating AI, which can then provide the information and adjust the level of detail.

[0050] The service provider can apply different service provision algorithms depending on the category of the analysis results at the time of provision. For example, the service provider can apply an image service provision algorithm to analysis results related to hairstyles. It can also apply a text service provision algorithm to analysis results related to lifestyles. Furthermore, it can apply an emotion service provision algorithm to analysis results related to customer emotions. This allows for the application of different service provision algorithms depending on the category of the analysis results. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the category of the analysis results into a generative AI, which can then provide the information and apply an appropriate algorithm.

[0051] The service provider can determine the priority of service provision based on the submission date of the analysis results. For example, the service provider may prioritize providing the most recent analysis results. It may also postpone providing older analysis results. Furthermore, the service provider can determine the order of service provision based on the submission date. This allows the service provider to determine the priority of service provision based on the submission date of the analysis results. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without a generating AI. For example, the service provider can input the submission date of the analysis results into a generating AI, which can then provide the information and determine the priority.

[0052] The service provider can adjust the order of provision based on the relevance of the analysis results at the time of provision. For example, the service provider can prioritize providing analysis results that are highly relevant. The service provider can also postpone providing analysis results that are less relevant. Furthermore, the service provider can determine the order of provision based on the relevance of the analysis results. This allows the service provider to adjust the order of provision based on the relevance of the analysis results. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without a generating AI. For example, the service provider can input the relevance of the analysis results into a generating AI, which can then provide the information and adjust the order.

[0053] The proposal unit can adjust the level of detail in its proposals based on the importance of the counseling results. For example, it can provide detailed proposals for important counseling results, and concise proposals for less important results. Furthermore, the proposal unit can prioritize proposals according to the importance of the counseling results, thereby adjusting the level of detail in the proposals based on the importance of the counseling results. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the counseling results into a generative AI, which can then provide information and adjust the level of detail.

[0054] The suggestion unit can apply different suggestion algorithms depending on the category of the counseling result when making a suggestion. For example, the suggestion unit can apply an image suggestion algorithm to counseling results related to hairstyles. It can also apply a text suggestion algorithm to counseling results related to lifestyles. Furthermore, it can apply an emotion suggestion algorithm to counseling results related to customer emotions. This allows for the application of different suggestion algorithms depending on the category of the counseling result. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the category of the counseling result into a generative AI, which can then provide information and apply an appropriate algorithm.

[0055] The proposal department can determine the priority of proposals based on the submission date of the counseling results. For example, the proposal department may prioritize the most recent counseling results. It may also postpone older counseling results. Furthermore, the proposal department can determine the order of proposals based on the submission date. This allows for the prioritization of proposals based on the submission date of the counseling results. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the submission date of the counseling results into a generative AI, which can then provide the information and determine the priority.

[0056] The proposal unit can adjust the order of proposals based on the relevance of the counseling results. For example, the proposal unit may prioritize proposing counseling results that are highly relevant. It can also postpone proposing less relevant counseling results. Furthermore, the proposal unit can determine the order of proposals based on the relevance of the counseling results. This allows the order of proposals to be adjusted based on the relevance of the counseling results. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the relevance of the counseling results into a generative AI, which can then provide the information and adjust the order.

[0057] The simulation unit can provide the optimal simulation by referring to the customer's past treatment history during the simulation. For example, the simulation unit provides relevant simulations based on the customer's past treatment history. The simulation unit can also simulate the optimal treatment method from the customer's past treatment history. Furthermore, the simulation unit can analyze the customer's past treatment history and provide the most effective simulation. This allows the simulation to provide the optimal simulation by referring to the customer's past treatment history. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the customer's past treatment history into a generative AI, which can then provide information and perform the optimal simulation.

[0058] The simulation unit can provide an optimal simulation by taking into account the customer's geographical location information during the simulation. For example, the simulation unit can provide a region-specific simulation based on the customer's place of residence. It can also provide a simulation regarding nearby services and facilities based on the customer's current location. Furthermore, the simulation unit can provide a simulation related to the customer's travel destination based on their travel destination. This allows the system to provide an optimal simulation by taking into account the customer's geographical location information. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the customer's geographical location information into a generative AI, which can then provide the information and perform an optimal simulation.

[0059] The sharing unit can share optimal information by referring to the customer's past treatment history during the sharing process. For example, the sharing unit can share relevant information based on the customer's past treatment history. The sharing unit can also share information about the optimal treatment method based on the customer's past treatment history. Furthermore, the sharing unit can analyze the customer's past treatment history and share the most effective information. This allows for the sharing of optimal information by referring to the customer's past treatment history. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing unit can input the customer's past treatment history into a generative AI, which then provides the information and shares the optimal information.

[0060] The sharing function can share optimal information by considering the customer's geographical location when sharing. For example, the sharing function can share region-specific information based on the customer's place of residence. It can also share information about nearby services and facilities based on the customer's current location. Furthermore, it can share information related to the customer's travel destination based on their travel destination. This allows for the sharing of optimal information by considering the customer's geographical location. Some or all of the above processing in the sharing function may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing function can input the customer's geographical location information into a generative AI, which then provides information and shares the optimal information.

[0061] The support unit can provide optimal support by referring to the customer's past treatment history during support. For example, the support unit can provide relevant support based on the customer's past treatment history. The support unit can also provide support regarding the most suitable treatment method based on the customer's past treatment history. Furthermore, the support unit can analyze the customer's past treatment history to provide the most effective support. This allows the support unit to provide optimal support by referring to the customer's past treatment history. Some or all of the above processes in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the customer's past treatment history into a generative AI, which can then provide information and provide optimal support.

[0062] The support department can provide optimal support by taking into account the customer's geographical location. For example, the support department can provide region-specific support based on the customer's place of residence. It can also provide support regarding nearby services and facilities based on the customer's current location. Furthermore, the support department can provide support related to the customer's travel destination based on their travel destination. This allows the support department to provide optimal support by taking into account the customer's geographical location. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can input the customer's geographical location information into a generative AI, which can then provide the information and provide optimal support.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The information gathering unit can also analyze a client's past counseling history and select the most appropriate method of information gathering. For example, it can automatically generate relevant questions based on information the client has previously provided. It can also analyze a client's past response patterns and determine the optimal order of questions. Furthermore, it can prioritize questions related to specific topics based on the client's past counseling history. This allows for the analysis of a client's past counseling history and the selection of the most effective information gathering method.

[0065] The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on important information, and a concise analysis on less important information. Furthermore, it can determine the priority of the analysis according to the importance of the information. This allows the level of detail of the analysis to be adjusted based on the importance of the information.

[0066] The service provider can also adjust the level of detail provided based on the importance of the analysis results. For example, detailed information can be provided for important analysis results, while concise information can be provided for less important results. Furthermore, the service provider can determine the priority of information provision according to the importance of the analysis results. This allows for adjustment of the level of detail provided based on the importance of the analysis results.

[0067] The suggestion function can also apply different suggestion algorithms depending on the category of the counseling results. For example, an image suggestion algorithm can be applied to counseling results related to hairstyles. A text suggestion algorithm can be applied to counseling results related to lifestyles. Furthermore, an emotion suggestion algorithm can be applied to counseling results related to customer emotions. This allows for the application of different suggestion algorithms depending on the category of the counseling results.

[0068] The simulation unit can also provide optimal simulations by taking into account the customer's geographical location. For example, it can provide region-specific simulations based on the customer's place of residence. It can also provide simulations related to nearby services and facilities based on the customer's current location. Furthermore, it can provide simulations related to travel destinations based on the customer's travel destinations. This allows for the provision of optimal simulations that take into account the customer's geographical location.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The collection unit collects customer information. This information includes, but is not limited to, personal information, purchase history, and preferences. For example, the collection unit collects basic customer information and preferences. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using methods such as data mining, statistical analysis, or machine learning, but is not limited to these examples. For example, the analysis unit analyzes the collected information to understand customer needs. Step 3: The service provider provides counseling results based on the analysis results performed by the analysis provider. The counseling results may be provided in the form of reports, advice, recommendations, etc., but are not limited to these examples. The service provider may, for example, provide the analysis results to the stylist. Step 4: The proposal department makes the best proposal based on the counseling results provided by the service department. The best proposal may be, but is not limited to, proposals based on customer needs or proposals based on past data. The proposal department makes the best proposal based on the counseling results provided, for example.

[0071] (Example of form 2) The counseling tool according to an embodiment of the present invention is a system that uses an AI agent to engage in natural dialogue with customers, eliciting their preferences, lifestyle, and concerns about their hairstyles. This counseling tool begins with the customer receiving counseling via a tablet or smart device. The AI ​​agent uses natural language processing and machine learning to accurately understand the customer's words and emotions, and provides the counseling results to the stylist. This allows the stylist to gain a deeper understanding of the customer's needs and propose the most suitable hairstyle. Furthermore, the AI ​​agent can continuously support both the customer and the hair salon (stylist) from the first visit, increasing engagement. For example, a customer can express a desire such as "I want to increase the volume of my hair." The AI ​​agent then elicits the customer's preferences, lifestyle, and concerns about their hairstyle. For example, if a customer expresses a concern such as "My hair has been damaged recently," the AI ​​agent accurately understands this concern and provides appropriate counseling. The AI ​​agent then provides the counseling results to the stylist. This allows the stylist to gain a deeper understanding of the customer's needs and propose the most suitable hairstyle. For example, the stylist can understand the condition of the customer's hair and propose an appropriate treatment. Furthermore, the AI ​​agent provides continuous support to both the customer and the hair salon (stylist) from the first visit. This enhances customer engagement. For example, when a customer returns, the AI ​​agent can make suggestions based on the results of their previous consultation. This system improves customer satisfaction and repeat business, and enhances the service level of the hair salon. For instance, even if a customer has difficulty expressing their wishes, the AI ​​agent can elicit those needs and provide them to the stylist, ensuring that the service meets the customer's expectations. In addition, the quality of consultations becomes more consistent and less dependent on the stylist's experience, improving the overall service level. As a result, the consultation tool can accurately understand customer needs and propose the optimal hairstyle.

[0072] The counseling tool according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a proposal unit. The collection unit collects customer information. Customer information includes, but is not limited to, personal information, purchase history, and preferences. The collection unit collects, for example, basic customer information and preferences. The analysis unit analyzes the information collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, and machine learning, but is not limited to, such methods. The analysis unit analyzes the collected information to understand the customer's needs. The provision unit provides counseling results based on the results analyzed by the analysis unit. The counseling results are provided in, for example, the form of a report, advice, or recommendation, but is not limited to, such forms. The provision unit provides, for example, the analysis results to a stylist. The proposal unit makes the best proposal based on the counseling results provided by the provision unit. The best proposal is made by, for example, a proposal based on the customer's needs, a proposal based on past data, or similar methods, but is not limited to, such methods. The proposal unit makes the best proposal based on the provided counseling results. As a result, the counseling tool according to the embodiment can collect, analyze, and provide customer information and make optimal suggestions. Some or all of the above-described processes in the collection unit, analysis unit, provision unit, and suggestion unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the customer's basic information and preferences as input to the AI ​​in order to collect customer information, the AI ​​can analyze the information, the provision unit can provide the analysis results to the stylist, and the suggestion unit can make optimal suggestions.

[0073] The data collection department collects customer information. This information includes, but is not limited to, personal information, purchase history, and preferences. Specifically, the data collection department collects basic information provided by customers (such as name, age, gender, and contact information), past purchase history (such as purchased products and services, purchase frequency, and purchase amount), and customer preferences (such as favorite colors, styles, brands, and hobbies). This information is either entered by the customer into an online form or automatically retrieved from a database of past transactions. The data collection department also collects customer wishes and requests. For example, it interviews customers about their purpose for seeking counseling, specific problems or concerns, and goals they wish to achieve. This allows the data collection department to understand the customer's detailed profile and needs. The collected information is stored in a secure database and managed so that the analytics department can access it. It is important for the data collection department to regularly update customer information to keep it up-to-date. For example, if a customer purchases a new product or their preferences change, quickly reflecting this information enables more accurate counseling. The data collection department adheres to strict information handling policies to protect customer privacy and collects information only with the customer's consent. This allows the data collection department to efficiently collect necessary information while gaining the trust of customers.

[0074] The analysis unit analyzes the information collected by the data collection unit. Analysis is performed using methods such as data mining, statistical analysis, and machine learning, but is not limited to these examples. Specifically, the analysis unit retrieves collected customer information from the database and performs data preprocessing. Preprocessing includes data cleaning (imputing missing values, removing outliers, etc.) and data normalization (scaling, encoding, etc.). Next, the analysis unit uses data mining techniques to extract customer purchasing patterns and preference trends. For example, it identifies frequently purchased items and items popular in specific seasons. It also uses statistical analysis to analyze the relationship between customer attributes (age, gender, region, etc.) and purchasing behavior. Furthermore, it uses machine learning algorithms to build models that predict customer needs. For example, it creates a model that predicts the next item a customer is likely to purchase based on their past purchase history and preferences. Based on these analysis results, the analysis unit understands customer needs and preferences and uses this information for individualized counseling. It is crucial for the analysis unit to analyze data in real time and reflect the latest information. For example, when a customer purchases a new product, this information can be immediately reflected in the analysis, enabling more accurate counseling. The analysis unit visualizes the analysis results and provides them to the service unit in an easy-to-understand format. This allows the analysis unit to effectively analyze the collected information and accurately grasp customer needs.

[0075] The service department provides counseling results based on the analysis conducted by the analysis department. These results may be provided in the form of reports, advice, or recommendations, but are not limited to these examples. Specifically, the service department receives the analysis results from the analysis department and creates reports to communicate them clearly to the customer. These reports include advice and recommendations based on the customer's needs and preferences. For example, they may include suggestions for products and services that match the customer's preferences, or advice tailored to their lifestyle. The service department effectively communicates the counseling results through communication with the customer. For example, they may provide counseling results through face-to-face counseling, online video calls, or email. The service department collects customer feedback to continuously improve the accuracy and effectiveness of the counseling results. For example, they collect information on how the customer reacted to the advice provided, whether they purchased the suggested products, and provide this feedback to the analysis department. This allows the service department to provide appropriate counseling results to customers and improve customer satisfaction. To protect customer privacy, the service department adheres to strict policies regarding the handling of counseling results and provides information only with the customer's consent. This allows the service department to gain customer trust and provide effective counseling.

[0076] The Proposal Department makes optimal suggestions based on the counseling results provided by the Service Department. These optimal suggestions may be based on customer needs, past data, or other methods, but are not limited to these. Specifically, the Proposal Department analyzes the counseling results received from the Service Department in detail and makes suggestions that best suit the customer's needs and preferences. For example, if a customer prefers a particular style or brand, the Proposal Department will suggest new products or services that match those preferences. It can also suggest products that are compatible with items the customer has previously purchased, based on past data. The Proposal Department utilizes AI to make optimal suggestions based on customer needs. For example, it uses machine learning algorithms to analyze customer purchase history and preferences and predict products they are most likely to purchase next. Furthermore, the Proposal Department collects customer feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, it collects information such as whether the customer purchased the suggested product and how they reacted to the suggestion, and provides this feedback to the Analysis Department. This allows the Proposal Department to make optimal suggestions to customers and improve customer satisfaction. To protect customer privacy, the Proposal Department adheres to strict policies regarding the handling of suggestion content and provides information only with the customer's consent. This allows the proposal department to make effective proposals while gaining the trust of customers.

[0077] The simulation unit can perform simulations to alleviate customers' anxieties before treatment. For example, the simulation unit provides simulations to alleviate anxieties regarding pain during treatment, uncertainty of results, and costs. For example, the simulation unit can visually simulate the treatment process to help customers understand the flow of the treatment. The simulation unit can also simulate the results after treatment and provide customers with an image of the post-treatment state. For example, the simulation unit can display the post-treatment hairstyle as a 3D model to provide customers with an image of the post-treatment state. This can alleviate customers' anxieties before treatment. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, to alleviate customers' anxieties before treatment, the simulation unit can input the treatment process and results into a generative AI, which can then perform the simulation.

[0078] The sharing function can share the customer's treatment history. For example, the sharing function can share the treatment history, such as the type of treatment, date and time, and results. For example, the sharing function can share the customer's treatment history with stylists, allowing stylists to suggest the most suitable treatment based on the customer's past treatment history. The sharing function can also share the customer's treatment history with other staff members, allowing staff to provide support based on the customer's treatment history. For example, the sharing function can save the customer's treatment history as digital data and share it as needed. This allows the customer's treatment history to be shared. Some or all of the above processing in the sharing function may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing function can input the customer's treatment history into a generative AI, which can then analyze and share the treatment history.

[0079] The support unit can provide support during the treatment. For example, the support unit can provide technical support and psychological support. For example, the support unit can answer the customer's questions during the treatment and explain the progress of the treatment. The support unit can also provide music or videos to help the customer relax during the treatment. For example, the support unit can play relaxing music to help the customer relax. The support unit can also provide counseling to provide psychological support to the customer during the treatment. For example, the support unit can provide counseling to alleviate the customer's anxiety. In this way, support can be provided during the treatment. Some or all of the above processes in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the customer's support during the treatment into a generative AI, and the generative AI can provide the support.

[0080] The data collection unit can collect basic customer information and preferences. For example, the data collection unit collects basic information such as the customer's name, age, gender, and contact information. The data collection unit can also collect preferences such as the desired treatment content and expected results. For example, the data collection unit collects the customer's desired hairstyle and treatment content. This allows the data collection unit to collect basic customer information and preferences. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input the customer's basic information and preferences into a generating AI, and the generating AI can collect the information.

[0081] The analysis unit can analyze the collected information and understand customer needs. The analysis unit analyzes the collected information using methods such as data mining, statistical analysis, and machine learning. The analysis unit understands customer needs such as the purpose of the treatment and the expected effects. This allows the analysis unit to understand customer needs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected information into a generative AI, which will analyze the information and understand customer needs.

[0082] The service provider can provide the analysis results to the stylist. The service provider can provide the analysis results in the form of reports, advice, recommendations, etc. The service provider can provide information to help the stylist understand the customer's needs and propose the most suitable treatment. This allows the service provider to provide the analysis results to the stylist. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without a generating AI. For example, the service provider can input the analysis results into a generating AI, and the generating AI can provide the information.

[0083] The proposal department can make optimal suggestions based on the provided counseling results. For example, the proposal department can make suggestions based on customer needs or based on past data. For example, the proposal department can suggest hairstyles and treatment methods that suit customer needs. This enables the proposal department to make optimal suggestions. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input the provided counseling results into a generative AI, which can then make optimal suggestions.

[0084] The data collection unit can estimate the customer's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the customer is relaxed, the data collection unit can ask detailed questions and collect more information. If the customer is tense, the data collection unit can start with simple questions and gradually move to more detailed ones. Furthermore, if the customer is in a hurry, the data collection unit can prioritize collecting important information. This allows the timing of information collection to be adjusted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and adjust the timing of information collection.

[0085] The data collection unit can analyze a customer's past counseling history and select the optimal information collection method. For example, the data collection unit can automatically generate relevant questions based on information the customer has previously provided. The data collection unit can also analyze the customer's past response patterns and determine the optimal order of questions. Furthermore, the data collection unit can prioritize questions related to specific topics based on the customer's past counseling history. This allows for the analysis of the customer's past counseling history and the selection of the optimal information collection method. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's past counseling history into a generative AI, which can analyze the information and select the optimal information collection method.

[0086] The data collection unit can filter information based on the customer's current lifestyle and areas of interest during the information gathering process. For example, the data collection unit can ask questions tailored to the customer's lifestyle and collect relevant information. The data collection unit can also prioritize questions on specific topics based on the customer's areas of interest. Furthermore, the data collection unit can omit unnecessary questions based on the customer's lifestyle and areas of interest. This allows for filtering based on the customer's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's lifestyle and areas of interest into a generative AI, which can then filter the information.

[0087] The data collection unit can estimate the customer's emotions and prioritize the information to collect based on the estimated emotions. For example, if the customer is stressed, the data collection unit will prioritize collecting important information. If the customer is relaxed, the data collection unit can also collect detailed information. Furthermore, if the customer is in a hurry, the data collection unit can prioritize collecting only the essential information. This allows the data collection unit to prioritize the information to collect based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and determine the priority of the information.

[0088] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location during data collection. For example, the data collection unit can collect region-specific information based on the customer's place of residence. It can also collect information about nearby services and facilities based on the customer's current location. Furthermore, it can collect information related to the customer's travel destination based on their travel destination. This allows for the priority collection of highly relevant information by considering the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input the customer's geographical location information into a generating AI, which can then collect the information.

[0089] The data collection unit can analyze the customer's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the content of the customer's social media posts and collect information on topics of interest. The data collection unit can also analyze the customer's followers and followed accounts and collect relevant information. Furthermore, the data collection unit can analyze the customer's social media activity history and collect information based on past interests. This allows for the analysis of the customer's social media activity and the collection of relevant information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's social media activity into a generative AI, which can then collect the information.

[0090] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can provide detailed analysis results. If the customer is tense, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, if the customer is in a hurry, the analysis unit can provide concise analysis results. This allows the presentation of the analysis to be adjusted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, to estimate the customer's emotions, the analysis unit can input the customer's facial expressions and voice data into the generative AI, which can then estimate the emotions and adjust the presentation of the analysis.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important information. It can also perform a concise analysis on less important information. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows the level of detail of the analysis to be adjusted based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the importance of the information into the generating AI, which can then analyze the information and adjust the level of detail.

[0092] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an image analysis algorithm to information about hairstyles. It can also apply a text analysis algorithm to information about lifestyles. Furthermore, it can apply an emotion analysis algorithm to information about customer emotions. This allows for the application of different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category of information into a generative AI, which can analyze the information and apply an appropriate algorithm.

[0093] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can perform a detailed analysis. If the customer is tense, the analysis unit can also perform a concise analysis. Furthermore, if the customer is in a hurry, the analysis unit can perform a short, to-the-point analysis. This allows the length of the analysis to be adjusted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, to estimate the customer's emotions, the analysis unit can input the customer's facial expressions and voice data into the generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0094] The analysis unit can determine the priority of analysis based on the information submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. It can also postpone the analysis of older information. Furthermore, the analysis unit can determine the order of analysis based on the submission date. This allows the analysis priority to be determined based on the information submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the information submission date into a generating AI, which can then analyze the information and determine the priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the information into a generative AI, which can then analyze the information and adjust the order.

[0096] The service provider can estimate the customer's emotions and adjust the presentation of the service based on the estimated emotions. For example, if the customer is relaxed, the service provider can provide detailed information. If the customer is tense, the service provider can provide concise and easy-to-understand information. Furthermore, if the customer is in a hurry, the service provider can provide concise information. This allows the service provider to adjust the presentation of the service based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, to estimate the customer's emotions, the service provider can input the customer's facial expressions and voice data into the generative AI, which can then estimate the emotions and adjust the presentation of the service.

[0097] The data delivery unit can adjust the level of detail provided based on the importance of the analysis results at the time of delivery. For example, the data delivery unit can provide detailed information for important analysis results. It can also provide concise information for less important analysis results. Furthermore, the data delivery unit can determine the priority of delivery according to the importance of the analysis results. This allows for adjustment of the level of detail provided based on the importance of the analysis results. Some or all of the above processing in the data delivery unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data delivery unit can input the importance of the analysis results into a generating AI, which can then provide the information and adjust the level of detail.

[0098] The service provider can apply different service provision algorithms depending on the category of the analysis results at the time of provision. For example, the service provider can apply an image service provision algorithm to analysis results related to hairstyles. It can also apply a text service provision algorithm to analysis results related to lifestyles. Furthermore, it can apply an emotion service provision algorithm to analysis results related to customer emotions. This allows for the application of different service provision algorithms depending on the category of the analysis results. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the category of the analysis results into a generative AI, which can then provide the information and apply an appropriate algorithm.

[0099] The service provider can estimate the customer's emotions and adjust the length of the service based on the estimated emotions. For example, if the customer is relaxed, the service provider can provide detailed information. If the customer is tense, the service provider can provide concise information. Furthermore, if the customer is in a hurry, the service provider can provide short, to-the-point information. This allows the length of the service to be adjusted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and adjust the length of the service.

[0100] The service provider can determine the priority of service provision based on the submission date of the analysis results. For example, the service provider may prioritize providing the most recent analysis results. It may also postpone providing older analysis results. Furthermore, the service provider can determine the order of service provision based on the submission date. This allows the service provider to determine the priority of service provision based on the submission date of the analysis results. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without a generating AI. For example, the service provider can input the submission date of the analysis results into a generating AI, which can then provide the information and determine the priority.

[0101] The service provider can adjust the order of provision based on the relevance of the analysis results at the time of provision. For example, the service provider can prioritize providing analysis results that are highly relevant. The service provider can also postpone providing analysis results that are less relevant. Furthermore, the service provider can determine the order of provision based on the relevance of the analysis results. This allows the service provider to adjust the order of provision based on the relevance of the analysis results. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without a generating AI. For example, the service provider can input the relevance of the analysis results into a generating AI, which can then provide the information and adjust the order.

[0102] The proposal unit can estimate the customer's emotions and adjust the way the proposal is presented based on those emotions. For example, if the customer is relaxed, the proposal unit can provide a detailed proposal. If the customer is tense, the proposal unit can provide a concise and easy-to-understand proposal. Furthermore, if the customer is in a hurry, the proposal unit can provide a concise proposal. This allows the proposal to be presented in a way that is tailored to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and adjust the way the proposal is presented.

[0103] The proposal unit can adjust the level of detail in its proposals based on the importance of the counseling results. For example, it can provide detailed proposals for important counseling results, and concise proposals for less important results. Furthermore, the proposal unit can prioritize proposals according to the importance of the counseling results, thereby adjusting the level of detail in the proposals based on the importance of the counseling results. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the importance of the counseling results into a generative AI, which can then provide information and adjust the level of detail.

[0104] The suggestion unit can apply different suggestion algorithms depending on the category of the counseling result when making a suggestion. For example, the suggestion unit can apply an image suggestion algorithm to counseling results related to hairstyles. It can also apply a text suggestion algorithm to counseling results related to lifestyles. Furthermore, it can apply an emotion suggestion algorithm to counseling results related to customer emotions. This allows for the application of different suggestion algorithms depending on the category of the counseling result. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the category of the counseling result into a generative AI, which can then provide information and apply an appropriate algorithm.

[0105] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the customer is relaxed, the suggestion unit can provide a detailed suggestion. If the customer is tense, the suggestion unit can provide a concise suggestion. Furthermore, if the customer is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. This allows the length of the suggestion to be adjusted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, to estimate the customer's emotions, the suggestion unit can input the customer's facial expressions and voice data into the generative AI, which can then estimate the emotions and adjust the length of the suggestion.

[0106] The proposal department can determine the priority of proposals based on the submission date of the counseling results. For example, the proposal department may prioritize the most recent counseling results. It may also postpone older counseling results. Furthermore, the proposal department can determine the order of proposals based on the submission date. This allows for the prioritization of proposals based on the submission date of the counseling results. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the submission date of the counseling results into a generative AI, which can then provide the information and determine the priority.

[0107] The proposal unit can adjust the order of proposals based on the relevance of the counseling results. For example, the proposal unit may prioritize proposing counseling results that are highly relevant. It can also postpone proposing less relevant counseling results. Furthermore, the proposal unit can determine the order of proposals based on the relevance of the counseling results. This allows the order of proposals to be adjusted based on the relevance of the counseling results. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the relevance of the counseling results into a generative AI, which can then provide the information and adjust the order.

[0108] The simulation unit can estimate the customer's emotions and adjust the simulation's presentation based on the estimated emotions. For example, if the customer is relaxed, the simulation unit can provide a detailed simulation. If the customer is tense, the simulation unit can provide a concise and easy-to-understand simulation. Furthermore, if the customer is in a hurry, the simulation unit can provide a to-the-point simulation. This allows the simulation's presentation to be adjusted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input customer facial expressions and voice data into the generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and adjust the simulation's presentation.

[0109] The simulation unit can provide the optimal simulation by referring to the customer's past treatment history during the simulation. For example, the simulation unit provides relevant simulations based on the customer's past treatment history. The simulation unit can also simulate the optimal treatment method from the customer's past treatment history. Furthermore, the simulation unit can analyze the customer's past treatment history and provide the most effective simulation. This allows the simulation to provide the optimal simulation by referring to the customer's past treatment history. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the customer's past treatment history into a generative AI, which can then provide information and perform the optimal simulation.

[0110] The simulation unit can estimate the customer's emotions and determine the priority of simulations based on the estimated emotions. For example, if the customer is relaxed, the simulation unit may prioritize providing a detailed simulation. It may also prioritize providing a concise simulation if the customer is tense. Furthermore, if the customer is in a hurry, the simulation unit may prioritize providing a to-the-point simulation. This allows the simulation unit to prioritize simulations based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, or not. For example, the simulation unit can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, which then estimates the emotions and determines the priority of simulations.

[0111] The simulation unit can provide an optimal simulation by taking into account the customer's geographical location information during the simulation. For example, the simulation unit can provide a region-specific simulation based on the customer's place of residence. It can also provide a simulation regarding nearby services and facilities based on the customer's current location. Furthermore, the simulation unit can provide a simulation related to the customer's travel destination based on their travel destination. This allows the system to provide an optimal simulation by taking into account the customer's geographical location information. Some or all of the above processing in the simulation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the simulation unit can input the customer's geographical location information into a generative AI, which can then provide the information and perform an optimal simulation.

[0112] The sharing unit can estimate the customer's emotions and adjust the way the information shared is presented based on the estimated emotions. For example, if the customer is relaxed, the sharing unit can share detailed information. If the customer is tense, it can also share concise and easy-to-understand information. Furthermore, if the customer is in a hurry, it can share concise information. This allows the sharing unit to adjust the way the information shared is presented based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not using AI. For example, the sharing unit can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and adjust the way the information shared is presented.

[0113] The sharing unit can share optimal information by referring to the customer's past treatment history during the sharing process. For example, the sharing unit can share relevant information based on the customer's past treatment history. The sharing unit can also share information about the optimal treatment method based on the customer's past treatment history. Furthermore, the sharing unit can analyze the customer's past treatment history and share the most effective information. This allows for the sharing of optimal information by referring to the customer's past treatment history. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing unit can input the customer's past treatment history into a generative AI, which then provides the information and shares the optimal information.

[0114] The sharing unit can estimate the customer's emotions and prioritize the information to share based on the estimated emotions. For example, if the customer is relaxed, the sharing unit may prioritize sharing detailed information. If the customer is tense, the sharing unit may prioritize sharing concise information. Furthermore, if the customer is in a hurry, the sharing unit may prioritize sharing concise information. This allows the sharing unit to prioritize the information to share based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not using AI. For example, the sharing unit can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and determine the priority of the information to share.

[0115] The sharing function can share optimal information by considering the customer's geographical location when sharing. For example, the sharing function can share region-specific information based on the customer's place of residence. It can also share information about nearby services and facilities based on the customer's current location. Furthermore, it can share information related to the customer's travel destination based on their travel destination. This allows for the sharing of optimal information by considering the customer's geographical location. Some or all of the above processing in the sharing function may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing function can input the customer's geographical location information into a generative AI, which then provides information and shares the optimal information.

[0116] The support unit can estimate the customer's emotions and adjust the way it provides support based on those emotions. For example, if the customer is relaxed, the support unit can provide detailed support. If the customer is stressed, the support unit can provide concise and easy-to-understand support. Furthermore, if the customer is in a hurry, the support unit can provide concise support. This allows the support unit to adjust its approach based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI or not. For example, the support unit can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and adjust the way it provides support.

[0117] The support unit can provide optimal support by referring to the customer's past treatment history during support. For example, the support unit can provide relevant support based on the customer's past treatment history. The support unit can also provide support regarding the most suitable treatment method based on the customer's past treatment history. Furthermore, the support unit can analyze the customer's past treatment history to provide the most effective support. This allows the support unit to provide optimal support by referring to the customer's past treatment history. Some or all of the above processes in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the customer's past treatment history into a generative AI, which can then provide information and provide optimal support.

[0118] The support department can estimate the customer's emotions and prioritize support based on those emotions. For example, if the customer is relaxed, the support department may prioritize detailed support. If the customer is stressed, the support department may prioritize concise support. Furthermore, if the customer is in a hurry, the support department may prioritize concise support. This allows the support department to prioritize support based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support department may be performed using AI or not. For example, the support department can input customer facial expressions and voice data into a generative AI to estimate the customer's emotions, and the generative AI can estimate the emotions and determine the support priority.

[0119] The support department can provide optimal support by taking into account the customer's geographical location. For example, the support department can provide region-specific support based on the customer's place of residence. It can also provide support regarding nearby services and facilities based on the customer's current location. Furthermore, the support department can provide support related to the customer's travel destination based on their travel destination. This allows the support department to provide optimal support by taking into account the customer's geographical location. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can input the customer's geographical location information into a generative AI, which can then provide the information and provide optimal support.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The analysis unit can also estimate the customer's emotions and prioritize the analysis based on those emotions. For example, if the customer is relaxed, a detailed analysis can be prioritized. If the customer is tense, a concise analysis can be prioritized. Furthermore, if the customer is in a hurry, a concise analysis can be prioritized. This allows the analysis to be prioritized based on the customer's emotions.

[0122] The service provider can also estimate the customer's emotions and adjust the format of the information provided based on those estimates. For example, if the customer is relaxed, the information can be provided in the form of a detailed report. If the customer is stressed, the information can be provided in the form of concise advice. Furthermore, if the customer is in a hurry, the information can be provided in the form of concise recommendations. This allows the service provider to tailor the format of the information provided based on the customer's emotions.

[0123] The proposal team can also estimate the customer's emotions and adjust the content of the proposal based on those estimates. For example, if the customer is relaxed, a detailed proposal can be made. If the customer is tense, a concise proposal can be made. Furthermore, if the customer is in a hurry, a to-the-point proposal can be made. In this way, the content of the proposal can be adjusted based on the customer's emotions.

[0124] The simulation unit can estimate the customer's emotions and adjust the simulation content based on those emotions. For example, if the customer is relaxed, a detailed simulation can be provided. If the customer is nervous, a concise simulation can be provided. Furthermore, if the customer is in a hurry, a simulation that gets straight to the point can be provided. This allows the simulation content to be adjusted based on the customer's emotions.

[0125] The sharing function can also estimate the customer's emotions and prioritize the information to share based on those emotions. For example, if the customer is relaxed, detailed information can be prioritized. If the customer is stressed, concise information can be prioritized. Furthermore, if the customer is in a hurry, concise information can be prioritized. This allows for the prioritization of information to be shared based on the customer's emotions.

[0126] The information gathering unit can also analyze a client's past counseling history and select the most appropriate method of information gathering. For example, it can automatically generate relevant questions based on information the client has previously provided. It can also analyze a client's past response patterns and determine the optimal order of questions. Furthermore, it can prioritize questions related to specific topics based on the client's past counseling history. This allows for the analysis of a client's past counseling history and the selection of the most effective information gathering method.

[0127] The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on important information, and a concise analysis on less important information. Furthermore, it can determine the priority of the analysis according to the importance of the information. This allows the level of detail of the analysis to be adjusted based on the importance of the information.

[0128] The service provider can also adjust the level of detail provided based on the importance of the analysis results. For example, detailed information can be provided for important analysis results, while concise information can be provided for less important results. Furthermore, the service provider can determine the priority of information provision according to the importance of the analysis results. This allows for adjustment of the level of detail provided based on the importance of the analysis results.

[0129] The suggestion function can also apply different suggestion algorithms depending on the category of the counseling results. For example, an image suggestion algorithm can be applied to counseling results related to hairstyles. A text suggestion algorithm can be applied to counseling results related to lifestyles. Furthermore, an emotion suggestion algorithm can be applied to counseling results related to customer emotions. This allows for the application of different suggestion algorithms depending on the category of the counseling results.

[0130] The simulation unit can also provide optimal simulations by taking into account the customer's geographical location. For example, it can provide region-specific simulations based on the customer's place of residence. It can also provide simulations related to nearby services and facilities based on the customer's current location. Furthermore, it can provide simulations related to travel destinations based on the customer's travel destinations. This allows for the provision of optimal simulations that take into account the customer's geographical location.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The collection unit collects customer information. This information includes, but is not limited to, personal information, purchase history, and preferences. For example, the collection unit collects basic customer information and preferences. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using methods such as data mining, statistical analysis, or machine learning, but is not limited to these examples. For example, the analysis unit analyzes the collected information to understand customer needs. Step 3: The service provider provides counseling results based on the analysis results performed by the analysis provider. The counseling results may be provided in the form of reports, advice, recommendations, etc., but are not limited to these examples. The service provider may, for example, provide the analysis results to the stylist. Step 4: The proposal department makes the best proposal based on the counseling results provided by the service department. The best proposal may be, but is not limited to, proposals based on customer needs or proposals based on past data. The proposal department makes the best proposal based on the counseling results provided, for example.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, proposal unit, simulation unit, sharing unit, and support unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer information using the camera 42 and microphone 38B of the smart device 14 and processes the information with the control unit 46A. The analysis unit analyzes the collected information with the specific processing unit 290 of the data processing unit 12. The provision unit provides the analysis results to the stylist with the specific processing unit 290 of the data processing unit 12. The proposal unit makes optimal suggestions with the control unit 46A of the smart device 14. The simulation unit simulates the treatment using the display 40A of the smart device 14. The sharing unit stores the treatment history in the database 24 of the data processing unit 12 and shares it as needed. The support unit provides support during the treatment using the speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, proposal unit, simulation unit, sharing unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects customer information using the camera 42 and microphone 238 of the smart glasses 214 and processes the information with the control unit 46A. The analysis unit analyzes the collected information with the specific processing unit 290 of the data processing unit 12. The provision unit provides the analysis results to the stylist with the specific processing unit 290 of the data processing unit 12. The proposal unit makes optimal suggestions with the control unit 46A of the smart glasses 214. The simulation unit simulates the procedure using the display of the smart glasses 214. The sharing unit stores the procedure history in the database 24 of the data processing unit 12 and shares it as needed. The support unit provides support during the procedure using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, proposal unit, simulation unit, sharing unit, and support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer information using the camera 42 and microphone 238 of the headset terminal 314 and processes the information with the control unit 46A. The analysis unit analyzes the collected information with the identification processing unit 290 of the data processing unit 12. The provision unit provides the analysis results to the stylist with the identification processing unit 290 of the data processing unit 12. The proposal unit makes optimal suggestions with the control unit 46A of the headset terminal 314. The simulation unit simulates the treatment using the display 343 of the headset terminal 314. The sharing unit stores the treatment history in the database 24 of the data processing unit 12 and shares it as needed. The support unit provides support during the treatment using the speaker 240 of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, proposal unit, simulation unit, sharing unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer information using the camera 42 and microphone 238 of the robot 414 and processes the information with the control unit 46A. The analysis unit analyzes the collected information with the specific processing unit 290 of the data processing unit 12. The provision unit provides the analysis results to the stylist with the specific processing unit 290 of the data processing unit 12. The proposal unit makes optimal suggestions with the control unit 46A of the robot 414. The simulation unit simulates the procedure using the display of the robot 414. The sharing unit stores the procedure history in the database 24 of the data processing unit 12 and shares it as needed. The support unit provides support during the procedure using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) A collection department that collects customer information, An analysis unit analyzes the information collected by the aforementioned collection unit, A provision unit provides counseling results based on the results analyzed by the aforementioned analysis unit, The system comprises a proposal unit that makes optimal suggestions based on the counseling results provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) It is equipped with a simulation unit to alleviate customers' anxieties before treatment. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a shared section for sharing customer treatment history. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a support unit to provide assistance during the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect basic customer information and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze the collected information to understand customer needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We provide the analysis results to the stylist. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, Based on the counseling results provided, we will make the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate customer emotions and adjust the timing of information gathering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We analyze the client's past counseling history and select the most suitable method for gathering information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, filtering is performed based on the customer's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate customer emotions and prioritize the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we prioritize collecting highly relevant information by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, we analyze customers' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate customer emotions and adjust the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates customer emotions and adjusts the length of the analysis based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, We estimate customer emotions and adjust the way we present our offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the data, we will adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the results, different provisioning algorithms will be applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, Estimate customer emotions and adjust the length of the service based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the data, the priority of provision will be determined based on the timing of the submission of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the data, the order of delivery will be adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, We estimate the customer's emotions and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the counseling results. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the counseling results. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Estimate the customer's emotions and adjust the length of the suggestion based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the counseling results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the counseling results. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned simulation unit, We estimate customer emotions and adjust the simulation's representation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned simulation unit, During the simulation, we refer to the customer's past treatment history to provide the optimal simulation. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned simulation unit, The system estimates customer emotions and prioritizes simulations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned simulation unit, During simulation, we provide an optimal simulation that takes into account the customer's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned shared portion is, We estimate customer emotions and adjust how we present shared information based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned shared portion is, When sharing information, we refer to the customer's past treatment history to share the most relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned shared portion is, We estimate customer emotions and prioritize the information to share based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned shared portion is, When sharing information, we share the most relevant information by considering the customer's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned support unit is We estimate customer emotions and adjust the way we express support based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned support unit is When providing support, we refer to the customer's past treatment history to provide the most suitable support. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned support unit is We estimate customer emotions and prioritize support based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned support unit is When providing support, we take the customer's geographical location into consideration to provide the most suitable support. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects customer information, An analysis unit analyzes the information collected by the aforementioned collection unit, A provision unit provides counseling results based on the results analyzed by the aforementioned analysis unit, The system comprises a proposal unit that makes optimal suggestions based on the counseling results provided by the aforementioned provision unit. A system characterized by the following features.

2. It is equipped with a simulation unit to alleviate customers' anxieties before treatment. The system according to feature 1.

3. It has a shared section for sharing customer treatment history. The system according to feature 1.

4. It is equipped with a support unit to provide assistance during the procedure. The system according to feature 1.

5. The aforementioned collection unit is Collect basic customer information and preferences. The system according to feature 1.

6. The aforementioned analysis unit, Analyze the collected information to understand customer needs. The system according to feature 1.

7. The aforementioned supply unit is, We provide the analysis results to the stylist. The system according to feature 1.

8. The aforementioned proposal section is, Based on the counseling results provided, we will make the most suitable suggestions. The system according to feature 1.

9. The aforementioned collection unit is We estimate customer emotions and adjust the timing of information gathering based on those estimated emotions. The system according to feature 1.

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