System

Abstract

An object of a system according to an embodiment is to propose an optimum service on the basis of a preference or a past use history of a customer and to improve convenience of the customer.SOLUTION: A system includes a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects a preference or a past use history of a customer. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimum service based on the data analyzed by the analysis unit. The support unit supports reservation and purchase of the service proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose optimal services based on customer preferences and past usage history, and there is room for improvement.

[0005] The system according to the embodiment aims to improve customer convenience by proposing optimal services based on the customer's preferences and past usage history. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects customer preferences or past usage history. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes optimal services based on the data analyzed by the analysis unit. The support unit supports reservations and purchases of services proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal services based on the customer's preferences and past usage history, thereby improving customer convenience. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The service proposal system according to the embodiment of the present invention is a system that analyzes customer preferences and past usage history, proposes optimal services, and supports reservations and purchases. As a result, the service proposal system can improve customer convenience.

[0029] A service proposal system according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects customer preferences or past usage history. For example, the data collection unit collects customer past purchase history and survey results. The data collection unit can also collect customer online behavior as log data. The data collection unit can also collect customer behavior data using sensors. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes customer preferences using statistical analysis. The analysis unit can also analyze data using machine learning algorithms. The analysis unit can also analyze customer needs using data mining technology. The proposal unit proposes optimal services based on the data analyzed by the analysis unit. For example, the proposal unit proposes new restaurants based on the customer's past usage history. The proposal unit can also propose events or products that the customer might be interested in. The proposal unit can also propose customized services based on the customer's preferences. The support unit supports reservations and purchases of services proposed by the proposal unit. For example, the support unit makes reservations for the proposed restaurants on the customer's behalf. The support unit can also support the customer in making a purchase of the proposed product. The support unit can also support the customer in making a reservation through an online reservation system. This allows the service proposal system according to the embodiment to improve customer convenience. For example, the customer can easily find a service that suits them and use the service without any hassle. Furthermore, the customer's satisfaction level increases when services that match their preferences are proposed.

[0030] The data collection unit analyzes the customer's social media activity and can grasp changes in preferences or interests in real time. For example, the data collection unit analyzes the customer's social media posts to grasp changes in interests and preferences in real time. For example, if a customer posts about a new hobby, related services can be suggested based on that information. The data collection unit also analyzes the customer's following and like history on social media to grasp changes in preferences. For example, if a customer starts following a particular brand or account, related products can be suggested based on that information. The data collection unit also analyzes the customer's social media activity over time to grasp changes in preferences. For example, it compares past posts with current posts to identify changes in preferences. In this way, by analyzing the customer's social media activity, it is possible to grasp changes in preferences and interests in real time.

[0031] The data collection unit can use the customer's location information to analyze the places visited or movement patterns to understand the customer's preferences or behavioral patterns. For example, the data collection unit collects the customer's location information and analyzes the places visited and movement patterns to understand the customer's preferences. For example, based on data on restaurants and shopping malls frequently visited by the customer, related services are suggested. The data collection unit also analyzes the customer's behavioral patterns based on the location information to understand changes in preferences. For example, when a customer visits a new place, related services are suggested based on that information. The data collection unit also analyzes the customer's location information over time to understand changes in behavioral patterns. For example, past movement history is compared with current movement history to identify changes in preferences. In this way, by using the customer's location information, the places visited and movement patterns can be analyzed to understand the customer's preferences and behavioral patterns.

[0032] The suggestion unit can predict future preferences and make proactive suggestions based on the customer's past service usage history. The suggestion unit, for example, analyzes the customer's past service usage history and develops an algorithm to predict future preferences. For example, it suggests new restaurants based on data on restaurants the customer has used in the past. The suggestion unit also predicts events and products that the customer might be interested in based on the past usage history and makes proactive suggestions. For example, it suggests related events based on data on events the customer has previously attended. The suggestion unit also analyzes the customer's usage history in chronological order to predict future preferences. For example, it compares past usage history with current usage history to identify changes in preferences. This improves customer convenience by predicting future preferences based on the customer's past service usage history and making proactive suggestions.

[0033] The suggestion unit can analyze the preference data of the customer's friends or family and suggest services that are optimal for group use. For example, the suggestion unit collects preference data of the customer's friends and family and suggests services that are optimal for group use. For example, it suggests restaurants and events that the whole family can enjoy. The suggestion unit also develops an algorithm that predicts the optimal services for group use based on the preference data of friends and family. For example, it suggests products that all friends are likely to be interested in. The suggestion unit also analyzes the group preference data over time to suggest the optimal services. For example, it compares past group usage history with current preference data to identify changes in preferences. In this way, it is possible to suggest the optimal services for group use by analyzing the preference data of the customer's friends and family.

[0034] The suggestion unit can suggest services from different cultural spheres or regions based on the customer's preference data, providing options from an international perspective. The suggestion unit, for example, develops an algorithm that suggests services from different cultural spheres or regions based on the customer's preference data. For example, it suggests overseas restaurants or events that the customer might be interested in. When suggesting services from different cultural spheres or regions, the suggestion unit also analyzes the customer's preference data and provides optimal options. For example, it suggests related overseas services based on data on services the customer has used in the past. The suggestion unit also analyzes the customer's preference data over time to suggest services from different cultural spheres or regions. For example, it compares past usage history with current preference data to identify changes in preferences. This makes it possible to suggest services from different cultural spheres or regions based on the customer's preference data, providing options from an international perspective.

[0035] The suggestion unit can analyze the customer's health data and suggest services according to their health condition. For example, the suggestion unit collects the customer's health data and develops an algorithm that suggests services according to their health condition. For example, it suggests relaxation services based on the customer's heart rate and sleep data. The suggestion unit also predicts services according to the customer's health condition based on the health data. For example, it suggests fitness services if the customer is feeling stressed. The suggestion unit also analyzes the customer's health data over time to suggest services according to their health condition. For example, it compares past health data with current health data to identify changes in their health condition. In this way, it is possible to suggest services according to their health condition by analyzing the customer's health data.

[0036] The support department can analyze a customer's calendar information and automatically suggest the optimal reservation time. For example, the support department collects a customer's calendar information and analyzes available times to suggest the optimal reservation time. For example, it suggests the optimal restaurant reservation time taking into consideration the customer's work and personal plans. The support department also automatically suggests reservation times that fit the customer's schedule based on the calendar information. For example, it suggests service at a time when the customer can relax, avoiding busy times. The support department also analyzes a customer's calendar information in real time and dynamically suggests the optimal reservation time. For example, it immediately suggests a new reservation time if plans change. In this way, the support department can automatically suggest the optimal reservation time by analyzing a customer's calendar information.

[0037] The support department can analyze a customer's payment history and suggest the optimal payment method or discount information. For example, the support department analyzes a customer's payment history and suggests the optimal payment method. For example, it suggests the most convenient payment method based on payment methods the customer has used in the past. The support department also suggests the optimal discount information for the customer based on the payment history. For example, it suggests new discount information based on coupons and discount information the customer has used in the past. The support department also analyzes a customer's payment history in chronological order and suggests the optimal payment method or discount information. For example, it compares past payment history with current payment history and makes the optimal suggestion. In this way, it is possible to suggest the optimal payment method or discount information by analyzing a customer's payment history.

[0038] The support department can propose special offers or services for repeat customers based on the customer's past reservation history. For example, the support department analyzes the customer's past reservation history and proposes special offers for repeat customers. For example, a discount is offered the next time the customer visits based on data on restaurants the customer has visited in the past. The support department also proposes services for repeat customers based on the past reservation history. For example, a special offer is offered the next time the customer visits based on data on services the customer has used in the past. The support department also analyzes the customer's reservation history in chronological order and proposes special offers and services for repeat customers. For example, the support department compares past reservation history with current reservation history and makes the most appropriate proposal. This makes it possible to propose special offers and services for repeat customers based on the customer's past reservation history.

[0039] The support unit can use the customer's location information to suggest nearby stores or services in real time and support instant reservations. The support unit, for example, collects the customer's location information and suggests nearby stores and services in real time. For example, it suggests restaurants or cafes close to the customer's current location. The support unit also supports the customer in making instant reservations for nearby stores and services based on the location information. For example, the customer can check the availability of stores close to their current location in real time and make an instant reservation. The support unit also analyzes the customer's location information in real time and dynamically suggests nearby stores and services. For example, it suggests nearby stores while the customer is on the move and supports instant reservations. In this way, by using the customer's location information, it is possible to suggest nearby stores and services in real time and support instant reservations.

[0040] The analysis unit can analyze customer feedback, automatically extract negative elements, and generate improvement proposals. The analysis unit, for example, develops an algorithm that analyzes customer feedback and automatically extracts negative elements. For example, it analyzes customer reviews and evaluation comments and identifies problems. The analysis unit also builds a system that automatically generates improvement proposals based on the negative elements. For example, it proposes improvements to services that customers are dissatisfied with. The analysis unit also analyzes customer feedback in chronological order and continuously extracts negative elements. For example, it compares past feedback with current feedback and identifies areas for improvement. In this way, the quality of service is improved by analyzing customer feedback, automatically extracting negative elements, and generating improvement proposals.

[0041] The analysis unit analyzes customer feedback in chronological order, and is able to continuously identify areas for improvement in the service. The analysis unit, for example, collects customer feedback in chronological order, and builds a system that continuously identifies areas for improvement in the service. For example, it periodically analyzes customer reviews and evaluation comments. The analysis unit also analyzes the feedback in chronological order, and identifies areas for improvement in the service. For example, it compares past feedback with current feedback, and extracts areas for improvement. The analysis unit also continuously collects customer feedback, and is able to continuously identify areas for improvement in the service. For example, it analyzes each time new feedback is added, and identifies areas for improvement. In this way, by analyzing customer feedback in chronological order, it is possible to continuously identify areas for improvement in the service.

[0042] The analysis unit can automatically translate customer feedback into different languages ​​and identify areas for improvement from an international perspective. The analysis unit, for example, builds a system that automatically translates customer feedback into different languages ​​and identifies areas for improvement from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The analysis unit also identifies areas for improvement from an international perspective based on the translated feedback. For example, it analyzes the opinions of customers from different cultural backgrounds and extracts areas for improvement in services. The analysis unit also integrates customer feedback into a multilingual system and identifies areas for improvement from an international perspective in real time. For example, it translates each time new feedback is added and identifies areas for improvement. In this way, it is possible to identify areas for improvement from an international perspective by automatically translating customer feedback into different languages.

[0043] The analysis unit can convert customer feedback into visual notes or mind maps to make it easier to understand visually. The analysis unit, for example, converts customer feedback into visual notes and builds a system to visually display them. For example, it could indicate important points with diagrams or icons. The analysis unit could also convert the feedback into a mind map format to visually organize related keywords and concepts. This allows the overall picture of the feedback to be understood at a glance. The analysis unit could also develop tools to automatically generate visual notes and mind maps, allowing users to easily display feedback visually. For example, it could provide a function to visualize feedback with drag and drop. This allows customer feedback to be converted into visual notes or mind maps to make it easier to understand visually.

[0044] The analysis unit tracks customer preference data over the long term and analyzes changes in preferences to predict future needs. The analysis unit, for example, builds a system that collects customer preference data over the long term and analyzes changes in preferences. For example, it identifies changes in preferences based on data on services a customer has used in the past and products they have purchased. The analysis unit also analyzes preference data over time to develop an algorithm that predicts future needs. For example, it compares past preference data with current preference data to predict future needs. The analysis unit also predicts future needs based on customer preference data and proactively suggests services. For example, if a customer starts a new hobby, it suggests services related to that hobby. In this way, it is possible to predict future needs by tracking customer preference data over the long term and analyzing changes in preferences.

[0045] The analysis unit can detect a customer's new hobbies or interests in real time and instantly suggest corresponding services. The analysis unit, for example, builds a system that detects a customer's new hobbies and interests in real time. For example, it analyzes a customer's social media posts and search history to identify new hobbies. The analysis unit also develops an algorithm that instantly suggests services based on new hobbies and interests. For example, if a customer posts about a new hobby, it suggests services related to that hobby. The analysis unit also analyzes a customer's new hobbies and interests in real time and dynamically adjusts the content of service suggestions. For example, if a customer starts a new hobby, it instantly suggests services related to that hobby. This makes it possible to detect a customer's new hobbies and interests in real time and instantly suggest corresponding services.

[0046] The analysis unit can propose services in different industries or fields based on customer preference data, thereby drawing out new interest. The analysis unit, for example, builds a system that proposes services in different industries or fields based on customer preference data. For example, it proposes services in new fields that the customer may be interested in. When proposing services in different industries or fields, the analysis unit also analyzes the customer preference data to provide optimal options. For example, it proposes services in new fields based on data on services the customer has used in the past. The analysis unit also analyzes the customer preference data in chronological order to propose services in different industries or fields. For example, it compares past usage history with current preference data to draw out new interest. In this way, it is possible to propose services in different industries or fields based on the customer preference data, thereby drawing out new interest.

[0047] The analysis unit can compare the customer's preference data with other customers, form groups with common interests, and propose services for the group. The analysis unit, for example, builds a system that compares the customer's preference data with other customers and forms groups with common interests. For example, it groups customers with the same hobbies and proposes services for the group. The analysis unit also develops an algorithm that proposes services for the group based on groups with common interests. For example, it proposes events where customers with the same hobbies gather. The analysis unit also analyzes the customer's preference data over time to form groups with common interests and propose services for the group. For example, it compares past preference data with current preference data to form groups. In this way, by comparing the customer's preference data with other customers, it is possible to form groups with common interests and propose services for the group.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The data collection unit can collect not only a customer's purchase history, but also data on the web pages the customer viewed and the ads the customer clicked on. For example, if a customer frequently visits a particular brand's website, services related to that brand can be suggested. The data collection unit can also collect content and comments the customer shared on social media to understand their preferences in more detail. For example, if a customer comments on a particular event, services related to that event can be suggested. Furthermore, the data collection unit can collect data on online forums and communities the customer participated in and analyze their preferences. This allows for a complete understanding of the customer's online behavior, enabling more accurate service suggestions.

[0050] The data collection unit can analyze not only a customer's social media activity but also their activity on other online platforms. For example, it can analyze the products and reviews a customer views on an online shopping site to understand changes in their preferences. The data collection unit can also analyze posts on online communities and forums in which the customer participates to understand changes in their interests and preferences in real time. Furthermore, the data collection unit can analyze the viewing history of the streaming services the customer uses to identify changes in their preferences. This allows for an analysis of the customer's overall online activity and enables more accurate service recommendations.

[0051] When using customer location information, the data collection unit can collect more detailed location data. For example, it can analyze the length of time a customer stays at a place they visit and the frequency of their visit, allowing for a more detailed understanding of their preferences. The data collection unit can also analyze a customer's movement patterns and identify the places and routes they visit during specific time periods. Furthermore, the data collection unit can collect environmental data (for example, weather and congestion) from the places they visit and analyze changes in their preferences. This allows for a multifaceted analysis of customer location information, enabling more accurate service proposals.

[0052] The suggestion unit can analyze more detailed data when predicting future preferences based on the customer's past service usage history. For example, it can analyze the frequency and duration of services used by the customer in the past to predict future preferences in more detail. The suggestion unit can also predict preferences related to specific seasons or events based on the customer's past usage history. Furthermore, the suggestion unit can predict future needs by analyzing the customer's usage history over time and identifying changes in preferences. This makes it possible to more accurately predict future preferences based on the customer's past service usage history and make proactive suggestions.

[0053] The suggestion unit can collect more detailed data when analyzing the preference data of the customer's friends and family. For example, it can collect the past service usage history and online behavior data of friends and family to understand the preferences of the entire group. The suggestion unit can also develop an algorithm that predicts services that the entire group will enjoy based on the preference data of friends and family. Furthermore, the suggestion unit can analyze the group's preference data over time and identify changes in preferences to suggest optimal services. This makes it possible to analyze the preference data of the customer's friends and family in detail and suggest optimal services that the entire group can enjoy.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection unit collects customer preferences or past usage history. For example, it collects the customer's past purchase history or survey results. The data collection unit can also collect the customer's online behavior as log data. Furthermore, the data collection unit can also collect customer behavior data using sensors. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes customer preferences using statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also analyze customer needs using data mining techniques. Step 3: The suggestion unit suggests optimal services based on the data analyzed by the analysis unit. For example, the suggestion unit may suggest new restaurants based on the customer's past usage history. The suggestion unit may also suggest events or products that the customer may be interested in. The suggestion unit may also suggest customized services based on the customer's preferences. Step 4: The support department supports the reservation or purchase of the service proposed by the proposal department. For example, the support department makes a reservation at a proposed restaurant on behalf of the customer. The support department can also support the purchase procedure of a proposed product. The support department can also support the customer in making a reservation through an online reservation system.

[0056] (Example 2) The service proposal system according to the embodiment of the present invention is a system that analyzes customer preferences and past usage history, proposes optimal services, and supports reservations and purchases. As a result, the service proposal system can improve customer convenience.

[0057] A service proposal system according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects customer preferences or past usage history. For example, the data collection unit collects customer past purchase history and survey results. The data collection unit can also collect customer online behavior as log data. The data collection unit can also collect customer behavior data using sensors. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes customer preferences using statistical analysis. The analysis unit can also analyze data using machine learning algorithms. The analysis unit can also analyze customer needs using data mining technology. The proposal unit proposes optimal services based on the data analyzed by the analysis unit. For example, the proposal unit proposes new restaurants based on the customer's past usage history. The proposal unit can also propose events or products that the customer might be interested in. The proposal unit can also propose customized services based on the customer's preferences. The support unit supports reservations and purchases of services proposed by the proposal unit. For example, the support unit makes reservations for the proposed restaurants on the customer's behalf. The support unit can also support the customer in making a purchase of the proposed product. The support unit can also support the customer in making a reservation through an online reservation system. This allows the service proposal system according to the embodiment to improve customer convenience. For example, the customer can easily find a service that suits them and use the service without any hassle. Furthermore, the customer's satisfaction level increases when services that match their preferences are proposed.

[0058] The data collection unit can collect biometric data of customers and analyze their emotional state. For example, the data collection unit monitors the customer's heart rate and electrodermal activity in real time and analyzes their emotional state based on that data. For example, it analyzes fluctuations in heart rate when the customer uses a specific service and collects data to elicit positive emotions. The data collection unit also collects biometric data of customers over the long term and analyzes changes in their emotional state. For example, it compares changes in heart rate and electrodermal activity before and after the customer uses a specific service to understand changes in preferences. The data collection unit also analyzes the customer's emotional state in real time based on the biometric data and reflects this in service suggestions. For example, if the customer is feeling stressed, it suggests a relaxation service. In this way, analyzing the customer's emotional state based on the customer's biometric data enables more accurate preference analysis.

[0059] The data collection unit analyzes the customer's social media activity and can grasp changes in preferences or interests in real time. For example, the data collection unit analyzes the customer's social media posts to grasp changes in interests and preferences in real time. For example, if a customer posts about a new hobby, related services can be suggested based on that information. The data collection unit also analyzes the customer's following and like history on social media to grasp changes in preferences. For example, if a customer starts following a particular brand or account, related products can be suggested based on that information. The data collection unit also analyzes the customer's social media activity over time to grasp changes in preferences. For example, it compares past posts with current posts to identify changes in preferences. In this way, by analyzing the customer's social media activity, it is possible to grasp changes in preferences and interests in real time.

[0060] The data collection unit can use the emotion estimation function to analyze emotions felt by customers when they use a service and collect data to elicit positive emotions. The data collection unit, for example, analyzes facial expressions and voices of customers when they use a service to estimate their emotional state. For example, it analyzes facial expressions of customers while they are eating at a restaurant to evaluate their satisfaction. The data collection unit also uses the emotion estimation function to compare the emotional state of customers before and after they use a service and collect data to elicit positive emotions. For example, it analyzes emotional changes before and after a customer participates in an event. The data collection unit also monitors the emotional state of customers in real time and reflects this in service suggestions. For example, it suggests relaxation services if a customer is feeling stressed. In this way, the emotion estimation function makes it possible to analyze emotions felt by customers when they use a service and collect data to elicit positive emotions.

[0061] The data collection unit can analyze the customer's voice data and estimate the emotion or stress level from the tone of voice or speaking style. The data collection unit, for example, collects the customer's voice data and estimates the emotional state by analyzing the tone of voice and speaking style. For example, the data collection unit analyzes the voice when the customer calls customer support and evaluates the stress level. The data collection unit also analyzes the customer's emotional state in real time based on the voice data and reflects this in service suggestions. For example, if the customer is relaxed, it suggests an entertainment service. The data collection unit also collects the customer's voice data over the long term and analyzes changes in the emotional state. For example, it compares changes in the tone of voice before and after the customer uses a specific service to understand changes in preferences. In this way, by analyzing the customer's voice data, it is possible to estimate the emotion or stress level from the tone of voice and speaking style.

[0062] The data collection unit can use the customer's location information to analyze the places visited or movement patterns to understand the customer's preferences or behavioral patterns. For example, the data collection unit collects the customer's location information and analyzes the places visited and movement patterns to understand the customer's preferences. For example, based on data on restaurants and shopping malls frequently visited by the customer, related services are suggested. The data collection unit also analyzes the customer's behavioral patterns based on the location information to understand changes in preferences. For example, when a customer visits a new place, related services are suggested based on that information. The data collection unit also analyzes the customer's location information over time to understand changes in behavioral patterns. For example, past movement history is compared with current movement history to identify changes in preferences. In this way, by using the customer's location information, the places visited and movement patterns can be analyzed to understand the customer's preferences and behavioral patterns.

[0063] The data collection unit can use the emotion estimation function to monitor the emotions of customers when they use a specific service in real time and reflect the results in data collection. For example, the data collection unit monitors the emotional state of customers by analyzing their facial expressions and voices in real time when they use a specific service. For example, the data collection unit analyzes the facial expressions of customers while they are watching a movie and evaluates their satisfaction level. The data collection unit also uses the emotion estimation function to compare the emotional state of customers before and after they use a service in real time and reflect the results in data collection. For example, the data collection unit analyzes the emotional changes before and after a customer participates in a sporting event. The data collection unit also monitors the emotional state of customers in real time and reflects the results in service suggestions. For example, if a customer is feeling stressed, the data collection unit suggests a relaxation service. In this way, by using the emotion estimation function, the emotions of customers when they use a specific service can be monitored in real time and reflected in data collection.

[0064] The suggestion unit can predict future preferences and make proactive suggestions based on the customer's past service usage history. The suggestion unit, for example, analyzes the customer's past service usage history and develops an algorithm to predict future preferences. For example, it suggests new restaurants based on data on restaurants the customer has used in the past. The suggestion unit also predicts events and products that the customer might be interested in based on the past usage history and makes proactive suggestions. For example, it suggests related events based on data on events the customer has previously attended. The suggestion unit also analyzes the customer's usage history in chronological order to predict future preferences. For example, it compares past usage history with current usage history to identify changes in preferences. This improves customer convenience by predicting future preferences based on the customer's past service usage history and making proactive suggestions.

[0065] The suggestion unit can analyze the preference data of the customer's friends or family and suggest services that are optimal for group use. For example, the suggestion unit collects preference data of the customer's friends and family and suggests services that are optimal for group use. For example, it suggests restaurants and events that the whole family can enjoy. The suggestion unit also develops an algorithm that predicts the optimal services for group use based on the preference data of friends and family. For example, it suggests products that all friends are likely to be interested in. The suggestion unit also analyzes the group preference data over time to suggest the optimal services. For example, it compares past group usage history with current preference data to identify changes in preferences. In this way, it is possible to suggest the optimal services for group use by analyzing the preference data of the customer's friends and family.

[0066] The suggestion unit uses the emotion estimation function to predict how a customer will feel about a proposed service and can make a proposal that will elicit the most positive response. The suggestion unit, for example, uses the emotion estimation function to develop an algorithm that predicts how a customer will feel about a proposed service. For example, the suggestion unit predicts an emotional response based on data on services the customer has used in the past. The suggestion unit also makes a proposal that will elicit the most positive response based on the customer's emotional response data. For example, the suggestion unit prioritizes proposing services that the customer has felt positive about in the past. The suggestion unit also uses the emotion estimation function to predict in real time how a customer will feel about a proposed service and dynamically adjusts the content of the proposal. For example, the suggestion unit modifies the proposal based on real-time feedback from the customer. In this way, the emotion estimation function can be used to predict how a customer will feel about a proposed service and can make a proposal that will elicit the most positive response.

[0067] The suggestion unit can suggest services from different cultural spheres or regions based on the customer's preference data, providing options from an international perspective. The suggestion unit, for example, develops an algorithm that suggests services from different cultural spheres or regions based on the customer's preference data. For example, it suggests overseas restaurants or events that the customer might be interested in. When suggesting services from different cultural spheres or regions, the suggestion unit also analyzes the customer's preference data and provides optimal options. For example, it suggests related overseas services based on data on services the customer has used in the past. The suggestion unit also analyzes the customer's preference data over time to suggest services from different cultural spheres or regions. For example, it compares past usage history with current preference data to identify changes in preferences. This makes it possible to suggest services from different cultural spheres or regions based on the customer's preference data, providing options from an international perspective.

[0068] The suggestion unit can analyze the customer's health data and suggest services according to their health condition. For example, the suggestion unit collects the customer's health data and develops an algorithm that suggests services according to their health condition. For example, it suggests relaxation services based on the customer's heart rate and sleep data. The suggestion unit also predicts services according to the customer's health condition based on the health data. For example, it suggests fitness services if the customer is feeling stressed. The suggestion unit also analyzes the customer's health data over time to suggest services according to their health condition. For example, it compares past health data with current health data to identify changes in their health condition. In this way, it is possible to suggest services according to their health condition by analyzing the customer's health data.

[0069] The suggestion unit can use the emotion estimation function to monitor the emotions of customers when they use a specific service in real time and dynamically adjust the content of suggestions. The suggestion unit, for example, uses the emotion estimation function to develop a system that monitors the emotions of customers when they use a specific service in real time. For example, the suggestion unit analyzes the customer's facial expressions and voice to evaluate their emotional state. The suggestion unit also monitors the customer's emotional state in real time and dynamically adjusts the content of suggestions. For example, if the customer is feeling stressed, the suggestion unit can suggest relaxation services. The suggestion unit also uses the emotion estimation function to analyze the emotions of customers when they use a specific service in real time and dynamically adjust the content of suggestions. For example, the suggestion unit modifies the suggestions based on real-time feedback from the customer. In this way, the emotion estimation function can be used to monitor the emotions of customers when they use a specific service in real time and dynamically adjust the content of suggestions.

[0070] The support department can analyze a customer's calendar information and automatically suggest the optimal reservation time. For example, the support department collects a customer's calendar information and analyzes available times to suggest the optimal reservation time. For example, it suggests the optimal restaurant reservation time taking into consideration the customer's work and personal plans. The support department also automatically suggests reservation times that fit the customer's schedule based on the calendar information. For example, it suggests service at a time when the customer can relax, avoiding busy times. The support department also analyzes a customer's calendar information in real time and dynamically suggests the optimal reservation time. For example, it immediately suggests a new reservation time if plans change. In this way, the support department can automatically suggest the optimal reservation time by analyzing a customer's calendar information.

[0071] The support department can analyze a customer's payment history and suggest the optimal payment method or discount information. For example, the support department analyzes a customer's payment history and suggests the optimal payment method. For example, it suggests the most convenient payment method based on payment methods the customer has used in the past. The support department also suggests the optimal discount information for the customer based on the payment history. For example, it suggests new discount information based on coupons and discount information the customer has used in the past. The support department also analyzes a customer's payment history in chronological order and suggests the optimal payment method or discount information. For example, it compares past payment history with current payment history and makes the optimal suggestion. In this way, it is possible to suggest the optimal payment method or discount information by analyzing a customer's payment history.

[0072] The support unit can use the emotion estimation function to provide support to reduce the stress level of customers when making reservations or purchasing procedures. For example, the support unit uses the emotion estimation function to analyze the stress level of customers when making reservations or purchasing procedures in real time. For example, the support unit analyzes the customer's facial expressions and voice to evaluate the stress level. The support unit also provides support to reduce the customer's stress level. For example, if the reservation procedure is complicated, the support unit suggests a simpler procedure. The support unit also uses the emotion estimation function to monitor the stress level of customers when making reservations or purchasing procedures in real time and dynamically adjust the support content. For example, the support is modified based on real-time feedback from the customer. In this way, the emotion estimation function can be used to provide support to reduce the stress level of customers when making reservations or purchasing procedures.

[0073] The support department can propose special offers or services for repeat customers based on the customer's past reservation history. For example, the support department analyzes the customer's past reservation history and proposes special offers for repeat customers. For example, a discount is offered the next time the customer visits based on data on restaurants the customer has visited in the past. The support department also proposes services for repeat customers based on the past reservation history. For example, a special offer is offered the next time the customer visits based on data on services the customer has used in the past. The support department also analyzes the customer's reservation history in chronological order and proposes special offers and services for repeat customers. For example, the support department compares past reservation history with current reservation history and makes the most appropriate proposal. This makes it possible to propose special offers and services for repeat customers based on the customer's past reservation history.

[0074] The support unit can use the customer's location information to suggest nearby stores or services in real time and support instant reservations. The support unit, for example, collects the customer's location information and suggests nearby stores and services in real time. For example, it suggests restaurants or cafes close to the customer's current location. The support unit also supports the customer in making instant reservations for nearby stores and services based on the location information. For example, the customer can check the availability of stores close to their current location in real time and make an instant reservation. The support unit also analyzes the customer's location information in real time and dynamically suggests nearby stores and services. For example, it suggests nearby stores while the customer is on the move and supports instant reservations. In this way, by using the customer's location information, it is possible to suggest nearby stores and services in real time and support instant reservations.

[0075] The support department can use the emotion estimation function to monitor the emotions of customers in real time when making reservations or purchasing procedures, and provide optimal support. For example, the support department develops a system that uses the emotion estimation function to monitor the emotions of customers in real time when making reservations or purchasing procedures. For example, the system analyzes the customer's facial expressions and voice to evaluate their emotional state. The support department also monitors the customer's emotional state in real time and provides optimal support. For example, if the customer is feeling stressed, the system suggests a simple procedure. The support department also uses the emotion estimation function to analyze the emotions of customers in real time when making reservations or purchasing procedures, and dynamically adjusts the support content. For example, the system modifies the support based on real-time feedback from the customer. In this way, the emotion estimation function can be used to monitor the emotions of customers in real time when making reservations or purchasing procedures, and provide optimal support.

[0076] The analysis unit can analyze customer feedback, automatically extract negative elements, and generate improvement proposals. The analysis unit, for example, develops an algorithm that analyzes customer feedback and automatically extracts negative elements. For example, it analyzes customer reviews and evaluation comments and identifies problems. The analysis unit also builds a system that automatically generates improvement proposals based on the negative elements. For example, it proposes improvements to services that customers are dissatisfied with. The analysis unit also analyzes customer feedback in chronological order and continuously extracts negative elements. For example, it compares past feedback with current feedback and identifies areas for improvement. In this way, the quality of service is improved by analyzing customer feedback, automatically extracting negative elements, and generating improvement proposals.

[0077] The analysis unit analyzes customer feedback in chronological order, and is able to continuously identify areas for improvement in the service. The analysis unit, for example, collects customer feedback in chronological order, and builds a system that continuously identifies areas for improvement in the service. For example, it periodically analyzes customer reviews and evaluation comments. The analysis unit also analyzes the feedback in chronological order, and identifies areas for improvement in the service. For example, it compares past feedback with current feedback, and extracts areas for improvement. The analysis unit also continuously collects customer feedback, and is able to continuously identify areas for improvement in the service. For example, it analyzes each time new feedback is added, and identifies areas for improvement. In this way, by analyzing customer feedback in chronological order, it is possible to continuously identify areas for improvement in the service.

[0078] The analysis unit can use the emotion estimation function to analyze the emotional aspects of the feedback and make improvement suggestions to elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to develop a system that analyzes the emotional aspects of customer feedback. For example, the analysis unit analyzes customer reviews and evaluation comments and evaluates the emotional state. The analysis unit also makes improvement suggestions to elicit positive emotions based on the emotional aspects of the feedback. For example, the analysis unit suggests service improvements to increase customer satisfaction. The analysis unit also uses the emotion estimation function to analyze the emotional aspects of the feedback in real time and dynamically adjust the improvement suggestions. For example, the analysis unit modifies the improvement points based on real-time customer feedback. In this way, the emotion estimation function can be used to analyze the emotional aspects of the feedback and make improvement suggestions to elicit positive emotions.

[0079] The analysis unit can automatically translate customer feedback into different languages ​​and identify areas for improvement from an international perspective. The analysis unit, for example, builds a system that automatically translates customer feedback into different languages ​​and identifies areas for improvement from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The analysis unit also identifies areas for improvement from an international perspective based on the translated feedback. For example, it analyzes the opinions of customers from different cultural backgrounds and extracts areas for improvement in services. The analysis unit also integrates customer feedback into a multilingual system and identifies areas for improvement from an international perspective in real time. For example, it translates each time new feedback is added and identifies areas for improvement. In this way, it is possible to identify areas for improvement from an international perspective by automatically translating customer feedback into different languages.

[0080] The analysis unit can convert customer feedback into visual notes or mind maps to make it easier to understand visually. The analysis unit, for example, converts customer feedback into visual notes and builds a system to visually display them. For example, it could indicate important points with diagrams or icons. The analysis unit could also convert the feedback into a mind map format to visually organize related keywords and concepts. This allows the overall picture of the feedback to be understood at a glance. The analysis unit could also develop tools to automatically generate visual notes and mind maps, allowing users to easily display feedback visually. For example, it could provide a function to visualize feedback with drag and drop. This allows customer feedback to be converted into visual notes or mind maps to make it easier to understand visually.

[0081] The analysis unit can use the emotion estimation function to collect the customer's emotional response to feedback and reflect it in the next proposal. The analysis unit, for example, uses the emotion estimation function to develop a system that collects the customer's emotional response to feedback in real time. For example, the analysis unit analyzes the customer's facial expressions and voice to evaluate their emotional state. The analysis unit also reflects this in the next proposal based on the customer's emotional response data. For example, the analysis unit prioritizes proposing services that the customer feels positive about. The analysis unit also uses the emotion estimation function to analyze the customer's emotional response to feedback in real time and dynamically adjusts the content of the next proposal. For example, the proposal is revised based on the customer's real-time feedback. In this way, the emotion estimation function can be used to collect the customer's emotional response to feedback and reflect it in the next proposal.

[0082] The analysis unit tracks customer preference data over the long term and analyzes changes in preferences to predict future needs. The analysis unit, for example, builds a system that collects customer preference data over the long term and analyzes changes in preferences. For example, it identifies changes in preferences based on data on services a customer has used in the past and products they have purchased. The analysis unit also analyzes preference data over time to develop an algorithm that predicts future needs. For example, it compares past preference data with current preference data to predict future needs. The analysis unit also predicts future needs based on customer preference data and proactively suggests services. For example, if a customer starts a new hobby, it suggests services related to that hobby. In this way, it is possible to predict future needs by tracking customer preference data over the long term and analyzing changes in preferences.

[0083] The analysis unit can detect a customer's new hobbies or interests in real time and instantly suggest corresponding services. The analysis unit, for example, builds a system that detects a customer's new hobbies and interests in real time. For example, it analyzes a customer's social media posts and search history to identify new hobbies. The analysis unit also develops an algorithm that instantly suggests services based on new hobbies and interests. For example, if a customer posts about a new hobby, it suggests services related to that hobby. The analysis unit also analyzes a customer's new hobbies and interests in real time and dynamically adjusts the content of service suggestions. For example, if a customer starts a new hobby, it instantly suggests services related to that hobby. This makes it possible to detect a customer's new hobbies and interests in real time and instantly suggest corresponding services.

[0084] The analysis unit can use the emotion estimation function to analyze the customer's emotional response to their new preferences and suggest optimal services. The analysis unit, for example, develops a system that uses the emotion estimation function to analyze the customer's emotional response to their new preferences. For example, the analysis unit analyzes facial expressions and voice when a customer posts about their new hobby to evaluate their emotional state. The analysis unit also suggests optimal services for the new preferences based on the customer's emotional response data. For example, if a customer has positive emotions about their new hobby, the analysis unit suggests services related to that hobby. The analysis unit also uses the emotion estimation function to analyze the customer's emotional response to their new preferences in real time and dynamically adjust the content of the service suggestions. For example, the suggestions are revised based on real-time customer feedback. In this way, the emotion estimation function can be used to analyze the customer's emotional response to their new preferences and suggest optimal services.

[0085] The analysis unit can propose services in different industries or fields based on customer preference data, thereby drawing out new interest. The analysis unit, for example, builds a system that proposes services in different industries or fields based on customer preference data. For example, it proposes services in new fields that the customer may be interested in. When proposing services in different industries or fields, the analysis unit also analyzes the customer preference data to provide optimal options. For example, it proposes services in new fields based on data on services the customer has used in the past. The analysis unit also analyzes the customer preference data in chronological order to propose services in different industries or fields. For example, it compares past usage history with current preference data to draw out new interest. In this way, it is possible to propose services in different industries or fields based on the customer preference data, thereby drawing out new interest.

[0086] The analysis unit can compare the customer's preference data with other customers, form groups with common interests, and propose services for the group. The analysis unit, for example, builds a system that compares the customer's preference data with other customers and forms groups with common interests. For example, it groups customers with the same hobbies and proposes services for the group. The analysis unit also develops an algorithm that proposes services for the group based on groups with common interests. For example, it proposes events where customers with the same hobbies gather. The analysis unit also analyzes the customer's preference data over time to form groups with common interests and propose services for the group. For example, it compares past preference data with current preference data to form groups. In this way, by comparing the customer's preference data with other customers, it is possible to form groups with common interests and propose services for the group.

[0087] The analysis unit uses the emotion estimation function to monitor the customer's emotional response to changes in preferences in real time and dynamically adjust the content of proposals. The analysis unit, for example, uses the emotion estimation function to develop a system that monitors the customer's emotional response to changes in preferences in real time. For example, the analysis unit analyzes the customer's facial expressions and voice to evaluate their emotional state. The analysis unit also proposes services that correspond to changes in preferences based on the customer's emotional response data. For example, if a customer has positive feelings about a new hobby, the analysis unit proposes services related to that hobby. The analysis unit also uses the emotion estimation function to analyze the customer's emotional response to changes in preferences in real time and dynamically adjust the content of proposals. For example, the proposals are revised based on the customer's real-time feedback. In this way, by using the emotion estimation function, the analysis unit can monitor the customer's emotional response to changes in preferences in real time and dynamically adjust the content of proposals.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The data collection unit can collect not only a customer's purchase history, but also data on the web pages the customer viewed and the ads the customer clicked on. For example, if a customer frequently visits a particular brand's website, services related to that brand can be suggested. The data collection unit can also collect content and comments the customer shared on social media to understand their preferences in more detail. For example, if a customer comments on a particular event, services related to that event can be suggested. Furthermore, the data collection unit can collect data on online forums and communities the customer participated in and analyze their preferences. This allows for a complete understanding of the customer's online behavior, enabling more accurate service suggestions.

[0090] When collecting a customer's biometric data, the data collection unit can obtain more detailed data. For example, it can collect the customer's electroencephalogram data and analyze the brain's response to a specific service. The data collection unit can also collect the customer's electromyogram data and analyze the muscle tension when using a specific service. Furthermore, the data collection unit can monitor the customer's skin temperature and sweat rate to analyze the customer's emotional state in more detail. This allows for multifaceted analysis of the customer's biometric data and more accurate emotion estimation.

[0091] The data collection unit can analyze not only a customer's social media activity but also their activity on other online platforms. For example, it can analyze the products and reviews a customer views on an online shopping site to understand changes in their preferences. The data collection unit can also analyze posts on online communities and forums in which the customer participates to understand changes in their interests and preferences in real time. Furthermore, the data collection unit can analyze the viewing history of the streaming services the customer uses to identify changes in their preferences. This allows for an analysis of the customer's overall online activity and enables more accurate service recommendations.

[0092] The data collection unit uses its emotion estimation function to not only analyze the emotions customers feel when using a service, but also analyze their expectations before using the service and their level of satisfaction after using it. For example, it can analyze a customer's facial expressions and voice when reserving a service to evaluate their expectations. The data collection unit can also collect feedback after using the service to evaluate their level of satisfaction. Furthermore, the data collection unit can compare the customer's emotional state before and after using the service to evaluate the effectiveness of the service. This allows for a multifaceted analysis of the customer's emotional state, enabling more accurate service suggestions.

[0093] When analyzing a customer's voice data, the data collection unit can analyze more detailed voice features. For example, it can analyze the pitch, rhythm, and speed of the customer's voice to estimate the customer's emotional state in more detail. The data collection unit can also collect customer voice data over the long term and analyze changes in the customer's emotional state over time. Furthermore, the data collection unit can compare voice data emitted by the customer in different situations to identify the customer's emotional state in a specific situation. This enables multifaceted analysis of the customer's voice data and more accurate emotion estimation.

[0094] When using customer location information, the data collection unit can collect more detailed location data. For example, it can analyze the length of time a customer stays at a place they visit and the frequency of their visit, allowing for a more detailed understanding of their preferences. The data collection unit can also analyze a customer's movement patterns and identify the places and routes they visit during specific time periods. Furthermore, the data collection unit can collect environmental data (for example, weather and congestion) from the places they visit and analyze changes in their preferences. This allows for a multifaceted analysis of customer location information, enabling more accurate service proposals.

[0095] The data collection unit uses its emotion estimation function to not only monitor in real time the emotions of customers when they use a specific service, but also the customer's expectations before using the service and their level of satisfaction after using it. For example, it can analyze in real time the customer's facial expressions and voice when reserving a service to evaluate their expectations. The data collection unit can also collect in real time feedback after using the service to evaluate their level of satisfaction. Furthermore, the data collection unit can compare in real time the customer's emotional state before and after using the service to evaluate the effectiveness of the service. This enables multifaceted analysis of the customer's emotional state in real time, enabling more accurate service suggestions.

[0096] The suggestion unit can analyze more detailed data when predicting future preferences based on the customer's past service usage history. For example, it can analyze the frequency and duration of services used by the customer in the past to predict future preferences in more detail. The suggestion unit can also predict preferences related to specific seasons or events based on the customer's past usage history. Furthermore, the suggestion unit can predict future needs by analyzing the customer's usage history over time and identifying changes in preferences. This makes it possible to more accurately predict future preferences based on the customer's past service usage history and make proactive suggestions.

[0097] The suggestion unit can collect more detailed data when analyzing the preference data of the customer's friends and family. For example, it can collect the past service usage history and online behavior data of friends and family to understand the preferences of the entire group. The suggestion unit can also develop an algorithm that predicts services that the entire group will enjoy based on the preference data of friends and family. Furthermore, the suggestion unit can analyze the group's preference data over time and identify changes in preferences to suggest optimal services. This makes it possible to analyze the preference data of the customer's friends and family in detail and suggest optimal services that the entire group can enjoy.

[0098] The suggestion unit can use the emotion estimation function to analyze more detailed data when predicting how a customer will feel about a proposed service. For example, the suggestion unit can more accurately predict emotional responses based on detailed data about services the customer has used in the past (e.g., usage time and frequency). The suggestion unit can also dynamically adjust the content of the proposal to elicit the most positive response based on the customer's emotional response data. Furthermore, the suggestion unit can instantly revise the content of the proposal based on real-time feedback from the customer and make the optimal proposal. As a result, the emotion estimation function can more accurately predict how a customer will feel about a proposed service and make a proposal that will elicit the most positive response.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The data collection unit collects customer preferences or past usage history. For example, it collects the customer's past purchase history or survey results. The data collection unit can also collect the customer's online behavior as log data. Furthermore, the data collection unit can also collect customer behavior data using sensors. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes customer preferences using statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also analyze customer needs using data mining techniques. Step 3: The suggestion unit suggests optimal services based on the data analyzed by the analysis unit. For example, the suggestion unit may suggest new restaurants based on the customer's past usage history. The suggestion unit may also suggest events or products that the customer may be interested in. The suggestion unit may also suggest customized services based on the customer's preferences. Step 4: The support department supports the reservation or purchase of the service proposed by the proposal department. For example, the support department makes a reservation at a proposed restaurant on behalf of the customer. The support department can also support the purchase procedure of a proposed product. The support department can also support the customer in making a reservation through an online reservation system.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects customer preferences or past usage history; an analysis unit that analyzes the data collected by the data collection unit; a proposal unit that proposes an optimal service based on the data analyzed by the analysis unit; a support unit that supports reservations and purchases of the services proposed by the proposal unit. A system characterized by:

2. The data collection unit Collecting customer biometric data and analyzing their emotional state 2. The system of claim 1.

3. The data collection unit Analyze your customers' social media activity to understand changes in their preferences or interests in real time 2. The system of claim 1.

4. The data collection unit Analyzing customer emotions when using services and collecting data to elicit positive emotions 2. The system of claim 1.

5. The data collection unit Analyzing customer voice data to infer emotion or stress level from tone of voice or speaking style 2. The system of claim 1.

6. The data collection unit Use customer location information to analyze places visited or travel patterns to understand preferences or behavioral patterns 2. The system of claim 1.

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