system

Abstract

The system according to the embodiment aims to analyze customer data and propose appropriate products and services to individual customers. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a feedback collection unit. The collection unit collects customer data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes appropriate products and services to individual customers based on the analysis results obtained by the analysis unit. The feedback collection unit collects customer feedback on the products and services proposed by the proposal unit.

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 have not yet effectively utilized customer data to propose appropriate products and services to individual customers, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze customer data and propose appropriate products and services to individual customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a feedback collection unit. The collection unit collects customer data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes appropriate products and services to individual customers based on the analysis results obtained by the analysis unit. The feedback collection unit collects customer feedback on the products and services proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze customer data and propose appropriate products and services to individual customers. [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 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) A customer data analysis system according to an embodiment of the present invention is a tool that connects companies and customers. This customer data analysis system focuses on the children's market and collects, analyzes, proposes, and collects feedback from customers. For example, the customer data analysis system uses a customer data collection unit to collect behavioral data from children and their parents. The collected data includes website browsing history, purchase history, event participation history, and the like. Next, the customer data analysis system uses an analysis unit to analyze the collected data using AI to understand customers' interests. For example, it analyzes what products a specific customer demographic is interested in. Next, the customer data analysis system uses a proposal unit to propose optimal products and services to individual customers based on the analysis results. For example, it proposes products that are likely to interest a specific customer demographic. Finally, the customer data analysis system uses a feedback collection unit to collect customer feedback and use it to improve services. For example, it collects reactions to proposed products and evaluations of events. This allows companies to better understand customer needs and develop effective marketing strategies. Customers also experience increased satisfaction when they are offered products and services tailored to their needs. This allows the customer data analysis system to help companies gain a deeper understanding of their customers' needs and develop effective marketing strategies.

[0029] A customer data analysis system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a feedback collection unit. The collection unit collects customer data. The customer data includes, but is not limited to, website browsing history, purchase history, and event participation history. For example, the collection unit tracks user behavior using cookies to collect website browsing history. The collection unit can also acquire information from an online shopping site database to collect purchase history. The collection unit can also acquire data from an event management system to collect event participation history. For example, the collection unit collects website browsing history using cookies to track user behavior. The collection unit can also acquire purchase history from an online shopping site database. The collection unit can also acquire event participation history from an event management system. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using a machine learning algorithm, but is not limited to, for example. For example, the analysis unit analyzes customer interests using a machine learning algorithm. The analysis unit can also analyze customer text data using natural language processing technology. The analysis unit can also segment customers using a clustering algorithm. For example, the analysis unit can analyze customer interests using a machine learning algorithm. The analysis unit can also analyze customer text data using natural language processing technology. The analysis unit can also segment customers using a clustering algorithm. The suggestion unit suggests optimal products and services for each customer based on the analysis results obtained by the analysis unit. The suggestion is made using, for example, a recommendation algorithm, but is not limited to such an example. For example, the suggestion unit can suggest optimal products to the customer using a recommendation algorithm. The suggestion unit can also suggest optimal services to the customer using a collaborative filtering algorithm. The suggestion unit can also suggest optimal products to the customer using a content-based filtering algorithm.For example, the suggestion unit uses a recommendation algorithm to suggest a product that is optimal for the customer. The suggestion unit can also use a collaborative filtering algorithm to suggest a service that is optimal for the customer. The suggestion unit can also use a content-based filtering algorithm to suggest a product that is optimal for the customer. The feedback collection unit collects customer feedback on the product or service suggested by the suggestion unit. The feedback is collected, for example, through questionnaires or reviews, but is not limited to these examples. For example, the feedback collection unit collects customer feedback using an online questionnaire. The feedback collection unit can also collect customer feedback through post-purchase reviews. The feedback collection unit can also collect feedback after participating in an event. For example, the feedback collection unit collects customer feedback using an online questionnaire. The feedback collection unit can also collect customer feedback through post-purchase reviews. The feedback collection unit can also collect feedback after participating in an event. As a result, the customer data analysis system according to the embodiment enables companies to deeply understand customer needs and develop effective marketing strategies.

[0030] The collection unit can collect data on website browsing history, purchase history, and event participation history. For example, the collection unit tracks user behavior using cookies to collect website browsing history. For example, the collection unit can collect browsing history and viewing time of specific pages. The collection unit can also acquire information from an online shopping site database to collect purchase history. For example, the collection unit can collect the type of product purchased and the date and time of purchase. The collection unit can also acquire data from an event management system to collect event participation history. For example, the collection unit can collect the type of event attended and the date and time of participation. By collecting customer behavior data, it is possible to understand the customer's interests. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the website browsing history collected using cookies into a generation AI and have the generation AI analyze the browsing history.

[0031] The analysis unit can analyze the collected data using machine learning to understand customer interests. The analysis unit can analyze customer interests using, for example, a machine learning algorithm. For example, the analysis unit can analyze customer behavioral data and identify interests using deep learning. The analysis unit can also classify customer behavioral data and understand interests using a support vector machine. The analysis unit can also segment customers and analyze the interests of each segment using a clustering algorithm. For example, the analysis unit can analyze customer behavioral data and identify interests using deep learning. The analysis unit can also classify customer behavioral data and understand interests using a support vector machine. The analysis unit can also segment customers and analyze the interests of each segment using a clustering algorithm. In this way, customer interests can be analyzed with high accuracy using machine learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis department can input collected data into the generation AI and have the generation AI perform an analysis of customer interests and concerns.

[0032] The suggestion unit can use an algorithm that suggests appropriate products and services to individual customers based on the analysis results. The suggestion unit can, for example, use a recommendation algorithm to suggest optimal products to customers. For example, the suggestion unit can use a collaborative filtering algorithm to suggest optimal services to customers. The suggestion unit can also use a content-based filtering algorithm to suggest optimal products to customers. For example, the suggestion unit can use a recommendation algorithm to suggest optimal products to customers. The suggestion unit can also use a collaborative filtering algorithm to suggest optimal services to customers. The suggestion unit can also use a content-based filtering algorithm to suggest optimal products to customers. This makes it possible to improve customer satisfaction by suggesting optimal products and services based on the analysis results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI and cause the generation AI to suggest optimal products and services.

[0033] The feedback collection unit can collect customer reactions to proposed products or services and evaluations of events. The feedback collection unit collects customer feedback using, for example, an online survey. For example, the feedback collection unit can collect customer feedback through reviews after purchases. The feedback collection unit can also collect feedback after participating in an event. For example, the feedback collection unit collects customer feedback using an online survey. The feedback collection unit can collect customer feedback through reviews after purchases. The feedback collection unit can also collect feedback after participating in an event. In this way, collecting customer feedback can be used to improve services. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the results of an online survey into a generation AI and cause the generation AI to analyze the feedback.

[0034] The collection unit can analyze the user's past behavioral history and select an appropriate data collection method. For example, the collection unit can prioritize collecting data from websites the user frequently visited in the past. For example, the collection unit can analyze the user's past browsing history and prioritize collecting data on specific pages. The collection unit can also collect related data based on the types of events the user has previously attended. For example, the collection unit can analyze the user's past event participation history and collect data on related events. The collection unit can also analyze the user's purchase history and collect data on products that the user may be interested in. For example, the collection unit can analyze the user's past purchase history and collect data on related products. This enables more effective data collection by analyzing the user's past behavioral history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past behavioral history into a generation AI and have the generation AI select an optimal data collection method.

[0035] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is currently raising a child, the collection unit prioritizes collecting childcare-related data. For example, the collection unit can analyze the user's living situation and collect childcare-related data. Furthermore, if the user has a specific hobby, the collection unit can also collect data related to the hobby. For example, the collection unit can collect relevant data based on the user's hobby. Furthermore, if the user lives in a specific area, the collection unit can also collect data related to the area. For example, the collection unit can collect area-related data based on the user's place of residence. This allows for filtering data based on the user's living situation and areas of interest, thereby collecting more relevant data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting event information for that area. For example, the collection unit can acquire the user's geographical location information using GPS data and collect event information for that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting tourist information for the travel destination. For example, the collection unit can acquire the user's geographical location information using address information and collect tourist information for the travel destination. Furthermore, when the user is in a specific store, the collection unit can prioritize collecting promotion information for that store. For example, the collection unit can acquire the user's geographical location information using Wi-Fi data and collect promotion information for that store. This allows for the collection of more relevant data by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and cause the generation AI to collect highly relevant data.

[0037] During data collection, the collection unit can analyze the user's social media activity and collect related data. For example, if the user uses a specific hashtag, the collection unit can collect data related to the hashtag. For example, the collection unit can analyze social media postings and collect data related to the specific hashtag. Furthermore, if the user is a member of a specific group, the collection unit can also collect data related to the group. For example, the collection unit can analyze group activity on social media and collect related data. Furthermore, if the user is participating in a specific event, the collection unit can also collect data related to the event. For example, the collection unit can analyze event participation information on social media and collect related data. This allows for the collection of more relevant data by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media postings to a generation AI and cause the generation AI to collect related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit performs a detailed analysis on data with high business impact and provides a detailed report. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit performs a simplified analysis on data with low business impact and provides a simple summary. The analysis unit can also perform an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit performs a moderate analysis on data with medium business impact and provides a report with a moderate level of detail. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a purchasing behavior analysis algorithm to purchase history data. For example, the analysis unit uses the purchase history data to apply a purchasing behavior analysis algorithm to analyze customer purchasing patterns. The analysis unit can also apply a browsing behavior analysis algorithm to website browsing history data. For example, the analysis unit uses the website browsing history data to apply a browsing behavior analysis algorithm to analyze customer browsing patterns. The analysis unit can also apply an event participation behavior analysis algorithm to event participation history data. For example, the analysis unit uses the event participation history data to apply an event participation behavior analysis algorithm to analyze customer event participation patterns. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes the analysis of the most recent data. For example, the analysis unit can prioritize the analysis of the most recent data based on the date and time when the data was collected. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit can analyze the most recent data while referring to past data based on the data collection period. The analysis unit can also focus on analyzing data from a specific period. For example, the analysis unit can focus on analyzing data from a specific collection period to understand trends for that period. This enables analysis that prioritizes the most recent data by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit can prioritize analysis of highly relevant data based on data correlation. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can postpone analysis of less relevant data based on data causality. The analysis unit can also moderately analyze data with a medium degree of relevance. For example, the analysis unit can moderately analyze data with a medium degree of relevance based on data correlation. This enables efficient data analysis by adjusting the order of analysis based on data relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data relevance to the generation AI and cause the generation AI to adjust the order of analysis.

[0042] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes a detailed suggestion for a product with high importance. For example, the suggestion unit makes a detailed suggestion for a product with high sales contribution and provides a detailed product description. The suggestion unit can also make a simplified suggestion for a product with low importance. For example, the suggestion unit makes a simplified suggestion for a product with low sales contribution and provides a simple product description. The suggestion unit can also make a suggestion with an appropriate level of detail for a product with medium importance. For example, the suggestion unit makes an appropriate suggestion for a product with medium sales contribution and provides an appropriate product description. This enables more effective suggestions by adjusting the level of detail of the suggestion based on the importance of the product. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the importance of the product to a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0043] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit applies a suggestion algorithm based on a child's age and interests to toys. For example, the suggestion unit applies a suggestion algorithm that takes into account a child's age and interests based on the toy category to suggest the most suitable toy. The suggestion unit can also apply a suggestion algorithm based on a child's learning progress and interests to education-related products. For example, the suggestion unit applies a suggestion algorithm that takes into account a child's learning progress and interests based on the category of education-related products to suggest the most suitable education-related product. The suggestion unit can also apply a suggestion algorithm based on seasons and trends to clothing. For example, the suggestion unit applies a suggestion algorithm that takes into account seasons and trends based on the clothing category to suggest the most suitable clothing. This improves the accuracy of suggestions by applying an appropriate suggestion algorithm depending on the product category. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the product category into a generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0044] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. The proposal unit, for example, prioritizes new products. For example, the proposal unit can prioritize new products based on the submission time of the product. The proposal unit can also propose seasonal products at an appropriate time. For example, the proposal unit can propose seasonal products at an appropriate time based on the submission time of the product. The proposal unit can also prioritize sale products. For example, the proposal unit can prioritize sale products based on the submission time of the product. This enables timely proposals by determining the priority of proposals based on the submission time of the product. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the submission time of the product to the generation AI and cause the generation AI to determine the priority of the proposals.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. The suggestion unit, for example, prioritizes suggesting highly relevant products. For example, the suggestion unit can prioritize suggesting highly relevant products based on the relevance of the products. The suggestion unit can also postpone products with low relevance. For example, the suggestion unit can postpone suggesting products with low relevance based on the relevance of the products. The suggestion unit can also moderately suggest products with medium relevance. For example, the suggestion unit can moderately suggest products with medium relevance based on the relevance of the products. This enables more effective suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the products to a generation AI and cause the generation AI to adjust the order of suggestions.

[0046] When collecting feedback, the feedback collection unit can analyze the user's past response history and select the optimal collection method. The feedback collection unit, for example, preferentially provides a feedback collection method that the user has used favorably in the past. For example, the feedback collection unit can analyze the past response history and provide the feedback collection method that the user has used favorably. The feedback collection unit can also select the most effective collection method from the user's past response history. For example, the feedback collection unit can analyze the past response history and select the most effective collection method. The feedback collection unit can also customize the collection method by referring to the user's past feedback content. For example, the feedback collection unit can analyze the past feedback content and customize the collection method. This enables more effective feedback collection by analyzing the past response history. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the past response history into a generation AI and cause the generation AI to select the optimal collection method.

[0047] When collecting feedback, the feedback collection unit can customize the means of collection based on the user's current living situation. For example, if the user is busy, the feedback collection unit can provide a feedback collection method that can be completed in a short time. For example, the feedback collection unit can analyze the user's living situation and provide a feedback collection method that can be completed in a short time. Furthermore, if the user is relaxed, the feedback collection unit can request detailed feedback. For example, the feedback collection unit can analyze the user's living situation and request detailed feedback. Furthermore, if the user is participating in a specific event, the feedback collection unit can provide a feedback collection method related to the event. For example, the feedback collection unit can analyze the user's living situation and provide a feedback collection method related to the specific event. This enables more effective feedback collection by customizing the means of collection based on the user's living situation. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input the user's living situation to the generation AI and cause the generation AI to customize the means of collection.

[0048] When collecting feedback, the feedback collection unit can select an optimal collection method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback collection unit provides a feedback collection method related to that area. For example, the feedback collection unit can acquire the user's geographical location information using GPS data and provide a feedback collection method related to the area. Furthermore, if the user is traveling, the feedback collection unit can provide a feedback collection method according to the situation at the travel destination. For example, the feedback collection unit can acquire the user's geographical location information using address information and provide a feedback collection method according to the situation at the travel destination. Furthermore, if the user is in a specific store, the feedback collection unit can provide a feedback collection method related to that store. For example, the feedback collection unit can acquire the user's geographical location information using Wi-Fi data and provide a feedback collection method related to that store. This enables more effective feedback collection by taking the geographical location information into consideration. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input GPS data to a generation AI and cause the generation AI to select an optimal collection method.

[0049] When collecting feedback, the feedback collection unit can analyze the user's social media activity and suggest a means of collection. For example, if the user uses a specific hashtag, the feedback collection unit can provide a feedback collection method related to the hashtag. For example, the feedback collection unit can analyze the content of social media posts and provide a feedback collection method related to the specific hashtag. Furthermore, if the user is participating in a specific group, the feedback collection unit can also provide a feedback collection method related to the group. For example, the feedback collection unit can analyze group activity on social media and provide a related feedback collection method. Furthermore, if the user is participating in a specific event, the feedback collection unit can also provide a feedback collection method related to the event. For example, the feedback collection unit can analyze event participation information on social media and provide a related feedback collection method. This enables more effective feedback collection by analyzing social media activity. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the content of social media posts to a generation AI and cause the generation AI to suggest a collection method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can monitor the user's device usage and determine the optimal timing for data collection. For example, the collection unit can identify the time periods when the user frequently uses a smartphone and collect data during those time periods. The collection unit can also identify the time periods when the user uses a personal computer and collect data during those time periods. Furthermore, the collection unit can collect data while avoiding time periods when the user is not using the device. This improves the efficiency of data collection by determining the optimal timing for data collection based on the user's device usage.

[0052] The analysis unit can predict future purchasing behavior based on a user's purchasing history. For example, the analysis unit can analyze past purchasing history and predict products that the user is likely to purchase next. The analysis unit can also analyze a user's purchasing patterns and predict purchasing behavior related to specific seasons or events. Furthermore, the analysis unit can compare the user's purchasing history with the purchasing histories of other users and make predictions based on the behavior of users with similar purchasing patterns. This allows for more effective marketing strategies to be developed by predicting future purchasing behavior based on a user's purchasing history.

[0053] The suggestion unit can customize the content of suggestions based on the user's past feedback. For example, the suggestion unit can analyze the past feedback, identify products and services that the user likes, and make suggestions based on the identified products and services. The suggestion unit can also extract the user's dissatisfaction points from the past feedback and make suggestions that improve on those points. Furthermore, the suggestion unit can suggest new products and services that the user may be interested in based on the past feedback. In this way, the accuracy of suggestions can be improved by customizing the content of suggestions based on the user's past feedback.

[0054] The collection unit can monitor the remaining battery level of the user's device and determine the optimal timing for data collection. For example, the collection unit can temporarily stop data collection when the battery level of the user's device is low and resume it after the battery is charged. The collection unit can also prioritize data collection when the user's device is charging. Furthermore, the collection unit can perform detailed data collection when the battery level of the user's device is sufficient. This improves the efficiency of data collection by determining the optimal timing for data collection based on the remaining battery level of the user's device.

[0055] The analysis unit can analyze a user's social media activity to identify the user's interests. For example, the analysis unit can analyze hashtags frequently used by the user to identify the user's interests. The analysis unit can also analyze groups the user joins and accounts the user follows to identify the user's interests. Furthermore, the analysis unit can analyze the content of the user's posts to identify the user's interests. This allows for the development of more effective marketing strategies by identifying the user's interests based on the user's social media activity.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects customer data. The customer data includes website browsing history, purchase history, event participation history, etc. The collection unit collects website browsing history using cookies, obtains purchase history from the online shopping site's database, and obtains event participation history from the event management system. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out using machine learning algorithms, natural language processing technology, clustering algorithms, etc. This allows the analysis of customer interests and segments the customers. Step 3: The proposal unit proposes optimal products and services for each customer based on the analysis results obtained by the analysis unit. The proposals are made using recommendation algorithms, collaborative filtering algorithms, content-based filtering algorithms, etc. Step 4: The feedback collection department collects customer feedback on the products and services proposed by the proposal department. Feedback is collected through online surveys, post-purchase reviews, and feedback after participating in events.

[0058] (Example 2) A customer data analysis system according to an embodiment of the present invention is a tool that connects companies and customers. This customer data analysis system focuses on the children's market and collects, analyzes, proposes, and collects feedback from customers. For example, the customer data analysis system uses a customer data collection unit to collect behavioral data from children and their parents. The collected data includes website browsing history, purchase history, event participation history, and the like. Next, the customer data analysis system uses an analysis unit to analyze the collected data using AI to understand customers' interests. For example, it analyzes what products a specific customer demographic is interested in. Next, the customer data analysis system uses a proposal unit to propose optimal products and services to individual customers based on the analysis results. For example, it proposes products that are likely to interest a specific customer demographic. Finally, the customer data analysis system uses a feedback collection unit to collect customer feedback and use it to improve services. For example, it collects reactions to proposed products and evaluations of events. This allows companies to better understand customer needs and develop effective marketing strategies. Customers also experience increased satisfaction when they are offered products and services tailored to their needs. This allows the customer data analysis system to help companies gain a deeper understanding of their customers' needs and develop effective marketing strategies.

[0059] A customer data analysis system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a feedback collection unit. The collection unit collects customer data. The customer data includes, but is not limited to, website browsing history, purchase history, and event participation history. For example, the collection unit tracks user behavior using cookies to collect website browsing history. The collection unit can also acquire information from an online shopping site database to collect purchase history. The collection unit can also acquire data from an event management system to collect event participation history. For example, the collection unit collects website browsing history using cookies to track user behavior. The collection unit can also acquire purchase history from an online shopping site database. The collection unit can also acquire event participation history from an event management system. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using a machine learning algorithm, but is not limited to, for example. For example, the analysis unit analyzes customer interests using a machine learning algorithm. The analysis unit can also analyze customer text data using natural language processing technology. The analysis unit can also segment customers using a clustering algorithm. For example, the analysis unit can analyze customer interests using a machine learning algorithm. The analysis unit can also analyze customer text data using natural language processing technology. The analysis unit can also segment customers using a clustering algorithm. The suggestion unit suggests optimal products and services for each customer based on the analysis results obtained by the analysis unit. The suggestion is made using, for example, a recommendation algorithm, but is not limited to such an example. For example, the suggestion unit can suggest optimal products to the customer using a recommendation algorithm. The suggestion unit can also suggest optimal services to the customer using a collaborative filtering algorithm. The suggestion unit can also suggest optimal products to the customer using a content-based filtering algorithm.For example, the suggestion unit uses a recommendation algorithm to suggest a product that is optimal for the customer. The suggestion unit can also use a collaborative filtering algorithm to suggest a service that is optimal for the customer. The suggestion unit can also use a content-based filtering algorithm to suggest a product that is optimal for the customer. The feedback collection unit collects customer feedback on the product or service suggested by the suggestion unit. The feedback is collected, for example, through questionnaires or reviews, but is not limited to these examples. For example, the feedback collection unit collects customer feedback using an online questionnaire. The feedback collection unit can also collect customer feedback through post-purchase reviews. The feedback collection unit can also collect feedback after participating in an event. For example, the feedback collection unit collects customer feedback using an online questionnaire. The feedback collection unit can also collect customer feedback through post-purchase reviews. The feedback collection unit can also collect feedback after participating in an event. As a result, the customer data analysis system according to the embodiment enables companies to deeply understand customer needs and develop effective marketing strategies.

[0060] The collection unit can collect data on website browsing history, purchase history, and event participation history. For example, the collection unit tracks user behavior using cookies to collect website browsing history. For example, the collection unit can collect browsing history and viewing time of specific pages. The collection unit can also acquire information from an online shopping site database to collect purchase history. For example, the collection unit can collect the type of product purchased and the date and time of purchase. The collection unit can also acquire data from an event management system to collect event participation history. For example, the collection unit can collect the type of event attended and the date and time of participation. By collecting customer behavior data, it is possible to understand the customer's interests. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the website browsing history collected using cookies into a generation AI and have the generation AI analyze the browsing history.

[0061] The analysis unit can analyze the collected data using machine learning to understand customer interests. The analysis unit can analyze customer interests using, for example, a machine learning algorithm. For example, the analysis unit can analyze customer behavioral data and identify interests using deep learning. The analysis unit can also classify customer behavioral data and understand interests using a support vector machine. The analysis unit can also segment customers and analyze the interests of each segment using a clustering algorithm. For example, the analysis unit can analyze customer behavioral data and identify interests using deep learning. The analysis unit can also classify customer behavioral data and understand interests using a support vector machine. The analysis unit can also segment customers and analyze the interests of each segment using a clustering algorithm. In this way, customer interests can be analyzed with high accuracy using machine learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis department can input collected data into the generation AI and have the generation AI perform an analysis of customer interests and concerns.

[0062] The suggestion unit can use an algorithm that suggests appropriate products and services to individual customers based on the analysis results. The suggestion unit can, for example, use a recommendation algorithm to suggest optimal products to customers. For example, the suggestion unit can use a collaborative filtering algorithm to suggest optimal services to customers. The suggestion unit can also use a content-based filtering algorithm to suggest optimal products to customers. For example, the suggestion unit can use a recommendation algorithm to suggest optimal products to customers. The suggestion unit can also use a collaborative filtering algorithm to suggest optimal services to customers. The suggestion unit can also use a content-based filtering algorithm to suggest optimal products to customers. This makes it possible to improve customer satisfaction by suggesting optimal products and services based on the analysis results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI and cause the generation AI to suggest optimal products and services.

[0063] The feedback collection unit can collect customer reactions to proposed products or services and evaluations of events. The feedback collection unit collects customer feedback using, for example, an online survey. For example, the feedback collection unit can collect customer feedback through reviews after purchases. The feedback collection unit can also collect feedback after participating in an event. For example, the feedback collection unit collects customer feedback using an online survey. The feedback collection unit can collect customer feedback through reviews after purchases. The feedback collection unit can also collect feedback after participating in an event. In this way, collecting customer feedback can be used to improve services. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the results of an online survey into a generation AI and cause the generation AI to analyze the feedback.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit temporarily stops data collection and resumes it when the user relaxes. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is excited, the collection unit can collect data in real time and immediately analyze it. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is relaxed, the collection unit can periodically collect data to grasp long-term trends. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. By adjusting the timing of data collection according to the user's emotions, more appropriate data can be collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0065] The collection unit can analyze the user's past behavioral history and select an appropriate data collection method. For example, the collection unit can prioritize collecting data from websites the user frequently visited in the past. For example, the collection unit can analyze the user's past browsing history and prioritize collecting data on specific pages. The collection unit can also collect related data based on the types of events the user has previously attended. For example, the collection unit can analyze the user's past event participation history and collect data on related events. The collection unit can also analyze the user's purchase history and collect data on products that the user may be interested in. For example, the collection unit can analyze the user's past purchase history and collect data on related products. This enables more effective data collection by analyzing the user's past behavioral history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past behavioral history into a generation AI and have the generation AI select an optimal data collection method.

[0066] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is currently raising a child, the collection unit prioritizes collecting childcare-related data. For example, the collection unit can analyze the user's living situation and collect childcare-related data. Furthermore, if the user has a specific hobby, the collection unit can also collect data related to the hobby. For example, the collection unit can collect relevant data based on the user's hobby. Furthermore, if the user lives in a specific area, the collection unit can also collect data related to the area. For example, the collection unit can collect area-related data based on the user's place of residence. This allows for filtering data based on the user's living situation and areas of interest, thereby collecting more relevant data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0067] The collection unit can estimate the user's emotions and prioritize data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting data related to relaxing content. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the collection unit can prioritize collecting entertainment-related data. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is relaxed, the collection unit can prioritize collecting education-related data. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This enables more effective data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0068] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting event information for that area. For example, the collection unit can acquire the user's geographical location information using GPS data and collect event information for that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting tourist information for the travel destination. For example, the collection unit can acquire the user's geographical location information using address information and collect tourist information for the travel destination. Furthermore, when the user is in a specific store, the collection unit can prioritize collecting promotion information for that store. For example, the collection unit can acquire the user's geographical location information using Wi-Fi data and collect promotion information for that store. This allows for the collection of more relevant data by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and cause the generation AI to collect highly relevant data.

[0069] During data collection, the collection unit can analyze the user's social media activity and collect related data. For example, if the user uses a specific hashtag, the collection unit can collect data related to the hashtag. For example, the collection unit can analyze social media postings and collect data related to the specific hashtag. Furthermore, if the user is a member of a specific group, the collection unit can also collect data related to the group. For example, the collection unit can analyze group activity on social media and collect related data. Furthermore, if the user is participating in a specific event, the collection unit can also collect data related to the event. For example, the collection unit can analyze event participation information on social media and collect related data. This allows for the collection of more relevant data by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media postings to a generation AI and cause the generation AI to collect related data.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, visually easy-to-understand analysis results. For example, the analysis unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide the analysis results using simple graphs and charts. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed report. Furthermore, if the user is excited, the analysis unit can provide interactive analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide the analysis results using an interactive dashboard. This allows the analysis results to be more easily understood by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to adjust the method of expressing the analysis.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit performs a detailed analysis on data with high business impact and provides a detailed report. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit performs a simplified analysis on data with low business impact and provides a simple summary. The analysis unit can also perform an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit performs a moderate analysis on data with medium business impact and provides a report with a moderate level of detail. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a purchasing behavior analysis algorithm to purchase history data. For example, the analysis unit uses the purchase history data to apply a purchasing behavior analysis algorithm to analyze customer purchasing patterns. The analysis unit can also apply a browsing behavior analysis algorithm to website browsing history data. For example, the analysis unit uses the website browsing history data to apply a browsing behavior analysis algorithm to analyze customer browsing patterns. The analysis unit can also apply an event participation behavior analysis algorithm to event participation history data. For example, the analysis unit uses the event participation history data to apply an event participation behavior analysis algorithm to analyze customer event participation patterns. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, the analysis unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a short, concise analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed report. Alternatively, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide the analysis result using a visually stimulating dashboard. This allows the analysis unit to adjust the length of the analysis based on the user's emotions and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to adjust the length of the analysis.

[0074] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes the analysis of the most recent data. For example, the analysis unit can prioritize the analysis of the most recent data based on the date and time when the data was collected. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit can analyze the most recent data while referring to past data based on the data collection period. The analysis unit can also focus on analyzing data from a specific period. For example, the analysis unit can focus on analyzing data from a specific collection period to understand trends for that period. This enables analysis that prioritizes the most recent data by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit can prioritize analysis of highly relevant data based on data correlation. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can postpone analysis of less relevant data based on data causality. The analysis unit can also moderately analyze data with a medium degree of relevance. For example, the analysis unit can moderately analyze data with a medium degree of relevance based on data correlation. This enables efficient data analysis by adjusting the order of analysis based on data relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data relevance to the generation AI and cause the generation AI to adjust the order of analysis.

[0076] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit provides simple, visually easy-to-understand suggestions. For example, the suggestion unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and provides suggestions using simple graphs and charts. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, the suggestion unit records the user's voice, estimates the user's emotions using voice analysis technology, and provides a detailed report. Furthermore, if the user is excited, the suggestion unit can provide interactive suggestions. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor, estimates the user's emotions using an emotion estimation algorithm, and provides suggestions using an interactive dashboard. This enables more effective suggestions by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of the user taken with a camera to the generation AI and cause the generation AI to adjust the presentation method of the suggestion.

[0077] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit makes a detailed suggestion for a product with high importance. For example, the suggestion unit makes a detailed suggestion for a product with high sales contribution and provides a detailed product description. The suggestion unit can also make a simplified suggestion for a product with low importance. For example, the suggestion unit makes a simplified suggestion for a product with low sales contribution and provides a simple product description. The suggestion unit can also make a suggestion with an appropriate level of detail for a product with medium importance. For example, the suggestion unit makes an appropriate suggestion for a product with medium sales contribution and provides an appropriate product description. This enables more effective suggestions by adjusting the level of detail of the suggestion based on the importance of the product. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the importance of the product to a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0078] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit applies a suggestion algorithm based on a child's age and interests to toys. For example, the suggestion unit applies a suggestion algorithm that takes into account a child's age and interests based on the toy category to suggest the most suitable toy. The suggestion unit can also apply a suggestion algorithm based on a child's learning progress and interests to education-related products. For example, the suggestion unit applies a suggestion algorithm that takes into account a child's learning progress and interests based on the category of education-related products to suggest the most suitable education-related product. The suggestion unit can also apply a suggestion algorithm based on seasons and trends to clothing. For example, the suggestion unit applies a suggestion algorithm that takes into account seasons and trends based on the clothing category to suggest the most suitable clothing. This improves the accuracy of suggestions by applying an appropriate suggestion algorithm depending on the product category. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the product category into a generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide short, concise suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, the suggestion unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed report. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. For example, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide suggestions using a visually stimulating dashboard. This allows for more appropriate suggestions by adjusting the length of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0080] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. The proposal unit, for example, prioritizes new products. For example, the proposal unit can prioritize new products based on the submission time of the product. The proposal unit can also propose seasonal products at an appropriate time. For example, the proposal unit can propose seasonal products at an appropriate time based on the submission time of the product. The proposal unit can also prioritize sale products. For example, the proposal unit can prioritize sale products based on the submission time of the product. This enables timely proposals by determining the priority of proposals based on the submission time of the product. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the submission time of the product to the generation AI and cause the generation AI to determine the priority of the proposals.

[0081] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. The suggestion unit, for example, prioritizes suggesting highly relevant products. For example, the suggestion unit can prioritize suggesting highly relevant products based on the relevance of the products. The suggestion unit can also postpone products with low relevance. For example, the suggestion unit can postpone suggesting products with low relevance based on the relevance of the products. The suggestion unit can also moderately suggest products with medium relevance. For example, the suggestion unit can moderately suggest products with medium relevance based on the relevance of the products. This enables more effective suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of the products to a generation AI and cause the generation AI to adjust the order of suggestions.

[0082] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, if the user is feeling stressed, the feedback collection unit collects feedback in the form of a simple questionnaire. For example, the feedback collection unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and collects feedback in the form of a simple questionnaire. Furthermore, if the user is relaxed, the feedback collection unit can request detailed feedback. For example, the feedback collection unit records the user's voice, estimates the user's emotions using voice analysis technology, and provides a detailed questionnaire. Furthermore, if the user is excited, the feedback collection unit can also provide an interactive feedback collection method. For example, the feedback collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the user's emotions using an emotion estimation algorithm, and provides an interactive feedback collection method. This enables more effective feedback collection by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit may input image data of the user taken with a camera into the generation AI and cause the generation AI to adjust the feedback collection method.

[0083] When collecting feedback, the feedback collection unit can analyze the user's past response history and select the optimal collection method. The feedback collection unit, for example, preferentially provides a feedback collection method that the user has used favorably in the past. For example, the feedback collection unit can analyze the past response history and provide the feedback collection method that the user has used favorably. The feedback collection unit can also select the most effective collection method from the user's past response history. For example, the feedback collection unit can analyze the past response history and select the most effective collection method. The feedback collection unit can also customize the collection method by referring to the user's past feedback content. For example, the feedback collection unit can analyze the past feedback content and customize the collection method. This enables more effective feedback collection by analyzing the past response history. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the past response history into a generation AI and cause the generation AI to select the optimal collection method.

[0084] When collecting feedback, the feedback collection unit can customize the means of collection based on the user's current living situation. For example, if the user is busy, the feedback collection unit can provide a feedback collection method that can be completed in a short time. For example, the feedback collection unit can analyze the user's living situation and provide a feedback collection method that can be completed in a short time. Furthermore, if the user is relaxed, the feedback collection unit can request detailed feedback. For example, the feedback collection unit can analyze the user's living situation and request detailed feedback. Furthermore, if the user is participating in a specific event, the feedback collection unit can provide a feedback collection method related to the event. For example, the feedback collection unit can analyze the user's living situation and provide a feedback collection method related to the specific event. This enables more effective feedback collection by customizing the means of collection based on the user's living situation. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input the user's living situation to the generation AI and cause the generation AI to customize the means of collection.

[0085] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated user emotions. For example, when the user is feeling stressed, the feedback collection unit prioritizes collecting feedback with high importance. For example, the feedback collection unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and prioritizes collecting feedback with high importance. The feedback collection unit can also collect detailed feedback when the user is relaxed. For example, the feedback collection unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed questionnaire. The feedback collection unit can also collect feedback immediately when the user is excited. For example, the feedback collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and collect feedback immediately. This enables more effective feedback collection by determining the priority of feedback collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input image data of a user taken with a camera to the generation AI, and have the generation AI determine the priority of feedback collection.

[0086] When collecting feedback, the feedback collection unit can select an optimal collection method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback collection unit provides a feedback collection method related to that area. For example, the feedback collection unit can acquire the user's geographical location information using GPS data and provide a feedback collection method related to the area. Furthermore, if the user is traveling, the feedback collection unit can provide a feedback collection method according to the situation at the travel destination. For example, the feedback collection unit can acquire the user's geographical location information using address information and provide a feedback collection method according to the situation at the travel destination. Furthermore, if the user is in a specific store, the feedback collection unit can provide a feedback collection method related to that store. For example, the feedback collection unit can acquire the user's geographical location information using Wi-Fi data and provide a feedback collection method related to that store. This enables more effective feedback collection by taking the geographical location information into consideration. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input GPS data to a generation AI and cause the generation AI to select an optimal collection method.

[0087] When collecting feedback, the feedback collection unit can analyze the user's social media activity and suggest a means of collection. For example, if the user uses a specific hashtag, the feedback collection unit can provide a feedback collection method related to the hashtag. For example, the feedback collection unit can analyze the content of social media posts and provide a feedback collection method related to the specific hashtag. Furthermore, if the user is participating in a specific group, the feedback collection unit can also provide a feedback collection method related to the group. For example, the feedback collection unit can analyze group activity on social media and provide a related feedback collection method. Furthermore, if the user is participating in a specific event, the feedback collection unit can also provide a feedback collection method related to the event. For example, the feedback collection unit can analyze event participation information on social media and provide a related feedback collection method. This enables more effective feedback collection by analyzing social media activity. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the content of social media posts to a generation AI and cause the generation AI to suggest a collection method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and feedback collection unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects customer data such as website browsing history, purchase history, and event participation history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services for each customer based on the analysis results. The feedback collection unit is realized, for example, by the control unit 46A of the smart device 14 and collects feedback from customers. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and feedback collection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects customer data such as website browsing history, purchase history, and event participation history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services for each customer based on the analysis results. The feedback collection unit is realized, for example, by the control unit 46A of the smart glasses 214 and collects feedback from customers. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and feedback collection unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects customer data such as website browsing history, purchase history, and event participation history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services for each customer based on the analysis results. The feedback collection unit is realized, for example, by the control unit 46A of the headset type terminal 314 and collects feedback from customers. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and feedback collection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects customer data such as website browsing history, purchase history, and event participation history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services for individual customers based on the analysis results. The feedback collection unit is realized, for example, by the control unit 46A of the robot 414 and collects feedback from customers.

[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 collection unit can monitor the user's device usage and determine the optimal timing for data collection. For example, the collection unit can identify the time periods when the user frequently uses a smartphone and collect data during those time periods. The collection unit can also identify the time periods when the user uses a personal computer and collect data during those time periods. Furthermore, the collection unit can collect data while avoiding time periods when the user is not using the device. This improves the efficiency of data collection by determining the optimal timing for data collection based on the user's device usage.

[0090] The analysis unit can predict future purchasing behavior based on a user's purchasing history. For example, the analysis unit can analyze past purchasing history and predict products that the user is likely to purchase next. The analysis unit can also analyze a user's purchasing patterns and predict purchasing behavior related to specific seasons or events. Furthermore, the analysis unit can compare the user's purchasing history with the purchasing histories of other users and make predictions based on the behavior of users with similar purchasing patterns. This allows for more effective marketing strategies to be developed by predicting future purchasing behavior based on a user's purchasing history.

[0091] The suggestion unit can customize the content of suggestions based on the user's past feedback. For example, the suggestion unit can analyze the past feedback, identify products and services that the user likes, and make suggestions based on the identified products and services. The suggestion unit can also extract the user's dissatisfaction points from the past feedback and make suggestions that improve on those points. Furthermore, the suggestion unit can suggest new products and services that the user may be interested in based on the past feedback. In this way, the accuracy of suggestions can be improved by customizing the content of suggestions based on the user's past feedback.

[0092] The feedback collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated user's emotions. For example, the feedback collection unit selects the timing to collect feedback when the user is relaxed. Furthermore, the feedback collection unit can temporarily postpone feedback collection when the user is stressed. Furthermore, the feedback collection unit can immediately collect feedback when the user is excited. This allows for more effective feedback collection by adjusting the timing of feedback collection according to the user's emotions.

[0093] The collection unit can estimate the user's emotions and select the type of data to collect based on the estimated user's emotions. For example, the collection unit can collect detailed behavioral data when the user is relaxed. Alternatively, the collection unit can collect only basic behavioral data when the user is stressed. Furthermore, the collection unit can collect real-time behavioral data when the user is excited. This allows for more appropriate data collection by selecting the type of data to collect depending on the user's emotions.

[0094] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit will prioritize analyzing data of high importance. Also, if the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide analysis results immediately. This allows for more effective data analysis by determining the priority of analysis based on the user's emotions.

[0095] The suggestion unit can estimate the user's emotions and adjust the timing of the suggestion based on the estimated user's emotions. For example, the suggestion unit selects the timing to make a suggestion when the user is relaxed. Furthermore, the suggestion unit can temporarily postpone the suggestion when the user is stressed. Furthermore, the suggestion unit can make an immediate suggestion when the user is excited. This allows for more effective suggestions by adjusting the timing of the suggestion according to the user's emotions.

[0096] The feedback collection unit can estimate the user's emotions and select a feedback collection method based on the estimated user's emotions. For example, if the user is relaxed, the feedback collection unit can collect feedback in the form of a detailed questionnaire. If the user is stressed, the feedback collection unit can collect feedback in the form of a simple questionnaire. Furthermore, if the user is excited, the feedback collection unit can provide an interactive feedback collection method. This allows for more effective feedback collection by selecting a feedback collection method according to the user's emotions.

[0097] The collection unit can monitor the remaining battery level of the user's device and determine the optimal timing for data collection. For example, the collection unit can temporarily stop data collection when the battery level of the user's device is low and resume it after the battery is charged. The collection unit can also prioritize data collection when the user's device is charging. Furthermore, the collection unit can perform detailed data collection when the battery level of the user's device is sufficient. This improves the efficiency of data collection by determining the optimal timing for data collection based on the remaining battery level of the user's device.

[0098] The analysis unit can analyze a user's social media activity to identify the user's interests. For example, the analysis unit can analyze hashtags frequently used by the user to identify the user's interests. The analysis unit can also analyze groups the user joins and accounts the user follows to identify the user's interests. Furthermore, the analysis unit can analyze the content of the user's posts to identify the user's interests. This allows for the development of more effective marketing strategies by identifying the user's interests based on the user's social media activity.

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

[0100] Step 1: The collection unit collects customer data. The customer data includes website browsing history, purchase history, event participation history, etc. The collection unit collects website browsing history using cookies, obtains purchase history from the online shopping site's database, and obtains event participation history from the event management system. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out using machine learning algorithms, natural language processing technology, clustering algorithms, etc. This allows the analysis of customer interests and segments the customers. Step 3: The proposal unit proposes optimal products and services for each customer based on the analysis results obtained by the analysis unit. The proposals are made using recommendation algorithms, collaborative filtering algorithms, content-based filtering algorithms, etc. Step 4: The feedback collection department collects customer feedback on the products and services proposed by the proposal department. Feedback is collected through online surveys, post-purchase reviews, and feedback after participating in events.

[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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[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] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0140] 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.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 collection unit that collects customer data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes appropriate products and services to individual customers based on the analysis results obtained by the analysis unit; a feedback collection unit that collects customer feedback regarding the products and services proposed by the proposal unit. A system characterized by:

2. The collecting unit Collecting data on website browsing history, purchase history, and event participation history 2. The system of claim 1.

3. The analysis unit Use machine learning to analyze collected data and understand customer interests 2. The system of claim 1.

4. The proposal unit Uses algorithms to suggest appropriate products and services to individual customers based on analysis results 2. The system of claim 1.

5. The feedback collection unit: Collect customer reactions to proposed products and services, and event ratings 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past behavioral history and select the appropriate data collection method 2. The system of claim 1.

8. The collecting unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

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