system

Abstract

The system according to the embodiment aims to optimize the content of a website for each individual user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a customization unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates content based on the analysis results obtained by the analysis unit. The customization unit customizes the content of a website based on the content generated by the generation 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 do not adequately optimize website content for individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the content of a website for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a customization unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates content based on the analysis results obtained by the analysis unit. The customization unit customizes the content of the website based on the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the content of a website for each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A website optimization system according to an embodiment of the present invention optimizes website structure and content for each customer. This website optimization system first collects customer web browsing data. This data is collected by combining data from multiple data sources. Next, the collected data is used to analyze customer attributes and web behavior history. Based on the analysis results, optimal content is automatically generated and the website content is customized. This system reduces employee analysis and improvement efforts, improves NPS (Net Promoter Score), and contributes to profits by improving conversion rates. It also reduces the cost of responding to inquiries after browsing the website. For example, a website optimization system collects customer web browsing data. This data is collected by combining data from multiple data sources. This allows for more diversified data collection. Next, the collected data is used to analyze customer attributes and web behavior history. For example, attribute information such as age, gender, and interests, as well as behavioral history such as which pages were viewed and which links were clicked, are analyzed. This allows for an understanding of customer interests. Based on the analysis results, optimal content is automatically generated. For example, articles and product information tailored to customer interests are automatically generated. This makes it possible to provide content that is appealing to customers. Furthermore, the content of the website can be customized. For example, the content and layout displayed can be changed based on the customer's attributes and behavioral history. This makes it possible to provide a website that is easy for customers to use. This system reduces the amount of analysis and improvement work required by employees. For example, it eliminates the need to manually analyze data and find areas for improvement. It also improves NPS. By providing content that is appealing to customers, satisfaction increases and NPS improves. It also improves conversion rates. By providing content that is appealing to customers, actions such as purchases and inquiries increase, improving conversion rates. This contributes to revenue. It also reduces the cost of responding to inquiries after web browsing.By providing a website that is easy for customers to use, the number of inquiries will decrease and response costs will be reduced. As a result, the website optimization system can reduce the amount of time employees spend on analysis and improvement, improve NPS, and increase conversion rates. It can also reduce the cost of responding to inquiries after web browsing.

[0029] A website optimization system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a customization unit. The collection unit collects data. For example, the collection unit can collect behavioral data of website visitors. For example, the collection unit can collect data such as which pages the visitors viewed, which links they clicked, and how long they stayed on the page. The collection unit can also collect attribute data of the visitors. For example, the collection unit can collect data such as the visitors' age, gender, and interests. The collection unit can collect data from websites using, for example, scraping technology. The collection unit can also acquire data through an API. For example, the collection unit can collect social media activity data of the visitors using social media APIs. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the visitors' attribute information and behavioral history based on the collected data. For example, the analysis unit can analyze the visitors' attribute information such as the visitors' age, gender, and interests. The analysis unit can also analyze the visitors' behavioral history such as which pages the visitors viewed and which links they clicked. The analysis unit can analyze data using, for example, statistical analysis techniques. The analysis unit can also analyze data by applying a machine learning algorithm. For example, the analysis unit can group visitors using a clustering algorithm and analyze the characteristics of each group. The generation unit generates content based on the analysis results obtained by the analysis unit. For example, the generation unit can automatically generate articles and product information based on the interests of visitors. For example, the generation unit can generate articles using natural language generation techniques. The generation unit can also generate product images using image generation techniques. For example, the generation unit can generate content that is likely to interest visitors based on the visitor's past behavioral history. The customization unit customizes the content of the website based on the content generated by the generation unit. For example, the customization unit can change the content and layout to be displayed based on the visitor's attributes and behavioral history.The customization unit can, for example, prioritize the display of content that is likely to interest a visitor. The customization unit can also dynamically change the layout of a website based on the visitor's attributes. For example, the customization unit can provide a visually appealing design to a young visitor and an easy-to-read layout to an elderly visitor. As a result, the website optimization system according to the embodiment automates data collection, analysis, content generation, and customization, enabling efficient website optimization.

[0030] The collection unit can collect data from multiple data sources. The collection unit can collect data from multiple data sources, such as social media, sensor data, and user-input data. For example, the collection unit can collect visitors' social media activity data using a social media API. The collection unit can also collect sensor data. For example, the collection unit can collect sensor data such as visitors' location information and device usage. The collection unit can also collect user-input data. For example, the collection unit can collect search keywords and form data entered by visitors on a website. By collecting data from multiple data sources, the collection unit can obtain more diversified data. 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 data acquired using a social media API into the generation AI and cause the generation AI to collect data.

[0031] The analysis unit can analyze customer attribute information and behavioral history based on the collected data. The analysis unit can, for example, analyze visitor attribute information and behavioral history based on the collected data. The analysis unit can, for example, analyze attribute information such as the visitor's age, gender, and interests. The analysis unit can also analyze behavioral history such as which pages the visitor viewed and which links they clicked. The analysis unit can, for example, analyze data using statistical analysis techniques. The analysis unit can also analyze data by applying machine learning algorithms. For example, the analysis unit can group visitors using a clustering algorithm and analyze the characteristics of each group. This allows the analysis unit to understand the visitor's interests. Some or all of the above-described 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 collected data into a generation AI and have the generation AI analyze the data.

[0032] The generation unit can automatically generate content based on the analysis results. The generation unit can automatically generate articles and product information based on, for example, the interests of visitors. The generation unit can generate articles using, for example, natural language generation technology. The generation unit can also generate product images using image generation technology. The generation unit can generate content that is likely to interest visitors based on, for example, the visitor's past behavior history. This allows the generation unit to provide content that is attractive to visitors. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate content.

[0033] The customization unit can customize the content of the website based on the generated content. The customization unit can change the content and layout to be displayed based on, for example, the visitor's attributes and behavioral history. The customization unit can, for example, prioritize displaying content that is likely to interest the visitor. The customization unit can also dynamically change the layout of the website based on the visitor's attributes. For example, the customization unit can provide a visually attractive design for young visitors and an easy-to-read layout for older visitors. This allows the customization unit to provide a website that is easy to use for visitors. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the generated content into a generation AI and have the generation AI customize the website.

[0034] The collection unit can analyze the user's past web browsing history and select the optimal data collection method. For example, the collection unit can prioritize data collection on pages frequently visited by the user. The collection unit can also collect detailed data on pages on which the user stays for a long time. Furthermore, the collection unit can focus on collecting data on pages visited by the user during specific time periods. This allows the collection unit to analyze the user's past web browsing history and select the optimal data collection method. 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 the user's web browsing history data into a generation AI and have the generation AI select the optimal data collection method.

[0035] The collection unit can filter data based on the user's current interests when collecting data. For example, the collection unit can preferentially collect data related to keywords recently searched by the user. The collection unit can also collect data related to product categories viewed by the user. Furthermore, the collection unit can collect data related to online events attended by the user. This allows the collection unit to filter data based on the user's current interests. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's interest data into a generation AI and have the generation AI perform data 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, the collection unit can prioritize collecting event information around the user's current location. Furthermore, if the user is traveling, the collection unit can prioritize collecting tourist information about the travel destination. Furthermore, if the user is interested in a particular region, the collection unit can prioritize collecting news about that region. This allows the collection unit to prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect data.

[0037] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect data related to content shared by the user on social media. The collection unit can also collect data based on the content posted by accounts the user follows. Furthermore, the collection unit can collect data related to topics of groups and communities in which the user participates. This allows the collection unit to analyze the user's social media activities and collect related data. 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 the user's social media activity data into a generation AI and cause the generation AI to collect 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 can perform a detailed analysis of data with high importance. Furthermore, the analysis unit can perform a simplified analysis of data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail of data with medium importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, 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] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a purchasing pattern analysis algorithm to product data. Furthermore, the analysis unit can apply a behavior prediction algorithm to user behavior data. Furthermore, the analysis unit can apply a sentiment analysis algorithm to social media data. This allows the analysis unit to apply different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data. Furthermore, the analysis unit can determine the analysis priority for past data according to its importance. Furthermore, the analysis unit can analyze data collected during a specific period taking into account trends during that period. This allows the analysis unit to determine the analysis priority based on the time when the data was collected. 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 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. For example, the analysis unit can prioritize analysis of highly relevant data. Furthermore, the analysis unit can postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0042] The generation unit can adjust the level of detail of the content to be generated based on the importance of the analysis result at the time of generation. For example, the generation unit can generate detailed content for analysis results with high importance. Furthermore, the generation unit can generate simplified content for analysis results with low importance. Furthermore, the generation unit can generate content with an appropriate level of detail for analysis results with medium importance. This allows the generation unit to adjust the level of detail of the content to be generated based on the importance of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the content.

[0043] During generation, the generation unit can apply different generation algorithms depending on the category of the analysis results. For example, for product data, the generation unit can generate content based on purchasing patterns. For user behavior data, the generation unit can generate content based on behavior prediction. For social media data, the generation unit can generate content based on sentiment analysis. This allows the generation unit to apply different generation algorithms depending on the category of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of the analysis results into the generation AI and cause the generation AI to apply the generation algorithm.

[0044] At the time of generation, the generation unit can determine the priority of content to be generated based on the time when the analysis results were collected. The generation unit can, for example, preferentially generate content based on the latest analysis results. The generation unit can also determine the order of content generation according to the importance of past analysis results. Furthermore, for analysis results collected during a specific period, the generation unit can generate content taking into account trends during that period. This allows the generation unit to determine the priority of content to be generated based on the time when the analysis results were collected. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the time when the analysis results were collected into the generation AI and have the generation AI determine the priority of the content.

[0045] The generation unit can adjust the order of content to be generated based on the relevance of the analysis results during generation. The generation unit can, for example, preferentially generate content based on highly relevant analysis results. The generation unit can also postpone generating content for less relevant analysis results. Furthermore, the generation unit can dynamically adjust the order of content generation according to the relevance of the analysis results. This allows the generation unit to adjust the order of content to be generated based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the analysis results into the generation AI and cause the generation AI to adjust the order of content.

[0046] During customization, the customization unit can adjust the level of detail of the customization based on the importance of the generated content. For example, the customization unit can perform detailed customization for content with high importance. Furthermore, the customization unit can perform simplified customization for content with low importance. Furthermore, the customization unit can perform customization with an appropriate level of detail for content with medium importance. This allows the customization unit to adjust the level of detail of the customization based on the importance of the generated content. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the importance of the generated content to the generation AI and cause the generation AI to adjust the level of detail of the customization.

[0047] During customization, the customization unit can apply different customization algorithms depending on the category of the generated content. For example, the customization unit can perform customization based on purchasing patterns for product data. For user behavior data, the customization unit can perform customization based on behavior prediction. For social media data, the customization unit can perform customization based on sentiment analysis. This allows the customization unit to apply different customization algorithms depending on the category of the generated content. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the category of the generated content to the generation AI and cause the generation AI to apply the customization algorithm.

[0048] During customization, the customization unit can adjust the order of customization based on the collection date of the generated content. For example, the customization unit can prioritize displaying the most recent content. Furthermore, the customization unit can determine the display order of past content according to its importance. Furthermore, the customization unit can display content collected during a specific period, taking into account trends during that period. This allows the customization unit to adjust the order of customization based on the collection date of the generated content. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the collection date of the generated content to the generation AI and cause the generation AI to adjust the order of customization.

[0049] During customization, the customization unit can adjust the order of customization based on the relevance of the generated content. For example, the customization unit can prioritize displaying highly relevant content. Furthermore, the customization unit can postpone displaying less relevant content. Furthermore, the customization unit can dynamically adjust the display order according to the relevance of the content. This allows the customization unit to adjust the order of customization based on the relevance of the generated content. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can input the relevance of the generated content to the generation AI and cause the generation AI to adjust the order of customization.

[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 adjust the data collection method based on the type of device and usage status of the user. For example, if the user is using a smartphone, the collection unit can reduce the frequency of data collection to reduce mobile data consumption. In addition, if the user is using a desktop, the collection unit can collect more detailed data. Furthermore, if the user is connected to Wi-Fi, the collection unit can increase the frequency of data collection and obtain more detailed data. This allows the collection unit to adjust the data collection method based on the type of device and usage status of the user.

[0052] The analysis unit can predict a user's future purchasing intentions based on the user's past purchase history. For example, the analysis unit can analyze the categories and price ranges of products the user has purchased in the past and predict the products the user is likely to purchase next. The analysis unit can also identify products that the user tends to purchase in a particular season and suggest promotions tailored to that season. Furthermore, the analysis unit can analyze reviews and ratings of products the user has purchased in the past and preferentially suggest products that provide high satisfaction. This allows the analysis unit to predict a user's future purchasing intentions based on the user's past purchase history.

[0053] The generation unit can automatically generate related content based on the user's browsing history. For example, the generation unit can generate new content related to articles or videos that the user has previously viewed. The generation unit can also automatically generate news articles or blog posts on topics that the user may be interested in. Furthermore, the generation unit can generate reviews and comparison articles of products that the user has previously viewed to serve as reference for purchasing. This allows the generation unit to automatically generate related content based on the user's browsing history.

[0054] The customization unit can display area-specific offers and promotions based on the user's geographic location information. For example, if the user is in a particular city, the customization unit can display information about events and sales taking place in that city. If the user is traveling, the customization unit can provide information about tourist attractions and restaurants in the travel destination. Furthermore, if the user is interested in a particular region, the customization unit can display news and articles related to that region. This allows the customization unit to display area-specific offers and promotions based on the user's geographic location information.

[0055] The collection unit can adjust the frequency of data collection based on the remaining battery power of the user's device. For example, when the remaining battery power of the user's device is low, the collection unit can reduce the frequency of data collection to reduce battery consumption. Furthermore, when the user's device is charging, the collection unit can increase the frequency of data collection to obtain detailed data. Furthermore, when the remaining battery power of the user's device is medium, the collection unit can collect data at an appropriate frequency. This allows the collection unit to adjust the frequency of data collection based on the remaining battery power of the user's device.

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

[0057] Step 1: The collection unit collects data. The collection unit may, for example, collect behavioral data of website visitors. The collection unit may collect data such as which pages visitors viewed, which links they clicked, and how long they stayed on the page. The collection unit may also collect visitor attribute data. For example, data such as the visitor's age, gender, and interests may be collected. The collection unit may, for example, collect data from the website using scraping technology. The collection unit may also obtain data through an API. For example, the collection unit may use a social media API to collect visitors' social media activity data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze visitor attribute information and behavioral history based on the collected data. The analysis unit can analyze attribute information such as the visitor's age, gender, and interests. The analysis unit can also analyze behavioral history such as which pages the visitor viewed and which links they clicked. The analysis unit can, for example, analyze the data using statistical analysis techniques. The analysis unit can also analyze the data by applying machine learning algorithms. For example, the analysis unit can group visitors using a clustering algorithm and analyze the characteristics of each group. Step 3: The generation unit generates content based on the analysis results obtained by the analysis unit. The generation unit can automatically generate articles and product information based on the visitor's interests, for example. The generation unit can generate articles using natural language generation technology. The generation unit can also generate product images using image generation technology. The generation unit can generate content that is likely to interest the visitor based on the visitor's past behavior history. Step 4: The customization unit customizes the content of the website based on the content generated by the generation unit. The customization unit can change the content and layout to be displayed based on, for example, the visitor's attributes and behavioral history. The customization unit can prioritize the display of content that is likely to interest the visitor. The customization unit can also dynamically change the layout of the website based on the visitor's attributes. For example, the customization unit can provide a visually appealing design for a young visitor and an easy-to-read layout for an elderly visitor.

[0058] (Example 2) A website optimization system according to an embodiment of the present invention optimizes website structure and content for each customer. This website optimization system first collects customer web browsing data. This data is collected by combining data from multiple data sources. Next, the collected data is used to analyze customer attributes and web behavior history. Based on the analysis results, optimal content is automatically generated and the website content is customized. This system reduces employee analysis and improvement efforts, improves NPS (Net Promoter Score), and contributes to profits by improving conversion rates. It also reduces the cost of responding to inquiries after browsing the website. For example, a website optimization system collects customer web browsing data. This data is collected by combining data from multiple data sources. This allows for more diversified data collection. Next, the collected data is used to analyze customer attributes and web behavior history. For example, attribute information such as age, gender, and interests, as well as behavioral history such as which pages were viewed and which links were clicked, are analyzed. This allows for an understanding of customer interests. Based on the analysis results, optimal content is automatically generated. For example, articles and product information tailored to customer interests are automatically generated. This makes it possible to provide content that is appealing to customers. Furthermore, the content of the website can be customized. For example, the content and layout displayed can be changed based on the customer's attributes and behavioral history. This makes it possible to provide a website that is easy for customers to use. This system reduces the amount of analysis and improvement work required by employees. For example, it eliminates the need to manually analyze data and find areas for improvement. It also improves NPS. By providing content that is appealing to customers, satisfaction increases and NPS improves. It also improves conversion rates. By providing content that is appealing to customers, actions such as purchases and inquiries increase, improving conversion rates. This contributes to revenue. It also reduces the cost of responding to inquiries after web browsing.By providing a website that is easy for customers to use, the number of inquiries will decrease and response costs will be reduced. As a result, the website optimization system can reduce the amount of time employees spend on analysis and improvement, improve NPS, and increase conversion rates. It can also reduce the cost of responding to inquiries after web browsing.

[0059] A website optimization system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a customization unit. The collection unit collects data. For example, the collection unit can collect behavioral data of website visitors. For example, the collection unit can collect data such as which pages the visitors viewed, which links they clicked, and how long they stayed on the page. The collection unit can also collect attribute data of the visitors. For example, the collection unit can collect data such as the visitors' age, gender, and interests. The collection unit can collect data from websites using, for example, scraping technology. The collection unit can also acquire data through an API. For example, the collection unit can collect social media activity data of the visitors using social media APIs. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the visitors' attribute information and behavioral history based on the collected data. For example, the analysis unit can analyze the visitors' attribute information such as the visitors' age, gender, and interests. The analysis unit can also analyze the visitors' behavioral history such as which pages the visitors viewed and which links they clicked. The analysis unit can analyze data using, for example, statistical analysis techniques. The analysis unit can also analyze data by applying a machine learning algorithm. For example, the analysis unit can group visitors using a clustering algorithm and analyze the characteristics of each group. The generation unit generates content based on the analysis results obtained by the analysis unit. For example, the generation unit can automatically generate articles and product information based on the interests of visitors. For example, the generation unit can generate articles using natural language generation techniques. The generation unit can also generate product images using image generation techniques. For example, the generation unit can generate content that is likely to interest visitors based on the visitor's past behavioral history. The customization unit customizes the content of the website based on the content generated by the generation unit. For example, the customization unit can change the content and layout to be displayed based on the visitor's attributes and behavioral history.The customization unit can, for example, prioritize the display of content that is likely to interest a visitor. The customization unit can also dynamically change the layout of a website based on the visitor's attributes. For example, the customization unit can provide a visually appealing design to a young visitor and an easy-to-read layout to an elderly visitor. As a result, the website optimization system according to the embodiment automates data collection, analysis, content generation, and customization, enabling efficient website optimization.

[0060] The collection unit can collect data from multiple data sources. The collection unit can collect data from multiple data sources, such as social media, sensor data, and user-input data. For example, the collection unit can collect visitors' social media activity data using a social media API. The collection unit can also collect sensor data. For example, the collection unit can collect sensor data such as visitors' location information and device usage. The collection unit can also collect user-input data. For example, the collection unit can collect search keywords and form data entered by visitors on a website. By collecting data from multiple data sources, the collection unit can obtain more diversified data. 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 data acquired using a social media API into the generation AI and cause the generation AI to collect data.

[0061] The analysis unit can analyze customer attribute information and behavioral history based on the collected data. The analysis unit can, for example, analyze visitor attribute information and behavioral history based on the collected data. The analysis unit can, for example, analyze attribute information such as the visitor's age, gender, and interests. The analysis unit can also analyze behavioral history such as which pages the visitor viewed and which links they clicked. The analysis unit can, for example, analyze data using statistical analysis techniques. The analysis unit can also analyze data by applying machine learning algorithms. For example, the analysis unit can group visitors using a clustering algorithm and analyze the characteristics of each group. This allows the analysis unit to understand the visitor's interests. Some or all of the above-described 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 collected data into a generation AI and have the generation AI analyze the data.

[0062] The generation unit can automatically generate content based on the analysis results. The generation unit can automatically generate articles and product information based on, for example, the interests of visitors. The generation unit can generate articles using, for example, natural language generation technology. The generation unit can also generate product images using image generation technology. The generation unit can generate content that is likely to interest visitors based on, for example, the visitor's past behavior history. This allows the generation unit to provide content that is attractive to visitors. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate content.

[0063] The customization unit can customize the content of the website based on the generated content. The customization unit can change the content and layout to be displayed based on, for example, the visitor's attributes and behavioral history. The customization unit can, for example, prioritize displaying content that is likely to interest the visitor. The customization unit can also dynamically change the layout of the website based on the visitor's attributes. For example, the customization unit can provide a visually attractive design for young visitors and an easy-to-read layout for older visitors. This allows the customization unit to provide a website that is easy to use for visitors. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the generated content into a generation AI and have the generation AI customize the website.

[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 can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to obtain more detailed data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. This allows the collection unit to adjust the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0065] The collection unit can analyze the user's past web browsing history and select the optimal data collection method. For example, the collection unit can prioritize data collection on pages frequently visited by the user. The collection unit can also collect detailed data on pages on which the user stays for a long time. Furthermore, the collection unit can focus on collecting data on pages visited by the user during specific time periods. This allows the collection unit to analyze the user's past web browsing history and select the optimal data collection method. 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 the user's web browsing history data into a generation AI and have the generation AI select the optimal data collection method.

[0066] The collection unit can filter data based on the user's current interests when collecting data. For example, the collection unit can preferentially collect data related to keywords recently searched by the user. The collection unit can also collect data related to product categories viewed by the user. Furthermore, the collection unit can collect data related to online events attended by the user. This allows the collection unit to filter data based on the user's current interests. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's interest data into a generation AI and have the generation AI perform data filtering.

[0067] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting the latest trend information. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed product reviews. Furthermore, if the user is stressed, the collection unit can prioritize collecting relaxing content. This allows the collection unit to determine the priority of data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation 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-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[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, the collection unit can prioritize collecting event information around the user's current location. Furthermore, if the user is traveling, the collection unit can prioritize collecting tourist information about the travel destination. Furthermore, if the user is interested in a particular region, the collection unit can prioritize collecting news about that region. This allows the collection unit to prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect data.

[0069] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect data related to content shared by the user on social media. The collection unit can also collect data based on the content posted by accounts the user follows. Furthermore, the collection unit can collect data related to topics of groups and communities in which the user participates. This allows the collection unit to analyze the user's social media activities and collect related data. 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 the user's social media activity data into a generation AI and cause the generation AI to collect data.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results using visually appealing graphs or charts when the user is excited. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of 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 can perform a detailed analysis of data with high importance. Furthermore, the analysis unit can perform a simplified analysis of data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail of data with medium importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, 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] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a purchasing pattern analysis algorithm to product data. Furthermore, the analysis unit can apply a behavior prediction algorithm to user behavior data. Furthermore, the analysis unit can apply a sentiment analysis algorithm to social media data. This allows the analysis unit to apply different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the 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 and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide the analysis result using visually appealing graphs or charts. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI 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. For example, the analysis unit can prioritize the analysis of the most recent data. Furthermore, the analysis unit can determine the analysis priority for past data according to its importance. Furthermore, the analysis unit can analyze data collected during a specific period taking into account trends during that period. This allows the analysis unit to determine the analysis priority based on the time when the data was collected. 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 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. For example, the analysis unit can prioritize analysis of highly relevant data. Furthermore, the analysis unit can postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0076] The generation unit can estimate the user's emotions and adjust the expression method of the generated content based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate content including detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate concise content that focuses on the main points. Furthermore, if the user is excited, the generation unit can generate content that adds visually appealing effects. This allows the generation unit to adjust the expression method of the generated content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression method of the content.

[0077] The generation unit can adjust the level of detail of the content to be generated based on the importance of the analysis result at the time of generation. For example, the generation unit can generate detailed content for analysis results with high importance. Furthermore, the generation unit can generate simplified content for analysis results with low importance. Furthermore, the generation unit can generate content with an appropriate level of detail for analysis results with medium importance. This allows the generation unit to adjust the level of detail of the content to be generated based on the importance of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the content.

[0078] During generation, the generation unit can apply different generation algorithms depending on the category of the analysis results. For example, for product data, the generation unit can generate content based on purchasing patterns. For user behavior data, the generation unit can generate content based on behavior prediction. For social media data, the generation unit can generate content based on sentiment analysis. This allows the generation unit to apply different generation algorithms depending on the category of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of the analysis results into the generation AI and cause the generation AI to apply the generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point content. Furthermore, if the user is relaxed, the generation unit can generate longer content with detailed explanations. Furthermore, if the user is excited, the generation unit can generate content with visually appealing effects. This allows the generation unit to adjust the length of the generated content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the content.

[0080] At the time of generation, the generation unit can determine the priority of content to be generated based on the time when the analysis results were collected. The generation unit can, for example, preferentially generate content based on the latest analysis results. The generation unit can also determine the order of content generation according to the importance of past analysis results. Furthermore, for analysis results collected during a specific period, the generation unit can generate content taking into account trends during that period. This allows the generation unit to determine the priority of content to be generated based on the time when the analysis results were collected. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the time when the analysis results were collected into the generation AI and have the generation AI determine the priority of the content.

[0081] The generation unit can adjust the order of content to be generated based on the relevance of the analysis results during generation. The generation unit can, for example, preferentially generate content based on highly relevant analysis results. The generation unit can also postpone generating content for less relevant analysis results. Furthermore, the generation unit can dynamically adjust the order of content generation according to the relevance of the analysis results. This allows the generation unit to adjust the order of content to be generated based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the analysis results into the generation AI and cause the generation AI to adjust the order of content.

[0082] The customization unit can estimate a user's emotions and adjust the customization method of the website based on the estimated user's emotions. For example, if the user is relaxed, the customization unit can provide a layout that displays detailed information. If the user is in a hurry, the customization unit can provide a concise layout that focuses on the main points. Furthermore, if the user is excited, the customization unit can provide a visually appealing design. This allows the customization unit to adjust the customization method of the website according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the customization unit can be performed using AI, for example, or without AI. For example, the customization unit can input user's emotion data into the generation AI and cause the generation AI to adjust the customization method of the website.

[0083] During customization, the customization unit can adjust the level of detail of the customization based on the importance of the generated content. For example, the customization unit can perform detailed customization for content with high importance. Furthermore, the customization unit can perform simplified customization for content with low importance. Furthermore, the customization unit can perform customization with an appropriate level of detail for content with medium importance. This allows the customization unit to adjust the level of detail of the customization based on the importance of the generated content. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the importance of the generated content to the generation AI and cause the generation AI to adjust the level of detail of the customization.

[0084] During customization, the customization unit can apply different customization algorithms depending on the category of the generated content. For example, the customization unit can perform customization based on purchasing patterns for product data. For user behavior data, the customization unit can perform customization based on behavior prediction. For social media data, the customization unit can perform customization based on sentiment analysis. This allows the customization unit to apply different customization algorithms depending on the category of the generated content. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the category of the generated content to the generation AI and cause the generation AI to apply the customization algorithm.

[0085] The customization unit can estimate a user's emotions and determine priorities for customizing the website based on the estimated user's emotions. For example, the customization unit can prioritize displaying detailed information when the user is relaxed. Furthermore, the customization unit can prioritize displaying concise information when the user is in a hurry. Furthermore, the customization unit can prioritize displaying visually appealing information when the user is excited. This allows the customization unit to determine priorities for customizing the website according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation 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 customization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the customization unit can input user's emotion data into the generation AI and have the generation AI determine the customization priorities.

[0086] During customization, the customization unit can adjust the order of customization based on the collection date of the generated content. For example, the customization unit can prioritize displaying the most recent content. Furthermore, the customization unit can determine the display order of past content according to its importance. Furthermore, the customization unit can display content collected during a specific period, taking into account trends during that period. This allows the customization unit to adjust the order of customization based on the collection date of the generated content. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the collection date of the generated content to the generation AI and cause the generation AI to adjust the order of customization.

[0087] During customization, the customization unit can adjust the order of customization based on the relevance of the generated content. For example, the customization unit can prioritize displaying highly relevant content. Furthermore, the customization unit can postpone displaying less relevant content. Furthermore, the customization unit can dynamically adjust the display order according to the relevance of the content. This allows the customization unit to adjust the order of customization based on the relevance of the generated content. Some or all of the above-described processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can input the relevance of the generated content to the generation AI and cause the generation AI to adjust the order of customization. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and customization 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 collects behavioral data of website visitors using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the visitor's attribute information and behavioral history based on the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates content based on the visitor's interests based on the analysis results. The customization unit is realized, for example, by the control unit 46A of the smart device 14 and customizes the content of the website based on the generated content. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and customization 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 collects behavioral data of website visitors using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the visitor's attribute information and behavioral history based on the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates content based on the visitor's interests based on the analysis results. The customization unit is realized, for example, by the control unit 46A of the smart glasses 214 and customizes the content of the website based on the generated content. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and customization unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects behavioral data of website visitors using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the visitor's attribute information and behavioral history based on the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates content based on the visitor's interests based on the analysis results. The customization unit is realized, for example, by the control unit 46A of the headset terminal 314, and customizes the content of the website based on the generated content. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and customization 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 collects behavioral data of website visitors using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the visitor's attribute information and behavioral history based on the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates content based on the visitor's interests based on the analysis results. The customization unit is realized, for example, by the control unit 46A of the robot 414, and customizes the content of the website based on the generated content.

[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 adjust the data collection method based on the type of device and usage status of the user. For example, if the user is using a smartphone, the collection unit can reduce the frequency of data collection to reduce mobile data consumption. In addition, if the user is using a desktop, the collection unit can collect more detailed data. Furthermore, if the user is connected to Wi-Fi, the collection unit can increase the frequency of data collection and obtain more detailed data. This allows the collection unit to adjust the data collection method based on the type of device and usage status of the user.

[0090] The analysis unit can predict a user's future purchasing intentions based on the user's past purchase history. For example, the analysis unit can analyze the categories and price ranges of products the user has purchased in the past and predict the products the user is likely to purchase next. The analysis unit can also identify products that the user tends to purchase in a particular season and suggest promotions tailored to that season. Furthermore, the analysis unit can analyze reviews and ratings of products the user has purchased in the past and preferentially suggest products that provide high satisfaction. This allows the analysis unit to predict a user's future purchasing intentions based on the user's past purchase history.

[0091] The generation unit can automatically generate related content based on the user's browsing history. For example, the generation unit can generate new content related to articles or videos that the user has previously viewed. The generation unit can also automatically generate news articles or blog posts on topics that the user may be interested in. Furthermore, the generation unit can generate reviews and comparison articles of products that the user has previously viewed to serve as reference for purchasing. This allows the generation unit to automatically generate related content based on the user's browsing history.

[0092] The customization unit can display area-specific offers and promotions based on the user's geographic location information. For example, if the user is in a particular city, the customization unit can display information about events and sales taking place in that city. If the user is traveling, the customization unit can provide information about tourist attractions and restaurants in the travel destination. Furthermore, if the user is interested in a particular region, the customization unit can display news and articles related to that region. This allows the customization unit to display area-specific offers and promotions based on the user's geographic location information.

[0093] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to obtain more detailed data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. This allows the collection unit to adjust the data collection method according to the user's emotions.

[0094] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Alternatively, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results using visually appealing graphs and charts when the user is excited. This allows the analysis unit to adjust the analysis method according to the user's emotions.

[0095] The generation unit can estimate the user's emotion and adjust the tone of the content to be generated based on the estimated user's emotion. For example, the generation unit can generate content with a calm tone when the user is relaxed. Furthermore, the generation unit can generate content with an energetic tone when the user is excited. Furthermore, the generation unit can generate content with a comforting tone when the user is sad. In this way, the generation unit can adjust the tone of the content to be generated according to the user's emotion.

[0096] The customization unit can estimate the user's emotions and adjust the way the website is customized based on the estimated user's emotions. For example, the customization unit can provide a layout that displays detailed information when the user is relaxed. Alternatively, the customization unit can provide a concise layout that focuses on the main points when the user is in a hurry. Furthermore, the customization unit can provide a visually appealing design when the user is excited. This allows the customization unit to adjust the way the website is customized according to the user's emotions.

[0097] The customization unit can estimate the user's emotions and determine the priority of website customization based on the estimated user's emotions. For example, the customization unit can prioritize displaying detailed information when the user is relaxed. Furthermore, the customization unit can prioritize displaying information that focuses on the main points when the user is in a hurry. Furthermore, the customization unit can prioritize displaying visually appealing information when the user is excited. In this way, the customization unit can determine the priority of website customization according to the user's emotions.

[0098] The collection unit can adjust the frequency of data collection based on the remaining battery power of the user's device. For example, when the remaining battery power of the user's device is low, the collection unit can reduce the frequency of data collection to reduce battery consumption. Furthermore, when the user's device is charging, the collection unit can increase the frequency of data collection to obtain detailed data. Furthermore, when the remaining battery power of the user's device is medium, the collection unit can collect data at an appropriate frequency. This allows the collection unit to adjust the frequency of data collection based on the remaining battery power of the user's device.

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

[0100] Step 1: The collection unit collects data. The collection unit may, for example, collect behavioral data of website visitors. The collection unit may collect data such as which pages visitors viewed, which links they clicked, and how long they stayed on the page. The collection unit may also collect visitor attribute data. For example, data such as the visitor's age, gender, and interests may be collected. The collection unit may, for example, collect data from the website using scraping technology. The collection unit may also obtain data through an API. For example, the collection unit may use a social media API to collect visitors' social media activity data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze visitor attribute information and behavioral history based on the collected data. The analysis unit can analyze attribute information such as the visitor's age, gender, and interests. The analysis unit can also analyze behavioral history such as which pages the visitor viewed and which links they clicked. The analysis unit can, for example, analyze the data using statistical analysis techniques. The analysis unit can also analyze the data by applying machine learning algorithms. For example, the analysis unit can group visitors using a clustering algorithm and analyze the characteristics of each group. Step 3: The generation unit generates content based on the analysis results obtained by the analysis unit. The generation unit can automatically generate articles and product information based on the visitor's interests, for example. The generation unit can generate articles using natural language generation technology. The generation unit can also generate product images using image generation technology. The generation unit can generate content that is likely to interest the visitor based on the visitor's past behavior history. Step 4: The customization unit customizes the content of the website based on the content generated by the generation unit. The customization unit can change the content and layout to be displayed based on, for example, the visitor's attributes and behavioral history. The customization unit can prioritize the display of content that is likely to interest the visitor. The customization unit can also dynamically change the layout of the website based on the visitor's attributes. For example, the customization unit can provide a visually appealing design for a young visitor and an easy-to-read layout for an elderly visitor.

[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 a 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, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[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 data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates content based on the analysis result obtained by the analysis unit; a customization unit that customizes the content of a website based on the content generated by the generation unit; Equipped with A system characterized by:

2. The collecting unit Collect data from multiple sources 2. The system of claim 1.

3. The analysis unit Analyze customer attribute information and behavioral history based on collected data 2. The system of claim 1.

4. The generation unit Automatically generate content based on analysis results 2. The system of claim 1.

5. The customization unit Customize your website content based on generated content 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 web browsing history and select the optimal data collection method 2. The system of claim 1.

8. The collecting unit At the time of data collection, filtering based on the user's current interests 2. The system of claim 1.

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