system

Abstract

The system according to the embodiment aims to analyze information collected from SNS and utilize it in a company's advertising strategy. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and an awarding unit. The collection unit collects information from SNS. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the results of the analysis by the analysis unit to a company. The awarding unit places advertisements based on the data provided by the provision unit and awards points based on the results.

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 technology leaves room for improvement in terms of effectively utilizing information collected from social media and linking it to a company's advertising strategy.

[0005] The system according to the embodiment aims to analyze information collected from SNS and utilize it in a company's advertising strategy. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an awarding unit. The collection unit collects information from SNS. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the results of the analysis by the analysis unit to the company. The awarding unit places advertisements based on the data provided by the provision unit and awards points based on the results. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information collected from SNS and utilize it in a company's advertising strategy. [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) An ecosystem according to an embodiment of the present invention processes and analyzes information collected from customers (users) via social networking sites (SNSs) and provides it to companies. This system collects information from customers via SNSs, analyzes the collected information, and provides it to companies. Companies use the provided data to place advertisements and generate revenue from advertising fees on e-commerce sites. Customers who provide information also receive points through an electronic payment system. For example, information is collected from customers via SNSs. Detailed data, such as the customer's behavior on the SNS and their interests, is collected. For example, data such as which posts the customer "liked" and which groups the customer joined, is collected. This allows for an understanding of customer behavior patterns and interests. The collected information is then analyzed. AI is used for the analysis, allowing for detailed analysis of customer behavior patterns and interests. For example, it is possible to identify customers with a high level of interest in a particular product and target them with appropriate advertisements. This allows companies to display personalized advertisements tailored to their needs. The analysis results are then provided to companies. Companies then place advertisements based on the provided data and generate revenue from advertising fees on e-commerce sites. For example, by targeting customers who are highly interested in a particular product with advertisements related to that product, the effectiveness of the advertisement can be maximized. Finally, points are awarded to customers who provide information. This allows customers to receive points in exchange for providing information, which can then be used for shopping on e-commerce sites. For example, if an advertisement is displayed based on the information provided by a customer and the customer clicks on the advertisement, points are awarded to the customer. In this way, an ecosystem is created in which customers, companies, and e-commerce sites all benefit. This allows the ecosystem to process and analyze information from customers collected through social media and provide it to companies, allowing them to place advertisements and award points.

[0029] The ecosystem according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an assignment unit. The collection unit collects information from social media. The collection unit collects detailed data on, for example, the behavior of customers on social media and their interests. For example, the collection unit collects data such as which posts customers have "liked" and which groups they have joined. This allows the behavioral patterns and interests of customers to be understood. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and performs a detailed analysis of the behavioral patterns and interests of customers. For example, the analysis unit can identify customers who are highly interested in a particular product and deliver appropriate advertisements to those customers. This allows companies to deliver personalized advertisements tailored to their needs. The provision unit provides the results of the analysis performed by the analysis unit to companies. For example, the provision unit provides the analysis results to companies, and the companies deliver advertisements based on the provided data. For example, the effectiveness of advertisements can be maximized by delivering advertisements related to a particular product to customers who are highly interested in that product. The granting unit places advertisements based on the data provided by the providing unit and grants points based on the results. The granting unit, for example, grants points to customers who provide information. As a result, customers can receive points in exchange for providing information, and can use the points for shopping on e-commerce sites, etc. For example, when an advertisement is placed based on information provided by a customer and the advertisement is clicked, points are granted to the customer. As a result, the ecosystem according to the embodiment processes and analyzes information from customers accumulated through SNS and provides it to companies, enabling advertisement placement and point granting.

[0030] The collection unit can collect detailed data on the actions a customer takes on the social networking site and their interests and concerns. Examples of such detailed data include, but are not limited to, click history, post content, and number of followers. For example, the collection unit can collect information on which posts a customer has "liked" on the social networking site. The collection unit can also collect information on which groups a customer is participating in. For example, the collection unit can collect information on the activities of groups the customer participates in. The collection unit can also collect information on the content of posts shared by customers. For example, the collection unit can collect information related to posts shared by customers. This allows for a detailed understanding of customer behavioral patterns and interests. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input customer behavioral data on the social networking site into AI, which can then analyze the customer's behavioral patterns and interests.

[0031] The analysis unit analyzes the collected data and can perform a detailed analysis of customer behavioral patterns and interests. Examples of behavioral pattern analysis include, but are not limited to, frequency analysis and sequence analysis. For example, the analysis unit performs a frequency analysis of customer behavioral patterns using the collected data. The analysis unit can also analyze customer behavioral patterns using sequence analysis. For example, the analysis unit analyzes the order and patterns of customer behavior. Examples of interest analysis include, but are not limited to, topic modeling and keyword extraction. For example, the analysis unit can analyze customer interests using topic modeling. The analysis unit can also analyze customer interests using keyword extraction. For example, the analysis unit extracts keywords from customer posts and analyzes their interests. This allows for a detailed analysis of customer behavioral patterns and interests. Some or all of the above-described processing performed by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into AI, which can then analyze behavioral patterns and interests.

[0032] The providing unit provides the analysis results to the company, and the company can place advertisements based on the provided data. The analysis results include, for example, statistical data, graphs, reports, etc., but are not limited to these examples. For example, the providing unit provides the analysis results to the company as statistical data. The providing unit can also provide the analysis results to the company as graphs. For example, the providing unit provides the analysis results in a graph format that is visually easy to understand. The providing unit can also provide the analysis results to the company as a report. For example, the providing unit provides the company with a report that details the analysis results. This allows the company to place advertisements based on the provided data. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results into AI, and the AI ​​can generate data to be provided to the company.

[0033] The awarding unit can award points to customers who provide information. Information provided includes, for example, answering a questionnaire, providing feedback, etc., but is not limited to these examples. The awarding unit, for example, awards points when a customer answers a questionnaire. The awarding unit can also award points when a customer provides feedback. For example, the awarding unit awards points based on the feedback provided by the customer. In this way, points can be awarded to customers who provide information. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input customer information provided data into AI, which can determine the timing and conditions for awarding points.

[0034] The awarding unit can award points when an advertisement is clicked. Conditions for when an advertisement is clicked include, for example, the number of clicks and the click rate, but are not limited to these examples. The awarding unit can award points, for example, when an advertisement reaches a certain number of clicks. The awarding unit can also award points when the click rate of the advertisement meets a certain standard. For example, the awarding unit awards points when the click rate of the advertisement is high. In this way, points can be awarded when an advertisement is clicked. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input advertisement click data into AI, which can determine the timing and conditions for awarding points.

[0035] The collection unit can analyze the user's past SNS activity history and select the optimal collection method. Examples of the optimal collection method include, but are not limited to, the frequency of data collection and the selection of a collection tool. For example, the collection unit prioritizes collecting posts that the user has frequently "liked" in the past. The collection unit can also collect activity details of groups the user has previously participated in. For example, the collection unit analyzes activity details of groups the user participates in and collects related information. The collection unit can also analyze content of posts the user has previously shared and collect related information. For example, the collection unit collects information related to posts the user has shared. This allows the user's past SNS activity history to be analyzed and the optimal collection method to be selected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past SNS activity data into AI, which then selects the optimal collection method.

[0036] When collecting information, the collection unit can filter the information based on the user's current interests and trends. Filtering criteria include, but are not limited to, keyword filtering and topic filtering. For example, the collection unit preferentially collects posts related to topics in which the user is currently interested. The collection unit can also filter and collect related information based on current trends. For example, the collection unit preferentially collects information related to current trends. The collection unit can also collect related information based on keywords recently searched by the user. For example, the collection unit collects information related to keywords recently searched by the user. This makes it possible to filter information based on the user's current interests and trends. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current interests and trend data into AI, which then performs filtering.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Methods for collecting geographical location information include, but are not limited to, GPS data, IP addresses, etc. The collection unit, for example, prioritizes collecting event information in the area where the user is currently located. The collection unit can also collect information on nearby stores and services based on the user's geographical location. For example, the collection unit collects information on nearby stores and services based on the user's geographical location. The collection unit can also prioritize collecting tourist information about the travel destination when the user is traveling. For example, when the user is traveling, the collection unit prioritizes collecting tourist information about the travel destination. This makes it possible to prioritize collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into AI, which can then prioritize collecting highly relevant information.

[0038] When collecting information, the collection unit can analyze the user's social media activity and collect related information. The content of the social media activity includes, for example, the content of posts, comments, the number of likes, etc., but is not limited to these examples. For example, the collection unit collects information related to posts that the user recently "liked." The collection unit can also collect activity content of groups in which the user participates. For example, the collection unit collects activity content of groups in which the user participates. The collection unit can also collect information related to posts shared by the user. For example, the collection unit collects information related to posts shared by the user. This makes it possible to analyze the user's social media activity and collect related information. Some or all of the above-mentioned processing in the collection unit may be performed using, or without using, AI. For example, the collection unit can input the user's social media activity data into AI, which then collects related information.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. Criteria for evaluating the importance of information include, but are not limited to, an impact score, a relevance score, and the like. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. For example, the analysis unit performs a concise analysis of information with low importance. The analysis unit can also determine the priority of the analysis based on the importance. For example, the analysis unit determines the priority of the analysis based on the importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the information. 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 information importance data to AI, which can adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. Types of analysis algorithms include, but are not limited to, clustering algorithms and classification algorithms. For example, the analysis unit can apply a purchasing behavior analysis algorithm to information about products. The analysis unit can also apply a customer satisfaction analysis algorithm to information about services. For example, the analysis unit can apply a customer satisfaction analysis algorithm to information about services. The analysis unit can also apply a willingness to participate analysis algorithm to information about events. For example, the analysis unit can apply a willingness to participate analysis algorithm to information about events. This makes it possible to apply different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input category data of the information into AI, which can then apply an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. Criteria for considering the time when the information was collected include, but are not limited to, the collection date and collection frequency. The analysis unit, for example, prioritizes the analysis of the most recent information. The analysis unit can also determine the priority of analysis for past information based on its importance. For example, the analysis unit can determine the priority of analysis for past information based on its importance. The analysis unit can also adjust the analysis schedule based on the time when the information was collected. For example, the analysis unit adjusts the analysis schedule based on the time when the information was collected. This makes it possible to determine the priority of analysis based on the time when the information 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 data on the time when the information was collected into AI, and the AI ​​can determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. Criteria for evaluating the relevance of the information include, but are not limited to, co-occurrence analysis, relevance scores, and the like. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit postpones analysis of less relevant information. The analysis unit can also adjust the analysis schedule based on the relevance of the information. For example, the analysis unit adjusts the analysis schedule based on the relevance of the information. This makes it possible to adjust the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input relevance data of the information into AI, which can then adjust the order of analysis.

[0043] The providing unit can adjust the level of detail of the data to be provided based on the company's needs when providing the data. Criteria for evaluating the company's needs include, but are not limited to, survey results, interview results, etc. For example, if the company needs detailed data, the providing unit can provide detailed analysis results. Furthermore, if the company needs concise data, the providing unit can provide analysis results that focus on the main points. For example, if the company needs concise data, the providing unit can provide analysis results that focus on the main points. Furthermore, the providing unit can adjust the level of detail of the data according to the company's needs. For example, the providing unit adjusts the level of detail of the data according to the company's needs. This makes it possible to adjust the level of detail of the data to be provided based on the company's needs. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input company needs data into AI and adjust the level of detail of the data provided by the AI.

[0044] The providing unit can apply an appropriate providing method depending on the company's industry and purpose when providing the data. Types of providing methods include, but are not limited to, report provision and API provision. For example, in the case of the retail industry, the providing unit can provide data on purchasing behavior. Furthermore, in the case of the service industry, the providing unit can also provide data on customer satisfaction. For example, in the case of the service industry, the providing unit can provide data on customer satisfaction. Furthermore, in the case of the event industry, the providing unit can also provide data on willingness to participate. For example, in the case of the event industry, the providing unit can provide data on willingness to participate. This makes it possible to apply the optimal providing method depending on the company's industry and purpose. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's industry and purpose data into AI, which can then apply the appropriate providing method.

[0045] The providing unit can provide optimal data by taking into account the geographical location information of the company when providing the data. Methods for collecting geographical location information include, but are not limited to, GPS data, IP addresses, etc. The providing unit can, for example, provide market data for the region where the company is located. The providing unit can also provide related consumer data based on the geographical location of the company. For example, the providing unit provides related consumer data based on the geographical location of the company. The providing unit can also provide optimal advertising placement data according to the geographical location of the company. For example, the providing unit provides optimal advertising placement data according to the geographical location of the company. This makes it possible to provide optimal data by taking into account the geographical location information of the company. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the geographical location data of the company into AI, which can then provide optimal data.

[0046] When providing the data, the providing unit can customize the data to be provided by referring to the company's past advertising history. The content of the advertising history includes, for example, past advertising campaigns, advertising performance data, etc., but is not limited to these examples. For example, the providing unit analyzes the effectiveness of advertisements previously placed by the company and provides optimal data. The providing unit can also provide related data based on the company's past advertising history. For example, the providing unit provides related data based on the company's past advertising history. The providing unit can also provide customized data by referring to the company's past advertising history. For example, the providing unit provides customized data by referring to the company's past advertising history. This makes it possible to customize the data to be provided by referring to the company's past advertising history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's advertising history data into AI and customize the data provided by the AI.

[0047] When awarding points, the awarding unit can analyze the user's past behavioral history and select the optimal awarding method. Criteria for selecting the optimal awarding method include, but are not limited to, the type of points and the awarding conditions. For example, the awarding unit can award bonus points if the user has used points frequently in the past. Furthermore, the awarding unit can also award a benefit to encourage use of points if the user has not used points frequently in the past. For example, the awarding unit can award a benefit to encourage use of points if the user has not used points frequently in the past. Furthermore, the awarding unit can also select the optimal point awarding method based on the user's past behavioral history. For example, the awarding unit selects the optimal point awarding method based on the user's past behavioral history. This allows the user's past behavioral history to be analyzed and the optimal point awarding method to be selected. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or may be performed without AI. For example, the awarding unit can input the user's past behavioral history data into AI, which can then select the optimal point awarding method.

[0048] When awarding points, the awarding unit can customize the awarding method based on the user's current interests. Criteria for customizing the awarding method include, but are not limited to, points, coupons, gifts, etc. For example, the awarding unit awards points for products in which the user is currently interested. The awarding unit can also award points for services in which the user is currently interested. For example, the awarding unit awards points for services in which the user is currently interested. The awarding unit can also customize the point awarding method based on the user's current interests. For example, the awarding unit customizes the point awarding method based on the user's current interests. This makes it possible to customize the point awarding method based on the user's current interests. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input the user's current interest data into AI, which can then customize the awarding method.

[0049] When awarding points, the awarding unit can select the optimal awarding method by taking into account the user's geographical location information. Methods for collecting geographical location information include, but are not limited to, GPS data, IP addresses, etc. The awarding unit, for example, awards points that can be used at stores in the user's current area. The awarding unit can also award points that can be used at nearby services based on the user's geographical location. For example, the awarding unit awards points that can be used at nearby services based on the user's geographical location. If the user is traveling, the awarding unit can also award points that can be used at the user's travel destination. For example, if the user is traveling, the awarding unit awards points that can be used at the user's travel destination. This allows the optimal point awarding method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the awarding unit may be performed using, or without, AI. For example, the awarding unit can input the user's geographical location data into AI, which can then select the optimal point awarding method.

[0050] When awarding points, the awarding unit can analyze the user's social media activity and suggest a method for awarding points. The content of social media activity includes, but is not limited to, the content of posts, comments, and the number of likes. For example, the awarding unit can award points for products related to posts that the user recently "liked." The awarding unit can also award points based on the activity content of a group the user participates in. For example, the awarding unit can award points based on the activity content of a group the user participates in. The awarding unit can also award points for services related to posts shared by the user. For example, the awarding unit can award points for services related to posts shared by the user. This makes it possible to analyze the user's social media activity and suggest an optimal method for awarding points. Some or all of the above-described processing in the awarding unit may be performed using, or without, AI. For example, the awarding unit can input the user's social media activity data into AI, which can then suggest a method for awarding points.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze the user's past purchase history and prioritize collection of information on related products and services. For example, it collects new product information related to products the user has purchased in the past. The collection unit can also collect campaign information related to services the user has used in the past. For example, it collects campaign information related to services the user has used in the past. The collection unit can also collect related review and rating information based on the user's purchase history. For example, it collects review and rating information on products the user has purchased in the past. This makes it possible to analyze the user's past purchase history and prioritize collection of related information.

[0053] The analysis unit can take into account trends based on seasons and events when analyzing user behavior patterns. For example, the analysis unit analyzes user purchasing behavior during a particular season. The analysis unit can also analyze user behavior patterns during particular event periods. For example, the analysis unit analyzes user behavior patterns during events such as Christmas and Valentine's Day. The analysis unit can also analyze user interests by taking into account trends based on seasons and events. For example, the analysis unit analyzes interest in outdoor gear in the summer. This makes it possible to analyze user behavior patterns and interests in detail by taking into account trends based on seasons and events.

[0054] The providing unit can customize and provide the analysis results based on the company's marketing strategy. For example, the providing unit provides analysis results tailored to the customer demographic targeted by the company. The providing unit can also customize the analysis results according to the objectives of the company's marketing campaign. For example, the providing unit provides analysis results tailored to a promotional campaign for a new product. The providing unit can also adjust the format and content of the analysis results based on the company's marketing strategy. For example, the providing unit adjusts the format and content of the analysis results based on the company's marketing strategy. This allows the analysis results to be customized and provided based on the company's marketing strategy.

[0055] The awarding unit can award points for specific products or services based on the user's purchase history. For example, the awarding unit awards additional points for products that the user has purchased in the past. The awarding unit can also award bonus points for services that the user has used in the past. For example, the awarding unit awards bonus points for services that the user has used in the past. The awarding unit can also award points for related products or services based on the user's purchase history. For example, the awarding unit awards points for new products that are related to products that the user has purchased in the past. This makes it possible to award points for specific products or services based on the user's purchase history.

[0056] The collection unit can prioritize collecting trend and event information specific to a region, taking into account the user's geographical location information. For example, the collection unit collects local event information for the region where the user is currently located. The collection unit can also collect trend information specific to a region based on the user's geographical location. For example, the collection unit collects trend information for the region where the user is currently located. The collection unit can also prioritize collecting tourist information for the travel destination, if the user is traveling. For example, the collection unit prioritizes collecting tourist information for the travel destination, if the user is traveling. This makes it possible to prioritize collecting trend and event information specific to a region, taking into account the user's geographical location information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects information from social media. For example, the collection unit collects detailed data on what actions customers take on social media and what interests and concerns they have. For example, it collects data such as which posts customers "like" and which groups they join. This allows the system to understand customers' behavioral patterns and interests. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and performs detailed analysis of customer behavior patterns and interests. For example, it can identify customers who are highly interested in a particular product and target them with appropriate advertisements. This enables companies to display personalized advertisements tailored to customer needs. Step 3: The providing unit provides the results of the analysis performed by the analysis unit to the company. For example, the providing unit provides the analysis results to the company, and the company places advertisements based on the provided data. For example, by placing advertisements related to a particular product for customers who have a high level of interest in that product, the effectiveness of the advertisement can be maximized. Step 4: The granting unit places an advertisement based on the data provided by the providing unit and grants points based on the results. The granting unit, for example, grants points to the customer who provided the information. This allows the customer to receive points in return for providing the information, and the points can be used for shopping on an e-commerce site, for example. For example, if an advertisement is placed based on the information provided by the customer and the advertisement is clicked, points are granted to the customer.

[0059] (Example 2) An ecosystem according to an embodiment of the present invention processes and analyzes information collected from customers (users) via social networking sites (SNSs) and provides it to companies. This system collects information from customers via SNSs, analyzes the collected information, and provides it to companies. Companies use the provided data to place advertisements and generate revenue from advertising fees on e-commerce sites. Customers who provide information also receive points through an electronic payment system. For example, information is collected from customers via SNSs. Detailed data, such as the customer's behavior on the SNS and their interests, is collected. For example, data such as which posts the customer "liked" and which groups the customer joined, is collected. This allows for an understanding of customer behavior patterns and interests. The collected information is then analyzed. AI is used for the analysis, allowing for detailed analysis of customer behavior patterns and interests. For example, it is possible to identify customers with a high level of interest in a particular product and target them with appropriate advertisements. This allows companies to display personalized advertisements tailored to their needs. The analysis results are then provided to companies. Companies then place advertisements based on the provided data and generate revenue from advertising fees on e-commerce sites. For example, by targeting customers who are highly interested in a particular product with advertisements related to that product, the effectiveness of the advertisement can be maximized. Finally, points are awarded to customers who provide information. This allows customers to receive points in exchange for providing information, which can then be used for shopping on e-commerce sites. For example, if an advertisement is displayed based on the information provided by a customer and the customer clicks on the advertisement, points are awarded to the customer. In this way, an ecosystem is created in which customers, companies, and e-commerce sites all benefit. This allows the ecosystem to process and analyze information from customers collected through social media and provide it to companies, allowing them to place advertisements and award points.

[0060] The ecosystem according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an assignment unit. The collection unit collects information from social media. The collection unit collects detailed data on, for example, the behavior of customers on social media and their interests. For example, the collection unit collects data such as which posts customers have "liked" and which groups they have joined. This allows the behavioral patterns and interests of customers to be understood. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and performs a detailed analysis of the behavioral patterns and interests of customers. For example, the analysis unit can identify customers who are highly interested in a particular product and deliver appropriate advertisements to those customers. This allows companies to deliver personalized advertisements tailored to their needs. The provision unit provides the results of the analysis performed by the analysis unit to companies. For example, the provision unit provides the analysis results to companies, and the companies deliver advertisements based on the provided data. For example, the effectiveness of advertisements can be maximized by delivering advertisements related to a particular product to customers who are highly interested in that product. The granting unit places advertisements based on the data provided by the providing unit and grants points based on the results. The granting unit, for example, grants points to customers who provide information. As a result, customers can receive points in exchange for providing information, and can use the points for shopping on e-commerce sites, etc. For example, when an advertisement is placed based on information provided by a customer and the advertisement is clicked, points are granted to the customer. As a result, the ecosystem according to the embodiment processes and analyzes information from customers accumulated through SNS and provides it to companies, enabling advertisement placement and point granting.

[0061] The collection unit can collect detailed data on the actions a customer takes on the social networking site and their interests and concerns. Examples of such detailed data include, but are not limited to, click history, post content, and number of followers. For example, the collection unit can collect information on which posts a customer has "liked" on the social networking site. The collection unit can also collect information on which groups a customer is participating in. For example, the collection unit can collect information on the activities of groups the customer participates in. The collection unit can also collect information on the content of posts shared by customers. For example, the collection unit can collect information related to posts shared by customers. This allows for a detailed understanding of customer behavioral patterns and interests. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input customer behavioral data on the social networking site into AI, which can then analyze the customer's behavioral patterns and interests.

[0062] The analysis unit analyzes the collected data and can perform a detailed analysis of customer behavioral patterns and interests. Examples of behavioral pattern analysis include, but are not limited to, frequency analysis and sequence analysis. For example, the analysis unit performs a frequency analysis of customer behavioral patterns using the collected data. The analysis unit can also analyze customer behavioral patterns using sequence analysis. For example, the analysis unit analyzes the order and patterns of customer behavior. Examples of interest analysis include, but are not limited to, topic modeling and keyword extraction. For example, the analysis unit can analyze customer interests using topic modeling. The analysis unit can also analyze customer interests using keyword extraction. For example, the analysis unit extracts keywords from customer posts and analyzes their interests. This allows for a detailed analysis of customer behavioral patterns and interests. Some or all of the above-described processing performed by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into AI, which can then analyze behavioral patterns and interests.

[0063] The providing unit provides the analysis results to the company, and the company can place advertisements based on the provided data. The analysis results include, for example, statistical data, graphs, reports, etc., but are not limited to these examples. For example, the providing unit provides the analysis results to the company as statistical data. The providing unit can also provide the analysis results to the company as graphs. For example, the providing unit provides the analysis results in a graph format that is visually easy to understand. The providing unit can also provide the analysis results to the company as a report. For example, the providing unit provides the company with a report that details the analysis results. This allows the company to place advertisements based on the provided data. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results into AI, and the AI ​​can generate data to be provided to the company.

[0064] The awarding unit can award points to customers who provide information. Information provided includes, for example, answering a questionnaire, providing feedback, etc., but is not limited to these examples. The awarding unit, for example, awards points when a customer answers a questionnaire. The awarding unit can also award points when a customer provides feedback. For example, the awarding unit awards points based on the feedback provided by the customer. In this way, points can be awarded to customers who provide information. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input customer information provided data into AI, which can determine the timing and conditions for awarding points.

[0065] The awarding unit can award points when an advertisement is clicked. Conditions for when an advertisement is clicked include, for example, the number of clicks and the click rate, but are not limited to these examples. The awarding unit can award points, for example, when an advertisement reaches a certain number of clicks. The awarding unit can also award points when the click rate of the advertisement meets a certain standard. For example, the awarding unit awards points when the click rate of the advertisement is high. In this way, points can be awarded when an advertisement is clicked. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input advertisement click data into AI, which can determine the timing and conditions for awarding points.

[0066] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. For example, the collection unit can estimate emotions by analyzing the user's text data. The collection unit can also estimate emotions by recognizing the user's facial expressions. For example, the collection unit can estimate emotions based on changes in the user's facial expressions. The collection unit can also estimate emotions by analyzing the user's voice data. For example, the collection unit can estimate emotions by analyzing the tone and speed of the user's voice. This makes it possible to adjust the timing of information collection based on the user's emotions. 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 emotional data into AI, which can then adjust the timing of information collection.

[0067] The collection unit can analyze the user's past SNS activity history and select the optimal collection method. Examples of the optimal collection method include, but are not limited to, the frequency of data collection and the selection of a collection tool. For example, the collection unit prioritizes collecting posts that the user has frequently "liked" in the past. The collection unit can also collect activity details of groups the user has previously participated in. For example, the collection unit analyzes activity details of groups the user participates in and collects related information. The collection unit can also analyze content of posts the user has previously shared and collect related information. For example, the collection unit collects information related to posts the user has shared. This allows the user's past SNS activity history to be analyzed and the optimal collection method to be selected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past SNS activity data into AI, which then selects the optimal collection method.

[0068] When collecting information, the collection unit can filter the information based on the user's current interests and trends. Filtering criteria include, but are not limited to, keyword filtering and topic filtering. For example, the collection unit preferentially collects posts related to topics in which the user is currently interested. The collection unit can also filter and collect related information based on current trends. For example, the collection unit preferentially collects information related to current trends. The collection unit can also collect related information based on keywords recently searched by the user. For example, the collection unit collects information related to keywords recently searched by the user. This makes it possible to filter information based on the user's current interests and trends. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current interests and trend data into AI, which then performs filtering.

[0069] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. Criteria for determining the priority of information include, but are not limited to, importance scores and relevance scores. For example, when the user is stressed, the collection unit can prioritize collecting relaxing information. Furthermore, when the user is excited, the collection unit can also prioritize collecting entertainment-related information. For example, when the user is excited, the collection unit can prioritize collecting entertainment-related information. Furthermore, when the user is tired, the collection unit can also prioritize collecting relaxing content. For example, when the user is tired, the collection unit can prioritize collecting relaxing content. In this way, the priority of information to be collected can be determined based on the user's emotions. 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 emotional data into AI, which can then prioritize the information.

[0070] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Methods for collecting geographical location information include, but are not limited to, GPS data, IP addresses, etc. The collection unit, for example, prioritizes collecting event information in the area where the user is currently located. The collection unit can also collect information on nearby stores and services based on the user's geographical location. For example, the collection unit collects information on nearby stores and services based on the user's geographical location. The collection unit can also prioritize collecting tourist information about the travel destination when the user is traveling. For example, when the user is traveling, the collection unit prioritizes collecting tourist information about the travel destination. This makes it possible to prioritize collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into AI, which can then prioritize collecting highly relevant information.

[0071] When collecting information, the collection unit can analyze the user's social media activity and collect related information. The content of the social media activity includes, for example, the content of posts, comments, the number of likes, etc., but is not limited to these examples. For example, the collection unit collects information related to posts that the user recently "liked." The collection unit can also collect activity content of groups in which the user participates. For example, the collection unit collects activity content of groups in which the user participates. The collection unit can also collect information related to posts shared by the user. For example, the collection unit collects information related to posts shared by the user. This makes it possible to analyze the user's social media activity and collect related information. Some or all of the above-mentioned processing in the collection unit may be performed using, or without using, AI. For example, the collection unit can input the user's social media activity data into AI, which then collects related information.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Examples of presentation methods of the analysis include, but are not limited to, graph display, text display, infographics, etc. 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. For example, 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 visually stimulating analysis results when the user is excited. For example, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows the presentation method of the analysis to be adjusted based on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's emotion data into AI, which can then adjust the presentation method of the analysis.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. Criteria for evaluating the importance of information include, but are not limited to, an impact score, a relevance score, and the like. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. For example, the analysis unit performs a concise analysis of information with low importance. The analysis unit can also determine the priority of the analysis based on the importance. For example, the analysis unit determines the priority of the analysis based on the importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the information. 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 information importance data to AI, which can adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. Types of analysis algorithms include, but are not limited to, clustering algorithms and classification algorithms. For example, the analysis unit can apply a purchasing behavior analysis algorithm to information about products. The analysis unit can also apply a customer satisfaction analysis algorithm to information about services. For example, the analysis unit can apply a customer satisfaction analysis algorithm to information about services. The analysis unit can also apply a willingness to participate analysis algorithm to information about events. For example, the analysis unit can apply a willingness to participate analysis algorithm to information about events. This makes it possible to apply different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input category data of the information into AI, which can then apply an appropriate analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. Criteria for adjusting the length of the analysis include, but are not limited to, the analysis time and the level of detail of the analysis. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, the analysis unit can provide a visually stimulating analysis result if the user is excited. This allows the length of the analysis to be adjusted based on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's emotion data into AI, which can then adjust the length of the analysis.

[0076] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. Criteria for considering the time when the information was collected include, but are not limited to, the collection date and collection frequency. The analysis unit, for example, prioritizes the analysis of the most recent information. The analysis unit can also determine the priority of analysis for past information based on its importance. For example, the analysis unit can determine the priority of analysis for past information based on its importance. The analysis unit can also adjust the analysis schedule based on the time when the information was collected. For example, the analysis unit adjusts the analysis schedule based on the time when the information was collected. This makes it possible to determine the priority of analysis based on the time when the information 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 data on the time when the information was collected into AI, and the AI ​​can determine the priority of analysis.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. Criteria for evaluating the relevance of the information include, but are not limited to, co-occurrence analysis, relevance scores, and the like. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit postpones analysis of less relevant information. The analysis unit can also adjust the analysis schedule based on the relevance of the information. For example, the analysis unit adjusts the analysis schedule based on the relevance of the information. This makes it possible to adjust the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input relevance data of the information into AI, which can then adjust the order of analysis.

[0078] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. Criteria for adjusting the method of providing information include, but are not limited to, email provision and dashboard provision. For example, when the user is relaxed, the providing unit can provide detailed information. Furthermore, when the user is in a hurry, the providing unit can provide concise information that focuses on the main points. For example, when the user is in a hurry, the providing unit can provide concise information that focuses on the main points. Furthermore, when the user is excited, the providing unit can provide visually stimulating information. For example, when the user is excited, the providing unit provides visually stimulating information. This makes it possible to adjust the method of providing information based on the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into AI, which can then adjust the method of providing information.

[0079] The providing unit can adjust the level of detail of the data to be provided based on the company's needs when providing the data. Criteria for evaluating the company's needs include, but are not limited to, survey results, interview results, etc. For example, if the company needs detailed data, the providing unit can provide detailed analysis results. Furthermore, if the company needs concise data, the providing unit can provide analysis results that focus on the main points. For example, if the company needs concise data, the providing unit can provide analysis results that focus on the main points. Furthermore, the providing unit can adjust the level of detail of the data according to the company's needs. For example, the providing unit adjusts the level of detail of the data according to the company's needs. This makes it possible to adjust the level of detail of the data to be provided based on the company's needs. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input company needs data into AI and adjust the level of detail of the data provided by the AI.

[0080] The providing unit can apply an appropriate providing method depending on the company's industry and purpose when providing the data. Types of providing methods include, but are not limited to, report provision and API provision. For example, in the case of the retail industry, the providing unit can provide data on purchasing behavior. Furthermore, in the case of the service industry, the providing unit can also provide data on customer satisfaction. For example, in the case of the service industry, the providing unit can provide data on customer satisfaction. Furthermore, in the case of the event industry, the providing unit can also provide data on willingness to participate. For example, in the case of the event industry, the providing unit can provide data on willingness to participate. This makes it possible to apply the optimal providing method depending on the company's industry and purpose. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's industry and purpose data into AI, which can then apply the appropriate providing method.

[0081] The providing unit can estimate the user's emotions and determine the priority of data to be provided based on the estimated user's emotions. Criteria for determining the priority of data include, but are not limited to, importance scores and relevance scores. For example, when the user is relaxed, the providing unit can prioritize providing detailed data. Furthermore, when the user is in a hurry, the providing unit can prioritize providing data that emphasizes the main points. For example, when the user is in a hurry, the providing unit can prioritize providing data that emphasizes the main points. Furthermore, when the user is excited, the providing unit can prioritize providing visually stimulating data. For example, when the user is excited, the providing unit prioritizes providing visually stimulating data. This makes it possible to determine the priority of data to be provided based on the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into AI and determine the priority of data to be provided by the AI.

[0082] The providing unit can provide optimal data by taking into account the geographical location information of the company when providing the data. Methods for collecting geographical location information include, but are not limited to, GPS data, IP addresses, etc. The providing unit can, for example, provide market data for the region where the company is located. The providing unit can also provide related consumer data based on the geographical location of the company. For example, the providing unit provides related consumer data based on the geographical location of the company. The providing unit can also provide optimal advertising placement data according to the geographical location of the company. For example, the providing unit provides optimal advertising placement data according to the geographical location of the company. This makes it possible to provide optimal data by taking into account the geographical location information of the company. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the geographical location data of the company into AI, which can then provide optimal data.

[0083] When providing the data, the providing unit can customize the data to be provided by referring to the company's past advertising history. The content of the advertising history includes, for example, past advertising campaigns, advertising performance data, etc., but is not limited to these examples. For example, the providing unit analyzes the effectiveness of advertisements previously placed by the company and provides optimal data. The providing unit can also provide related data based on the company's past advertising history. For example, the providing unit provides related data based on the company's past advertising history. The providing unit can also provide customized data by referring to the company's past advertising history. For example, the providing unit provides customized data by referring to the company's past advertising history. This makes it possible to customize the data to be provided by referring to the company's past advertising history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's advertising history data into AI and customize the data provided by the AI.

[0084] The assigning unit can estimate the user's emotions and adjust the timing of point awarding based on the estimated user's emotions. Criteria for adjusting the timing of point awarding include, but are not limited to, immediately after an action or after a certain period of time. For example, when the user is relaxed, the assigning unit can flexibly adjust the timing of point awarding. Furthermore, when the user is in a hurry, the assigning unit can immediately award points. For example, when the user is in a hurry, the assigning unit can immediately award points. Furthermore, when the user is excited, the assigning unit can adjust the timing of point awarding in real time. For example, when the user is excited, the assigning unit adjusts the timing of point awarding in real time. This allows the timing of point awarding to be adjusted based on the user's emotions. Some or all of the above-described processing in the assigning unit may be performed using, or without, AI. For example, the assigning unit can input the user's emotion data into AI, which can then adjust the timing of point awarding.

[0085] When awarding points, the awarding unit can analyze the user's past behavioral history and select the optimal awarding method. Criteria for selecting the optimal awarding method include, but are not limited to, the type of points and the awarding conditions. For example, the awarding unit can award bonus points if the user has used points frequently in the past. Furthermore, the awarding unit can also award a benefit to encourage use of points if the user has not used points frequently in the past. For example, the awarding unit can award a benefit to encourage use of points if the user has not used points frequently in the past. Furthermore, the awarding unit can also select the optimal point awarding method based on the user's past behavioral history. For example, the awarding unit selects the optimal point awarding method based on the user's past behavioral history. This allows the user's past behavioral history to be analyzed and the optimal point awarding method to be selected. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or may be performed without AI. For example, the awarding unit can input the user's past behavioral history data into AI, which can then select the optimal point awarding method.

[0086] When awarding points, the awarding unit can customize the awarding method based on the user's current interests. Criteria for customizing the awarding method include, but are not limited to, points, coupons, gifts, etc. For example, the awarding unit awards points for products in which the user is currently interested. The awarding unit can also award points for services in which the user is currently interested. For example, the awarding unit awards points for services in which the user is currently interested. The awarding unit can also customize the point awarding method based on the user's current interests. For example, the awarding unit customizes the point awarding method based on the user's current interests. This makes it possible to customize the point awarding method based on the user's current interests. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input the user's current interest data into AI, which can then customize the awarding method.

[0087] The assigning unit can estimate the user's emotions and determine the priority of point assignment based on the estimated user's emotions. Criteria for determining the priority of point assignment include, but are not limited to, the importance and relevance of actions. For example, when the user is relaxed, the assigning unit flexibly adjusts the priority of point assignment. The assigning unit can also immediately assign points when the user is in a hurry. For example, when the user is in a hurry, the assigning unit immediately assigns points. The assigning unit can also adjust the priority of point assignment in real time when the user is excited. For example, when the user is excited, the assigning unit adjusts the priority of point assignment in real time. This makes it possible to determine the priority of point assignment based on the user's emotions. Some or all of the above-described processing in the assigning unit may be performed using, for example, AI, or may be performed without using AI. For example, the assigning unit can input the user's emotion data into AI, which can then determine the priority of point assignment.

[0088] When awarding points, the awarding unit can select the optimal awarding method by taking into account the user's geographical location information. Methods for collecting geographical location information include, but are not limited to, GPS data, IP addresses, etc. The awarding unit, for example, awards points that can be used at stores in the user's current area. The awarding unit can also award points that can be used at nearby services based on the user's geographical location. For example, the awarding unit awards points that can be used at nearby services based on the user's geographical location. If the user is traveling, the awarding unit can also award points that can be used at the user's travel destination. For example, if the user is traveling, the awarding unit awards points that can be used at the user's travel destination. This allows the optimal point awarding method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the awarding unit may be performed using, or without, AI. For example, the awarding unit can input the user's geographical location data into AI, which can then select the optimal point awarding method.

[0089] When awarding points, the awarding unit can analyze the user's social media activity and suggest a method for awarding points. The content of social media activity includes, but is not limited to, the content of posts, comments, and the number of likes. For example, the awarding unit can award points for products related to posts that the user recently "liked." The awarding unit can also award points based on the activity content of a group the user participates in. For example, the awarding unit can award points based on the activity content of a group the user participates in. The awarding unit can also award points for services related to posts shared by the user. For example, the awarding unit can award points for services related to posts shared by the user. This makes it possible to analyze the user's social media activity and suggest an optimal method for awarding points. Some or all of the above-described processing in the awarding unit may be performed using, or without, AI. For example, the awarding unit can input the user's social media activity data into AI, which can then suggest a method for awarding points. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and award 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 customer behavior data using the camera 42 and microphone 38B of the smart device 14, and grasps the customer's behavioral patterns and interests and concerns using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and analyzes the customer's behavioral patterns and interests and concerns in detail. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the analysis results to the company. The award unit is realized, for example, by the control unit 46A of the smart device 14, and awards points to customers who provide information. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and award 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 customer behavior data using the camera 42 and microphone 238 of the smart glasses 214, and grasps the customer's behavioral patterns and interests and concerns using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and performs detailed analysis of the customer's behavioral patterns and interests and concerns. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides the analysis results to the company. The award unit, realized, for example, by the control unit 46A of the smart glasses 214, awards points to customers who provide information. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and award 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 customer behavior data using the camera 42 and microphone 238 of the headset terminal 314, and grasps the customer's behavioral patterns and interests and concerns using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to perform a detailed analysis of the customer's behavioral patterns and interests and concerns. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the analysis results to the company. The award unit is realized, for example, by the control unit 46A of the headset terminal 314, and awards points to customers who provide information. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and awarding 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 customer behavior data using the camera 42 and microphone 238 of the robot 414, and grasps the customer's behavioral patterns and interests and concerns using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and performs a detailed analysis of the customer's behavioral patterns and interests and concerns. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the analysis results to the company. The awarding unit is realized, for example, by the control unit 46A of the robot 414, and awards points to customers who provide information.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The collection unit can analyze the user's past purchase history and prioritize collection of information on related products and services. For example, it collects new product information related to products the user has purchased in the past. The collection unit can also collect campaign information related to services the user has used in the past. For example, it collects campaign information related to services the user has used in the past. The collection unit can also collect related review and rating information based on the user's purchase history. For example, it collects review and rating information on products the user has purchased in the past. This makes it possible to analyze the user's past purchase history and prioritize collection of related information.

[0092] The analysis unit can take into account trends based on seasons and events when analyzing user behavior patterns. For example, the analysis unit analyzes user purchasing behavior during a particular season. The analysis unit can also analyze user behavior patterns during particular event periods. For example, the analysis unit analyzes user behavior patterns during events such as Christmas and Valentine's Day. The analysis unit can also analyze user interests by taking into account trends based on seasons and events. For example, the analysis unit analyzes interest in outdoor gear in the summer. This makes it possible to analyze user behavior patterns and interests in detail by taking into account trends based on seasons and events.

[0093] The providing unit can customize and provide the analysis results based on the company's marketing strategy. For example, the providing unit provides analysis results tailored to the customer demographic targeted by the company. The providing unit can also customize the analysis results according to the objectives of the company's marketing campaign. For example, the providing unit provides analysis results tailored to a promotional campaign for a new product. The providing unit can also adjust the format and content of the analysis results based on the company's marketing strategy. For example, the providing unit adjusts the format and content of the analysis results based on the company's marketing strategy. This allows the analysis results to be customized and provided based on the company's marketing strategy.

[0094] The awarding unit can award points for specific products or services based on the user's purchase history. For example, the awarding unit awards additional points for products that the user has purchased in the past. The awarding unit can also award bonus points for services that the user has used in the past. For example, the awarding unit awards bonus points for services that the user has used in the past. The awarding unit can also award points for related products or services based on the user's purchase history. For example, the awarding unit awards points for new products that are related to products that the user has purchased in the past. This makes it possible to award points for specific products or services based on the user's purchase history.

[0095] The collection unit can estimate the user's emotions and adjust the frequency of information collection based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of information collection can be reduced. The collection unit can also increase the frequency of information collection when the user is relaxed. For example, the collection unit increases the frequency of information collection when the user is relaxed. The collection unit can also adjust the frequency of information collection in real time when the user is excited. For example, the collection unit adjusts the frequency of information collection in real time when the user is excited. This makes it possible to adjust the frequency of information collection based on the user's emotions.

[0096] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis is performed. Furthermore, the analysis unit can also perform a brief analysis when the user is in a hurry. For example, if the user is in a hurry, the analysis unit can perform a brief analysis. Furthermore, the analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This makes it possible to adjust the accuracy of the analysis based on the user's emotions.

[0097] The providing unit can estimate the user's emotions and adjust the format of the data to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit provides data in a detailed report format. Furthermore, when the user is in a hurry, the providing unit can provide concise data that focuses on the main points. For example, when the user is in a hurry, the providing unit provides concise data that focuses on the main points. Furthermore, when the user is excited, the providing unit can provide visually stimulating data. For example, when the user is excited, the providing unit provides visually stimulating data. In this way, the format of the data to be provided can be adjusted based on the user's emotions.

[0098] The awarding unit can estimate the user's emotions and adjust the conditions for awarding points based on the estimated user's emotions. For example, if the user is relaxed, the conditions for awarding points are relaxed. Furthermore, the awarding unit can also tighten the conditions for awarding points when the user is in a hurry. For example, if the user is in a hurry, the awarding unit tightens the conditions for awarding points. Furthermore, the awarding unit can also adjust the conditions for awarding points in real time when the user is excited. For example, if the user is excited, the awarding unit adjusts the conditions for awarding points in real time. In this way, the conditions for awarding points can be adjusted based on the user's emotions.

[0099] The assigning unit can estimate the user's emotion and select the type of point to be assigned based on the estimated user's emotion. For example, if the user is relaxed, the assigning unit assigns relaxation-related points. Furthermore, if the user is in a hurry, the assigning unit can assign immediately usable points. For example, if the user is in a hurry, the assigning unit assigns immediately usable points. Furthermore, if the user is excited, the assigning unit can assign entertainment-related points. For example, if the user is excited, the assigning unit assigns entertainment-related points. In this way, the type of point to be assigned can be selected based on the user's emotion.

[0100] The collection unit can prioritize collecting trend and event information specific to a region, taking into account the user's geographical location information. For example, the collection unit collects local event information for the region where the user is currently located. The collection unit can also collect trend information specific to a region based on the user's geographical location. For example, the collection unit collects trend information for the region where the user is currently located. The collection unit can also prioritize collecting tourist information for the travel destination, if the user is traveling. For example, the collection unit prioritizes collecting tourist information for the travel destination, if the user is traveling. This makes it possible to prioritize collecting trend and event information specific to a region, taking into account the user's geographical location information.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects information from social media. For example, the collection unit collects detailed data on what actions customers take on social media and what interests and concerns they have. For example, it collects data such as which posts customers "like" and which groups they join. This allows the system to understand customers' behavioral patterns and interests. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and performs detailed analysis of customer behavior patterns and interests. For example, it can identify customers who are highly interested in a particular product and target them with appropriate advertisements. This enables companies to display personalized advertisements tailored to customer needs. Step 3: The providing unit provides the results of the analysis performed by the analysis unit to the company. For example, the providing unit provides the analysis results to the company, and the company places advertisements based on the provided data. For example, by placing advertisements related to a particular product for customers who have a high level of interest in that product, the effectiveness of the advertisement can be maximized. Step 4: The granting unit places an advertisement based on the data provided by the providing unit and grants points based on the results. The granting unit, for example, grants points to the customer who provided the information. This allows the customer to receive points in return for providing the information, and the points can be used for shopping on an e-commerce site, for example. For example, if an advertisement is placed based on the information provided by the customer and the advertisement is clicked, points are granted to the customer.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0126] 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.

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

[0128] 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).

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

[0130] 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.

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

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

[0133] 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.

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

[0135] 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.

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

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0142] 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.

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

[0144] 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).

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 department that collects information from SNS, an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides the results of the analysis by the analysis unit to a company; an awarding unit that places an advertisement based on the data provided by the providing unit and awards points based on the results of the advertisement. A system characterized by:

2. The collecting unit Collect detailed data on how customers behave on social media and what their interests and concerns are.

2. The system of claim 1.

3. The analysis unit Analyze the collected data and conduct detailed analysis of customer behavior patterns and interests.

2. The system of claim 1.

4. The providing unit The analysis results are provided to companies, who then place advertisements based on the data provided.

2. The system of claim 1.

5. The granting unit Award points to customers who provide information 2. The system of claim 1.

6. The granting unit Award points when ads are clicked 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past social media activity history and select the appropriate collection method 2. The system of claim 1.

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