system

Abstract

The system according to this embodiment aims to perform data analysis and propose countermeasures to improve engagement between companies and consumers or products. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an alert unit, and a proposal unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The alert unit issues an alert based on the analysis results obtained by the analysis unit. The proposal unit proposes causes and countermeasures based on the alert issued by the alert unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that sufficient data analysis and countermeasure proposals for continuously improving the engagement between enterprises and consumers or products were not carried out.

[0005] The system according to the embodiment aims to perform data analysis and countermeasure proposals for improving the engagement between enterprises and consumers or products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an alert unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The alert unit issues alerts based on the analysis results obtained by the analysis unit. The proposal unit proposes causes and countermeasures based on the alerts issued by the alert unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform data analysis and propose countermeasures to improve engagement between companies and consumers or products. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the EngageAI system of the present invention is a system that periodically surveys the engagement between a company and consumers or products, and uses AI to analyze the data and generate reports. The EngageAI system allows companies to periodically conduct surveys about consumers and products using an online survey platform. For example, a survey is created that includes questions about consumer satisfaction and product evaluations, and distributed to registered users of the online survey platform. This allows companies to collect feedback from a wide range of consumers. Next, the collected survey data is analyzed by the AI. The AI ​​analyzes the survey response data and calculates engagement indicators. For example, it calculates indicators such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows companies to understand the current state of consumer and product engagement. If a particular indicator declines, the AI ​​issues an alert. For example, if the NPS in the food category falls to 25, the AI ​​issues an alert and notifies the company. Furthermore, the AI ​​suggests possible causes and countermeasures. For example, if delivery delays or dissatisfaction with the quality of customer support are the main causes, the AI ​​suggests that improvements to the delivery process and the quality of customer support are necessary. Companies can use this service on an annual contract to continuously improve customer engagement. For example, by conducting regular surveys and monitoring engagement metrics based on data analyzed by AI, companies can quickly identify problems and take corrective action. This can improve customer satisfaction and loyalty. In this way, the EngageAI system can efficiently improve engagement between companies and consumers, or between companies and products.

[0029] The EngageAI system according to this embodiment comprises a data collection unit, an analysis unit, an alert unit, and a suggestion unit. The data collection unit collects data. The data collection unit can, for example, collect data using an online survey platform. The data collection unit can, for example, create surveys regarding consumer satisfaction and product evaluations and distribute them to registered users of the online survey platform. This allows the data collection unit to collect feedback from a wide range of consumers. The data collection unit can, for example, collect survey response data and store it in a database. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use AI to analyze survey response data and calculate engagement indicators. For example, the analysis unit calculates indicators such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows the analysis unit to understand the current state of consumer and product engagement. The alert unit issues alerts based on the analysis results obtained by the analysis unit. The alert unit can, for example, issue an alert when a specific indicator declines. For example, the alert unit issues an alert and notifies the company if the NPS in the food category falls to 25. The suggestion unit proposes causes and countermeasures based on the alert issued by the alert unit. The suggestion unit can, for example, use AI to propose possible causes and countermeasures. For example, if the suggestion unit determines that the main causes are delivery delays or dissatisfaction with the quality of customer support, it may suggest that improvements to the delivery process and the quality of customer support are necessary. This allows the EngageAI system according to the embodiment to efficiently collect, analyze, issue alerts, and propose causes and countermeasures. Some or all of the above-described processes in the collection unit, analysis unit, alert unit, and suggestion unit may be performed using AI, for example, or not using AI. For example, the collection unit can input data collected using an online survey platform into AI and have the AI ​​perform data analysis.

[0030] The data collection unit collects data. For example, the data collection unit can collect data using an online survey platform. Specifically, the data collection unit creates surveys on consumer satisfaction and product evaluations and distributes them to registered users of the online survey platform. The survey design is carefully considered, taking into account the types and order of questions and the answer format, to make it easy for respondents to answer. For example, by combining multiple-choice questions and open-ended questions, both quantitative and qualitative data can be collected. The data collection unit collects the survey response data in real time and stores it in a database. The database is designed to efficiently manage the response data and make it available for subsequent analysis and processing. Furthermore, the data collection unit can improve the response rate by monitoring the distribution status and response rate of the survey and sending reminders as needed. This allows the data collection unit to efficiently collect feedback from a wide range of consumers and ensure the quality and quantity of data.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses AI to analyze survey response data and calculate engagement metrics. Specifically, the AI ​​uses natural language processing technology to analyze free-response answers and performs sentiment analysis and topic modeling. This allows for a quantitative evaluation of consumer opinions and emotions. For multiple-choice response data, statistical methods are used to calculate metrics such as NPS (Net Promoter Score) and customer loyalty. Furthermore, the AI ​​can also predict future engagement fluctuations by considering past data and trends. For example, it can predict engagement fluctuations at specific times by considering the impact of seasons and campaigns, providing companies with information to take countermeasures in advance. This allows the analysis department to understand the current state of consumer and product engagement and predict future risks and opportunities.

[0032] The alert unit issues alerts based on the analysis results obtained by the analysis unit. For example, the alert unit can issue an alert when a specific metric declines. Specifically, if the NPS in the food category falls to 25, the alert unit will issue an alert and notify the company. Alerts are sent using multiple communication methods, such as email, SMS, and push notifications. This allows company personnel to quickly understand the situation and take appropriate action. The alert unit can also set notification priorities according to the importance and urgency of the alert. For example, in the event of a serious problem such as a sharp decline in NPS or a significant decrease in customer loyalty, an alert will be issued immediately and notified directly to the company's management. This allows the alert unit to help companies respond quickly and appropriately, preventing a decline in engagement.

[0033] The Proposal Department proposes causes and countermeasures based on alerts issued by the Alert Department. For example, the Proposal Department can use AI to propose potential causes and countermeasures. Specifically, the AI ​​refers to past data and similar cases to identify the root cause of the problem. For instance, if delivery delays or dissatisfaction with the quality of customer support are the main causes, the AI ​​will identify these factors and propose specific improvement measures. The Proposal Department might suggest improvements to the delivery process or enhancements to customer support quality. Furthermore, the Proposal Department can evaluate the feasibility and effectiveness of proposals and prioritize them. For example, considering cost and resource constraints, it can select and propose the most effective and feasible countermeasures to the company. The Proposal Department can also monitor the implementation status and effectiveness of proposals and make additional suggestions or modifications as needed. This allows the Proposal Department to help companies resolve problems quickly and effectively and improve engagement.

[0034] The Survey Department conducts surveys. The Survey Department can, for example, create surveys using an online survey platform and distribute surveys that include questions about consumers and products. For example, the Survey Department can create surveys that include questions about consumer satisfaction and product evaluations and distribute them to registered users of an online survey platform. This allows the Survey Department to collect feedback from a wide range of consumers. The Survey Department can, for example, collect survey response data and store it in a database. This allows the Survey Department to collect data through surveys. Some or all of the above processes in the Survey Department may be performed using AI, for example, or not using AI. For example, the Survey Department can input data collected using an online survey platform into an AI and have the AI ​​perform data analysis.

[0035] The metrics unit calculates engagement metrics. For example, the metrics unit can use AI to analyze survey response data and calculate engagement metrics. For example, the metrics unit calculates metrics such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows the metrics unit to understand the current state of consumer and product engagement. For example, the metrics unit can collect survey response data and store it in a database. This allows the metrics unit to calculate engagement metrics. Some or all of the above processing in the metrics unit may be performed using AI, or not using AI. For example, the metrics unit can input data collected using an online survey platform into an AI and have the AI ​​perform data analysis.

[0036] The Contracts Department manages annual contracts. For example, the Contracts Department can manage contracts for companies to use the EngageAI system on an annual basis. For example, the Contracts Department manages contract periods and terms to ensure that companies can continuously use the EngageAI system. In this way, the Contracts Department can manage annual contracts. Some or all of the above processes in the Contracts Department may be performed using AI, for example, or not using AI. For example, the Contracts Department can input contract information into AI and have the AI ​​perform contract management.

[0037] The data collection unit can collect data using an online survey platform. For example, the data collection unit can create surveys using an online survey platform and distribute surveys that include questions about consumers and products. For example, the data collection unit can create surveys that include questions about consumer satisfaction and product evaluations and distribute them to registered users of the online survey platform. This allows the data collection unit to collect feedback from a wide range of consumers. For example, the data collection unit can collect survey response data and store it in a database. This allows the data collection unit to collect data using an online survey platform. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data collected using an online survey platform into an AI and have the AI ​​perform data analysis.

[0038] The analysis department can analyze survey response data and calculate engagement metrics. For example, the analysis department can use AI to analyze survey response data and calculate engagement metrics. For instance, the analysis department can calculate metrics such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows the analysis department to understand the current state of consumer and product engagement. The analysis department can collect survey response data and store it in a database. This allows the analysis department to analyze the survey response data and calculate engagement metrics. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input data collected using an online survey platform into an AI and have the AI ​​perform the data analysis.

[0039] The alert unit can issue an alert when a specific indicator falls. For example, the alert unit can issue an alert and notify the company if the NPS in the food category falls to 25. Thus, the alert unit can issue an alert when a specific indicator falls. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​issue an alert.

[0040] The proposal department can propose causes and countermeasures based on alerts. For example, the proposal department can use AI to propose possible causes and countermeasures. For example, if the main causes are delivery delays or dissatisfaction with the quality of customer support, the proposal department may propose improvements to the delivery process and the quality of customer support. In this way, the proposal department can propose causes and countermeasures based on alerts. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input alerts issued by the alert department into the AI ​​and have the AI ​​execute suggestions for causes and countermeasures.

[0041] The data collection unit can analyze past survey response history and select the most suitable question format. For example, if a user has shown a high response rate to multiple-choice questions in the past, the data collection unit can prioritize multiple-choice questions. For example, if a user has provided detailed answers to open-ended questions in the past, the data collection unit can prioritize open-ended questions. For example, the data collection unit can prioritize question formats that a user has completed quickly in the past. In this way, the data collection unit can analyze past survey response history and select the most suitable question format. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past survey response history into AI and have the AI ​​select the most suitable question format.

[0042] The data collection unit can customize the content of survey questions based on the user's current areas of interest when distributing them. For example, the data collection unit can prioritize distributing questions about products the user has recently purchased. For example, the data collection unit can distribute questions related to keywords the user has recently searched for. For example, the data collection unit can distribute questions about events the user has recently attended. This allows the data collection unit to customize the content of questions based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user area of ​​interest data into AI and have the AI ​​perform the customization of the questions.

[0043] The data collection unit can prioritize the delivery of highly relevant questions when distributing surveys, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit can deliver questions related to that region. For example, if the user is traveling, the data collection unit can deliver questions related to their travel destination. For example, if the user is in a specific store, the data collection unit can deliver questions related to that store. In this way, the data collection unit can prioritize the delivery of highly relevant questions, taking into account the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​deliver the questions.

[0044] The data collection unit can analyze a user's social media activity and deliver relevant questions when distributing a survey. For example, the data collection unit can deliver questions about products the user has recently mentioned on social media. For example, the data collection unit can deliver questions about online events the user has recently attended. For example, the data collection unit can deliver questions related to accounts the user has recently followed. This allows the data collection unit to analyze a user's social media activity and deliver relevant questions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into AI and have AI deliver the questions.

[0045] The analysis unit can adjust the level of detail of its analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For example, it can perform a simplified analysis on less important data. For example, it can perform a moderate level of detail on data of moderate importance. In this way, the analysis unit can adjust the level of detail of its analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0046] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, the analysis department can apply a sentiment analysis algorithm to consumer satisfaction data. For example, the analysis department can apply a rating score analysis algorithm to product evaluation data. For example, the analysis department can apply a loyalty score analysis algorithm to customer loyalty data. In this way, the analysis department can apply different analysis algorithms depending on the data category. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the data category into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0047] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department may prioritize the analysis of the most recent data. For example, the analysis department may postpone the analysis of older data. For example, the analysis department may analyze data with a medium submission date with a medium priority. This allows the analysis department to determine the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into the AI ​​and have the AI ​​perform the determination of the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may postpone the analysis of data with low relevance. For example, the analysis unit may analyze data with moderate relevance with moderate priority. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0049] The alert unit can adjust the level of detail of an alert based on the importance of a specific indicator when an alert is issued. For example, the alert unit can issue a detailed alert for an indicator of high importance. For example, the alert unit can issue a simplified alert for an indicator of low importance. For example, the alert unit can issue an alert with a moderate level of detail for an indicator of medium importance. In this way, the alert unit can adjust the level of detail of an alert based on the importance of a specific indicator. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the importance of a specific indicator into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the alert.

[0050] The alert unit can apply different alert algorithms depending on the metric category when an alert is issued. For example, the alert unit can apply a sentiment analysis algorithm to a consumer satisfaction metric. For example, the alert unit can apply a rating score analysis algorithm to a product evaluation metric. For example, the alert unit can apply a loyalty score analysis algorithm to a customer loyalty metric. In this way, the alert unit can apply different alert algorithms depending on the metric category. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the metric category into the AI ​​and have the AI ​​execute the application of the alert algorithm.

[0051] The alert unit can determine the priority of alerts based on the submission timing of the indicators when an alert is issued. For example, the alert unit may issue alerts preferentially for the most recent indicators. For example, the alert unit may issue alerts later for older indicators. For example, the alert unit may issue alerts with a medium priority for indicators that have been submitted at a moderate time. In this way, the alert unit can determine the priority of alerts based on the submission timing of the indicators. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit may input the submission timing of the indicators into the AI ​​and have the AI ​​perform the determination of the alert priority.

[0052] The alert unit can adjust the order of alerts based on the relevance of the indicators when an alert is issued. For example, the alert unit can issue alerts preferentially for indicators with high relevance. For example, the alert unit can issue alerts later for indicators with low relevance. For example, the alert unit can issue alerts with medium relevance with medium priority. In this way, the alert unit can adjust the order of alerts based on the relevance of the indicators. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the relevance of the indicators into the AI ​​and have the AI ​​perform the adjustment of the order of alerts.

[0053] The proposal department can adjust the level of detail of a proposal based on the importance of the cause. For example, the proposal department can provide detailed proposals for causes with high importance. For example, it can provide simplified proposals for causes with low importance. For example, it can provide proposals with a moderate level of detail for causes with moderate importance. In this way, the proposal department can adjust the level of detail of a proposal based on the importance of the cause. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the cause into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the proposal.

[0054] The proposal department can apply different proposal algorithms depending on the category of the cause when making a proposal. For example, for delivery delays, the proposal department can make suggestions to improve the delivery process. For example, for dissatisfaction with the quality of customer support, the proposal department can make suggestions to improve the quality of customer support. For example, for dissatisfaction with product quality, the proposal department can make suggestions to improve quality control. In this way, the proposal department can apply different proposal algorithms depending on the category of the cause. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the category of the cause into the AI ​​and have the AI ​​execute the application of the proposal algorithm.

[0055] The proposal department can determine the priority of proposals based on the submission date of the cause. For example, the proposal department can prioritize proposals for the most recent cause. For example, it can postpone proposals for older causes. For example, it can give a moderate priority to proposals for causes that were submitted at a moderate time. In this way, the proposal department can determine the priority of proposals based on the submission date of the cause. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission date of the cause into the AI ​​and have the AI ​​perform the determination of the proposal priority.

[0056] The proposal unit can adjust the order of proposals based on the relevance of the causes. For example, the proposal unit can prioritize proposals for causes with high relevance. For example, the proposal unit can postpone proposals for causes with low relevance. For example, the proposal unit can give a medium priority to proposals for causes with moderate relevance. In this way, the proposal unit can adjust the order of proposals based on the relevance of the causes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the causes into the AI ​​and have the AI ​​perform the adjustment of the order of proposals.

[0057] The survey department can select the most appropriate questions by referring to past survey response history when conducting a survey. For example, the survey department may prioritize question formats in which users have previously provided detailed answers. For example, the survey department may prioritize question formats in which users have previously shown a high response rate. For example, the survey department may prioritize question formats in which users have previously completed answers in a short amount of time. In this way, the survey department can select the most appropriate questions by referring to past survey response history. Some or all of the above processing in the survey department may be performed using AI, for example, or without using AI. For example, the survey department may input past survey response history into AI and have the AI ​​perform the selection of the most appropriate questions.

[0058] The survey department can select the most appropriate questions when conducting a survey, taking into account the user's geographical location. For example, if the user is in a specific region, the survey department can deliver questions related to that region. For example, if the user is traveling, the survey department can deliver questions related to their travel destination. For example, if the user is in a specific store, the survey department can deliver questions related to that store. In this way, the survey department can select the most appropriate questions, taking into account the user's geographical location. Some or all of the above processing in the survey department may be performed using AI, for example, or not using AI. For example, the survey department can input the user's geographical location information into AI and have the AI ​​select the most appropriate questions.

[0059] The indicator unit can adjust the level of detail of an indicator based on the importance of the data when calculating an indicator. For example, the indicator unit can calculate a detailed indicator for data with high importance. For example, the indicator unit can calculate a simplified indicator for data with low importance. For example, the indicator unit can calculate an indicator with a moderate level of detail for data with moderate importance. In this way, the indicator unit can adjust the level of detail of an indicator based on the importance of the data. Some or all of the above processing in the indicator unit may be performed using AI, for example, or without AI. For example, the indicator unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the indicator.

[0060] The indicator unit can determine the priority of indicators based on the data submission timing when calculating indicators. For example, the indicator unit can calculate indicators with priority given to the most recent data. For example, the indicator unit can calculate indicators with priority given to older data. For example, the indicator unit can calculate indicators with a moderate priority given to data submitted at a moderate time. In this way, the indicator unit can determine the priority of indicators based on the data submission timing. Some or all of the above processing in the indicator unit may be performed using AI, for example, or without AI. For example, the indicator unit can input the data submission timing into AI and have AI perform the determination of indicator priority.

[0061] The contract department can propose the optimal contract plan by referring to past contract history during contract management. For example, the contract department can propose the optimal contract plan based on contract plans previously used by the user. For example, the contract department can propose the most cost-effective contract plan based on the user's past contract history. For example, the contract department can analyze the user's past contract history and propose the most suitable contract plan. In this way, the contract department can propose the optimal contract plan by referring to past contract history. Some or all of the above processes in the contract department may be performed using AI, for example, or not using AI. For example, the contract department can input past contract history into AI and have the AI ​​propose the optimal contract plan.

[0062] The Contracts Department can propose the optimal contract plan when managing contracts, taking into account the user's geographical location. For example, if the user is in a specific region, the Contracts Department can propose a contract plan related to that region. For example, if the user is traveling, the Contracts Department can propose a contract plan related to the travel destination. For example, if the user is in a specific store, the Contracts Department can propose a contract plan related to that store. In this way, the Contracts Department can propose the optimal contract plan considering the user's geographical location. Some or all of the above processing in the Contracts Department may be performed using AI, for example, or not using AI. For example, the Contracts Department can input the user's geographical location information into AI and have the AI ​​propose the optimal contract plan.

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

[0064] The EngageAI system can further include a survey unit that analyzes the user's past survey response history and selects the most suitable question format. For example, if a user has a high response rate to multiple-choice questions in the past, multiple-choice questions can be prioritized. If a user has provided detailed answers to open-ended questions in the past, open-ended questions can be prioritized. Question formats that a user has completed quickly in the past can also be prioritized. This allows the survey unit to analyze past survey response history and select the most suitable question format. Some or all of the above processing in the survey unit may be performed using AI, for example, or without AI. For example, the survey unit can input past survey response history into AI and have AI select the most suitable question format.

[0065] The EngageAI system may also include a survey unit that prioritizes the delivery of highly relevant questions by considering the user's geographical location. For example, if a user is in a specific region, questions related to that region can be delivered. If a user is traveling, questions related to their travel destination can be delivered. If a user is in a specific store, questions related to that store can be delivered. This allows the survey unit to prioritize the delivery of highly relevant questions by considering the user's geographical location. Some or all of the above processing in the survey unit may be performed using AI, for example, or without AI. For example, the survey unit can input the user's geographical location information into the AI ​​and have the AI ​​deliver the questions.

[0066] The EngageAI system may further include a survey unit that analyzes the user's social media activity and delivers relevant questions. For example, it can deliver questions about products the user has recently mentioned on social media. It can deliver questions about online events the user has recently attended. It can deliver questions related to accounts the user has recently followed. This allows the survey unit to analyze the user's social media activity and deliver relevant questions. Some or all of the above processing in the survey unit may be performed using AI, for example, or not using AI. For example, the survey unit can input the user's social media activity data into the AI ​​and have the AI ​​deliver the questions.

[0067] The EngageAI system may further include an analysis unit that adjusts the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and a moderate level of detail on data of moderate importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0068] The EngageAI system may further include an analysis unit that applies different analysis algorithms depending on the data category. For example, a sentiment analysis algorithm can be applied to consumer satisfaction data. A rating score analysis algorithm can be applied to product evaluation data. A loyalty score analysis algorithm can be applied to customer loyalty data. This allows the analysis unit to apply different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

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

[0070] Step 1: The data collection unit collects data. The data collection unit can collect data using, for example, an online survey platform. The data collection unit can create surveys on consumer satisfaction and product evaluations and distribute them to registered users of the online survey platform. This allows the data collection unit to collect feedback from a wide range of consumers. The data collection unit can collect survey response data and store it in a database. Step 2: The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses AI to analyze survey response data and calculate engagement metrics. For instance, the analysis department calculates metrics such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows the analysis department to understand the current state of consumer and product engagement. Step 3: The alert unit issues alerts based on the analysis results obtained by the analysis unit. The alert unit can issue alerts, for example, when a specific indicator declines. For example, the alert unit will issue an alert and notify the company if the NPS in the food category falls to 25. Step 4: The proposal team proposes causes and countermeasures based on the alerts issued by the alert team. The proposal team can, for example, use AI to propose possible causes and countermeasures. For example, if the main causes are delivery delays or dissatisfaction with the quality of customer support, the proposal team may suggest improvements to the delivery process or enhancements to the quality of customer support.

[0071] (Example of form 2) An embodiment of the EngageAI system of the present invention is a system that periodically surveys the engagement between a company and consumers or products, and uses AI to analyze the data and generate reports. The EngageAI system allows companies to periodically conduct surveys about consumers and products using an online survey platform. For example, a survey is created that includes questions about consumer satisfaction and product evaluations, and distributed to registered users of the online survey platform. This allows companies to collect feedback from a wide range of consumers. Next, the collected survey data is analyzed by the AI. The AI ​​analyzes the survey response data and calculates engagement indicators. For example, it calculates indicators such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows companies to understand the current state of consumer and product engagement. If a particular indicator declines, the AI ​​issues an alert. For example, if the NPS in the food category falls to 25, the AI ​​issues an alert and notifies the company. Furthermore, the AI ​​suggests possible causes and countermeasures. For example, if delivery delays or dissatisfaction with the quality of customer support are the main causes, the AI ​​suggests that improvements to the delivery process and the quality of customer support are necessary. Companies can use this service on an annual contract to continuously improve customer engagement. For example, by conducting regular surveys and monitoring engagement metrics based on data analyzed by AI, companies can quickly identify problems and take corrective action. This can improve customer satisfaction and loyalty. In this way, the EngageAI system can efficiently improve engagement between companies and consumers, or between companies and products.

[0072] The EngageAI system according to this embodiment comprises a data collection unit, an analysis unit, an alert unit, and a suggestion unit. The data collection unit collects data. The data collection unit can, for example, collect data using an online survey platform. The data collection unit can, for example, create surveys regarding consumer satisfaction and product evaluations and distribute them to registered users of the online survey platform. This allows the data collection unit to collect feedback from a wide range of consumers. The data collection unit can, for example, collect survey response data and store it in a database. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use AI to analyze survey response data and calculate engagement indicators. For example, the analysis unit calculates indicators such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows the analysis unit to understand the current state of consumer and product engagement. The alert unit issues alerts based on the analysis results obtained by the analysis unit. The alert unit can, for example, issue an alert when a specific indicator declines. For example, the alert unit issues an alert and notifies the company if the NPS in the food category falls to 25. The suggestion unit proposes causes and countermeasures based on the alert issued by the alert unit. The suggestion unit can, for example, use AI to propose possible causes and countermeasures. For example, if the suggestion unit determines that the main causes are delivery delays or dissatisfaction with the quality of customer support, it may suggest that improvements to the delivery process and the quality of customer support are necessary. This allows the EngageAI system according to the embodiment to efficiently collect, analyze, issue alerts, and propose causes and countermeasures. Some or all of the above-described processes in the collection unit, analysis unit, alert unit, and suggestion unit may be performed using AI, for example, or not using AI. For example, the collection unit can input data collected using an online survey platform into AI and have the AI ​​perform data analysis.

[0073] The data collection unit collects data. For example, the data collection unit can collect data using an online survey platform. Specifically, the data collection unit creates surveys on consumer satisfaction and product evaluations and distributes them to registered users of the online survey platform. The survey design is carefully considered, taking into account the types and order of questions and the answer format, to make it easy for respondents to answer. For example, by combining multiple-choice questions and open-ended questions, both quantitative and qualitative data can be collected. The data collection unit collects the survey response data in real time and stores it in a database. The database is designed to efficiently manage the response data and make it available for subsequent analysis and processing. Furthermore, the data collection unit can improve the response rate by monitoring the distribution status and response rate of the survey and sending reminders as needed. This allows the data collection unit to efficiently collect feedback from a wide range of consumers and ensure the quality and quantity of data.

[0074] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses AI to analyze survey response data and calculate engagement metrics. Specifically, the AI ​​uses natural language processing technology to analyze free-response answers and performs sentiment analysis and topic modeling. This allows for a quantitative evaluation of consumer opinions and emotions. For multiple-choice response data, statistical methods are used to calculate metrics such as NPS (Net Promoter Score) and customer loyalty. Furthermore, the AI ​​can also predict future engagement fluctuations by considering past data and trends. For example, it can predict engagement fluctuations at specific times by considering the impact of seasons and campaigns, providing companies with information to take countermeasures in advance. This allows the analysis department to understand the current state of consumer and product engagement and predict future risks and opportunities.

[0075] The alert unit issues alerts based on the analysis results obtained by the analysis unit. For example, the alert unit can issue an alert when a specific metric declines. Specifically, if the NPS in the food category falls to 25, the alert unit will issue an alert and notify the company. Alerts are sent using multiple communication methods, such as email, SMS, and push notifications. This allows company personnel to quickly understand the situation and take appropriate action. The alert unit can also set notification priorities according to the importance and urgency of the alert. For example, in the event of a serious problem such as a sharp decline in NPS or a significant decrease in customer loyalty, an alert will be issued immediately and notified directly to the company's management. This allows the alert unit to help companies respond quickly and appropriately, preventing a decline in engagement.

[0076] The Proposal Department proposes causes and countermeasures based on alerts issued by the Alert Department. For example, the Proposal Department can use AI to propose potential causes and countermeasures. Specifically, the AI ​​refers to past data and similar cases to identify the root cause of the problem. For instance, if delivery delays or dissatisfaction with the quality of customer support are the main causes, the AI ​​will identify these factors and propose specific improvement measures. The Proposal Department might suggest improvements to the delivery process or enhancements to customer support quality. Furthermore, the Proposal Department can evaluate the feasibility and effectiveness of proposals and prioritize them. For example, considering cost and resource constraints, it can select and propose the most effective and feasible countermeasures to the company. The Proposal Department can also monitor the implementation status and effectiveness of proposals and make additional suggestions or modifications as needed. This allows the Proposal Department to help companies resolve problems quickly and effectively and improve engagement.

[0077] The Survey Department conducts surveys. The Survey Department can, for example, create surveys using an online survey platform and distribute surveys that include questions about consumers and products. For example, the Survey Department can create surveys that include questions about consumer satisfaction and product evaluations and distribute them to registered users of an online survey platform. This allows the Survey Department to collect feedback from a wide range of consumers. The Survey Department can, for example, collect survey response data and store it in a database. This allows the Survey Department to collect data through surveys. Some or all of the above processes in the Survey Department may be performed using AI, for example, or not using AI. For example, the Survey Department can input data collected using an online survey platform into an AI and have the AI ​​perform data analysis.

[0078] The metrics unit calculates engagement metrics. For example, the metrics unit can use AI to analyze survey response data and calculate engagement metrics. For example, the metrics unit calculates metrics such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows the metrics unit to understand the current state of consumer and product engagement. For example, the metrics unit can collect survey response data and store it in a database. This allows the metrics unit to calculate engagement metrics. Some or all of the above processing in the metrics unit may be performed using AI, or not using AI. For example, the metrics unit can input data collected using an online survey platform into an AI and have the AI ​​perform data analysis.

[0079] The Contracts Department manages annual contracts. For example, the Contracts Department can manage contracts for companies to use the EngageAI system on an annual basis. For example, the Contracts Department manages contract periods and terms to ensure that companies can continuously use the EngageAI system. In this way, the Contracts Department can manage annual contracts. Some or all of the above processes in the Contracts Department may be performed using AI, for example, or not using AI. For example, the Contracts Department can input contract information into AI and have the AI ​​perform contract management.

[0080] The data collection unit can collect data using an online survey platform. For example, the data collection unit can create surveys using an online survey platform and distribute surveys that include questions about consumers and products. For example, the data collection unit can create surveys that include questions about consumer satisfaction and product evaluations and distribute them to registered users of the online survey platform. This allows the data collection unit to collect feedback from a wide range of consumers. For example, the data collection unit can collect survey response data and store it in a database. This allows the data collection unit to collect data using an online survey platform. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data collected using an online survey platform into an AI and have the AI ​​perform data analysis.

[0081] The analysis department can analyze survey response data and calculate engagement metrics. For example, the analysis department can use AI to analyze survey response data and calculate engagement metrics. For instance, the analysis department can calculate metrics such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows the analysis department to understand the current state of consumer and product engagement. The analysis department can collect survey response data and store it in a database. This allows the analysis department to analyze the survey response data and calculate engagement metrics. Some or all of the above-described processes in the analysis department may be performed using AI, or not. For example, the analysis department can input data collected using an online survey platform into an AI and have the AI ​​perform the data analysis.

[0082] The alert unit can issue an alert when a specific indicator falls. For example, the alert unit can issue an alert and notify the company if the NPS in the food category falls to 25. Thus, the alert unit can issue an alert when a specific indicator falls. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​issue an alert.

[0083] The proposal department can propose causes and countermeasures based on alerts. For example, the proposal department can use AI to propose possible causes and countermeasures. For example, if the main causes are delivery delays or dissatisfaction with the quality of customer support, the proposal department may propose improvements to the delivery process and the quality of customer support. In this way, the proposal department can propose causes and countermeasures based on alerts. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input alerts issued by the alert department into the AI ​​and have the AI ​​execute suggestions for causes and countermeasures.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of survey delivery based on the estimated emotions. For example, if the user is stressed, the data collection unit can deliver the survey during a relaxed time. For example, if the user is relaxed, the data collection unit can deliver the survey immediately. For example, if the user is busy, the data collection unit can deliver the survey during a free time. In this way, the data collection unit can adjust the timing of survey delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of survey delivery.

[0085] The data collection unit can analyze past survey response history and select the most suitable question format. For example, if a user has shown a high response rate to multiple-choice questions in the past, the data collection unit can prioritize multiple-choice questions. For example, if a user has provided detailed answers to open-ended questions in the past, the data collection unit can prioritize open-ended questions. For example, the data collection unit can prioritize question formats that a user has completed quickly in the past. In this way, the data collection unit can analyze past survey response history and select the most suitable question format. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past survey response history into AI and have the AI ​​select the most suitable question format.

[0086] The data collection unit can customize the content of survey questions based on the user's current areas of interest when distributing them. For example, the data collection unit can prioritize distributing questions about products the user has recently purchased. For example, the data collection unit can distribute questions related to keywords the user has recently searched for. For example, the data collection unit can distribute questions about events the user has recently attended. This allows the data collection unit to customize the content of questions based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user area of ​​interest data into AI and have the AI ​​perform the customization of the questions.

[0087] The data collection unit can estimate the user's emotions and determine the priority of surveys based on the estimated emotions. For example, if the user is excited, the data collection unit can immediately deliver a survey. For example, if the user is relaxed, the data collection unit can deliver a survey that can be postponed. For example, if the user is stressed, the data collection unit can prioritize delivering high-priority surveys. In this way, the data collection unit can determine the priority of surveys based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​perform the task of determining the priority of surveys.

[0088] The data collection unit can prioritize the delivery of highly relevant questions when distributing surveys, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit can deliver questions related to that region. For example, if the user is traveling, the data collection unit can deliver questions related to their travel destination. For example, if the user is in a specific store, the data collection unit can deliver questions related to that store. In this way, the data collection unit can prioritize the delivery of highly relevant questions, taking into account the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​deliver the questions.

[0089] The data collection unit can analyze a user's social media activity and deliver relevant questions when distributing a survey. For example, the data collection unit can deliver questions about products the user has recently mentioned on social media. For example, the data collection unit can deliver questions about online events the user has recently attended. For example, the data collection unit can deliver questions related to accounts the user has recently followed. This allows the data collection unit to analyze a user's social media activity and deliver relevant questions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into AI and have AI deliver the questions.

[0090] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis. For example, if the user is in a hurry, the analysis unit can perform a simplified data analysis. For example, if the user is excited, the analysis unit can perform a visually stimulating data analysis. In this way, the analysis unit can adjust the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the data analysis method.

[0091] The analysis unit can adjust the level of detail of its analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For example, it can perform a simplified analysis on less important data. For example, it can perform a moderate level of detail on data of moderate importance. In this way, the analysis unit can adjust the level of detail of its analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0092] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, the analysis department can apply a sentiment analysis algorithm to consumer satisfaction data. For example, the analysis department can apply a rating score analysis algorithm to product evaluation data. For example, the analysis department can apply a loyalty score analysis algorithm to customer loyalty data. In this way, the analysis department can apply different analysis algorithms depending on the data category. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the data category into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the display method of the analysis results.

[0094] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department may prioritize the analysis of the most recent data. For example, the analysis department may postpone the analysis of older data. For example, the analysis department may analyze data with a medium submission date with a medium priority. This allows the analysis department to determine the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into the AI ​​and have the AI ​​perform the determination of the analysis priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may postpone the analysis of data with low relevance. For example, the analysis unit may analyze data with moderate relevance with moderate priority. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0096] The alert unit can estimate the user's emotions and adjust how it issues alerts based on those emotions. For example, if the user is tense, the alert unit can issue an alert in a calm tone. If the user is relaxed, the alert unit can issue an alert in a cheerful tone. If the user is in a hurry, the alert unit can issue a quick and concise alert. This allows the alert unit to adjust how it issues alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into an AI and have the AI ​​adjust how it issues alerts.

[0097] The alert unit can adjust the level of detail of an alert based on the importance of a specific indicator when an alert is issued. For example, the alert unit can issue a detailed alert for an indicator of high importance. For example, the alert unit can issue a simplified alert for an indicator of low importance. For example, the alert unit can issue an alert with a moderate level of detail for an indicator of medium importance. In this way, the alert unit can adjust the level of detail of an alert based on the importance of a specific indicator. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the importance of a specific indicator into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the alert.

[0098] The alert unit can apply different alert algorithms depending on the metric category when an alert is issued. For example, the alert unit can apply a sentiment analysis algorithm to a consumer satisfaction metric. For example, the alert unit can apply a rating score analysis algorithm to a product evaluation metric. For example, the alert unit can apply a loyalty score analysis algorithm to a customer loyalty metric. In this way, the alert unit can apply different alert algorithms depending on the metric category. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the metric category into the AI ​​and have the AI ​​execute the application of the alert algorithm.

[0099] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is excited, the alert unit will immediately issue an alert. For example, if the user is relaxed, the alert unit can issue an alert that can be postponed. For example, if the user is stressed, the alert unit can prioritize issuing high-priority alerts. In this way, the alert unit can determine the priority of alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not using AI. For example, the alert unit can input user emotion data into an AI and have the AI ​​determine the priority of alerts.

[0100] The alert unit can determine the priority of alerts based on the submission timing of the indicators when an alert is issued. For example, the alert unit may issue alerts preferentially for the most recent indicators. For example, the alert unit may issue alerts later for older indicators. For example, the alert unit may issue alerts with a medium priority for indicators that have been submitted at a moderate time. In this way, the alert unit can determine the priority of alerts based on the submission timing of the indicators. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit may input the submission timing of the indicators into the AI ​​and have the AI ​​perform the determination of the alert priority.

[0101] The alert unit can adjust the order of alerts based on the relevance of the indicators when an alert is issued. For example, the alert unit can issue alerts preferentially for indicators with high relevance. For example, the alert unit can issue alerts later for indicators with low relevance. For example, the alert unit can issue alerts with medium relevance with medium priority. In this way, the alert unit can adjust the order of alerts based on the relevance of the indicators. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the relevance of the indicators into the AI ​​and have the AI ​​perform the adjustment of the order of alerts.

[0102] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present suggestions in a calm tone. If the user is relaxed, the suggestion unit can present suggestions in a cheerful tone. If the user is in a hurry, the suggestion unit can present suggestions quickly and concisely. This allows the suggestion unit to adjust the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the way it presents suggestions.

[0103] The proposal department can adjust the level of detail of a proposal based on the importance of the cause. For example, the proposal department can provide detailed proposals for causes with high importance. For example, it can provide simplified proposals for causes with low importance. For example, it can provide proposals with a moderate level of detail for causes with moderate importance. In this way, the proposal department can adjust the level of detail of a proposal based on the importance of the cause. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the cause into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the proposal.

[0104] The proposal department can apply different proposal algorithms depending on the category of the cause when making a proposal. For example, for delivery delays, the proposal department can make suggestions to improve the delivery process. For example, for dissatisfaction with the quality of customer support, the proposal department can make suggestions to improve the quality of customer support. For example, for dissatisfaction with product quality, the proposal department can make suggestions to improve quality control. In this way, the proposal department can apply different proposal algorithms depending on the category of the cause. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the category of the cause into the AI ​​and have the AI ​​execute the application of the proposal algorithm.

[0105] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is excited, the suggestion unit will immediately make suggestions. For example, if the user is relaxed, the suggestion unit can make suggestions that can be postponed. For example, if the user is stressed, the suggestion unit can prioritize high-priority suggestions. In this way, the suggestion unit can determine the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​determine the priority of suggestions.

[0106] The proposal department can determine the priority of proposals based on the submission date of the cause. For example, the proposal department can prioritize proposals for the most recent cause. For example, it can postpone proposals for older causes. For example, it can give a moderate priority to proposals for causes that were submitted at a moderate time. In this way, the proposal department can determine the priority of proposals based on the submission date of the cause. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission date of the cause into the AI ​​and have the AI ​​perform the determination of the proposal priority.

[0107] The proposal unit can adjust the order of proposals based on the relevance of the causes. For example, the proposal unit can prioritize proposals for causes with high relevance. For example, the proposal unit can postpone proposals for causes with low relevance. For example, the proposal unit can give a medium priority to proposals for causes with moderate relevance. In this way, the proposal unit can adjust the order of proposals based on the relevance of the causes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the causes into the AI ​​and have the AI ​​perform the adjustment of the order of proposals.

[0108] The survey unit can estimate the user's emotions and adjust the survey questions based on the estimated emotions. For example, if the user is relaxed, the survey unit can provide a survey with detailed questions. For example, if the user is in a hurry, the survey unit can provide a survey with simplified questions. For example, if the user is excited, the survey unit can provide a survey with visually stimulating questions. In this way, the survey unit can adjust the survey questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the survey unit may be performed using AI, for example, or not using AI. For example, the survey unit can input user emotion data into AI and have the AI ​​adjust the survey questions.

[0109] The survey department can select the most appropriate questions by referring to past survey response history when conducting a survey. For example, the survey department may prioritize question formats in which users have previously provided detailed answers. For example, the survey department may prioritize question formats in which users have previously shown a high response rate. For example, the survey department may prioritize question formats in which users have previously completed answers in a short amount of time. In this way, the survey department can select the most appropriate questions by referring to past survey response history. Some or all of the above processing in the survey department may be performed using AI, for example, or without using AI. For example, the survey department may input past survey response history into AI and have the AI ​​perform the selection of the most appropriate questions.

[0110] The survey unit can estimate the user's emotions and determine the priority of surveys based on the estimated emotions. For example, if the user is excited, the survey unit can immediately deliver a survey. For example, if the user is relaxed, the survey unit can deliver a survey that can be postponed. For example, if the user is stressed, the survey unit can prioritize delivering high-priority surveys. In this way, the survey unit can determine the priority of surveys based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the survey unit may be performed using AI, for example, or not using AI. For example, the survey unit can input user emotion data into AI and have the AI ​​perform the determination of survey priorities.

[0111] The survey department can select the most appropriate questions when conducting a survey, taking into account the user's geographical location. For example, if the user is in a specific region, the survey department can deliver questions related to that region. For example, if the user is traveling, the survey department can deliver questions related to their travel destination. For example, if the user is in a specific store, the survey department can deliver questions related to that store. In this way, the survey department can select the most appropriate questions, taking into account the user's geographical location. Some or all of the above processing in the survey department may be performed using AI, for example, or not using AI. For example, the survey department can input the user's geographical location information into AI and have the AI ​​select the most appropriate questions.

[0112] The metrics unit can estimate the user's emotions and adjust the method of calculating metrics based on the estimated user emotions. For example, if the user is relaxed, the metrics unit can calculate detailed metrics. For example, if the user is in a hurry, the metrics unit can calculate simplified metrics. For example, if the user is excited, the metrics unit can calculate visually stimulating metrics. In this way, the metrics unit can adjust the method of calculating metrics based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the metrics unit may be performed using AI or not using AI. For example, the metrics unit can input user emotion data into AI and have the AI ​​adjust the method of calculating metrics.

[0113] The indicator unit can adjust the level of detail of an indicator based on the importance of the data when calculating an indicator. For example, the indicator unit can calculate a detailed indicator for data with high importance. For example, the indicator unit can calculate a simplified indicator for data with low importance. For example, the indicator unit can calculate an indicator with a moderate level of detail for data with moderate importance. In this way, the indicator unit can adjust the level of detail of an indicator based on the importance of the data. Some or all of the above processing in the indicator unit may be performed using AI, for example, or without AI. For example, the indicator unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the indicator.

[0114] The metrics unit can estimate the user's emotions and determine the priority of metrics based on the estimated user emotions. For example, if the user is excited, the metrics unit can immediately calculate metrics. For example, if the user is relaxed, the metrics unit can calculate metrics that can be postponed. For example, if the user is stressed, the metrics unit can prioritize calculating high-importance metrics. In this way, the metrics unit can determine the priority of metrics based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the metrics unit may be performed using AI, for example, or not using AI. For example, the metrics unit can input user emotion data into AI and have the AI ​​perform the determination of metric priority.

[0115] The indicator unit can determine the priority of indicators based on the data submission timing when calculating indicators. For example, the indicator unit can calculate indicators with priority given to the most recent data. For example, the indicator unit can calculate indicators with priority given to older data. For example, the indicator unit can calculate indicators with a moderate priority given to data submitted at a moderate time. In this way, the indicator unit can determine the priority of indicators based on the data submission timing. Some or all of the above processing in the indicator unit may be performed using AI, for example, or without AI. For example, the indicator unit can input the data submission timing into AI and have AI perform the determination of indicator priority.

[0116] The Contracts Department can estimate the user's emotions and adjust the contract renewal timing based on the estimated emotions. For example, if the user is relaxed, the Contracts Department can notify the user of the contract renewal earlier. For example, if the user is in a hurry, the Contracts Department can delay the notification of contract renewal. For example, if the user is excited, the Contracts Department can notify the user of the contract renewal immediately. In this way, the Contracts Department can adjust the contract renewal timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Contracts Department may be performed using AI or not using AI. For example, the Contracts Department can input user emotion data into an AI and have the AI ​​perform the adjustment of the contract renewal timing.

[0117] The contract department can propose the optimal contract plan by referring to past contract history during contract management. For example, the contract department can propose the optimal contract plan based on contract plans previously used by the user. For example, the contract department can propose the most cost-effective contract plan based on the user's past contract history. For example, the contract department can analyze the user's past contract history and propose the most suitable contract plan. In this way, the contract department can propose the optimal contract plan by referring to past contract history. Some or all of the above processes in the contract department may be performed using AI, for example, or not using AI. For example, the contract department can input past contract history into AI and have the AI ​​propose the optimal contract plan.

[0118] The Contracts Department can estimate a user's emotions and determine contract priorities based on those emotions. For example, if a user is excited, the Contracts Department will immediately renew a contract. If a user is relaxed, the Contracts Department can renew contracts that can be postponed. If a user is stressed, the Contracts Department can prioritize renewing high-priority contracts. This allows the Contracts Department to determine contract priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Contracts Department may be performed using AI or not. For example, the Contracts Department can input user emotion data into an AI and have the AI ​​determine contract priorities.

[0119] The Contracts Department can propose the optimal contract plan when managing contracts, taking into account the user's geographical location. For example, if the user is in a specific region, the Contracts Department can propose a contract plan related to that region. For example, if the user is traveling, the Contracts Department can propose a contract plan related to the travel destination. For example, if the user is in a specific store, the Contracts Department can propose a contract plan related to that store. In this way, the Contracts Department can propose the optimal contract plan considering the user's geographical location. Some or all of the above processing in the Contracts Department may be performed using AI, for example, or not using AI. For example, the Contracts Department can input the user's geographical location information into AI and have the AI ​​propose the optimal contract plan.

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

[0121] The EngageAI system may further include a survey unit that estimates the user's emotions and customizes the survey questions based on those emotions. For example, if the user is relaxed, a survey with detailed questions may be provided. If the user is in a hurry, a survey with simplified questions may be provided. If the user is excited, a survey with visually stimulating questions may be provided. This allows the survey unit to adjust the survey questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the survey unit may be performed using AI or not using AI. For example, the survey unit may input user emotion data into an AI and have the AI ​​adjust the survey questions.

[0122] The EngageAI system can further include a survey unit that analyzes the user's past survey response history and selects the most suitable question format. For example, if a user has a high response rate to multiple-choice questions in the past, multiple-choice questions can be prioritized. If a user has provided detailed answers to open-ended questions in the past, open-ended questions can be prioritized. Question formats that a user has completed quickly in the past can also be prioritized. This allows the survey unit to analyze past survey response history and select the most suitable question format. Some or all of the above processing in the survey unit may be performed using AI, for example, or without AI. For example, the survey unit can input past survey response history into AI and have AI select the most suitable question format.

[0123] The EngageAI system may also include a survey unit that prioritizes the delivery of highly relevant questions by considering the user's geographical location. For example, if a user is in a specific region, questions related to that region can be delivered. If a user is traveling, questions related to their travel destination can be delivered. If a user is in a specific store, questions related to that store can be delivered. This allows the survey unit to prioritize the delivery of highly relevant questions by considering the user's geographical location. Some or all of the above processing in the survey unit may be performed using AI, for example, or without AI. For example, the survey unit can input the user's geographical location information into the AI ​​and have the AI ​​deliver the questions.

[0124] The EngageAI system may further include a survey unit that analyzes the user's social media activity and delivers relevant questions. For example, it can deliver questions about products the user has recently mentioned on social media. It can deliver questions about online events the user has recently attended. It can deliver questions related to accounts the user has recently followed. This allows the survey unit to analyze the user's social media activity and deliver relevant questions. Some or all of the above processing in the survey unit may be performed using AI, for example, or not using AI. For example, the survey unit can input the user's social media activity data into the AI ​​and have the AI ​​deliver the questions.

[0125] The EngageAI system may further include a data collection unit that estimates the user's emotions and adjusts the timing of survey delivery based on the estimated emotions. For example, if a user is stressed, the survey can be delivered during a relaxed time. If a user is relaxed, the survey can be delivered immediately. If a user is busy, the survey can be delivered during a free time. This allows the data collection unit to adjust the timing of survey delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of survey delivery.

[0126] The EngageAI system may further include a data collection unit that estimates the user's emotions and determines the priority of surveys based on the estimated emotions. For example, if a user is excited, a survey can be delivered immediately. If a user is relaxed, a survey that can be postponed can be delivered. If a user is stressed, a high-priority survey can be prioritized. This allows the data collection unit to determine the priority of surveys based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform the task of determining the priority of surveys.

[0127] The EngageAI system may further include an analysis unit that estimates the user's emotions and adjusts the data analysis method based on the estimated emotions. For example, if the user is relaxed, a detailed data analysis can be performed. If the user is in a hurry, a simplified data analysis can be performed. If the user is excited, a visually stimulating data analysis can be performed. This allows the analysis unit to adjust the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the data analysis method.

[0128] The EngageAI system may further include an analysis unit that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. This allows the analysis unit to adjust the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the AI ​​and have the AI ​​adjust the display method of the analysis results.

[0129] The EngageAI system may further include an analysis unit that adjusts the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and a moderate level of detail on data of moderate importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0130] The EngageAI system may further include an analysis unit that applies different analysis algorithms depending on the data category. For example, a sentiment analysis algorithm can be applied to consumer satisfaction data. A rating score analysis algorithm can be applied to product evaluation data. A loyalty score analysis algorithm can be applied to customer loyalty data. This allows the analysis unit to apply different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

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

[0132] Step 1: The data collection unit collects data. The data collection unit can collect data using, for example, an online survey platform. The data collection unit can create surveys on consumer satisfaction and product evaluations and distribute them to registered users of the online survey platform. This allows the data collection unit to collect feedback from a wide range of consumers. The data collection unit can collect survey response data and store it in a database. Step 2: The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses AI to analyze survey response data and calculate engagement metrics. For instance, the analysis department calculates metrics such as NPS (Net Promoter Score) and customer loyalty based on consumer satisfaction and product evaluations. This allows the analysis department to understand the current state of consumer and product engagement. Step 3: The alert unit issues alerts based on the analysis results obtained by the analysis unit. The alert unit can issue alerts, for example, when a specific indicator declines. For example, the alert unit will issue an alert and notify the company if the NPS in the food category falls to 25. Step 4: The proposal team proposes causes and countermeasures based on the alerts issued by the alert team. The proposal team can, for example, use AI to propose possible causes and countermeasures. For example, if the main causes are delivery delays or dissatisfaction with the quality of customer support, the proposal team may suggest improvements to the delivery process or enhancements to the quality of customer support.

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

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

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

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, alert unit, proposal unit, survey unit, indicator unit, and contract unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The alert unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and issues an alert when a specific indicator falls. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes causes and countermeasures. The survey unit is implemented by, for example, the control unit 46A of the smart device 14 and creates and distributes surveys. The indicator unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates engagement indicators. The contract section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and manages annual contracts. The correspondence between each section and the devices and control units is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, alert unit, proposal unit, survey unit, indicator unit, and contract unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The alert unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and issues an alert when a specific indicator falls. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes causes and countermeasures. The survey unit is implemented, for example, by the control unit 46A of the smart glasses 214 and creates and distributes surveys. The indicator unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and calculates engagement indicators. The contract section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and manages annual contracts. The correspondence between each section and the devices and control units is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the data collection unit, analysis unit, alert unit, proposal unit, survey unit, indicator unit, and contract unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The alert unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and issues an alert when a specific indicator falls. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes causes and countermeasures. The survey unit is implemented by, for example, the control unit 46A of the headset terminal 314 and creates and distributes surveys. The indicator unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates engagement indicators. The contract section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and manages annual contracts. The correspondence between each section and the devices and control units is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the data collection unit, analysis unit, alert unit, proposal unit, survey unit, indicator unit, and contract unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The alert unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and issues an alert when a specific indicator falls. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes causes and countermeasures. The survey unit is implemented by, for example, the control unit 46A of the robot 414 and creates and distributes surveys. The indicator unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates engagement indicators. The contract section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and manages annual contracts. The correspondence between each section and the devices and control units is not limited to the example described above, and various changes are possible.

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

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

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

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

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

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

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

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0204] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, An alert unit that issues an alert based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that proposes causes and countermeasures based on alerts issued by the alert unit. A system characterized by the following features. (Note 2) The company has a department dedicated to conducting surveys. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a metrics section for calculating engagement metrics. The system described in Appendix 1, characterized by the features described herein. (Note 4) The company has a contracts department that manages annual contracts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We collect data using an online survey platform. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is We analyze survey response data to calculate engagement metrics. The system described in Appendix 1, characterized by the features described herein. (Note 7) The alert unit is, An alert is issued when a specific indicator falls. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, Based on the alert, we will suggest the cause and countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of survey delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze past survey response history to select the most suitable question format. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When distributing surveys, customize the questions based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system estimates user sentiment and prioritizes survey questions based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When distributing surveys, the system prioritizes sending questions that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When distributing surveys, we analyze users' social media activity and distribute relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, It estimates the user's emotions and adjusts how alerts are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, When an alert is issued, adjust the level of detail of the alert based on the importance of specific metrics. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, When an alert is issued, different alert algorithms are applied depending on the metric category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The alert unit is, When an alert is issued, the priority of the alert is determined based on when the metrics were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The alert unit is, When an alert is issued, the order of the alerts will be adjusted based on the relevance of the indicators. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the cause. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the cause. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the cause was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the causes. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned survey department, The system estimates the user's emotions and adjusts the survey questions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned survey department, When conducting a survey, the most appropriate questions are selected by referring to past survey response history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned survey department, The system estimates user sentiment and prioritizes survey questions based on the estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned survey department, When conducting a survey, select the most appropriate questions by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned indicator unit is We estimate the user's emotions and adjust the metric calculation method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned indicator unit is When calculating metrics, adjust the level of detail of the metrics based on the importance of the data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned indicator unit is We estimate user sentiment and determine metric priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned indicator unit is When calculating metrics, the priority of metrics is determined based on the timing of data submission. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned contracts department, It estimates the user's emotions and adjusts the contract renewal timing based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned contracts department, When managing contracts, we refer to past contract history to propose the most suitable contract plan. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned contracts department, It estimates user sentiment and determines contract priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned contracts department, When managing contracts, we propose the optimal contract plan considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, An alert unit that issues an alert based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that proposes causes and countermeasures based on alerts issued by the alert unit. A system characterized by the following features.

2. The company has a department dedicated to conducting surveys. The system according to feature 1.

3. It includes a metrics section for calculating engagement metrics. The system according to feature 1.

4. The company has a contracts department that manages annual contracts. The system according to feature 1.

5. The aforementioned collection unit is We collect data using an online survey platform. The system according to feature 1.

6. The aforementioned analysis unit is We analyze survey response data to calculate engagement metrics. The system according to feature 1.

7. The alert unit is, An alert is issued when a specific indicator falls. The system according to feature 1.

8. The aforementioned proposal section is, Based on the alert, we will suggest the cause and countermeasures. The system according to feature 1.

9. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of survey delivery based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned collection unit is Analyze past survey response history to select the most suitable question format. The system according to feature 1.

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