system

Abstract

The system according to this embodiment aims to analyze large amounts of data in the retail industry and identify and solve problems in stores. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a discovery unit, a proposal unit, and a support unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The discovery unit discovers problems based on the analysis results obtained by the analysis unit. The proposal unit proposes improvement measures for the problems discovered by the discovery unit. The support unit collects necessary information from store employees.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 is a problem that it is difficult to systematically integrate a large amount of data in the retail industry and visualize all customer information.

[0005] The system according to the embodiment aims to analyze a large amount of data in the retail industry and discover and solve store problems.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a discovery unit, a proposal unit, and a support unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The discovery unit discovers problems based on the analysis results obtained by the analysis unit. The proposal unit proposes improvement measures for the problems discovered by the discovery unit. The support unit collects necessary information from store employees. [Effects of the Invention]

[0007] The system according to this embodiment can analyze large amounts of data in the retail industry and identify and solve store-related problems. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 reception 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 reception 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) The AI ​​agent system according to an embodiment of the present invention is a system that supports data analysis and problem solving in the retail industry. This AI agent system collects various data held by stores, and by analyzing it with AI, it identifies store problems and proposes specific improvement measures. Furthermore, it proposes methods for effectively collecting necessary information from store staff and supports the optimization of store operations. For example, the AI ​​agent system collects data held by stores. This data includes the number of customers, sales data, inventory information, and staff meeting minutes. Next, the AI ​​analyzes the collected data. The AI ​​analyzes quantitative data such as the number of customers and purchase history, and detects trends and anomalies in time-series data. It also analyzes meeting minutes and conversation logs of store staff, and evaluates staff motivation and customer service attitudes through keyword extraction and sentiment analysis. Based on the analysis results, it automatically presents specific store problems. For example, if customers enter the store but do not reach the register, problems such as poor store flow, unclear product placement, and a decline in the quality of customer service may be pointed out as issues. In addition, by analyzing the complaints and grievances of part-time staff from meeting minutes, it is possible to discover problems such as insufficient training and working environment issues. Furthermore, the system provides solutions to challenges and supports the optimization of store operations. For example, it may suggest revising the store layout, strengthening staff training, and revising customer service manuals. It also lists information and questions to collect from store staff and proposes effective interviewing methods. This system allows for the rapid identification of problems that might otherwise be overlooked through data analysis, and reduces the burden on staff by automating information collection and analysis. Moreover, by implementing appropriate improvement measures, store performance is expected to improve, leading to increased sales and customer satisfaction. In this way, the AI ​​agent system can support the optimization of store operations by analyzing store data, identifying challenges, and proposing improvement measures.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a discovery unit, a proposal unit, and a support unit. The data collection unit collects data. The data collection unit can collect data such as the number of customers, sales data, inventory information, and staff meeting minutes. The data collection unit can count the number of customers using sensors or cameras, for example. The data collection unit can also acquire sales data from a POS system. Furthermore, the data collection unit can acquire inventory information from an inventory management system. For example, the data collection unit can count the number of customers in real time using sensors. The data collection unit can automatically acquire sales data from a POS system. The data collection unit can periodically acquire inventory information from an inventory management system. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze quantitative data such as the number of customers and purchase history, and can detect trends and outliers in time-series data. For example, the analysis unit can analyze trends in time-series data to understand increases and decreases in the number of customers. The analysis unit can also detect outliers and identify abnormal sales fluctuations. Furthermore, the analysis unit can analyze meeting minutes and conversation logs of store staff, and evaluate staff motivation and customer service attitudes through keyword extraction and sentiment analysis. For example, the analysis unit can extract keywords from meeting minutes to understand staff concerns. The analysis unit can analyze conversation logs to evaluate staff emotions. The analysis unit can evaluate staff motivation through sentiment analysis. The discovery unit discovers issues based on the analysis results obtained by the analysis unit. For example, the discovery unit can automatically present specific issues for the store based on the analysis results. For example, the discovery unit can detect a decrease in the number of customers and point out problems with the store's traffic flow. The discovery unit can also detect anomalies in sales data and point out problems with product placement. Furthermore, the discovery unit can analyze the dissatisfaction of part-time staff from meeting minutes to discover problems with insufficient training and working conditions. For example, the discovery unit can detect a decrease in the number of customers and propose a review of the store's traffic flow. The discovery unit can detect anomalies in sales data and propose improvements to product placement.The Discovery Department can analyze the dissatisfaction of part-time staff from meeting minutes and propose strengthening the training program. The Proposal Department proposes solutions to the issues identified by the Discovery Department. For example, the Proposal Department can set priorities for each issue and propose countermeasures. For example, the Proposal Department can propose a review of the store layout. The Proposal Department can also propose strengthening staff training. Furthermore, the Proposal Department can propose revising the customer service manual. For example, the Proposal Department can propose a review of the store layout and optimize the placement of products. The Proposal Department can propose strengthening staff training to improve customer service skills. The Proposal Department can propose revising the customer service manual to improve the quality of customer service. The Support Department collects necessary information from store employees. For example, the Support Department can list the information and questions to be collected from store employees and propose effective interview methods. For example, the Support Department can propose methods for collecting customer feedback from store employees. The Support Department can also propose methods for collecting sales data from store employees. Furthermore, the Support Department can propose methods for collecting inventory information from store employees. For example, the support department can create questionnaires to collect customer feedback from store employees. The support department can create reports to collect sales data from store employees. The support department can create checklists to collect inventory information from store employees. In this way, the AI ​​agent system according to the embodiment can help optimize store operations by analyzing store data, identifying problems, and proposing improvement measures.

[0030] The data collection unit collects data. For example, the data collection unit can collect data such as the number of customers, sales data, inventory information, and staff meeting minutes. For example, the data collection unit can count the number of customers using sensors and cameras. Specifically, it uses cameras and infrared sensors installed at the store entrance and on each floor to track customer movements in real time and accurately count the number of customers. The data collection unit can also acquire sales data from the POS system. The POS system records detailed sales information for each product, allowing for the automatic collection of sales data by time of day and by product. Furthermore, the data collection unit can acquire inventory information from the inventory management system. The inventory management system updates product inbound and outbound information in real time, allowing for accurate understanding of the current inventory status. For example, the data collection unit can count the number of customers in real time using sensors. The data collection unit can automatically acquire sales data from the POS system. The data collection unit can periodically acquire inventory information from the inventory management system. This allows the data collection unit to efficiently collect a variety of data necessary for store operations and enable real-time situational awareness. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and discovery units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze quantitative data such as the number of customers and purchase history, and detect trends and outliers in time-series data. Specifically, it can analyze customer data in time series to understand increases and decreases in customer numbers on specific days of the week or time slots. Furthermore, by analyzing purchase history, it can identify trends in popular products and seasonal best-selling items. The analysis unit can, for example, analyze trends in time-series data to understand increases and decreases in customer numbers. It can also detect outliers and identify abnormal sales fluctuations. In addition, the analysis unit can analyze meeting minutes and conversation logs of store staff, and evaluate staff motivation and customer service attitudes through keyword extraction and sentiment analysis. For example, the analysis unit can extract keywords from meeting minutes to understand staff interests. It can analyze conversation logs to evaluate staff emotions. It can evaluate staff motivation through sentiment analysis. This allows the analytics unit to analyze collected data from multiple perspectives and provide important insights into store operations. Furthermore, the analytics unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in customer numbers during specific seasons or events based on past customer traffic data and formulate future countermeasures. In addition, the analytics unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analytics unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The discovery unit identifies problems based on the analysis results obtained by the analysis unit. For example, the discovery unit can automatically present specific store issues based on the analysis results. Specifically, it can detect a decrease in the number of customers and point out problems with the store's layout. For example, if the number of customers decreases during a specific time period, the cause may be a complex layout or inappropriate product placement. The discovery unit can also detect anomalies in sales data and point out problems with product placement. For example, if a particular product has extremely low sales compared to other products, it may be due to poor placement of that product. Furthermore, the discovery unit can analyze the dissatisfaction of part-time staff from meeting minutes and identify problems with insufficient training or the working environment. For example, keywords that frequently appear in meeting minutes and the results of sentiment analysis can reveal that staff members are dissatisfied with specific tasks. Based on this information, the discovery unit can propose strengthening training programs or improving the working environment. For example, the discovery unit can detect a decrease in the number of customers and propose a review of the store's layout. The discovery unit can detect anomalies in sales data and propose improvements to product placement. The discovery department can analyze the dissatisfaction of part-time staff from meeting minutes and propose improvements to the training program. This allows the discovery department to quickly identify specific issues based on the analysis results and contribute to improving store operations.

[0033] The proposal department proposes solutions to address the issues identified by the discovery department. For example, the proposal department can prioritize each issue and propose countermeasures. Specifically, it can propose a review of the store layout. For instance, if a decrease in customer traffic is due to problems with the store's flow, the proposal department can propose optimizing product placement and changing the layout to allow customers to move more smoothly. The proposal department can also propose strengthening staff training. For example, if there are problems with staff customer service attitudes, the proposal department can propose introducing a training program to improve customer service skills. Furthermore, the proposal department can propose revising the customer service manual. For example, based on customer feedback, it can propose reviewing the current customer service manual and incorporating more effective customer service methods. The proposal department presents these proposals as concrete action plans and provides them in an actionable form. For example, the proposal department can propose a review of the store layout and optimize product placement. The proposal department can propose strengthening staff training to improve customer service skills. The proposal department can propose revising the customer service manual to improve the quality of customer service. In this way, the proposal department can provide concrete and actionable solutions to the identified issues and support the optimization of store operations.

[0034] The support department collects necessary information from store staff. For example, the support department can list the information and questions that should be collected from store staff and propose effective interviewing methods. Specifically, it can propose methods for collecting customer feedback from store staff. For example, a simple questionnaire could be conducted when customers visit the store to collect information on customer satisfaction and areas for improvement. The support department can also propose methods for collecting sales data from store staff. For example, each store staff member could record their sales data in a daily report, and by aggregating this data, the sales performance of each individual employee could be understood. Furthermore, the support department can propose methods for collecting inventory information from store staff. For example, by conducting regular inventory checks and compiling the results into a report, the inventory situation could be accurately understood. The support department provides these information collection methods as specific tools and formats to help store staff collect information efficiently. For example, the support department can create questionnaires to collect customer feedback from store staff. The support department can create reports to collect sales data from store staff. The support department can create checklists to collect inventory information from store staff. This allows the support department to efficiently collect necessary information from store staff and use it to improve store operations. Furthermore, the support department can analyze the collected information to clearly identify challenges and areas for improvement in store operations. As a result, the support department can help optimize store operations and improve store performance.

[0035] The analysis unit can analyze quantitative data such as the number of customers and purchase history, and detect trends and outliers in time-series data. For example, the analysis unit can analyze time-series data of the number of customers to grasp trends. For example, the analysis unit can analyze time-series data of purchase history to detect outliers. For example, the analysis unit can analyze increases and decreases in the number of customers to grasp trends. The analysis unit can analyze outliers in purchase history to detect abnormal purchasing patterns. The analysis unit can analyze time-series data of the number of customers to grasp trends. The analysis unit can analyze time-series data of purchase history to detect outliers. As a result, by analyzing quantitative data, trends and outliers can be detected, and store issues can be identified.

[0036] The analysis unit can analyze meeting minutes and conversation logs of store staff, and evaluate staff motivation and customer service attitudes through keyword extraction and sentiment analysis. For example, the analysis unit can extract keywords from meeting minutes to understand staff concerns. For example, the analysis unit can analyze conversation logs to evaluate staff emotions. For example, the analysis unit can evaluate staff motivation through sentiment analysis. The analysis unit can extract keywords from meeting minutes to understand staff concerns. The analysis unit can analyze conversation logs to evaluate staff emotions. The analysis unit can evaluate staff motivation through sentiment analysis. This allows for the evaluation of staff motivation and customer service attitudes through the analysis of meeting minutes and conversation logs, which can be used to improve store operations.

[0037] The detection unit can automatically identify specific issues for the store based on the analysis results. For example, the detection unit can detect a decrease in the number of customers and point out problems with the store's layout. For example, the detection unit can detect anomalies in sales data and point out problems with product placement. For example, the detection unit can analyze the complaints of part-time staff from meeting minutes and discover problems with insufficient training or working conditions. The detection unit can detect a decrease in the number of customers and propose a review of the store's layout. The detection unit can detect anomalies in sales data and propose improvements to product placement. The detection unit can analyze the complaints of part-time staff from meeting minutes and propose strengthening the training program. In this way, by presenting specific issues based on the analysis results, the store's problems can be clearly identified.

[0038] The proposal department can prioritize issues and propose solutions. For example, the proposal department can propose a review of the store layout. For example, the proposal department can propose strengthening staff training. For example, the proposal department can propose revising the customer service manual. The proposal department can propose a review of the store layout and optimize the placement of products. The proposal department can propose strengthening staff training to improve customer service skills. The proposal department can propose revising the customer service manual to improve the quality of customer service. In this way, by prioritizing issues and proposing solutions, it is possible to support efficient problem solving.

[0039] The support department can list the information and questions to be collected from store staff and propose effective interview methods. For example, the support department can propose methods for collecting customer feedback from store staff. For example, the support department can propose methods for collecting sales data from store staff. For example, the support department can propose methods for collecting inventory information from store staff. The support department can create questionnaires for collecting customer feedback from store staff. The support department can create reports for collecting sales data from store staff. The support department can create checklists for collecting inventory information from store staff. This allows for effective information gathering from store staff, thereby supporting the optimization of store operations.

[0040] The data collection unit can analyze the store's past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past data collection history and apply it to current data collection. For example, the data collection unit can analyze past data collection history and optimize collection frequency and timing. For example, the data collection unit can set priorities for data to be collected based on past data collection history. This enables efficient data collection by selecting the optimal collection method through analysis of past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter data based on the current store conditions or specific events during data collection. For example, the data collection unit can adjust the timing of data collection based on store congestion levels. For example, the data collection unit can prioritize the collection of relevant data based on specific events (sales, promotions, etc.). For example, the data collection unit can collect data outside of store operating hours to minimize the impact on operations. This allows for the collection of highly relevant data by filtering data based on store conditions and events. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the current store conditions or specific events into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of stores during data collection. For example, the data collection unit can prioritize the collection of region-specific data based on the store's location. For example, the data collection unit can collect relevant data by considering the surrounding environment of the store (traffic volume, competing stores, etc.). For example, the data collection unit can adjust the type of data to be collected according to the store's location conditions (urban, suburban, etc.). This allows for the priority collection of highly relevant data by considering geographical location information. 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 store's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0043] The data collection unit can analyze the store's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the store's social media posts to understand customer reactions and trends. For example, the data collection unit can collect customer feedback on social media and reflect it in store operations. For example, the data collection unit can collect social media engagement data to optimize marketing strategies. In this way, by analyzing social media activity, relevant data can be collected and used to improve store operations. 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 store's social media activity data into a generating AI and have the generating AI collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to improve accuracy. For example, the analysis unit can perform a simplified analysis on less important data to prioritize efficiency. For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This makes efficient analysis possible by adjusting the level of detail of the analysis according to 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 a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply statistical analysis algorithms to quantitative data. For example, the analysis unit can apply natural language processing algorithms to text data. For example, the analysis unit can apply image recognition algorithms to image data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes 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 a generating AI and have the generating AI select an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. For example, the analysis unit can analyze historical data to grasp long-term trends. For example, the analysis unit can optimally allocate analysis resources according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. 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 data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to improve accuracy. For example, the analysis unit can postpone the analysis of less relevant data to prioritize efficiency. For example, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This enables efficient analysis by adjusting 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 without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The discovery unit can improve the accuracy of its discovery by considering the interrelationships of data when discovering a problem. For example, the discovery unit can analyze the interrelationships of data and discover related problems. For example, the discovery unit can set priority for problems based on the interrelationships of data. For example, the discovery unit can improve the accuracy of problem discovery by considering the interrelationships of data. As a result, the accuracy of discovery is improved by considering the interrelationships of data. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the interrelationships of data into a generating AI and have the generating AI perform the task of improving the accuracy of discovery.

[0049] The discovery unit can perform discovery by considering the attribute information of the data submitter when discovering a problem. For example, the discovery unit can evaluate the importance of a problem based on the data submitter's position and experience. For example, the discovery unit can set the priority of problems by considering the attribute information of the data submitter. For example, the discovery unit can improve the accuracy of problem discovery based on the attribute information of the data submitter. As a result, the accuracy of discovery is improved by considering the attribute information of the data submitter. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without using AI. For example, the discovery unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the task of improving the accuracy of discovery.

[0050] The discovery unit can perform problem identification while considering the geographical distribution of the data. For example, the discovery unit can analyze the geographical distribution of the data and identify region-specific problems. For example, the discovery unit can set priority levels for problems based on the geographical distribution. For example, the discovery unit can improve the accuracy of problem identification by considering the geographical distribution. This allows for the identification of region-specific problems by considering the geographical distribution of the data. Some or all of the above-described processes in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the task of improving the accuracy of the discovery.

[0051] The discovery unit can improve the accuracy of its discovery by referring to relevant literature when it discovers a problem. For example, the discovery unit can identify the background and causes of a problem by referring to relevant literature. For example, the discovery unit can set priority levels for problems based on relevant literature. For example, the discovery unit can improve the accuracy of problem discovery by considering relevant literature. As a result, the accuracy of discovery is improved by referring to relevant literature. Some or all of the above processes in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input relevant literature into a generation AI and have the generation AI perform the task of improving the accuracy of discovery.

[0052] The proposal function can adjust the level of detail of a proposal based on the importance of the issue. For example, for important issues, the proposal function can provide a detailed proposal and show specific solutions. For less important issues, the proposal function can provide a simplified proposal, prioritizing efficiency. The proposal function can also optimally allocate resources to the proposal according to the importance of the issue. This allows for efficient proposals by adjusting the level of detail according to the importance of the issue. Some or all of the above processes in the proposal function may be performed using AI, or not. For example, the proposal function can input the importance of the issue into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0053] The proposal unit can apply different proposal algorithms depending on the category of the problem when making a proposal. For example, the proposal unit can apply a marketing algorithm to a problem related to sales promotion. For example, the proposal unit can apply an inventory optimization algorithm to a problem related to inventory management. For example, the proposal unit can apply an education program algorithm to a problem related to staff training. By applying the appropriate proposal algorithm according to the category of the problem, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the problem into a generating AI and have the generating AI select an appropriate proposal algorithm.

[0054] The proposal department can determine the priority of proposals based on when the problem was discovered. For example, the proposal department can prioritize proposals for the most recent problems to encourage a quick response. For example, the proposal department can propose long-term solutions for past problems. For example, the proposal department can optimally allocate resources for proposals according to when the problem was discovered. This enables efficient proposals by determining the priority of proposals based on when the problem was discovered. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the timing of problem discovery into a generating AI and have the generating AI determine the priority of proposals.

[0055] The proposal unit can adjust the order of proposals based on the relevance of the issues during the proposal process. For example, the proposal unit can prioritize proposals for highly relevant issues and provide effective solutions. For example, the proposal unit can postpone less relevant issues to prioritize efficiency. For example, the proposal unit can optimally allocate resources for proposals according to the relevance of the issues. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the issues. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the issues into a generating AI and have the generating AI adjust the order of proposals.

[0056] The support department can select the optimal information collection method by referring to the past interview history of store employees when collecting information. For example, the support department can identify the most effective collection method from past interview history and apply it to current information collection. For example, the support department can analyze past interview history and optimize the frequency and timing of collection. For example, the support department can set priorities for the information to be collected based on past interview history. This makes it possible to select the optimal information collection method by referring to past interview history and to collect information efficiently. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past interview history into a generating AI and have the generating AI select the optimal collection method.

[0057] The support unit can select the optimal data collection method when gathering information, taking into account the geographical location of the store employees. For example, the support unit can prioritize the collection of region-specific information based on the store employees' location. For example, the support unit can collect relevant information by considering the store employees' surrounding environment (traffic volume, competing stores, etc.). For example, the support unit can adjust the type of information collected according to the store employees' location conditions (urban, suburban, etc.). This allows for efficient information gathering by selecting the optimal data collection method while considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the store employees' geographical location information into a generating AI and have the generating AI select the optimal data collection method.

[0058] The support department can analyze employees' social media activities and collect relevant information during information gathering. For example, the support department can analyze employees' social media posts to understand customer reactions and trends. For example, the support department can collect customer feedback on social media and reflect it in store operations. For example, the support department can collect social media engagement data to optimize marketing strategies. In this way, by analyzing social media activities, relevant information can be collected and used to improve store operations. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input employees' social media activity data into a generating AI and have the generating AI collect relevant information.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can analyze the store's past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past data collection history and apply it to current data collection. For example, the data collection unit can analyze past data collection history and optimize collection frequency and timing. For example, the data collection unit can set priorities for data to be collected based on past data collection history. This enables efficient data collection by selecting the optimal collection method through analysis of past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0061] The proposal function can adjust the level of detail of a proposal based on the importance of the issue. For example, for important issues, the proposal function can provide a detailed proposal and show specific solutions. For less important issues, the proposal function can provide a simplified proposal, prioritizing efficiency. The proposal function can also optimally allocate resources to the proposal according to the importance of the issue. This allows for efficient proposals by adjusting the level of detail according to the importance of the issue. Some or all of the above processes in the proposal function may be performed using AI, or not. For example, the proposal function can input the importance of the issue into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0062] The data collection unit can filter data based on the current store conditions or specific events during data collection. For example, the data collection unit can adjust the timing of data collection based on store congestion levels. For example, the data collection unit can prioritize the collection of relevant data based on specific events (sales, promotions, etc.). For example, the data collection unit can collect data outside of store operating hours to minimize the impact on operations. This allows for the collection of highly relevant data by filtering data based on store conditions and events. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the current store conditions or specific events into a generating AI and have the generating AI perform the filtering.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to improve accuracy. For example, the analysis unit can perform a simplified analysis on less important data to prioritize efficiency. For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This makes efficient analysis possible by adjusting the level of detail of the analysis according to 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 a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0064] The discovery unit can improve the accuracy of its discovery by considering the interrelationships of data when discovering a problem. For example, the discovery unit can analyze the interrelationships of data and discover related problems. For example, the discovery unit can set priority for problems based on the interrelationships of data. For example, the discovery unit can improve the accuracy of problem discovery by considering the interrelationships of data. As a result, the accuracy of discovery is improved by considering the interrelationships of data. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the interrelationships of data into a generating AI and have the generating AI perform the task of improving the accuracy of discovery.

[0065] The support department can select the optimal information collection method by referring to the past interview history of store employees when collecting information. For example, the support department can identify the most effective collection method from past interview history and apply it to current information collection. For example, the support department can analyze past interview history and optimize the frequency and timing of collection. For example, the support department can set priorities for the information to be collected based on past interview history. This makes it possible to select the optimal information collection method by referring to past interview history and to collect information efficiently. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past interview history into a generating AI and have the generating AI select the optimal collection method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data. The data collection unit can collect data such as the number of customers, sales data, inventory information, and staff meeting minutes. The data collection unit can count the number of customers using sensors or cameras, for example. The data collection unit can also acquire sales data from a POS system. Furthermore, the data collection unit can acquire inventory information from an inventory management system. For example, the data collection unit can count the number of customers in real time using sensors. The data collection unit can automatically acquire sales data from a POS system. The data collection unit can periodically acquire inventory information from an inventory management system. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze quantitative data such as the number of customers and purchase history, and can detect trends and outliers in time-series data. For example, the analysis unit can analyze trends in time-series data to understand increases and decreases in the number of customers. The analysis unit can also detect outliers and identify abnormal fluctuations in sales. Furthermore, the analysis unit can analyze meeting minutes and conversation logs of store staff, and evaluate staff motivation and customer service attitudes through keyword extraction and sentiment analysis. For example, the analysis unit can extract keywords from meeting minutes to understand staff concerns. The analysis unit can analyze conversation logs to evaluate staff emotions. The analysis unit can evaluate staff motivation through sentiment analysis. Step 3: The discovery unit identifies issues based on the analysis results obtained by the analysis unit. For example, the discovery unit can automatically present specific issues of the store based on the analysis results. For example, the discovery unit can detect a decrease in the number of customers and point out problems with the store's layout. The discovery unit can also detect anomalies in sales data and point out problems with product placement. Furthermore, the discovery unit can analyze the dissatisfaction of part-time staff from meeting minutes and discover problems with insufficient training or working conditions. For example, the discovery unit can detect a decrease in the number of customers and propose a review of the store's layout. The discovery unit can detect anomalies in sales data and propose improvements to product placement. The discovery unit can analyze the dissatisfaction of part-time staff from meeting minutes and propose strengthening the training program. Step 4: The proposal department proposes solutions to the issues identified by the discovery department. For example, the proposal department can set priorities for each issue and propose countermeasures. For example, the proposal department can propose a review of the store layout. The proposal department can also propose strengthening staff training. Furthermore, the proposal department can propose revising the customer service manual. For example, the proposal department can propose a review of the store layout and optimize the placement of products. The proposal department can propose strengthening staff training to improve customer service skills. The proposal department can propose revising the customer service manual to improve the quality of customer service. Step 5: The support department collects necessary information from the staff. The support department can, for example, list the information and questions to be collected from the staff and propose effective interview methods. The support department can, for example, propose methods for collecting customer feedback from the staff. The support department can also propose methods for collecting sales data from the staff. Furthermore, the support department can propose methods for collecting inventory information from the staff. For example, the support department can create questionnaires to collect customer feedback from the staff. The support department can create reports to collect sales data from the staff. The support department can create checklists to collect inventory information from the staff.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that supports data analysis and problem solving in the retail industry. This AI agent system collects various data held by stores, and by analyzing it with AI, it identifies store problems and proposes specific improvement measures. Furthermore, it proposes methods for effectively collecting necessary information from store staff and supports the optimization of store operations. For example, the AI ​​agent system collects data held by stores. This data includes the number of customers, sales data, inventory information, and staff meeting minutes. Next, the AI ​​analyzes the collected data. The AI ​​analyzes quantitative data such as the number of customers and purchase history, and detects trends and anomalies in time-series data. It also analyzes meeting minutes and conversation logs of store staff, and evaluates staff motivation and customer service attitudes through keyword extraction and sentiment analysis. Based on the analysis results, it automatically presents specific store problems. For example, if customers enter the store but do not reach the register, problems such as poor store flow, unclear product placement, and a decline in the quality of customer service may be pointed out as issues. In addition, by analyzing the complaints and grievances of part-time staff from meeting minutes, it is possible to discover problems such as insufficient training and working environment issues. Furthermore, the system provides solutions to challenges and supports the optimization of store operations. For example, it may suggest revising the store layout, strengthening staff training, and revising customer service manuals. It also lists information and questions to collect from store staff and proposes effective interviewing methods. This system allows for the rapid identification of problems that might otherwise be overlooked through data analysis, and reduces the burden on staff by automating information collection and analysis. Moreover, by implementing appropriate improvement measures, store performance is expected to improve, leading to increased sales and customer satisfaction. In this way, the AI ​​agent system can support the optimization of store operations by analyzing store data, identifying challenges, and proposing improvement measures.

[0069] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a discovery unit, a proposal unit, and a support unit. The data collection unit collects data. The data collection unit can collect data such as the number of customers, sales data, inventory information, and staff meeting minutes. The data collection unit can count the number of customers using sensors or cameras, for example. The data collection unit can also acquire sales data from a POS system. Furthermore, the data collection unit can acquire inventory information from an inventory management system. For example, the data collection unit can count the number of customers in real time using sensors. The data collection unit can automatically acquire sales data from a POS system. The data collection unit can periodically acquire inventory information from an inventory management system. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze quantitative data such as the number of customers and purchase history, and can detect trends and outliers in time-series data. For example, the analysis unit can analyze trends in time-series data to understand increases and decreases in the number of customers. The analysis unit can also detect outliers and identify abnormal sales fluctuations. Furthermore, the analysis unit can analyze meeting minutes and conversation logs of store staff, and evaluate staff motivation and customer service attitudes through keyword extraction and sentiment analysis. For example, the analysis unit can extract keywords from meeting minutes to understand staff concerns. The analysis unit can analyze conversation logs to evaluate staff emotions. The analysis unit can evaluate staff motivation through sentiment analysis. The discovery unit discovers issues based on the analysis results obtained by the analysis unit. For example, the discovery unit can automatically present specific issues for the store based on the analysis results. For example, the discovery unit can detect a decrease in the number of customers and point out problems with the store's traffic flow. The discovery unit can also detect anomalies in sales data and point out problems with product placement. Furthermore, the discovery unit can analyze the dissatisfaction of part-time staff from meeting minutes to discover problems with insufficient training and working conditions. For example, the discovery unit can detect a decrease in the number of customers and propose a review of the store's traffic flow. The discovery unit can detect anomalies in sales data and propose improvements to product placement.The Discovery Department can analyze the dissatisfaction of part-time staff from meeting minutes and propose strengthening the training program. The Proposal Department proposes solutions to the issues identified by the Discovery Department. For example, the Proposal Department can set priorities for each issue and propose countermeasures. For example, the Proposal Department can propose a review of the store layout. The Proposal Department can also propose strengthening staff training. Furthermore, the Proposal Department can propose revising the customer service manual. For example, the Proposal Department can propose a review of the store layout and optimize the placement of products. The Proposal Department can propose strengthening staff training to improve customer service skills. The Proposal Department can propose revising the customer service manual to improve the quality of customer service. The Support Department collects necessary information from store employees. For example, the Support Department can list the information and questions to be collected from store employees and propose effective interview methods. For example, the Support Department can propose methods for collecting customer feedback from store employees. The Support Department can also propose methods for collecting sales data from store employees. Furthermore, the Support Department can propose methods for collecting inventory information from store employees. For example, the support department can create questionnaires to collect customer feedback from store employees. The support department can create reports to collect sales data from store employees. The support department can create checklists to collect inventory information from store employees. In this way, the AI ​​agent system according to the embodiment can help optimize store operations by analyzing store data, identifying problems, and proposing improvement measures.

[0070] The data collection unit collects data. For example, the data collection unit can collect data such as the number of customers, sales data, inventory information, and staff meeting minutes. For example, the data collection unit can count the number of customers using sensors and cameras. Specifically, it uses cameras and infrared sensors installed at the store entrance and on each floor to track customer movements in real time and accurately count the number of customers. The data collection unit can also acquire sales data from the POS system. The POS system records detailed sales information for each product, allowing for the automatic collection of sales data by time of day and by product. Furthermore, the data collection unit can acquire inventory information from the inventory management system. The inventory management system updates product inbound and outbound information in real time, allowing for accurate understanding of the current inventory status. For example, the data collection unit can count the number of customers in real time using sensors. The data collection unit can automatically acquire sales data from the POS system. The data collection unit can periodically acquire inventory information from the inventory management system. This allows the data collection unit to efficiently collect a variety of data necessary for store operations and enable real-time situational awareness. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and discovery units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze quantitative data such as the number of customers and purchase history, and detect trends and outliers in time-series data. Specifically, it can analyze customer data in time series to understand increases and decreases in customer numbers on specific days of the week or time slots. Furthermore, by analyzing purchase history, it can identify trends in popular products and seasonal best-selling items. The analysis unit can, for example, analyze trends in time-series data to understand increases and decreases in customer numbers. It can also detect outliers and identify abnormal sales fluctuations. In addition, the analysis unit can analyze meeting minutes and conversation logs of store staff, and evaluate staff motivation and customer service attitudes through keyword extraction and sentiment analysis. For example, the analysis unit can extract keywords from meeting minutes to understand staff interests. It can analyze conversation logs to evaluate staff emotions. It can evaluate staff motivation through sentiment analysis. This allows the analytics unit to analyze collected data from multiple perspectives and provide important insights into store operations. Furthermore, the analytics unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in customer numbers during specific seasons or events based on past customer traffic data and formulate future countermeasures. In addition, the analytics unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analytics unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0072] The discovery unit identifies problems based on the analysis results obtained by the analysis unit. For example, the discovery unit can automatically present specific store issues based on the analysis results. Specifically, it can detect a decrease in the number of customers and point out problems with the store's layout. For example, if the number of customers decreases during a specific time period, the cause may be a complex layout or inappropriate product placement. The discovery unit can also detect anomalies in sales data and point out problems with product placement. For example, if a particular product has extremely low sales compared to other products, it may be due to poor placement of that product. Furthermore, the discovery unit can analyze the dissatisfaction of part-time staff from meeting minutes and identify problems with insufficient training or the working environment. For example, keywords that frequently appear in meeting minutes and the results of sentiment analysis can reveal that staff members are dissatisfied with specific tasks. Based on this information, the discovery unit can propose strengthening training programs or improving the working environment. For example, the discovery unit can detect a decrease in the number of customers and propose a review of the store's layout. The discovery unit can detect anomalies in sales data and propose improvements to product placement. The discovery department can analyze the dissatisfaction of part-time staff from meeting minutes and propose improvements to the training program. This allows the discovery department to quickly identify specific issues based on the analysis results and contribute to improving store operations.

[0073] The proposal department proposes solutions to address the issues identified by the discovery department. For example, the proposal department can prioritize each issue and propose countermeasures. Specifically, it can propose a review of the store layout. For instance, if a decrease in customer traffic is due to problems with the store's flow, the proposal department can propose optimizing product placement and changing the layout to allow customers to move more smoothly. The proposal department can also propose strengthening staff training. For example, if there are problems with staff customer service attitudes, the proposal department can propose introducing a training program to improve customer service skills. Furthermore, the proposal department can propose revising the customer service manual. For example, based on customer feedback, it can propose reviewing the current customer service manual and incorporating more effective customer service methods. The proposal department presents these proposals as concrete action plans and provides them in an actionable form. For example, the proposal department can propose a review of the store layout and optimize product placement. The proposal department can propose strengthening staff training to improve customer service skills. The proposal department can propose revising the customer service manual to improve the quality of customer service. In this way, the proposal department can provide concrete and actionable solutions to the identified issues and support the optimization of store operations.

[0074] The support department collects necessary information from store staff. For example, the support department can list the information and questions that should be collected from store staff and propose effective interviewing methods. Specifically, it can propose methods for collecting customer feedback from store staff. For example, a simple questionnaire could be conducted when customers visit the store to collect information on customer satisfaction and areas for improvement. The support department can also propose methods for collecting sales data from store staff. For example, each store staff member could record their sales data in a daily report, and by aggregating this data, the sales performance of each individual employee could be understood. Furthermore, the support department can propose methods for collecting inventory information from store staff. For example, by conducting regular inventory checks and compiling the results into a report, the inventory situation could be accurately understood. The support department provides these information collection methods as specific tools and formats to help store staff collect information efficiently. For example, the support department can create questionnaires to collect customer feedback from store staff. The support department can create reports to collect sales data from store staff. The support department can create checklists to collect inventory information from store staff. This allows the support department to efficiently collect necessary information from store staff and use it to improve store operations. Furthermore, the support department can analyze the collected information to clearly identify challenges and areas for improvement in store operations. As a result, the support department can help optimize store operations and improve store performance.

[0075] The analysis unit can analyze quantitative data such as the number of customers and purchase history, and detect trends and outliers in time-series data. For example, the analysis unit can analyze time-series data of the number of customers to grasp trends. For example, the analysis unit can analyze time-series data of purchase history to detect outliers. For example, the analysis unit can analyze increases and decreases in the number of customers to grasp trends. The analysis unit can analyze outliers in purchase history to detect abnormal purchasing patterns. The analysis unit can analyze time-series data of the number of customers to grasp trends. The analysis unit can analyze time-series data of purchase history to detect outliers. As a result, by analyzing quantitative data, trends and outliers can be detected, and store issues can be identified.

[0076] The analysis unit can analyze meeting minutes and conversation logs of store staff, and evaluate staff motivation and customer service attitudes through keyword extraction and sentiment analysis. For example, the analysis unit can extract keywords from meeting minutes to understand staff concerns. For example, the analysis unit can analyze conversation logs to evaluate staff emotions. For example, the analysis unit can evaluate staff motivation through sentiment analysis. The analysis unit can extract keywords from meeting minutes to understand staff concerns. The analysis unit can analyze conversation logs to evaluate staff emotions. The analysis unit can evaluate staff motivation through sentiment analysis. This allows for the evaluation of staff motivation and customer service attitudes through the analysis of meeting minutes and conversation logs, which can be used to improve store operations.

[0077] The detection unit can automatically identify specific issues for the store based on the analysis results. For example, the detection unit can detect a decrease in the number of customers and point out problems with the store's layout. For example, the detection unit can detect anomalies in sales data and point out problems with product placement. For example, the detection unit can analyze the complaints of part-time staff from meeting minutes and discover problems with insufficient training or working conditions. The detection unit can detect a decrease in the number of customers and propose a review of the store's layout. The detection unit can detect anomalies in sales data and propose improvements to product placement. The detection unit can analyze the complaints of part-time staff from meeting minutes and propose strengthening the training program. In this way, by presenting specific issues based on the analysis results, the store's problems can be clearly identified.

[0078] The proposal department can prioritize issues and propose solutions. For example, the proposal department can propose a review of the store layout. For example, the proposal department can propose strengthening staff training. For example, the proposal department can propose revising the customer service manual. The proposal department can propose a review of the store layout and optimize the placement of products. The proposal department can propose strengthening staff training to improve customer service skills. The proposal department can propose revising the customer service manual to improve the quality of customer service. In this way, by prioritizing issues and proposing solutions, it is possible to support efficient problem solving.

[0079] The support department can list the information and questions to be collected from store staff and propose effective interview methods. For example, the support department can propose methods for collecting customer feedback from store staff. For example, the support department can propose methods for collecting sales data from store staff. For example, the support department can propose methods for collecting inventory information from store staff. The support department can create questionnaires for collecting customer feedback from store staff. The support department can create reports for collecting sales data from store staff. The support department can create checklists for collecting inventory information from store staff. This allows for effective information gathering from store staff, thereby supporting the optimization of store operations.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can collect detailed data to obtain more information. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can analyze the store's past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past data collection history and apply it to current data collection. For example, the data collection unit can analyze past data collection history and optimize collection frequency and timing. For example, the data collection unit can set priorities for data to be collected based on past data collection history. This enables efficient data collection by selecting the optimal collection method through analysis of past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0082] The data collection unit can filter data based on the current store conditions or specific events during data collection. For example, the data collection unit can adjust the timing of data collection based on store congestion levels. For example, the data collection unit can prioritize the collection of relevant data based on specific events (sales, promotions, etc.). For example, the data collection unit can collect data outside of store operating hours to minimize the impact on operations. This allows for the collection of highly relevant data by filtering data based on store conditions and events. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the current store conditions or specific events into a generating AI and have the generating AI perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can collect detailed data to improve the accuracy of the analysis. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This enables efficient data collection by prioritizing data according to 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 or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of stores during data collection. For example, the data collection unit can prioritize the collection of region-specific data based on the store's location. For example, the data collection unit can collect relevant data by considering the surrounding environment of the store (traffic volume, competing stores, etc.). For example, the data collection unit can adjust the type of data to be collected according to the store's location conditions (urban, suburban, etc.). This allows for the priority collection of highly relevant data by considering geographical location information. 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 store's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0085] The data collection unit can analyze the store's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the store's social media posts to understand customer reactions and trends. For example, the data collection unit can collect customer feedback on social media and reflect it in store operations. For example, the data collection unit can collect social media engagement data to optimize marketing strategies. In this way, by analyzing social media activity, relevant data can be collected and used to improve store operations. 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 store's social media activity data into a generating AI and have the generating AI collect relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. This allows for a deeper understanding of the analysis results by adjusting the presentation of the analysis according to 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to improve accuracy. For example, the analysis unit can perform a simplified analysis on less important data to prioritize efficiency. For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This makes efficient analysis possible by adjusting the level of detail of the analysis according to 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 a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply statistical analysis algorithms to quantitative data. For example, the analysis unit can apply natural language processing algorithms to text data. For example, the analysis unit can apply image recognition algorithms to image data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes 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 a generating AI and have the generating AI select an appropriate analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows for efficient analysis by adjusting the length of the analysis according to 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. For example, the analysis unit can analyze historical data to grasp long-term trends. For example, the analysis unit can optimally allocate analysis resources according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. 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 data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to improve accuracy. For example, the analysis unit can postpone the analysis of less relevant data to prioritize efficiency. For example, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This enables efficient analysis by adjusting 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 without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The discovery unit can estimate the user's emotions and adjust the problem identification criteria based on the estimated user emotions. For example, if the user is tense, the discovery unit can provide simple and highly visible problem identification criteria. For example, if the user is relaxed, the discovery unit can provide detailed problem identification criteria to deepen understanding. For example, if the user is in a hurry, the discovery unit can provide concise problem identification criteria that get straight to the point. This allows for efficient problem identification by adjusting the problem identification criteria according to 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 discovery unit may be performed using AI, for example, or not using AI. For example, the discovery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The discovery unit can improve the accuracy of its discovery by considering the interrelationships of data when discovering a problem. For example, the discovery unit can analyze the interrelationships of data and discover related problems. For example, the discovery unit can set priority for problems based on the interrelationships of data. For example, the discovery unit can improve the accuracy of problem discovery by considering the interrelationships of data. As a result, the accuracy of discovery is improved by considering the interrelationships of data. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the interrelationships of data into a generating AI and have the generating AI perform the task of improving the accuracy of discovery.

[0094] The discovery unit can perform discovery by considering the attribute information of the data submitter when discovering a problem. For example, the discovery unit can evaluate the importance of a problem based on the data submitter's position and experience. For example, the discovery unit can set the priority of problems by considering the attribute information of the data submitter. For example, the discovery unit can improve the accuracy of problem discovery based on the attribute information of the data submitter. As a result, the accuracy of discovery is improved by considering the attribute information of the data submitter. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without using AI. For example, the discovery unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the task of improving the accuracy of discovery.

[0095] The discovery unit can estimate the user's emotions and adjust the display method of the discovered tasks based on the estimated user emotions. For example, if the user is nervous, the discovery unit can provide a simple and highly visible display method. For example, if the user is relaxed, the discovery unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the discovery unit can provide a display method that gets straight to the point. This allows for efficient task display by adjusting the task display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The discovery unit can perform problem identification while considering the geographical distribution of the data. For example, the discovery unit can analyze the geographical distribution of the data and identify region-specific problems. For example, the discovery unit can set priority levels for problems based on the geographical distribution. For example, the discovery unit can improve the accuracy of problem identification by considering the geographical distribution. This allows for the identification of region-specific problems by considering the geographical distribution of the data. Some or all of the above-described processes in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the task of improving the accuracy of the discovery.

[0097] The discovery unit can improve the accuracy of its discovery by referring to relevant literature when it discovers a problem. For example, the discovery unit can identify the background and causes of a problem by referring to relevant literature. For example, the discovery unit can set priority levels for problems based on relevant literature. For example, the discovery unit can improve the accuracy of problem discovery by considering relevant literature. As a result, the accuracy of discovery is improved by referring to relevant literature. Some or all of the above processes in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input relevant literature into a generation AI and have the generation AI perform the task of improving the accuracy of discovery.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions to deepen understanding. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. This allows for efficient suggestions by adjusting the way suggestions are presented according to 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 a generative AI and have the generative AI perform emotion estimation.

[0099] The proposal function can adjust the level of detail of a proposal based on the importance of the issue. For example, for important issues, the proposal function can provide a detailed proposal and show specific solutions. For less important issues, the proposal function can provide a simplified proposal, prioritizing efficiency. The proposal function can also optimally allocate resources to the proposal according to the importance of the issue. This allows for efficient proposals by adjusting the level of detail according to the importance of the issue. Some or all of the above processes in the proposal function may be performed using AI, or not. For example, the proposal function can input the importance of the issue into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0100] The proposal unit can apply different proposal algorithms depending on the category of the problem when making a proposal. For example, the proposal unit can apply a marketing algorithm to a problem related to sales promotion. For example, the proposal unit can apply an inventory optimization algorithm to a problem related to inventory management. For example, the proposal unit can apply an education program algorithm to a problem related to staff training. By applying the appropriate proposal algorithm according to the category of the problem, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the problem into a generating AI and have the generating AI select an appropriate proposal algorithm.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, efficient suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as 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. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The proposal department can determine the priority of proposals based on when the problem was discovered. For example, the proposal department can prioritize proposals for the most recent problems to encourage a quick response. For example, the proposal department can propose long-term solutions for past problems. For example, the proposal department can optimally allocate resources for proposals according to when the problem was discovered. This enables efficient proposals by determining the priority of proposals based on when the problem was discovered. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the timing of problem discovery into a generating AI and have the generating AI determine the priority of proposals.

[0103] The proposal unit can adjust the order of proposals based on the relevance of the issues during the proposal process. For example, the proposal unit can prioritize proposals for highly relevant issues and provide effective solutions. For example, the proposal unit can postpone less relevant issues to prioritize efficiency. For example, the proposal unit can optimally allocate resources for proposals according to the relevance of the issues. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the issues. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the issues into a generating AI and have the generating AI adjust the order of proposals.

[0104] The support unit can estimate the user's emotions and adjust the information gathering method based on the estimated emotions. For example, if the user is nervous, the support unit can provide a simple and highly visible information gathering method. For example, if the user is relaxed, the support unit can provide a detailed information gathering method to deepen understanding. For example, if the user is in a hurry, the support unit can provide a concise information gathering method that gets straight to the point. This allows for efficient information gathering by adjusting the information gathering method according to 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 support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The support department can select the optimal information collection method by referring to the past interview history of store employees when collecting information. For example, the support department can identify the most effective collection method from past interview history and apply it to current information collection. For example, the support department can analyze past interview history and optimize the frequency and timing of collection. For example, the support department can set priorities for the information to be collected based on past interview history. This makes it possible to select the optimal information collection method by referring to past interview history and to collect information efficiently. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past interview history into a generating AI and have the generating AI select the optimal collection method.

[0106] The support unit can estimate the user's emotions and determine the priority of information gathering based on the estimated emotions. For example, if the user is stressed, the support unit can prioritize collecting only important information. For example, if the user is relaxed, the support unit can collect detailed information to improve the accuracy of the analysis. For example, if the user is in a hurry, the support unit can prioritize information that can be collected quickly. This enables efficient information gathering by determining the priority of information gathering according to 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 support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The support unit can select the optimal data collection method when gathering information, taking into account the geographical location of the store employees. For example, the support unit can prioritize the collection of region-specific information based on the store employees' location. For example, the support unit can collect relevant information by considering the store employees' surrounding environment (traffic volume, competing stores, etc.). For example, the support unit can adjust the type of information collected according to the store employees' location conditions (urban, suburban, etc.). This allows for efficient information gathering by selecting the optimal data collection method while considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the store employees' geographical location information into a generating AI and have the generating AI select the optimal data collection method.

[0108] The support department can analyze employees' social media activities and collect relevant information during information gathering. For example, the support department can analyze employees' social media posts to understand customer reactions and trends. For example, the support department can collect customer feedback on social media and reflect it in store operations. For example, the support department can collect social media engagement data to optimize marketing strategies. In this way, by analyzing social media activities, relevant information can be collected and used to improve store operations. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input employees' social media activity data into a generating AI and have the generating AI collect relevant information.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize the analysis of only the most important data. For example, if the user is relaxed, the analysis unit can analyze detailed data to improve the accuracy of the analysis. For example, if the user is in a hurry, the analysis unit can prioritize data that can be analyzed quickly. This enables efficient data analysis by determining the priority of analysis according to 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 without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The data collection unit can analyze the store's past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past data collection history and apply it to current data collection. For example, the data collection unit can analyze past data collection history and optimize collection frequency and timing. For example, the data collection unit can set priorities for data to be collected based on past data collection history. This enables efficient data collection by selecting the optimal collection method through analysis of past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0112] The discovery unit can estimate the user's emotions and adjust the problem identification criteria based on the estimated user emotions. For example, if the user is tense, the discovery unit can provide simple and highly visible problem identification criteria. For example, if the user is relaxed, the discovery unit can provide detailed problem identification criteria to deepen understanding. For example, if the user is in a hurry, the discovery unit can provide concise problem identification criteria that get straight to the point. This allows for efficient problem identification by adjusting the problem identification criteria according to 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 discovery unit may be performed using AI, for example, or not using AI. For example, the discovery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The proposal function can adjust the level of detail of a proposal based on the importance of the issue. For example, for important issues, the proposal function can provide a detailed proposal and show specific solutions. For less important issues, the proposal function can provide a simplified proposal, prioritizing efficiency. The proposal function can also optimally allocate resources to the proposal according to the importance of the issue. This allows for efficient proposals by adjusting the level of detail according to the importance of the issue. Some or all of the above processes in the proposal function may be performed using AI, or not. For example, the proposal function can input the importance of the issue into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0114] The support unit can estimate the user's emotions and adjust the information gathering method based on the estimated emotions. For example, if the user is nervous, the support unit can provide a simple and highly visible information gathering method. For example, if the user is relaxed, the support unit can provide a detailed information gathering method to deepen understanding. For example, if the user is in a hurry, the support unit can provide a concise information gathering method that gets straight to the point. This allows for efficient information gathering by adjusting the information gathering method according to 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 support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The data collection unit can filter data based on the current store conditions or specific events during data collection. For example, the data collection unit can adjust the timing of data collection based on store congestion levels. For example, the data collection unit can prioritize the collection of relevant data based on specific events (sales, promotions, etc.). For example, the data collection unit can collect data outside of store operating hours to minimize the impact on operations. This allows for the collection of highly relevant data by filtering data based on store conditions and events. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the current store conditions or specific events into a generating AI and have the generating AI perform the filtering.

[0116] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to improve accuracy. For example, the analysis unit can perform a simplified analysis on less important data to prioritize efficiency. For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This makes efficient analysis possible by adjusting the level of detail of the analysis according to 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 a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0117] The discovery unit can improve the accuracy of its discovery by considering the interrelationships of data when discovering a problem. For example, the discovery unit can analyze the interrelationships of data and discover related problems. For example, the discovery unit can set priority for problems based on the interrelationships of data. For example, the discovery unit can improve the accuracy of problem discovery by considering the interrelationships of data. As a result, the accuracy of discovery is improved by considering the interrelationships of data. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the interrelationships of data into a generating AI and have the generating AI perform the task of improving the accuracy of discovery.

[0118] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions to deepen understanding. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. This allows for efficient suggestions by adjusting the way suggestions are presented according to 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 a generative AI and have the generative AI perform emotion estimation.

[0119] The support department can select the optimal information collection method by referring to the past interview history of store employees when collecting information. For example, the support department can identify the most effective collection method from past interview history and apply it to current information collection. For example, the support department can analyze past interview history and optimize the frequency and timing of collection. For example, the support department can set priorities for the information to be collected based on past interview history. This makes it possible to select the optimal information collection method by referring to past interview history and to collect information efficiently. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past interview history into a generating AI and have the generating AI select the optimal collection method.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The data collection unit collects data. The data collection unit can collect data such as the number of customers, sales data, inventory information, and staff meeting minutes. The data collection unit can count the number of customers using sensors or cameras, for example. The data collection unit can also acquire sales data from a POS system. Furthermore, the data collection unit can acquire inventory information from an inventory management system. For example, the data collection unit can count the number of customers in real time using sensors. The data collection unit can automatically acquire sales data from a POS system. The data collection unit can periodically acquire inventory information from an inventory management system. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze quantitative data such as the number of customers and purchase history, and can detect trends and outliers in time-series data. For example, the analysis unit can analyze trends in time-series data to understand increases and decreases in the number of customers. The analysis unit can also detect outliers and identify abnormal fluctuations in sales. Furthermore, the analysis unit can analyze meeting minutes and conversation logs of store staff, and evaluate staff motivation and customer service attitudes through keyword extraction and sentiment analysis. For example, the analysis unit can extract keywords from meeting minutes to understand staff concerns. The analysis unit can analyze conversation logs to evaluate staff emotions. The analysis unit can evaluate staff motivation through sentiment analysis. Step 3: The discovery unit identifies issues based on the analysis results obtained by the analysis unit. For example, the discovery unit can automatically present specific issues of the store based on the analysis results. For example, the discovery unit can detect a decrease in the number of customers and point out problems with the store's layout. The discovery unit can also detect anomalies in sales data and point out problems with product placement. Furthermore, the discovery unit can analyze the dissatisfaction of part-time staff from meeting minutes and discover problems with insufficient training or working conditions. For example, the discovery unit can detect a decrease in the number of customers and propose a review of the store's layout. The discovery unit can detect anomalies in sales data and propose improvements to product placement. The discovery unit can analyze the dissatisfaction of part-time staff from meeting minutes and propose strengthening the training program. Step 4: The proposal department proposes solutions to the issues identified by the discovery department. For example, the proposal department can set priorities for each issue and propose countermeasures. For example, the proposal department can propose a review of the store layout. The proposal department can also propose strengthening staff training. Furthermore, the proposal department can propose revising the customer service manual. For example, the proposal department can propose a review of the store layout and optimize the placement of products. The proposal department can propose strengthening staff training to improve customer service skills. The proposal department can propose revising the customer service manual to improve the quality of customer service. Step 5: The support department collects necessary information from the staff. The support department can, for example, list the information and questions to be collected from the staff and propose effective interview methods. The support department can, for example, propose methods for collecting customer feedback from the staff. The support department can also propose methods for collecting sales data from the staff. Furthermore, the support department can propose methods for collecting inventory information from the staff. For example, the support department can create questionnaires to collect customer feedback from the staff. The support department can create reports to collect sales data from the staff. The support department can create checklists to collect inventory information from the staff.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the data collection unit, analysis unit, discovery unit, proposal unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit counts the number of customers using the sensors and cameras of the smart device 14 and acquires sales data from the POS system. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and detects trends and anomalies in the time-series data. The discovery unit automatically presents specific issues for the store based on the analysis results, and the proposal unit proposes improvement measures for the discovered issues. The support unit proposes methods for collecting necessary information from store employees and supports the optimization of store operations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, discovery unit, proposal unit, and support 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 counts the number of customers using the sensors and cameras of the smart glasses 214 and acquires sales data from the POS system. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and detects trends and anomalies in the time-series data. The discovery unit automatically presents specific issues for the store based on the analysis results, and the proposal unit proposes improvement measures for the discovered issues. The support unit proposes methods for collecting necessary information from store employees and supports the optimization of store operations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, discovery unit, proposal unit, and support unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit counts the number of customers using the sensors and camera of the headset terminal 314 and acquires sales data from the POS system. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and detects trends and anomalies in the time-series data. The discovery unit automatically presents specific issues for the store based on the analysis results, and the proposal unit proposes improvement measures for the discovered issues. The support unit proposes methods for collecting necessary information from store employees and supports the optimization of store operations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, proposal unit, and support unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit counts the number of customers using the sensors and cameras of the robot 414 and acquires sales data from the POS system. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and detects trends and anomalies in the time-series data. The discovery unit automatically presents specific issues for the store based on the analysis results, and the proposal unit proposes improvement measures for the discovered issues. The support unit proposes methods for collecting necessary information from store employees and supports the optimization of store operations. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A discovery unit that identifies problems based on the analysis results obtained by the aforementioned analysis unit, A proposal unit that proposes improvement measures for the issues discovered by the discovery unit, It includes a support department that collects necessary information from store staff. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze quantitative data such as the number of customers and purchase history to detect trends and anomalies in time-series data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze meeting minutes and conversation logs of store staff, extract keywords, and perform sentiment analysis to evaluate staff motivation and customer service attitudes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned detection unit is Based on the analysis results, it automatically identifies specific challenges for the store. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Prioritize each issue and propose solutions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, We will list the information and questions that should be collected from store staff and propose effective interviewing methods. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the store's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the current status of the store or specific events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of the stores. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze the store's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, 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 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, 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 19) The aforementioned detection unit is We estimate user emotions and adjust the criteria for identifying problems based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned detection unit is When identifying problems, consider the interrelationships between data to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned detection unit is When identifying problems, consider the attribute information of the data submitters. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned detection unit is It estimates the user's emotions and adjusts how the identified issues are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned detection unit is When identifying problems, consider the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned detection unit is When identifying problems, refer to relevant literature to improve the accuracy of the findings. The system described in Appendix 1, characterized by the features described herein. (Note 25) 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 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the issue. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the problem was discovered. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit, It estimates the user's emotions and adjusts the information gathering method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit, When gathering information, refer to the store clerk's past interview history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit, It estimates the user's emotions and determines the priority of information gathering based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit, When gathering information, the optimal collection method is selected by considering the geographical location of the store staff. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, When gathering information, we analyze the social media activity of store employees and collect relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 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, A discovery unit that identifies problems based on the analysis results obtained by the aforementioned analysis unit, A proposal unit that proposes improvement measures for the issues discovered by the discovery unit, It includes a support department that collects necessary information from store staff. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze quantitative data such as the number of customers and purchase history to detect trends and anomalies in time-series data. The system according to feature 1.

3. The aforementioned analysis unit, We analyze meeting minutes and conversation logs of store staff, extract keywords, and perform sentiment analysis to evaluate staff motivation and customer service attitudes. The system according to feature 1.

4. The aforementioned detection unit is Based on the analysis results, it automatically identifies specific challenges for the store. The system according to feature 1.

5. The aforementioned proposal section is, Prioritize each issue and propose solutions. The system according to feature 1.

6. The aforementioned support unit, We will list the information and questions that should be collected from store staff and propose effective interviewing methods. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the store's past data collection history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the current status of the store or specific events. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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