system

Abstract

The system according to this embodiment aims to enable small business operators to effectively utilize data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit creates visualization results and performance summaries based on the analysis results obtained by the analysis unit. The provision unit presents the results generated by the generation unit to the business owner.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, small business operators do not have dedicated data organization and analysis resources for effectively utilizing data, resulting in an opportunity loss for data utilization.

[0005] The system according to the embodiment aims to enable small business operators to effectively utilize data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The generation unit creates visualization results and performance summaries based on the analysis results obtained by the analysis unit. The provision unit presents the results generated by the generation unit to the business owner. [Effects of the Invention]

[0007] The system according to this embodiment can enable small business operators to effectively utilize data. [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 applicable 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The agent-based question-answering service according to an embodiment of the present invention is a system that enables small business operators to effectively utilize diverse data generated in their operations. This system supports the analysis and visualization of data generated in daily operations, such as sales and POS data. When a business owner inputs questions or instructions in natural language, the agent creates and presents visualization results and performance summaries. For example, questions such as "Which customer segments are seeing increased sales?" or instructions such as "Create a monthly summary" can be given. The agent prioritizes usability on mobile devices and enables immediate analysis by digitizing paper-based data using OCR. This allows small business operators to reduce the loss of opportunities for data utilization without having a dedicated data organization or analytical resources. It also reduces implementation costs and personnel costs for users. Furthermore, by learning industry-specific data and evolving autonomously, the agent supports small business employees in taking their first steps in data utilization, creating business value and optimizing cost allocation. For example, when a business owner inputs sales data and asks, "Which customer segments are seeing increased sales?", the agent analyzes the data and presents visualization results. Furthermore, when instructed to "create a monthly summary," the agent creates a performance summary and presents it to the business owner. This allows the business owner to make data-driven decisions. In addition, the agent prioritizes usability on mobile devices and enables immediate analysis by digitizing paper-based data using OCR. For example, a paper slip can be photographed with a camera, digitized using OCR, and then input into the agent. This allows small business owners to easily utilize data. In this way, the present invention provides an agent-based question-answering service that enables small business owners to effectively utilize data, reduces lost opportunities for data utilization, and lowers implementation costs and personnel costs for utilization staff. As a result, the agent-based question-answering service can support small business owners in effectively utilizing data, creating business value, and optimizing cost allocation.

[0029] The agent-based question answering service according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The collection unit can collect data such as sales data, customer data, and inventory data. The collection unit can also digitize paper-based data using OCR. For example, the collection unit scans paper slips and converts them into digital data using OCR technology. The collection unit can also digitize paper slips photographed with a camera using OCR. For example, the collection unit photographs paper slips using a smartphone camera and converts them into digital data using OCR technology. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze data generated in daily operations, such as sales data and POS data. The analysis unit can also analyze data trends and patterns using AI. For example, the analysis unit analyzes sales data to understand increases and decreases in sales and customer purchasing trends. The generation unit creates visualization results and performance summaries based on the analysis results obtained by the analysis unit. The generation unit can create visualization results such as graphs, charts, and dashboards. The generation unit can also automatically visualize analysis results using AI. For example, the generation unit can graph sales data and visually display sales trends. The delivery unit presents the results generated by the generation unit to the business owner. For example, the delivery unit can present the generated visualization results and performance summaries to the business owner. The delivery unit can also present results using mobile devices. For example, the delivery unit can display the generated results using a smartphone or tablet. As a result, the agent-based question answering service according to this embodiment can help small business operators effectively utilize data to create business value and optimize cost allocation.

[0030] The data collection unit collects data. For example, it can collect sales data, customer data, and inventory data. Specifically, sales data is automatically acquired from POS systems and online sales platforms, customer data is collected from customer management systems and CRM tools, and inventory data is acquired from warehouse management systems and inventory management software. This data is collected in real time via APIs and stored in a central database. The data collection unit can also digitize paper-based data using OCR. For example, the unit scans paper slips and converts them into digital data using OCR technology. OCR technology uses character recognition algorithms to convert information on paper slips into text data and stores it in the database. Furthermore, the data collection unit can digitize paper slips photographed with a camera using OCR. For example, the unit photographs paper slips using a smartphone camera and converts them into digital data using OCR technology. Images captured by the smartphone camera are sent to an OCR service in the cloud and converted into text data. This allows the data collection unit to efficiently digitize paper-based data and achieve centralized data management. Furthermore, the data collection unit can also collect data from social media and websites. For example, it can use social media APIs to collect customer feedback and reviews, and use web analytics tools to analyze website traffic data. This allows the data collection unit to gather a wide range of data from diverse data sources and build a foundation for business intelligence.

[0031] The Analysis Department analyzes data collected by the Data Collection Department. For example, the Analysis Department can analyze data generated in daily operations, such as sales data and POS data. Specifically, it can analyze sales data over time to understand sales fluctuations and seasonal trends. By analyzing POS data, it can analyze sales trends for specific products or categories in detail, which can be used to optimize inventory management and marketing strategies. The Analysis Department can also use AI to analyze data trends and patterns. For example, it can use machine learning algorithms to analyze sales data to understand sales fluctuations and customer purchasing trends. AI can predict future sales based on past data, contributing to demand forecasting and inventory management optimization. Furthermore, by analyzing customer data, it can improve customer segmentation and retention rates. For example, it can analyze customer purchase history and behavioral data to identify customer preferences and purchasing patterns. This allows for targeted marketing and personalized promotions, improving customer satisfaction. In addition, the Analysis Department can use anomaly detection algorithms to detect data anomalies and fraudulent activity. For example, it becomes possible to detect unusual patterns or fraudulent transactions in sales data early and take appropriate measures. This allows the analytics department to ensure data reliability and security, and support the healthy operation of the business.

[0032] The generation unit creates visualization results and performance summaries based on the analysis results obtained by the analysis unit. The generation unit can create visualization results such as graphs, charts, and dashboards. Specifically, it can create line graphs and bar graphs based on sales data to visually display sales trends and comparisons. It can also create pie charts and heatmaps based on customer data to visualize customer segmentation and purchasing trends. The generation unit can also automatically visualize analysis results using AI. For example, it can automatically summarize analysis results using natural language processing technology and generate them as text reports. This allows business owners to quickly understand the analysis results and use them to aid in decision-making. Furthermore, the generation unit can create interactive dashboards, allowing users to freely explore data and perform detailed analysis. For example, users can select specific periods or product categories on the dashboard to analyze sales and customer data in detail. This allows the generation unit to provide flexible data visualizations tailored to user needs and support the acquisition of business insights. Additionally, the generation unit can automatically update the generated visualization results and performance summaries, providing information based on the latest data. This allows business owners to make decisions based on the latest information at all times.

[0033] The service provider presents the results generated by the data generation unit to the business owner. For example, the service provider can present the generated visualization results and performance summaries to the business owner. Specifically, it can display generated graphs and charts on the business owner's device to provide information visually. The service provider can also present results using mobile devices. For example, it can display generated results using a smartphone or tablet. This allows business owners to check data in real time, even when on the go, and make quick decisions. Furthermore, the service provider can send the generated results to the business owner via email or notification. For example, it can send periodic reports via email to provide important information in a timely manner. The service provider can also provide results verbally using a voice assistant. For example, when a business owner asks a voice assistant for the latest sales data, the voice assistant will respond verbally with the generated results. This allows the service provider to provide information to business owners in a variety of ways, improving convenience. Furthermore, the service provider can collect feedback from business owners and continuously improve the accuracy and usefulness of the information it provides. For example, based on feedback from business owners, it can review the content and format of the reports it provides to make the information more user-friendly. This allows the service provider to offer information tailored to the needs of business owners and support their business success.

[0034] The data collection unit can digitize paper-based data using OCR. For example, the data collection unit can scan paper slips and convert them into digital data using OCR technology. The data collection unit can also digitize paper-based data such as handwritten notes and contracts using OCR technology. For example, the data collection unit can scan handwritten notes and convert them into text data using OCR technology. The data collection unit can also scan contracts and convert them into digital data using OCR technology. This enables immediate analysis by digitizing paper-based data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data obtained by scanning paper slips into a generation AI and have the generation AI generate text data from the image data.

[0035] The data collection unit can digitize paper documents photographed with a camera using OCR. For example, the data collection unit can photograph a paper document using a smartphone camera and convert it into digital data using OCR technology. The data collection unit can also digitize paper documents such as receipts and invoices using OCR technology. For example, the data collection unit can photograph a receipt with a camera and convert it into text data using OCR technology. The data collection unit can also photograph an invoice with a camera and convert it into digital data using OCR technology. This makes it easy to collect data by digitizing paper documents photographed with a camera. 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 image data of a paper document photographed with a camera into a generation AI and have the generation AI perform the generation of text data from the image data.

[0036] The analysis department can analyze data generated in daily operations, such as sales data and POS data. For example, the analysis department can analyze sales data to understand increases and decreases in sales and customer purchasing trends. The analysis department can also analyze POS data to understand point-of-sale information and product scan data. For example, the analysis department can analyze POS data to understand customer purchase history and product sales performance. Furthermore, the analysis department can use AI to analyze data trends and patterns. For example, the analysis department can input sales data into AI and have the AI ​​analyze increases and decreases in sales and customer purchasing trends. This allows for increased operational efficiency by analyzing data generated in daily operations. Some or all of the above-mentioned processes in the analysis department may be performed using AI or not. For example, the analysis department can input sales data into AI and have the AI ​​analyze increases and decreases in sales and customer purchasing trends.

[0037] The generation unit can create visualization results and performance summaries based on the analysis results. For example, the generation unit can create visualization results such as graphs, charts, and dashboards. The generation unit can also graph sales data and visually display sales trends. For example, the generation unit can graph sales data and visually display increases and decreases in sales. Furthermore, the generation unit can automatically visualize analysis results using AI. For example, the generation unit can input sales data into AI and graph sales trends. This deepens the understanding of the data by creating visualization results and performance summaries based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input sales data into AI and graph sales trends.

[0038] The service provider can present the generated results to the business owner. For example, the service provider can present the generated visualization results and performance summaries to the business owner. The service provider can also present the results using mobile devices. For example, the service provider can display the generated results using a smartphone or tablet. Furthermore, the service provider can automatically present the generated results using AI. For example, the service provider can input the generated visualization results into AI and present them to the business owner. This enables data-driven decision-making by presenting the generated results to the business owner. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated visualization results into AI and present them to the business owner.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize collecting the types of data that the user has frequently collected in the past. The data collection unit can also suggest the optimal collection timing based on the user's past data collection history. For example, the data collection unit analyzes the user's past data collection history and selects an efficient collection method. This enables efficient data collection by analyzing the user's past data collection history. 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 the user's past data collection history into AI and have the AI ​​select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can collect only the necessary data according to the user's work situation. This allows the collection of only the necessary data by filtering the data according to the user's work situation and areas of interest. 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 the user's work situation data into AI and have the AI ​​perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter and collect highly relevant data based on the user's current location. For example, the data collection unit will prioritize the collection of highly relevant data by considering the user's travel history. This enables efficient data collection by collecting highly relevant data based on the user's geographical location information. 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 the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also collect data related to topics of interest from the user's social media activity. For example, the data collection unit can collect relevant data based on the activity of the user's social media followers and friends. This makes it possible to collect data that is tailored to the user's interests by collecting relevant data based on the user's social media activity. 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 the user's social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0043] 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 high-importance data. The analysis unit can also perform a simplified analysis on low-importance data. For example, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. This allows for efficient data analysis 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 or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sales analysis algorithm to sales data. The analysis unit can also apply a POS analysis algorithm to POS data. For example, the analysis unit can apply a customer analysis algorithm to customer data. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of the appropriate analysis algorithm.

[0045] The analysis department can prioritize analysis based on the data submission date. For example, the analysis department may prioritize analyzing the most recent data. It may also prioritize analyzing data with an approaching submission deadline. For example, the analysis department adjusts the order of analysis according to the data submission date. This enables efficient data analysis by prioritizing analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. For example, the analysis unit optimizes the order of analysis according to the relevance of the data. This allows for efficient data 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 or not. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0047] The generation unit can adjust the level of detail of the generated data based on the importance of the analysis results during generation. For example, the generation unit can perform detailed visualizations for analysis results of high importance, and simplified visualizations for analysis results of low importance. For example, the generation unit can adjust the depth and scope of the generation according to the importance of the analysis results. This allows for efficient data generation by adjusting the level of detail of the generated data according to the importance of the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not be performed using AI. For example, the generation unit can input the importance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the generated data.

[0048] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit can apply a sales visualization algorithm to sales data. The generation unit can also apply a POS visualization algorithm to POS data. For example, the generation unit can apply a customer visualization algorithm to customer data. By applying the appropriate generation algorithm according to the data category, highly accurate generation results can be obtained. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the data category into the AI ​​and have the AI ​​execute the application of the appropriate generation algorithm.

[0049] The generation unit can determine the generation priority based on the data submission timing during generation. For example, the generation unit may prioritize the visualization of the most recent data. The generation unit may also prioritize the visualization of data with approaching submission deadlines. For example, the generation unit may adjust the generation order according to the data submission timing. This enables efficient data generation by determining the generation priority based on the data submission timing. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the data submission timing into the AI ​​and have the AI ​​determine the generation priority.

[0050] The generation unit can adjust the generation order based on the relevance of the data during generation. For example, the generation unit may prioritize the visualization of highly relevant data. It can also postpone the visualization of less relevant data. For example, the generation unit optimizes the generation order according to the relevance of the data. This allows for efficient data generation by adjusting the generation order based on the relevance of the data. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the generation order.

[0051] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method based on the user's past operation history. For example, the service provider may analyze the user's operation history and select an efficient display method. This allows the service provider to provide an efficient display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input user operation history data into AI and have the AI ​​select the optimal display method.

[0052] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a desktop, the service provider can provide a display method that includes detailed information. This enables efficient display by providing the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0053] The service provider can select the optimal display method at the time of delivery, taking into account the user's network connection status. For example, if the user is connected to a high-speed network, the service provider can provide a high-resolution display method. If the user is connected to a low-speed network, the service provider can also provide a lightweight display method. For example, the service provider can automatically adjust the display method according to the user's network connection status. This enables efficient display by providing the optimal display method based on the user's network connection status. Some or all of the above processing in the service provider may be performed using AI, or it may be performed without AI. For example, the service provider can input the user's network connection status into the AI ​​and have the AI ​​select the optimal display method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize the collection of data types that the user has frequently collected in the past. It can also suggest the optimal collection timing based on the user's past data collection history. Furthermore, it can analyze the user's past data collection history and select an efficient collection method. This enables efficient data collection by analyzing the user's past data collection history.

[0056] The data collection unit can filter data based on the user's current work situation and areas of interest during data collection. For example, it can prioritize the collection of data related to projects the user is currently working on. It can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, it can collect only the necessary data according to the user's work situation. In this way, by filtering data according to the user's work situation and areas of interest, only the necessary data can be collected.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if a user is in a specific region, it can prioritize the collection of data related to that region. It can also filter and collect highly relevant data based on the user's current location. Furthermore, it can prioritize the collection of highly relevant data by considering the user's travel history. This enables efficient data collection by collecting highly relevant data based on the user's geographical location.

[0058] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, it can collect relevant data based on information shared by the user on social media. It can also collect data related to topics of interest from the user's social media activity. Furthermore, it can collect relevant data based on the activity of the user's social media followers and friends. This enables data collection tailored to the user's interests by collecting relevant data based on the user's social media activity.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail according to the importance of the data.

[0060] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a sales analysis algorithm to sales data, a POS analysis algorithm to POS data, and a customer analysis algorithm to customer data. By applying the appropriate analysis algorithm according to the data category, it is possible to obtain highly accurate analysis results.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data. The data collection unit can collect data such as sales data, customer data, and inventory data. The data collection unit can also digitize paper-based data using OCR. For example, the data collection unit can scan paper slips and convert them into digital data using OCR technology. The data collection unit can also digitize paper slips that have been photographed with a camera using OCR. For example, the data collection unit can photograph paper slips using a smartphone camera and convert them into digital data using OCR technology. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department can analyze data generated in daily operations, such as sales data and POS data. The analysis department can also use AI to analyze data trends and patterns. For example, the analysis department can analyze sales data to understand increases and decreases in sales and customer purchasing trends. Step 3: The generation unit creates visualization results and performance summaries based on the analysis results obtained by the analysis unit. The generation unit can create visualization results such as graphs, charts, and dashboards. The generation unit can also automatically visualize analysis results using AI. For example, the generation unit can graph sales data and visually display sales trends. Step 4: The delivery unit presents the results generated by the generation unit to the business owner. The delivery unit can, for example, present the generated visualization results or performance summaries to the business owner. The delivery unit can also present the results using mobile devices. For example, the delivery unit can display the generated results using a smartphone or tablet.

[0063] (Example of form 2) The agent-based question-answering service according to an embodiment of the present invention is a system that enables small business operators to effectively utilize diverse data generated in their operations. This system supports the analysis and visualization of data generated in daily operations, such as sales and POS data. When a business owner inputs questions or instructions in natural language, the agent creates and presents visualization results and performance summaries. For example, questions such as "Which customer segments are seeing increased sales?" or instructions such as "Create a monthly summary" can be given. The agent prioritizes usability on mobile devices and enables immediate analysis by digitizing paper-based data using OCR. This allows small business operators to reduce the loss of opportunities for data utilization without having a dedicated data organization or analytical resources. It also reduces implementation costs and personnel costs for users. Furthermore, by learning industry-specific data and evolving autonomously, the agent supports small business employees in taking their first steps in data utilization, creating business value and optimizing cost allocation. For example, when a business owner inputs sales data and asks, "Which customer segments are seeing increased sales?", the agent analyzes the data and presents visualization results. Furthermore, when instructed to "create a monthly summary," the agent creates a performance summary and presents it to the business owner. This allows the business owner to make data-driven decisions. In addition, the agent prioritizes usability on mobile devices and enables immediate analysis by digitizing paper-based data using OCR. For example, a paper slip can be photographed with a camera, digitized using OCR, and then input into the agent. This allows small business owners to easily utilize data. In this way, the present invention provides an agent-based question-answering service that enables small business owners to effectively utilize data, reduces lost opportunities for data utilization, and lowers implementation costs and personnel costs for utilization staff. As a result, the agent-based question-answering service can support small business owners in effectively utilizing data, creating business value, and optimizing cost allocation.

[0064] The agent-based question answering service according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The collection unit can collect data such as sales data, customer data, and inventory data. The collection unit can also digitize paper-based data using OCR. For example, the collection unit scans paper slips and converts them into digital data using OCR technology. The collection unit can also digitize paper slips photographed with a camera using OCR. For example, the collection unit photographs paper slips using a smartphone camera and converts them into digital data using OCR technology. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze data generated in daily operations, such as sales data and POS data. The analysis unit can also analyze data trends and patterns using AI. For example, the analysis unit analyzes sales data to understand increases and decreases in sales and customer purchasing trends. The generation unit creates visualization results and performance summaries based on the analysis results obtained by the analysis unit. The generation unit can create visualization results such as graphs, charts, and dashboards. The generation unit can also automatically visualize analysis results using AI. For example, the generation unit can graph sales data and visually display sales trends. The delivery unit presents the results generated by the generation unit to the business owner. For example, the delivery unit can present the generated visualization results and performance summaries to the business owner. The delivery unit can also present results using mobile devices. For example, the delivery unit can display the generated results using a smartphone or tablet. As a result, the agent-based question answering service according to this embodiment can help small business operators effectively utilize data to create business value and optimize cost allocation.

[0065] The data collection unit collects data. For example, it can collect sales data, customer data, and inventory data. Specifically, sales data is automatically acquired from POS systems and online sales platforms, customer data is collected from customer management systems and CRM tools, and inventory data is acquired from warehouse management systems and inventory management software. This data is collected in real time via APIs and stored in a central database. The data collection unit can also digitize paper-based data using OCR. For example, the unit scans paper slips and converts them into digital data using OCR technology. OCR technology uses character recognition algorithms to convert information on paper slips into text data and stores it in the database. Furthermore, the data collection unit can digitize paper slips photographed with a camera using OCR. For example, the unit photographs paper slips using a smartphone camera and converts them into digital data using OCR technology. Images captured by the smartphone camera are sent to an OCR service in the cloud and converted into text data. This allows the data collection unit to efficiently digitize paper-based data and achieve centralized data management. Furthermore, the data collection unit can also collect data from social media and websites. For example, it can use social media APIs to collect customer feedback and reviews, and use web analytics tools to analyze website traffic data. This allows the data collection unit to gather a wide range of data from diverse data sources and build a foundation for business intelligence.

[0066] The Analysis Department analyzes data collected by the Data Collection Department. For example, the Analysis Department can analyze data generated in daily operations, such as sales data and POS data. Specifically, it can analyze sales data over time to understand sales fluctuations and seasonal trends. By analyzing POS data, it can analyze sales trends for specific products or categories in detail, which can be used to optimize inventory management and marketing strategies. The Analysis Department can also use AI to analyze data trends and patterns. For example, it can use machine learning algorithms to analyze sales data to understand sales fluctuations and customer purchasing trends. AI can predict future sales based on past data, contributing to demand forecasting and inventory management optimization. Furthermore, by analyzing customer data, it can improve customer segmentation and retention rates. For example, it can analyze customer purchase history and behavioral data to identify customer preferences and purchasing patterns. This allows for targeted marketing and personalized promotions, improving customer satisfaction. In addition, the Analysis Department can use anomaly detection algorithms to detect data anomalies and fraudulent activity. For example, it becomes possible to detect unusual patterns or fraudulent transactions in sales data early and take appropriate measures. This allows the analytics department to ensure data reliability and security, and support the healthy operation of the business.

[0067] The generation unit creates visualization results and performance summaries based on the analysis results obtained by the analysis unit. The generation unit can create visualization results such as graphs, charts, and dashboards. Specifically, it can create line graphs and bar graphs based on sales data to visually display sales trends and comparisons. It can also create pie charts and heatmaps based on customer data to visualize customer segmentation and purchasing trends. The generation unit can also automatically visualize analysis results using AI. For example, it can automatically summarize analysis results using natural language processing technology and generate them as text reports. This allows business owners to quickly understand the analysis results and use them to aid in decision-making. Furthermore, the generation unit can create interactive dashboards, allowing users to freely explore data and perform detailed analysis. For example, users can select specific periods or product categories on the dashboard to analyze sales and customer data in detail. This allows the generation unit to provide flexible data visualizations tailored to user needs and support the acquisition of business insights. Additionally, the generation unit can automatically update the generated visualization results and performance summaries, providing information based on the latest data. This allows business owners to make decisions based on the latest information at all times.

[0068] The service provider presents the results generated by the data generation unit to the business owner. For example, the service provider can present the generated visualization results and performance summaries to the business owner. Specifically, it can display generated graphs and charts on the business owner's device to provide information visually. The service provider can also present results using mobile devices. For example, it can display generated results using a smartphone or tablet. This allows business owners to check data in real time, even when on the go, and make quick decisions. Furthermore, the service provider can send the generated results to the business owner via email or notification. For example, it can send periodic reports via email to provide important information in a timely manner. The service provider can also provide results verbally using a voice assistant. For example, when a business owner asks a voice assistant for the latest sales data, the voice assistant will respond verbally with the generated results. This allows the service provider to provide information to business owners in a variety of ways, improving convenience. Furthermore, the service provider can collect feedback from business owners and continuously improve the accuracy and usefulness of the information it provides. For example, based on feedback from business owners, it can review the content and format of the reports it provides to make the information more user-friendly. This allows the service provider to offer information tailored to the needs of business owners and support their business success.

[0069] The data collection unit can digitize paper-based data using OCR. For example, the data collection unit can scan paper slips and convert them into digital data using OCR technology. The data collection unit can also digitize paper-based data such as handwritten notes and contracts using OCR technology. For example, the data collection unit can scan handwritten notes and convert them into text data using OCR technology. The data collection unit can also scan contracts and convert them into digital data using OCR technology. This enables immediate analysis by digitizing paper-based data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data obtained by scanning paper slips into a generation AI and have the generation AI generate text data from the image data.

[0070] The data collection unit can digitize paper documents photographed with a camera using OCR. For example, the data collection unit can photograph a paper document using a smartphone camera and convert it into digital data using OCR technology. The data collection unit can also digitize paper documents such as receipts and invoices using OCR technology. For example, the data collection unit can photograph a receipt with a camera and convert it into text data using OCR technology. The data collection unit can also photograph an invoice with a camera and convert it into digital data using OCR technology. This makes it easy to collect data by digitizing paper documents photographed with a camera. 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 image data of a paper document photographed with a camera into a generation AI and have the generation AI perform the generation of text data from the image data.

[0071] The analysis department can analyze data generated in daily operations, such as sales data and POS data. For example, the analysis department can analyze sales data to understand increases and decreases in sales and customer purchasing trends. The analysis department can also analyze POS data to understand point-of-sale information and product scan data. For example, the analysis department can analyze POS data to understand customer purchase history and product sales performance. Furthermore, the analysis department can use AI to analyze data trends and patterns. For example, the analysis department can input sales data into AI and have the AI ​​analyze increases and decreases in sales and customer purchasing trends. This allows for increased operational efficiency by analyzing data generated in daily operations. Some or all of the above-mentioned processes in the analysis department may be performed using AI or not. For example, the analysis department can input sales data into AI and have the AI ​​analyze increases and decreases in sales and customer purchasing trends.

[0072] The generation unit can create visualization results and performance summaries based on the analysis results. For example, the generation unit can create visualization results such as graphs, charts, and dashboards. The generation unit can also graph sales data and visually display sales trends. For example, the generation unit can graph sales data and visually display increases and decreases in sales. Furthermore, the generation unit can automatically visualize analysis results using AI. For example, the generation unit can input sales data into AI and graph sales trends. This deepens the understanding of the data by creating visualization results and performance summaries based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input sales data into AI and graph sales trends.

[0073] The service provider can present the generated results to the business owner. For example, the service provider can present the generated visualization results and performance summaries to the business owner. The service provider can also present the results using mobile devices. For example, the service provider can display the generated results using a smartphone or tablet. Furthermore, the service provider can automatically present the generated results using AI. For example, the service provider can input the generated visualization results into AI and present them to the business owner. This enables data-driven decision-making by presenting the generated results to the business owner. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated visualization results into AI and present them to the business owner.

[0074] 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 user's burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. This reduces the user's burden by adjusting the timing of data collection 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. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0075] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize collecting the types of data that the user has frequently collected in the past. The data collection unit can also suggest the optimal collection timing based on the user's past data collection history. For example, the data collection unit analyzes the user's past data collection history and selects an efficient collection method. This enables efficient data collection by analyzing the user's past data collection history. 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 the user's past data collection history into AI and have the AI ​​select the optimal collection method.

[0076] The data collection unit can filter data based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can collect only the necessary data according to the user's work situation. This allows the collection of only the necessary data by filtering the data according to the user's work situation and areas of interest. 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 the user's work situation data into AI and have the AI ​​perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect 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 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter and collect highly relevant data based on the user's current location. For example, the data collection unit will prioritize the collection of highly relevant data by considering the user's travel history. This enables efficient data collection by collecting highly relevant data based on the user's geographical location information. 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 the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also collect data related to topics of interest from the user's social media activity. For example, the data collection unit can collect relevant data based on the activity of the user's social media followers and friends. This makes it possible to collect data that is tailored to the user's interests by collecting relevant data based on the user's social media activity. 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 the user's social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0080] 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. If the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be made easy for the user to understand. 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 or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] 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 high-importance data. The analysis unit can also perform a simplified analysis on low-importance data. For example, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. This allows for efficient data analysis 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 or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sales analysis algorithm to sales data. The analysis unit can also apply a POS analysis algorithm to POS data. For example, the analysis unit can apply a customer analysis algorithm to customer data. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of the appropriate analysis algorithm.

[0083] 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 tense, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is in a hurry, the analysis unit can simplify the analysis for quick understanding. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0084] The analysis department can prioritize analysis based on the data submission date. For example, the analysis department may prioritize analyzing the most recent data. It may also prioritize analyzing data with an approaching submission deadline. For example, the analysis department adjusts the order of analysis according to the data submission date. This enables efficient data analysis by prioritizing analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. For example, the analysis unit optimizes the order of analysis according to the relevance of the data. This allows for efficient data 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 or not. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0086] The generation unit can estimate the user's emotions and adjust the presentation of the generated visualizations and summaries based on the estimated emotions. For example, if the user is tense, the generation unit can provide simple and highly visible visualizations. If the user is relaxed, the generation unit can also provide detailed visualizations. For example, if the user is in a hurry, the generation unit can provide concise visualizations. By adjusting the presentation of visualizations and summaries according to the user's emotions, results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0087] The generation unit can adjust the level of detail of the generated data based on the importance of the analysis results during generation. For example, the generation unit can perform detailed visualizations for analysis results of high importance, and simplified visualizations for analysis results of low importance. For example, the generation unit can adjust the depth and scope of the generation according to the importance of the analysis results. This allows for efficient data generation by adjusting the level of detail of the generated data according to the importance of the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not be performed using AI. For example, the generation unit can input the importance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the generated data.

[0088] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit can apply a sales visualization algorithm to sales data. The generation unit can also apply a POS visualization algorithm to POS data. For example, the generation unit can apply a customer visualization algorithm to customer data. By applying the appropriate generation algorithm according to the data category, highly accurate generation results can be obtained. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the data category into the AI ​​and have the AI ​​execute the application of the appropriate generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length of the visualization results and summaries it generates based on the estimated emotions. For example, if the user is tense, the generation unit can provide short, concise visualization results. If the user is relaxed, the generation unit can also provide detailed visualization results. For example, if the user is in a hurry, the generation unit can simplify the visualization results for quick understanding. By adjusting the length of the visualization results and summaries according to the user's emotions, the optimal result for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0090] The generation unit can determine the generation priority based on the data submission timing during generation. For example, the generation unit may prioritize the visualization of the most recent data. The generation unit may also prioritize the visualization of data with approaching submission deadlines. For example, the generation unit may adjust the generation order according to the data submission timing. This enables efficient data generation by determining the generation priority based on the data submission timing. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the data submission timing into the AI ​​and have the AI ​​determine the generation priority.

[0091] The generation unit can adjust the generation order based on the relevance of the data during generation. For example, the generation unit may prioritize the visualization of highly relevant data. It can also postpone the visualization of less relevant data. For example, the generation unit optimizes the generation order according to the relevance of the data. This allows for efficient data generation by adjusting the generation order based on the relevance of the data. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the generation order.

[0092] The service provider can estimate the user's emotions and adjust the display method of the results based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the display method of the results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. 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 service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also suggest the optimal display method based on the user's past operation history. For example, the service provider may analyze the user's operation history and select an efficient display method. This allows the service provider to provide an efficient display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input user operation history data into AI and have the AI ​​select the optimal display method.

[0094] The service provider can estimate the user's emotions and adjust the operation procedures for the results provided based on the estimated emotions. For example, if the user is nervous, the service provider may simplify the operation procedures. If the user is relaxed, the service provider may also provide detailed operation procedures. For example, if the user is in a hurry, the service provider may adjust the procedures to allow for quick operation. This allows for optimal operation for the user by adjusting the operation procedures for the results provided 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 service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0095] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a desktop, the service provider can provide a display method that includes detailed information. This enables efficient display by providing the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0096] The service provider can select the optimal display method at the time of delivery, taking into account the user's network connection status. For example, if the user is connected to a high-speed network, the service provider can provide a high-resolution display method. If the user is connected to a low-speed network, the service provider can also provide a lightweight display method. For example, the service provider can automatically adjust the display method according to the user's network connection status. This enables efficient display by providing the optimal display method based on the user's network connection status. Some or all of the above processing in the service provider may be performed using AI, or it may be performed without AI. For example, the service provider can input the user's network connection status into the AI ​​and have the AI ​​select the optimal display method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the unit can adjust the timing of data collection to quickly collect the necessary data. In this way, by adjusting the timing of data collection according to the user's emotions, the user's burden can be reduced.

[0099] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize the collection of data types that the user has frequently collected in the past. It can also suggest the optimal collection timing based on the user's past data collection history. Furthermore, it can analyze the user's past data collection history and select an efficient collection method. This enables efficient data collection by analyzing the user's past data collection history.

[0100] The data collection unit can filter data based on the user's current work situation and areas of interest during data collection. For example, it can prioritize the collection of data related to projects the user is currently working on. It can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, it can collect only the necessary data according to the user's work situation. In this way, by filtering data according to the user's work situation and areas of interest, only the necessary data can be collected.

[0101] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit can prioritize collecting high-priority data. If the user is relaxed, the unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the unit can prioritize collecting data that can be retrieved quickly. In this way, by prioritizing the data to be collected according to the user's emotions, important data can be collected preferentially.

[0102] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if a user is in a specific region, it can prioritize the collection of data related to that region. It can also filter and collect highly relevant data based on the user's current location. Furthermore, it can prioritize the collection of highly relevant data by considering the user's travel history. This enables efficient data collection by collecting highly relevant data based on the user's geographical location.

[0103] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, it can collect relevant data based on information shared by the user on social media. It can also collect data related to topics of interest from the user's social media activity. Furthermore, it can collect relevant data based on the activity of the user's social media followers and friends. This enables data collection tailored to the user's interests by collecting relevant data based on the user's social media activity.

[0104] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand results. If the user is relaxed, the analysis unit can provide detailed results. Furthermore, if the user is in a hurry, the analysis unit can provide concise results. By adjusting the presentation of the analysis according to the user's emotions, the system can provide analysis results that are easy for the user to understand.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail according to the importance of the data.

[0106] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a sales analysis algorithm to sales data, a POS analysis algorithm to POS data, and a customer analysis algorithm to customer data. By applying the appropriate analysis algorithm according to the data category, it is possible to obtain highly accurate analysis results.

[0107] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on those emotions. For example, if the user is nervous, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can simplify the analysis for quick understanding. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects data. The data collection unit can collect data such as sales data, customer data, and inventory data. The data collection unit can also digitize paper-based data using OCR. For example, the data collection unit can scan paper slips and convert them into digital data using OCR technology. The data collection unit can also digitize paper slips that have been photographed with a camera using OCR. For example, the data collection unit can photograph paper slips using a smartphone camera and convert them into digital data using OCR technology. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department can analyze data generated in daily operations, such as sales data and POS data. The analysis department can also use AI to analyze data trends and patterns. For example, the analysis department can analyze sales data to understand increases and decreases in sales and customer purchasing trends. Step 3: The generation unit creates visualization results and performance summaries based on the analysis results obtained by the analysis unit. The generation unit can create visualization results such as graphs, charts, and dashboards. The generation unit can also automatically visualize analysis results using AI. For example, the generation unit can graph sales data and visually display sales trends. Step 4: The delivery unit presents the results generated by the generation unit to the business owner. The delivery unit can, for example, present the generated visualization results or performance summaries to the business owner. The delivery unit can also present the results using mobile devices. For example, the delivery unit can display the generated results using a smartphone or tablet.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 or scanner of the smart device 14 to digitize paper slips using OCR and converts them into digital data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates visualization results and performance summaries based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and presents the generated results to the business owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 or scanner of the smart glasses 214 to digitize paper slips using OCR and converts them into digital data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates visualization results and performance summaries based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and presents the generated results to the business owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and scanner of the headset terminal 314 to digitize paper slips using OCR and converts them into digital data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates visualization results and performance summaries based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and presents the generated results to the business owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and scanner of the robot 414 to digitize paper slips using OCR and converts them into digital data by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates visualization results and performance summaries based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and presents the generated results to the business owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit creates visualization results and performance summaries based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that presents the results generated by the generation unit to the business owner. A system characterized by the following features. (Note 2) The aforementioned collection unit is Converting paper-based data into digital data using OCR. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Converting paper slips photographed with a camera into digital data using OCR. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Analyze data generated in daily operations, such as sales data and POS data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the analysis results, create visualizations and performance summaries. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Present the generated results to the business owner. 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 user's past data collection history and select the optimal 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 user's current work situation and areas of interest. 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 user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is 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 is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way visualization results and summaries are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the visualizations and summaries generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the generation priority is determined based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation order is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the action steps provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's network connection status. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 generation unit creates visualization results and performance summaries based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that presents the results generated by the generation unit to the business owner. A system characterized by the following features.

2. The aforementioned collection unit is Converting paper-based data into digital data using OCR. The system according to feature 1.

3. The aforementioned collection unit is Converting paper slips photographed with a camera into digital data using OCR. The system according to feature 1.

4. The aforementioned analysis unit is Analyze data generated in daily operations, such as sales data and POS data. The system according to feature 1.

5. The generating unit is Based on the analysis results, create visualizations and performance summaries. The system according to feature 1.

6. The aforementioned supply unit is, Present the generated results to the business owner. 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 user's past data collection history and select the optimal collection method. The system according to feature 1.

Images