system

Abstract

The system according to this embodiment aims to solve all productivity challenges. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal solution based on the analysis results obtained by the analysis unit. The execution unit executes the solution proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a risk that productivity may decrease as the working population decreases.

[0005] The system according to the embodiment aims to solve any productivity problems.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal solution based on the analysis result obtained by the analysis unit. The execution unit executes the solution proposed by the proposal unit.

Effects of the Invention

[0007] The system according to this embodiment can solve all productivity challenges. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 productivity improvement system according to an embodiment of the present invention is a mechanism that uses an AI agent to solve all productivity challenges in order to address the decline in productivity due to the decrease in the working population. The productivity improvement system aims for the AI ​​agent to replace various tasks and perform them with a quality exceeding that of humans. For example, the productivity improvement system collects and analyzes various business data within a company. For example, it identifies specific business challenges such as the lack of manpower and the standardization of workflows faced by sales managers, and the time and cost issues of market research faced by product planning managers. Next, the productivity improvement system proposes the optimal solution for the identified challenges. For example, to a sales manager, it proposes that the AI ​​agent replace part of the sales activities to reduce manpower. Also, to a product planning manager, the AI ​​agent analyzes market research data in real time and proposes an efficient research method. Furthermore, the productivity improvement system implements the proposed solution. For example, when the AI ​​agent replaces sales activities, it automatically creates meeting minutes and analyzes customer behavior logs. Also, in product planning, the AI ​​agent formulates a new product concept based on market research data and supports the progress of the project. This allows companies to compensate for the decline in productivity caused by a shrinking workforce and achieve operational efficiency and quality improvement. For example, by having AI agents take over sales activities, sales managers can focus on more strategic tasks. Also, with the support of AI agents, product planning managers can conduct rapid and accurate market research and efficiently advance new product development. In this way, by utilizing AI agents, companies can improve productivity and operational efficiency, thereby enhancing their competitiveness. Thus, productivity improvement systems can compensate for the decline in productivity caused by a shrinking workforce and achieve operational efficiency and quality improvement.

[0029] The productivity improvement system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an execution unit. The data collection unit collects data. For example, the data collection unit collects various business data within a company. For example, the data collection unit collects data to identify specific business challenges, such as the lack of manpower and workflow standardization problems faced by sales managers, or the time and cost issues of market research faced by product planning managers. For example, the data collection unit can collect sales activity data and market research data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected sales activity data to identify areas for improvement to streamline sales activities. The analysis unit can also analyze the collected market research data to grasp market trends. For example, the analysis unit can perform sales trend analysis and customer segmentation. The proposal unit proposes optimal solutions based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes solutions for sales activities. For example, the proposal unit can propose improvements to the sales process or optimization of customer targeting. The proposal unit can also propose solutions for market research. For example, the proposal department can propose market launch strategies for new products or methods for understanding consumer needs. The execution department implements the solutions proposed by the proposal department. The execution department can implement solutions for sales activities, for example, by conducting sales training or introducing sales tools. The execution department can also implement solutions for market research, for example, by conducting test marketing for new products or collecting consumer feedback. In this way, the productivity improvement system according to the embodiment can efficiently solve productivity challenges by consistently performing data collection, analysis, proposal, and execution.

[0030] The data collection department collects data. For example, the data collection department collects various business data within a company. Specifically, the data collection department collects data to identify specific business challenges, such as the lack of manpower and workflow standardization problems faced by sales department managers, or the time and cost issues of market research faced by product planning managers. For example, the data collection department can collect sales activity data and market research data. Sales activity data includes sales representatives' visit history, negotiation progress, closing rates, and customer feedback. This data is automatically collected from CRM systems and sales support tools that sales representatives input into their daily work. Market research data includes consumer survey results, competitor activities, and industry trends. This data is collected through online survey tools and reports provided by market research companies. The data collection department centrally manages this data and stores it in a database. The database is built on the cloud and the data is updated in real time. This allows the data collection department to always have access to the latest data and quickly provide the data that the analysis department and proposal department need. Furthermore, the data collection unit also performs data quality control, conducting checks to ensure data accuracy and consistency. For example, it detects data duplication and missing data, and corrects or supplements them as needed. This allows the data collection unit to provide reliable data and improve the overall accuracy and efficiency of the system.

[0031] The analysis department analyzes data collected by the data collection department. For example, the analysis department analyzes collected sales activity data to identify areas for improvement in sales efficiency. Specifically, the analysis department uses AI to analyze data and identify bottlenecks and inefficiencies in the sales process. For example, AI analyzes sales representatives' visit history and negotiation progress to identify the reasons for low closing rates. AI also analyzes customer feedback using natural language processing technology to extract customer needs and points of dissatisfaction. This allows the analysis department to specifically identify areas for improvement in sales activities and propose measures for efficiency. Furthermore, the analysis department can analyze collected market research data to understand market trends. For example, AI analyzes consumer survey results to identify changes in consumer purchasing intent and preferences. It also analyzes the activities of competitors and industry trends to evaluate the company's market position. This allows the analysis department to accurately grasp market trends and support strategic decision-making. In addition, the analysis department can utilize historical data and statistical information to conduct long-term trend analysis and forecasts. For example, by using past sales data to forecast future sales and developing measures to respond to fluctuations in demand, the analysis department can contribute not only to identifying short-term areas for improvement but also to long-term strategic planning, thereby supporting the overall productivity improvement of the system.

[0032] The Proposal Department proposes optimal solutions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes solutions for sales activities. Specifically, the Proposal Department can propose improvements to the sales process and optimization of customer targeting. For example, the Proposal Department can optimize the visit schedules of sales representatives and propose efficient visit routes. It can also perform customer segmentation and propose effective approaches to target customers. This can improve the efficiency of sales activities and increase the closing rate. Furthermore, the Proposal Department can also propose solutions for market research. For example, the Proposal Department can propose market launch strategies for new products and methods for understanding consumer needs. Specifically, the Proposal Department proposes the design and implementation methods of consumer surveys and collects real consumer voices. It can also propose market entry strategies that take into account the trends of competitors and develop measures to enhance the competitiveness of its own products. In this way, the Proposal Department can provide concrete solutions in sales activities and market research and support the improvement of corporate productivity. Furthermore, the Proposal Department evaluates the feasibility and effectiveness of the proposals and provides specific instructions to the Implementation Department. This strengthens collaboration between the Proposal Department and the Implementation Department and ensures the implementation of the proposals.

[0033] The Execution Department implements the solutions proposed by the Proposal Department. For example, the Execution Department implements solutions related to sales activities. Specifically, the Execution Department can conduct sales training and introduce sales tools. For instance, the Execution Department can provide sales representatives with training on efficient sales methods and customer service to improve their sales skills. They can also introduce sales support tools and CRM systems to help streamline sales activities. This allows the Execution Department to concretely implement the proposed solutions and improve sales activities. Furthermore, the Execution Department can also implement solutions related to market research. For example, the Execution Department can conduct test marketing of new products and collect consumer feedback. Specifically, the Execution Department can conduct test sales of new products in target markets and collect consumer reactions. They can also conduct consumer surveys and interviews to understand product evaluations and areas for improvement. This allows the Execution Department to concretely implement market research solutions and prepare for the market launch of new products. Furthermore, the Execution Department evaluates the results of the implementation and provides feedback, reporting to the Proposal Department and Analysis Department. This allows the implementation unit to understand the status and effects of the proposed changes and contribute to the overall improvement of the system.

[0034] The data collection unit can collect sales activity data. For example, the data collection unit can collect customer visit records, negotiation history, sales data, etc. For example, the data collection unit can collect data to identify problems such as insufficient manpower or workflow standardization issues faced by sales managers in order to collect sales activity data. This makes it possible to improve the efficiency of sales activities by collecting sales activity data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use AI to automatically collect customer visit records, negotiation history, and sales data in order to collect sales activity data.

[0035] The data collection unit can collect market research data. For example, the data collection unit can collect market research data. For example, the data collection unit can collect survey results, competitor analysis data, consumer behavior data, etc. For example, in order to collect market research data, the data collection unit can collect data to identify the time and cost problems of market research faced by product planning managers. This makes market research more efficient by collecting market research data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, in order to collect market research data, the data collection unit can use AI to automatically collect survey results, competitor analysis data, and consumer behavior data.

[0036] The analysis department can analyze sales activity data. For example, the analysis department can analyze sales activity data. For example, the analysis department can perform sales trend analysis and customer segmentation. For example, in order to analyze sales activity data, the analysis department can analyze data to identify problems such as insufficient manpower and standardized workflows faced by sales department managers. By analyzing sales activity data, it is possible to identify areas for improvement in sales activities. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can use AI to automatically perform sales trend analysis and customer segmentation in order to analyze sales activity data.

[0037] The analysis department can analyze market research data. For example, the analysis department can analyze market research data. For example, the analysis department can perform consumer behavior analysis and competitor analysis. For example, in order to analyze market research data, the analysis department can analyze data to identify the time and cost issues of market research faced by product planning managers. This allows for an understanding of market trends by analyzing market research data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can use AI to automatically perform consumer behavior analysis and competitor analysis in order to analyze market research data.

[0038] The proposal department can propose solutions for sales activities. For example, the proposal department can propose solutions for sales activities. For example, the proposal department can propose improvements to the sales process or optimization of customer targeting. For example, in order to propose solutions for sales activities, the proposal department can analyze data to identify problems such as insufficient manpower or standardized workflows faced by sales department managers, and propose solutions based on the results. In this way, by proposing solutions for sales activities, the efficiency of sales activities can be improved. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, in order to propose solutions for sales activities, the proposal department can use AI to automatically propose improvements to the sales process or optimization of customer targeting.

[0039] The proposal department can propose solutions for market research. For example, the proposal department can propose market launch strategies for new products and methods for understanding consumer needs. For example, in order to propose market research solutions, the proposal department can analyze data to identify the time and cost problems of market research faced by product planning managers and propose solutions based on the results. This makes market research more efficient by proposing market research solutions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, in order to propose market research solutions, the proposal department can use AI to automatically propose market launch strategies for new products and methods for understanding consumer needs.

[0040] The execution unit can implement solutions for sales activities. For example, the execution unit can implement solutions for sales activities. For example, the execution unit can conduct sales training or introduce sales tools. For example, in order to implement solutions for sales activities, the execution unit can analyze data to identify problems such as insufficient manpower or workflow standardization issues faced by sales department managers, and then implement solutions based on the results. This makes it possible to improve the efficiency of sales activities by implementing solutions for sales activities. Some or all of the above processes in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can use AI to automatically conduct sales training or introduce sales tools in order to implement solutions for sales activities.

[0041] The execution unit can implement market research solutions. For example, the execution unit can implement market research solutions. For example, the execution unit can conduct test marketing of new products and collect consumer feedback. For example, in order to implement market research solutions, the execution unit can analyze data to identify the time and cost problems of market research faced by product planning managers and implement solutions based on the results. This makes market research more efficient by implementing market research solutions. Some or all of the above processes in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can use AI to automatically conduct test marketing of new products and collect consumer feedback in order to implement market research solutions.

[0042] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can analyze past data collection history and select the most efficient collection method. For example, the data collection unit can optimize the collection frequency based on past data collection history. For example, the data collection unit can determine the priority of data to be collected by referring to past data collection history. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to optimize the data collection method in order to analyze past data collection history.

[0043] The data collection unit can filter data based on the user's current work status and areas of interest during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current work status. For example, the data collection unit can filter the data to be collected based on the user's areas of interest. For example, the data collection unit can adjust the types of data collected according to the user's work progress. This allows for the collection of highly relevant data by filtering the data based on the user's work status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the details of the work schedule and work content in order to understand the user's current work status and areas of interest, and then filter the data based on the results.

[0044] 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, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can determine the priority of data to be collected by referring to the user's movement history. For example, the data collection unit can filter the data to be collected by considering the user's geographical location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to utilize GPS data or location services to consider the user's geographical location information and collect data based on the results.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media activity and prioritize the collection of relevant data. For example, the data collection unit can filter the collected data based on the user's interests on social media. For example, the data collection unit can determine the priority of data to be collected by referring to the user's social media activity history. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the content of posts and followers in order to analyze a user's social media activity, and collect data based on the results.

[0046] 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. For example, the analysis unit can perform a simplified analysis on low-importance data. For example, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows for efficient 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, for example, or without AI. For example, the analysis unit can use AI to analyze the impact and reliability of the data in order to evaluate the importance of the data, and adjust the level of detail of the analysis based on the results.

[0047] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, the analysis department can apply a specific analysis algorithm to sales data. For example, the analysis department can apply a different analysis algorithm to market research data. For example, the analysis department can select the optimal analysis algorithm depending on the data category. This enables highly accurate analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, in order to classify the data categories, the analysis department can use AI to classify the data by industry, application, and data format, and then apply the optimal analysis algorithm based on the results.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may determine the priority of analysis by referring to past data. For example, the analysis unit may adjust the order of analysis based on the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may use AI to analyze the freshness and frequency of data collection in order to consider the data collection timing, and then determine the priority of analysis based on the results.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can optimize the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can use AI to analyze the correlation and co-occurrence frequency of data in order to evaluate the relevance of the data, and adjust the order of analysis based on the results.

[0050] The proposal department can adjust the level of detail of a proposal based on the importance of the solution. For example, the proposal department can provide a detailed proposal for a high-importance solution. For example, it can provide a simplified proposal for a low-importance solution. The proposal department can adjust the depth of the proposal according to the importance of the solution. This allows for efficient proposals by adjusting the level of detail according to the importance of the solution. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to analyze the impact, feasibility, and cost-effectiveness of a solution in order to evaluate its importance, and adjust the level of detail of the proposal based on the results.

[0051] The proposal department can apply different proposal algorithms depending on the category of the solution when making a proposal. For example, the proposal department can apply a specific proposal algorithm to sales activities. For example, the proposal department can apply a different proposal algorithm to market research. For example, the proposal department can select the optimal proposal algorithm depending on the category of the solution. This makes it possible to make highly accurate proposals by applying the optimal proposal algorithm according to the category of the solution. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, in order to classify the category of solutions, the proposal department can use AI to classify solutions by industry, application, and technology, and then apply the optimal proposal algorithm based on the results.

[0052] The proposal department can prioritize proposals based on the timing of solution submission. For example, the proposal department may prioritize solutions that are urgent. The proposal department can prioritize proposals based on the submission date. The proposal department can adjust the order of proposals according to the timing of solution submission. This allows for more efficient proposals by prioritizing proposals based on the timing of solution submission. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department may use AI to analyze submission deadlines and frequencies to consider the timing of solution submission, and then prioritize proposals based on the results.

[0053] The proposal department can adjust the order of proposals based on the relevance of the solutions when making a proposal. For example, the proposal department can prioritize proposing highly relevant solutions. For example, the proposal department can postpone proposing less relevant solutions. For example, the proposal department can optimize the order of proposals based on the relevance of the solutions. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the solutions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can use AI to analyze the correlation and co-occurrence frequency of solutions in order to evaluate the relevance of the solutions, and adjust the order of proposals based on the results.

[0054] The execution unit can analyze the user's past work history during execution to select the optimal execution method. For example, the execution unit can analyze the user's past work history and select the most efficient execution method. For example, the execution unit can optimize the execution procedure based on the user's past work history. For example, the execution unit can determine the priority of the tasks to be executed by referring to the user's past work history. This allows the optimal execution method to be selected by analyzing the user's past work history. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to analyze work records and work results in order to analyze the user's past work history, and select the optimal execution method based on the results.

[0055] The execution unit can customize the means of execution at runtime based on the user's current work status. For example, the execution unit can select the optimal means of execution based on the user's current work status. For example, the execution unit can customize the means of execution according to the user's work progress. For example, the execution unit can adjust the means of execution considering the user's current work status. This enables efficient execution by customizing the means of execution based on the user's current work status. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to analyze the details of the work schedule and work content in order to understand the user's current work status, and customize the means of execution based on the results.

[0056] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, the execution unit can select the optimal execution method based on the user's current location. For example, the execution unit can optimize the execution method by referring to the user's travel history. For example, the execution unit can adjust the execution means, taking into account the user's geographical location information. This allows the execution unit to select the optimal execution method by taking into account the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can use AI to utilize GPS data and location information services to take into account the user's geographical location information, and select the optimal execution method based on the results.

[0057] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can analyze the user's social media activity and propose relevant means of execution. For example, the execution unit can filter means of execution based on the user's interests on social media. For example, the execution unit can determine the priority of means of execution by referring to the user's social media activity history. In this way, by analyzing the user's social media activity, the optimal means of execution can be proposed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, in order to analyze the user's social media activity, the execution unit can use AI to analyze the content of posts and the number of followers, and propose means of execution based on the results.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit can analyze past data collection history and select the optimal collection method. For example, it can analyze past data collection history and select the most efficient collection method. It can also optimize the collection frequency based on past data collection history. Furthermore, it can determine the priority of data to be collected by referring to past data collection history. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0060] 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, it can adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data.

[0061] The proposal department can adjust the level of detail in a proposal based on the importance of the solution. For example, it can provide detailed proposals for high-priority solutions and simplified proposals for low-priority solutions. Furthermore, it can adjust the depth of the proposal according to the importance of the solution. This allows for more efficient proposals by adjusting the level of detail according to the importance of the solution.

[0062] The execution unit can analyze the user's past work history during execution to select the optimal execution method. For example, it can analyze the user's past work history and select the most efficient execution method. It can also optimize the execution procedure based on the user's past work history. Furthermore, it can determine the priority of the tasks to be executed by referring to the user's past work history. In this way, the optimal execution method can be selected by analyzing the user's past work history.

[0063] The execution unit can customize the execution method based on the user's current work status during execution. For example, it can select the optimal execution method based on the user's current work status. It can also customize the execution method according to the user's work progress. Furthermore, it can adjust the execution method considering the user's current work status. This allows for efficient execution by customizing the execution method based on the user's current work status.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection department collects data. For example, it collects various business data within the company to identify specific business challenges, such as the lack of manpower and workflow standardization problems faced by sales department managers, or the time and cost issues of market research faced by product planning managers. The data collection department can collect sales activity data and market research data. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they analyze collected sales activity data to identify areas for improvement in sales efficiency. They can also analyze collected market research data to understand market trends. The analysis department can perform sales trend analysis and customer segmentation. Step 3: The proposal department proposes the optimal solution based on the analysis results obtained by the analysis department. For example, as a solution for sales activities, they may propose improvements to the sales process or optimization of customer targeting. As a solution for market research, they may propose new product launch strategies or methods for understanding consumer needs. Step 4: The implementation team carries out the solutions proposed by the proposal team. For example, solutions for sales activities could include conducting sales training or introducing sales tools. Solutions for market research could include test marketing of new products or collecting consumer feedback.

[0066] (Example of form 2) The productivity improvement system according to an embodiment of the present invention is a mechanism that uses an AI agent to solve all productivity challenges in order to address the decline in productivity due to the decrease in the working population. The productivity improvement system aims for the AI ​​agent to replace various tasks and perform them with a quality exceeding that of humans. For example, the productivity improvement system collects and analyzes various business data within a company. For example, it identifies specific business challenges such as the lack of manpower and the standardization of workflows faced by sales managers, and the time and cost issues of market research faced by product planning managers. Next, the productivity improvement system proposes the optimal solution for the identified challenges. For example, to a sales manager, it proposes that the AI ​​agent replace part of the sales activities to reduce manpower. Also, to a product planning manager, the AI ​​agent analyzes market research data in real time and proposes an efficient research method. Furthermore, the productivity improvement system implements the proposed solution. For example, when the AI ​​agent replaces sales activities, it automatically creates meeting minutes and analyzes customer behavior logs. Also, in product planning, the AI ​​agent formulates a new product concept based on market research data and supports the progress of the project. This allows companies to compensate for the decline in productivity caused by a shrinking workforce and achieve operational efficiency and quality improvement. For example, by having AI agents take over sales activities, sales managers can focus on more strategic tasks. Also, with the support of AI agents, product planning managers can conduct rapid and accurate market research and efficiently advance new product development. In this way, by utilizing AI agents, companies can improve productivity and operational efficiency, thereby enhancing their competitiveness. Thus, productivity improvement systems can compensate for the decline in productivity caused by a shrinking workforce and achieve operational efficiency and quality improvement.

[0067] The productivity improvement system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an execution unit. The data collection unit collects data. For example, the data collection unit collects various business data within a company. For example, the data collection unit collects data to identify specific business challenges, such as the lack of manpower and workflow standardization problems faced by sales managers, or the time and cost issues of market research faced by product planning managers. For example, the data collection unit can collect sales activity data and market research data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected sales activity data to identify areas for improvement to streamline sales activities. The analysis unit can also analyze the collected market research data to grasp market trends. For example, the analysis unit can perform sales trend analysis and customer segmentation. The proposal unit proposes optimal solutions based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes solutions for sales activities. For example, the proposal unit can propose improvements to the sales process or optimization of customer targeting. The proposal unit can also propose solutions for market research. For example, the proposal department can propose market launch strategies for new products or methods for understanding consumer needs. The execution department implements the solutions proposed by the proposal department. The execution department can implement solutions for sales activities, for example, by conducting sales training or introducing sales tools. The execution department can also implement solutions for market research, for example, by conducting test marketing for new products or collecting consumer feedback. In this way, the productivity improvement system according to the embodiment can efficiently solve productivity challenges by consistently performing data collection, analysis, proposal, and execution.

[0068] The data collection department collects data. For example, the data collection department collects various business data within a company. Specifically, the data collection department collects data to identify specific business challenges, such as the lack of manpower and workflow standardization problems faced by sales department managers, or the time and cost issues of market research faced by product planning managers. For example, the data collection department can collect sales activity data and market research data. Sales activity data includes sales representatives' visit history, negotiation progress, closing rates, and customer feedback. This data is automatically collected from CRM systems and sales support tools that sales representatives input into their daily work. Market research data includes consumer survey results, competitor activities, and industry trends. This data is collected through online survey tools and reports provided by market research companies. The data collection department centrally manages this data and stores it in a database. The database is built on the cloud and the data is updated in real time. This allows the data collection department to always have access to the latest data and quickly provide the data that the analysis department and proposal department need. Furthermore, the data collection unit also performs data quality control, conducting checks to ensure data accuracy and consistency. For example, it detects data duplication and missing data, and corrects or supplements them as needed. This allows the data collection unit to provide reliable data and improve the overall accuracy and efficiency of the system.

[0069] The analysis department analyzes data collected by the data collection department. For example, the analysis department analyzes collected sales activity data to identify areas for improvement in sales efficiency. Specifically, the analysis department uses AI to analyze data and identify bottlenecks and inefficiencies in the sales process. For example, AI analyzes sales representatives' visit history and negotiation progress to identify the reasons for low closing rates. AI also analyzes customer feedback using natural language processing technology to extract customer needs and points of dissatisfaction. This allows the analysis department to specifically identify areas for improvement in sales activities and propose measures for efficiency. Furthermore, the analysis department can analyze collected market research data to understand market trends. For example, AI analyzes consumer survey results to identify changes in consumer purchasing intent and preferences. It also analyzes the activities of competitors and industry trends to evaluate the company's market position. This allows the analysis department to accurately grasp market trends and support strategic decision-making. In addition, the analysis department can utilize historical data and statistical information to conduct long-term trend analysis and forecasts. For example, by using past sales data to forecast future sales and developing measures to respond to fluctuations in demand, the analysis department can contribute not only to identifying short-term areas for improvement but also to long-term strategic planning, thereby supporting the overall productivity improvement of the system.

[0070] The Proposal Department proposes optimal solutions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes solutions for sales activities. Specifically, the Proposal Department can propose improvements to the sales process and optimization of customer targeting. For example, the Proposal Department can optimize the visit schedules of sales representatives and propose efficient visit routes. It can also perform customer segmentation and propose effective approaches to target customers. This can improve the efficiency of sales activities and increase the closing rate. Furthermore, the Proposal Department can also propose solutions for market research. For example, the Proposal Department can propose market launch strategies for new products and methods for understanding consumer needs. Specifically, the Proposal Department proposes the design and implementation methods of consumer surveys and collects real consumer voices. It can also propose market entry strategies that take into account the trends of competitors and develop measures to enhance the competitiveness of its own products. In this way, the Proposal Department can provide concrete solutions in sales activities and market research and support the improvement of corporate productivity. Furthermore, the Proposal Department evaluates the feasibility and effectiveness of the proposals and provides specific instructions to the Implementation Department. This strengthens collaboration between the Proposal Department and the Implementation Department and ensures the implementation of the proposals.

[0071] The Execution Department implements the solutions proposed by the Proposal Department. For example, the Execution Department implements solutions related to sales activities. Specifically, the Execution Department can conduct sales training and introduce sales tools. For instance, the Execution Department can provide sales representatives with training on efficient sales methods and customer service to improve their sales skills. They can also introduce sales support tools and CRM systems to help streamline sales activities. This allows the Execution Department to concretely implement the proposed solutions and improve sales activities. Furthermore, the Execution Department can also implement solutions related to market research. For example, the Execution Department can conduct test marketing of new products and collect consumer feedback. Specifically, the Execution Department can conduct test sales of new products in target markets and collect consumer reactions. They can also conduct consumer surveys and interviews to understand product evaluations and areas for improvement. This allows the Execution Department to concretely implement market research solutions and prepare for the market launch of new products. Furthermore, the Execution Department evaluates the results of the implementation and provides feedback, reporting to the Proposal Department and Analysis Department. This allows the implementation unit to understand the status and effects of the proposed changes and contribute to the overall improvement of the system.

[0072] The data collection unit can collect sales activity data. For example, the data collection unit can collect customer visit records, negotiation history, sales data, etc. For example, the data collection unit can collect data to identify problems such as insufficient manpower or workflow standardization issues faced by sales managers in order to collect sales activity data. This makes it possible to improve the efficiency of sales activities by collecting sales activity data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use AI to automatically collect customer visit records, negotiation history, and sales data in order to collect sales activity data.

[0073] The data collection unit can collect market research data. For example, the data collection unit can collect market research data. For example, the data collection unit can collect survey results, competitor analysis data, consumer behavior data, etc. For example, in order to collect market research data, the data collection unit can collect data to identify the time and cost problems of market research faced by product planning managers. This makes market research more efficient by collecting market research data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, in order to collect market research data, the data collection unit can use AI to automatically collect survey results, competitor analysis data, and consumer behavior data.

[0074] The analysis department can analyze sales activity data. For example, the analysis department can analyze sales activity data. For example, the analysis department can perform sales trend analysis and customer segmentation. For example, in order to analyze sales activity data, the analysis department can analyze data to identify problems such as insufficient manpower and standardized workflows faced by sales department managers. By analyzing sales activity data, it is possible to identify areas for improvement in sales activities. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can use AI to automatically perform sales trend analysis and customer segmentation in order to analyze sales activity data.

[0075] The analysis department can analyze market research data. For example, the analysis department can analyze market research data. For example, the analysis department can perform consumer behavior analysis and competitor analysis. For example, in order to analyze market research data, the analysis department can analyze data to identify the time and cost issues of market research faced by product planning managers. This allows for an understanding of market trends by analyzing market research data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can use AI to automatically perform consumer behavior analysis and competitor analysis in order to analyze market research data.

[0076] The proposal department can propose solutions for sales activities. For example, the proposal department can propose solutions for sales activities. For example, the proposal department can propose improvements to the sales process or optimization of customer targeting. For example, in order to propose solutions for sales activities, the proposal department can analyze data to identify problems such as insufficient manpower or standardized workflows faced by sales department managers, and propose solutions based on the results. In this way, by proposing solutions for sales activities, the efficiency of sales activities can be improved. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, in order to propose solutions for sales activities, the proposal department can use AI to automatically propose improvements to the sales process or optimization of customer targeting.

[0077] The proposal department can propose solutions for market research. For example, the proposal department can propose market launch strategies for new products and methods for understanding consumer needs. For example, in order to propose market research solutions, the proposal department can analyze data to identify the time and cost problems of market research faced by product planning managers and propose solutions based on the results. This makes market research more efficient by proposing market research solutions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, in order to propose market research solutions, the proposal department can use AI to automatically propose market launch strategies for new products and methods for understanding consumer needs.

[0078] The execution unit can implement solutions for sales activities. For example, the execution unit can implement solutions for sales activities. For example, the execution unit can conduct sales training or introduce sales tools. For example, in order to implement solutions for sales activities, the execution unit can analyze data to identify problems such as insufficient manpower or workflow standardization issues faced by sales department managers, and then implement solutions based on the results. This makes it possible to improve the efficiency of sales activities by implementing solutions for sales activities. Some or all of the above processes in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can use AI to automatically conduct sales training or introduce sales tools in order to implement solutions for sales activities.

[0079] The execution unit can implement market research solutions. For example, the execution unit can implement market research solutions. For example, the execution unit can conduct test marketing of new products and collect consumer feedback. For example, in order to implement market research solutions, the execution unit can analyze data to identify the time and cost problems of market research faced by product planning managers and implement solutions based on the results. This makes market research more efficient by implementing market research solutions. Some or all of the above processes in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can use AI to automatically conduct test marketing of new products and collect consumer feedback in order to implement market research solutions.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. For example, if the user is concentrating, the data collection unit can collect data in a way that does not interrupt the user's concentration. For example, if the user is tired, the data collection unit can collect data while the user is taking a break. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the timing of data collection based on the results.

[0081] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can analyze past data collection history and select the most efficient collection method. For example, the data collection unit can optimize the collection frequency based on past data collection history. For example, the data collection unit can determine the priority of data to be collected by referring to past data collection history. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to optimize the data collection method in order to analyze past data collection history.

[0082] The data collection unit can filter data based on the user's current work status and areas of interest during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current work status. For example, the data collection unit can filter the data to be collected based on the user's areas of interest. For example, the data collection unit can adjust the types of data collected according to the user's work progress. This allows for the collection of highly relevant data by filtering the data based on the user's work status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the details of the work schedule and work content in order to understand the user's current work status and areas of interest, and then filter the data based on the results.

[0083] 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 postpone collecting less important data. For example, if the user is relaxed, the data collection unit can prioritize collecting more important data. For example, if the user is focused, the data collection unit can adjust the priority of the data to be collected. This enables efficient data collection by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and determine the priority of data to collect based on the results.

[0084] 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, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can determine the priority of data to be collected by referring to the user's movement history. For example, the data collection unit can filter the data to be collected by considering the user's geographical location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to utilize GPS data or location services to consider the user's geographical location information and collect data based on the results.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media activity and prioritize the collection of relevant data. For example, the data collection unit can filter the collected data based on the user's interests on social media. For example, the data collection unit can determine the priority of data to be collected by referring to the user's social media activity history. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the content of posts and followers in order to analyze a user's social media activity, and collect data based on the results.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visual presentation. For example, if the user is relaxed, the analysis unit can provide a presentation that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a presentation that gets straight to the point. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are 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 using AI. For example, the analysis unit can use AI to perform facial recognition and voice analysis to estimate the user's emotions and adjust the presentation of the analysis based on the results.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data. For example, the analysis unit can perform a simplified analysis on low-importance data. For example, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows for efficient 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, for example, or without AI. For example, the analysis unit can use AI to analyze the impact and reliability of the data in order to evaluate the importance of the data, and adjust the level of detail of the analysis based on the results.

[0088] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, the analysis department can apply a specific analysis algorithm to sales data. For example, the analysis department can apply a different analysis algorithm to market research data. For example, the analysis department can select the optimal analysis algorithm depending on the data category. This enables highly accurate analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, in order to classify the data categories, the analysis department can use AI to classify the data by industry, application, and data format, and then apply the optimal analysis algorithm based on the results.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. If the user is excited, the analysis unit can provide an analysis with visually stimulating effects. 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the length of the analysis based on the results.

[0090] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may determine the priority of analysis by referring to past data. For example, the analysis unit may adjust the order of analysis based on the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may use AI to analyze the freshness and frequency of data collection in order to consider the data collection timing, and then determine the priority of analysis based on the results.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can optimize the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can use AI to analyze the correlation and co-occurrence frequency of data in order to evaluate the relevance of the data, and adjust the order of analysis based on the results.

[0092] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily visible presentation. For example, if the user is relaxed, the suggestion unit can provide a presentation that includes detailed information. For example, if the user is in a hurry, the suggestion unit can provide a presentation that gets straight to the point. By adjusting the presentation of the suggestion according to the user's emotions, the suggestion unit can provide suggestions that are 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 suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the way the suggestion is presented based on the results.

[0093] The proposal department can adjust the level of detail of a proposal based on the importance of the solution. For example, the proposal department can provide a detailed proposal for a high-importance solution. For example, it can provide a simplified proposal for a low-importance solution. The proposal department can adjust the depth of the proposal according to the importance of the solution. This allows for efficient proposals by adjusting the level of detail according to the importance of the solution. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use AI to analyze the impact, feasibility, and cost-effectiveness of a solution in order to evaluate its importance, and adjust the level of detail of the proposal based on the results.

[0094] The proposal department can apply different proposal algorithms depending on the category of the solution when making a proposal. For example, the proposal department can apply a specific proposal algorithm to sales activities. For example, the proposal department can apply a different proposal algorithm to market research. For example, the proposal department can select the optimal proposal algorithm depending on the category of the solution. This makes it possible to make highly accurate proposals by applying the optimal proposal algorithm according to the category of the solution. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, in order to classify the category of solutions, the proposal department can use AI to classify solutions by industry, application, and technology, and then apply the optimal proposal algorithm based on the results.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion according to the user's emotions, the suggestion unit can provide the most suitable suggestion for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the length of the suggestion based on the results.

[0096] The proposal department can prioritize proposals based on the timing of solution submission. For example, the proposal department may prioritize solutions that are urgent. The proposal department can prioritize proposals based on the submission date. The proposal department can adjust the order of proposals according to the timing of solution submission. This allows for more efficient proposals by prioritizing proposals based on the timing of solution submission. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department may use AI to analyze submission deadlines and frequencies to consider the timing of solution submission, and then prioritize proposals based on the results.

[0097] The proposal department can adjust the order of proposals based on the relevance of the solutions when making a proposal. For example, the proposal department can prioritize proposing highly relevant solutions. For example, the proposal department can postpone proposing less relevant solutions. For example, the proposal department can optimize the order of proposals based on the relevance of the solutions. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the solutions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can use AI to analyze the correlation and co-occurrence frequency of solutions in order to evaluate the relevance of the solutions, and adjust the order of proposals based on the results.

[0098] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated emotions. For example, if the user is nervous, the execution unit may execute in a simple and highly visible manner. For example, if the user is relaxed, the execution unit may execute in a manner that includes detailed information. For example, if the user is in a hurry, the execution unit may execute in a quick and concise manner. This allows the execution method to be adjusted according to the user's emotions, thereby providing the user with the optimal execution method. 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 execution unit may be performed using AI, for example, or without AI. For example, the execution unit may use AI to perform facial recognition or voice analysis to estimate the user's emotions and adjust the execution method based on the results.

[0099] The execution unit can analyze the user's past work history during execution to select the optimal execution method. For example, the execution unit can analyze the user's past work history and select the most efficient execution method. For example, the execution unit can optimize the execution procedure based on the user's past work history. For example, the execution unit can determine the priority of the tasks to be executed by referring to the user's past work history. This allows the optimal execution method to be selected by analyzing the user's past work history. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to analyze work records and work results in order to analyze the user's past work history, and select the optimal execution method based on the results.

[0100] The execution unit can customize the means of execution at runtime based on the user's current work status. For example, the execution unit can select the optimal means of execution based on the user's current work status. For example, the execution unit can customize the means of execution according to the user's work progress. For example, the execution unit can adjust the means of execution considering the user's current work status. This enables efficient execution by customizing the means of execution based on the user's current work status. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can use AI to analyze the details of the work schedule and work content in order to understand the user's current work status, and customize the means of execution based on the results.

[0101] The execution unit can estimate the user's emotions and determine the priority of tasks based on the estimated emotions. For example, if the user is stressed, the execution unit will postpone less important tasks. For example, if the user is relaxed, the execution unit can prioritize high-importance tasks. For example, if the user is focused, the execution unit can adjust the priority of tasks. This enables efficient execution by determining the priority of tasks 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 execution unit may be performed using AI, or not using AI. For example, the execution unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and determine the priority of tasks based on the results.

[0102] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, the execution unit can select the optimal execution method based on the user's current location. For example, the execution unit can optimize the execution method by referring to the user's travel history. For example, the execution unit can adjust the execution means, taking into account the user's geographical location information. This allows the execution unit to select the optimal execution method by taking into account the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can use AI to utilize GPS data and location information services to take into account the user's geographical location information, and select the optimal execution method based on the results.

[0103] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can analyze the user's social media activity and propose relevant means of execution. For example, the execution unit can filter means of execution based on the user's interests on social media. For example, the execution unit can determine the priority of means of execution by referring to the user's social media activity history. In this way, by analyzing the user's social media activity, the optimal means of execution can be proposed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, in order to analyze the user's social media activity, the execution unit can use AI to analyze the content of posts and the number of followers, and propose means of execution based on the results.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if a user is stressed, data can be collected during times when the user is relaxed. If a user is concentrating, data can be collected to avoid interrupting their concentration. Furthermore, if a user is tired, data can be collected while they are taking a break. By adjusting the timing of data collection according to the user's emotions, efficient data collection becomes possible.

[0106] 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, it can provide a simple and highly visual presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0107] The proposal function can estimate the user's emotions and adjust the presentation of the proposal based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the presentation of the proposal according to the user's emotions, it can provide a proposal that is easy for the user to understand.

[0108] The execution unit can estimate the user's emotions and adjust the execution method based on those emotions. For example, if the user is nervous, the execution can be performed in a simple and highly visible manner. If the user is relaxed, the execution can be performed in a manner that includes detailed information. Furthermore, if the user is in a hurry, the execution can be performed in a quick and concise manner. In this way, by adjusting the execution method according to the user's emotions, the system can provide the user with the most optimal execution method.

[0109] The execution unit can estimate the user's emotions and determine the priority of tasks based on those emotions. For example, if the user is stressed, less important tasks can be postponed. Conversely, if the user is relaxed, more important tasks can be prioritized. Furthermore, if the user is focused, the priority of tasks can be adjusted. This allows for more efficient execution by determining the priority of tasks according to the user's emotions.

[0110] The data collection unit can analyze past data collection history and select the optimal collection method. For example, it can analyze past data collection history and select the most efficient collection method. It can also optimize the collection frequency based on past data collection history. Furthermore, it can determine the priority of data to be collected by referring to past data collection history. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0111] 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, it can adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data.

[0112] The proposal department can adjust the level of detail in a proposal based on the importance of the solution. For example, it can provide detailed proposals for high-priority solutions and simplified proposals for low-priority solutions. Furthermore, it can adjust the depth of the proposal according to the importance of the solution. This allows for more efficient proposals by adjusting the level of detail according to the importance of the solution.

[0113] The execution unit can analyze the user's past work history during execution to select the optimal execution method. For example, it can analyze the user's past work history and select the most efficient execution method. It can also optimize the execution procedure based on the user's past work history. Furthermore, it can determine the priority of the tasks to be executed by referring to the user's past work history. In this way, the optimal execution method can be selected by analyzing the user's past work history.

[0114] The execution unit can customize the execution method based on the user's current work status during execution. For example, it can select the optimal execution method based on the user's current work status. It can also customize the execution method according to the user's work progress. Furthermore, it can adjust the execution method considering the user's current work status. This allows for efficient execution by customizing the execution method based on the user's current work status.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The data collection department collects data. For example, it collects various business data within the company to identify specific business challenges, such as the lack of manpower and workflow standardization problems faced by sales department managers, or the time and cost issues of market research faced by product planning managers. The data collection department can collect sales activity data and market research data. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they analyze collected sales activity data to identify areas for improvement in sales efficiency. They can also analyze collected market research data to understand market trends. The analysis department can perform sales trend analysis and customer segmentation. Step 3: The proposal department proposes the optimal solution based on the analysis results obtained by the analysis department. For example, as a solution for sales activities, they may propose improvements to the sales process or optimization of customer targeting. As a solution for market research, they may propose new product launch strategies or methods for understanding consumer needs. Step 4: The implementation team carries out the solutions proposed by the proposal team. For example, solutions for sales activities could include conducting sales training or introducing sales tools. Solutions for market research could include test marketing of new products or collecting consumer feedback.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects various business data within the company using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to identify areas for improvement to streamline operations. The proposal unit proposes an optimal solution based on the analysis results using the specific processing unit 290 of the data processing unit 12. The execution unit executes the proposed solution using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and execution unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects various business data within the company using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and identifies areas for improvement to streamline operations. The proposal unit proposes an optimal solution based on the analysis results by the specific processing unit 290 of the data processing unit 12. The execution unit executes the proposed solution by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

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

[0140] The 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.

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

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

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

[0144] Figure 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.

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

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

[0147] In the 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the collection unit collects various business data within the company using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to identify areas for improvement to streamline operations. The proposal unit proposes an optimal solution based on the analysis results using the specific processing unit 290 of the data processing unit 12. The execution unit executes the proposed solution using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

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

[0156] The 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.

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects various business data within the company using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and identifies areas for improvement to streamline operations. The proposal unit proposes an optimal solution based on the analysis results by the specific processing unit 290 of the data processing unit 12. The execution unit executes the proposed solution by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that proposes the optimal solution based on the analysis results obtained by the aforementioned analysis unit, The system comprises an execution unit that executes the solution proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect sales activity data The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect market research data The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Analyzing sales activity data The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyzing market research data The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose solutions for sales activities. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose solutions for market research. The system described in Appendix 1, characterized by the features described herein. (Note 8) The execution unit is, Implement solutions for sales activities The system described in Appendix 1, characterized by the features described herein. (Note 9) The execution unit is, Implement market research solutions The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) 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 14) 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 15) 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 16) 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 17) 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 18) 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 19) 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 20) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the solution. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the solution. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the solutions will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the solutions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, During execution, the system analyzes the user's past work history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, At runtime, the execution method is customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The execution unit is, During execution, the system selects the optimal execution method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 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 proposal unit that proposes the optimal solution based on the analysis results obtained by the aforementioned analysis unit, The system comprises an execution unit that executes the solution proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect sales activity data The system according to feature 1.

3. The aforementioned collection unit is Collect market research data The system according to feature 1.

4. The aforementioned analysis unit is Analyzing sales activity data The system according to feature 1.

5. The aforementioned analysis unit is Analyzing market research data The system according to feature 1.

6. The aforementioned proposal section is, We propose solutions for sales activities. The system according to feature 1.

7. The aforementioned proposal section is, We propose solutions for market research. The system according to feature 1.

8. The execution unit is, Implement solutions for sales activities The system according to feature 1.

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