system

Abstract

The system according to this embodiment aims to integrate and analyze internal and external data to provide optimal sales strategies and market forecasts. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects internal and external data. The analysis unit integrates and analyzes the data collected by the data collection unit. The proposal unit provides sales strategies and market forecasts based on the analysis results obtained by the analysis unit. The feedback unit provides feedback on the results of the strategies provided by the proposal unit to improve the accuracy of the AI ​​model.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the process of integrating and analyzing internal and external data of a company to provide sales strategies and market forecasts is not fully automated, and there is room for improvement.

[0005] The system according to the embodiment aims to integrate and analyze internal and external data of a company to provide an optimal sales strategy and market forecast.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects internal and external data. The analysis unit integrates and analyzes the data collected by the data collection unit. The proposal unit provides sales strategies and market forecasts based on the analysis results obtained by the analysis unit. The feedback unit provides feedback on the results of the strategies provided by the proposal unit to improve the accuracy of the AI ​​model. [Effects of the Invention]

[0007] The system according to this embodiment can integrate and analyze internal and external data to provide optimal sales strategies and market forecasts. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that integrates and analyzes internal and external data to provide sales strategies and market forecasts. This AI agent system collects internal and external data, and the AI ​​agent integrates and analyzes this data. Next, based on the analysis results, it provides sales strategies and market forecasts. Furthermore, it provides feedback on the results of the implemented strategies and improves the accuracy of the AI ​​model, enabling it to always provide optimal proposals. For example, the AI ​​agent system collects internal and external data. In this case, it collects data such as sales materials, internal services, external data, and internal success stories. For example, sales materials include records of past sales activities and customer information. Internal services include information on services provided by each department within the company. External data includes information on competitors and market trends. Internal success stories include past successful sales strategies and business plans. Next, the AI ​​agent integrates and analyzes the collected data. The AI ​​agent analyzes the collected data using natural language processing technology and generates sales strategies and market forecasts. For example, sales strategies include personalized sales proposals, competitive analysis reports, and customer needs forecasts. Market forecasts include market trend reports, recommendations for new business opportunities, risk assessments, and countermeasures. Furthermore, the results of implemented strategies are fed back to improve the accuracy of the AI ​​model. Specifically, the results of implemented sales strategies and market forecasts are collected and fed back to the AI ​​agent. The AI ​​agent learns from the feedback and improves the accuracy of future proposals. This ensures that optimal proposals are always made. This system allows sales representatives, business planners, HR departments, finance teams, and management to solve problems such as inefficient analysis due to scattered data and time-consuming strategy formulation, and to support rapid and effective decision-making. For example, sales representatives can make effective proposals to customers using personalized sales proposals generated by the AI ​​agent. Business planners can develop effective business plans based on market trend reports and recommendations for new business opportunities.The HR department can recruit the right talent based on recruitment market trend reports and talent matching reports. The finance team can develop effective financial strategies based on financial forecast reports and investment opportunity analyses. Management can enhance the company's competitiveness based on data-driven decision-making provided by AI agents. This allows the AI ​​agent system to integrate and analyze internal and external data to provide sales strategies and market forecasts.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects internal and external data. Internal and external data includes, but is not limited to, sales materials, internal services, external data, and internal success stories. For example, the data collection unit collects records of past sales activities and customer information as sales materials. The data collection unit can also collect information on services provided by various departments within the company as internal services. Furthermore, the data collection unit can collect information on competitors and market trends as external data. The data collection unit can also collect past successful sales strategies and business plans as internal success stories. For example, the data collection unit digitizes sales materials and stores them in a database. Information on internal services is collected and integrated from data provided by each department. External data is collected from the internet and market research reports. Internal success stories are collected from past project data and reports. The analysis unit integrates and analyzes the data collected by the data collection unit. The analysis is performed using, for example, natural language processing technology, but is not limited to this example. For example, the analytics department uses text mining techniques to analyze data and extract important information. The analytics department can also classify data and discover patterns using machine learning algorithms. Furthermore, the analytics department can analyze data correlations using data mining techniques. For instance, the analytics department uses text mining techniques to extract customer needs from sales materials. They use machine learning algorithms to classify customer data and identify customer segments. They use data mining techniques to analyze the correlation between internal success stories and market trends. The proposal department provides sales strategies and market forecasts based on the analysis results obtained by the analytics department. Sales strategies and market forecasts include, but are not limited to, personalized sales proposals, competitive analysis reports, customer needs forecasts, market trend reports, new business opportunity recommendations, and risk assessments and countermeasures. For example, the proposal department can generate personalized sales proposals and provide them to sales representatives. They can also generate competitive analysis reports and provide them to management.Furthermore, the proposal department can also generate customer needs forecasts and provide them to the marketing department. For example, the proposal department can analyze customer data to create personalized sales proposals and develop proposals tailored to individual needs. To generate competitive analysis reports, it can analyze competitor data to identify competitors' strengths and weaknesses. To generate customer needs forecasts, it can analyze customer purchase history and behavioral data to predict future needs. The feedback department provides feedback on the results of strategies provided by the proposal department to improve the accuracy of the AI ​​model. Feedback is provided, but is not limited to, collecting the results of implemented sales strategies and market forecasts and feeding them back to the AI ​​agent. For example, the feedback department collects the results of implemented sales strategies and feeds them back to the AI ​​agent. The feedback department can also collect the results of market forecasts and feed them back to the AI ​​agent. Furthermore, the feedback department can collect customer reactions and feedback and feed them back to the AI ​​agent. For example, the feedback department receives reports from sales representatives to collect the results of implemented sales strategies. It collects market research data to collect market forecast results. It utilizes customer surveys and feedback forms to collect customer reactions and feedback. As a result, the AI ​​agent system according to this embodiment can integrate and analyze internal and external data to provide sales strategies and market forecasts.

[0030] The data collection department collects internal and external data. This data includes, but is not limited to, sales materials, internal services, external data, and internal success stories. For example, the department collects records of past sales activities and customer information as sales materials. Specifically, this includes proposals and contracts created by sales representatives, email correspondence with customers, and records of business negotiations. This data is digitized and stored in a database. The data collection department can use OCR (optical character recognition) and natural language processing technologies to automatically collect this data. The department can also collect information on services provided by various departments within the company as internal services. Examples include technical support information provided by the technology department, product information provided by the product development department, and employee skill information provided by the human resources department. This information is automatically collected and integrated from each department's database. Furthermore, the data collection department can collect information on competitors and market trends as external data. Examples include competitor product information, pricing information, marketing strategies, and industry news and reports. This data is collected from publicly available information on the internet and market research reports. The data collection unit can automatically collect this data using web scraping technology and APIs. It can also collect past successful sales strategies and business plans as internal success stories. This includes, for example, detailed reports on successful projects, analyses of success factors, and customer feedback. This data is collected from past project databases and reports. The data collection unit centrally manages this data and can integrate it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The Analysis Department integrates and analyzes the data collected by the Data Collection Department. Analysis is performed using, but is not limited to, natural language processing techniques. Specifically, it analyzes data using text mining techniques to extract important information. For example, it extracts customer needs and interests from sales materials to create customer profiles. It can also classify data and discover patterns using machine learning algorithms. For example, it analyzes customer data to identify customer segments and develop optimal sales strategies for each segment. Furthermore, it can analyze data correlations using data mining techniques. For example, it analyzes the correlation between internal success stories and market trends to identify success factors. The Analysis Department utilizes these techniques to analyze collected data from multiple perspectives and extract information necessary for sales strategies and market forecasts. In addition, the Analysis Department can automate data analysis using AI and provide analysis results in real time. For example, it uses natural language processing techniques to extract customer needs from sales materials in real time and provide them to sales representatives. It also uses machine learning algorithms to classify customer data in real time and identify customer segments. By using data mining techniques, the company analyzes the correlation between internal success stories and market trends in real time. This allows the analytics department to quickly and accurately analyze data and provide the information necessary for sales strategies and market forecasts. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past sales data, they can predict risk fluctuations in specific regions or time periods and formulate future countermeasures. In addition, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analytics department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The Proposal Department provides sales strategies and market forecasts based on the analysis results obtained by the Analysis Department. These strategies and forecasts include, but are not limited to, personalized sales proposals, competitive analysis reports, customer needs forecasts, market trend reports, new business opportunity recommendations, and risk assessments and countermeasures. Specifically, the Proposal Department generates personalized sales proposals and provides them to sales representatives. For example, it analyzes customer data to create proposals tailored to individual needs. It proposes optimal products and services based on customers' past purchase history and behavioral data. The Proposal Department can also generate competitive analysis reports and provide them to management. For example, it analyzes competitor data to identify their strengths and weaknesses. It analyzes the characteristics of competitors' products and services and incorporates them into its own strategy. Furthermore, the Proposal Department can generate customer needs forecasts and provide them to the marketing department. For example, it analyzes customer purchase history and behavioral data to predict future needs. It understands customer interests and purchasing intent and incorporates them into marketing strategies. The Proposal Department utilizes AI technology to automatically generate these proposals. For example, natural language generation technology can be used to automatically create personalized sales proposals. Machine learning algorithms can be used to automatically generate competitive analysis reports. Data mining technology can be used to automatically generate customer needs forecasts. This allows the proposal department to provide sales strategies and market forecasts quickly and accurately. Furthermore, the proposal department can continuously revise proposals based on real-time updated data to respond to the latest situations. For example, if a customer's purchase history or behavioral data is updated, the proposal department can immediately incorporate the new data and update the proposal. The proposal department can also make more accurate proposals by considering regional characteristics and historical data. As a result, the proposal department can always provide highly accurate sales strategies and market forecasts based on the latest information, supporting quick and appropriate responses.

[0033] The Feedback Department provides feedback on the results of strategies provided by the Proposal Department to improve the accuracy of the AI ​​model. Feedback is provided, for example, by collecting the results of implemented sales strategies and market forecasts and feeding them back to the AI ​​agent. Specifically, the Feedback Department collects the results of implemented sales strategies and feeds them back to the AI ​​agent. For example, it receives reports from sales representatives and evaluates the effectiveness of the implemented strategies. It analyzes the results of sales activities and customer reactions to identify areas for improvement in the strategies. The Feedback Department can also collect the results of market forecasts and feed them back to the AI ​​agent. For example, it collects market research data and evaluates the accuracy of the forecasts. It analyzes market trends and competitor movements to identify areas for improvement in the forecasting model. Furthermore, the Feedback Department can also collect customer reactions and feedback and feed them back to the AI ​​agent. For example, it uses customer surveys and feedback forms to collect customer opinions and requests. It analyzes customer satisfaction and dissatisfaction to identify areas for service improvement. The Feedback Department provides this feedback to the AI ​​agent to improve the accuracy of the AI ​​model. For example, it uses machine learning algorithms to analyze the feedback data and adjust the model parameters. It uses data mining techniques to analyze the correlations of the feedback data and identify areas for improvement in the model. This allows the feedback unit to continuously improve the accuracy of the AI ​​agent, enabling it to provide more effective sales strategies and market forecasts. Furthermore, by adjusting the frequency and method of feedback collection, the feedback unit can respond flexibly to specific situations and conditions. This allows the feedback unit to collect feedback efficiently and effectively, improving the performance of the AI ​​agent.

[0034] The proposal department can generate personalized sales proposals. For example, it can analyze customer data and create proposals tailored to individual needs. For example, it can suggest products and services that a customer might be interested in based on their past purchase history. The proposal department can also analyze customer behavior data and create proposals that will capture the customer's attention. Furthermore, the proposal department can improve the content of proposals based on customer feedback. For example, it can analyze a customer's purchase history and suggest related products and services based on products and services the customer has purchased in the past. It can analyze customer behavior data and make suggestions that are likely to be of interest based on products and services the customer has viewed on the website. It can improve the content of proposals based on customer feedback to make more effective proposals. In this way, by generating personalized sales proposals, it becomes possible to make effective proposals to customers.

[0035] The proposal department can generate competitive analysis reports. For example, the proposal department can collect data on competitors and identify their strengths and weaknesses. For example, the proposal department can analyze the characteristics of competitors' products and services and compare them with its own products. The proposal department can also analyze competitors' market share and growth rates to understand their trends. Furthermore, the proposal department can analyze competitors' strategies and measures and reflect them in its own strategy. For example, the proposal department can analyze the characteristics of competitors' products and services and make proposals that emphasize the strengths of its own products. It can analyze competitors' market share and growth rates and formulate market strategies based on their trends. By analyzing competitors' strategies and measures and reflecting them in its own strategy, it can improve its competitiveness. In this way, by generating competitive analysis reports, it is possible to understand competitor information and formulate strategies.

[0036] The proposal department can generate customer needs forecasts. For example, the proposal department analyzes customer data to predict customer needs. For example, the proposal department predicts products and services that customers will need in the future based on customer purchase history and behavioral data. The proposal department can also understand customer needs based on customer feedback. Furthermore, the proposal department can predict the needs of each customer segment based on customer attribute data. For example, the proposal department analyzes customer purchase history and predicts future needs based on products and services that customers have purchased in the past. It analyzes customer behavioral data and predicts future needs based on products and services that customers have viewed on websites. It understands customer needs based on customer feedback and makes appropriate proposals. It predicts the needs of each customer segment based on customer attribute data and makes customized proposals for each segment. In this way, by generating customer needs forecasts, it becomes possible to understand customer needs and make appropriate proposals.

[0037] The proposal department can generate market trend reports. For example, the proposal department can collect market data and analyze trends. For example, the proposal department can analyze market growth rates and market share to understand trends. The proposal department can also analyze the activities of competitors and predict market changes. Furthermore, the proposal department can analyze consumer behavior data to understand market needs. For example, the proposal department can analyze market growth rates and predict future market size. It can analyze market share to understand the activities of competitors. It can analyze consumer behavior data to understand consumer needs. By generating market trend reports, it is possible to understand market trends and formulate strategies.

[0038] The proposal department can generate recommendations for new business opportunities. For example, the proposal department can analyze market data to identify new business opportunities. It can also identify growth markets and untapped markets and evaluate the potential of new businesses. Furthermore, the proposal department can analyze the activities of competitors to find new business opportunities. In addition, it can analyze consumer needs and propose new business ideas. For example, the proposal department can identify growth markets and evaluate the potential of new businesses. It can identify untapped markets and find new business opportunities. It can analyze the activities of competitors and propose new business ideas. It can analyze consumer needs and propose new business ideas. By generating recommendations for new business opportunities, the proposal department can discover new business opportunities.

[0039] The proposal department can generate risk assessments and countermeasures. For example, the proposal department can collect risk data and assess risks. For example, the proposal department can assess business risks and market risks and analyze the impact of those risks. The proposal department can also assess the probability of risks occurring and determine the priority of risks. Furthermore, the proposal department can propose countermeasures for risks. For example, the proposal department can assess business risks and analyze their impact. It can assess market risks and assess the probability of those risks occurring. It can determine the priority of risks and propose countermeasures for those risks. By generating risk assessments and countermeasures, appropriate responses to risks become possible.

[0040] The proposal department can generate recruitment market trend reports. For example, the proposal department can collect recruitment market data and analyze trends. For example, the proposal department can analyze the growth rate and recruitment needs of the recruitment market to understand trends. The proposal department can also analyze the recruitment trends of competitors and predict changes in the recruitment market. Furthermore, the proposal department can analyze job seeker behavior data to understand the needs of the recruitment market. For example, the proposal department can analyze the growth rate of the recruitment market and predict future recruitment needs. It can analyze recruitment needs and understand the recruitment trends of competitors. It can analyze job seeker behavior data and understand the needs of the recruitment market. By generating recruitment market trend reports, the proposal department can understand the trends in the recruitment market and recruit the right talent.

[0041] The proposal department can generate a talent suitability matching report. For example, the proposal department can collect job seeker data and evaluate their suitability. For example, the proposal department can evaluate job seekers' skills and experience to determine their suitability. Furthermore, the proposal department can evaluate job seekers' personality and values ​​to determine their suitability. In addition, the proposal department can evaluate suitability based on job seekers' feedback. For example, the proposal department can evaluate job seekers' skills and experience to determine their suitability. It can evaluate job seekers' personality and values ​​to determine their suitability. It can evaluate suitability based on job seekers' feedback. By generating a talent suitability matching report, it is possible to find suitable personnel.

[0042] The proposal department can generate employee engagement analyses. For example, the proposal department can collect employee data and evaluate engagement. For example, the proposal department can evaluate employee satisfaction and motivation and determine engagement. The proposal department can also evaluate employee performance data and determine engagement. Furthermore, the proposal department can evaluate engagement based on employee feedback. For example, the proposal department can evaluate employee satisfaction and motivation and determine engagement. For example, it can evaluate employee performance data and determine engagement. For example, it can evaluate engagement based on employee feedback. By generating employee engagement analyses, it is possible to understand and improve employee engagement.

[0043] The proposal department can generate financial forecast reports. For example, the proposal department collects financial data and makes forecasts. For example, the proposal department analyzes sales data and cost data to predict future financial conditions. Furthermore, the proposal department can analyze investment data to predict investment returns. In addition, the proposal department can analyze economic data to predict economic impacts. For example, the proposal department analyzes sales data to predict future sales; analyzes cost data to predict future costs; analyzes investment data to predict investment returns; and analyzes economic data to predict economic impacts. This allows for the development of financial strategies by generating financial forecast reports.

[0044] The proposal unit can generate investment opportunity analyses. For example, the proposal unit can collect investment data and evaluate investment opportunities. For example, the proposal unit can evaluate investment returns and risks to determine investment opportunities. Furthermore, the proposal unit can analyze market trends for investments and determine investment opportunities. In addition, the proposal unit can evaluate investment portfolios and determine investment opportunities. For example, the proposal unit can evaluate investment returns and risks to determine investment opportunities. It can analyze market trends for investments and determine investment opportunities. It can evaluate investment portfolios and determine investment opportunities. By generating investment opportunity analyses, investment strategies can be formulated.

[0045] The proposal department can generate cost reduction proposals. For example, the proposal department can collect cost data and evaluate the potential for reduction. For example, the proposal department can analyze the breakdown of costs and identify areas for reduction. Furthermore, the proposal department can propose cost reduction measures and develop implementation plans. In addition, the proposal department can evaluate the effectiveness of cost reductions and suggest areas for improvement. For example, the proposal department can analyze the breakdown of costs and identify areas for reduction. It can propose cost reduction measures and develop implementation plans. It can evaluate the effectiveness of cost reductions and suggest areas for improvement. By generating cost reduction proposals, the department can develop strategies for cost reduction.

[0046] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the unit can identify the most effective collection method from past data collection history and apply a similar method. The unit can also analyze past data collection history to identify areas for improvement in the collection method and optimize it. Furthermore, the unit can customize the collection method based on past data collection history and select the optimal method for specific situations. For example, the unit can identify the most effective collection method from past data collection history and apply a similar method. The unit can analyze past data collection history to identify areas for improvement in the collection method and optimize it. The unit can customize the collection method based on past data collection history and select the optimal method for specific situations. This allows the optimal collection method to be selected by analyzing past data collection history.

[0047] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting only data related to the user's current projects. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. The data collection unit can also dynamically filter and collect necessary data according to the user's project progress. For example, the data collection unit can prioritize collecting only data related to the user's current projects. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can dynamically filter and collect necessary data according to the user's project progress. This allows for the collection of highly relevant data by filtering data based on the user's current projects and areas of interest.

[0048] 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 market data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. The data collection unit can also prioritize the collection of regional competitive information by considering the user's location information. For example, the data collection unit can prioritize the collection of market data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, the data collection unit can prioritize the collection of regional competitive information by considering the user's location information. This makes it possible to collect region-specific data by collecting data while considering the user's geographical location information.

[0049] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect relevant competitor information based on users' interests on social media. The data collection unit can also analyze users' social media activity history and collect relevant customer needs data. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect relevant competitor information based on users' interests on social media. For example, the data collection unit can analyze users' social media activity history and collect relevant customer needs data. In this way, relevant data can be collected by analyzing users' social media activity.

[0050] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit will perform a detailed analysis on high-importance data and a concise analysis on low-importance data. Furthermore, the analysis unit can dynamically adjust the depth of the analysis according to the importance of the data. For example, the analysis unit will perform a detailed analysis on high-importance data and a concise analysis on low-importance data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data.

[0051] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, the analysis department can apply a trend analysis algorithm to market data. For example, the analysis department can apply a clustering algorithm to customer data. Furthermore, the analysis department can also apply a competitive analysis algorithm to competitor data. For example, the analysis department can apply a trend analysis algorithm to market data. For example, the analysis department can apply a clustering algorithm to customer data. For example, the analysis department can apply a competitive analysis algorithm to competitor data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category.

[0052] The analysis department can determine the priority of analysis based on the data collection timing. For example, the analysis department may prioritize the analysis of the most recent data. For example, the analysis department may prioritize the most recent data while also referring to historical data. The analysis department can also dynamically adjust the priority of analysis according to the data collection timing. For example, the analysis department may prioritize the analysis of the most recent data. For example, the analysis department may prioritize the analysis of the most recent data while also referring to historical data. The analysis department can dynamically adjust the priority of analysis according to the data collection timing. This allows the analysis department to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing.

[0053] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data. For example, the analysis unit can postpone analyzing less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data. For example, the analysis unit can postpone analyzing less relevant data. The analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0054] The proposal department can adjust the level of detail of a proposal based on its importance. For example, it provides detailed explanations for high-importance proposals and concise explanations for low-importance proposals. Furthermore, the proposal department can dynamically adjust the level of detail of a proposal according to its importance. For example, it provides detailed explanations for high-importance proposals and concise explanations for low-importance proposals. By dynamically adjusting the level of detail based on the importance of the proposal, it can provide detailed explanations for important proposals.

[0055] The proposal department can apply different proposal algorithms depending on the category of the proposal content. For example, the proposal department can apply a personalized proposal algorithm to sales strategies. For example, the proposal department can apply a trend analysis algorithm to market forecasts. Furthermore, the proposal department can also apply a risk analysis algorithm to risk assessments. For example, the proposal department can apply a personalized proposal algorithm to sales strategies. For example, the proposal department can apply a trend analysis algorithm to market forecasts. For example, the proposal department can apply a risk analysis algorithm to risk assessments. By applying the most suitable proposal algorithm according to the category of the proposal content, the accuracy of the proposal is improved.

[0056] The proposal department can determine the priority of proposals based on the submission timing of each proposal. For example, the proposal department will prioritize proposals that require immediate attention. The proposal department can dynamically adjust priorities according to the submission timing of each proposal. The proposal department can also provide proposals at the optimal time, taking into account the submission timing of each proposal. For example, the proposal department will prioritize proposals that require immediate attention. The proposal department can dynamically adjust priorities according to the submission timing of each proposal. The proposal department can provide proposals at the optimal time, taking into account the submission timing of each proposal. This allows for prioritizing proposals based on the submission timing of each proposal, thereby ensuring that proposals requiring immediate attention are provided first.

[0057] The proposal department can adjust the order of proposals based on the relevance of their content when submitting proposals. For example, the proposal department may prioritize highly relevant proposals. For example, the proposal department may postpone less relevant proposals. The proposal department can also dynamically adjust the order of proposals according to the relevance of their content. For example, the proposal department may prioritize highly relevant proposals. For example, the proposal department may postpone less relevant proposals. The proposal department dynamically adjusts the order of proposals according to the relevance of their content. This allows the proposal department to prioritize highly relevant proposals by adjusting the order of proposals based on the relevance of their content.

[0058] The feedback unit can optimize the feedback algorithm by referring to past feedback data during the feedback process. For example, the feedback unit can analyze past feedback data to identify the optimal feedback method. The feedback unit can also improve the feedback algorithm based on past feedback data. Furthermore, the feedback unit can improve the accuracy of feedback by referring to past feedback data. For example, the feedback unit can analyze past feedback data to identify the optimal feedback method. The feedback unit can improve the feedback algorithm based on past feedback data. The feedback unit can improve the accuracy of feedback by referring to past feedback data. In this way, by referring to past feedback data, the feedback algorithm can be optimized and the accuracy of feedback can be improved.

[0059] The feedback department can apply different feedback methods depending on the category of the feedback content. For example, the feedback department provides detailed feedback, including specific areas for improvement, for sales strategies. For example, the feedback department provides feedback, including an evaluation of forecast accuracy and areas for improvement, for market forecasts. Furthermore, the feedback department can also provide feedback, including a reassessment of risks and proposed countermeasures, for risk assessments. By applying the most appropriate feedback method according to the category of the feedback content, the accuracy of the feedback is improved.

[0060] The feedback unit can weight feedback based on when it is submitted. For example, the feedback unit prioritizes providing feedback of high urgency. The feedback unit can dynamically adjust the weighting according to the submission date of the feedback. Furthermore, the feedback unit can provide feedback at the optimal time, taking into account the submission date. For example, the feedback unit prioritizes providing feedback of high urgency. The feedback unit dynamically adjusts the weighting according to the submission date of the feedback. The feedback unit can provide feedback at the optimal time, taking into account the submission date of the feedback. This allows for prioritizing the provision of feedback of high urgency by weighting it based on the submission date.

[0061] The feedback unit can adjust the order of feedback based on the relevance of the feedback content. For example, the feedback unit may prioritize providing highly relevant feedback. For example, the feedback unit may postpone providing less relevant feedback. The feedback unit can also dynamically adjust the order of feedback according to the relevance of the feedback content. For example, the feedback unit may prioritize providing highly relevant feedback. For example, the feedback unit may postpone providing less relevant feedback. The feedback unit dynamically adjusts the order of feedback according to the relevance of the feedback content. This allows the system to prioritize providing highly relevant feedback by adjusting the order of feedback based on the relevance of the feedback content.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the unit can identify the most effective collection method from past data collection history and apply a similar method. The unit can also analyze past data collection history to identify areas for improvement in the collection method and optimize it. Furthermore, the unit can customize the collection method based on past data collection history and select the optimal method for specific situations. For example, the unit can identify the most effective collection method from past data collection history and apply a similar method. The unit can analyze past data collection history to identify areas for improvement in the collection method and optimize it. The unit can customize the collection method based on past data collection history and select the optimal method for specific situations. This allows the optimal collection method to be selected by analyzing past data collection history.

[0064] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting only data related to the user's current projects. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. The data collection unit can also dynamically filter and collect necessary data according to the user's project progress. For example, the data collection unit can prioritize collecting only data related to the user's current projects. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can dynamically filter and collect necessary data according to the user's project progress. This allows for the collection of highly relevant data by filtering data based on the user's current projects and areas of interest.

[0065] 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 market data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. The data collection unit can also prioritize the collection of regional competitive information by considering the user's location information. For example, the data collection unit can prioritize the collection of market data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, the data collection unit can prioritize the collection of regional competitive information by considering the user's location information. This makes it possible to collect region-specific data by collecting data while considering the user's geographical location information.

[0066] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect relevant competitor information based on users' interests on social media. The data collection unit can also analyze users' social media activity history and collect relevant customer needs data. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect relevant competitor information based on users' interests on social media. For example, the data collection unit can analyze users' social media activity history and collect relevant customer needs data. In this way, relevant data can be collected by analyzing users' social media activity.

[0067] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit will perform a detailed analysis on high-importance data and a concise analysis on low-importance data. Furthermore, the analysis unit can dynamically adjust the depth of the analysis according to the importance of the data. For example, the analysis unit will perform a detailed analysis on high-importance data and a concise analysis on low-importance data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data.

[0068] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, the analysis department can apply a trend analysis algorithm to market data. For example, the analysis department can apply a clustering algorithm to customer data. Furthermore, the analysis department can also apply a competitive analysis algorithm to competitor data. For example, the analysis department can apply a trend analysis algorithm to market data. For example, the analysis department can apply a clustering algorithm to customer data. For example, the analysis department can apply a competitive analysis algorithm to competitor data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category.

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

[0070] Step 1: The data collection department collects internal and external data. This includes sales materials, internal services, external data, and internal success stories. For example, the data collection department collects records of past sales activities and customer information as sales materials, and information on services provided by various departments within the company as internal services. Furthermore, the data collection department collects information on competitors and market trends as external data, and collects past successful sales strategies and business plans as internal success stories. Step 2: The analysis unit integrates and analyzes the data collected by the collection unit. The analysis is performed using natural language processing techniques, text mining techniques, machine learning algorithms, and data mining techniques. For example, the analysis unit uses text mining techniques to analyze the data and extract important information. It uses machine learning algorithms to classify the data and discover patterns. It uses data mining techniques to analyze the correlations between the data. Step 3: The proposal department provides sales strategies and market forecasts based on the analysis results obtained by the analysis department. These sales strategies and market forecasts include personalized sales proposals, competitive analysis reports, customer needs forecasts, market trend reports, new business opportunity recommendations, risk assessments, and countermeasures. For example, the proposal department generates personalized sales proposals and provides them to sales representatives. It generates competitive analysis reports and provides them to management. It generates customer needs forecasts and provides them to the marketing department. Step 4: The Feedback Department provides feedback on the results of the strategies provided by the Proposal Department to improve the accuracy of the AI ​​model. Feedback is provided by collecting the results of the implemented sales strategies and market forecasts and feeding them back to the AI ​​agent. For example, the Feedback Department collects the results of the implemented sales strategies and feeds them back to the AI ​​agent. It collects the results of market forecasts and feeds them back to the AI ​​agent. It collects customer reactions and feedback and feeds them back to the AI ​​agent.

[0071] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that integrates and analyzes internal and external data to provide sales strategies and market forecasts. This AI agent system collects internal and external data, and the AI ​​agent integrates and analyzes this data. Next, based on the analysis results, it provides sales strategies and market forecasts. Furthermore, it provides feedback on the results of the implemented strategies and improves the accuracy of the AI ​​model, enabling it to always provide optimal proposals. For example, the AI ​​agent system collects internal and external data. In this case, it collects data such as sales materials, internal services, external data, and internal success stories. For example, sales materials include records of past sales activities and customer information. Internal services include information on services provided by each department within the company. External data includes information on competitors and market trends. Internal success stories include past successful sales strategies and business plans. Next, the AI ​​agent integrates and analyzes the collected data. The AI ​​agent analyzes the collected data using natural language processing technology and generates sales strategies and market forecasts. For example, sales strategies include personalized sales proposals, competitive analysis reports, and customer needs forecasts. Market forecasts include market trend reports, recommendations for new business opportunities, risk assessments, and countermeasures. Furthermore, the results of implemented strategies are fed back to improve the accuracy of the AI ​​model. Specifically, the results of implemented sales strategies and market forecasts are collected and fed back to the AI ​​agent. The AI ​​agent learns from the feedback and improves the accuracy of future proposals. This ensures that optimal proposals are always made. This system allows sales representatives, business planners, HR departments, finance teams, and management to solve problems such as inefficient analysis due to scattered data and time-consuming strategy formulation, and to support rapid and effective decision-making. For example, sales representatives can make effective proposals to customers using personalized sales proposals generated by the AI ​​agent. Business planners can develop effective business plans based on market trend reports and recommendations for new business opportunities.The HR department can recruit the right talent based on recruitment market trend reports and talent matching reports. The finance team can develop effective financial strategies based on financial forecast reports and investment opportunity analyses. Management can enhance the company's competitiveness based on data-driven decision-making provided by AI agents. This allows the AI ​​agent system to integrate and analyze internal and external data to provide sales strategies and market forecasts.

[0072] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects internal and external data. Internal and external data includes, but is not limited to, sales materials, internal services, external data, and internal success stories. For example, the data collection unit collects records of past sales activities and customer information as sales materials. The data collection unit can also collect information on services provided by various departments within the company as internal services. Furthermore, the data collection unit can collect information on competitors and market trends as external data. The data collection unit can also collect past successful sales strategies and business plans as internal success stories. For example, the data collection unit digitizes sales materials and stores them in a database. Information on internal services is collected and integrated from data provided by each department. External data is collected from the internet and market research reports. Internal success stories are collected from past project data and reports. The analysis unit integrates and analyzes the data collected by the data collection unit. The analysis is performed using, for example, natural language processing technology, but is not limited to this example. For example, the analytics department uses text mining techniques to analyze data and extract important information. The analytics department can also classify data and discover patterns using machine learning algorithms. Furthermore, the analytics department can analyze data correlations using data mining techniques. For instance, the analytics department uses text mining techniques to extract customer needs from sales materials. They use machine learning algorithms to classify customer data and identify customer segments. They use data mining techniques to analyze the correlation between internal success stories and market trends. The proposal department provides sales strategies and market forecasts based on the analysis results obtained by the analytics department. Sales strategies and market forecasts include, but are not limited to, personalized sales proposals, competitive analysis reports, customer needs forecasts, market trend reports, new business opportunity recommendations, and risk assessments and countermeasures. For example, the proposal department can generate personalized sales proposals and provide them to sales representatives. They can also generate competitive analysis reports and provide them to management.Furthermore, the proposal department can also generate customer needs forecasts and provide them to the marketing department. For example, the proposal department can analyze customer data to create personalized sales proposals and develop proposals tailored to individual needs. To generate competitive analysis reports, it can analyze competitor data to identify competitors' strengths and weaknesses. To generate customer needs forecasts, it can analyze customer purchase history and behavioral data to predict future needs. The feedback department provides feedback on the results of strategies provided by the proposal department to improve the accuracy of the AI ​​model. Feedback is provided, but is not limited to, collecting the results of implemented sales strategies and market forecasts and feeding them back to the AI ​​agent. For example, the feedback department collects the results of implemented sales strategies and feeds them back to the AI ​​agent. The feedback department can also collect the results of market forecasts and feed them back to the AI ​​agent. Furthermore, the feedback department can collect customer reactions and feedback and feed them back to the AI ​​agent. For example, the feedback department receives reports from sales representatives to collect the results of implemented sales strategies. It collects market research data to collect market forecast results. It utilizes customer surveys and feedback forms to collect customer reactions and feedback. As a result, the AI ​​agent system according to this embodiment can integrate and analyze internal and external data to provide sales strategies and market forecasts.

[0073] The data collection department collects internal and external data. This data includes, but is not limited to, sales materials, internal services, external data, and internal success stories. For example, the department collects records of past sales activities and customer information as sales materials. Specifically, this includes proposals and contracts created by sales representatives, email correspondence with customers, and records of business negotiations. This data is digitized and stored in a database. The data collection department can use OCR (optical character recognition) and natural language processing technologies to automatically collect this data. The department can also collect information on services provided by various departments within the company as internal services. Examples include technical support information provided by the technology department, product information provided by the product development department, and employee skill information provided by the human resources department. This information is automatically collected and integrated from each department's database. Furthermore, the data collection department can collect information on competitors and market trends as external data. Examples include competitor product information, pricing information, marketing strategies, and industry news and reports. This data is collected from publicly available information on the internet and market research reports. The data collection unit can automatically collect this data using web scraping technology and APIs. It can also collect past successful sales strategies and business plans as internal success stories. This includes, for example, detailed reports on successful projects, analyses of success factors, and customer feedback. This data is collected from past project databases and reports. The data collection unit centrally manages this data and can integrate it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The Analysis Department integrates and analyzes the data collected by the Data Collection Department. Analysis is performed using, but is not limited to, natural language processing techniques. Specifically, it analyzes data using text mining techniques to extract important information. For example, it extracts customer needs and interests from sales materials to create customer profiles. It can also classify data and discover patterns using machine learning algorithms. For example, it analyzes customer data to identify customer segments and develop optimal sales strategies for each segment. Furthermore, it can analyze data correlations using data mining techniques. For example, it analyzes the correlation between internal success stories and market trends to identify success factors. The Analysis Department utilizes these techniques to analyze collected data from multiple perspectives and extract information necessary for sales strategies and market forecasts. In addition, the Analysis Department can automate data analysis using AI and provide analysis results in real time. For example, it uses natural language processing techniques to extract customer needs from sales materials in real time and provide them to sales representatives. It also uses machine learning algorithms to classify customer data in real time and identify customer segments. By using data mining techniques, the company analyzes the correlation between internal success stories and market trends in real time. This allows the analytics department to quickly and accurately analyze data and provide the information necessary for sales strategies and market forecasts. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past sales data, they can predict risk fluctuations in specific regions or time periods and formulate future countermeasures. In addition, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analytics department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0075] The Proposal Department provides sales strategies and market forecasts based on the analysis results obtained by the Analysis Department. These strategies and forecasts include, but are not limited to, personalized sales proposals, competitive analysis reports, customer needs forecasts, market trend reports, new business opportunity recommendations, and risk assessments and countermeasures. Specifically, the Proposal Department generates personalized sales proposals and provides them to sales representatives. For example, it analyzes customer data to create proposals tailored to individual needs. It proposes optimal products and services based on customers' past purchase history and behavioral data. The Proposal Department can also generate competitive analysis reports and provide them to management. For example, it analyzes competitor data to identify their strengths and weaknesses. It analyzes the characteristics of competitors' products and services and incorporates them into its own strategy. Furthermore, the Proposal Department can generate customer needs forecasts and provide them to the marketing department. For example, it analyzes customer purchase history and behavioral data to predict future needs. It understands customer interests and purchasing intent and incorporates them into marketing strategies. The Proposal Department utilizes AI technology to automatically generate these proposals. For example, natural language generation technology can be used to automatically create personalized sales proposals. Machine learning algorithms can be used to automatically generate competitive analysis reports. Data mining technology can be used to automatically generate customer needs forecasts. This allows the proposal department to provide sales strategies and market forecasts quickly and accurately. Furthermore, the proposal department can continuously revise proposals based on real-time updated data to respond to the latest situations. For example, if a customer's purchase history or behavioral data is updated, the proposal department can immediately incorporate the new data and update the proposal. The proposal department can also make more accurate proposals by considering regional characteristics and historical data. As a result, the proposal department can always provide highly accurate sales strategies and market forecasts based on the latest information, supporting quick and appropriate responses.

[0076] The Feedback Department provides feedback on the results of strategies provided by the Proposal Department to improve the accuracy of the AI ​​model. Feedback is provided, for example, by collecting the results of implemented sales strategies and market forecasts and feeding them back to the AI ​​agent. Specifically, the Feedback Department collects the results of implemented sales strategies and feeds them back to the AI ​​agent. For example, it receives reports from sales representatives and evaluates the effectiveness of the implemented strategies. It analyzes the results of sales activities and customer reactions to identify areas for improvement in the strategies. The Feedback Department can also collect the results of market forecasts and feed them back to the AI ​​agent. For example, it collects market research data and evaluates the accuracy of the forecasts. It analyzes market trends and competitor movements to identify areas for improvement in the forecasting model. Furthermore, the Feedback Department can also collect customer reactions and feedback and feed them back to the AI ​​agent. For example, it uses customer surveys and feedback forms to collect customer opinions and requests. It analyzes customer satisfaction and dissatisfaction to identify areas for service improvement. The Feedback Department provides this feedback to the AI ​​agent to improve the accuracy of the AI ​​model. For example, it uses machine learning algorithms to analyze the feedback data and adjust the model parameters. It uses data mining techniques to analyze the correlations of the feedback data and identify areas for improvement in the model. This allows the feedback unit to continuously improve the accuracy of the AI ​​agent, enabling it to provide more effective sales strategies and market forecasts. Furthermore, by adjusting the frequency and method of feedback collection, the feedback unit can respond flexibly to specific situations and conditions. This allows the feedback unit to collect feedback efficiently and effectively, improving the performance of the AI ​​agent.

[0077] The proposal department can generate personalized sales proposals. For example, it can analyze customer data and create proposals tailored to individual needs. For example, it can suggest products and services that a customer might be interested in based on their past purchase history. The proposal department can also analyze customer behavior data and create proposals that will capture the customer's attention. Furthermore, the proposal department can improve the content of proposals based on customer feedback. For example, it can analyze a customer's purchase history and suggest related products and services based on products and services the customer has purchased in the past. It can analyze customer behavior data and make suggestions that are likely to be of interest based on products and services the customer has viewed on the website. It can improve the content of proposals based on customer feedback to make more effective proposals. In this way, by generating personalized sales proposals, it becomes possible to make effective proposals to customers.

[0078] The proposal department can generate competitive analysis reports. For example, the proposal department can collect data on competitors and identify their strengths and weaknesses. For example, the proposal department can analyze the characteristics of competitors' products and services and compare them with its own products. The proposal department can also analyze competitors' market share and growth rates to understand their trends. Furthermore, the proposal department can analyze competitors' strategies and measures and reflect them in its own strategy. For example, the proposal department can analyze the characteristics of competitors' products and services and make proposals that emphasize the strengths of its own products. It can analyze competitors' market share and growth rates and formulate market strategies based on their trends. By analyzing competitors' strategies and measures and reflecting them in its own strategy, it can improve its competitiveness. In this way, by generating competitive analysis reports, it is possible to understand competitor information and formulate strategies.

[0079] The proposal department can generate customer needs forecasts. For example, the proposal department analyzes customer data to predict customer needs. For example, the proposal department predicts products and services that customers will need in the future based on customer purchase history and behavioral data. The proposal department can also understand customer needs based on customer feedback. Furthermore, the proposal department can predict the needs of each customer segment based on customer attribute data. For example, the proposal department analyzes customer purchase history and predicts future needs based on products and services that customers have purchased in the past. It analyzes customer behavioral data and predicts future needs based on products and services that customers have viewed on websites. It understands customer needs based on customer feedback and makes appropriate proposals. It predicts the needs of each customer segment based on customer attribute data and makes customized proposals for each segment. In this way, by generating customer needs forecasts, it becomes possible to understand customer needs and make appropriate proposals.

[0080] The proposal department can generate market trend reports. For example, the proposal department can collect market data and analyze trends. For example, the proposal department can analyze market growth rates and market share to understand trends. The proposal department can also analyze the activities of competitors and predict market changes. Furthermore, the proposal department can analyze consumer behavior data to understand market needs. For example, the proposal department can analyze market growth rates and predict future market size. It can analyze market share to understand the activities of competitors. It can analyze consumer behavior data to understand consumer needs. By generating market trend reports, it is possible to understand market trends and formulate strategies.

[0081] The proposal department can generate recommendations for new business opportunities. For example, the proposal department can analyze market data to identify new business opportunities. It can also identify growth markets and untapped markets and evaluate the potential of new businesses. Furthermore, the proposal department can analyze the activities of competitors to find new business opportunities. In addition, it can analyze consumer needs and propose new business ideas. For example, the proposal department can identify growth markets and evaluate the potential of new businesses. It can identify untapped markets and find new business opportunities. It can analyze the activities of competitors and propose new business ideas. It can analyze consumer needs and propose new business ideas. By generating recommendations for new business opportunities, the proposal department can discover new business opportunities.

[0082] The proposal department can generate risk assessments and countermeasures. For example, the proposal department can collect risk data and assess risks. For example, the proposal department can assess business risks and market risks and analyze the impact of those risks. The proposal department can also assess the probability of risks occurring and determine the priority of risks. Furthermore, the proposal department can propose countermeasures for risks. For example, the proposal department can assess business risks and analyze their impact. It can assess market risks and assess the probability of those risks occurring. It can determine the priority of risks and propose countermeasures for those risks. By generating risk assessments and countermeasures, appropriate responses to risks become possible.

[0083] The proposal department can generate recruitment market trend reports. For example, the proposal department can collect recruitment market data and analyze trends. For example, the proposal department can analyze the growth rate and recruitment needs of the recruitment market to understand trends. The proposal department can also analyze the recruitment trends of competitors and predict changes in the recruitment market. Furthermore, the proposal department can analyze job seeker behavior data to understand the needs of the recruitment market. For example, the proposal department can analyze the growth rate of the recruitment market and predict future recruitment needs. It can analyze recruitment needs and understand the recruitment trends of competitors. It can analyze job seeker behavior data and understand the needs of the recruitment market. By generating recruitment market trend reports, the proposal department can understand the trends in the recruitment market and recruit the right talent.

[0084] The proposal department can generate a talent suitability matching report. For example, the proposal department can collect job seeker data and evaluate their suitability. For example, the proposal department can evaluate job seekers' skills and experience to determine their suitability. Furthermore, the proposal department can evaluate job seekers' personality and values ​​to determine their suitability. In addition, the proposal department can evaluate suitability based on job seekers' feedback. For example, the proposal department can evaluate job seekers' skills and experience to determine their suitability. It can evaluate job seekers' personality and values ​​to determine their suitability. It can evaluate suitability based on job seekers' feedback. By generating a talent suitability matching report, it is possible to find suitable personnel.

[0085] The proposal department can generate employee engagement analyses. For example, the proposal department can collect employee data and evaluate engagement. For example, the proposal department can evaluate employee satisfaction and motivation and determine engagement. The proposal department can also evaluate employee performance data and determine engagement. Furthermore, the proposal department can evaluate engagement based on employee feedback. For example, the proposal department can evaluate employee satisfaction and motivation and determine engagement. For example, it can evaluate employee performance data and determine engagement. For example, it can evaluate engagement based on employee feedback. By generating employee engagement analyses, it is possible to understand and improve employee engagement.

[0086] The proposal department can generate financial forecast reports. For example, the proposal department collects financial data and makes forecasts. For example, the proposal department analyzes sales data and cost data to predict future financial conditions. Furthermore, the proposal department can analyze investment data to predict investment returns. In addition, the proposal department can analyze economic data to predict economic impacts. For example, the proposal department analyzes sales data to predict future sales; analyzes cost data to predict future costs; analyzes investment data to predict investment returns; and analyzes economic data to predict economic impacts. This allows for the development of financial strategies by generating financial forecast reports.

[0087] The proposal unit can generate investment opportunity analyses. For example, the proposal unit can collect investment data and evaluate investment opportunities. For example, the proposal unit can evaluate investment returns and risks to determine investment opportunities. Furthermore, the proposal unit can analyze market trends for investments and determine investment opportunities. In addition, the proposal unit can evaluate investment portfolios and determine investment opportunities. For example, the proposal unit can evaluate investment returns and risks to determine investment opportunities. It can analyze market trends for investments and determine investment opportunities. It can evaluate investment portfolios and determine investment opportunities. By generating investment opportunity analyses, investment strategies can be formulated.

[0088] The proposal department can generate cost reduction proposals. For example, the proposal department can collect cost data and evaluate the potential for reduction. For example, the proposal department can analyze the breakdown of costs and identify areas for reduction. Furthermore, the proposal department can propose cost reduction measures and develop implementation plans. In addition, the proposal department can evaluate the effectiveness of cost reductions and suggest areas for improvement. For example, the proposal department can analyze the breakdown of costs and identify areas for reduction. It can propose cost reduction measures and develop implementation plans. It can evaluate the effectiveness of cost reductions and suggest areas for improvement. By generating cost reduction proposals, the department can develop strategies for cost reduction.

[0089] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in the user's facial expressions and adjust the timing of data collection. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in the user's facial expressions and reduce the frequency of data collection if the user is stressed, thereby reducing the user's burden. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions.

[0090] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the unit can identify the most effective collection method from past data collection history and apply a similar method. The unit can also analyze past data collection history to identify areas for improvement in the collection method and optimize it. Furthermore, the unit can customize the collection method based on past data collection history and select the optimal method for specific situations. For example, the unit can identify the most effective collection method from past data collection history and apply a similar method. The unit can analyze past data collection history to identify areas for improvement in the collection method and optimize it. The unit can customize the collection method based on past data collection history and select the optimal method for specific situations. This allows the optimal collection method to be selected by analyzing past data collection history.

[0091] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting only data related to the user's current projects. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. The data collection unit can also dynamically filter and collect necessary data according to the user's project progress. For example, the data collection unit can prioritize collecting only data related to the user's current projects. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can dynamically filter and collect necessary data according to the user's project progress. This allows for the collection of highly relevant data by filtering data based on the user's current projects and areas of interest.

[0092] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, the unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The unit can also calculate an emotion score based on changes in the user's facial expressions and prioritize the data to collect. Furthermore, the unit can record the user's voice and estimate their emotions using voice analysis technology. In addition, the unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on changes in the user's facial expressions and prioritize collecting high-priority data if the user is stressed. If the user is relaxed, the unit prioritizes collecting detailed data. If the user is in a hurry, the unit prioritizes collecting data that can be collected quickly. This allows for the priority collection of important data by determining the data to collect according to the user's emotions.

[0093] 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 market data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. The data collection unit can also prioritize the collection of regional competitive information by considering the user's location information. For example, the data collection unit can prioritize the collection of market data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, the data collection unit can prioritize the collection of regional competitive information by considering the user's location information. This makes it possible to collect region-specific data by collecting data while considering the user's geographical location information.

[0094] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect relevant competitor information based on users' interests on social media. The data collection unit can also analyze users' social media activity history and collect relevant customer needs data. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect relevant competitor information based on users' interests on social media. For example, the data collection unit can analyze users' social media activity history and collect relevant customer needs data. In this way, relevant data can be collected by analyzing users' social media activity.

[0095] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions and adjust the presentation of the analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions and provide a simple and highly visual analysis result if the user is tense. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is in a hurry, the analysis unit provides a concise analysis result that gets straight to the point. In this way, 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.

[0096] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit will perform a detailed analysis on high-importance data and a concise analysis on low-importance data. Furthermore, the analysis unit can dynamically adjust the depth of the analysis according to the importance of the data. For example, the analysis unit will perform a detailed analysis on high-importance data and a concise analysis on low-importance data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data.

[0097] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, the analysis department can apply a trend analysis algorithm to market data. For example, the analysis department can apply a clustering algorithm to customer data. Furthermore, the analysis department can also apply a competitive analysis algorithm to competitor data. For example, the analysis department can apply a trend analysis algorithm to market data. For example, the analysis department can apply a clustering algorithm to customer data. For example, the analysis department can apply a competitive analysis algorithm to competitor data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions and adjust the length of the analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions and provide a short, concise analysis result if the user is in a hurry. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is excited, the analysis unit provides an analysis result with visually stimulating effects. In this way, by adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with the most optimal analysis result.

[0099] The analysis department can determine the priority of analysis based on the data collection timing. For example, the analysis department may prioritize the analysis of the most recent data. For example, the analysis department may prioritize the most recent data while also referring to historical data. The analysis department can also dynamically adjust the priority of analysis according to the data collection timing. For example, the analysis department may prioritize the analysis of the most recent data. For example, the analysis department may prioritize the analysis of the most recent data while also referring to historical data. The analysis department can dynamically adjust the priority of analysis according to the data collection timing. This allows the analysis department to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing.

[0100] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data. For example, the analysis unit can postpone analyzing less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data. For example, the analysis unit can postpone analyzing less relevant data. The analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0101] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expressions and adjust the way it presents suggestions. The suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expressions and provide simple, highly visible suggestions if the user is tense. If the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, the suggestion unit provides concise suggestions that get straight to the point. By adjusting the way it presents suggestions according to the user's emotions, it can provide suggestions that are easy for the user to understand.

[0102] The proposal department can adjust the level of detail of a proposal based on its importance. For example, it provides detailed explanations for high-importance proposals and concise explanations for low-importance proposals. Furthermore, the proposal department can dynamically adjust the level of detail of a proposal according to its importance. For example, it provides detailed explanations for high-importance proposals and concise explanations for low-importance proposals. By dynamically adjusting the level of detail based on the importance of the proposal, it can provide detailed explanations for important proposals.

[0103] The proposal department can apply different proposal algorithms depending on the category of the proposal content. For example, the proposal department can apply a personalized proposal algorithm to sales strategies. For example, the proposal department can apply a trend analysis algorithm to market forecasts. Furthermore, the proposal department can also apply a risk analysis algorithm to risk assessments. For example, the proposal department can apply a personalized proposal algorithm to sales strategies. For example, the proposal department can apply a trend analysis algorithm to market forecasts. For example, the proposal department can apply a risk analysis algorithm to risk assessments. By applying the most suitable proposal algorithm according to the category of the proposal content, the accuracy of the proposal is improved.

[0104] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expressions and adjust the length of the suggestions. The suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expressions and provide short, concise suggestions if the user is in a hurry. If the user is relaxed, the suggestion unit provides detailed suggestions. If the user is excited, the suggestion unit provides suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user.

[0105] The proposal department can determine the priority of proposals based on the submission timing of each proposal. For example, the proposal department will prioritize proposals that require immediate attention. The proposal department can dynamically adjust priorities according to the submission timing of each proposal. The proposal department can also provide proposals at the optimal time, taking into account the submission timing of each proposal. For example, the proposal department will prioritize proposals that require immediate attention. The proposal department can dynamically adjust priorities according to the submission timing of each proposal. The proposal department can provide proposals at the optimal time, taking into account the submission timing of each proposal. This allows for prioritizing proposals based on the submission timing of each proposal, thereby ensuring that proposals requiring immediate attention are provided first.

[0106] The proposal department can adjust the order of proposals based on the relevance of their content when submitting proposals. For example, the proposal department may prioritize highly relevant proposals. For example, the proposal department may postpone less relevant proposals. The proposal department can also dynamically adjust the order of proposals according to the relevance of their content. For example, the proposal department may prioritize highly relevant proposals. For example, the proposal department may postpone less relevant proposals. The proposal department dynamically adjusts the order of proposals according to the relevance of their content. This allows the proposal department to prioritize highly relevant proposals by adjusting the order of proposals based on the relevance of their content.

[0107] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, the feedback unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the feedback unit can calculate an emotion score based on changes in the user's facial expressions and adjust the feedback method. The feedback unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the feedback unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the feedback unit calculates an emotion score based on changes in the user's facial expressions and provides simple, highly visible feedback if the user is tense. If the user is relaxed, the feedback unit provides detailed feedback. If the user is in a hurry, the feedback unit provides concise, to-the-point feedback. In this way, by adjusting the feedback method according to the user's emotions, it is possible to provide feedback that is easy for the user to understand.

[0108] The feedback unit can optimize the feedback algorithm by referring to past feedback data during the feedback process. For example, the feedback unit can analyze past feedback data to identify the optimal feedback method. The feedback unit can also improve the feedback algorithm based on past feedback data. Furthermore, the feedback unit can improve the accuracy of feedback by referring to past feedback data. For example, the feedback unit can analyze past feedback data to identify the optimal feedback method. The feedback unit can improve the feedback algorithm based on past feedback data. The feedback unit can improve the accuracy of feedback by referring to past feedback data. In this way, by referring to past feedback data, the feedback algorithm can be optimized and the accuracy of feedback can be improved.

[0109] The feedback department can apply different feedback methods depending on the category of the feedback content. For example, the feedback department provides detailed feedback, including specific areas for improvement, for sales strategies. For example, the feedback department provides feedback, including an evaluation of forecast accuracy and areas for improvement, for market forecasts. Furthermore, the feedback department can also provide feedback, including a reassessment of risks and proposed countermeasures, for risk assessments. By applying the most appropriate feedback method according to the category of the feedback content, the accuracy of the feedback is improved.

[0110] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, the feedback unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the feedback unit can calculate an emotion score based on changes in the user's facial expressions and determine the priority of feedback. The feedback unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the feedback unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the feedback unit calculates an emotion score based on changes in the user's facial expressions and prioritizes providing high-priority feedback if the user is stressed. If the user is relaxed, the feedback unit prioritizes providing detailed feedback. If the user is in a hurry, the feedback unit prioritizes providing feedback that can be delivered quickly. In this way, by prioritizing feedback according to the user's emotions, important feedback can be provided preferentially.

[0111] The feedback unit can weight feedback based on when it is submitted. For example, the feedback unit prioritizes providing feedback of high urgency. The feedback unit can dynamically adjust the weighting according to the submission date of the feedback. Furthermore, the feedback unit can provide feedback at the optimal time, taking into account the submission date. For example, the feedback unit prioritizes providing feedback of high urgency. The feedback unit dynamically adjusts the weighting according to the submission date of the feedback. The feedback unit can provide feedback at the optimal time, taking into account the submission date of the feedback. This allows for prioritizing the provision of feedback of high urgency by weighting it based on the submission date.

[0112] The feedback unit can adjust the order of feedback based on the relevance of the feedback content. For example, the feedback unit may prioritize providing highly relevant feedback. For example, the feedback unit may postpone providing less relevant feedback. The feedback unit can also dynamically adjust the order of feedback according to the relevance of the feedback content. For example, the feedback unit may prioritize providing highly relevant feedback. For example, the feedback unit may postpone providing less relevant feedback. The feedback unit dynamically adjusts the order of feedback according to the relevance of the feedback content. This allows the system to prioritize providing highly relevant feedback by adjusting the order of feedback based on the relevance of the feedback content.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in the user's facial expressions and adjust the timing of data collection. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in the user's facial expressions and reduce the frequency of data collection if the user is stressed, thereby reducing the user's burden. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions.

[0115] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the unit can identify the most effective collection method from past data collection history and apply a similar method. The unit can also analyze past data collection history to identify areas for improvement in the collection method and optimize it. Furthermore, the unit can customize the collection method based on past data collection history and select the optimal method for specific situations. For example, the unit can identify the most effective collection method from past data collection history and apply a similar method. The unit can analyze past data collection history to identify areas for improvement in the collection method and optimize it. The unit can customize the collection method based on past data collection history and select the optimal method for specific situations. This allows the optimal collection method to be selected by analyzing past data collection history.

[0116] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting only data related to the user's current projects. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. The data collection unit can also dynamically filter and collect necessary data according to the user's project progress. For example, the data collection unit can prioritize collecting only data related to the user's current projects. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can dynamically filter and collect necessary data according to the user's project progress. This allows for the collection of highly relevant data by filtering data based on the user's current projects and areas of interest.

[0117] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, the unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The unit can also calculate an emotion score based on changes in the user's facial expressions and prioritize the data to collect. Furthermore, the unit can record the user's voice and estimate their emotions using voice analysis technology. In addition, the unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on changes in the user's facial expressions and prioritize collecting high-priority data if the user is stressed. If the user is relaxed, the unit prioritizes collecting detailed data. If the user is in a hurry, the unit prioritizes collecting data that can be collected quickly. This allows for the priority collection of important data by determining the data to collect according to the user's emotions.

[0118] 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 market data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. The data collection unit can also prioritize the collection of regional competitive information by considering the user's location information. For example, the data collection unit can prioritize the collection of market data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, the data collection unit can prioritize the collection of regional competitive information by considering the user's location information. This makes it possible to collect region-specific data by collecting data while considering the user's geographical location information.

[0119] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect relevant competitor information based on users' interests on social media. The data collection unit can also analyze users' social media activity history and collect relevant customer needs data. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect relevant competitor information based on users' interests on social media. For example, the data collection unit can analyze users' social media activity history and collect relevant customer needs data. In this way, relevant data can be collected by analyzing users' social media activity.

[0120] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions and adjust the presentation of the analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions and provide a simple and highly visual analysis result if the user is tense. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is in a hurry, the analysis unit provides a concise analysis result that gets straight to the point. In this way, 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.

[0121] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit will perform a detailed analysis on high-importance data and a concise analysis on low-importance data. Furthermore, the analysis unit can dynamically adjust the depth of the analysis according to the importance of the data. For example, the analysis unit will perform a detailed analysis on high-importance data and a concise analysis on low-importance data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data.

[0122] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, the analysis department can apply a trend analysis algorithm to market data. For example, the analysis department can apply a clustering algorithm to customer data. Furthermore, the analysis department can also apply a competitive analysis algorithm to competitor data. For example, the analysis department can apply a trend analysis algorithm to market data. For example, the analysis department can apply a clustering algorithm to customer data. For example, the analysis department can apply a competitive analysis algorithm to competitor data. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category.

[0123] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions and adjust the length of the analysis. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and cutaneous electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expressions and provide a short, concise analysis result if the user is in a hurry. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is excited, the analysis unit provides an analysis result with visually stimulating effects. In this way, by adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with the most optimal analysis result.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The data collection department collects internal and external data. This includes sales materials, internal services, external data, and internal success stories. For example, the data collection department collects records of past sales activities and customer information as sales materials, and information on services provided by various departments within the company as internal services. Furthermore, the data collection department collects information on competitors and market trends as external data, and collects past successful sales strategies and business plans as internal success stories. Step 2: The analysis unit integrates and analyzes the data collected by the collection unit. The analysis is performed using natural language processing techniques, text mining techniques, machine learning algorithms, and data mining techniques. For example, the analysis unit uses text mining techniques to analyze the data and extract important information. It uses machine learning algorithms to classify the data and discover patterns. It uses data mining techniques to analyze the correlations between the data. Step 3: The proposal department provides sales strategies and market forecasts based on the analysis results obtained by the analysis department. These sales strategies and market forecasts include personalized sales proposals, competitive analysis reports, customer needs forecasts, market trend reports, new business opportunity recommendations, risk assessments, and countermeasures. For example, the proposal department generates personalized sales proposals and provides them to sales representatives. It generates competitive analysis reports and provides them to management. It generates customer needs forecasts and provides them to the marketing department. Step 4: The Feedback Department provides feedback on the results of the strategies provided by the Proposal Department to improve the accuracy of the AI ​​model. Feedback is provided by collecting the results of the implemented sales strategies and market forecasts and feeding them back to the AI ​​agent. For example, the Feedback Department collects the results of the implemented sales strategies and feeds them back to the AI ​​agent. It collects the results of market forecasts and feeds them back to the AI ​​agent. It collects customer reactions and feedback and feeds them back to the AI ​​agent.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects internal and external data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides sales strategies and market forecasts based on the analysis results. The feedback unit is implemented by the control unit 46A of the smart device 14 and collects the results of the implemented strategies and provides feedback to improve the accuracy of the AI ​​model. 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.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 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.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the 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.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 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.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects internal and external data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides sales strategies and market forecasts based on the analysis results. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and collects the results of the implemented strategies and provides feedback to improve the accuracy of the AI ​​model. 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.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects internal and external data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides sales strategies and market forecasts based on the analysis results. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and collects the results of the implemented strategies and provides feedback to improve the accuracy of the AI ​​model. 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.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects internal and external data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides sales strategies and market forecasts based on the analysis results. The feedback unit is implemented by the control unit 46A of the robot 414 and collects the results of the implemented strategies and provides feedback to improve the accuracy of the AI ​​model. 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) The data collection department collects data from both inside and outside the company, An analysis unit integrates and analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis department, the proposal department provides sales strategies and market forecasts. The system includes a feedback unit that provides feedback on the results of the strategy provided by the proposed unit and improves the accuracy of the AI ​​model. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Generate personalized sales proposals The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Generate a competitive analysis report The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Generate customer needs predictions The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Generate a market trend report The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Generate recommendations for new business opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Generate risk assessments and countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, Generate a recruitment market trend report The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, Generate a talent suitability matching report. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, Generate employee engagement analysis The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, Generate financial forecast reports The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, Generate investment opportunity analysis The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, Generate a cost reduction proposal The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) 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 16) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) 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 19) 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 20) 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 21) 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 22) 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 23) 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 24) 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 25) 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 26) 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 27) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the proposed content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When submitting a proposal, a different proposal algorithm is applied depending on the category of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 29) 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 30) The aforementioned proposal section is, When submitting a proposal, the priority of the proposals will be determined based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When submitting proposals, adjust the order of the proposals based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is During the feedback process, the feedback algorithm is optimized by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is When providing feedback, apply different feedback methods depending on the category of the feedback content. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, weight the feedback based on when it was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned feedback unit is When providing feedback, adjust the order of feedback based on the relevance of the feedback content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 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. The data collection department collects data from both inside and outside the company, An analysis unit integrates and analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis department, the proposal department provides sales strategies and market forecasts. The system includes a feedback unit that provides feedback on the results of the strategy provided by the proposed unit and improves the accuracy of the AI ​​model. A system characterized by the following features.

2. The aforementioned proposal section is, Generate personalized sales proposals The system according to feature 1.

3. The aforementioned proposal section is, Generate a competitive analysis report The system according to feature 1.

4. The aforementioned proposal section is, Generate customer needs predictions The system according to feature 1.

5. The aforementioned proposal section is, Generate a market trend report The system according to feature 1.

6. The aforementioned proposal section is, Generate recommendations for new business opportunities. The system according to feature 1.

7. The aforementioned proposal section is, Generate risk assessments and countermeasures. The system according to feature 1.

8. The aforementioned proposal section is, Generate a recruitment market trend report The system according to feature 1.

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