system

Abstract

The system according to this embodiment aims to propose a strategic vision based on past data and to support the formulation process for each department. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects historical data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a strategic vision based on the analysis results obtained by the analysis unit. The support unit assists each department in formulating a strategic vision based on the strategic vision proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor 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 proposal of a strategic vision based on past data and the support for the planning work of each department have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to propose a strategic vision based on past data and support the planning work of each department.

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 support unit. The data collection unit collects historical data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a strategic vision based on the analysis results obtained by the analysis unit. The support unit assists each department in formulating a strategic vision based on the strategic vision proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose a strategic vision based on past data and support the formulation process for each department. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The strategic proposal system according to an embodiment of the present invention learns data on the company's direction and management strategies over the past ten years, interprets trends and market developments in the telecommunications industry and their impact on each department within the company, and proposes an organizational strategic vision based on the results. The strategic proposal system also uses AI to assist in the formulation process for each department. First, the strategic proposal system collects data on the company's direction and management strategies over the past ten years. This includes the company's annual reports, minutes of management meetings, and internal strategic documents. Next, the AI ​​learns from the collected data and analyzes trends and market developments in the telecommunications industry. For example, advances in telecommunications technology, the actions of competitors, and consumer behavior patterns are among the subjects of analysis. Based on the analysis results, the AI ​​interprets the impact on each department. For example, it analyzes how the introduction of new telecommunications technology will affect the sales department, and how the strategies of competitors will affect the marketing department. This makes it possible to grasp the specific impact on each department. Next, the strategic proposal system proposes an organizational strategic vision based on the analysis results. This strategic vision will take into account the company's direction and management strategies over the past ten years and will be consistent. For example, the system proposes strategies that build upon past successful strategies while adapting to new trends and market dynamics. Furthermore, the strategy proposal system also supports the formulation process for each department. Specifically, it devises measures for each department and automatically outputs them as reports. For instance, it proposes a sales strategy to the sales department in conjunction with the introduction of new communication technologies, and a marketing strategy to the marketing department based on the actions of competitors. This system enables business executives and those in positions to formulate strategic visions within organizations working in the telecommunications industry to efficiently grasp the latest technologies and market trends and formulate a consistent strategic vision. In addition, since AI also supports the formulation of specific measures for each department, it is expected that the execution of strategies will proceed smoothly. In this way, the strategy proposal system can analyze trends and market dynamics in the telecommunications industry based on past data, propose strategic visions for each department, and support the formulation process.

[0029] The strategic proposal system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects historical data. For example, the data collection unit collects company annual reports, minutes of management meetings, and internal strategic documents. The data collection unit stores this data in digital format and makes it available to the analysis unit. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes advances in communication technology, the trends of competitors, and consumer behavior patterns. Based on this data, the analysis unit interprets the impact on each department. The proposal unit proposes a strategic vision based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a strategy that follows successful strategies from the past while responding to new trends and market developments. The support unit supports the formulation work for each department based on the strategic vision proposed by the proposal unit. For example, the support unit devises measures for each department and automatically outputs them as a report. As a result, the strategic proposal system according to the embodiment can analyze trends and market movements in the telecommunications industry based on past data, propose strategic visions for each department, and support the formulation process.

[0030] The data collection department collects historical data. For example, it collects company annual reports, minutes of management meetings, and internal strategic documents. Since this data may exist in paper or digital formats, the department can use OCR (Optical Character Recognition) technology to digitize paper data. Furthermore, the department performs data verification and cleansing to ensure data reliability and consistency, such as removing duplicate data and supplementing incomplete data. The department stores this data digitally, making it available to the analysis department. The data is stored in cloud storage or databases and is encrypted and access-controlled for security. The department regularly updates the data to maintain the most up-to-date information. The department can also collaborate with external data sources, collecting external data such as industry reports and market research data. This allows the department to integrate internal and external data to build more comprehensive datasets.

[0031] The Analysis Department analyzes the data collected by the Data Collection Department. For example, the Analysis Department analyzes advancements in communication technology, competitor activity, and consumer behavior patterns. Based on this data, the Analysis Department interprets its impact on each department. Specifically, it utilizes AI-based data mining techniques to extract useful patterns and trends from large amounts of data. For example, it uses natural language processing (NLP) techniques to extract important keywords and phrases from management meeting minutes and strategic documents, and performs text analysis. It also uses machine learning algorithms to predict future trends from past data. For example, it analyzes consumer behavior patterns to predict future market trends. Furthermore, the Analysis Department uses data visualization tools to visually display analysis results as graphs and charts. This allows personnel in each department to intuitively understand data trends and anomalies. The Analysis Department regularly updates analysis results to provide the latest information. Additionally, the Analysis Department uses anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes a strategic vision based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes strategies that build upon past successful strategies while responding to new trends and market developments. Specifically, it utilizes AI-based simulation technology to examine multiple strategic scenarios. For example, it assumes different market conditions and competitive situations and simulates the effectiveness of strategies for each scenario. Based on these simulation results, the Proposal Department selects the most effective strategy. The Proposal Department also evaluates the feasibility and risks of the strategies and proposes risk management measures. For example, it evaluates the risks associated with the introduction of new technologies and market fluctuations and takes countermeasures. The Proposal Department translates the strategic vision into concrete action plans and provides implementation instructions to each department. This allows the Proposal Department to make concrete strategic proposals based on analysis results and improve the overall competitiveness of the company. Furthermore, the Proposal Department monitors the progress of the proposed strategies and modifies or improves them as needed. This allows the Proposal Department to always make flexible strategic proposals based on the latest information and support the sustainable growth of the company.

[0033] The Support Department assists each department in formulating its own strategies based on the strategic vision proposed by the Proposal Department. For example, the Support Department devises measures for each department and automatically outputs them as reports. Specifically, it utilizes AI-powered project management tools to automatically generate tasks and schedules for each department. For example, it considers the resources and capabilities of each department to make optimal task assignments. The Support Department also monitors progress in real time and takes immediate action if delays or problems occur. For example, it visualizes the progress of tasks and reallocates resources or changes task priorities if delays occur. Furthermore, the Support Department collects feedback from each department and improves the measures. For example, it evaluates the effectiveness of the measures based on feedback from each department and modifies or improves the measures as needed. The Support Department centrally manages this information and reports it regularly to management. This allows the Support Department to effectively support the implementation of the proposed strategic vision and support the achievement of the company's overall goals. In addition, the Support Department facilitates communication between departments and promotes information sharing. For example, it holds regular meetings and workshops to share the progress and challenges of each department. This will allow the support department to strengthen the collaborative system across the entire company and facilitate the smooth implementation of strategies.

[0034] The data collection unit can collect company annual reports, minutes of management meetings, and internal strategic documents. For example, the data collection unit can collect company annual reports, including financial data, performance reports, and future outlooks. The data collection unit can collect minutes of management meetings, including meeting agendas, decisions, and participants' opinions. The data collection unit can collect internal strategic documents, including strategic plans, goal setting, and implementation plans. This allows for more accurate analysis by collecting data on the company's past direction and management strategy. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can scan annual reports and minutes and convert them into text data using AI.

[0035] The analysis unit can analyze advancements in communication technology, competitor activities, and consumer behavior patterns. For example, the analysis unit can analyze advancements in communication technology, including new communication protocols, hardware advancements, and software updates. The analysis unit can analyze competitor activities, including new products, marketing strategies, and performance. The analysis unit can analyze consumer behavior patterns, including purchase history, website browsing history, and survey results. This allows for the analysis of trends and market developments in the telecommunications industry, enabling the understanding of their impact on each department. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into an AI, which can then analyze the data and output results.

[0036] The proposal department can propose strategies that build upon past successful strategies while also addressing new trends and market dynamics. For example, based on past successful strategies, the proposal department can propose strategies that increase sales, improve customer satisfaction, and reduce costs. It can also analyze new trends and market dynamics, including the introduction of new technologies, growth areas in the market, and changes in consumer preferences. This allows the proposal department to propose strategies that address new trends and market dynamics while building upon past successes. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input historical data and new trends into an AI, which can then propose the optimal strategy.

[0037] The support department can devise strategies for each department and automatically output them as reports. For example, the support department can devise strategies such as marketing campaigns, product development plans, and resource allocation. The support department outputs these strategies as reports in formats such as PDF, Excel, and web dashboards. This allows for the smooth execution of strategies by automatically outputting specific strategies for each department as reports. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can entrust the strategy devising to AI, and the AI ​​can automatically generate reports.

[0038] The data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of data sources and prioritize the collection of data from reliable sources. The data collection unit can check the consistency of the data and prioritize the collection of consistent data. The data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. This improves the accuracy of the analysis results by prioritizing the collection of highly reliable data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can entrust the evaluation of data sources to AI, which can then select highly reliable data.

[0039] The data collection unit can dynamically change the types of data it collects based on the company's current strategic objectives. For example, if the company's strategic objectives change, the data collection unit will automatically change the types of data it collects. If a new project is launched, the data collection unit will prioritize collecting data related to that project. If market trends change, the data collection unit will collect data that corresponds to those changes. This allows for more appropriate data collection by changing the types of data collected according to the company's strategic objectives. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input changes in strategic objectives into the AI, and the AI ​​can dynamically change the types of data it collects.

[0040] The data collection unit can divide the scope of data to be collected according to the company's geographical locations. For example, the data collection unit can set different data collection criteria for each geographical location to efficiently collect data. The data collection unit adjusts the types of data to be collected, taking into account the characteristics of each geographical location. The data collection unit collects data that reflects market trends for each geographical location. By collecting data for each geographical location, it becomes possible to collect data that takes into account the characteristics of each region. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can set data collection criteria for each geographical location in AI, and the AI ​​can automatically collect the data.

[0041] The data collection unit can collect data in formats that include not only text data, but also audio and video data. For example, the data collection unit can collect audio data from meetings in addition to text data and use it for analysis. The data collection unit can also collect video data from video conferences and use non-verbal information for analysis. The data collection unit can convert audio data to text and integrate it with the text data for analysis. This allows for the use of a wider variety of data for analysis by collecting not only text data, but also audio and video data. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not be performed using AI. For example, the data collection unit can input audio data and video data into a generating AI, which can then analyze the data.

[0042] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between data during the analysis. For example, the analysis unit can analyze the correlations between data to obtain more accurate results. The analysis unit integrates information from different data sources and analyzes their interrelationships. The analysis unit analyzes time-series data and considers temporal correlations. By considering the interrelationships between data, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have AI analyze the correlations between data and output results that the AI ​​has taken into account.

[0043] The analysis unit can perform analysis while considering the temporal changes in the data. For example, the analysis unit can analyze temporal trends based on past data. The analysis unit can perform analysis while considering seasonal variations in the data. The analysis unit can analyze long-term changes in the data and make future predictions. By considering the temporal changes in the data, it becomes possible to perform analysis that reflects trends and long-term changes. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input time-series data into AI, and the AI ​​can perform analysis that takes temporal changes into account.

[0044] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can analyze geographical data to understand the characteristics of each region. The analysis unit can analyze the correlation of data while considering the geographical distribution. The analysis unit can analyze geographical trends and formulate strategies for each region. This makes it possible to perform analysis that reflects the characteristics of each region by considering the geographical distribution. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical data into AI, and the AI ​​can perform analysis while considering the geographical distribution.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant external data (e.g., industry reports and market research data) during the analysis process. For example, the analysis unit may refer to industry reports and reflect them in the analysis results. The analysis unit may refer to market research data to improve the accuracy of the analysis. The analysis unit may integrate external data to perform a more comprehensive analysis. This enables a more comprehensive analysis by referring to relevant external data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input external data into AI, which can then analyze the data and output results.

[0046] The proposal department can propose the optimal strategy by comparing past success and failure cases when making a proposal. For example, the proposal department can propose the optimal strategy for similar situations based on past success cases. The proposal department can analyze past failure cases and propose strategies to avoid the same mistakes. The proposal department compares success and failure cases and proposes the most effective strategy. In this way, by comparing past success and failure cases, the most effective strategy can be proposed. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input past success and failure cases into AI, and the AI ​​can propose the optimal strategy.

[0047] The proposal department can propose customized strategies that take into account the characteristics of each department. For example, the proposal department can propose the optimal strategy considering the work content of each department. The proposal department can propose an actionable strategy considering the resource situation of each department. The proposal department can propose an effective strategy based on the past performance of each department. In this way, by taking into account the characteristics of each department, it is possible to propose more actionable and effective strategies. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input characteristic data for each department into the AI, and the AI ​​can propose a customized strategy.

[0048] The proposal department can propose different strategies for each of the company's geographical locations when making proposals. For example, the proposal department can propose the optimal strategy considering the market trends of each geographical location. The proposal department can propose a customized strategy considering the characteristics of each geographical location. The proposal department can propose an effective strategy considering the competitive situation of each geographical location. In this way, by proposing different strategies for each geographical location, it is possible to formulate strategies that take into account the characteristics of each region. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input data for each geographical location into the AI, and the AI ​​can propose a strategy that takes into account the characteristics of each region.

[0049] The proposal department can improve the accuracy of its proposals by referring to relevant external data (e.g., industry reports and market research data) when making proposals. For example, the proposal department may refer to industry reports and incorporate them into its proposals. The proposal department may refer to market research data to improve the accuracy of its proposals. The proposal department may integrate external data to make more comprehensive proposals. This allows for more comprehensive and accurate proposals by referring to relevant external data. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department may input external data into an AI, which may analyze the data and output proposals.

[0050] The support department can devise optimal measures by referring to the past performance data of each department when providing support. For example, the support department can analyze the past performance data of each department and propose optimal measures. The support department can devise optimal measures for similar situations based on past success stories. The support department can analyze past failure stories and devise measures to avoid the same mistakes. In this way, by referring to the past performance data of each department, more effective measures can be devised. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input past performance data into AI, and the AI ​​can devise optimal measures.

[0051] The support department can customize measures when providing support, taking into account the resource situation of each department. For example, the support department can understand the resource situation of each department and propose feasible measures. If resources are insufficient, the support department can propose efficient resource allocation. If resources are abundant, the support department can propose more proactive measures. In this way, by considering the resource situation of each department, it is possible to propose feasible and effective measures. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input resource situation data into AI, and the AI ​​can propose customized measures.

[0052] The support department can devise different measures for each department's geographical location when providing support. For example, the support department can propose the optimal measures considering the market trends of each geographical location. The support department can propose customized measures considering the characteristics of each geographical location. The support department can propose effective measures considering the competitive situation of each geographical location. In this way, by devising different measures for each geographical location, it is possible to propose measures that take into account the characteristics of each region. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input data for each geographical location into AI, and the AI ​​can propose measures that take into account the characteristics of each region.

[0053] The support department can improve the accuracy of its strategies by referring to relevant external data (e.g., industry reports and market research data) during the support process. For example, the support department may refer to industry reports and incorporate them into its strategies. The support department may refer to market research data to improve the accuracy of its strategies. The support department may integrate external data to devise more comprehensive strategies. This allows the support department to devise more comprehensive and accurate strategies by referring to relevant external data. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department may input external data into an AI, which may analyze the data and output strategies.

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

[0055] The strategy proposal system may further include a feedback collection unit. The feedback collection unit collects feedback from each department and evaluates the implementation status and effectiveness of the proposed strategy. For example, it collects feedback from the sales department to evaluate how the introduction of new communication technology has affected sales activities. It also collects feedback from the marketing department to evaluate how effective the marketing strategy based on the actions of competitors has been. This allows the strategy proposal system to understand the implementation status of the proposed strategy and modify the strategy as needed. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input feedback from each department into an AI, which can analyze the feedback and suggest modifications to the strategy.

[0056] The strategy proposal system may further include a simulation unit. The simulation unit simulates the proposed strategy in a virtual environment and evaluates its effects in advance. For example, it may simulate the impact of introducing new communication technology on the sales department and evaluate the expected increase in sales and improvement in customer satisfaction. It may also simulate marketing strategies and predict the market's reaction to the actions of competitors. This allows the strategy proposal system to evaluate the effectiveness of the proposed strategy in advance and minimize risks. Some or all of the above-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input the proposed strategy into an AI, which can then perform the simulation in a virtual environment.

[0057] The strategy proposal system may further include a risk assessment unit. The risk assessment unit evaluates the risks associated with the proposed strategy and proposes risk management measures. For example, it evaluates the technical and market risks associated with the introduction of new communication technologies and proposes measures to mitigate those risks. It also evaluates the competitive and brand risks associated with marketing strategies and proposes measures to minimize those risks. This allows the strategy proposal system to pre-evaluate the risks of the proposed strategy and implement risk management measures. Some or all of the above-described processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input the proposed strategy into an AI, which can then evaluate the risks and propose management measures.

[0058] The strategy proposal system may also include a training section. The training section trains employees in each department on how to implement the proposed strategies. For example, it trains sales department employees on sales techniques related to the introduction of new communication technologies, and trains marketing department employees on how to implement marketing strategies. This allows the strategy proposal system to provide training to ensure the smooth implementation of the proposed strategies. Some or all of the above processes in the training section may be performed using AI or not. For example, the training section can input the proposed strategy implementation methods into an AI, which can then generate a training program.

[0059] The strategy proposal system may also include a monitoring unit. The monitoring unit monitors the implementation status of the proposed strategy in real time and proposes modifications as needed. For example, it may monitor whether the introduction of new communication technology is progressing as planned and quickly propose countermeasures if problems arise. It may also monitor the implementation status of marketing strategies and modify them in accordance with the actions of competitors. This allows the strategy proposal system to grasp the implementation status of the proposed strategy in real time and respond flexibly. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the implementation status of the proposed strategy into the AI, which can then perform real-time monitoring.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection department collects historical data. For example, it collects company annual reports, minutes of management meetings, and internal strategic documents, and stores this data in digital format. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes advances in communication technology, the activities of competitors, and consumer behavior patterns to understand their impact on each department. Step 3: The proposal team proposes a strategic vision based on the analysis results obtained by the analysis team. For example, they might propose a strategy that follows past successful strategies while also addressing new trends and market developments. Step 4: The support department assists each department in formulating its own strategies based on the strategic vision proposed by the proposal department. For example, it devises measures for each department and automatically outputs them as reports.

[0062] (Example of form 2) The strategic proposal system according to an embodiment of the present invention learns data on the company's direction and management strategies over the past ten years, interprets trends and market developments in the telecommunications industry and their impact on each department within the company, and proposes an organizational strategic vision based on the results. The strategic proposal system also uses AI to assist in the formulation process for each department. First, the strategic proposal system collects data on the company's direction and management strategies over the past ten years. This includes the company's annual reports, minutes of management meetings, and internal strategic documents. Next, the AI ​​learns from the collected data and analyzes trends and market developments in the telecommunications industry. For example, advances in telecommunications technology, the actions of competitors, and consumer behavior patterns are among the subjects of analysis. Based on the analysis results, the AI ​​interprets the impact on each department. For example, it analyzes how the introduction of new telecommunications technology will affect the sales department, and how the strategies of competitors will affect the marketing department. This makes it possible to grasp the specific impact on each department. Next, the strategic proposal system proposes an organizational strategic vision based on the analysis results. This strategic vision will take into account the company's direction and management strategies over the past ten years and will be consistent. For example, the system proposes strategies that build upon past successful strategies while adapting to new trends and market dynamics. Furthermore, the strategy proposal system also supports the formulation process for each department. Specifically, it devises measures for each department and automatically outputs them as reports. For instance, it proposes a sales strategy to the sales department in conjunction with the introduction of new communication technologies, and a marketing strategy to the marketing department based on the actions of competitors. This system enables business executives and those in positions to formulate strategic visions within organizations working in the telecommunications industry to efficiently grasp the latest technologies and market trends and formulate a consistent strategic vision. In addition, since AI also supports the formulation of specific measures for each department, it is expected that the execution of strategies will proceed smoothly. In this way, the strategy proposal system can analyze trends and market dynamics in the telecommunications industry based on past data, propose strategic visions for each department, and support the formulation process.

[0063] The strategic proposal system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a support unit. The data collection unit collects historical data. For example, the data collection unit collects company annual reports, minutes of management meetings, and internal strategic documents. The data collection unit stores this data in digital format and makes it available to the analysis unit. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes advances in communication technology, the trends of competitors, and consumer behavior patterns. Based on this data, the analysis unit interprets the impact on each department. The proposal unit proposes a strategic vision based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a strategy that follows successful strategies from the past while responding to new trends and market developments. The support unit supports the formulation work for each department based on the strategic vision proposed by the proposal unit. For example, the support unit devises measures for each department and automatically outputs them as a report. As a result, the strategic proposal system according to the embodiment can analyze trends and market movements in the telecommunications industry based on past data, propose strategic visions for each department, and support the formulation process.

[0064] The data collection department collects historical data. For example, it collects company annual reports, minutes of management meetings, and internal strategic documents. Since this data may exist in paper or digital formats, the department can use OCR (Optical Character Recognition) technology to digitize paper data. Furthermore, the department performs data verification and cleansing to ensure data reliability and consistency, such as removing duplicate data and supplementing incomplete data. The department stores this data digitally, making it available to the analysis department. The data is stored in cloud storage or databases and is encrypted and access-controlled for security. The department regularly updates the data to maintain the most up-to-date information. The department can also collaborate with external data sources, collecting external data such as industry reports and market research data. This allows the department to integrate internal and external data to build more comprehensive datasets.

[0065] The Analysis Department analyzes the data collected by the Data Collection Department. For example, the Analysis Department analyzes advancements in communication technology, competitor activity, and consumer behavior patterns. Based on this data, the Analysis Department interprets its impact on each department. Specifically, it utilizes AI-based data mining techniques to extract useful patterns and trends from large amounts of data. For example, it uses natural language processing (NLP) techniques to extract important keywords and phrases from management meeting minutes and strategic documents, and performs text analysis. It also uses machine learning algorithms to predict future trends from past data. For example, it analyzes consumer behavior patterns to predict future market trends. Furthermore, the Analysis Department uses data visualization tools to visually display analysis results as graphs and charts. This allows personnel in each department to intuitively understand data trends and anomalies. The Analysis Department regularly updates analysis results to provide the latest information. Additionally, the Analysis Department uses anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The Proposal Department proposes a strategic vision based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes strategies that build upon past successful strategies while responding to new trends and market developments. Specifically, it utilizes AI-based simulation technology to examine multiple strategic scenarios. For example, it assumes different market conditions and competitive situations and simulates the effectiveness of strategies for each scenario. Based on these simulation results, the Proposal Department selects the most effective strategy. The Proposal Department also evaluates the feasibility and risks of the strategies and proposes risk management measures. For example, it evaluates the risks associated with the introduction of new technologies and market fluctuations and takes countermeasures. The Proposal Department translates the strategic vision into concrete action plans and provides implementation instructions to each department. This allows the Proposal Department to make concrete strategic proposals based on analysis results and improve the overall competitiveness of the company. Furthermore, the Proposal Department monitors the progress of the proposed strategies and modifies or improves them as needed. This allows the Proposal Department to always make flexible strategic proposals based on the latest information and support the sustainable growth of the company.

[0067] The Support Department assists each department in formulating its own strategies based on the strategic vision proposed by the Proposal Department. For example, the Support Department devises measures for each department and automatically outputs them as reports. Specifically, it utilizes AI-powered project management tools to automatically generate tasks and schedules for each department. For example, it considers the resources and capabilities of each department to make optimal task assignments. The Support Department also monitors progress in real time and takes immediate action if delays or problems occur. For example, it visualizes the progress of tasks and reallocates resources or changes task priorities if delays occur. Furthermore, the Support Department collects feedback from each department and improves the measures. For example, it evaluates the effectiveness of the measures based on feedback from each department and modifies or improves the measures as needed. The Support Department centrally manages this information and reports it regularly to management. This allows the Support Department to effectively support the implementation of the proposed strategic vision and support the achievement of the company's overall goals. In addition, the Support Department facilitates communication between departments and promotes information sharing. For example, it holds regular meetings and workshops to share the progress and challenges of each department. This will allow the support department to strengthen the collaborative system across the entire company and facilitate the smooth implementation of strategies.

[0068] The data collection unit can collect company annual reports, minutes of management meetings, and internal strategic documents. For example, the data collection unit can collect company annual reports, including financial data, performance reports, and future outlooks. The data collection unit can collect minutes of management meetings, including meeting agendas, decisions, and participants' opinions. The data collection unit can collect internal strategic documents, including strategic plans, goal setting, and implementation plans. This allows for more accurate analysis by collecting data on the company's past direction and management strategy. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can scan annual reports and minutes and convert them into text data using AI.

[0069] The analysis unit can analyze advancements in communication technology, competitor activities, and consumer behavior patterns. For example, the analysis unit can analyze advancements in communication technology, including new communication protocols, hardware advancements, and software updates. The analysis unit can analyze competitor activities, including new products, marketing strategies, and performance. The analysis unit can analyze consumer behavior patterns, including purchase history, website browsing history, and survey results. This allows for the analysis of trends and market developments in the telecommunications industry, enabling the understanding of their impact on each department. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into an AI, which can then analyze the data and output results.

[0070] The proposal department can propose strategies that build upon past successful strategies while also addressing new trends and market dynamics. For example, based on past successful strategies, the proposal department can propose strategies that increase sales, improve customer satisfaction, and reduce costs. It can also analyze new trends and market dynamics, including the introduction of new technologies, growth areas in the market, and changes in consumer preferences. This allows the proposal department to propose strategies that address new trends and market dynamics while building upon past successes. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input historical data and new trends into an AI, which can then propose the optimal strategy.

[0071] The support department can devise strategies for each department and automatically output them as reports. For example, the support department can devise strategies such as marketing campaigns, product development plans, and resource allocation. The support department outputs these strategies as reports in formats such as PDF, Excel, and web dashboards. This allows for the smooth execution of strategies by automatically outputting specific strategies for each department as reports. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can entrust the strategy devising to AI, and the AI ​​can automatically generate reports.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection to alleviate the user's burden. If the user is relaxed, the data collection unit performs detailed data collection to obtain more information. If the user is in a hurry, the data collection unit prioritizes collecting only important data and processes it quickly. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the timing of data collection based on the result.

[0073] The data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of data sources and prioritize the collection of data from reliable sources. The data collection unit can check the consistency of the data and prioritize the collection of consistent data. The data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. This improves the accuracy of the analysis results by prioritizing the collection of highly reliable data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can entrust the evaluation of data sources to AI, which can then select highly reliable data.

[0074] The data collection unit can dynamically change the types of data it collects based on the company's current strategic objectives. For example, if the company's strategic objectives change, the data collection unit will automatically change the types of data it collects. If a new project is launched, the data collection unit will prioritize collecting data related to that project. If market trends change, the data collection unit will collect data that corresponds to those changes. This allows for more appropriate data collection by changing the types of data collected according to the company's strategic objectives. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input changes in strategic objectives into the AI, and the AI ​​can dynamically change the types of data it collects.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI, which will estimate the emotions and determine the priority of data to collect based on the result.

[0076] The data collection unit can divide the scope of data to be collected according to the company's geographical locations. For example, the data collection unit can set different data collection criteria for each geographical location to efficiently collect data. The data collection unit adjusts the types of data to be collected, taking into account the characteristics of each geographical location. The data collection unit collects data that reflects market trends for each geographical location. By collecting data for each geographical location, it becomes possible to collect data that takes into account the characteristics of each region. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can set data collection criteria for each geographical location in AI, and the AI ​​can automatically collect the data.

[0077] The data collection unit can collect data in formats that include not only text data, but also audio and video data. For example, the data collection unit can collect audio data from meetings in addition to text data and use it for analysis. The data collection unit can also collect video data from video conferences and use non-verbal information for analysis. The data collection unit can convert audio data to text and integrate it with the text data for analysis. This allows for the use of a wider variety of data for analysis by collecting not only text data, but also audio and video data. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not be performed using AI. For example, the data collection unit can input audio data and video data into a generating AI, which can then analyze the data.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit provides a display method that includes detailed information. If the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI, the generative AI can estimate the emotions, and the display method of the analysis results can be adjusted based on the result.

[0079] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between data during the analysis. For example, the analysis unit can analyze the correlations between data to obtain more accurate results. The analysis unit integrates information from different data sources and analyzes their interrelationships. The analysis unit analyzes time-series data and considers temporal correlations. By considering the interrelationships between data, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have AI analyze the correlations between data and output results that the AI ​​has taken into account.

[0080] The analysis unit can perform analysis while considering the temporal changes in the data. For example, the analysis unit can analyze temporal trends based on past data. The analysis unit can perform analysis while considering seasonal variations in the data. The analysis unit can analyze long-term changes in the data and make future predictions. By considering the temporal changes in the data, it becomes possible to perform analysis that reflects trends and long-term changes. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input time-series data into AI, and the AI ​​can perform analysis that takes temporal changes into account.

[0081] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying only important analysis results. If the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. If the user is in a hurry, the analysis unit will prioritize displaying analysis results that can be quickly understood. In this way, important information can be prioritized by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user facial expression data into a generative AI, the generative AI can estimate emotions, and the analysis results can be prioritized based on the result.

[0082] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can analyze geographical data to understand the characteristics of each region. The analysis unit can analyze the correlation of data while considering the geographical distribution. The analysis unit can analyze geographical trends and formulate strategies for each region. This makes it possible to perform analysis that reflects the characteristics of each region by considering the geographical distribution. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical data into AI, and the AI ​​can perform analysis while considering the geographical distribution.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant external data (e.g., industry reports and market research data) during the analysis process. For example, the analysis unit may refer to industry reports and reflect them in the analysis results. The analysis unit may refer to market research data to improve the accuracy of the analysis. The analysis unit may integrate external data to perform a more comprehensive analysis. This enables a more comprehensive analysis by referring to relevant external data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input external data into AI, which can then analyze the data and output results.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit will present simple and easily visible suggestions. If the user is relaxed, the suggestion unit will present suggestions that include detailed information. If the user is in a hurry, the suggestion unit will present suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, the generative AI can estimate the emotions, and the way suggestions are presented can be adjusted based on the result.

[0085] The proposal department can propose the optimal strategy by comparing past success and failure cases when making a proposal. For example, the proposal department can propose the optimal strategy for similar situations based on past success cases. The proposal department can analyze past failure cases and propose strategies to avoid the same mistakes. The proposal department compares success and failure cases and proposes the most effective strategy. In this way, by comparing past success and failure cases, the most effective strategy can be proposed. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input past success and failure cases into AI, and the AI ​​can propose the optimal strategy.

[0086] The proposal department can propose customized strategies that take into account the characteristics of each department. For example, the proposal department can propose the optimal strategy considering the work content of each department. The proposal department can propose an actionable strategy considering the resource situation of each department. The proposal department can propose an effective strategy based on the past performance of each department. In this way, by taking into account the characteristics of each department, it is possible to propose more actionable and effective strategies. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input characteristic data for each department into the AI, and the AI ​​can propose a customized strategy.

[0087] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize only important suggestions. If the user is relaxed, the suggestion unit will prioritize detailed suggestions. If the user is in a hurry, the suggestion unit will prioritize suggestions that can be quickly understood. This allows for prioritizing important suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which will estimate the emotions and determine the priority of suggestions based on the result.

[0088] The proposal department can propose different strategies for each of the company's geographical locations when making proposals. For example, the proposal department can propose the optimal strategy considering the market trends of each geographical location. The proposal department can propose a customized strategy considering the characteristics of each geographical location. The proposal department can propose an effective strategy considering the competitive situation of each geographical location. In this way, by proposing different strategies for each geographical location, it is possible to formulate strategies that take into account the characteristics of each region. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input data for each geographical location into the AI, and the AI ​​can propose a strategy that takes into account the characteristics of each region.

[0089] The proposal department can improve the accuracy of its proposals by referring to relevant external data (e.g., industry reports and market research data) when making proposals. For example, the proposal department may refer to industry reports and incorporate them into its proposals. The proposal department may refer to market research data to improve the accuracy of its proposals. The proposal department may integrate external data to make more comprehensive proposals. This allows for more comprehensive and accurate proposals by referring to relevant external data. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department may input external data into an AI, which may analyze the data and output proposals.

[0090] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is nervous, the support unit provides a simple and easily visible support method. If the user is relaxed, the support unit provides a support method that includes detailed information. If the user is in a hurry, the support unit provides a concise support method. By adjusting the support method according to the user's emotions, the support unit can provide the optimal support for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the user's facial expression data into a generative AI, which can estimate the emotion and adjust the support method based on the result.

[0091] The support department can devise optimal measures by referring to the past performance data of each department when providing support. For example, the support department can analyze the past performance data of each department and propose optimal measures. The support department can devise optimal measures for similar situations based on past success stories. The support department can analyze past failure stories and devise measures to avoid the same mistakes. In this way, by referring to the past performance data of each department, more effective measures can be devised. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input past performance data into AI, and the AI ​​can devise optimal measures.

[0092] The support department can customize measures when providing support, taking into account the resource situation of each department. For example, the support department can understand the resource situation of each department and propose feasible measures. If resources are insufficient, the support department can propose efficient resource allocation. If resources are abundant, the support department can propose more proactive measures. In this way, by considering the resource situation of each department, it is possible to propose feasible and effective measures. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input resource situation data into AI, and the AI ​​can propose customized measures.

[0093] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize only important support. If the user is relaxed, the support unit will prioritize detailed support. If the user is in a hurry, the support unit will prioritize support that can be quickly understood. In this way, by determining the priority of support according to the user's emotions, important support can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user facial expression data into a generative AI, the generative AI can estimate emotions, and the support priority can be determined based on the result.

[0094] The support department can devise different measures for each department's geographical location when providing support. For example, the support department can propose the optimal measures considering the market trends of each geographical location. The support department can propose customized measures considering the characteristics of each geographical location. The support department can propose effective measures considering the competitive situation of each geographical location. In this way, by devising different measures for each geographical location, it is possible to propose measures that take into account the characteristics of each region. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input data for each geographical location into AI, and the AI ​​can propose measures that take into account the characteristics of each region.

[0095] The support department can improve the accuracy of its strategies by referring to relevant external data (e.g., industry reports and market research data) during the support process. For example, the support department may refer to industry reports and incorporate them into its strategies. The support department may refer to market research data to improve the accuracy of its strategies. The support department may integrate external data to devise more comprehensive strategies. This allows the support department to devise more comprehensive and accurate strategies by referring to relevant external data. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department may input external data into an AI, which may analyze the data and output strategies.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The strategy proposal system may further include a feedback collection unit. The feedback collection unit collects feedback from each department and evaluates the implementation status and effectiveness of the proposed strategy. For example, it collects feedback from the sales department to evaluate how the introduction of new communication technology has affected sales activities. It also collects feedback from the marketing department to evaluate how effective the marketing strategy based on the actions of competitors has been. This allows the strategy proposal system to understand the implementation status of the proposed strategy and modify the strategy as needed. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input feedback from each department into an AI, which can analyze the feedback and suggest modifications to the strategy.

[0098] The strategy proposal system may further include a simulation unit. The simulation unit simulates the proposed strategy in a virtual environment and evaluates its effects in advance. For example, it may simulate the impact of introducing new communication technology on the sales department and evaluate the expected increase in sales and improvement in customer satisfaction. It may also simulate marketing strategies and predict the market's reaction to the actions of competitors. This allows the strategy proposal system to evaluate the effectiveness of the proposed strategy in advance and minimize risks. Some or all of the above-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input the proposed strategy into an AI, which can then perform the simulation in a virtual environment.

[0099] The strategy proposal system may further include a risk assessment unit. The risk assessment unit evaluates the risks associated with the proposed strategy and proposes risk management measures. For example, it evaluates the technical and market risks associated with the introduction of new communication technologies and proposes measures to mitigate those risks. It also evaluates the competitive and brand risks associated with marketing strategies and proposes measures to minimize those risks. This allows the strategy proposal system to pre-evaluate the risks of the proposed strategy and implement risk management measures. Some or all of the above-described processes in the risk assessment unit may be performed using AI or not. For example, the risk assessment unit can input the proposed strategy into an AI, which can then evaluate the risks and propose management measures.

[0100] The strategy proposal system may also include a training section. The training section trains employees in each department on how to implement the proposed strategies. For example, it trains sales department employees on sales techniques related to the introduction of new communication technologies, and trains marketing department employees on how to implement marketing strategies. This allows the strategy proposal system to provide training to ensure the smooth implementation of the proposed strategies. Some or all of the above processes in the training section may be performed using AI or not. For example, the training section can input the proposed strategy implementation methods into an AI, which can then generate a training program.

[0101] The strategy proposal system may also include a monitoring unit. The monitoring unit monitors the implementation status of the proposed strategy in real time and proposes modifications as needed. For example, it may monitor whether the introduction of new communication technology is progressing as planned and quickly propose countermeasures if problems arise. It may also monitor the implementation status of marketing strategies and modify them in accordance with the actions of competitors. This allows the strategy proposal system to grasp the implementation status of the proposed strategy in real time and respond flexibly. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the implementation status of the proposed strategy into the AI, which can then perform real-time monitoring.

[0102] The strategy suggestion system can estimate the user's emotions and customize its suggestions based on those emotions. For example, if the user is stressed, it might suggest a simple and easy-to-implement strategy. If the user is relaxed, it might suggest a strategy with detailed information. If the user is in a hurry, it might suggest a strategy that can be implemented quickly. In this way, the strategy suggestion system can customize its suggestions according to the user's emotions and provide the optimal strategy for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and customize the suggestions based on the result.

[0103] The strategic suggestion system can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the frequency of suggestions can be reduced to lessen the user's burden. If the user is relaxed, detailed suggestions can be made more frequently to provide more information. If the user is in a hurry, only important suggestions can be made quickly. In this way, the strategic suggestion system can adjust the timing of suggestions according to the user's emotions and make suggestions at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the timing of suggestions based on the result.

[0104] The strategic suggestion system can estimate the user's emotions and adjust the format of its suggestions based on those emotions. For example, if the user is stressed, it can provide suggestions using visually easy-to-understand graphs and charts. If the user is relaxed, it can provide detailed text-based suggestions. If the user is in a hurry, it can provide suggestions in a concise, bullet-point format. In this way, the strategic suggestion system can adjust the format of its suggestions according to the user's emotions, making them easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the format of the suggestions based on the result.

[0105] The strategy suggestion system can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, it can suggest a low-risk strategy. If the user is relaxed, it can suggest a challenging strategy. If the user is in a hurry, it can suggest a strategy that can be implemented quickly. In this way, the strategy suggestion system can adjust the content of its suggestions according to the user's emotions and provide the user with the optimal strategy. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the content of its suggestions based on the result.

[0106] The strategic suggestion system can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, only important suggestions will be prioritized. If the user is relaxed, detailed suggestions will be prioritized. If the user is in a hurry, suggestions that can be quickly understood will be prioritized. In this way, the strategic suggestion system can prioritize suggestions according to the user's emotions and prioritize important suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of suggestions based on the result.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection department collects historical data. For example, it collects company annual reports, minutes of management meetings, and internal strategic documents, and stores this data in digital format. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes advances in communication technology, the activities of competitors, and consumer behavior patterns to understand their impact on each department. Step 3: The proposal team proposes a strategic vision based on the analysis results obtained by the analysis team. For example, they might propose a strategy that follows past successful strategies while also addressing new trends and market developments. Step 4: The support department assists each department in formulating its own strategies based on the strategic vision proposed by the proposal department. For example, it devises measures for each department and automatically outputs them as reports.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and stores it in digital format by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a strategic vision based on the analysis results. The support unit is implemented in the specific processing unit 46A of the smart device 14 and supports the formulation work of each department. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and stores it in digital format by the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and proposes a strategic vision based on the analysis results. The support unit is implemented, for example, in the control unit 46A of the smart glasses 214 and supports the formulation work of each department. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and stores it in digital format by the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a strategic vision based on the analysis results. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and supports the formulation work of each department. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[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 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.

[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 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.

[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 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and stores it in digital format by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a strategic vision based on the analysis results. The support unit is implemented, for example, by the control unit 46A of the robot 414 and supports the formulation work of each department. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects past data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a strategic vision, The system includes a support department that assists each department in formulating a strategic vision based on the strategic vision proposed by the aforementioned proposal department. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect company annual reports, management meeting minutes, and internal strategic documents. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze advancements in communication technology, competitor trends, and consumer behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose strategies that build upon past successful strategies while adapting to new trends and market developments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, The system devises measures for each department and automatically outputs them as a report. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is The data to be collected is evaluated for reliability, and reliable data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The types of data collected are dynamically changed based on the company's current strategic objectives. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is The scope of data to be collected will be divided and collected according to the company's geographical locations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The data to be collected should include not only text data, but also audio and video data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the analysis should take into account the temporal changes in the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we refer to relevant external data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned proposal section is, When making a proposal, we compare past success stories and failures to suggest the optimal strategy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we will propose a customized strategy that takes into account the characteristics of each department. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we propose different strategies for each of the company's geographical locations. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we refer to relevant external data to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit, It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, When providing support, we devise optimal measures by referring to past performance data from each department. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, When providing support, customize measures to take into account the resource situation of each department. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, It estimates the user's emotions and determines the priority of support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, When providing support, different measures will be devised for each department's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, When providing support, we refer to relevant external data to improve the accuracy of the measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects past data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a strategic vision, The system includes a support department that assists each department in formulating a strategic vision based on the strategic vision proposed by the aforementioned proposal department. A system characterized by the following features.

2. The aforementioned collection unit is Collect company annual reports, management meeting minutes, and internal strategic documents. The system according to feature 1.

3. The aforementioned analysis unit, Analyze advancements in communication technology, competitor trends, and consumer behavior patterns. The system according to feature 1.

4. The aforementioned proposal section is, We propose strategies that build upon past successful strategies while adapting to new trends and market developments. The system according to feature 1.

5. The aforementioned support unit, The system devises measures for each department and automatically outputs them as a report. The system according to feature 1.

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

7. The aforementioned collection unit is The data to be collected is evaluated for reliability, and reliable data is prioritized for collection. The system according to feature 1.

8. The aforementioned collection unit is The types of data collected are dynamically changed based on the company's current strategic objectives. The system according to feature 1.

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