system

Abstract

The system according to this embodiment aims to efficiently perform various tasks in policy formulation. [Solution] The system according to the embodiment comprises a reception unit, a concretization unit, a creation unit, an implementation unit, an analysis unit, a creation unit, and an evaluation unit. The reception unit inputs policy proposals. The concretization unit concretizes customer benefits based on the policy proposals input by the reception unit. The creation unit creates questionnaires based on the customer benefits concretized by the concretization unit. The implementation unit conducts the questionnaires created by the creation unit. The analysis unit analyzes the results of the questionnaires conducted by the implementation unit. The creation unit creates policies based on the results analyzed by the analysis unit. The evaluation unit evaluates the policies created by the creation unit. The evaluation unit evaluates the profitability of the policies evaluated by the evaluation unit. The evaluation unit evaluates the compliance of the policies evaluated by the evaluation unit with respect to laws and regulations.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that in policy making, many operations such as needs investigation, income and expenditure calculation, and law confirmation are required, and it is difficult to perform them efficiently.

[0005] The system according to the embodiment aims to efficiently perform various operations in policy making.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a concretization unit, a creation unit, an implementation unit, an analysis unit, another creation unit, and an evaluation unit. The reception unit receives policy proposals. The concretization unit concretizes customer benefits based on the policy proposals received by the reception unit. The creation unit creates questionnaires based on the customer benefits concretized by the concretization unit. The implementation unit conducts the questionnaires created by the creation unit. The analysis unit analyzes the results of the questionnaires conducted by the implementation unit. The creation unit creates policies based on the results analyzed by the analysis unit. The evaluation unit evaluates the policies created by the creation unit. The evaluation unit evaluates the profitability of the policies evaluated by the evaluation unit. The evaluation unit evaluates the compliance of the policies evaluated by the evaluation unit with respect to laws and regulations. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform various tasks in policy formulation. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 policy planning support system according to an embodiment of the present invention is a system that efficiently concretizes policy proposals and evaluates customer satisfaction, revenue and expenditure forecasts, and legal compliance. The policy planning support system provides a mechanism in which, simply by inputting a policy proposal, the AI ​​concretizes the policy and evaluates customer satisfaction, revenue and expenditure forecasts, and legal compliance. The policy planning support system can efficiently perform tasks from inputting a policy proposal to concretizing customer benefits, creating and conducting questionnaires, analyzing results, creating policies, and evaluating them. For example, the policy planning support system accepts the input of a rough policy proposal from the user. For example, if the user inputs a policy proposal such as "I want to distribute shares to communication service users," AI agent A concretizes customer benefits such as receiving shares the more they use the communication service, becoming a shareholder, receiving dividend income, and receiving capital gains. Next, AI agent B creates a questionnaire based on these benefits, and AI agent C conducts a virtual questionnaire. AI agent D analyzes the questionnaire results to understand customer needs. For example, the analysis yields results such as 82% finding it highly attractive, effectiveness if usage exceeds 10%, projected new customer acquisition rate of 70%, projected cancellation prevention rate of 80%, and projected net increase rate of 5%. Next, the policy planning support system uses AI agent E to create a "stock dividend plan" based on these analysis results. Specifically, this plan involves distributing stocks equivalent to 10% of the monthly usage amount. The distributed stocks can be traded or paid as dividends. The policy planning support system uses AI agent F to evaluate customers and AI agent G to evaluate profitability. Finally, the policy planning support system uses AI agent H to evaluate legal compliance, providing results such as no issues with the prize limit, the need to open a securities account, and the risk of dilution of existing stocks. In this way, the policy planning support system streamlines policy planning and provides an AI system that evaluates customer satisfaction, projected profitability, and legal compliance. This allows the policy planning support system to efficiently concretize policy proposals and evaluate customer satisfaction, projected profitability, and legal compliance.

[0029] The policy planning support system according to this embodiment comprises a reception unit, a concretization unit, a creation unit, an implementation unit, an analysis unit, a creation unit, an evaluation unit, an evaluation unit, and an evaluation unit. The reception unit inputs policy proposals. Policy proposals include, but are not limited to, business policies and marketing policies. For example, the reception unit allows users to input policy proposals in text format. The reception unit also allows policy proposals to be input using voice input. For example, the reception unit uses voice recognition technology to convert the user's voice into text and inputs it as a policy proposal. The concretization unit concretizes customer benefits based on the policy proposals input by the reception unit. Customer benefits include, but are not limited to, cost reduction and improved convenience. For example, the concretization unit concretizes customer benefits using AI. For example, the concretization unit extracts and concretizes the specific benefits that customers can obtain based on the policy proposals. The creation unit creates questionnaires based on the customer benefits concretized by the concretization unit. The survey includes, but is not limited to, questions and answer formats. The creation department creates the survey using, for example, AI. For example, the creation department creates questions to collect customer opinions based on customer benefits. The implementation department conducts the survey created by the creation department. The implementation department conducts, for example, a virtual survey. A virtual survey includes, but is not limited to, an online survey or a web survey. For example, the implementation department conducts a virtual survey using, for example, AI. For example, the implementation department conducts an online survey to collect customer opinions. The analysis department analyzes the survey results conducted by the implementation department. The analysis includes, but is not limited to, statistical analysis or text mining. For example, the analysis department analyzes the survey results using, for example, AI. For example, the analysis department statistically analyzes the survey results to understand customer needs. The creation department creates measures based on the results analyzed by the analysis department. Measures include, but are not limited to, implementation plans and effectiveness measurements. The creation department creates measures using, for example, AI. For example, the planning department creates specific measures based on the analysis results.The evaluation department evaluates the policies created by the creation department. The evaluation includes, but is not limited to, customer evaluation, revenue and expenditure evaluation, and legal evaluation. For example, the evaluation department may use AI to evaluate the policies. For example, the evaluation department may conduct customer evaluations to assess the effectiveness of the policies. The evaluation department may conduct revenue and expenditure evaluations to assess the economic effects of the policies. The evaluation department may conduct legal evaluations to confirm that the policies comply with the law. As a result, the policy planning support system according to this embodiment can efficiently perform tasks from inputting policy proposals to concretizing customer benefits, creating and conducting questionnaires, analyzing results, creating policies, and evaluating them.

[0030] The reception desk receives input for proposed policies. These policies include, but are not limited to, business policies and marketing policies. The reception desk allows users to input policy proposals in text format. It also allows input using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text and input it as a policy proposal. Specifically, the speech recognition technology incorporates natural language processing technology, enabling accurate recognition and transcription of the user's speech. This allows users to input policy proposals using only their voice, without using a keyboard. Furthermore, the reception desk has a function to automatically classify the inputted policy proposals and assign them to the appropriate categories. For example, it classifies them into categories such as business policies, marketing policies, and technology policies to ensure smooth subsequent processing. The reception desk also has a function to present relevant information and past examples in relation to the policy proposals entered by the user. This allows users to obtain reference information when inputting policy proposals, enabling them to create more specific and effective policy proposals.

[0031] The concretization unit concretizes customer benefits based on the policy proposals entered by the reception unit. Customer benefits include, but are not limited to, cost reduction and improved convenience. The concretization unit concretizes customer benefits using, for example, AI. For example, the concretization unit extracts and concretizes the specific benefits that customers can receive based on the policy proposal. Specifically, the AI ​​analyzes the content of the policy proposal, refers to past data and case studies, and clarifies the benefits for the customer. For example, if the policy proposal is for cost reduction, the AI ​​analyzes the results of similar past measures and shows the expected amount of cost reduction in specific figures. If the policy proposal is for improved convenience, it shows specifically what kind of convenience will be improved based on customer usage and feedback. Furthermore, the concretization unit is equipped with tools to present customer benefits in an easy-to-understand visual way. For example, it uses graphs and charts to visually represent customer benefits so that users can easily understand them. In this way, the concretization unit can show the effects of the policy proposal concretely and clearly and provide information for users to judge the effectiveness of the measures.

[0032] The creation department creates questionnaires based on the customer benefits specified by the specification department. The questionnaires include, but are not limited to, questions and answer formats. For example, the creation department can use AI to create questionnaires. Specifically, the creation department creates questions to collect customer opinions based on the customer benefits. The AI ​​analyzes the customer benefits and automatically generates related questions. For example, if the question concerns cost reduction measures, it generates specific questions such as, "Please tell us about your current cost reduction efforts," or "What level of cost reduction can be expected?" It also automatically selects the most suitable answer format, such as multiple-choice or open-ended. Furthermore, the creation department automatically adjusts the design and layout of the questionnaire to make it easy for users to answer. This allows the creation department to quickly and efficiently create questionnaires and prepare them for collecting customer opinions.

[0033] The implementation department conducts the questionnaires created by the creation department. The implementation department conducts virtual questionnaires, for example. Virtual questionnaires include, but are not limited to, online questionnaires and web surveys. The implementation department conducts virtual questionnaires, for example, using AI. For example, the implementation department conducts online questionnaires to collect customer opinions. Specifically, the AI ​​automatically selects the recipients of the questionnaire and delivers it at the optimal time. For example, it analyzes customers' past response history and behavior patterns to identify the times and days of the week when response rates are high. The AI ​​also monitors the progress of the questionnaire in real time and takes action such as sending reminders if the number of responses is low. Furthermore, the implementation department automatically collects the questionnaire response data and stores it in a database. This allows the implementation department to conduct questionnaires efficiently and collect customer opinions.

[0034] The Analysis Department analyzes the results of surveys conducted by the Implementation Department. This analysis includes, but is not limited to, statistical analysis and text mining. For example, the Analysis Department uses AI to analyze survey results. Specifically, the Analysis Department statistically analyzes survey results to understand customer needs. More precisely, the AI ​​analyzes survey response data to extract response trends and patterns. For example, for multiple-choice responses, it analyzes the distribution of responses to identify which option is most popular. For open-ended responses, it uses text mining techniques to analyze the content and extract common keywords and themes. Furthermore, the Analysis Department has tools to present survey results visually and clearly. For example, it uses graphs and charts to visually represent survey results, making them easily understandable to users. This allows the Analysis Department to quickly and accurately analyze survey results and understand customer needs.

[0035] The planning department creates measures based on the results of the analysis conducted by the analysis department. These measures include, but are not limited to, implementation plans and effectiveness measurements. The planning department can, for example, use AI to create measures. For example, the planning department creates specific measures based on the analysis results. Specifically, the AI ​​analyzes the analysis results and automatically generates optimal measures. For example, it creates development plans for new products and services based on customer needs. It also automatically sets indicators and evaluation methods for measuring the effectiveness of the measures. Furthermore, the planning department automatically adjusts the resources and schedules necessary for implementing the measures and creates actionable plans. This allows the planning department to create measures quickly and efficiently and prepare for implementation.

[0036] The evaluation department evaluates the measures created by the development department. Evaluations include, but are not limited to, customer evaluations, revenue / expense evaluations, and legal evaluations. The evaluation department may, for example, use AI to evaluate measures. For instance, the evaluation department conducts customer evaluations to assess the effectiveness of the measures. Specifically, the AI ​​analyzes customer feedback and quantitatively evaluates the effectiveness of the measures. For example, it evaluates the effectiveness of measures based on indicators such as customer satisfaction and repeat purchase rates. Furthermore, for revenue / expense evaluations, it analyzes the costs and revenues associated with implementing the measures to assess their economic impact. In addition, for legal evaluations, it verifies whether the measures comply with relevant laws and regulations. This allows the evaluation department to comprehensively evaluate the effectiveness of measures and clearly identify areas for improvement and challenges.

[0037] The evaluation department conducts revenue and expenditure evaluations to assess the economic effects of the measures. Specifically, AI analyzes the costs and revenues associated with the implementation of the measures and evaluates their economic effects. For example, it compares revenue and expenditure data before and after the implementation of the measures to quantitatively evaluate the effects of cost reduction and revenue increase. It also considers the risks and uncertainties associated with the implementation of the measures to comprehensively evaluate the economic effects. Furthermore, the evaluation department is equipped with tools to present the economic effects of the measures in an easy-to-understand visual way. For example, it uses graphs and charts to visually represent revenue and expenditure data, making it easy for users to understand. This allows the evaluation department to accurately evaluate the economic effects of the measures and provide information to judge the effectiveness of the measures.

[0038] The evaluation department conducts legal assessments to verify that policies comply with the law. Specifically, AI analyzes the content of policies and evaluates them against relevant laws and regulations. For example, it verifies whether policies comply with laws such as the Personal Information Protection Act and the Labor Standards Act. It also evaluates legal risks and compliance issues associated with the implementation of policies. Furthermore, the evaluation department has tools to present the results of legal assessments in an easy-to-understand manner. For example, it compiles the results of legal assessments in a report format, visually representing the legal compliance of policies. This allows the evaluation department to accurately assess whether policies comply with the law and provide information to minimize legal risks.

[0039] The concretization unit can concretize customer benefits. The concretization unit can concretize customer benefits using, for example, AI. For example, the concretization unit extracts and concretizes the specific benefits that customers can obtain based on the proposed measures. For example, the concretization unit can concretize customer benefits such as cost reduction, improved convenience, and increased revenue. For example, the concretization unit shows in detail the specific benefits that customers can obtain by using the service. The concretization unit can also use graphs and charts to visually represent customer benefits. By concretizing customer benefits in this way, the specificity of the measures is improved. Some or all of the above-described processes in the concretization unit may be performed using, for example, AI, or not using AI. For example, the concretization unit can have a generating AI perform the process of extracting and concretizing the specific benefits that customers can obtain based on the proposed measures.

[0040] The creation unit can create questionnaires. The creation unit can create questionnaires using, for example, AI. For example, the creation unit can create questions to collect customer opinions based on customer benefits. For example, the creation unit can include customer satisfaction, intention to use, and areas for improvement as questionnaire questions. For example, the creation unit can set the questionnaire response format to multiple choice, open-ended, etc. For example, the creation unit can use graphs and charts to visually represent the questionnaire questions. In this way, customer opinions can be collected by creating questionnaires. Some or all of the above processes in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit can have a generating AI perform the process of creating questions to collect customer opinions based on customer benefits.

[0041] The implementation department can conduct virtual surveys. The implementation department can conduct virtual surveys using, for example, AI. For example, the implementation department can conduct online surveys to collect customer opinions. The implementation department can also conduct web surveys to collect customer opinions. For example, the implementation department can use email distribution, website forms, mobile apps, etc., as methods for conducting virtual surveys. The implementation department can also collect and analyze the results of virtual surveys in real time. This makes it possible to efficiently collect customer opinions by conducting virtual surveys. Some or all of the above processes in the implementation department may be performed using, for example, AI, or not using AI. For example, the implementation department can have a generating AI perform the process of conducting online surveys and collecting customer opinions.

[0042] The analysis department can analyze survey results. The analysis department can analyze survey results using, for example, AI. For example, the analysis department can statistically analyze survey results to understand customer needs. The analysis department can also analyze open-ended responses using, for example, text mining technology. The analysis department can also use graphs and charts to visually represent survey results. For example, the analysis department can understand customer satisfaction, usage intentions, and areas for improvement based on the survey results. In this way, customer needs can be understood by analyzing the survey results. Some or all of the above processes in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can have a generating AI perform the process of statistically analyzing survey results and understanding customer needs.

[0043] The creation unit can create policies. The creation unit can create policies using, for example, AI. For example, the creation unit can create specific policies based on analysis results. For example, the content of the policies can include, for example, implementation plans, effectiveness measurement, and resource allocation. The creation unit can also use, for example, graphs and charts to visually represent the content of the policies. This allows for the creation of concrete action plans by creating policies. Some or all of the above processes in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit can have a generating AI perform the process of creating specific policies based on analysis results.

[0044] The evaluation unit can perform customer evaluations. The evaluation unit can perform customer evaluations using, for example, AI. For example, the evaluation unit can conduct customer satisfaction surveys and collect customer feedback. The evaluation unit can evaluate the effectiveness of measures based on customer feedback. The evaluation unit can also use graphs and charts to visually represent the results of customer evaluations. In this way, the effectiveness of measures can be evaluated by performing customer evaluations. Some or all of the above processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can have a generating AI perform the process of conducting customer satisfaction surveys and collecting customer feedback.

[0045] The evaluation unit can perform a revenue-expense evaluation. The evaluation unit can perform a revenue-expense evaluation using, for example, AI. For example, the evaluation unit calculates the revenue and expenses of a policy and evaluates the revenue and expenses. The evaluation unit can also use graphs or charts to visually represent the results of the revenue-expense evaluation. The evaluation unit can use, for example, revenue, expenses, profit margins, etc., as criteria for the revenue-expense evaluation. In this way, the economic effect of a policy can be evaluated by performing a revenue-expense evaluation. Some or all of the above processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can have a generating AI perform the process of calculating the revenue and expenses of a policy and evaluating the revenue and expenses.

[0046] The evaluation unit can perform legal compliance evaluations. The evaluation unit can perform legal compliance evaluations using, for example, AI. For example, the evaluation unit can verify whether a policy complies with relevant laws and regulations. The evaluation unit can also use graphs and charts to visually represent the results of the legal compliance evaluation. The evaluation unit can use, for example, relevant laws and regulations, compliance checks, etc., as criteria for legal compliance evaluation. This allows the evaluation unit to verify whether a policy complies with laws and regulations. Some or all of the above processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can have a generating AI perform the process of verifying whether a policy complies with relevant laws and regulations.

[0047] The reception desk can analyze a user's past policy proposal submission history and select the optimal input method. For example, the reception desk can use AI to analyze a user's past policy proposal submission history. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can analyze the content of policy proposals previously submitted by the user and provide templates for inputting similar policy proposals. For example, the reception desk can predict and suggest input methods to be used at specific times based on the user's past submission history. In this way, by analyzing past submission history, the reception desk can provide the user with the optimal input method. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can have a generating AI perform the process of analyzing a user's past policy proposal submission history and selecting the optimal input method.

[0048] The reception unit can filter policy proposals based on the user's current projects and areas of interest when they are entered. For example, the reception unit can use AI to analyze the user's current projects and areas of interest and filter relevant policy proposals. For example, the reception unit can prioritize displaying policy proposals related to the projects the user is currently working on. The reception unit can also filter and display relevant policy proposals based on the user's areas of interest. For example, the reception unit can analyze the user's past project history and suggest relevant policy proposals. This allows users to input highly relevant policy proposals by filtering based on their current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have a generating AI perform the process of analyzing the user's current projects and areas of interest and filtering relevant policy proposals.

[0049] The reception unit can prioritize inputting highly relevant policy proposals by considering the user's geographical location information when inputting policy proposals. The reception unit can, for example, use AI to analyze the user's geographical location information and filter relevant policy proposals. For example, the reception unit can prioritize displaying policy proposals related to the user's current location. The reception unit can also, for example, analyze the user's past location information and suggest relevant policy proposals. The reception unit can also, for example, filter and display region-specific policy proposals based on the user's current location information. This allows for the input of highly relevant policy proposals by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have a generating AI perform the process of analyzing the user's geographical location information and filtering relevant policy proposals.

[0050] The reception unit can analyze the user's social media activity and input relevant policy proposals when inputting policy proposals. The reception unit can, for example, use AI to analyze the user's social media activity. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant policy proposals. The reception unit can also, for example, analyze the activity of the user's followers and friends on social media and suggest relevant policy proposals. The reception unit can also, for example, analyze the user's interests on social media and filter and display relevant policy proposals. This allows relevant policy proposals to be input by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have a generating AI perform the process of analyzing the user's social media activity and inputting relevant policy proposals.

[0051] The concretization unit can adjust the level of detail in the concretization of customer benefits based on the importance of the proposed measures. For example, the concretization unit can use AI to evaluate the importance of the proposed measures and adjust the level of detail. For example, the concretization unit can concretize detailed customer benefits for highly important measures. For example, the concretization unit can concretize concretized concretized customer benefits in a concise manner for less important measures. The concretization unit can also adjust the level of detail in stages according to the importance of the proposed measures. By adjusting the level of detail based on the importance of the proposed measures, more effective customer benefits can be concretized. Some or all of the above-described processes in the concretization unit may be performed using AI, for example, or without AI. For example, the concretization unit can have a generating AI perform the process of evaluating the importance of the proposed measures and adjusting the level of detail.

[0052] The concretization unit can apply different concretization algorithms depending on the category of the proposed measures when concretizing customer benefits. For example, the concretization unit can use AI to analyze the category of the proposed measures and apply an appropriate concretization algorithm. For example, for marketing measures, the concretization unit can apply a concretization algorithm that increases customer purchasing intent. For example, for service improvement measures, the concretization unit can also apply a concretization algorithm that improves customer satisfaction. For example, for new product development measures, the concretization unit can also apply a concretization algorithm that attracts customer interest. By applying a concretization algorithm according to the category of the proposed measures, more appropriate customer benefits can be concretized. Some or all of the above-described processes in the concretization unit may be performed using AI, for example, or without AI. For example, the concretization unit can have a generating AI perform the process of analyzing the category of the proposed measures and applying an appropriate concretization algorithm.

[0053] The concretization unit can determine the priority of concretization based on the submission timing of policy proposals when concretizing customer benefits. For example, the concretization unit can use AI to evaluate the submission timing of policy proposals and determine the priority of concretization. For example, the concretization unit can prioritize concretizing customer benefits for policy proposals that are submitted early. For example, the concretization unit can postpone concretizing customer benefits for policy proposals that are submitted late. For example, the concretization unit can also adjust the priority of concretization in stages according to the submission timing. This allows for efficient concretization of customer benefits by determining the priority of concretization based on the submission timing of policy proposals. Some or all of the above-described processes in the concretization unit may be performed using AI, for example, or without AI. For example, the concretization unit can have a generating AI perform the process of evaluating the submission timing of policy proposals and determining the priority of concretization.

[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 reception desk can analyze a user's past policy proposal submission history and select the most suitable input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze the content of past policy proposals and provide templates for similar proposals. Furthermore, the reception desk can predict and suggest input methods that a user will use at specific times based on their past submission history. This allows the system to provide users with the most suitable input method by analyzing their past submission history.

[0056] The concretization unit can adjust the level of detail in the concretization of customer benefits based on the importance of the proposed measures. For example, the concretization unit uses AI to evaluate the importance of proposed measures and adjusts the level of detail accordingly. For highly important measures, the concretization unit can concretize detailed customer benefits. For less important measures, it can concretize concretized customer benefits in a concise manner. Furthermore, the concretization unit can adjust the level of detail in stages according to the importance of the proposed measures. By adjusting the level of detail based on the importance of the proposed measures, more effective customer benefits can be concretized.

[0057] The creation unit can analyze a user's past survey response history when creating a survey and select the most suitable questions. For example, it can analyze the content of surveys a user has previously answered and suggest questions for similar surveys. It can also predict response trends to specific questions based on a user's past response history and adjust the questions accordingly. Furthermore, it can optimize the survey response format based on the user's past response history. This allows the system to provide users with the most suitable questions by analyzing their past response history.

[0058] The implementation team can filter virtual surveys based on users' current projects and areas of interest. For example, the implementation team can use AI to analyze users' current projects and areas of interest and prioritize relevant surveys. They can also filter and conduct surveys based on users' areas of interest. Furthermore, they can analyze users' past project history and suggest relevant surveys. This allows for the implementation of highly relevant surveys by filtering based on current projects and areas of interest.

[0059] The analytics department can analyze users' social media activity and supplement relevant data when analyzing survey results. For example, the analytics department can use AI to analyze the content of users' social media posts and supplement data related to the survey results. Furthermore, the analytics department can analyze the activity of users' followers and friends on social media and supplement data related to the survey results. In addition, the analytics department can analyze users' interests on social media and supplement data related to the survey results. This allows for a more accurate analysis of survey results by analyzing social media activity.

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

[0061] Step 1: The reception desk inputs the proposed measures. These measures include business strategies and marketing strategies. Users can input their proposed measures in text format, and voice input is also available. Using speech recognition technology, the user's voice can be converted into text and entered as a proposed measure. Step 2: The concretization department concretizes the customer benefits based on the policy proposal entered by the reception department. Customer benefits include cost reduction and improved convenience. The concretization department uses AI to extract and concretize the specific benefits that customers can obtain based on the policy proposal. Step 3: The creation team creates a questionnaire based on the customer benefits specified by the specification team. The questionnaire includes questions and answer formats. The creation team uses AI to create questions to collect customer opinions based on customer benefits. Step 4: The implementation department conducts the questionnaire created by the development department. The implementation department conducts a virtual questionnaire, including online questionnaires and web surveys. The virtual questionnaire is conducted using AI to collect customer opinions. Step 5: The analysis department analyzes the survey results conducted by the implementation department. This analysis includes statistical analysis and text mining. AI is used to analyze the survey results and understand customer needs. Step 6: The planning department creates measures based on the results analyzed by the analysis department. These measures include implementation plans and effectiveness measurements. AI is used to create specific measures based on the analysis results. Step 7: The evaluation department evaluates the measures created by the development department. The evaluation includes customer evaluation, revenue and expenditure evaluation, and legal evaluation. AI is used to evaluate the measures, customer evaluation is conducted, and the effectiveness of the measures is assessed. Revenue and expenditure evaluation is conducted to assess the economic effect of the measures. Legal evaluation is conducted to confirm that the measures comply with the law.

[0062] (Example of form 2) The policy planning support system according to an embodiment of the present invention is a system that efficiently concretizes policy proposals and evaluates customer satisfaction, revenue and expenditure forecasts, and legal compliance. The policy planning support system provides a mechanism in which, simply by inputting a policy proposal, the AI ​​concretizes the policy and evaluates customer satisfaction, revenue and expenditure forecasts, and legal compliance. The policy planning support system can efficiently perform tasks from inputting a policy proposal to concretizing customer benefits, creating and conducting questionnaires, analyzing results, creating policies, and evaluating them. For example, the policy planning support system accepts the input of a rough policy proposal from the user. For example, if the user inputs a policy proposal such as "I want to distribute shares to communication service users," AI agent A concretizes customer benefits such as receiving shares the more they use the communication service, becoming a shareholder, receiving dividend income, and receiving capital gains. Next, AI agent B creates a questionnaire based on these benefits, and AI agent C conducts a virtual questionnaire. AI agent D analyzes the questionnaire results to understand customer needs. For example, the analysis yields results such as 82% finding it highly attractive, effectiveness if usage exceeds 10%, projected new customer acquisition rate of 70%, projected cancellation prevention rate of 80%, and projected net increase rate of 5%. Next, the policy planning support system uses AI agent E to create a "stock dividend plan" based on these analysis results. Specifically, this plan involves distributing stocks equivalent to 10% of the monthly usage amount. The distributed stocks can be traded or paid as dividends. The policy planning support system uses AI agent F to evaluate customers and AI agent G to evaluate profitability. Finally, the policy planning support system uses AI agent H to evaluate legal compliance, providing results such as no issues with the prize limit, the need to open a securities account, and the risk of dilution of existing stocks. In this way, the policy planning support system streamlines policy planning and provides an AI system that evaluates customer satisfaction, projected profitability, and legal compliance. This allows the policy planning support system to efficiently concretize policy proposals and evaluate customer satisfaction, projected profitability, and legal compliance.

[0063] The policy planning support system according to this embodiment comprises a reception unit, a concretization unit, a creation unit, an implementation unit, an analysis unit, a creation unit, an evaluation unit, an evaluation unit, and an evaluation unit. The reception unit inputs policy proposals. Policy proposals include, but are not limited to, business policies and marketing policies. For example, the reception unit allows users to input policy proposals in text format. The reception unit also allows policy proposals to be input using voice input. For example, the reception unit uses voice recognition technology to convert the user's voice into text and inputs it as a policy proposal. The concretization unit concretizes customer benefits based on the policy proposals input by the reception unit. Customer benefits include, but are not limited to, cost reduction and improved convenience. For example, the concretization unit concretizes customer benefits using AI. For example, the concretization unit extracts and concretizes the specific benefits that customers can obtain based on the policy proposals. The creation unit creates questionnaires based on the customer benefits concretized by the concretization unit. The survey includes, but is not limited to, questions and answer formats. The creation department creates the survey using, for example, AI. For example, the creation department creates questions to collect customer opinions based on customer benefits. The implementation department conducts the survey created by the creation department. The implementation department conducts, for example, a virtual survey. A virtual survey includes, but is not limited to, an online survey or a web survey. For example, the implementation department conducts a virtual survey using, for example, AI. For example, the implementation department conducts an online survey to collect customer opinions. The analysis department analyzes the survey results conducted by the implementation department. The analysis includes, but is not limited to, statistical analysis or text mining. For example, the analysis department analyzes the survey results using, for example, AI. For example, the analysis department statistically analyzes the survey results to understand customer needs. The creation department creates measures based on the results analyzed by the analysis department. Measures include, but are not limited to, implementation plans and effectiveness measurements. The creation department creates measures using, for example, AI. For example, the planning department creates specific measures based on the analysis results.The evaluation department evaluates the policies created by the creation department. The evaluation includes, but is not limited to, customer evaluation, revenue and expenditure evaluation, and legal evaluation. For example, the evaluation department may use AI to evaluate the policies. For example, the evaluation department may conduct customer evaluations to assess the effectiveness of the policies. The evaluation department may conduct revenue and expenditure evaluations to assess the economic effects of the policies. The evaluation department may conduct legal evaluations to confirm that the policies comply with the law. As a result, the policy planning support system according to this embodiment can efficiently perform tasks from inputting policy proposals to concretizing customer benefits, creating and conducting questionnaires, analyzing results, creating policies, and evaluating them.

[0064] The reception desk receives input for proposed policies. These policies include, but are not limited to, business policies and marketing policies. The reception desk allows users to input policy proposals in text format. It also allows input using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text and input it as a policy proposal. Specifically, the speech recognition technology incorporates natural language processing technology, enabling accurate recognition and transcription of the user's speech. This allows users to input policy proposals using only their voice, without using a keyboard. Furthermore, the reception desk has a function to automatically classify the inputted policy proposals and assign them to the appropriate categories. For example, it classifies them into categories such as business policies, marketing policies, and technology policies to ensure smooth subsequent processing. The reception desk also has a function to present relevant information and past examples in relation to the policy proposals entered by the user. This allows users to obtain reference information when inputting policy proposals, enabling them to create more specific and effective policy proposals.

[0065] The concretization unit concretizes customer benefits based on the policy proposals entered by the reception unit. Customer benefits include, but are not limited to, cost reduction and improved convenience. The concretization unit concretizes customer benefits using, for example, AI. For example, the concretization unit extracts and concretizes the specific benefits that customers can receive based on the policy proposal. Specifically, the AI ​​analyzes the content of the policy proposal, refers to past data and case studies, and clarifies the benefits for the customer. For example, if the policy proposal is for cost reduction, the AI ​​analyzes the results of similar past measures and shows the expected amount of cost reduction in specific figures. If the policy proposal is for improved convenience, it shows specifically what kind of convenience will be improved based on customer usage and feedback. Furthermore, the concretization unit is equipped with tools to present customer benefits in an easy-to-understand visual way. For example, it uses graphs and charts to visually represent customer benefits so that users can easily understand them. In this way, the concretization unit can show the effects of the policy proposal concretely and clearly and provide information for users to judge the effectiveness of the measures.

[0066] The creation department creates questionnaires based on the customer benefits specified by the specification department. The questionnaires include, but are not limited to, questions and answer formats. For example, the creation department can use AI to create questionnaires. Specifically, the creation department creates questions to collect customer opinions based on the customer benefits. The AI ​​analyzes the customer benefits and automatically generates related questions. For example, if the question concerns cost reduction measures, it generates specific questions such as, "Please tell us about your current cost reduction efforts," or "What level of cost reduction can be expected?" It also automatically selects the most suitable answer format, such as multiple-choice or open-ended. Furthermore, the creation department automatically adjusts the design and layout of the questionnaire to make it easy for users to answer. This allows the creation department to quickly and efficiently create questionnaires and prepare them for collecting customer opinions.

[0067] The implementation department conducts the questionnaires created by the creation department. The implementation department conducts virtual questionnaires, for example. Virtual questionnaires include, but are not limited to, online questionnaires and web surveys. The implementation department conducts virtual questionnaires, for example, using AI. For example, the implementation department conducts online questionnaires to collect customer opinions. Specifically, the AI ​​automatically selects the recipients of the questionnaire and delivers it at the optimal time. For example, it analyzes customers' past response history and behavior patterns to identify the times and days of the week when response rates are high. The AI ​​also monitors the progress of the questionnaire in real time and takes action such as sending reminders if the number of responses is low. Furthermore, the implementation department automatically collects the questionnaire response data and stores it in a database. This allows the implementation department to conduct questionnaires efficiently and collect customer opinions.

[0068] The Analysis Department analyzes the results of surveys conducted by the Implementation Department. This analysis includes, but is not limited to, statistical analysis and text mining. For example, the Analysis Department uses AI to analyze survey results. Specifically, the Analysis Department statistically analyzes survey results to understand customer needs. More precisely, the AI ​​analyzes survey response data to extract response trends and patterns. For example, for multiple-choice responses, it analyzes the distribution of responses to identify which option is most popular. For open-ended responses, it uses text mining techniques to analyze the content and extract common keywords and themes. Furthermore, the Analysis Department has tools to present survey results visually and clearly. For example, it uses graphs and charts to visually represent survey results, making them easily understandable to users. This allows the Analysis Department to quickly and accurately analyze survey results and understand customer needs.

[0069] The planning department creates measures based on the results of the analysis conducted by the analysis department. These measures include, but are not limited to, implementation plans and effectiveness measurements. The planning department can, for example, use AI to create measures. For example, the planning department creates specific measures based on the analysis results. Specifically, the AI ​​analyzes the analysis results and automatically generates optimal measures. For example, it creates development plans for new products and services based on customer needs. It also automatically sets indicators and evaluation methods for measuring the effectiveness of the measures. Furthermore, the planning department automatically adjusts the resources and schedules necessary for implementing the measures and creates actionable plans. This allows the planning department to create measures quickly and efficiently and prepare for implementation.

[0070] The evaluation department evaluates the measures created by the development department. Evaluations include, but are not limited to, customer evaluations, revenue / expense evaluations, and legal evaluations. The evaluation department may, for example, use AI to evaluate measures. For instance, the evaluation department conducts customer evaluations to assess the effectiveness of the measures. Specifically, the AI ​​analyzes customer feedback and quantitatively evaluates the effectiveness of the measures. For example, it evaluates the effectiveness of measures based on indicators such as customer satisfaction and repeat purchase rates. Furthermore, for revenue / expense evaluations, it analyzes the costs and revenues associated with implementing the measures to assess their economic impact. In addition, for legal evaluations, it verifies whether the measures comply with relevant laws and regulations. This allows the evaluation department to comprehensively evaluate the effectiveness of measures and clearly identify areas for improvement and challenges.

[0071] The evaluation department conducts revenue and expenditure evaluations to assess the economic effects of the measures. Specifically, AI analyzes the costs and revenues associated with the implementation of the measures and evaluates their economic effects. For example, it compares revenue and expenditure data before and after the implementation of the measures to quantitatively evaluate the effects of cost reduction and revenue increase. It also considers the risks and uncertainties associated with the implementation of the measures to comprehensively evaluate the economic effects. Furthermore, the evaluation department is equipped with tools to present the economic effects of the measures in an easy-to-understand visual way. For example, it uses graphs and charts to visually represent revenue and expenditure data, making it easy for users to understand. This allows the evaluation department to accurately evaluate the economic effects of the measures and provide information to judge the effectiveness of the measures.

[0072] The evaluation department conducts legal assessments to verify that policies comply with the law. Specifically, AI analyzes the content of policies and evaluates them against relevant laws and regulations. For example, it verifies whether policies comply with laws such as the Personal Information Protection Act and the Labor Standards Act. It also evaluates legal risks and compliance issues associated with the implementation of policies. Furthermore, the evaluation department has tools to present the results of legal assessments in an easy-to-understand manner. For example, it compiles the results of legal assessments in a report format, visually representing the legal compliance of policies. This allows the evaluation department to accurately assess whether policies comply with the law and provide information to minimize legal risks.

[0073] The concretization unit can concretize customer benefits. The concretization unit can concretize customer benefits using, for example, AI. For example, the concretization unit extracts and concretizes the specific benefits that customers can obtain based on the proposed measures. For example, the concretization unit can concretize customer benefits such as cost reduction, improved convenience, and increased revenue. For example, the concretization unit shows in detail the specific benefits that customers can obtain by using the service. The concretization unit can also use graphs and charts to visually represent customer benefits. By concretizing customer benefits in this way, the specificity of the measures is improved. Some or all of the above-described processes in the concretization unit may be performed using, for example, AI, or not using AI. For example, the concretization unit can have a generating AI perform the process of extracting and concretizing the specific benefits that customers can obtain based on the proposed measures.

[0074] The creation unit can create questionnaires. The creation unit can create questionnaires using, for example, AI. For example, the creation unit can create questions to collect customer opinions based on customer benefits. For example, the creation unit can include customer satisfaction, intention to use, and areas for improvement as questionnaire questions. For example, the creation unit can set the questionnaire response format to multiple choice, open-ended, etc. For example, the creation unit can use graphs and charts to visually represent the questionnaire questions. In this way, customer opinions can be collected by creating questionnaires. Some or all of the above processes in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit can have a generating AI perform the process of creating questions to collect customer opinions based on customer benefits.

[0075] The implementation department can conduct virtual surveys. The implementation department can conduct virtual surveys using, for example, AI. For example, the implementation department can conduct online surveys to collect customer opinions. The implementation department can also conduct web surveys to collect customer opinions. For example, the implementation department can use email distribution, website forms, mobile apps, etc., as methods for conducting virtual surveys. The implementation department can also collect and analyze the results of virtual surveys in real time. This makes it possible to efficiently collect customer opinions by conducting virtual surveys. Some or all of the above processes in the implementation department may be performed using, for example, AI, or not using AI. For example, the implementation department can have a generating AI perform the process of conducting online surveys and collecting customer opinions.

[0076] The analysis department can analyze survey results. The analysis department can analyze survey results using, for example, AI. For example, the analysis department can statistically analyze survey results to understand customer needs. The analysis department can also analyze open-ended responses using, for example, text mining technology. The analysis department can also use graphs and charts to visually represent survey results. For example, the analysis department can understand customer satisfaction, usage intentions, and areas for improvement based on the survey results. In this way, customer needs can be understood by analyzing the survey results. Some or all of the above processes in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can have a generating AI perform the process of statistically analyzing survey results and understanding customer needs.

[0077] The creation unit can create policies. The creation unit can create policies using, for example, AI. For example, the creation unit can create specific policies based on analysis results. For example, the content of the policies can include, for example, implementation plans, effectiveness measurement, and resource allocation. The creation unit can also use, for example, graphs and charts to visually represent the content of the policies. This allows for the creation of concrete action plans by creating policies. Some or all of the above processes in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit can have a generating AI perform the process of creating specific policies based on analysis results.

[0078] The evaluation unit can perform customer evaluations. The evaluation unit can perform customer evaluations using, for example, AI. For example, the evaluation unit can conduct customer satisfaction surveys and collect customer feedback. The evaluation unit can evaluate the effectiveness of measures based on customer feedback. The evaluation unit can also use graphs and charts to visually represent the results of customer evaluations. In this way, the effectiveness of measures can be evaluated by performing customer evaluations. Some or all of the above processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can have a generating AI perform the process of conducting customer satisfaction surveys and collecting customer feedback.

[0079] The evaluation unit can perform a revenue-expense evaluation. The evaluation unit can perform a revenue-expense evaluation using, for example, AI. For example, the evaluation unit calculates the revenue and expenses of a policy and evaluates the revenue and expenses. The evaluation unit can also use graphs or charts to visually represent the results of the revenue-expense evaluation. The evaluation unit can use, for example, revenue, expenses, profit margins, etc., as criteria for the revenue-expense evaluation. In this way, the economic effect of a policy can be evaluated by performing a revenue-expense evaluation. Some or all of the above processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can have a generating AI perform the process of calculating the revenue and expenses of a policy and evaluating the revenue and expenses.

[0080] The evaluation unit can perform legal compliance evaluations. The evaluation unit can perform legal compliance evaluations using, for example, AI. For example, the evaluation unit can verify whether a policy complies with relevant laws and regulations. The evaluation unit can also use graphs and charts to visually represent the results of the legal compliance evaluation. The evaluation unit can use, for example, relevant laws and regulations, compliance checks, etc., as criteria for legal compliance evaluation. This allows the evaluation unit to verify whether a policy complies with laws and regulations. Some or all of the above processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can have a generating AI perform the process of verifying whether a policy complies with relevant laws and regulations.

[0081] The reception unit can estimate the user's emotions and adjust the timing of policy proposal input based on the estimated emotions. The reception unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. The reception unit can also collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for input of policy proposals at a more appropriate time by adjusting the timing of policy proposal input according to the user's emotions. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have a generative AI perform the process of estimating the user's emotions and adjusting the timing of policy proposal input based on the estimated emotions.

[0082] The reception desk can analyze a user's past policy proposal submission history and select the optimal input method. For example, the reception desk can use AI to analyze a user's past policy proposal submission history. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can analyze the content of policy proposals previously submitted by the user and provide templates for inputting similar policy proposals. For example, the reception desk can predict and suggest input methods to be used at specific times based on the user's past submission history. In this way, by analyzing past submission history, the reception desk can provide the user with the optimal input method. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can have a generating AI perform the process of analyzing a user's past policy proposal submission history and selecting the optimal input method.

[0083] The reception unit can filter policy proposals based on the user's current projects and areas of interest when they are entered. For example, the reception unit can use AI to analyze the user's current projects and areas of interest and filter relevant policy proposals. For example, the reception unit can prioritize displaying policy proposals related to the projects the user is currently working on. The reception unit can also filter and display relevant policy proposals based on the user's areas of interest. For example, the reception unit can analyze the user's past project history and suggest relevant policy proposals. This allows users to input highly relevant policy proposals by filtering based on their current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have a generating AI perform the process of analyzing the user's current projects and areas of interest and filtering relevant policy proposals.

[0084] The reception unit can estimate the user's emotions and determine the priority of proposed measures based on the estimated emotions. The reception unit estimates the user's emotions using an emotion estimation function, for example, an emotion engine or generative AI. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. The reception unit can also collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for the input of more appropriate measures by determining the priority of proposed measures according to the user's emotions. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have a generative AI perform the process of estimating the user's emotions and determining the priority of proposed measures based on the estimated emotions.

[0085] The reception unit can prioritize inputting highly relevant policy proposals by considering the user's geographical location information when inputting policy proposals. The reception unit can, for example, use AI to analyze the user's geographical location information and filter relevant policy proposals. For example, the reception unit can prioritize displaying policy proposals related to the user's current location. The reception unit can also, for example, analyze the user's past location information and suggest relevant policy proposals. The reception unit can also, for example, filter and display region-specific policy proposals based on the user's current location information. This allows for the input of highly relevant policy proposals by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have a generating AI perform the process of analyzing the user's geographical location information and filtering relevant policy proposals.

[0086] The reception unit can analyze the user's social media activity and input relevant policy proposals when inputting policy proposals. The reception unit can, for example, use AI to analyze the user's social media activity. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant policy proposals. The reception unit can also, for example, analyze the activity of the user's followers and friends on social media and suggest relevant policy proposals. The reception unit can also, for example, analyze the user's interests on social media and filter and display relevant policy proposals. This allows relevant policy proposals to be input by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have a generating AI perform the process of analyzing the user's social media activity and inputting relevant policy proposals.

[0087] The concretization unit can estimate the user's emotions and adjust the method of concretizing customer benefits based on the estimated user emotions. The concretization unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, the concretization unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The concretization unit can also record the user's voice and estimate emotions using voice analysis technology. The concretization unit can also collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. By doing so, more appropriate customer benefits can be concretized by adjusting the method of concretizing customer benefits according to the user's emotions. Some or all of the above-described processes in the concretization unit may be performed using AI, for example, or without AI. For example, the concretization unit can have a generative AI perform the process of estimating the user's emotions and adjusting the method of concretizing customer benefits based on the estimated user emotions.

[0088] The concretization unit can adjust the level of detail in the concretization of customer benefits based on the importance of the proposed measures. For example, the concretization unit can use AI to evaluate the importance of the proposed measures and adjust the level of detail. For example, the concretization unit can concretize detailed customer benefits for highly important measures. For example, the concretization unit can concretize concretized concretized customer benefits in a concise manner for less important measures. The concretization unit can also adjust the level of detail in stages according to the importance of the proposed measures. By adjusting the level of detail based on the importance of the proposed measures, more effective customer benefits can be concretized. Some or all of the above-described processes in the concretization unit may be performed using AI, for example, or without AI. For example, the concretization unit can have a generating AI perform the process of evaluating the importance of the proposed measures and adjusting the level of detail.

[0089] The concretization unit can apply different concretization algorithms depending on the category of the proposed measures when concretizing customer benefits. For example, the concretization unit can use AI to analyze the category of the proposed measures and apply an appropriate concretization algorithm. For example, for marketing measures, the concretization unit can apply a concretization algorithm that increases customer purchasing intent. For example, for service improvement measures, the concretization unit can also apply a concretization algorithm that improves customer satisfaction. For example, for new product development measures, the concretization unit can also apply a concretization algorithm that attracts customer interest. By applying a concretization algorithm according to the category of the proposed measures, more appropriate customer benefits can be concretized. Some or all of the above-described processes in the concretization unit may be performed using AI, for example, or without AI. For example, the concretization unit can have a generating AI perform the process of analyzing the category of the proposed measures and applying an appropriate concretization algorithm.

[0090] The concretization unit can estimate the user's emotions and adjust the length of the concretization of customer benefits based on the estimated user emotions. The concretization unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, the concretization unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The concretization unit can also record the user's voice and estimate the emotions using voice analysis technology. The concretization unit can also collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for the concretization of more appropriate customer benefits by adjusting the length of the concretization of customer benefits according to the user's emotions. Some or all of the above-described processes in the concretization unit may be performed using AI, for example, or without AI. For example, the concretization unit can have a generative AI perform the process of estimating the user's emotions and adjusting the length of the concretization of customer benefits based on the estimated user emotions.

[0091] The concretization unit can determine the priority of concretization based on the submission timing of policy proposals when concretizing customer benefits. For example, the concretization unit can use AI to evaluate the submission timing of policy proposals and determine the priority of concretization. For example, the concretization unit can prioritize concretizing customer benefits for policy proposals that are submitted early. For example, the concretization unit can postpone concretizing customer benefits for policy proposals that are submitted late. For example, the concretization unit can also adjust the priority of concretization in stages according to the submission timing. This allows for efficient concretization of customer benefits by determining the priority of concretization based on the submission timing of policy proposals. Some or all of the above-described processes in the concretization unit may be performed using AI, for example, or without AI. For example, the concretization unit can have a generating AI perform the process of evaluating the submission timing of policy proposals and determining the priority of concretization.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The reception desk can analyze a user's past policy proposal submission history and select the most suitable input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze the content of past policy proposals and provide templates for similar proposals. Furthermore, the reception desk can predict and suggest input methods that a user will use at specific times based on their past submission history. This allows the system to provide users with the most suitable input method by analyzing their past submission history.

[0094] The concretization unit can adjust the level of detail in the concretization of customer benefits based on the importance of the proposed measures. For example, the concretization unit uses AI to evaluate the importance of proposed measures and adjusts the level of detail accordingly. For highly important measures, the concretization unit can concretize detailed customer benefits. For less important measures, it can concretize concretized customer benefits in a concise manner. Furthermore, the concretization unit can adjust the level of detail in stages according to the importance of the proposed measures. By adjusting the level of detail based on the importance of the proposed measures, more effective customer benefits can be concretized.

[0095] The creation unit can analyze a user's past survey response history when creating a survey and select the most suitable questions. For example, it can analyze the content of surveys a user has previously answered and suggest questions for similar surveys. It can also predict response trends to specific questions based on a user's past response history and adjust the questions accordingly. Furthermore, it can optimize the survey response format based on the user's past response history. This allows the system to provide users with the most suitable questions by analyzing their past response history.

[0096] The implementation team can filter virtual surveys based on users' current projects and areas of interest. For example, the implementation team can use AI to analyze users' current projects and areas of interest and prioritize relevant surveys. They can also filter and conduct surveys based on users' areas of interest. Furthermore, they can analyze users' past project history and suggest relevant surveys. This allows for the implementation of highly relevant surveys by filtering based on current projects and areas of interest.

[0097] The analytics department can analyze users' social media activity and supplement relevant data when analyzing survey results. For example, the analytics department can use AI to analyze the content of users' social media posts and supplement data related to the survey results. Furthermore, the analytics department can analyze the activity of users' followers and friends on social media and supplement data related to the survey results. In addition, the analytics department can analyze users' interests on social media and supplement data related to the survey results. This allows for a more accurate analysis of survey results by analyzing social media activity.

[0098] The reception desk can estimate the user's emotions and adjust the timing of policy proposal input based on the estimated emotions. For example, the reception desk can estimate the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. The reception desk can also capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. Furthermore, the reception desk can record the user's voice and estimate emotions using voice analysis technology. In addition, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. By adjusting the timing of policy proposal input according to the user's emotions, policy proposals can be entered at a more appropriate time.

[0099] The concretization unit can estimate the user's emotions and adjust the method of concretizing customer benefits based on the estimated user emotions. For example, the concretization unit can estimate the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. The concretization unit can also capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. Furthermore, the concretization unit can record the user's voice and estimate emotions using voice analysis technology. In addition, the concretization unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. By adjusting the method of concretizing customer benefits according to the user's emotions, more appropriate customer benefits can be concretized.

[0100] The creation unit can estimate the user's emotions when creating a questionnaire and adjust the content of the questions based on those estimated emotions. For example, the creation unit can estimate the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. The creation unit can also capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, the creation unit can record the user's voice and estimate their emotions using voice analysis technology. In addition, the creation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the creation of more appropriate questionnaires by adjusting the content of the questions according to the user's emotions.

[0101] The implementation unit can estimate the user's emotions during the virtual survey and adjust the survey method based on the estimated emotions. For example, the implementation unit can estimate the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. The implementation unit can also capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, the implementation unit can record the user's voice and estimate their emotions using voice analysis technology. In addition, the implementation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a more appropriate survey to be conducted by adjusting the survey method according to the user's emotions.

[0102] The analysis unit can estimate the user's emotions when analyzing survey results and adjust the analysis method based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. The analysis unit can also capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. In addition, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the analysis method according to the user's emotions, a more appropriate analysis of survey results can be achieved.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The reception desk inputs the proposed measures. These measures include business strategies and marketing strategies. Users can input their proposed measures in text format, and voice input is also available. Using speech recognition technology, the user's voice can be converted into text and entered as a proposed measure. Step 2: The concretization department concretizes the customer benefits based on the policy proposal entered by the reception department. Customer benefits include cost reduction and improved convenience. The concretization department uses AI to extract and concretize the specific benefits that customers can obtain based on the policy proposal. Step 3: The creation team creates a questionnaire based on the customer benefits specified by the specification team. The questionnaire includes questions and answer formats. The creation team uses AI to create questions to collect customer opinions based on customer benefits. Step 4: The implementation department conducts the questionnaire created by the development department. The implementation department conducts a virtual questionnaire, including online questionnaires and web surveys. The virtual questionnaire is conducted using AI to collect customer opinions. Step 5: The analysis department analyzes the survey results conducted by the implementation department. This analysis includes statistical analysis and text mining. AI is used to analyze the survey results and understand customer needs. Step 6: The planning department creates measures based on the results analyzed by the analysis department. These measures include implementation plans and effectiveness measurements. AI is used to create specific measures based on the analysis results. Step 7: The evaluation department evaluates the measures created by the development department. The evaluation includes customer evaluation, revenue and expenditure evaluation, and legal evaluation. AI is used to evaluate the measures, customer evaluation is conducted, and the effectiveness of the measures is assessed. Revenue and expenditure evaluation is conducted to assess the economic effect of the measures. Legal evaluation is conducted to confirm that the measures comply with the law.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the reception unit, concretization unit, creation unit, implementation unit, analysis unit, and evaluation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing users to input policy proposals in text format or by voice input. The concretization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, using AI to concretize customer benefits. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, creating questionnaires. The implementation unit is implemented by, for example, the control unit 46A of the smart device 14, conducting virtual questionnaires. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, analyzing the questionnaire results. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, performing customer evaluation, revenue and expenditure evaluation, and legal evaluation of the policy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, concretization unit, creation unit, implementation unit, analysis unit, and evaluation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input policy proposals in text format or by voice input. The concretization unit is implemented by the specific processing unit 290 of the data processing unit 12, using AI to concretize customer benefits. The creation unit is implemented by the specific processing unit 290 of the data processing unit 12, creating questionnaires. The implementation unit is implemented by the control unit 46A of the smart glasses 214, conducting virtual questionnaires. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the questionnaire results. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, performing customer evaluation, revenue and expenditure evaluation, and legal evaluation of the policy. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, concretization unit, creation unit, implementation unit, analysis unit, and evaluation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input policy proposals in text format or by voice input. The concretization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, using AI to concretize customer benefits. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, creating questionnaires. The implementation unit is implemented by, for example, the control unit 46A of the headset terminal 314, conducting virtual questionnaires. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, analyzing the questionnaire results. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, performing customer evaluation, revenue and expenditure evaluation, and legal evaluation of the policy. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In 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.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 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.

[0157] Each of the multiple elements described above, including the reception unit, concretization unit, creation unit, implementation unit, analysis unit, and evaluation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing users to input policy proposals in text format or by voice input. The concretization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, using AI to concretize customer benefits. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, creating questionnaires. The implementation unit is implemented by, for example, the control unit 46A of the robot 414, conducting virtual questionnaires. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, analyzing the questionnaire results. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, performing customer evaluation, revenue and expenditure evaluation, and legal evaluation of the policy. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) The reception desk for entering policy proposals, Based on the policy proposals entered by the reception unit, there is a concretization unit that concretizes the customer benefits, A creation unit creates a questionnaire based on the customer benefits specified by the concretization unit, An implementation unit that conducts the questionnaire created by the aforementioned creation unit, An analysis unit analyzes the results of the questionnaire conducted by the aforementioned implementation unit, A creation unit that creates policies based on the results of the analysis performed by the aforementioned analysis unit, An evaluation unit that evaluates the measures created by the creation unit, An evaluation unit that evaluates the revenue and expenditure of the measures evaluated by the aforementioned evaluation unit, The system comprises an evaluation unit that evaluates compliance with laws and regulations of the measures evaluated by the aforementioned evaluation unit. A system characterized by the following features. (Note 2) The aforementioned concrete part is, Specify the benefits for the customer. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned creation unit, Create a survey The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned implementation unit is Conduct a virtual survey The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyzing the survey results The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned creation unit, Creating policies The system described in Appendix 1, characterized by the features described herein. (Note 7) The evaluation unit described above, Conduct customer evaluations The system described in Appendix 1, characterized by the features described herein. (Note 8) The evaluation unit described above, Conduct a financial assessment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The evaluation unit described above, Conduct a legal assessment The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of policy proposal input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyze the user's past policy proposal submission history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When inputting policy proposals, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and determines the priority of proposed measures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When inputting policy proposals, the system prioritizes inputting highly relevant policy proposals by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When entering policy proposals, the system analyzes users' social media activity and inputs relevant policy proposals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned concrete part is, We estimate the user's emotions and adjust how we specify customer benefits based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned concrete part is, When specifying customer benefits, adjust the level of detail based on the importance of the proposed measures. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned concrete part is, When concretizing customer benefits, different concretization algorithms are applied depending on the category of the proposed measures. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned concrete part is, The system estimates the user's emotions and adjusts the length of detailing customer benefits based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned concrete part is, When concretizing customer benefits, prioritize implementation based on the timing of proposal submission. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk for entering policy proposals, Based on the policy proposals entered by the reception unit, there is a concretization unit that concretizes the customer benefits, A creation unit creates a questionnaire based on the customer benefits specified by the concretization unit, An implementation unit that conducts the questionnaire created by the aforementioned creation unit, An analysis unit analyzes the results of the questionnaire conducted by the aforementioned implementation unit, A creation unit that creates policies based on the results of the analysis performed by the aforementioned analysis unit, An evaluation unit that evaluates the measures created by the creation unit, An evaluation unit that evaluates the revenue and expenditure of the measures evaluated by the aforementioned evaluation unit, The system comprises an evaluation unit that evaluates compliance with laws and regulations of the measures evaluated by the aforementioned evaluation unit. A system characterized by the following features.

2. The aforementioned concrete part is, Specify the benefits for the customer. The system according to feature 1.

3. The aforementioned creation unit, Create a survey The system according to feature 1.

4. The aforementioned implementation unit is Conduct a virtual survey The system according to feature 1.

5. The aforementioned analysis unit is Analyzing the survey results The system according to feature 1.

6. The aforementioned creation unit, Creating policies The system according to feature 1.

7. The evaluation unit described above, Conduct customer evaluations The system according to feature 1.

8. The evaluation unit described above, Conduct a financial assessment. The system according to feature 1.

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