system

Abstract

The system according to this embodiment aims to efficiently elicit excellent ideas during product planning discussions and meetings. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a strategy planning unit, and a milestone creation unit. The collection unit collects information from discussions and meetings regarding product planning. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The strategy planning unit formulates a strategy based on the content proposed by the proposal unit. The milestone creation unit creates milestones based on the strategy formulated by the strategy planning unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently draw out excellent ideas in discussions and meetings for product planning.

[0005] The system according to the embodiment aims to efficiently draw out excellent ideas in discussions and meetings for product planning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a strategy planning unit, and a milestone creation unit. The data collection unit collects information from discussions and meetings regarding product planning. The analysis unit analyzes the information collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The strategy planning unit formulates a strategy based on the content proposed by the proposal unit. The milestone creation unit creates milestones based on the strategy formulated by the strategy planning unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently elicit excellent ideas during product planning discussions and meetings. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The product planning support system according to an embodiment of the present invention is a system that supports the efficiency of brainstorming and the extraction of excellent ideas by having a planning AI participate in product planning discussions and meetings, making suggestions in real time. The product planning support system involves the planning AI in product planning discussions and meetings. For example, by having the AI ​​make suggestions in real time during a meeting, participants can gain new perspectives and ideas. This improves the efficiency of brainstorming and extracts excellent ideas. Next, the planning AI analyzes product and service examples that have been created through collaboration between companies. For example, by extracting and analyzing past success and failure cases from a database, it is possible to understand what kind of collaboration has been effective. Furthermore, the planning AI proposes collaborative products and services that match the strengths and characteristics of the company. For example, if a company has excellent technological capabilities, it can propose new products and services that utilize that technology. It also proposes appropriate companies as collaboration partners. For example, synergistic effects can be expected when a company with excellent technological capabilities collaborates with a company with excellent marketing capabilities. In addition, the planning AI also supports the strategic planning and creation of milestones leading up to the collaboration. For example, it can clarify the goals and steps to be achieved in a collaboration and create a concrete action plan based on them. This makes the collaboration process smoother and increases the probability of success. In this way, utilizing planning AI streamlines discussions and meetings for product planning, and helps to elicit excellent ideas. It also promotes collaboration between companies, and is expected to lead to the creation of new products and services. Thus, product planning support systems can streamline discussions and meetings for product planning and help to elicit excellent ideas.

[0029] The product planning support system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a strategy planning unit, and a milestone creation unit. The collection unit collects information from product planning discussions and meetings. The collection unit can collect information in various formats and types, such as meeting minutes, email correspondence, and audio recordings. The collection unit collects information based on specific methods and criteria, such as manual collection, automated collection methods, and collection frequency. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information based on specific methods and criteria, such as text mining, data analysis techniques, and algorithms used. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit makes proposals based on specific criteria and methods, such as evaluation criteria for proposal content and proposal format. The strategy planning unit formulates strategies based on the content proposed by the proposal unit. The strategy planning unit formulates strategies based on specific planning methods and criteria, such as short-term strategies, long-term strategies, and risk assessments. The milestone creation unit creates milestones based on the strategy formulated by the strategy planning unit. The milestone creation unit creates milestones based on specific creation methods and criteria, such as achievement targets, deadlines, and evaluation criteria. As a result, the product planning support system according to this embodiment can streamline product planning discussions and meetings and elicit excellent ideas.

[0030] The data collection department gathers information from product planning discussions and meetings. This department can collect information in various formats and types, such as meeting minutes, email correspondence, and audio recordings. Specifically, meeting minutes are text data recorded in real time during meetings, detailing participants' statements and the progress of discussions. Email correspondence is collected from emails exchanged between project members and used to understand the background and intent of discussions. Audio recordings capture the audio of meetings and discussions, which are later converted to text for analysis. The data collection department can manually collect this information by manually recording meeting minutes and manually organizing emails. Alternatively, automated collection methods include using speech recognition technology to automatically transcribe meeting audio and automatically classifying and organizing email content. The frequency of collection is determined based on specific criteria, such as collecting information in real time after each meeting or periodically collecting and updating email content. This allows the data collection department to efficiently gather diverse information related to product planning and provide the data necessary for the next analysis stage.

[0031] The analysis department analyzes the information collected by the data collection department. The analysis department analyzes the information based on specific methods and criteria, such as text mining, data analysis techniques, and the algorithms used. Specifically, it uses text mining techniques to extract important keywords and topics from meeting minutes and email content, clarifying the focus and issues of discussions. As for data analysis techniques, it uses statistical analysis and machine learning algorithms to analyze patterns and trends in the collected data and derive important insights for product planning. For example, it can use clustering algorithms to classify the content of discussions by theme and evaluate the importance and relevance of each theme. Furthermore, it can use natural language processing techniques to perform sentiment analysis on text data, identifying positive and negative opinions within discussions and evaluating the direction of the plan. This allows the analysis department to analyze the collected information from multiple perspectives and provide important insights for product planning. In addition, the analysis department can integrate historical data and external market data to perform more accurate analysis. For example, it can refer to data from past successful product plans and analyze similarities and differences with current plans to assess the likelihood of success. Furthermore, by incorporating external market data, it becomes possible to understand the actions of competitors and market trends, and to evaluate the competitiveness of the project. This allows the analytics department to support data-driven decision-making in product planning and improve the success rate of the project.

[0032] The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department makes proposals based on specific criteria and methods, such as evaluation criteria for proposal content and proposal format. Specifically, based on the analysis results, they propose the direction of product planning and specific ideas. Evaluation criteria for proposal content include feasibility of the plan, market demand, and points of differentiation from competitors. In terms of proposal format, the proposal content is summarized in presentation materials or reports and shared with stakeholders. For example, the proposal department proposes new product concepts and functions based on market needs and trends derived from the analysis results. They can also propose differentiation strategies that take into account the trends of competitors and marketing strategies for target customer segments. This allows the proposal department to translate the analysis results into concrete action plans and show concrete steps toward realizing product planning. Furthermore, the proposal department can collect feedback on the proposal content and continuously improve the accuracy and effectiveness of the proposal. For example, they can collect opinions and evaluations of stakeholders on the proposal content and review the content and format of the proposal. The proposal department can also monitor the implementation status of the proposal content and revise or update the proposal content as needed. This allows the proposal department to consistently provide highly accurate proposals based on the latest information, thereby supporting the success of product planning.

[0033] The Strategy Planning Department formulates strategies based on the proposals submitted by the Proposal Department. The Strategy Planning Department formulates strategies based on specific planning methods and criteria, such as short-term strategies, long-term strategies, and risk assessments. Specifically, it develops concrete strategies for realizing product plans based on the proposed content. Short-term strategies include determining specific action plans and resource allocations in the initial stages of product development. Long-term strategies include formulating marketing strategies and sales plans that consider the entire product lifecycle. Risk assessments identify potential risks in product planning and implement countermeasures. For example, it evaluates risks related to competitor activities and market fluctuations and develops countermeasures. The Strategy Planning Department also evaluates the feasibility of the proposed content and estimates the necessary resources and schedule. This allows the Strategy Planning Department to formulate concrete strategies for realizing product plans and support their success. Furthermore, the Strategy Planning Department monitors the implementation status of strategies and can modify and update them as needed. For example, it flexibly reviews strategies in response to problems and changes that arise during the implementation process. Furthermore, the Strategy Planning Department will maintain close communication with stakeholders to promote the sharing and understanding of the strategy. This will enable the Strategy Planning Department to formulate concrete strategies for realizing product plans and support the success of those plans.

[0034] The milestone creation department creates milestones based on the strategy formulated by the strategy planning department. The milestone creation department creates milestones based on specific creation methods and criteria, such as achievement targets, deadlines, and evaluation criteria. Specifically, based on the strategy, it sets specific achievement targets and deadlines for each stage of product planning. Achievement targets include specific deliverables and progress status for each phase of product development. Deadlines include the completion date of each phase and the schedule of important events. Evaluation criteria include specific indicators and standards for evaluating the achievement status of each milestone. For example, in the initial stages of product development, the achievement targets are the confirmation of the concept and the completion of the prototype, and deadlines are set for these. In addition, evaluation criteria include specific indicators for evaluating the quality of the prototype and market response. This allows the milestone creation department to specifically manage the progress of product planning and show specific steps toward the success of the plan. Furthermore, the milestone creation department can monitor the achievement status of milestones and revise or update them as needed. For example, it can review deadlines or set new achievement targets depending on the progress. Furthermore, the milestone creation department maintains close communication with stakeholders to facilitate the sharing and understanding of milestones. This allows the milestone creation department to concretely manage the progress of product planning and outline specific steps toward the success of the plan.

[0035] The proposal department can analyze examples of products and services created through inter-company collaborations and propose collaborative products and services that match the strengths and characteristics of those companies. For example, the proposal department can extract and analyze past success and failure cases from a database to understand what kinds of collaborations have been effective. For example, a company with strong technological capabilities can propose new products and services that leverage its technology. Similarly, a company with strong marketing capabilities can propose new products and services that leverage its marketing strategy. This allows the proposal of collaborative products and services that match the strengths and characteristics of each company. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input examples of products and services created through inter-company collaborations into a generating AI and have the generating AI produce proposals for collaborative products and services that match the strengths and characteristics of each company.

[0036] The proposal department can suggest suitable companies as collaboration partners. The proposal department can select suitable companies based on criteria such as industry relevance, company size, and past performance. For example, the proposal department can expect synergistic effects from a collaboration between a company with strong technological capabilities and a company with strong marketing capabilities. Furthermore, the proposal department can open up new markets from a collaboration between a company with strong brand power and a company with strong manufacturing capabilities. This allows the proposal department to suggest suitable companies as collaboration partners. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input company information into a generating AI and have the generating AI generate proposals for suitable companies as collaboration partners.

[0037] The Strategy Planning Department can formulate strategies leading up to collaboration. For example, the Strategy Planning Department can formulate strategies based on criteria such as identifying stakeholders, managing risks, and setting timelines. For example, the Strategy Planning Department can clarify the goals of the collaboration and set steps to achieve them. Furthermore, the Strategy Planning Department can assess risks and formulate risk management plans. This allows for the formulation of strategies leading up to collaboration. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or not. For example, the Strategy Planning Department can input collaboration goals and risk information into a generating AI and have the generating AI formulate the strategy.

[0038] The milestone creation unit can clarify the goals and steps to be achieved in a collaboration and create a concrete action plan based on them. The milestone creation unit can create milestones based on criteria such as short-term goals, long-term goals, and specific actions for each step. The milestone creation unit can create a concrete action plan to achieve the collaboration goals. The milestone creation unit can also evaluate the progress of each step and revise the action plan as needed. This makes the collaboration progress more smoothly and increases the probability of success. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or not. For example, the milestone creation unit can input the collaboration goals and step information into a generating AI and have the generating AI create the action plan.

[0039] The data collection unit can analyze past discussion history and select the optimal data collection method. For example, the data collection unit can identify an effective data collection method for a specific topic from past discussion history and apply a similar method. The data collection unit can also analyze past discussion history and select a method that facilitated smooth discussion. Furthermore, based on past discussion history, the data collection unit can select a data collection method that elicited a positive response from participants. This allows the optimal data collection method to be selected based on past discussion history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past discussion history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The collection unit can filter discussions based on the participants' areas of expertise and interests. For example, the collection unit can prioritize collecting discussions that are highly relevant based on the participants' areas of expertise. It can also collect discussions that are of interest based on the participants' areas of interest. Furthermore, the collection unit can combine the participants' areas of expertise and areas of interest to collect the most relevant discussions. This allows for filtering of discussions based on the participants' areas of expertise and interests. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the participants' areas of expertise and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The collection unit can prioritize collecting discussions that are highly relevant, taking into account the geographical location information of the participants. For example, the collection unit can prioritize collecting discussions related to a region based on the geographical location information of the participants. The collection unit can also prioritize collecting discussions related to regional trends, taking into account the geographical location information of the participants. Furthermore, the collection unit can prioritize collecting discussions related to regional issues, taking into account the geographical location information of the participants. This allows for the priority collection of highly relevant discussions, taking into account the geographical location information of the participants. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the geographical location information of the participants into a generating AI and have the generating AI perform the collection of highly relevant discussions.

[0042] The collection unit can analyze participants' social media activity and collect relevant discussions when collecting discussions. For example, the collection unit can analyze participants' social media activity and collect discussions they are interested in. The collection unit can also collect discussions related to trends based on participants' social media activity. Furthermore, the collection unit can collect discussions that are of interest, taking into account participants' social media activity. This allows for the collection of relevant discussions by analyzing participants' social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input participants' social media activity data into a generating AI and have the generating AI perform the collection of relevant discussions.

[0043] The analysis unit can adjust the level of detail of its analysis based on the importance of each discussion. For example, it can perform a detailed analysis on important discussions and a simplified analysis on less important discussions. Furthermore, it can adjust the level of detail of its analysis in stages according to the importance of each discussion. This allows the level of detail of the analysis to be adjusted based on the importance of each discussion. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input discussion importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the discussion during the analysis. For example, the analysis unit can apply a technical analysis algorithm to a technical discussion. It can also apply a marketing analysis algorithm to a marketing discussion. Furthermore, it can apply a financial analysis algorithm to a financial discussion. This allows for the application of different analysis algorithms depending on the category of the discussion. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input discussion category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on when the arguments were submitted. For example, the analysis unit may prioritize the analysis of recently submitted arguments. It can also postpone the analysis of older arguments. Furthermore, the analysis unit can adjust the priority of analysis in stages based on the submission date. This allows the analysis priority to be determined based on when the arguments were submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission date data of the arguments into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the arguments during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant arguments. It can also postpone the analysis of less relevant arguments. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the arguments. This allows the order of analysis to be adjusted based on the relevance of the arguments. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance data of the arguments into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0047] The proposal department can adjust the level of detail in a proposal based on the company's strengths and characteristics. For example, the proposal department can provide a proposal that includes technical details to a company with strong technological capabilities. It can also provide a proposal that includes marketing strategies to a company with strong marketing capabilities. Furthermore, it can provide a proposal that includes financial details to a company with strong financial capabilities. This allows the proposal department to adjust the level of detail based on the company's strengths and characteristics. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the company's strengths and characteristics into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0048] The proposal unit can apply different proposal algorithms depending on the company category when making proposals. For example, the proposal unit can apply a proposal algorithm related to manufacturing processes to manufacturing companies. It can also apply a proposal algorithm related to service provision to service companies. Furthermore, it can apply a proposal algorithm related to sales strategies to retail companies. This allows for the application of different proposal algorithms depending on the company category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input company category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0049] The proposal department can determine the priority of proposals based on the submission timing of companies. For example, the proposal department may prioritize recently submitted proposals. It may also postpone the provision of older proposals. Furthermore, the proposal department may adjust the priority of proposals in stages based on the submission timing. This allows for the determination of proposal priority based on the submission timing of companies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input company submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0050] The proposal department can adjust the order of proposals based on the relevance of the companies when making proposals. For example, the proposal department may prioritize providing proposals with high relevance. It may also postpone providing proposals with low relevance. Furthermore, the proposal department may adjust the order of proposals in stages based on the relevance of the companies. This allows for adjusting the order of proposals based on the relevance of the companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input company relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0051] The Strategy Planning Department can select the optimal strategy by referring to past success and failure cases when formulating a strategy. For example, the Strategy Planning Department can select a similar strategy based on past success cases. It can also analyze past failure cases and select a strategy to avoid the same mistakes. Furthermore, the Strategy Planning Department can compare success and failure cases to select the optimal strategy. This allows for the selection of the optimal strategy based on past success and failure cases. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or without AI. For example, the Strategy Planning Department can input data on past success and failure cases into a generating AI and have the generating AI select the optimal strategy.

[0052] The Strategy Planning Department can customize strategies based on the company's current situation when formulating them. For example, the Strategy Planning Department can customize feasible strategies based on the company's financial situation. It can also customize strategies to enhance competitiveness based on the company's market position. Furthermore, the Strategy Planning Department can customize efficient strategies based on the company's resources. This allows for the customization of strategies based on the company's current situation. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or not. For example, the Strategy Planning Department can input data on the company's current situation into a generating AI and have the generating AI perform the strategy customization.

[0053] The Strategy Planning Department can select the optimal strategy when formulating a strategy, taking into account the company's geographical location. For example, the Strategy Planning Department can select a region-related strategy based on the company's geographical location. It can also select a strategy related to regional trends, taking into account the company's geographical location. Furthermore, it can select a strategy related to regional issues, taking into account the company's geographical location. This allows for the selection of the optimal strategy, taking into account the company's geographical location. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or without AI. For example, the Strategy Planning Department can input the company's geographical location into a generating AI and have the generating AI select the optimal strategy.

[0054] The Strategy Planning Department can analyze a company's social media activities and propose strategies when formulating strategies. For example, the Strategy Planning Department can analyze a company's social media activities and propose strategies that are of interest to the company. Furthermore, the Strategy Planning Department can propose strategies related to trends based on the company's social media activities. In addition, the Strategy Planning Department can propose strategies that are engaging, taking into account the company's social media activities. This allows for the analysis of a company's social media activities and the proposal of strategies. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or not. For example, the Strategy Planning Department can input company social media activity data into a generating AI and have the generating AI generate strategy proposals.

[0055] The milestone creation unit can create optimal milestones by referring to past project data when creating milestones. For example, the milestone creation unit can create similar milestones based on data from past successful projects. It can also analyze data from past failed projects and create milestones to avoid the same mistakes. Furthermore, the milestone creation unit can compare successful and unsuccessful cases to create optimal milestones. This allows for the creation of optimal milestones based on past project data. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or without AI. For example, the milestone creation unit can input past project data into a generation AI and have the generation AI create optimal milestones.

[0056] The milestone creation unit can customize milestones based on the company's current situation when creating them. For example, the milestone creation unit can customize actionable milestones based on the company's financial situation. It can also customize milestones that enhance competitiveness based on the company's market position. Furthermore, it can customize efficient milestones based on the company's resources. This allows for the customization of milestones based on the company's current situation. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or not. For example, the milestone creation unit can input data on the company's current situation into a generating AI and have the generating AI perform the milestone customization.

[0057] The milestone creation unit can create optimal milestones by considering the company's geographical location information. For example, the milestone creation unit can create regionally relevant milestones based on the company's geographical location information. It can also create milestones related to regional trends by considering the company's geographical location information. Furthermore, it can create milestones related to regional issues based on the company's geographical location information. This allows for the creation of optimal milestones by considering the company's geographical location information. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or without AI. For example, the milestone creation unit can input the company's geographical location information into a generation AI and have the generation AI create optimal milestones.

[0058] The milestone creation unit can analyze a company's social media activities and propose milestones when creating them. For example, the milestone creation unit can analyze a company's social media activities and propose milestones of interest. It can also propose milestones related to trends based on a company's social media activities. Furthermore, the milestone creation unit can propose milestones that are of interest, taking into account a company's social media activities. In this way, milestones can be proposed by analyzing a company's social media activities. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or not using AI. For example, the milestone creation unit can input a company's social media activity data into a generating AI and have the generating AI execute the milestone proposal.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can analyze past discussion history and select the optimal data collection method. For example, it can identify an effective data collection method for a specific topic from past discussion history and apply a similar method. It can also analyze past discussion history and select a method that facilitated smooth discussion. Furthermore, it can select a data collection method that elicited a positive response from participants based on past discussion history. This allows for the selection of the optimal data collection method based on past discussion history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past discussion history data into a generating AI and have the generating AI select the optimal data collection method.

[0061] The collection unit can filter discussions based on the participants' areas of expertise and interests. For example, it can prioritize collecting discussions that are highly relevant based on the participants' areas of expertise. It can also collect discussions that are of interest based on the participants' areas of interest. Furthermore, it can combine the participants' areas of expertise and areas of interest to collect the most suitable discussions. This allows for filtering of discussions based on the participants' areas of expertise and interests. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the participants' areas of expertise and areas of interest into a generating AI and have the generating AI perform the filtering.

[0062] The collection unit can prioritize collecting discussions that are highly relevant, taking into account the geographical location information of the participants. For example, it can prioritize collecting discussions related to a specific region based on the participants' geographical location information. It can also prioritize collecting discussions related to regional trends, taking into account the participants' geographical location information. Furthermore, it can prioritize collecting discussions related to regional issues, taking into account the participants' geographical location information. This allows for the priority collection of highly relevant discussions, taking into account the participants' geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the participants' geographical location information into a generating AI and have the generating AI perform the collection of highly relevant discussions.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the discussions. For example, it can perform a detailed analysis on important discussions and a simplified analysis on less important discussions. Furthermore, it can adjust the level of detail of the analysis in stages according to the importance of the discussions. This allows the level of detail of the analysis to be adjusted based on the importance of the discussions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input discussion importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0064] The proposal department can adjust the level of detail in a proposal based on the company's strengths and characteristics. For example, a company with strong technological capabilities can receive a proposal that includes technical details. Similarly, a company with strong marketing capabilities can receive a proposal that includes marketing strategies. Furthermore, a company with strong financial capabilities can receive a proposal that includes financial details. This allows for adjustment of the level of detail in a proposal based on the company's strengths and characteristics. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the company's strengths and characteristics into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection department gathers information from product planning discussions and meetings. The collection department can collect information in various formats and types, such as meeting minutes, email correspondence, and audio recordings. The collection department collects information based on specific methods and criteria, such as manual collection, automated collection methods, and collection frequency. Step 2: The analysis department analyzes the information collected by the collection department. The analysis department analyzes the information based on specific methods and criteria, such as text mining, data analysis techniques, and the algorithms used. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department makes proposals based on specific criteria and methods, such as evaluation criteria for the proposal content and the format of the proposal. Step 4: The Strategy Planning Department formulates a strategy based on the proposals submitted by the Proposal Department. The Strategy Planning Department formulates a strategy based on specific planning methods and criteria, such as short-term strategies, long-term strategies, and risk assessments. Step 5: The milestone creation department creates milestones based on the strategy developed by the strategy planning department. The milestone creation department creates milestones based on specific creation methods and criteria such as achievement targets, deadlines, and evaluation criteria.

[0067] (Example of form 2) The product planning support system according to an embodiment of the present invention is a system that supports the efficiency of brainstorming and the extraction of excellent ideas by having a planning AI participate in product planning discussions and meetings, making suggestions in real time. The product planning support system involves the planning AI in product planning discussions and meetings. For example, by having the AI ​​make suggestions in real time during a meeting, participants can gain new perspectives and ideas. This improves the efficiency of brainstorming and extracts excellent ideas. Next, the planning AI analyzes product and service examples that have been created through collaboration between companies. For example, by extracting and analyzing past success and failure cases from a database, it is possible to understand what kind of collaboration has been effective. Furthermore, the planning AI proposes collaborative products and services that match the strengths and characteristics of the company. For example, if a company has excellent technological capabilities, it can propose new products and services that utilize that technology. It also proposes appropriate companies as collaboration partners. For example, synergistic effects can be expected when a company with excellent technological capabilities collaborates with a company with excellent marketing capabilities. In addition, the planning AI also supports the strategic planning and creation of milestones leading up to the collaboration. For example, it can clarify the goals and steps to be achieved in a collaboration and create a concrete action plan based on them. This makes the collaboration process smoother and increases the probability of success. In this way, utilizing planning AI streamlines discussions and meetings for product planning, and helps to elicit excellent ideas. It also promotes collaboration between companies, and is expected to lead to the creation of new products and services. Thus, product planning support systems can streamline discussions and meetings for product planning and help to elicit excellent ideas.

[0068] The product planning support system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a strategy planning unit, and a milestone creation unit. The collection unit collects information from product planning discussions and meetings. The collection unit can collect information in various formats and types, such as meeting minutes, email correspondence, and audio recordings. The collection unit collects information based on specific methods and criteria, such as manual collection, automated collection methods, and collection frequency. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information based on specific methods and criteria, such as text mining, data analysis techniques, and algorithms used. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit makes proposals based on specific criteria and methods, such as evaluation criteria for proposal content and proposal format. The strategy planning unit formulates strategies based on the content proposed by the proposal unit. The strategy planning unit formulates strategies based on specific planning methods and criteria, such as short-term strategies, long-term strategies, and risk assessments. The milestone creation unit creates milestones based on the strategy formulated by the strategy planning unit. The milestone creation unit creates milestones based on specific creation methods and criteria, such as achievement targets, deadlines, and evaluation criteria. As a result, the product planning support system according to this embodiment can streamline product planning discussions and meetings and elicit excellent ideas.

[0069] The data collection department gathers information from product planning discussions and meetings. This department can collect information in various formats and types, such as meeting minutes, email correspondence, and audio recordings. Specifically, meeting minutes are text data recorded in real time during meetings, detailing participants' statements and the progress of discussions. Email correspondence is collected from emails exchanged between project members and used to understand the background and intent of discussions. Audio recordings capture the audio of meetings and discussions, which are later converted to text for analysis. The data collection department can manually collect this information by manually recording meeting minutes and manually organizing emails. Alternatively, automated collection methods include using speech recognition technology to automatically transcribe meeting audio and automatically classifying and organizing email content. The frequency of collection is determined based on specific criteria, such as collecting information in real time after each meeting or periodically collecting and updating email content. This allows the data collection department to efficiently gather diverse information related to product planning and provide the data necessary for the next analysis stage.

[0070] The analysis department analyzes the information collected by the data collection department. The analysis department analyzes the information based on specific methods and criteria, such as text mining, data analysis techniques, and the algorithms used. Specifically, it uses text mining techniques to extract important keywords and topics from meeting minutes and email content, clarifying the focus and issues of discussions. As for data analysis techniques, it uses statistical analysis and machine learning algorithms to analyze patterns and trends in the collected data and derive important insights for product planning. For example, it can use clustering algorithms to classify the content of discussions by theme and evaluate the importance and relevance of each theme. Furthermore, it can use natural language processing techniques to perform sentiment analysis on text data, identifying positive and negative opinions within discussions and evaluating the direction of the plan. This allows the analysis department to analyze the collected information from multiple perspectives and provide important insights for product planning. In addition, the analysis department can integrate historical data and external market data to perform more accurate analysis. For example, it can refer to data from past successful product plans and analyze similarities and differences with current plans to assess the likelihood of success. Furthermore, by incorporating external market data, it becomes possible to understand the actions of competitors and market trends, and to evaluate the competitiveness of the project. This allows the analytics department to support data-driven decision-making in product planning and improve the success rate of the project.

[0071] The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department makes proposals based on specific criteria and methods, such as evaluation criteria for proposal content and proposal format. Specifically, based on the analysis results, they propose the direction of product planning and specific ideas. Evaluation criteria for proposal content include feasibility of the plan, market demand, and points of differentiation from competitors. In terms of proposal format, the proposal content is summarized in presentation materials or reports and shared with stakeholders. For example, the proposal department proposes new product concepts and functions based on market needs and trends derived from the analysis results. They can also propose differentiation strategies that take into account the trends of competitors and marketing strategies for target customer segments. This allows the proposal department to translate the analysis results into concrete action plans and show concrete steps toward realizing product planning. Furthermore, the proposal department can collect feedback on the proposal content and continuously improve the accuracy and effectiveness of the proposal. For example, they can collect opinions and evaluations of stakeholders on the proposal content and review the content and format of the proposal. The proposal department can also monitor the implementation status of the proposal content and revise or update the proposal content as needed. This allows the proposal department to consistently provide highly accurate proposals based on the latest information, thereby supporting the success of product planning.

[0072] The Strategy Planning Department formulates strategies based on the proposals submitted by the Proposal Department. The Strategy Planning Department formulates strategies based on specific planning methods and criteria, such as short-term strategies, long-term strategies, and risk assessments. Specifically, it develops concrete strategies for realizing product plans based on the proposed content. Short-term strategies include determining specific action plans and resource allocations in the initial stages of product development. Long-term strategies include formulating marketing strategies and sales plans that consider the entire product lifecycle. Risk assessments identify potential risks in product planning and implement countermeasures. For example, it evaluates risks related to competitor activities and market fluctuations and develops countermeasures. The Strategy Planning Department also evaluates the feasibility of the proposed content and estimates the necessary resources and schedule. This allows the Strategy Planning Department to formulate concrete strategies for realizing product plans and support their success. Furthermore, the Strategy Planning Department monitors the implementation status of strategies and can modify and update them as needed. For example, it flexibly reviews strategies in response to problems and changes that arise during the implementation process. Furthermore, the Strategy Planning Department will maintain close communication with stakeholders to promote the sharing and understanding of the strategy. This will enable the Strategy Planning Department to formulate concrete strategies for realizing product plans and support the success of those plans.

[0073] The milestone creation department creates milestones based on the strategy formulated by the strategy planning department. The milestone creation department creates milestones based on specific creation methods and criteria, such as achievement targets, deadlines, and evaluation criteria. Specifically, based on the strategy, it sets specific achievement targets and deadlines for each stage of product planning. Achievement targets include specific deliverables and progress status for each phase of product development. Deadlines include the completion date of each phase and the schedule of important events. Evaluation criteria include specific indicators and standards for evaluating the achievement status of each milestone. For example, in the initial stages of product development, the achievement targets are the confirmation of the concept and the completion of the prototype, and deadlines are set for these. In addition, evaluation criteria include specific indicators for evaluating the quality of the prototype and market response. This allows the milestone creation department to specifically manage the progress of product planning and show specific steps toward the success of the plan. Furthermore, the milestone creation department can monitor the achievement status of milestones and revise or update them as needed. For example, it can review deadlines or set new achievement targets depending on the progress. Furthermore, the milestone creation department maintains close communication with stakeholders to facilitate the sharing and understanding of milestones. This allows the milestone creation department to concretely manage the progress of product planning and outline specific steps toward the success of the plan.

[0074] The proposal department can analyze examples of products and services created through inter-company collaborations and propose collaborative products and services that match the strengths and characteristics of those companies. For example, the proposal department can extract and analyze past success and failure cases from a database to understand what kinds of collaborations have been effective. For example, a company with strong technological capabilities can propose new products and services that leverage its technology. Similarly, a company with strong marketing capabilities can propose new products and services that leverage its marketing strategy. This allows the proposal of collaborative products and services that match the strengths and characteristics of each company. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input examples of products and services created through inter-company collaborations into a generating AI and have the generating AI produce proposals for collaborative products and services that match the strengths and characteristics of each company.

[0075] The proposal department can suggest suitable companies as collaboration partners. The proposal department can select suitable companies based on criteria such as industry relevance, company size, and past performance. For example, the proposal department can expect synergistic effects from a collaboration between a company with strong technological capabilities and a company with strong marketing capabilities. Furthermore, the proposal department can open up new markets from a collaboration between a company with strong brand power and a company with strong manufacturing capabilities. This allows the proposal department to suggest suitable companies as collaboration partners. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input company information into a generating AI and have the generating AI generate proposals for suitable companies as collaboration partners.

[0076] The Strategy Planning Department can formulate strategies leading up to collaboration. For example, the Strategy Planning Department can formulate strategies based on criteria such as identifying stakeholders, managing risks, and setting timelines. For example, the Strategy Planning Department can clarify the goals of the collaboration and set steps to achieve them. Furthermore, the Strategy Planning Department can assess risks and formulate risk management plans. This allows for the formulation of strategies leading up to collaboration. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or not. For example, the Strategy Planning Department can input collaboration goals and risk information into a generating AI and have the generating AI formulate the strategy.

[0077] The milestone creation unit can clarify the goals and steps to be achieved in a collaboration and create a concrete action plan based on them. The milestone creation unit can create milestones based on criteria such as short-term goals, long-term goals, and specific actions for each step. The milestone creation unit can create a concrete action plan to achieve the collaboration goals. The milestone creation unit can also evaluate the progress of each step and revise the action plan as needed. This makes the collaboration progress more smoothly and increases the probability of success. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or not. For example, the milestone creation unit can input the collaboration goals and step information into a generating AI and have the generating AI create the action plan.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of discussion collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit can temporarily suspend discussion collection and provide a relaxing environment. Conversely, if the user is focused, the data collection unit can continue collecting discussion to ensure that important points are not missed. Furthermore, if the user is tired, the data collection unit can collect discussion in a short amount of time to efficiently acquire information. This allows the timing of discussion collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit can analyze past discussion history and select the optimal data collection method. For example, the data collection unit can identify an effective data collection method for a specific topic from past discussion history and apply a similar method. The data collection unit can also analyze past discussion history and select a method that facilitated smooth discussion. Furthermore, based on past discussion history, the data collection unit can select a data collection method that elicited a positive response from participants. This allows the optimal data collection method to be selected based on past discussion history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past discussion history data into a generating AI and have the generating AI select the optimal data collection method.

[0080] The collection unit can filter discussions based on the participants' areas of expertise and interests. For example, the collection unit can prioritize collecting discussions that are highly relevant based on the participants' areas of expertise. It can also collect discussions that are of interest based on the participants' areas of interest. Furthermore, the collection unit can combine the participants' areas of expertise and areas of interest to collect the most relevant discussions. This allows for filtering of discussions based on the participants' areas of expertise and interests. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the participants' areas of expertise and areas of interest into a generating AI and have the generating AI perform the filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of discussions to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting important discussions. If the user is relaxed, the data collection unit may also prioritize collecting interesting discussions. Furthermore, if the user is tired, the data collection unit may prioritize collecting discussions that can be collected in a short amount of time. This allows the priority of discussions to be collected to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The collection unit can prioritize collecting discussions that are highly relevant, taking into account the geographical location information of the participants. For example, the collection unit can prioritize collecting discussions related to a region based on the geographical location information of the participants. The collection unit can also prioritize collecting discussions related to regional trends, taking into account the geographical location information of the participants. Furthermore, the collection unit can prioritize collecting discussions related to regional issues, taking into account the geographical location information of the participants. This allows for the priority collection of highly relevant discussions, taking into account the geographical location information of the participants. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the geographical location information of the participants into a generating AI and have the generating AI perform the collection of highly relevant discussions.

[0083] The collection unit can analyze participants' social media activity and collect relevant discussions when collecting discussions. For example, the collection unit can analyze participants' social media activity and collect discussions they are interested in. The collection unit can also collect discussions related to trends based on participants' social media activity. Furthermore, the collection unit can collect discussions that are of interest, taking into account participants' social media activity. This allows for the collection of relevant discussions by analyzing participants' social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input participants' social media activity data into a generating AI and have the generating AI perform the collection of relevant discussions.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visual presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can adjust the level of detail of its analysis based on the importance of each discussion. For example, it can perform a detailed analysis on important discussions and a simplified analysis on less important discussions. Furthermore, it can adjust the level of detail of its analysis in stages according to the importance of each discussion. This allows the level of detail of the analysis to be adjusted based on the importance of each discussion. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input discussion importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the discussion during the analysis. For example, the analysis unit can apply a technical analysis algorithm to a technical discussion. It can also apply a marketing analysis algorithm to a marketing discussion. Furthermore, it can apply a financial analysis algorithm to a financial discussion. This allows for the application of different analysis algorithms depending on the category of the discussion. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input discussion category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can determine the priority of analysis based on when the arguments were submitted. For example, the analysis unit may prioritize the analysis of recently submitted arguments. It can also postpone the analysis of older arguments. Furthermore, the analysis unit can adjust the priority of analysis in stages based on the submission date. This allows the analysis priority to be determined based on when the arguments were submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission date data of the arguments into a generating AI and have the generating AI perform the determination of the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the arguments during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant arguments. It can also postpone the analysis of less relevant arguments. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the arguments. This allows the order of analysis to be adjusted based on the relevance of the arguments. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance data of the arguments into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, it can also provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide concise suggestions. This allows the suggestion unit to adjust the way suggestions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The proposal department can adjust the level of detail in a proposal based on the company's strengths and characteristics. For example, the proposal department can provide a proposal that includes technical details to a company with strong technological capabilities. It can also provide a proposal that includes marketing strategies to a company with strong marketing capabilities. Furthermore, it can provide a proposal that includes financial details to a company with strong financial capabilities. This allows the proposal department to adjust the level of detail based on the company's strengths and characteristics. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the company's strengths and characteristics into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0092] The proposal unit can apply different proposal algorithms depending on the company category when making proposals. For example, the proposal unit can apply a proposal algorithm related to manufacturing processes to manufacturing companies. It can also apply a proposal algorithm related to service provision to service companies. Furthermore, it can apply a proposal algorithm related to sales strategies to retail companies. This allows for the application of different proposal algorithms depending on the company category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input company category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows the length of suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The proposal department can determine the priority of proposals based on the submission timing of companies. For example, the proposal department may prioritize recently submitted proposals. It may also postpone the provision of older proposals. Furthermore, the proposal department may adjust the priority of proposals in stages based on the submission timing. This allows for the determination of proposal priority based on the submission timing of companies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input company submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0095] The proposal department can adjust the order of proposals based on the relevance of the companies when making proposals. For example, the proposal department may prioritize providing proposals with high relevance. It may also postpone providing proposals with low relevance. Furthermore, the proposal department may adjust the order of proposals in stages based on the relevance of the companies. This allows for adjusting the order of proposals based on the relevance of the companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input company relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0096] The strategy planning unit can estimate the user's emotions and adjust the strategy planning method based on the estimated user emotions. For example, if the user is stressed, the strategy planning unit can provide a simple and highly visible strategy. If the user is relaxed, the strategy planning unit can also provide a strategy that includes detailed information. Furthermore, if the user is in a hurry, the strategy planning unit can provide a concise strategy. This allows the strategy planning method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy planning unit may be performed using AI, for example, or not using AI. For example, the strategy planning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The Strategy Planning Department can select the optimal strategy by referring to past success and failure cases when formulating a strategy. For example, the Strategy Planning Department can select a similar strategy based on past success cases. It can also analyze past failure cases and select a strategy to avoid the same mistakes. Furthermore, the Strategy Planning Department can compare success and failure cases to select the optimal strategy. This allows for the selection of the optimal strategy based on past success and failure cases. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or without AI. For example, the Strategy Planning Department can input data on past success and failure cases into a generating AI and have the generating AI select the optimal strategy.

[0098] The Strategy Planning Department can customize strategies based on the company's current situation when formulating them. For example, the Strategy Planning Department can customize feasible strategies based on the company's financial situation. It can also customize strategies to enhance competitiveness based on the company's market position. Furthermore, the Strategy Planning Department can customize efficient strategies based on the company's resources. This allows for the customization of strategies based on the company's current situation. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or not. For example, the Strategy Planning Department can input data on the company's current situation into a generating AI and have the generating AI perform the strategy customization.

[0099] The strategy planning unit can estimate the user's emotions and determine the priority of strategies based on the estimated emotions. For example, if the user is excited, the strategy planning unit will prioritize important strategies. If the user is relaxed, the strategy planning unit may also prioritize detailed strategies. Furthermore, if the user is tired, the strategy planning unit may prioritize strategies that can be formulated quickly. This allows the priority of strategies to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy planning unit may be performed using AI, or not using AI. For example, the strategy planning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The Strategy Planning Department can select the optimal strategy when formulating a strategy, taking into account the company's geographical location. For example, the Strategy Planning Department can select a region-related strategy based on the company's geographical location. It can also select a strategy related to regional trends, taking into account the company's geographical location. Furthermore, it can select a strategy related to regional issues, taking into account the company's geographical location. This allows for the selection of the optimal strategy, taking into account the company's geographical location. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or without AI. For example, the Strategy Planning Department can input the company's geographical location into a generating AI and have the generating AI select the optimal strategy.

[0101] The Strategy Planning Department can analyze a company's social media activities and propose strategies when formulating strategies. For example, the Strategy Planning Department can analyze a company's social media activities and propose strategies that are of interest to the company. Furthermore, the Strategy Planning Department can propose strategies related to trends based on the company's social media activities. In addition, the Strategy Planning Department can propose strategies that are engaging, taking into account the company's social media activities. This allows for the analysis of a company's social media activities and the proposal of strategies. Some or all of the above processes in the Strategy Planning Department may be performed using AI, for example, or not. For example, the Strategy Planning Department can input company social media activity data into a generating AI and have the generating AI generate strategy proposals.

[0102] The milestone creation unit can estimate the user's emotions and adjust the milestone creation method based on the estimated user emotions. For example, if the user is nervous, the milestone creation unit can provide simple and highly visible milestones. If the user is relaxed, the milestone creation unit can also provide milestones that include detailed information. Furthermore, if the user is in a hurry, the milestone creation unit can provide milestones that get straight to the point. This allows the milestone creation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the milestone creation unit may be performed using AI or not using AI. For example, the milestone creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The milestone creation unit can create optimal milestones by referring to past project data when creating milestones. For example, the milestone creation unit can create similar milestones based on data from past successful projects. It can also analyze data from past failed projects and create milestones to avoid the same mistakes. Furthermore, the milestone creation unit can compare successful and unsuccessful cases to create optimal milestones. This allows for the creation of optimal milestones based on past project data. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or without AI. For example, the milestone creation unit can input past project data into a generation AI and have the generation AI create optimal milestones.

[0104] The milestone creation unit can customize milestones based on the company's current situation when creating them. For example, the milestone creation unit can customize actionable milestones based on the company's financial situation. It can also customize milestones that enhance competitiveness based on the company's market position. Furthermore, it can customize efficient milestones based on the company's resources. This allows for the customization of milestones based on the company's current situation. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or not. For example, the milestone creation unit can input data on the company's current situation into a generating AI and have the generating AI perform the milestone customization.

[0105] The milestone creation unit can estimate the user's emotions and determine the priority of milestones based on the estimated emotions. For example, if the user is excited, the milestone creation unit will prioritize creating important milestones. It can also prioritize creating detailed milestones if the user is relaxed. Furthermore, if the user is tired, it can prioritize creating milestones that can be completed quickly. This allows for the prioritization of milestones according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the milestone creation unit may be performed using AI, or not. For example, the milestone creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The milestone creation unit can create optimal milestones by considering the company's geographical location information. For example, the milestone creation unit can create regionally relevant milestones based on the company's geographical location information. It can also create milestones related to regional trends by considering the company's geographical location information. Furthermore, it can create milestones related to regional issues based on the company's geographical location information. This allows for the creation of optimal milestones by considering the company's geographical location information. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or without AI. For example, the milestone creation unit can input the company's geographical location information into a generation AI and have the generation AI create optimal milestones.

[0107] The milestone creation unit can analyze a company's social media activities and propose milestones when creating them. For example, the milestone creation unit can analyze a company's social media activities and propose milestones of interest. It can also propose milestones related to trends based on a company's social media activities. Furthermore, the milestone creation unit can propose milestones that are of interest, taking into account a company's social media activities. In this way, milestones can be proposed by analyzing a company's social media activities. Some or all of the above processes in the milestone creation unit may be performed using AI, for example, or not using AI. For example, the milestone creation unit can input a company's social media activity data into a generating AI and have the generating AI execute the milestone proposal.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The data collection unit can estimate the user's emotions and adjust the timing of discussion collection based on the estimated emotions. For example, if the user is stressed, the data collection can be temporarily suspended to provide a relaxing environment. If the user is focused, the data collection can continue to ensure that important points are not missed. Furthermore, if the user is tired, the data collection can be completed in a short time to efficiently acquire information. This allows the timing of discussion collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visual presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can provide simple and highly visible suggestions. If the user is relaxed, it can provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide suggestions that get straight to the point. This allows the suggestion unit to adjust its presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The strategy planning department can estimate the user's emotions and adjust the strategy planning method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible strategy. If the user is relaxed, it can provide a strategy that includes detailed information. Furthermore, if the user is in a hurry, it can provide a strategy that gets straight to the point. This allows the strategy planning method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy planning department may be performed using AI, for example, or not using AI. For example, the strategy planning department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The milestone creation unit can estimate the user's emotions and adjust the milestone creation method based on the estimated user emotions. For example, if the user is nervous, it can provide simple and highly visible milestones. If the user is relaxed, it can provide milestones that include detailed information. Furthermore, if the user is in a hurry, it can provide milestones that get straight to the point. This allows the milestone creation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the milestone creation unit may be performed using AI or not using AI. For example, the milestone creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The data collection unit can analyze past discussion history and select the optimal data collection method. For example, it can identify an effective data collection method for a specific topic from past discussion history and apply a similar method. It can also analyze past discussion history and select a method that facilitated smooth discussion. Furthermore, it can select a data collection method that elicited a positive response from participants based on past discussion history. This allows for the selection of the optimal data collection method based on past discussion history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past discussion history data into a generating AI and have the generating AI select the optimal data collection method.

[0115] The collection unit can filter discussions based on the participants' areas of expertise and interests. For example, it can prioritize collecting discussions that are highly relevant based on the participants' areas of expertise. It can also collect discussions that are of interest based on the participants' areas of interest. Furthermore, it can combine the participants' areas of expertise and areas of interest to collect the most suitable discussions. This allows for filtering of discussions based on the participants' areas of expertise and interests. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the participants' areas of expertise and areas of interest into a generating AI and have the generating AI perform the filtering.

[0116] The collection unit can prioritize collecting discussions that are highly relevant, taking into account the geographical location information of the participants. For example, it can prioritize collecting discussions related to a specific region based on the participants' geographical location information. It can also prioritize collecting discussions related to regional trends, taking into account the participants' geographical location information. Furthermore, it can prioritize collecting discussions related to regional issues, taking into account the participants' geographical location information. This allows for the priority collection of highly relevant discussions, taking into account the participants' geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the participants' geographical location information into a generating AI and have the generating AI perform the collection of highly relevant discussions.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the discussions. For example, it can perform a detailed analysis on important discussions and a simplified analysis on less important discussions. Furthermore, it can adjust the level of detail of the analysis in stages according to the importance of the discussions. This allows the level of detail of the analysis to be adjusted based on the importance of the discussions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input discussion importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0118] The proposal department can adjust the level of detail in a proposal based on the company's strengths and characteristics. For example, a company with strong technological capabilities can receive a proposal that includes technical details. Similarly, a company with strong marketing capabilities can receive a proposal that includes marketing strategies. Furthermore, a company with strong financial capabilities can receive a proposal that includes financial details. This allows for adjustment of the level of detail in a proposal based on the company's strengths and characteristics. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the company's strengths and characteristics into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The collection department gathers information from product planning discussions and meetings. The collection department can collect information in various formats and types, such as meeting minutes, email correspondence, and audio recordings. The collection department collects information based on specific methods and criteria, such as manual collection, automated collection methods, and collection frequency. Step 2: The analysis department analyzes the information collected by the collection department. The analysis department analyzes the information based on specific methods and criteria, such as text mining, data analysis techniques, and the algorithms used. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department makes proposals based on specific criteria and methods, such as evaluation criteria for the proposal content and the format of the proposal. Step 4: The Strategy Planning Department formulates a strategy based on the proposals submitted by the Proposal Department. The Strategy Planning Department formulates a strategy based on specific planning methods and criteria, such as short-term strategies, long-term strategies, and risk assessments. Step 5: The milestone creation department creates milestones based on the strategy developed by the strategy planning department. The milestone creation department creates milestones based on specific creation methods and criteria such as achievement targets, deadlines, and evaluation criteria.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, strategy planning unit, and milestone creation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects meeting minutes and audio recordings using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects the information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected information using text mining and data analysis methods. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and makes proposals based on the analysis results. The strategy planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and formulates a strategy based on the proposed content. The milestone creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and creates milestones based on the formulated strategy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0140] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, strategy planning unit, and milestone creation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects meeting minutes and audio recordings using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects the information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected information using text mining and data analysis methods. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes proposals based on the analysis results. The strategy planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and formulates a strategy based on the proposed content. The milestone creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and creates milestones based on the formulated strategy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[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 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.

[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 (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).

[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] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, strategy planning unit, and milestone creation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects meeting minutes and audio recordings using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects the information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected information using text mining and data analysis methods. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and makes proposals based on the analysis results. The strategy planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and formulates a strategy based on the proposed content. The milestone creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and creates milestones based on the formulated strategy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, strategy planning unit, and milestone creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect meeting minutes and audio recordings, and the control unit 46A collects the information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected information using text mining and data analysis techniques. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and makes proposals based on the analysis results. The strategy planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and formulates a strategy based on the proposed content. The milestone creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and creates milestones based on the formulated strategy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The collection department gathers information from discussions and meetings regarding product planning, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit makes proposals based on the analysis results obtained by the aforementioned analysis unit, The Strategic Planning Department formulates strategies based on the proposals made by the aforementioned Proposal Department, The system includes a milestone creation unit that creates milestones based on the strategy formulated by the aforementioned strategy planning unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We analyze examples of products and services created through collaborations between companies and propose collaborative products and services that match the strengths and characteristics of those companies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose suitable companies as collaboration partners. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Strategic Planning Department, Develop a strategy leading up to the collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned milestone creation unit is: Clearly define the goals and steps to be achieved in the collaboration, and then create a concrete action plan based on them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate user sentiment and adjust the timing of discussion collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past discussion history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting discussions, filter them based on the participants' areas of expertise and interests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates user sentiment and determines the priority of discussions to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting discussions, the system prioritizes collecting highly relevant discussions by considering the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting discussions, analyze participants' social media activity and gather relevant discussions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the arguments were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the arguments. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the company's strengths and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting proposals, different proposal algorithms are applied depending on the company category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, we prioritize them based on the submission timing of each company. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the companies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Strategic Planning Department, We estimate user emotions and adjust our strategy planning methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Strategic Planning Department, When formulating a strategy, the optimal strategy is selected by referring to past success stories and failures. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Strategic Planning Department, When formulating a strategy, customize it based on the company's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Strategic Planning Department, We estimate user sentiment and prioritize strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Strategic Planning Department, When formulating a strategy, the optimal strategy should be selected by considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Strategic Planning Department, When formulating a strategy, we analyze a company's social media activities and propose a strategy. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned milestone creation unit is: We estimate user sentiment and adjust how milestones are created based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned milestone creation unit is: When creating milestones, refer to past project data to create the most suitable milestones. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned milestone creation unit is: When creating milestones, customize them based on the company's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned milestone creation unit is: The system estimates user sentiment and prioritizes milestones based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned milestone creation unit is: When creating milestones, take into account the company's geographical location to create the most suitable milestones. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned milestone creation unit is: When creating milestones, we analyze the company's social media activities and propose milestones accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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 collection department gathers information from discussions and meetings regarding product planning, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit makes proposals based on the analysis results obtained by the aforementioned analysis unit, The Strategic Planning Department formulates strategies based on the proposals made by the aforementioned Proposal Department, The system includes a milestone creation unit that creates milestones based on the strategy formulated by the aforementioned strategy planning unit. A system characterized by the following features.

2. The aforementioned proposal section is, We analyze examples of products and services created through collaborations between companies and propose collaborative products and services that match the strengths and characteristics of those companies. The system according to feature 1.

3. The aforementioned proposal section is, We propose suitable companies as collaboration partners. The system according to feature 1.

4. The aforementioned Strategic Planning Department, Develop a strategy leading up to the collaboration. The system according to feature 1.

5. The aforementioned milestone creation unit is: Clearly define the goals and steps to be achieved in the collaboration, and then create a concrete action plan based on them. The system according to feature 1.

6. The aforementioned collection unit is We estimate user sentiment and adjust the timing of discussion collection based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned collection unit is Analyze past discussion history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting discussions, filter them based on the participants' areas of expertise and interests. The system according to feature 1.

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