system

Abstract

The system according to the embodiment aims to improve the fairness and efficiency of the idea evaluation process. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The evaluation unit performs evaluation based on the analysis results obtained by the analysis unit. The provision unit provides the evaluation results obtained by the evaluation unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technologies, it is difficult to ensure fairness and efficiency in the idea evaluation process, and there is a risk that good ideas may be overlooked.

[0005] The system according to the embodiment aims to improve the fairness and efficiency of the idea evaluation process. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The evaluation unit performs evaluation based on the analysis result obtained by the analysis unit. The provision unit provides the evaluation result obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the fairness and efficiency of the idea evaluation process. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An idea evaluation system according to an embodiment of the present invention analyzes data from past idea contests and trains AI to automatically evaluate new ideas. This idea evaluation system collects data from past idea contests and uses data analysis technology to gain a deep understanding of the content of each idea. Next, AI quantitatively evaluates the similarity to past outstanding ideas, novelty, and areas for improvement. This improves the fairness and efficiency of the evaluation process, enabling early discovery of outstanding ideas without overlooking them. Furthermore, AI provides accurate feedback and advice to idea proposers, helping them refine their ideas and improving the overall quality of their ideas. For example, AI can provide proposers with ideas for improvement and strengthening based on the evaluation results. This allows proposers to receive specific advice on how to improve their ideas. For example, AI can evaluate new ideas based on past data and suggest specific areas for improvement to the proposer. This mechanism streamlines and maintains fairness in the evaluation process for idea contests. Furthermore, proposers can refine their ideas based on feedback from AI, improving the overall quality of their ideas. This allows the idea evaluation system to automatically evaluate new ideas by analyzing data from past idea contests and training AI.

[0029] The idea evaluation system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data from past idea contests. The collected data includes, but is not limited to, text data, numerical data, and image data. The collection unit collects data from, for example, a public database on the Internet. The collection unit can also filter the data based on a specific theme or category. For example, the collection unit can collect only ideas related to environmental protection and exclude data on other themes. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, statistical analysis and the application of machine learning algorithms. For example, the analysis unit can analyze text data using natural language processing technology. The analysis unit can also analyze image data using image recognition technology. The evaluation unit performs evaluation based on the analysis results obtained by the analysis unit. Examples of the evaluation include, but are not limited to, scoring systems and evaluation indices. The evaluation unit evaluates, for example, similarity to past best ideas. The evaluation unit can also evaluate novelty and areas for improvement. The providing unit provides the evaluation results obtained by the evaluation unit. Examples of providing include, but are not limited to, report format, dashboard display, and the like. The providing unit provides the evaluation results to the proposer, for example, in report format. The providing unit can also display the evaluation results on a dashboard. This allows the idea evaluation system according to the embodiment to efficiently collect, analyze, evaluate, and provide data.

[0030] The idea evaluation system includes a feedback unit that provides feedback based on the evaluation results. The feedback unit provides feedback to the proposer based on the evaluation results obtained by the evaluation unit. The feedback includes, but is not limited to, for example, text comments, numerical evaluations, and graph displays. For example, the feedback unit suggests specific improvements based on the evaluation results. The feedback unit can also suggest points that should be strengthened based on the evaluation results. For example, the feedback unit provides specific advice to the proposer based on the evaluation results, such as "It would be good to improve this part." The feedback unit can also provide specific advice to the proposer based on the evaluation results, such as "It would be good to strengthen this part." This makes it possible to provide appropriate feedback based on the evaluation results.

[0031] The idea evaluation system includes a similarity evaluation unit that evaluates the similarity of a new idea based on past data. The similarity evaluation unit evaluates the similarity of a new idea based on past data. Methods for evaluating similarity include, but are not limited to, cosine similarity and Euclidean distance. The similarity evaluation unit evaluates the similarity between a new idea and a past best idea using, for example, cosine similarity. The similarity evaluation unit can also evaluate the similarity between a new idea and a past best idea using Euclidean distance. For example, the similarity evaluation unit calculates feature vectors of the new idea and a past best idea and evaluates the cosine similarity between the vectors. The similarity evaluation unit can also calculate feature vectors of the new idea and a past best idea and evaluate the Euclidean distance between the vectors. This allows for an accurate evaluation of the similarity of a new idea.

[0032] The idea evaluation system includes a novelty evaluation unit that evaluates novelty. The novelty evaluation unit evaluates the novelty of a new idea. Novelty evaluation includes, but is not limited to, patent search results, technical evaluation, and the like. The novelty evaluation unit evaluates the novelty of a new idea based on, for example, patent search results. The novelty evaluation unit can also evaluate the novelty of a new idea based on technical evaluation. For example, the novelty evaluation unit searches a patent database to check whether there are any patents similar to the new idea. The novelty evaluation unit can also perform technical evaluation to evaluate how different the new idea is from existing technology. This allows the novelty of the new idea to be accurately evaluated.

[0033] The idea evaluation system includes an improvement evaluation unit that evaluates improvements. The improvement evaluation unit evaluates improvements of an idea. Evaluation of improvements includes, for example, functional improvements, performance improvements, etc., but is not limited to these examples. The improvement evaluation unit evaluates improvements of an idea based on, for example, functional improvements. The improvement evaluation unit can also evaluate improvements of an idea based on performance improvements. For example, the improvement evaluation unit evaluates functional improvements of an idea and evaluates how effective the improvements are. The improvement evaluation unit can also evaluate performance improvements of an idea and evaluate how effective the improvements are. This allows for accurate evaluation of improvements of an idea.

[0034] The feedback unit can provide feedback to the proposer based on the evaluation results obtained by the evaluation unit. The feedback unit provides feedback to the proposer based on the evaluation results obtained by the evaluation unit. Feedback includes, for example, text comments, numerical evaluations, and graph displays, but is not limited to these examples. The feedback unit, for example, suggests specific improvements based on the evaluation results. The feedback unit can also suggest points to be strengthened based on the evaluation results. For example, the feedback unit can provide specific advice to the proposer, such as "You should improve this part," based on the evaluation results. The feedback unit can also provide specific advice to the proposer, such as "You should strengthen this part," based on the evaluation results. This makes it possible to provide appropriate feedback to the proposer based on the evaluation results.

[0035] When collecting data on past idea contests, the collection unit may filter the data based on a specific theme or category. When collecting data on past idea contests, the collection unit may filter the data based on a specific theme or category. Examples of themes and categories include, but are not limited to, environmental protection, technological innovation, and education. For example, the collection unit may collect only ideas related to environmental protection and exclude data on other themes. The collection unit may also prioritize collecting ideas related to technological innovation and postpone data on other categories. Furthermore, the collection unit may filter education-related ideas and concentrate on collecting data related to a specific educational field. For example, when collecting ideas related to environmental protection, the collection unit may filter data using related keywords. Furthermore, when collecting ideas related to technological innovation, the collection unit may prioritize collecting data related to a specific technical field. Furthermore, when collecting education-related ideas, the collection unit may concentrate on collecting data related to a specific educational field. This allows data to be filtered based on a specific theme or category.

[0036] The collection unit can evaluate the reliability of data when collecting data and prioritize collecting highly reliable data. The collection unit evaluates the reliability of data when collecting data and prioritize collecting highly reliable data. Evaluation of reliability includes, but is not limited to, for example, the reliability of the data source, the consistency of the data, and the update frequency of the data. For example, the collection unit can check the origin of the data and prioritize collecting data from highly reliable sources. The collection unit can also evaluate the consistency of the data and prioritize collecting consistent data. Furthermore, the collection unit can check the update frequency of the data and prioritize collecting the latest data. For example, the collection unit can check the reliability of the data provider to evaluate the reliability of the data source. Furthermore, the collection unit can perform a data consistency check to evaluate the consistency of the data. Furthermore, the collection unit can check the recency of the data to evaluate the update frequency of the data. This allows highly reliable data to be collected preferentially.

[0037] The collection unit can prioritize collecting highly relevant data by taking geographical information into consideration when collecting data. The collection unit prioritizes collecting highly relevant data by taking geographical information into consideration when collecting data. Geographical information includes, but is not limited to, GPS data, address information, and the like. For example, the collection unit prioritizes collecting ideas related to a specific region. The collection unit can also prioritize collecting data from geographically nearby regions. Furthermore, the collection unit can prioritize collecting highly relevant data by taking geographical trends into consideration. For example, the collection unit filters data using related keywords when collecting ideas related to a specific region. Furthermore, the collection unit can prioritize collecting data related to a specific region when collecting data from geographically nearby regions. Furthermore, the collection unit can refer to the latest geographical trend information when collecting highly relevant data by taking geographical trends into consideration. This makes it possible to collect highly relevant data by taking geographical information into consideration.

[0038] The collection unit may collect data from social media and online forums during data collection to understand idea trends. The collection unit may collect data from social media and online forums during data collection to understand idea trends. Examples of social media and online forums include, but are not limited to, Twitter (formerly Twitter®) and Reddit®. For example, the collection unit may analyze social media hashtags to collect trending ideas. The collection unit may also analyze online forum posts to collect popular ideas. Furthermore, the collection unit may analyze social media comments to understand user interests. For example, the collection unit may filter data using related keywords when analyzing social media hashtags. Furthermore, the collection unit may preferentially collect data related to specific topics when analyzing online forum posts. Furthermore, the collection unit may use sentiment analysis technology to understand user interests when analyzing social media comments. This allows data from social media and online forums to be collected to understand idea trends.

[0039] The analysis unit can apply different analysis algorithms based on the content of the data during analysis. The analysis unit applies different analysis algorithms based on the content of the data during analysis. Examples of analysis algorithms include, but are not limited to, natural language processing algorithms, image recognition algorithms, and statistical analysis algorithms. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a statistical analysis algorithm to numerical data. For example, when analyzing text data, the analysis unit analyzes the meaning of the text using a natural language processing algorithm. When analyzing image data, the analysis unit can also analyze the content of the image using an image recognition algorithm. Furthermore, when analyzing numerical data, the analysis unit can also analyze the trend of the data using a statistical analysis algorithm. This makes it possible to apply an appropriate analysis algorithm based on the content of the data.

[0040] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of data during analysis. The analysis unit improves the accuracy of the analysis by taking into account the interrelationships of data during analysis. Examples of interrelation analysis include, but are not limited to, correlation analysis and network analysis. For example, the analysis unit analyzes the interrelationships between text data and image data to improve the accuracy. The analysis unit can also analyze the interrelationships between numerical data and text data to improve the accuracy. Furthermore, the analysis unit can analyze the interrelationships between image data and numerical data to improve the accuracy. For example, when analyzing the interrelationships between text data and image data, the analysis unit associates the content of the text data with the content of the image data. Furthermore, when analyzing the interrelationships between numerical data and text data, the analysis unit can associate the trend of the numerical data with the content of the text data. Furthermore, when analyzing the interrelationships between image data and numerical data, the analysis unit can associate the content of the image data with the trend of the numerical data. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships of data.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. During analysis, the analysis unit determines the priority of analysis based on the time of data submission. The submission time includes, for example, the submission date and time, the submission order, etc., but is not limited to these examples. For example, the analysis unit prioritizes analyzing the latest data. The analysis unit can also prioritize analyzing data with an upcoming submission deadline. Furthermore, the analysis unit can prioritize analyzing the latest data, leaving older data for later analysis. For example, the analysis unit determines the priority of data based on the submission date and time. The analysis unit can also determine the priority of data based on the submission order. In this way, the priority of analysis can be determined based on the time of data submission.

[0042] The analysis unit can improve the accuracy of the analysis by referring to related literature and research data during the analysis. The analysis unit can improve the accuracy of the analysis by referring to related literature and research data during the analysis. Literature and research data include, but are not limited to, academic papers, technical reports, and market research data, for example. The analysis unit can improve the accuracy of the analysis by referring to related academic papers, for example. The analysis unit can also improve the accuracy of the analysis by referring to related patent data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to related market research data. For example, when referring to academic papers, the analysis unit can perform the analysis based on related research results. Furthermore, when referring to patent data, the analysis unit can perform the analysis based on related technical information. Furthermore, when referring to market research data, the analysis unit can perform the analysis based on related market trends. In this way, the accuracy of the analysis can be improved by referring to related literature and research data.

[0043] The evaluation unit can quantitatively evaluate the similarity to past best ideas during evaluation. The evaluation unit quantitatively evaluates the similarity to past best ideas during evaluation. Methods for evaluating similarity include, but are not limited to, cosine similarity and Euclidean distance. For example, the evaluation unit extracts features of past best ideas and evaluates their similarity to the new idea. The evaluation unit can also evaluate the similarity of the new idea based on the evaluation scores of the past best ideas. Furthermore, the evaluation unit can extract keywords from past best ideas and evaluate their similarity to the new idea. For example, the evaluation unit calculates feature vectors of past best ideas and evaluates the cosine similarity between the vectors. The evaluation unit can also evaluate the similarity of the new idea based on the evaluation scores of the past best ideas. Furthermore, the evaluation unit can extract keywords from past best ideas and evaluate their similarity to the new idea. This allows for quantitative evaluation of the similarity to past best ideas.

[0044] The evaluation unit can apply multiple evaluation criteria during the evaluation to evaluate novelty and improvements in detail. The evaluation unit can apply multiple evaluation criteria during the evaluation to evaluate novelty and improvements in detail. Examples of evaluation criteria include, but are not limited to, novelty, uniqueness, and practicality. For example, the evaluation unit can separately apply criteria for evaluating novelty and criteria for evaluating improvements. The evaluation unit can also apply multiple criteria for comprehensively evaluating novelty and improvements. Furthermore, the evaluation unit can apply criteria for quantitatively evaluating novelty and improvements. For example, the evaluation unit can apply criteria for evaluating novelty to evaluate the uniqueness of the new idea. The evaluation unit can also apply criteria for evaluating improvements to evaluate the improvements of the idea. Furthermore, the evaluation unit can apply criteria for comprehensively evaluating novelty and improvements to perform a comprehensive evaluation of the new idea. In this way, multiple evaluation criteria can be applied to evaluate novelty and improvements in detail.

[0045] The evaluation unit can perform the evaluation by taking into consideration attribute information of the idea submitter when evaluating the idea. The evaluation unit performs the evaluation by taking into consideration attribute information of the idea submitter when evaluating the idea. Attribute information includes, for example, age, occupation, field of expertise, etc., but is not limited to these examples. The evaluation unit can perform the evaluation by taking into consideration, for example, the submitter's field of expertise. The evaluation unit can also perform the evaluation by taking into consideration the submitter's years of experience. Furthermore, the evaluation unit can also perform the evaluation by taking into consideration the submitter's past performance. For example, the evaluation unit can perform the evaluation based on the submitter's field of expertise and consider specialized knowledge in that field. The evaluation unit can also perform the evaluation based on the submitter's years of experience and consider knowledge based on that experience. Furthermore, the evaluation unit can perform the evaluation based on the submitter's past performance and consider reliability based on that performance. In this way, by performing the evaluation by taking into consideration the submitter's attribute information, a more appropriate evaluation is possible.

[0046] The evaluation unit can improve the accuracy of the evaluation by referring to related market data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to related market data during the evaluation. Market data includes, but is not limited to, market research reports, sales data, and consumer survey data, for example. The evaluation unit can improve the accuracy of the evaluation by referring to related market trend data, for example. The evaluation unit can also improve the accuracy of the evaluation by referring to related competing product data. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to related consumer survey data. For example, when referring to market trend data, the evaluation unit can make an evaluation based on the latest market trends. Furthermore, when referring to competing product data, the evaluation unit can make an evaluation based on the characteristics of competing products. Furthermore, when referring to consumer survey data, the evaluation unit can make an evaluation based on consumer opinions. In this way, the accuracy of the evaluation can be improved by referring to related market data.

[0047] The providing unit can propose specific improvements and points to be strengthened based on the evaluation results when providing the information. The providing unit proposes specific improvements and points to be strengthened based on the evaluation results when providing the information. The improvements and points to be strengthened include, but are not limited to, functional improvements, performance improvements, and design improvements, for example. The providing unit, for example, proposes specific improvements based on the evaluation results. The providing unit can also propose points to be strengthened based on the evaluation results. Furthermore, the providing unit can propose a specific action plan for improvement based on the evaluation results. For example, the providing unit provides the proposer with specific advice such as "It would be good to improve this part" based on the evaluation results. The providing unit can also provide the proposer with specific advice such as "It would be good to strengthen this part" based on the evaluation results. Furthermore, the providing unit can provide the proposer with a specific action plan such as "It would be good to take the following action to improve this part" based on the evaluation results. This makes it possible to propose specific improvements and points to be strengthened based on the evaluation results.

[0048] The providing unit can provide optimal information by referring to the user's past feedback history when providing the information. The providing unit can provide optimal information by referring to the user's past feedback history when providing the information. The feedback history includes, for example, past comments, evaluation results, and areas for improvement, but is not limited to these examples. For example, the providing unit can provide optimal information by referring to the user's past feedback history. The providing unit can also analyze the user's past feedback history and suggest areas for improvement. Furthermore, the providing unit can suggest areas to be improved based on the user's past feedback history. For example, when referring to the user's past feedback history, the providing unit provides optimal information based on past comments. Furthermore, when referring to the user's past evaluation results, the providing unit can suggest areas to be improved based on the past evaluation results. Furthermore, when referring to the user's past improvements, the providing unit can suggest areas to be improved based on the past improvements. In this way, optimal information can be provided by referring to the user's past feedback history.

[0049] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display information. The providing unit selects the optimal display method by taking into consideration the user's device information when providing the display information. Device information includes, but is not limited to, the device type, OS, browser, etc. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. For example, the providing unit can provide a display method tailored to the smartphone screen size based on the user's device information. Furthermore, the providing unit can provide a display method optimized for a large tablet screen based on the user's device information. Furthermore, the providing unit can provide a display method optimized for a small smartwatch screen based on the user's device information. This makes it possible to select the optimal display method by taking into consideration the user's device information.

[0050] The providing unit can improve the accuracy of the information by referring to related trend information when providing the information. The providing unit can improve the accuracy of the information by referring to related trend information when providing the information. Trend information includes, for example, the latest technology trends, market trends, consumer trends, etc., but is not limited to these examples. The providing unit can improve the accuracy of the information by referring to related market trend information, for example. The providing unit can also improve the accuracy of the information by referring to related technology trend information. Furthermore, the providing unit can improve the accuracy of the information by referring to related consumer trend information. For example, when referring to market trend information, the providing unit can provide information based on the latest market trends. Furthermore, when referring to technology trend information, the providing unit can provide information based on the latest technology trends. Furthermore, when referring to consumer trend information, the providing unit can provide information based on the latest consumer trends. In this way, the accuracy of the information can be improved by referring to related trend information.

[0051] The feedback unit can propose specific improvements based on the evaluation results when providing feedback. The feedback unit can propose specific improvements based on the evaluation results when providing feedback. Improvements include, but are not limited to, functional improvements, performance improvements, and design improvements, for example. The feedback unit can propose specific improvements based on the evaluation results. The feedback unit can also propose points to be strengthened based on the evaluation results. The feedback unit can also propose a specific action plan for improvement based on the evaluation results. For example, the feedback unit can provide the proposer with specific advice such as "It would be good to improve this part" based on the evaluation results. The feedback unit can also provide the proposer with specific advice such as "It would be good to strengthen this part" based on the evaluation results. The feedback unit can also provide the proposer with a specific action plan such as "It would be good to take the following action to improve this part" based on the evaluation results. In this way, specific improvements can be proposed based on the evaluation results.

[0052] The feedback unit can provide optimal feedback by referring to the user's past idea submission history when providing feedback. The feedback unit can provide optimal feedback by referring to the user's past idea submission history when providing feedback. The idea submission history includes, for example, but is not limited to, previously submitted ideas, evaluation results, and improvements. For example, the feedback unit can provide optimal feedback by referring to the user's past idea submission history. The feedback unit can also analyze the user's past idea submission history and suggest improvements. Furthermore, the feedback unit can suggest areas to be improved based on the user's past idea submission history. For example, when referring to the user's past idea submission history, the feedback unit can provide optimal feedback based on the previously submitted ideas. Furthermore, when referring to the user's past evaluation results, the feedback unit can suggest areas to be improved based on the past evaluation results. Furthermore, when referring to the user's past improvements, the feedback unit can suggest areas to be improved based on the past improvements. In this way, optimal feedback can be provided by referring to the user's past idea submission history.

[0053] The feedback unit may provide optimal feedback by taking into consideration the geographical information of the user when providing feedback. The feedback unit may provide optimal feedback by taking into consideration the geographical information of the user when providing feedback. Examples of geographical information include, but are not limited to, GPS data and address information. For example, the feedback unit may refer to the geographical information of the user and provide feedback specific to a region. The feedback unit may also provide feedback tailored to regional characteristics based on the geographical information of the user. Furthermore, the feedback unit may provide feedback based on regional trends by taking into consideration the geographical information of the user. For example, the feedback unit may provide feedback specific to a region when referring to the geographical information of the user. The feedback unit may also provide feedback tailored to regional characteristics based on the geographical information of the user. Furthermore, the feedback unit may provide feedback based on regional trends by taking into consideration the geographical information of the user. This makes it possible to provide optimal feedback by taking into consideration the geographical information of the user.

[0054] The feedback unit may improve the accuracy of the feedback by referring to related market data when providing feedback. The feedback unit may improve the accuracy of the feedback by referring to related market data when providing feedback. Market data includes, but is not limited to, market research reports, sales data, and consumer survey data. For example, the feedback unit may improve the accuracy of the feedback by referring to related market trend data. The feedback unit may also improve the accuracy of the feedback by referring to related competitive product data. The feedback unit may also improve the accuracy of the feedback by referring to related consumer survey data. For example, the feedback unit may provide feedback based on the latest market trends when referring to market trend data. The feedback unit may also provide feedback based on the characteristics of competitive products when referring to competitive product data. The feedback unit may also provide feedback based on consumer opinions when referring to consumer survey data. This makes it possible to improve the accuracy of the feedback by referring to related market data.

[0055] The similarity evaluation unit can evaluate the similarity with past best ideas in detail during the similarity evaluation. The similarity evaluation unit evaluates the similarity with past best ideas in detail during the similarity evaluation. Methods for evaluating similarity include, but are not limited to, cosine similarity and Euclidean distance. For example, the similarity evaluation unit extracts features of past best ideas and evaluates their similarity with the new idea. The similarity evaluation unit can also evaluate the similarity of the new idea based on the evaluation scores of the past best ideas. Furthermore, the similarity evaluation unit can extract keywords from past best ideas and evaluate their similarity with the new idea. For example, the similarity evaluation unit calculates feature vectors of past best ideas and evaluates the cosine similarity between the vectors. The similarity evaluation unit can also evaluate the similarity of the new idea based on the evaluation scores of the past best ideas. Furthermore, the similarity evaluation unit can extract keywords from past best ideas and evaluate their similarity with the new idea. This allows for a detailed evaluation of the similarity with past best ideas.

[0056] The similarity evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between ideas when evaluating the similarity. The similarity evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between ideas when evaluating the similarity. Evaluation of the interrelationships includes, for example, correlation analysis, network analysis, etc., but is not limited to these examples. The similarity evaluation unit can, for example, analyze the interrelationships between text data and image data to improve the accuracy. The similarity evaluation unit can also analyze the interrelationships between numerical data and text data to improve the accuracy. Furthermore, the similarity evaluation unit can analyze the interrelationships between image data and numerical data to improve the accuracy. For example, when analyzing the interrelationships between text data and image data, the similarity evaluation unit can analyze the content of the text data by associating it with the content of the image data. Furthermore, when analyzing the interrelationships between numerical data and text data, the similarity evaluation unit can analyze the trend of the numerical data by associating it with the content of the text data. Furthermore, when analyzing the interrelationships between image data and numerical data, the similarity evaluation unit can analyze the content of the image data by associating it with the trend of the numerical data. In this way, the accuracy of the evaluation can be improved by taking into account the interrelationships between ideas.

[0057] The similarity evaluation unit can perform the similarity evaluation while taking into account attribute information of the idea submitter. The similarity evaluation unit performs the similarity evaluation while taking into account attribute information of the idea submitter. Attribute information includes, for example, age, occupation, and field of expertise, but is not limited to these examples. The similarity evaluation unit can perform the evaluation while taking into account, for example, the submitter's field of expertise. The similarity evaluation unit can also perform the evaluation while taking into account the submitter's years of experience. The similarity evaluation unit can also perform the evaluation while taking into account the submitter's past achievements. For example, the similarity evaluation unit can perform the evaluation based on the submitter's field of expertise and consider specialized knowledge in that field. The similarity evaluation unit can also perform the evaluation based on the submitter's years of experience and consider knowledge based on that experience. The similarity evaluation unit can also perform the evaluation based on the submitter's past achievements and consider reliability based on that achievement. This allows for a more appropriate evaluation by performing the evaluation while taking into account the submitter's attribute information.

[0058] The similarity evaluation unit can improve the accuracy of the evaluation by referring to related literature and research data when evaluating the similarity. The similarity evaluation unit can improve the accuracy of the evaluation by referring to related literature and research data when evaluating the similarity. Literature and research data include, but are not limited to, academic papers, technical reports, and market research data. For example, the similarity evaluation unit can improve the accuracy of the evaluation by referring to related academic papers. The similarity evaluation unit can also improve the accuracy of the evaluation by referring to related patent data. The similarity evaluation unit can also improve the accuracy of the evaluation by referring to related market research data. For example, when referring to academic papers, the similarity evaluation unit can make an evaluation based on related research results. When referring to patent data, the similarity evaluation unit can make an evaluation based on related technical information. When referring to market research data, the similarity evaluation unit can make an evaluation based on related market trends. This makes it possible to improve the accuracy of the evaluation by referring to related literature and research data.

[0059] The novelty evaluation unit can evaluate the uniqueness of the idea in detail during the novelty evaluation. The novelty evaluation unit evaluates the uniqueness of the idea in detail during the novelty evaluation. The evaluation of uniqueness includes, for example, technical uniqueness, uniqueness of the business model, etc., but is not limited to these examples. The novelty evaluation unit, for example, applies criteria for evaluating the uniqueness of the idea. The novelty evaluation unit can also apply criteria for quantitatively evaluating the uniqueness of the idea. Furthermore, the novelty evaluation unit can also apply criteria for comprehensively evaluating the uniqueness of the idea. For example, the novelty evaluation unit can apply criteria for evaluating technical uniqueness to evaluate the technical novelty of the idea. The novelty evaluation unit can also apply criteria for evaluating the uniqueness of the business model to evaluate the novelty of the business model of the idea. Furthermore, the novelty evaluation unit can apply criteria for comprehensively evaluating the technical uniqueness and the uniqueness of the business model to evaluate the overall uniqueness of the idea. This allows the uniqueness of the idea to be evaluated in detail.

[0060] The novelty evaluation unit can apply multiple evaluation criteria to clarify the differences from past ideas when evaluating novelty. The novelty evaluation unit applies multiple evaluation criteria to clarify the differences from past ideas when evaluating novelty. The evaluation criteria include, but are not limited to, novelty, uniqueness, and practicality, for example. The novelty evaluation unit applies, for example, criteria to evaluate the differences from past ideas. The novelty evaluation unit can also apply criteria to quantitatively evaluate the differences from past ideas. Furthermore, the novelty evaluation unit can also apply criteria to comprehensively evaluate the differences from past ideas. For example, the novelty evaluation unit can apply criteria to evaluate the differences from past ideas and evaluate the uniqueness of the new idea. The novelty evaluation unit can also apply criteria to quantitatively evaluate the differences from past ideas and evaluate the uniqueness of the new idea. Furthermore, the novelty evaluation unit can apply criteria to comprehensively evaluate the differences from past ideas and evaluate the overall uniqueness of the new idea. In this way, multiple evaluation criteria can be applied to clarify the differences from past ideas.

[0061] The novelty evaluation unit can perform the novelty evaluation by taking into consideration attribute information of the submitter of the idea. The novelty evaluation unit performs the novelty evaluation by taking into consideration attribute information of the submitter of the idea. Attribute information includes, for example, age, occupation, field of expertise, etc., but is not limited to these examples. The novelty evaluation unit can perform the evaluation by taking into consideration, for example, the submitter's field of expertise. The novelty evaluation unit can also perform the evaluation by taking into consideration the submitter's years of experience. Furthermore, the novelty evaluation unit can also perform the evaluation by taking into consideration the submitter's past achievements. For example, the novelty evaluation unit can perform the evaluation based on the submitter's field of expertise and consider specialized knowledge in that field. The novelty evaluation unit can also perform the evaluation based on the submitter's years of experience and consider knowledge based on that experience. Furthermore, the novelty evaluation unit can perform the evaluation based on the submitter's past achievements and consider reliability based on that achievement. As a result, by performing the evaluation by taking into consideration the submitter's attribute information, a more appropriate evaluation is possible.

[0062] The novelty evaluation unit can improve the accuracy of the evaluation by referring to related market data when evaluating novelty. The novelty evaluation unit can improve the accuracy of the evaluation by referring to related market data when evaluating novelty. Market data includes, but is not limited to, market research reports, sales data, and consumer survey data, for example. The novelty evaluation unit can improve the accuracy of the evaluation by referring to related market trend data, for example. The novelty evaluation unit can also improve the accuracy of the evaluation by referring to related competing product data. The novelty evaluation unit can also improve the accuracy of the evaluation by referring to related consumer survey data. For example, when referring to market trend data, the novelty evaluation unit can make an evaluation based on the latest market trends. When referring to competing product data, the novelty evaluation unit can make an evaluation based on the characteristics of competing products. When referring to consumer survey data, the novelty evaluation unit can make an evaluation based on consumer opinions. This makes it possible to improve the accuracy of the evaluation by referring to related market data.

[0063] The improvement evaluation unit can evaluate the improvement points of the idea in detail during the improvement evaluation. The improvement evaluation unit evaluates the improvement points of the idea in detail during the improvement evaluation. Evaluation of the improvement points includes, for example, functional improvement, performance improvement, design improvement, etc., but is not limited to these examples. The improvement evaluation unit, for example, applies criteria for evaluating the improvement points of the idea. The improvement evaluation unit can also apply criteria for quantitatively evaluating the improvement points of the idea. Furthermore, the improvement evaluation unit can apply criteria for comprehensively evaluating the improvement points of the idea. For example, the improvement evaluation unit can apply criteria for evaluating functional improvement to evaluate functional improvement points of the idea. The improvement evaluation unit can also apply criteria for evaluating performance improvement to evaluate performance improvement points of the idea. Furthermore, the improvement evaluation unit can apply criteria for evaluating design improvement to evaluate design improvement points of the idea. This allows the improvement points of the idea to be evaluated in detail.

[0064] The improvement point evaluation unit can improve the accuracy of the evaluation when evaluating the improvement points by referring to the improvement points of past ideas. The improvement point evaluation unit improves the accuracy of the evaluation when evaluating the improvement points by referring to the improvement points of past ideas. Reference to the improvement points includes, for example, past evaluation results, improvement history, etc., but is not limited to these examples. The improvement point evaluation unit, for example, improves the accuracy of the evaluation by referring to the improvement points of past ideas. The improvement point evaluation unit can also evaluate the improvement points of a new idea based on the improvement points of past ideas. Furthermore, the improvement point evaluation unit can analyze the improvement points of past ideas to improve the accuracy of the evaluation. For example, when referring to the improvement points of past ideas, the improvement point evaluation unit performs the evaluation based on the past evaluation results. Furthermore, when referring to the past improvement history, the improvement point evaluation unit can perform the evaluation based on the improvement history. Furthermore, when analyzing the past improvements, the improvement point evaluation unit can perform the evaluation based on the trend of the improvements. In this way, the accuracy of the evaluation can be improved by referring to the improvements of past ideas.

[0065] The improvement point evaluation unit can perform the evaluation by taking into consideration attribute information of the idea submitter when evaluating the improvement points. The improvement point evaluation unit performs the evaluation by taking into consideration attribute information of the idea submitter when evaluating the improvement points. Attribute information includes, for example, age, occupation, field of expertise, etc., but is not limited to these examples. The improvement point evaluation unit can perform the evaluation by taking into consideration, for example, the submitter's field of expertise. The improvement point evaluation unit can also perform the evaluation by taking into consideration the submitter's years of experience. Furthermore, the improvement point evaluation unit can also perform the evaluation by taking into consideration the submitter's past performance. For example, the improvement point evaluation unit can perform the evaluation based on the submitter's field of expertise and consider specialized knowledge in that field. The improvement point evaluation unit can also perform the evaluation based on the submitter's years of experience and consider knowledge based on that experience. Furthermore, the improvement point evaluation unit can perform the evaluation based on the submitter's past performance and consider reliability based on that performance. In this way, by performing the evaluation by taking into consideration the submitter's attribute information, a more appropriate evaluation is possible.

[0066] The improvement evaluation unit can improve the accuracy of the evaluation by referring to related literature and research data when evaluating the improvement points. The improvement evaluation unit can improve the accuracy of the evaluation by referring to related literature and research data when evaluating the improvement points. Literature and research data include, but are not limited to, academic papers, technical reports, and market research data, for example. The improvement evaluation unit can improve the accuracy of the evaluation by referring to related academic papers. The improvement evaluation unit can also improve the accuracy of the evaluation by referring to related patent data. The improvement evaluation unit can also improve the accuracy of the evaluation by referring to related market research data. For example, when referring to academic papers, the improvement evaluation unit can make an evaluation based on related research results. When referring to patent data, the improvement evaluation unit can make an evaluation based on related technical information. When referring to market research data, the improvement evaluation unit can make an evaluation based on related market trends. This makes it possible to improve the accuracy of the evaluation by referring to related literature and research data.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] During data collection, the collection unit can also collect data from social media and online forums to understand idea trends. For example, it can analyze social media hashtags to collect trending ideas. It can also analyze posts on online forums to collect popular ideas. It can also analyze social media comments to understand user interests. This makes it possible to collect data from social media and online forums to understand idea trends.

[0069] During analysis, the analysis unit can apply different analysis algorithms based on the content of the data. For example, it can apply a natural language processing algorithm to text data, an image recognition algorithm to image data, or a statistical analysis algorithm to numerical data. This allows it to apply the appropriate analysis algorithm based on the content of the data.

[0070] The evaluation unit may refer to related market data during evaluation to improve the accuracy of the evaluation. For example, related market trend data may be referenced to improve the accuracy of the evaluation. The evaluation unit may also refer to related competing product data to improve the accuracy of the evaluation. Furthermore, the evaluation unit may also refer to related consumer survey data to improve the accuracy of the evaluation. Thus, the accuracy of the evaluation can be improved by referring to related market data.

[0071] When providing the display, the providing unit can also select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. This makes it possible to select the optimal display method by taking into consideration the user's device information.

[0072] The feedback unit may improve the accuracy of the feedback by referring to related market data when providing feedback. For example, the feedback unit may improve the accuracy of the feedback by referring to related market trend data. The feedback unit may also improve the accuracy of the feedback by referring to related competing product data. The feedback unit may also improve the accuracy of the feedback by referring to related consumer survey data. In this way, the accuracy of the feedback can be improved by referring to related market data.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The collection unit collects data from past idea contests. The collected data may include, for example, text data, numerical data, and image data. The collection unit may collect data from public databases on the Internet and filter the data based on specific themes or categories. For example, the collection unit may collect only ideas related to environmental protection and exclude data on other themes. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis may include statistical analysis, application of machine learning algorithms, analysis of text data using natural language processing technology, analysis of image data using image recognition technology, etc. Step 3: The evaluation department evaluates the ideas based on the analysis results obtained by the analysis department. The evaluation includes a scoring system, evaluation criteria, evaluation of similarity to previous excellent ideas, evaluation of novelty and areas for improvement, etc. Step 4: The providing unit provides the evaluation results obtained by the evaluation unit. The providing may include a report format, a dashboard display, etc. For example, the evaluation results may be provided to the proposer in a report format or displayed on a dashboard.

[0075] (Example 2) An idea evaluation system according to an embodiment of the present invention analyzes data from past idea contests and trains AI to automatically evaluate new ideas. This idea evaluation system collects data from past idea contests and uses data analysis technology to gain a deep understanding of the content of each idea. Next, AI quantitatively evaluates the similarity to past outstanding ideas, novelty, and areas for improvement. This improves the fairness and efficiency of the evaluation process, enabling early discovery of outstanding ideas without overlooking them. Furthermore, AI provides accurate feedback and advice to idea proposers, helping them refine their ideas and improving the overall quality of their ideas. For example, AI can provide proposers with ideas for improvement and strengthening based on the evaluation results. This allows proposers to receive specific advice on how to improve their ideas. For example, AI can evaluate new ideas based on past data and suggest specific areas for improvement to the proposer. This mechanism streamlines and maintains fairness in the evaluation process for idea contests. Furthermore, proposers can refine their ideas based on feedback from AI, improving the overall quality of their ideas. This allows the idea evaluation system to automatically evaluate new ideas by analyzing data from past idea contests and training AI.

[0076] The idea evaluation system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data from past idea contests. The collected data includes, but is not limited to, text data, numerical data, and image data. The collection unit collects data from, for example, a public database on the Internet. The collection unit can also filter the data based on a specific theme or category. For example, the collection unit can collect only ideas related to environmental protection and exclude data on other themes. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, statistical analysis and the application of machine learning algorithms. For example, the analysis unit can analyze text data using natural language processing technology. The analysis unit can also analyze image data using image recognition technology. The evaluation unit performs evaluation based on the analysis results obtained by the analysis unit. Examples of the evaluation include, but are not limited to, scoring systems and evaluation indices. The evaluation unit evaluates, for example, similarity to past best ideas. The evaluation unit can also evaluate novelty and areas for improvement. The providing unit provides the evaluation results obtained by the evaluation unit. Examples of providing include, but are not limited to, report format, dashboard display, and the like. The providing unit provides the evaluation results to the proposer, for example, in report format. The providing unit can also display the evaluation results on a dashboard. This allows the idea evaluation system according to the embodiment to efficiently collect, analyze, evaluate, and provide data.

[0077] The idea evaluation system includes a feedback unit that provides feedback based on the evaluation results. The feedback unit provides feedback to the proposer based on the evaluation results obtained by the evaluation unit. The feedback includes, but is not limited to, for example, text comments, numerical evaluations, and graph displays. For example, the feedback unit suggests specific improvements based on the evaluation results. The feedback unit can also suggest points that should be strengthened based on the evaluation results. For example, the feedback unit provides specific advice to the proposer based on the evaluation results, such as "It would be good to improve this part." The feedback unit can also provide specific advice to the proposer based on the evaluation results, such as "It would be good to strengthen this part." This makes it possible to provide appropriate feedback based on the evaluation results.

[0078] The idea evaluation system includes a similarity evaluation unit that evaluates the similarity of a new idea based on past data. The similarity evaluation unit evaluates the similarity of a new idea based on past data. Methods for evaluating similarity include, but are not limited to, cosine similarity and Euclidean distance. The similarity evaluation unit evaluates the similarity between a new idea and a past best idea using, for example, cosine similarity. The similarity evaluation unit can also evaluate the similarity between a new idea and a past best idea using Euclidean distance. For example, the similarity evaluation unit calculates feature vectors of the new idea and a past best idea and evaluates the cosine similarity between the vectors. The similarity evaluation unit can also calculate feature vectors of the new idea and a past best idea and evaluate the Euclidean distance between the vectors. This allows for an accurate evaluation of the similarity of a new idea.

[0079] The idea evaluation system includes a novelty evaluation unit that evaluates novelty. The novelty evaluation unit evaluates the novelty of a new idea. Novelty evaluation includes, but is not limited to, patent search results, technical evaluation, and the like. The novelty evaluation unit evaluates the novelty of a new idea based on, for example, patent search results. The novelty evaluation unit can also evaluate the novelty of a new idea based on technical evaluation. For example, the novelty evaluation unit searches a patent database to check whether there are any patents similar to the new idea. The novelty evaluation unit can also perform technical evaluation to evaluate how different the new idea is from existing technology. This allows the novelty of the new idea to be accurately evaluated.

[0080] The idea evaluation system includes an improvement evaluation unit that evaluates improvements. The improvement evaluation unit evaluates improvements of an idea. Evaluation of improvements includes, for example, functional improvements, performance improvements, etc., but is not limited to these examples. The improvement evaluation unit evaluates improvements of an idea based on, for example, functional improvements. The improvement evaluation unit can also evaluate improvements of an idea based on performance improvements. For example, the improvement evaluation unit evaluates functional improvements of an idea and evaluates how effective the improvements are. The improvement evaluation unit can also evaluate performance improvements of an idea and evaluate how effective the improvements are. This allows for accurate evaluation of improvements of an idea.

[0081] The feedback unit can provide feedback to the proposer based on the evaluation results obtained by the evaluation unit. The feedback unit provides feedback to the proposer based on the evaluation results obtained by the evaluation unit. Feedback includes, for example, text comments, numerical evaluations, and graph displays, but is not limited to these examples. The feedback unit, for example, suggests specific improvements based on the evaluation results. The feedback unit can also suggest points to be strengthened based on the evaluation results. For example, the feedback unit can provide specific advice to the proposer, such as "You should improve this part," based on the evaluation results. The feedback unit can also provide specific advice to the proposer, such as "You should strengthen this part," based on the evaluation results. This makes it possible to provide appropriate feedback to the proposer based on the evaluation results.

[0082] The collection unit can estimate a user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit can estimate a user's emotion and adjust the timing of data collection based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is relaxed, the collection unit can immediately collect data to efficiently acquire information. Furthermore, when the user is feeling stressed, the collection unit can temporarily delay data collection to reduce the user's burden. Furthermore, when the user is concentrating, the collection unit can collect data in the background so as not to interfere with the user's work. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can analyze the user's text data and estimate the emotion. This allows the timing of data collection to be adjusted according to the user's emotion.

[0083] When collecting data on past idea contests, the collection unit may filter the data based on a specific theme or category. When collecting data on past idea contests, the collection unit may filter the data based on a specific theme or category. Examples of themes and categories include, but are not limited to, environmental protection, technological innovation, and education. For example, the collection unit may collect only ideas related to environmental protection and exclude data on other themes. The collection unit may also prioritize collecting ideas related to technological innovation and postpone data on other categories. Furthermore, the collection unit may filter education-related ideas and concentrate on collecting data related to a specific educational field. For example, when collecting ideas related to environmental protection, the collection unit may filter data using related keywords. Furthermore, when collecting ideas related to technological innovation, the collection unit may prioritize collecting data related to a specific technical field. Furthermore, when collecting education-related ideas, the collection unit may concentrate on collecting data related to a specific educational field. This allows data to be filtered based on a specific theme or category.

[0084] The collection unit can evaluate the reliability of data when collecting data and prioritize collecting highly reliable data. The collection unit evaluates the reliability of data when collecting data and prioritize collecting highly reliable data. Evaluation of reliability includes, but is not limited to, for example, the reliability of the data source, the consistency of the data, and the update frequency of the data. For example, the collection unit can check the origin of the data and prioritize collecting data from highly reliable sources. The collection unit can also evaluate the consistency of the data and prioritize collecting consistent data. Furthermore, the collection unit can check the update frequency of the data and prioritize collecting the latest data. For example, the collection unit can check the reliability of the data provider to evaluate the reliability of the data source. Furthermore, the collection unit can perform a data consistency check to evaluate the consistency of the data. Furthermore, the collection unit can check the recency of the data to evaluate the update frequency of the data. This allows highly reliable data to be collected preferentially.

[0085] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, when the user is excited, the collection unit can prioritize collecting the latest trend data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting past data. Furthermore, when the user is stressed, the collection unit can postpone collecting less important data. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can analyze the user's text data and estimate the emotion. In this way, the priority of data can be determined according to the user's emotions.

[0086] The collection unit can prioritize collecting highly relevant data by taking geographical information into consideration when collecting data. The collection unit prioritizes collecting highly relevant data by taking geographical information into consideration when collecting data. Geographical information includes, but is not limited to, GPS data, address information, and the like. For example, the collection unit prioritizes collecting ideas related to a specific region. The collection unit can also prioritize collecting data from geographically nearby regions. Furthermore, the collection unit can prioritize collecting highly relevant data by taking geographical trends into consideration. For example, the collection unit filters data using related keywords when collecting ideas related to a specific region. Furthermore, the collection unit can prioritize collecting data related to a specific region when collecting data from geographically nearby regions. Furthermore, the collection unit can refer to the latest geographical trend information when collecting highly relevant data by taking geographical trends into consideration. This makes it possible to collect highly relevant data by taking geographical information into consideration.

[0087] The collection unit may collect data from social media and online forums during data collection to identify idea trends. Examples of social media and online forums include, but are not limited to, Twitter (formerly known as X) and Reddit. For example, the collection unit may analyze social media hashtags to collect trending ideas. The collection unit may also analyze online forum posts to collect popular ideas. Furthermore, the collection unit may analyze social media comments to identify user interests. For example, the collection unit may filter data using related keywords when analyzing social media hashtags. Furthermore, the collection unit may prioritize the collection of data related to specific topics when analyzing online forum posts. Furthermore, the collection unit may use sentiment analysis technology to identify user interests when analyzing social media comments. This allows data from social media and online forums to be collected and idea trends to be identified.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to provide deep insights. If the user is in a hurry, the analysis unit can perform a concise analysis to provide results that focus on the main points. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can analyze the user's text data and estimate the emotions. This allows the analysis method to be adjusted according to the user's emotions.

[0089] The analysis unit can apply different analysis algorithms based on the content of the data during analysis. The analysis unit applies different analysis algorithms based on the content of the data during analysis. Examples of analysis algorithms include, but are not limited to, natural language processing algorithms, image recognition algorithms, and statistical analysis algorithms. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a statistical analysis algorithm to numerical data. For example, when analyzing text data, the analysis unit analyzes the meaning of the text using a natural language processing algorithm. When analyzing image data, the analysis unit can also analyze the content of the image using an image recognition algorithm. Furthermore, when analyzing numerical data, the analysis unit can also analyze the trend of the data using a statistical analysis algorithm. This makes it possible to apply an appropriate analysis algorithm based on the content of the data.

[0090] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of data during analysis. The analysis unit improves the accuracy of the analysis by taking into account the interrelationships of data during analysis. Examples of interrelation analysis include, but are not limited to, correlation analysis and network analysis. For example, the analysis unit analyzes the interrelationships between text data and image data to improve the accuracy. The analysis unit can also analyze the interrelationships between numerical data and text data to improve the accuracy. Furthermore, the analysis unit can analyze the interrelationships between image data and numerical data to improve the accuracy. For example, when analyzing the interrelationships between text data and image data, the analysis unit associates the content of the text data with the content of the image data. Furthermore, when analyzing the interrelationships between numerical data and text data, the analysis unit can associate the trend of the numerical data with the content of the text data. Furthermore, when analyzing the interrelationships between image data and numerical data, the analysis unit can associate the content of the image data with the trend of the numerical data. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships of data.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can analyze the user's text data and estimate the emotions. This allows the display method of the analysis results to be adjusted according to the user's emotions.

[0092] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. During analysis, the analysis unit determines the priority of analysis based on the time of data submission. The submission time includes, for example, the submission date and time, the submission order, etc., but is not limited to these examples. For example, the analysis unit prioritizes analyzing the latest data. The analysis unit can also prioritize analyzing data with an upcoming submission deadline. Furthermore, the analysis unit can prioritize analyzing the latest data, leaving older data for later analysis. For example, the analysis unit determines the priority of data based on the submission date and time. The analysis unit can also determine the priority of data based on the submission order. In this way, the priority of analysis can be determined based on the time of data submission.

[0093] The analysis unit can improve the accuracy of the analysis by referring to related literature and research data during the analysis. The analysis unit can improve the accuracy of the analysis by referring to related literature and research data during the analysis. Literature and research data include, but are not limited to, academic papers, technical reports, and market research data, for example. The analysis unit can improve the accuracy of the analysis by referring to related academic papers, for example. The analysis unit can also improve the accuracy of the analysis by referring to related patent data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to related market research data. For example, when referring to academic papers, the analysis unit can perform the analysis based on related research results. Furthermore, when referring to patent data, the analysis unit can perform the analysis based on related technical information. Furthermore, when referring to market research data, the analysis unit can perform the analysis based on related market trends. In this way, the accuracy of the analysis can be improved by referring to related literature and research data.

[0094] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, the evaluation unit can apply detailed evaluation criteria when the user is relaxed. Furthermore, the evaluation unit can apply concise evaluation criteria when the user is in a hurry. Furthermore, the evaluation unit can apply visually appealing evaluation criteria when the user is excited. For example, the evaluation unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the evaluation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the evaluation unit can analyze the user's text data and estimate the emotion. This allows the evaluation criteria to be adjusted according to the user's emotions.

[0095] The evaluation unit can quantitatively evaluate the similarity to past best ideas during evaluation. The evaluation unit quantitatively evaluates the similarity to past best ideas during evaluation. Methods for evaluating similarity include, but are not limited to, cosine similarity and Euclidean distance. For example, the evaluation unit extracts features of past best ideas and evaluates their similarity to the new idea. The evaluation unit can also evaluate the similarity of the new idea based on the evaluation scores of the past best ideas. Furthermore, the evaluation unit can extract keywords from past best ideas and evaluate their similarity to the new idea. For example, the evaluation unit calculates feature vectors of past best ideas and evaluates the cosine similarity between the vectors. The evaluation unit can also evaluate the similarity of the new idea based on the evaluation scores of the past best ideas. Furthermore, the evaluation unit can extract keywords from past best ideas and evaluate their similarity to the new idea. This allows for quantitative evaluation of the similarity to past best ideas.

[0096] The evaluation unit can apply multiple evaluation criteria during the evaluation to evaluate novelty and improvements in detail. The evaluation unit can apply multiple evaluation criteria during the evaluation to evaluate novelty and improvements in detail. Examples of evaluation criteria include, but are not limited to, novelty, uniqueness, and practicality. For example, the evaluation unit can separately apply criteria for evaluating novelty and criteria for evaluating improvements. The evaluation unit can also apply multiple criteria for comprehensively evaluating novelty and improvements. Furthermore, the evaluation unit can apply criteria for quantitatively evaluating novelty and improvements. For example, the evaluation unit can apply criteria for evaluating novelty to evaluate the uniqueness of the new idea. The evaluation unit can also apply criteria for evaluating improvements to evaluate the improvements of the idea. Furthermore, the evaluation unit can apply criteria for comprehensively evaluating novelty and improvements to perform a comprehensive evaluation of the new idea. In this way, multiple evaluation criteria can be applied to evaluate novelty and improvements in detail.

[0097] The evaluation unit can estimate the user's emotions and adjust the display order of the evaluation results based on the estimated user emotions. The evaluation unit can estimate the user's emotions and adjust the display order of the evaluation results based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, when the user is relaxed, the evaluation unit can prioritize displaying detailed evaluation results. Furthermore, when the user is in a hurry, the evaluation unit can prioritize displaying evaluation results that focus on the main points. Furthermore, when the user is excited, the evaluation unit can prioritize displaying visually appealing evaluation results. For example, the evaluation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Furthermore, the evaluation unit can record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the evaluation unit can analyze the user's text data and estimate the emotions. This allows the display order of the evaluation results to be adjusted according to the user's emotions.

[0098] The evaluation unit can perform the evaluation by taking into consideration attribute information of the idea submitter when evaluating the idea. The evaluation unit performs the evaluation by taking into consideration attribute information of the idea submitter when evaluating the idea. Attribute information includes, for example, age, occupation, field of expertise, etc., but is not limited to these examples. The evaluation unit can perform the evaluation by taking into consideration, for example, the submitter's field of expertise. The evaluation unit can also perform the evaluation by taking into consideration the submitter's years of experience. Furthermore, the evaluation unit can also perform the evaluation by taking into consideration the submitter's past performance. For example, the evaluation unit can perform the evaluation based on the submitter's field of expertise and consider specialized knowledge in that field. The evaluation unit can also perform the evaluation based on the submitter's years of experience and consider knowledge based on that experience. Furthermore, the evaluation unit can perform the evaluation based on the submitter's past performance and consider reliability based on that performance. In this way, by performing the evaluation by taking into consideration the submitter's attribute information, a more appropriate evaluation is possible.

[0099] The evaluation unit can improve the accuracy of the evaluation by referring to related market data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to related market data during the evaluation. Market data includes, but is not limited to, market research reports, sales data, and consumer survey data, for example. The evaluation unit can improve the accuracy of the evaluation by referring to related market trend data, for example. The evaluation unit can also improve the accuracy of the evaluation by referring to related competing product data. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to related consumer survey data. For example, when referring to market trend data, the evaluation unit can make an evaluation based on the latest market trends. Furthermore, when referring to competing product data, the evaluation unit can make an evaluation based on the characteristics of competing products. Furthermore, when referring to consumer survey data, the evaluation unit can make an evaluation based on consumer opinions. In this way, the accuracy of the evaluation can be improved by referring to related market data.

[0100] The providing unit can estimate the user's emotion and adjust the content of the information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the content of the information to be provided based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, the providing unit can provide detailed information when the user is relaxed. Furthermore, the providing unit can provide information that focuses on the main points when the user is in a hurry. Furthermore, the providing unit can provide visually appealing information when the user is excited. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can analyze the user's text data and estimate the emotion. This makes it possible to adjust the content of the information to be provided according to the user's emotion.

[0101] The providing unit can propose specific improvements and points to be strengthened based on the evaluation results when providing the information. The providing unit proposes specific improvements and points to be strengthened based on the evaluation results when providing the information. The improvements and points to be strengthened include, but are not limited to, functional improvements, performance improvements, and design improvements, for example. The providing unit, for example, proposes specific improvements based on the evaluation results. The providing unit can also propose points to be strengthened based on the evaluation results. Furthermore, the providing unit can propose a specific action plan for improvement based on the evaluation results. For example, the providing unit provides the proposer with specific advice such as "It would be good to improve this part" based on the evaluation results. The providing unit can also provide the proposer with specific advice such as "It would be good to strengthen this part" based on the evaluation results. Furthermore, the providing unit can provide the proposer with a specific action plan such as "It would be good to take the following action to improve this part" based on the evaluation results. This makes it possible to propose specific improvements and points to be strengthened based on the evaluation results.

[0102] The providing unit can provide optimal information by referring to the user's past feedback history when providing the information. The providing unit can provide optimal information by referring to the user's past feedback history when providing the information. The feedback history includes, for example, past comments, evaluation results, and areas for improvement, but is not limited to these examples. For example, the providing unit can provide optimal information by referring to the user's past feedback history. The providing unit can also analyze the user's past feedback history and suggest areas for improvement. Furthermore, the providing unit can suggest areas to be improved based on the user's past feedback history. For example, when referring to the user's past feedback history, the providing unit provides optimal information based on past comments. Furthermore, when referring to the user's past evaluation results, the providing unit can suggest areas to be improved based on the past evaluation results. Furthermore, when referring to the user's past improvements, the providing unit can suggest areas to be improved based on the past improvements. In this way, optimal information can be provided by referring to the user's past feedback history.

[0103] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing information that focuses on the main points. Furthermore, when the user is excited, the providing unit can prioritize providing visually appealing information. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can analyze the user's text data and estimate the emotion. This makes it possible to determine the priority of information to be provided according to the user's emotions.

[0104] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display information. The providing unit selects the optimal display method by taking into consideration the user's device information when providing the display information. Device information includes, but is not limited to, the device type, OS, browser, etc. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. For example, the providing unit can provide a display method tailored to the smartphone screen size based on the user's device information. Furthermore, the providing unit can provide a display method optimized for a large tablet screen based on the user's device information. Furthermore, the providing unit can provide a display method optimized for a small smartwatch screen based on the user's device information. This makes it possible to select the optimal display method by taking into consideration the user's device information.

[0105] The providing unit can improve the accuracy of the information by referring to related trend information when providing the information. The providing unit can improve the accuracy of the information by referring to related trend information when providing the information. Trend information includes, for example, the latest technology trends, market trends, consumer trends, etc., but is not limited to these examples. The providing unit can improve the accuracy of the information by referring to related market trend information, for example. The providing unit can also improve the accuracy of the information by referring to related technology trend information. Furthermore, the providing unit can improve the accuracy of the information by referring to related consumer trend information. For example, when referring to market trend information, the providing unit can provide information based on the latest market trends. Furthermore, when referring to technology trend information, the providing unit can provide information based on the latest technology trends. Furthermore, when referring to consumer trend information, the providing unit can provide information based on the latest consumer trends. In this way, the accuracy of the information can be improved by referring to related trend information.

[0106] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, the feedback unit can provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide feedback that is concise when the user is in a hurry. Furthermore, the feedback unit can provide visually appealing feedback when the user is excited. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the feedback unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the feedback unit can analyze the user's text data and estimate the emotion. This makes it possible to adjust the content of the feedback according to the user's emotion.

[0107] The feedback unit can propose specific improvements based on the evaluation results when providing feedback. The feedback unit can propose specific improvements based on the evaluation results when providing feedback. Improvements include, but are not limited to, functional improvements, performance improvements, and design improvements, for example. The feedback unit can propose specific improvements based on the evaluation results. The feedback unit can also propose points to be strengthened based on the evaluation results. The feedback unit can also propose a specific action plan for improvement based on the evaluation results. For example, the feedback unit can provide the proposer with specific advice such as "It would be good to improve this part" based on the evaluation results. The feedback unit can also provide the proposer with specific advice such as "It would be good to strengthen this part" based on the evaluation results. The feedback unit can also provide the proposer with a specific action plan such as "It would be good to take the following action to improve this part" based on the evaluation results. In this way, specific improvements can be proposed based on the evaluation results.

[0108] The feedback unit can provide optimal feedback by referring to the user's past idea submission history when providing feedback. The feedback unit can provide optimal feedback by referring to the user's past idea submission history when providing feedback. The idea submission history includes, for example, but is not limited to, previously submitted ideas, evaluation results, and improvements. For example, the feedback unit can provide optimal feedback by referring to the user's past idea submission history. The feedback unit can also analyze the user's past idea submission history and suggest improvements. Furthermore, the feedback unit can suggest areas to be improved based on the user's past idea submission history. For example, when referring to the user's past idea submission history, the feedback unit can provide optimal feedback based on the previously submitted ideas. Furthermore, when referring to the user's past evaluation results, the feedback unit can suggest areas to be improved based on the past evaluation results. Furthermore, when referring to the user's past improvements, the feedback unit can suggest areas to be improved based on the past improvements. In this way, optimal feedback can be provided by referring to the user's past idea submission history.

[0109] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, if the user is relaxed, the feedback unit can prioritize providing detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can prioritize providing feedback that focuses on the main points. Furthermore, if the user is excited, the feedback unit can prioritize providing visually appealing feedback. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the feedback unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the feedback unit can analyze the user's text data and estimate the emotion. In this way, the priority of feedback can be determined according to the user's emotions.

[0110] The feedback unit may provide optimal feedback by taking into consideration the geographical information of the user when providing feedback. The feedback unit may provide optimal feedback by taking into consideration the geographical information of the user when providing feedback. Examples of geographical information include, but are not limited to, GPS data and address information. For example, the feedback unit may refer to the geographical information of the user and provide feedback specific to a region. The feedback unit may also provide feedback tailored to regional characteristics based on the geographical information of the user. Furthermore, the feedback unit may provide feedback based on regional trends by taking into consideration the geographical information of the user. For example, the feedback unit may provide feedback specific to a region when referring to the geographical information of the user. The feedback unit may also provide feedback tailored to regional characteristics based on the geographical information of the user. Furthermore, the feedback unit may provide feedback based on regional trends by taking into consideration the geographical information of the user. This makes it possible to provide optimal feedback by taking into consideration the geographical information of the user.

[0111] The feedback unit may improve the accuracy of the feedback by referring to related market data when providing feedback. The feedback unit may improve the accuracy of the feedback by referring to related market data when providing feedback. Market data includes, but is not limited to, market research reports, sales data, and consumer survey data. For example, the feedback unit may improve the accuracy of the feedback by referring to related market trend data. The feedback unit may also improve the accuracy of the feedback by referring to related competitive product data. The feedback unit may also improve the accuracy of the feedback by referring to related consumer survey data. For example, the feedback unit may provide feedback based on the latest market trends when referring to market trend data. The feedback unit may also provide feedback based on the characteristics of competitive products when referring to competitive product data. The feedback unit may also provide feedback based on consumer opinions when referring to consumer survey data. This makes it possible to improve the accuracy of the feedback by referring to related market data.

[0112] The similarity evaluation unit can estimate the user's emotion and adjust the similarity evaluation criteria based on the estimated user's emotion. The similarity evaluation unit can estimate the user's emotion and adjust the similarity evaluation criteria based on the estimated user's emotion. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, the similarity evaluation unit can apply detailed similarity evaluation criteria when the user is relaxed. The similarity evaluation unit can also apply simple similarity evaluation criteria when the user is in a hurry. Furthermore, the similarity evaluation unit can apply visually appealing similarity evaluation criteria when the user is excited. For example, the similarity evaluation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The similarity evaluation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the similarity evaluation unit can analyze the user's text data and estimate the emotion. This allows the similarity evaluation criteria to be adjusted according to the user's emotion.

[0113] The similarity evaluation unit can evaluate the similarity with past best ideas in detail during the similarity evaluation. The similarity evaluation unit evaluates the similarity with past best ideas in detail during the similarity evaluation. Methods for evaluating similarity include, but are not limited to, cosine similarity and Euclidean distance. For example, the similarity evaluation unit extracts features of past best ideas and evaluates their similarity with the new idea. The similarity evaluation unit can also evaluate the similarity of the new idea based on the evaluation scores of the past best ideas. Furthermore, the similarity evaluation unit can extract keywords from past best ideas and evaluate their similarity with the new idea. For example, the similarity evaluation unit calculates feature vectors of past best ideas and evaluates the cosine similarity between the vectors. The similarity evaluation unit can also evaluate the similarity of the new idea based on the evaluation scores of the past best ideas. Furthermore, the similarity evaluation unit can extract keywords from past best ideas and evaluate their similarity with the new idea. This allows for a detailed evaluation of the similarity with past best ideas.

[0114] The similarity evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between ideas when evaluating the similarity. The similarity evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between ideas when evaluating the similarity. Evaluation of the interrelationships includes, for example, correlation analysis, network analysis, etc., but is not limited to these examples. The similarity evaluation unit can, for example, analyze the interrelationships between text data and image data to improve the accuracy. The similarity evaluation unit can also analyze the interrelationships between numerical data and text data to improve the accuracy. Furthermore, the similarity evaluation unit can analyze the interrelationships between image data and numerical data to improve the accuracy. For example, when analyzing the interrelationships between text data and image data, the similarity evaluation unit can analyze the content of the text data by associating it with the content of the image data. Furthermore, when analyzing the interrelationships between numerical data and text data, the similarity evaluation unit can analyze the trend of the numerical data by associating it with the content of the text data. Furthermore, when analyzing the interrelationships between image data and numerical data, the similarity evaluation unit can analyze the content of the image data by associating it with the trend of the numerical data. In this way, the accuracy of the evaluation can be improved by taking into account the interrelationships between ideas.

[0115] The similarity evaluation unit can estimate the user's emotion and adjust the display order of the similarity evaluation results based on the estimated user's emotion. The similarity evaluation unit can estimate the user's emotion and adjust the display order of the similarity evaluation results based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is relaxed, the similarity evaluation unit can prioritize displaying detailed similarity evaluation results. Furthermore, when the user is in a hurry, the similarity evaluation unit can prioritize displaying similarity evaluation results that focus on the main points. Furthermore, when the user is excited, the similarity evaluation unit can prioritize displaying visually appealing similarity evaluation results. For example, the similarity evaluation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the similarity evaluation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the similarity evaluation unit can analyze the user's text data and estimate the emotion. This allows the display order of the similarity evaluation results to be adjusted according to the user's emotion.

[0116] The similarity evaluation unit can perform the similarity evaluation while taking into account attribute information of the idea submitter. The similarity evaluation unit performs the similarity evaluation while taking into account attribute information of the idea submitter. Attribute information includes, for example, age, occupation, and field of expertise, but is not limited to these examples. The similarity evaluation unit can perform the evaluation while taking into account, for example, the submitter's field of expertise. The similarity evaluation unit can also perform the evaluation while taking into account the submitter's years of experience. The similarity evaluation unit can also perform the evaluation while taking into account the submitter's past achievements. For example, the similarity evaluation unit can perform the evaluation based on the submitter's field of expertise and consider specialized knowledge in that field. The similarity evaluation unit can also perform the evaluation based on the submitter's years of experience and consider knowledge based on that experience. The similarity evaluation unit can also perform the evaluation based on the submitter's past achievements and consider reliability based on that achievement. This allows for a more appropriate evaluation by performing the evaluation while taking into account the submitter's attribute information.

[0117] The similarity evaluation unit can improve the accuracy of the evaluation by referring to related literature and research data when evaluating the similarity. The similarity evaluation unit can improve the accuracy of the evaluation by referring to related literature and research data when evaluating the similarity. Literature and research data include, but are not limited to, academic papers, technical reports, and market research data. For example, the similarity evaluation unit can improve the accuracy of the evaluation by referring to related academic papers. The similarity evaluation unit can also improve the accuracy of the evaluation by referring to related patent data. The similarity evaluation unit can also improve the accuracy of the evaluation by referring to related market research data. For example, when referring to academic papers, the similarity evaluation unit can make an evaluation based on related research results. When referring to patent data, the similarity evaluation unit can make an evaluation based on related technical information. When referring to market research data, the similarity evaluation unit can make an evaluation based on related market trends. This makes it possible to improve the accuracy of the evaluation by referring to related literature and research data.

[0118] The novelty evaluation unit can estimate the user's emotion and adjust the novelty evaluation criteria based on the estimated user's emotion. The novelty evaluation unit can estimate the user's emotion and adjust the novelty evaluation criteria based on the estimated user's emotion. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, the novelty evaluation unit can apply detailed novelty evaluation criteria when the user is relaxed. Furthermore, the novelty evaluation unit can apply simple novelty evaluation criteria when the user is in a hurry. Furthermore, the novelty evaluation unit can apply visually appealing novelty evaluation criteria when the user is excited. For example, the novelty evaluation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the novelty evaluation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the novelty evaluation unit can analyze the user's text data and estimate the emotion. This allows the novelty evaluation criteria to be adjusted according to the user's emotion.

[0119] The novelty evaluation unit can evaluate the uniqueness of the idea in detail during the novelty evaluation. The novelty evaluation unit evaluates the uniqueness of the idea in detail during the novelty evaluation. The evaluation of uniqueness includes, for example, technical uniqueness, uniqueness of the business model, etc., but is not limited to these examples. The novelty evaluation unit, for example, applies criteria for evaluating the uniqueness of the idea. The novelty evaluation unit can also apply criteria for quantitatively evaluating the uniqueness of the idea. Furthermore, the novelty evaluation unit can also apply criteria for comprehensively evaluating the uniqueness of the idea. For example, the novelty evaluation unit can apply criteria for evaluating technical uniqueness to evaluate the technical novelty of the idea. The novelty evaluation unit can also apply criteria for evaluating the uniqueness of the business model to evaluate the novelty of the business model of the idea. Furthermore, the novelty evaluation unit can apply criteria for comprehensively evaluating the technical uniqueness and the uniqueness of the business model to evaluate the overall uniqueness of the idea. This allows the uniqueness of the idea to be evaluated in detail.

[0120] The novelty evaluation unit can apply multiple evaluation criteria to clarify the differences from past ideas when evaluating novelty. The novelty evaluation unit applies multiple evaluation criteria to clarify the differences from past ideas when evaluating novelty. The evaluation criteria include, but are not limited to, novelty, uniqueness, and practicality, for example. The novelty evaluation unit applies, for example, criteria to evaluate the differences from past ideas. The novelty evaluation unit can also apply criteria to quantitatively evaluate the differences from past ideas. Furthermore, the novelty evaluation unit can also apply criteria to comprehensively evaluate the differences from past ideas. For example, the novelty evaluation unit can apply criteria to evaluate the differences from past ideas and evaluate the uniqueness of the new idea. The novelty evaluation unit can also apply criteria to quantitatively evaluate the differences from past ideas and evaluate the uniqueness of the new idea. Furthermore, the novelty evaluation unit can apply criteria to comprehensively evaluate the differences from past ideas and evaluate the overall uniqueness of the new idea. In this way, multiple evaluation criteria can be applied to clarify the differences from past ideas.

[0121] The novelty evaluation unit can estimate a user's emotion and adjust the display order of the novelty evaluation results based on the estimated user's emotion. The novelty evaluation unit can estimate a user's emotion and adjust the display order of the novelty evaluation results based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. For example, when a user is relaxed, the novelty evaluation unit can prioritize displaying detailed novelty evaluation results. Furthermore, when a user is in a hurry, the novelty evaluation unit can prioritize displaying novelty evaluation results that focus on the main points. Furthermore, when a user is excited, the novelty evaluation unit can prioritize displaying visually appealing novelty evaluation results. For example, the novelty evaluation unit can capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the novelty evaluation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the novelty evaluation unit can analyze the user's text data and estimate the emotion. This allows the display order of the novelty evaluation results to be adjusted according to the user's emotion.

[0122] The novelty evaluation unit can perform the novelty evaluation by taking into consideration attribute information of the submitter of the idea. The novelty evaluation unit performs the novelty evaluation by taking into consideration attribute information of the submitter of the idea. Attribute information includes, for example, age, occupation, field of expertise, etc., but is not limited to these examples. The novelty evaluation unit can perform the evaluation by taking into consideration, for example, the submitter's field of expertise. The novelty evaluation unit can also perform the evaluation by taking into consideration the submitter's years of experience. Furthermore, the novelty evaluation unit can also perform the evaluation by taking into consideration the submitter's past achievements. For example, the novelty evaluation unit can perform the evaluation based on the submitter's field of expertise and consider specialized knowledge in that field. The novelty evaluation unit can also perform the evaluation based on the submitter's years of experience and consider knowledge based on that experience. Furthermore, the novelty evaluation unit can perform the evaluation based on the submitter's past achievements and consider reliability based on that achievement. As a result, by performing the evaluation by taking into consideration the submitter's attribute information, a more appropriate evaluation is possible.

[0123] The novelty evaluation unit can improve the accuracy of the evaluation by referring to related market data when evaluating novelty. The novelty evaluation unit can improve the accuracy of the evaluation by referring to related market data when evaluating novelty. Market data includes, but is not limited to, market research reports, sales data, and consumer survey data, for example. The novelty evaluation unit can improve the accuracy of the evaluation by referring to related market trend data, for example. The novelty evaluation unit can also improve the accuracy of the evaluation by referring to related competing product data. The novelty evaluation unit can also improve the accuracy of the evaluation by referring to related consumer survey data. For example, when referring to market trend data, the novelty evaluation unit can make an evaluation based on the latest market trends. When referring to competing product data, the novelty evaluation unit can make an evaluation based on the characteristics of competing products. When referring to consumer survey data, the novelty evaluation unit can make an evaluation based on consumer opinions. This makes it possible to improve the accuracy of the evaluation by referring to related market data.

[0124] The improvement evaluation unit can estimate the user's emotions and adjust the criteria for evaluating the improvement points based on the estimated user emotions. The improvement evaluation unit can estimate the user's emotions and adjust the criteria for evaluating the improvement points based on the estimated user emotions. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, the improvement evaluation unit can apply detailed improvement evaluation criteria when the user is relaxed. Furthermore, the improvement evaluation unit can apply concise improvement evaluation criteria when the user is in a hurry. Furthermore, the improvement evaluation unit can apply visually appealing improvement evaluation criteria when the user is excited. For example, the improvement evaluation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the improvement evaluation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the improvement evaluation unit can analyze the user's text data and estimate the emotion. In this way, the criteria for evaluating the improvement points can be adjusted according to the user's emotions.

[0125] The improvement evaluation unit can evaluate the improvement points of the idea in detail during the improvement evaluation. The improvement evaluation unit evaluates the improvement points of the idea in detail during the improvement evaluation. Evaluation of the improvement points includes, for example, functional improvement, performance improvement, design improvement, etc., but is not limited to these examples. The improvement evaluation unit, for example, applies criteria for evaluating the improvement points of the idea. The improvement evaluation unit can also apply criteria for quantitatively evaluating the improvement points of the idea. Furthermore, the improvement evaluation unit can apply criteria for comprehensively evaluating the improvement points of the idea. For example, the improvement evaluation unit can apply criteria for evaluating functional improvement to evaluate functional improvement points of the idea. The improvement evaluation unit can also apply criteria for evaluating performance improvement to evaluate performance improvement points of the idea. Furthermore, the improvement evaluation unit can apply criteria for evaluating design improvement to evaluate design improvement points of the idea. This allows the improvement points of the idea to be evaluated in detail.

[0126] The improvement point evaluation unit can improve the accuracy of the evaluation when evaluating the improvement points by referring to the improvement points of past ideas. The improvement point evaluation unit improves the accuracy of the evaluation when evaluating the improvement points by referring to the improvement points of past ideas. Reference to the improvement points includes, for example, past evaluation results, improvement history, etc., but is not limited to these examples. The improvement point evaluation unit, for example, improves the accuracy of the evaluation by referring to the improvement points of past ideas. The improvement point evaluation unit can also evaluate the improvement points of a new idea based on the improvement points of past ideas. Furthermore, the improvement point evaluation unit can analyze the improvement points of past ideas to improve the accuracy of the evaluation. For example, when referring to the improvement points of past ideas, the improvement point evaluation unit performs the evaluation based on the past evaluation results. Furthermore, when referring to the past improvement history, the improvement point evaluation unit can perform the evaluation based on the improvement history. Furthermore, when analyzing the past improvements, the improvement point evaluation unit can perform the evaluation based on the trend of the improvements. In this way, the accuracy of the evaluation can be improved by referring to the improvements of past ideas.

[0127] The improvement evaluation unit can estimate the user's emotions and adjust the display order of the improvement evaluation results based on the estimated user emotions. The improvement evaluation unit can estimate the user's emotions and adjust the display order of the improvement evaluation results based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, if the user is relaxed, the improvement evaluation unit can prioritize displaying detailed improvement evaluation results. Furthermore, if the user is in a hurry, the improvement evaluation unit can prioritize displaying improvement evaluation results that focus on the main points. Furthermore, if the user is excited, the improvement evaluation unit can prioritize displaying visually appealing improvement evaluation results. For example, the improvement evaluation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the improvement evaluation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the improvement evaluation unit can analyze the user's text data and estimate the emotion. This allows the display order of the improvement evaluation results to be adjusted according to the user's emotions.

[0128] The improvement point evaluation unit can perform the evaluation by taking into consideration attribute information of the idea submitter when evaluating the improvement points. The improvement point evaluation unit performs the evaluation by taking into consideration attribute information of the idea submitter when evaluating the improvement points. Attribute information includes, for example, age, occupation, field of expertise, etc., but is not limited to these examples. The improvement point evaluation unit can perform the evaluation by taking into consideration, for example, the submitter's field of expertise. The improvement point evaluation unit can also perform the evaluation by taking into consideration the submitter's years of experience. Furthermore, the improvement point evaluation unit can also perform the evaluation by taking into consideration the submitter's past performance. For example, the improvement point evaluation unit can perform the evaluation based on the submitter's field of expertise and consider specialized knowledge in that field. The improvement point evaluation unit can also perform the evaluation based on the submitter's years of experience and consider knowledge based on that experience. Furthermore, the improvement point evaluation unit can perform the evaluation based on the submitter's past performance and consider reliability based on that performance. In this way, by performing the evaluation by taking into consideration the submitter's attribute information, a more appropriate evaluation is possible.

[0129] The improvement evaluation unit can improve the accuracy of the evaluation by referring to related literature and research data when evaluating the improvement points. The improvement evaluation unit can improve the accuracy of the evaluation by referring to related literature and research data when evaluating the improvement points. Literature and research data include, but are not limited to, academic papers, technical reports, and market research data, for example. The improvement evaluation unit can improve the accuracy of the evaluation by referring to related academic papers. The improvement evaluation unit can also improve the accuracy of the evaluation by referring to related patent data. The improvement evaluation unit can also improve the accuracy of the evaluation by referring to related market research data. For example, when referring to academic papers, the improvement evaluation unit can make an evaluation based on related research results. When referring to patent data, the improvement evaluation unit can make an evaluation based on related technical information. When referring to market research data, the improvement evaluation unit can make an evaluation based on related market trends. This makes it possible to improve the accuracy of the evaluation by referring to related literature and research data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, provision unit, feedback unit, similarity evaluation unit, novelty evaluation unit, and improvement evaluation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and filters the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and performs evaluation based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides the evaluation results to the proposer. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on the evaluation results. The similarity evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the similarity of new ideas. The novelty evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the novelty of new ideas. The improvement evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates improvements of the idea. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, provision unit, feedback unit, similarity evaluation unit, novelty evaluation unit, and improvement evaluation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and filters the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and performs evaluation based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the evaluation results to the proposer. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on the evaluation results. The similarity evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the similarity of new ideas. The novelty evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the novelty of new ideas. The improvement evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates improvements of the idea. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, provision unit, feedback unit, similarity evaluation unit, novelty evaluation unit, and improvement evaluation unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset type terminal 314 and filters the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and performs evaluation based on the analysis results. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the evaluation results to the proposer. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on the evaluation results. The similarity evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the similarity of new ideas. The novelty evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the novelty of the new idea. The improvement evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the improvement of the idea. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, provision unit, feedback unit, similarity evaluation unit, novelty evaluation unit, and improvement evaluation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and filters the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and performs evaluation based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides the evaluation results to the proposer. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on the evaluation results. The similarity evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the similarity of new ideas. The novelty evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the novelty of new ideas. The improvement evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates improvements of the idea.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The analysis unit can also estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide deep insights. If the user is in a hurry, a concise analysis can be performed to provide results that focus on the main points. Furthermore, if the user is excited, a visually appealing analysis result can be provided. This allows the analysis method to be adjusted according to the user's emotions.

[0132] The evaluation unit can also estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is relaxed, detailed evaluation criteria can be applied. If the user is in a hurry, simple evaluation criteria can be applied. Furthermore, if the user is excited, visually attractive evaluation criteria can be applied. In this way, the evaluation criteria can be adjusted according to the user's emotions.

[0133] The providing unit can also estimate the user's emotions and adjust the content of the information to be provided based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, information that focuses on the main points can be provided. Furthermore, if the user is excited, visually appealing information can be provided. In this way, the content of the information to be provided can be adjusted according to the user's emotions.

[0134] The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, feedback that is concise can be provided. Furthermore, if the user is excited, visually appealing feedback can be provided. In this way, the content of the feedback can be adjusted according to the user's emotions.

[0135] The similarity evaluation unit can also estimate the user's emotion and adjust the similarity evaluation criteria based on the estimated user's emotion. For example, if the user is relaxed, a detailed similarity evaluation criterion can be applied. If the user is in a hurry, a simple similarity evaluation criterion can be applied. Furthermore, if the user is excited, a visually appealing similarity evaluation criterion can be applied. In this way, the similarity evaluation criteria can be adjusted according to the user's emotion.

[0136] During data collection, the collection unit can also collect data from social media and online forums to understand idea trends. For example, it can analyze social media hashtags to collect trending ideas. It can also analyze posts on online forums to collect popular ideas. It can also analyze social media comments to understand user interests. This makes it possible to collect data from social media and online forums to understand idea trends.

[0137] During analysis, the analysis unit can apply different analysis algorithms based on the content of the data. For example, it can apply a natural language processing algorithm to text data, an image recognition algorithm to image data, or a statistical analysis algorithm to numerical data. This allows it to apply the appropriate analysis algorithm based on the content of the data.

[0138] The evaluation unit may refer to related market data during evaluation to improve the accuracy of the evaluation. For example, related market trend data may be referenced to improve the accuracy of the evaluation. The evaluation unit may also refer to related competing product data to improve the accuracy of the evaluation. Furthermore, the evaluation unit may also refer to related consumer survey data to improve the accuracy of the evaluation. Thus, the accuracy of the evaluation can be improved by referring to related market data.

[0139] When providing the display, the providing unit can also select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. This makes it possible to select the optimal display method by taking into consideration the user's device information.

[0140] The feedback unit may improve the accuracy of the feedback by referring to related market data when providing feedback. For example, the feedback unit may improve the accuracy of the feedback by referring to related market trend data. The feedback unit may also improve the accuracy of the feedback by referring to related competing product data. The feedback unit may also improve the accuracy of the feedback by referring to related consumer survey data. In this way, the accuracy of the feedback can be improved by referring to related market data.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The collection unit collects data from past idea contests. The collected data may include, for example, text data, numerical data, and image data. The collection unit may collect data from public databases on the Internet and filter the data based on specific themes or categories. For example, the collection unit may collect only ideas related to environmental protection and exclude data on other themes. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis may include statistical analysis, application of machine learning algorithms, analysis of text data using natural language processing technology, analysis of image data using image recognition technology, etc. Step 3: The evaluation department evaluates the ideas based on the analysis results obtained by the analysis department. The evaluation includes a scoring system, evaluation criteria, evaluation of similarity to previous excellent ideas, evaluation of novelty and areas for improvement, etc. Step 4: The providing unit provides the evaluation results obtained by the evaluation unit. The providing may include a report format, a dashboard display, etc. For example, the evaluation results may be provided to the proposer in a report format or displayed on a dashboard.

[0143] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0145] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0165] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 7, a 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.

[0181] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0183] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0185] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0186] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0187] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0188] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0189] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0193] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0194] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0198] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0199] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0200] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0204] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0205] 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.

[0206] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0207] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0208] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0209] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0211] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0212] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0213] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0214] [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; an evaluation unit that performs evaluation based on the analysis result obtained by the analysis unit; a providing unit that provides the evaluation result obtained by the evaluation unit; Equipped with A system characterized by:

2. A feedback unit is provided to provide feedback based on the evaluation results.

2. The system of claim 1.

3. Equipped with a similarity evaluation unit that evaluates the similarity of new ideas based on past data 2. The system of claim 1.

4. Equipped with a novelty evaluation unit that evaluates novelty 2. The system of claim 1.

5. Equipped with an improvement evaluation section that evaluates improvements 2. The system of claim 1.

6. The feedback unit Providing feedback to the proposer based on the evaluation results obtained by the evaluation unit 3. The system of claim 2.

7. The system according to claim 1 , wherein the collection unit estimates a user's emotion and adjusts the timing of data collection based on the estimated user's emotion.

8. The collecting unit When collecting data from past idea contests, filter it based on specific themes or categories.

2. The system of claim 1.

9. The collecting unit When collecting data, evaluate the reliability of the data and prioritize collecting reliable data.

2. The system of claim 1.

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