system

Abstract

The system according to the embodiment aims to efficiently generate ideas and evaluate them. [Solution] A system according to an embodiment includes a receiving unit, a target receiving unit, a generation unit, an evaluation unit, and a display unit. The receiving unit inputs an idea generation category. The target receiving unit inputs an idea generation target. The generation unit generates ideas based on the information input by the receiving unit and the target receiving unit. The evaluation unit evaluates the ideas generated by the generation unit. The display unit displays the evaluation results obtained by the evaluation unit in a ranking format.

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] Conventional techniques make it difficult to efficiently generate and evaluate ideas, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently generate ideas and evaluate them. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a target receiving unit, a generation unit, an evaluation unit, and a display unit. The receiving unit inputs an idea generation category. The target receiving unit inputs an idea generation target. The generation unit generates ideas based on the information input by the receiving unit and the target receiving unit. The evaluation unit evaluates the ideas generated by the generation unit. The display unit displays the evaluation results obtained by the evaluation unit in a ranking format. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate ideas and evaluate them. [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) In an idea generation system according to an embodiment of the present invention, a user inputs an idea generation category and a target, evaluates the generated ideas, and displays the results in a ranking format. The idea generation system generates ideas for each target by inputting an idea generation category (e.g., a product or service) and then an idea generation target (e.g., an engineer, a marketer, or a consumer). The user can evaluate (rank) the generated ideas. Furthermore, by setting the value of the evaluation for whom, the results are displayed in a ranking format. For example, in the idea generation system, a user inputs categories such as "new smartphone apps" or "environmentally friendly products." This information is input to the generation AI. Next, the user inputs targets such as "engineers," "marketers," or "consumers." This allows the generation AI to generate ideas from different perspectives for each target. The user sets evaluation criteria for the generated ideas, such as "innovativeness," "feasibility," and "marketability," and evaluates them. This evaluation can also be supported by the generation AI. Furthermore, the user can set criteria such as "value for engineers" or "value for consumers." Based on these settings, the generation AI then displays the results in a ranking format. This allows the idea generation system to easily generate and evaluate ideas from various perspectives. For example, by generating ideas for a new smartphone app from the perspectives of engineers, marketers, and consumers and evaluating them from each perspective, it is possible to find the most valuable ideas.

[0029] An idea generation system according to an embodiment includes a receiving unit, a target receiving unit, a generation unit, an evaluation unit, and a display unit. The receiving unit receives an idea generation category input by a user. For example, the user can input categories such as "new smartphone app" or "environmentally friendly product." The target receiving unit receives an idea generation target input by a user. For example, the user can input targets such as "engineer," "marketer," or "consumer." The generation unit generates ideas based on the information input by the receiving unit and the target receiving unit using a generation AI. For example, the generation AI can generate ideas based on past data or trend analysis. The evaluation unit evaluates the ideas generated by the generation unit. For example, evaluation criteria such as "innovativeness," "feasibility," and "marketability" can be set for each idea and evaluation can be performed. The display unit displays the evaluation results obtained by the evaluation unit in a ranking format. For example, the user can set who the evaluation is valuable to and display the results in a ranking format based on the setting. This allows the idea generation system according to an embodiment to allow a user to input an idea generation category and a target, evaluate generated ideas, and display the results in a ranking format.

[0030] The generation unit can generate ideas based on past data or trend analysis. The generation unit, for example, generates ideas based on past data. For example, it analyzes past ideas and trend data and optimizes the generation algorithm. The generation unit can also generate ideas based on trend analysis. For example, it can perform time series analysis and market research to generate ideas that match current market trends. Furthermore, the generation unit can optimize the generation algorithm based on past data and trend analysis. For example, it extracts patterns of successful ideas in the past and optimizes the generation algorithm. In this way, more accurate ideas can be provided by generating ideas based on past data and trend analysis.

[0031] The evaluation department can set evaluation criteria for each idea, such as "innovativeness," "feasibility," and "marketability," and conduct an evaluation. For example, the evaluation department can evaluate the "innovativeness" of each idea. For example, the evaluation can be based on novelty and technological advancement. The evaluation department can also evaluate the "feasibility" of each idea. For example, the evaluation can be based on technical feasibility and cost. Furthermore, the evaluation department can also evaluate the "marketability" of each idea. For example, the evaluation can be based on market size and the competitive environment. In this way, by setting evaluation criteria for each idea and conducting an evaluation, the value of the idea can be objectively evaluated.

[0032] The display unit can set for whom an evaluation is valuable and display the results in a ranking format based on the setting. The display unit can set, for example, for whom the evaluation is valuable. For example, it can set "valuable to engineers" or "valuable to consumers." The display unit displays the results in a ranking format based on the setting. For example, it can display the ranking in descending order of score. The display unit can also display rankings by category. For example, it can display rankings by category such as technology category, business category, and creative category. In this way, by setting for whom the evaluation is valuable and displaying the results in a ranking format, the user can find the most valuable ideas.

[0033] The reception unit can analyze the user's past input history and select a category input method. The reception unit, for example, analyzes the user's past input history. For example, the analysis can be performed based on past input data and usage history. The reception unit selects the optimal category input method based on the analysis results. For example, categories that the user has frequently used in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest categories to be used in a specific time period based on the user's past input history. In this way, it is possible to provide the optimal category input method by analyzing the user's past input history.

[0034] The receiving unit can filter the idea generation categories based on the user's current project or area of ​​interest when the user inputs the category. The receiving unit performs filtering based on the user's current project or area of ​​interest, for example. For example, categories related to the project the user is currently working on can be preferentially displayed. Also, related categories can be filtered and displayed based on the user's area of ​​interest. Furthermore, related categories can be suggested with reference to the user's past project history. In this way, highly relevant categories can be provided by filtering based on the user's current project or area of ​​interest.

[0035] The receiving unit can select the optimum input means according to the user's input method when inputting an idea generation category. The receiving unit selects the optimum input means according to the user's input method, for example. For example, if the user prefers voice input, the receiving unit can provide voice input with priority. Also, if the user prefers text input, the receiving unit can provide text input with priority. Furthermore, if the user prefers image input, the receiving unit can provide image input with priority. This improves user convenience by providing the optimum input means according to the user's input method.

[0036] When inputting idea generation categories, the reception unit can preferentially input highly relevant categories taking into account the user's geographical location information. The reception unit, for example, preferentially inputs highly relevant categories taking into account the user's geographical location information. For example, when the user is in a specific area, categories related to that area can be preferentially displayed. Also, when the user is traveling, categories related to the travel destination can be preferentially displayed. Furthermore, when the user is at home, categories that can be performed at home can be preferentially displayed. In this way, highly relevant categories can be provided by taking into account the user's geographical location information.

[0037] When an idea generation category is input, the reception unit can analyze the user's social media activity and input related categories. The reception unit, for example, analyzes the user's social media activity. For example, the analysis can be performed based on the content of posts and follower information. The reception unit inputs related categories based on the analysis results. For example, it can display categories related to topics that the user frequently mentions on social media. It can also suggest related categories based on the activity of the user's friends on social media. Furthermore, it can analyze the content of the user's social media posts and suggest related categories. In this way, it is possible to provide related categories by analyzing the user's social media activity.

[0038] The receiving unit can customize the input method by reflecting the user's past feedback when inputting an idea generation category. The receiving unit, for example, reflects the user's past feedback. For example, it can suggest the optimal input method based on feedback provided by the user in the past. The receiving unit customizes the input method based on the past feedback. For example, it can provide a specific input method with priority based on the user's past feedback. It can also analyze the user's feedback and customize the input method. In this way, it is possible to provide the optimal input method by reflecting the user's past feedback.

[0039] The target receiving unit can analyze the user's past target input history and select the optimal target input method. The target receiving unit, for example, analyzes the user's past target input history. For example, the analysis can be performed based on past target data and usage history. The target receiving unit selects the optimal target input method based on the analysis results. For example, targets that the user has frequently used in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest targets to be used in a specific time period from the user's past input history. In this way, it is possible to provide the optimal target input method by analyzing the user's past target input history.

[0040] When a target is input, the target receiving unit can filter the target based on the user's current project or area of ​​interest. The target receiving unit performs filtering based on, for example, the user's current project or area of ​​interest. For example, targets related to a project the user is currently working on can be preferentially displayed. Related targets can also be filtered and displayed based on the user's area of ​​interest. Furthermore, related targets can be suggested by referring to the user's past project history. In this way, highly relevant targets can be provided by filtering based on the user's current project or area of ​​interest.

[0041] When inputting a target, the target receiving unit can select the optimum input means according to the user's input method. The target receiving unit selects the optimum input means according to the user's input method, for example. For example, if the user prefers voice input, it can provide voice input preferentially. Also, if the user prefers text input, it can provide text input preferentially. Furthermore, if the user prefers image input, it can provide image input preferentially. This provides the optimum input means according to the user's input method, thereby improving user convenience.

[0042] When inputting a target, the target receiving unit can preferentially input highly relevant targets taking into account the user's geographical location information. The target receiving unit, for example, preferentially inputs highly relevant targets taking into account the user's geographical location information. For example, if the user is in a specific area, targets related to that area can be preferentially displayed. Also, if the user is traveling, targets related to the travel destination can be preferentially displayed. Furthermore, if the user is at home, targets that can be performed at home can be preferentially displayed. In this way, highly relevant targets can be provided by taking into account the user's geographical location information.

[0043] When a target is input, the target receiving unit can analyze the user's social media activity and input related targets. The target receiving unit, for example, analyzes the user's social media activity. For example, the analysis can be performed based on the content of posts and follower information. The target receiving unit inputs related targets based on the analysis results. For example, it can display targets related to topics that the user frequently mentions on social media. It can also suggest related targets based on the activity of the user's friends on social media. Furthermore, it can analyze the content of the user's social media posts and suggest related targets. In this way, it is possible to provide related targets by analyzing the user's social media activity.

[0044] The target receiving unit can customize the input method by reflecting the user's past feedback when inputting a target. The target receiving unit, for example, reflects the user's past feedback. For example, it can suggest the optimal input method based on feedback provided by the user in the past. The target receiving unit customizes the input method based on the past feedback. For example, it can provide a specific input method with priority based on the user's past feedback. It can also analyze the user's feedback and customize the input method. In this way, it is possible to provide the optimal input method by reflecting the user's past feedback.

[0045] The generation unit can optimize the generation algorithm based on past data and trend analysis when generating ideas. The generation unit, for example, optimizes the generation algorithm based on past data. For example, it can analyze past ideas and trend data to optimize the generation algorithm. The generation unit can also optimize the generation algorithm based on trend analysis. For example, it can perform time series analysis and market research to optimize the generation algorithm in line with current market trends. Furthermore, the generation unit can optimize the generation algorithm based on past data and trend analysis. For example, it can extract patterns of successful ideas in the past and optimize the generation algorithm. In this way, by optimizing the generation algorithm based on past data and trend analysis, it is possible to provide more accurate ideas.

[0046] The generation unit can improve the accuracy of idea generation by taking into account the target's attribute information when generating ideas. The generation unit generates ideas by taking into account the target's attribute information, for example. For example, it can generate ideas tailored to the target's age group. It can also generate related ideas based on the target's occupation. It can also generate optimal ideas based on the target's interests and concerns. In this way, by taking into account the target's attribute information, it is possible to provide more accurate ideas.

[0047] When generating ideas, the generation unit can improve the accuracy of generation by referring to the user's past idea generation history. The generation unit, for example, generates ideas by referring to the user's past idea generation history. For example, it can generate similar ideas based on ideas previously generated by the user. It can also extract patterns of successful ideas from the user's past idea generation history and optimize the generation algorithm. Furthermore, it can analyze the user's past idea generation history and build an algorithm to avoid unsuccessful ideas. In this way, by referring to the user's past idea generation history, it is possible to provide more accurate ideas.

[0048] The generation unit can generate ideas taking into account the geographical distribution of the targets. For example, the generation unit generates ideas taking into account the geographical distribution of the targets. For example, if the targets are concentrated in a specific region, it can generate ideas related to that region. Also, if the targets are distributed across multiple regions, it can generate ideas suitable for each region. Furthermore, it can analyze the geographical distribution of the targets and generate optimal ideas. In this way, it is possible to provide more appropriate ideas by taking into account the geographical distribution of the targets.

[0049] The generation unit can improve the accuracy of idea generation by referring to related literature when generating ideas. The generation unit, for example, generates ideas by referring to related literature. For example, it can refer to related academic papers to generate ideas that incorporate the latest research results. It can also refer to related patent documents to generate ideas that utilize existing technologies. It can also refer to related industry reports to generate ideas based on market trends. In this way, by referring to related literature, it is possible to provide more accurate ideas.

[0050] The generation unit can generate ideas taking market value into consideration. The generation unit generates ideas taking market value into consideration, for example. For example, it can generate ideas with high market value as a priority. It can also eliminate ideas with low market value and generate ideas efficiently. Furthermore, it can evaluate market value and generate optimal ideas. In this way, by taking market value into consideration, it is possible to provide ideas with higher value.

[0051] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between ideas during evaluation. The evaluation unit, for example, analyzes the relevance between ideas. For example, it can highly evaluate ideas that complement each other. The evaluation unit can also analyze the competitive relationships between ideas. For example, it can appropriately evaluate competing ideas. Furthermore, the evaluation unit can also evaluate the overall balance by taking into account the interrelationships between ideas. In this way, by taking into account the interrelationships between ideas, it is possible to perform a more accurate evaluation.

[0052] The evaluation unit can make an evaluation taking into consideration attribute information of the idea submitter when making the evaluation. The evaluation unit makes an evaluation taking into consideration attribute information of the idea submitter, for example. For example, the evaluation unit can take into consideration the submitter's expertise and highly evaluate specialized ideas. It can also take into consideration the submitter's experience and highly evaluate ideas based on experience. It can also take into consideration the submitter's past performance and highly evaluate ideas based on performance. In this way, by taking into consideration attribute information of the idea submitter, a more appropriate evaluation can be made.

[0053] The evaluation unit can weight the evaluation based on the frequency of idea submission during evaluation. The evaluation unit weights the evaluation based on, for example, the frequency of idea submission. For example, the evaluation can be weighted by placing more emphasis on ideas that are submitted more frequently. The evaluation can also be weighted by placing less emphasis on ideas that are submitted less frequently. Furthermore, a balanced evaluation can be performed by taking the frequency of submission into consideration. Thus, by weighting the evaluation based on the frequency of idea submission, a more balanced evaluation can be performed.

[0054] The evaluation unit can perform evaluation taking into consideration the geographical distribution of ideas when performing evaluation. The evaluation unit performs evaluation taking into consideration, for example, the geographical distribution of ideas. For example, ideas that are geographically widespread can be given a high rating. Also, ideas that are geographically limited can be given a low rating. Furthermore, a balanced evaluation can be performed by taking into consideration the geographical distribution of ideas. In this way, a more balanced evaluation can be performed by taking into consideration the geographical distribution of ideas.

[0055] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the idea during the evaluation. The evaluation unit, for example, performs the evaluation by referring to related literature. For example, the evaluation unit can refer to related academic papers to perform an evaluation that incorporates the latest research results. The evaluation unit can also refer to related patent documents to perform an evaluation that utilizes existing technology. Furthermore, the evaluation unit can refer to related industry reports to perform an evaluation based on market trends. In this way, by referring to related literature, a more accurate evaluation can be performed.

[0056] The evaluation unit can perform evaluation taking into consideration the market value of the idea when performing the evaluation. The evaluation unit performs evaluation taking into consideration, for example, market value. For example, it can prioritize evaluation of ideas with high market value. It can also exclude ideas with low market value and perform evaluation efficiently. Furthermore, it can evaluate market value and select the most suitable idea. In this way, by taking market value into consideration, it is possible to provide ideas with higher value.

[0057] The display unit can optimize the display algorithm by referring to past ranking data when displaying rankings. The display unit, for example, optimizes the display algorithm by referring to past ranking data. For example, the display unit can extract the most successful display pattern based on the past ranking data and optimize the display algorithm. It can also analyze past ranking data and provide a display method that matches the user's preferences. It can also refer to past ranking data and select the optimal display method. In this way, by referring to past ranking data, it is possible to provide a more appropriate ranking display.

[0058] The display unit can customize the display content by taking into account the user's attribute information when displaying the rankings. The display unit customizes the ranking display by taking into account the user's attribute information, for example. For example, it can provide a ranking display tailored to the user's age group. It can also provide a related ranking display based on the user's occupation. It can also provide an optimal ranking display based on the user's interests and concerns. In this way, it is possible to provide a more appropriate ranking display by taking into account the user's attribute information.

[0059] The display unit can improve the display method by reflecting the user's past feedback when displaying the rankings. The display unit, for example, reflects the user's past feedback. For example, it can suggest an optimal display method based on feedback provided by the user in the past. The display unit improves the display method based on the past feedback. For example, it can provide a specific display method with priority based on the user's past feedback. It can also analyze the user's feedback and customize the display method. In this way, it is possible to provide a more appropriate ranking display by reflecting the user's past feedback.

[0060] When displaying rankings, the display unit can select the optimal display method taking into account the user's geographical location information. The display unit selects the optimal display method taking into account, for example, the user's geographical location information. For example, if the user is in a specific area, rankings related to that area can be displayed preferentially. Also, if the user is traveling, rankings related to the travel destination can be displayed preferentially. Furthermore, if the user is at home, rankings that can be performed at home can be displayed preferentially. In this way, by taking into account the user's geographical location information, a more appropriate ranking display can be provided.

[0061] When displaying rankings, the display unit can analyze the user's social media activity and customize the display content. The display unit, for example, analyzes the user's social media activity. For example, the display unit can perform analysis based on the content of posts and follower information. The display unit customizes the display content based on the analysis results. For example, the display unit can display rankings related to topics that the user frequently mentions on social media. It can also suggest related rankings based on the activity of the user's friends on social media. It can also analyze the content of the user's social media posts and suggest related rankings. In this way, it is possible to provide a more appropriate ranking display by analyzing the user's social media activity.

[0062] When displaying rankings, the display unit can select the optimal display method by taking into consideration the user's device information. The display unit selects the optimal display method by taking into consideration, for example, the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Also, if the user is using a tablet, it can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, it can provide a simple and highly visible display method. In this way, by taking into consideration the user's device information, it is possible to provide a more appropriate ranking display.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The reception unit can analyze the user's past idea generation history and understand trends in the ideas the user has generated in the past. For example, the user's preferences and areas of expertise can be identified based on the categories and evaluation results of the ideas the user has generated in the past. This allows the reception unit to suggest the most suitable idea generation category for the user. The reception unit can also analyze the success rate of ideas the user has generated in the past and extract patterns of successful ideas. This can provide support for the user to generate ideas that are more likely to be successful. Furthermore, it can analyze the factors that caused the failure of ideas the user has generated in the past and provide advice to avoid similar failures. This makes it possible to utilize the user's past idea generation history to support more effective idea generation.

[0065] The generation unit can increase the variety of ideas generated based on the user's past idea generation history. For example, it can analyze the patterns of ideas generated by the user in the past and generate ideas from different perspectives. It can also extract the success factors of ideas generated by the user in the past and generate ideas with similar success factors. It can also analyze the failure factors of ideas generated by the user in the past and generate ideas to avoid failure. This makes it possible to provide a wider variety of ideas by utilizing the user's past idea generation history.

[0066] When evaluating ideas, the evaluation unit can customize the evaluation criteria by referring to the user's past evaluation history. For example, it can extract the characteristics of ideas that the user has previously rated highly and highly rate ideas with similar characteristics. It can also analyze the characteristics of ideas that the user has previously rated low and highly rate ideas with similar characteristics. Furthermore, it can preferentially apply specific evaluation criteria based on the user's past evaluation history. This allows for more appropriate evaluation by referring to the user's past evaluation history.

[0067] The display unit can customize the ranking display method based on the user's past ranking display history. For example, it can provide a display method that the user has used favorably in the past with priority. It can also provide a more appropriate display method by excluding display methods that the user has avoided in the past. Furthermore, it can extract specific display patterns from the user's past ranking display history and suggest the optimal display method. In this way, it is possible to provide a more appropriate ranking display by utilizing the user's past ranking display history.

[0068] The receiving unit can support input of idea generation categories based on the user's current project or area of ​​interest. For example, categories related to the project the user is currently working on can be preferentially displayed. Also, related categories can be filtered and displayed based on the user's area of ​​interest. Furthermore, related categories can be suggested based on the user's past project history. This makes it possible to provide more appropriate categories based on the user's current project or area of ​​interest.

[0069] When generating ideas, the generation unit can take into account the latest trends in the relevant industry. For example, it can generate ideas that incorporate the latest technological trends. It can also generate ideas that reflect the market trends in the industry. It can also analyze the trends of competitors and generate ideas to counter them. This makes it possible to provide more competitive ideas by taking into account the latest trends in the relevant industry.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit receives an input of an idea generation category from the user. For example, the user can input categories such as "new smartphone apps" or "environmentally friendly products." Step 2: The target receiving unit receives an input of an idea generation target from the user. For example, the user can input targets such as "engineers," "marketers," and "consumers." Step 3: The generation unit uses the generation AI to generate ideas based on the information input by the reception unit and the target reception unit. For example, the generation AI can generate ideas based on past data and trend analysis. Step 4: The evaluation unit evaluates the ideas generated by the generation unit. For example, evaluation items such as "innovativeness," "feasibility," and "marketability" can be set for each idea and used to evaluate it. Step 5: The display unit displays the evaluation results obtained by the evaluation unit in a ranking format. For example, it is possible to set who the evaluation is valuable to and display the results in a ranking format based on the setting.

[0072] (Example 2) In an idea generation system according to an embodiment of the present invention, a user inputs an idea generation category and a target, evaluates the generated ideas, and displays the results in a ranking format. The idea generation system generates ideas for each target by inputting an idea generation category (e.g., a product or service) and then an idea generation target (e.g., an engineer, a marketer, or a consumer). The user can evaluate (rank) the generated ideas. Furthermore, by setting the value of the evaluation for whom, the results are displayed in a ranking format. For example, in the idea generation system, a user inputs categories such as "new smartphone apps" or "environmentally friendly products." This information is input to the generation AI. Next, the user inputs targets such as "engineers," "marketers," or "consumers." This allows the generation AI to generate ideas from different perspectives for each target. The user sets evaluation criteria for the generated ideas, such as "innovativeness," "feasibility," and "marketability," and evaluates them. This evaluation can also be supported by the generation AI. Furthermore, the user can set criteria such as "value for engineers" or "value for consumers." Based on these settings, the generation AI then displays the results in a ranking format. This allows the idea generation system to easily generate and evaluate ideas from various perspectives. For example, by generating ideas for a new smartphone app from the perspectives of engineers, marketers, and consumers and evaluating them from each perspective, it is possible to find the most valuable ideas.

[0073] An idea generation system according to an embodiment includes a receiving unit, a target receiving unit, a generation unit, an evaluation unit, and a display unit. The receiving unit receives an idea generation category input by a user. For example, the user can input categories such as "new smartphone app" or "environmentally friendly product." The target receiving unit receives an idea generation target input by a user. For example, the user can input targets such as "engineer," "marketer," or "consumer." The generation unit generates ideas based on the information input by the receiving unit and the target receiving unit using a generation AI. For example, the generation AI can generate ideas based on past data or trend analysis. The evaluation unit evaluates the ideas generated by the generation unit. For example, evaluation criteria such as "innovativeness," "feasibility," and "marketability" can be set for each idea and evaluation can be performed. The display unit displays the evaluation results obtained by the evaluation unit in a ranking format. For example, the user can set who the evaluation is valuable to and display the results in a ranking format based on the setting. This allows the idea generation system according to an embodiment to allow a user to input an idea generation category and a target, evaluate generated ideas, and display the results in a ranking format.

[0074] The generation unit can generate ideas based on past data or trend analysis. The generation unit, for example, generates ideas based on past data. For example, it analyzes past ideas and trend data and optimizes the generation algorithm. The generation unit can also generate ideas based on trend analysis. For example, it can perform time series analysis and market research to generate ideas that match current market trends. Furthermore, the generation unit can optimize the generation algorithm based on past data and trend analysis. For example, it extracts patterns of successful ideas in the past and optimizes the generation algorithm. In this way, more accurate ideas can be provided by generating ideas based on past data and trend analysis.

[0075] The evaluation department can set evaluation criteria for each idea, such as "innovativeness," "feasibility," and "marketability," and conduct an evaluation. For example, the evaluation department can evaluate the "innovativeness" of each idea. For example, the evaluation can be based on novelty and technological advancement. The evaluation department can also evaluate the "feasibility" of each idea. For example, the evaluation can be based on technical feasibility and cost. Furthermore, the evaluation department can also evaluate the "marketability" of each idea. For example, the evaluation can be based on market size and the competitive environment. In this way, by setting evaluation criteria for each idea and conducting an evaluation, the value of the idea can be objectively evaluated.

[0076] The display unit can set for whom an evaluation is valuable and display the results in a ranking format based on the setting. The display unit can set, for example, for whom the evaluation is valuable. For example, it can set "valuable to engineers" or "valuable to consumers." The display unit displays the results in a ranking format based on the setting. For example, it can display the ranking in descending order of score. The display unit can also display rankings by category. For example, it can display rankings by category such as technology category, business category, and creative category. In this way, by setting for whom the evaluation is valuable and displaying the results in a ranking format, the user can find the most valuable ideas.

[0077] The reception unit can estimate the user's emotions and adjust the timing of inputting the idea generation categories based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit can estimate the user's emotions using facial expression recognition or voice analysis. The reception unit adjusts the timing of inputting the idea generation categories based on the estimated user emotions. For example, if the user is feeling stressed, the system delays the input timing to provide a relaxing environment. Also, if the user is concentrating, the system prompts the user to input immediately, allowing the user to efficiently input the idea generation categories. Furthermore, if the user is tired, the system can adjust the input timing and suggest taking a break. In this way, by adjusting the input timing of the idea generation categories according to the user's emotions, ideas can be input at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The reception unit can analyze the user's past input history and select a category input method. The reception unit, for example, analyzes the user's past input history. For example, the analysis can be performed based on past input data and usage history. The reception unit selects the optimal category input method based on the analysis results. For example, categories that the user has frequently used in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest categories to be used in a specific time period based on the user's past input history. In this way, it is possible to provide the optimal category input method by analyzing the user's past input history.

[0079] The receiving unit can filter the idea generation categories based on the user's current project or area of ​​interest when the user inputs the category. The receiving unit performs filtering based on the user's current project or area of ​​interest, for example. For example, categories related to the project the user is currently working on can be preferentially displayed. Also, related categories can be filtered and displayed based on the user's area of ​​interest. Furthermore, related categories can be suggested with reference to the user's past project history. In this way, highly relevant categories can be provided by filtering based on the user's current project or area of ​​interest.

[0080] The receiving unit can select the optimum input means according to the user's input method when inputting an idea generation category. The receiving unit selects the optimum input means according to the user's input method, for example. For example, if the user prefers voice input, the receiving unit can provide voice input with priority. Also, if the user prefers text input, the receiving unit can provide text input with priority. Furthermore, if the user prefers image input, the receiving unit can provide image input with priority. This improves user convenience by providing the optimum input means according to the user's input method.

[0081] The reception unit can estimate the user's emotions and determine the priority of categories to be input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit can estimate the user's emotions using facial expression recognition or voice analysis. The reception unit determines the priority of categories to be input based on the estimated user emotions. For example, if the user is excited, creative categories can be displayed preferentially. Also, if the user is relaxed, categories suitable for research and analysis can be displayed preferentially. Furthermore, if the user is stressed, simple and intuitive categories can be displayed preferentially. In this way, by determining the priority of categories to be input according to the user's emotions, more appropriate categories can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] When inputting idea generation categories, the reception unit can preferentially input highly relevant categories taking into account the user's geographical location information. The reception unit, for example, preferentially inputs highly relevant categories taking into account the user's geographical location information. For example, when the user is in a specific area, categories related to that area can be preferentially displayed. Also, when the user is traveling, categories related to the travel destination can be preferentially displayed. Furthermore, when the user is at home, categories that can be performed at home can be preferentially displayed. In this way, highly relevant categories can be provided by taking into account the user's geographical location information.

[0083] When an idea generation category is input, the reception unit can analyze the user's social media activity and input related categories. The reception unit, for example, analyzes the user's social media activity. For example, the analysis can be performed based on the content of posts and follower information. The reception unit inputs related categories based on the analysis results. For example, it can display categories related to topics that the user frequently mentions on social media. It can also suggest related categories based on the activity of the user's friends on social media. Furthermore, it can analyze the content of the user's social media posts and suggest related categories. In this way, it is possible to provide related categories by analyzing the user's social media activity.

[0084] The receiving unit can customize the input method by reflecting the user's past feedback when inputting an idea generation category. The receiving unit, for example, reflects the user's past feedback. For example, it can suggest the optimal input method based on feedback provided by the user in the past. The receiving unit customizes the input method based on the past feedback. For example, it can provide a specific input method with priority based on the user's past feedback. It can also analyze the user's feedback and customize the input method. In this way, it is possible to provide the optimal input method by reflecting the user's past feedback.

[0085] The target reception unit can estimate the user's emotions and adjust the input timing of the target based on the estimated user's emotions. The target reception unit, for example, estimates the user's emotions. For example, the user's emotions can be estimated using facial expression recognition or voice analysis. The target reception unit adjusts the input timing of the target based on the estimated user's emotions. For example, if the user is stressed, the system delays the input timing to provide a relaxing environment. Also, if the user is concentrating, the system prompts the user to input immediately, allowing the user to input the target efficiently. Furthermore, if the user is tired, the system can adjust the input timing and suggest taking a break. In this way, by adjusting the input timing of the target according to the user's emotions, the target can be input at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The target receiving unit can analyze the user's past target input history and select the optimal target input method. The target receiving unit, for example, analyzes the user's past target input history. For example, the analysis can be performed based on past target data and usage history. The target receiving unit selects the optimal target input method based on the analysis results. For example, targets that the user has frequently used in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest targets to be used in a specific time period from the user's past input history. In this way, it is possible to provide the optimal target input method by analyzing the user's past target input history.

[0087] When a target is input, the target receiving unit can filter the target based on the user's current project or area of ​​interest. The target receiving unit performs filtering based on, for example, the user's current project or area of ​​interest. For example, targets related to a project the user is currently working on can be preferentially displayed. Related targets can also be filtered and displayed based on the user's area of ​​interest. Furthermore, related targets can be suggested by referring to the user's past project history. In this way, highly relevant targets can be provided by filtering based on the user's current project or area of ​​interest.

[0088] When inputting a target, the target receiving unit can select the optimum input means according to the user's input method. The target receiving unit selects the optimum input means according to the user's input method, for example. For example, if the user prefers voice input, it can provide voice input preferentially. Also, if the user prefers text input, it can provide text input preferentially. Furthermore, if the user prefers image input, it can provide image input preferentially. This provides the optimum input means according to the user's input method, thereby improving user convenience.

[0089] The target receiving unit can estimate the user's emotions and determine the priority of targets to be input based on the estimated user emotions. The target receiving unit, for example, estimates the user's emotions. For example, the user's emotions can be estimated using facial expression recognition or voice analysis. The target receiving unit determines the priority of targets to be input based on the estimated user emotions. For example, if the user is excited, creative targets can be displayed preferentially. Also, if the user is relaxed, targets suitable for research or analysis can be displayed preferentially. Furthermore, if the user is stressed, simple and intuitive targets can be displayed preferentially. In this way, by determining the priority of targets to be input according to the user's emotions, more appropriate targets can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] When inputting a target, the target receiving unit can preferentially input highly relevant targets taking into account the user's geographical location information. The target receiving unit, for example, preferentially inputs highly relevant targets taking into account the user's geographical location information. For example, if the user is in a specific area, targets related to that area can be preferentially displayed. Also, if the user is traveling, targets related to the travel destination can be preferentially displayed. Furthermore, if the user is at home, targets that can be performed at home can be preferentially displayed. In this way, highly relevant targets can be provided by taking into account the user's geographical location information.

[0091] When a target is input, the target receiving unit can analyze the user's social media activity and input related targets. The target receiving unit, for example, analyzes the user's social media activity. For example, the analysis can be performed based on the content of posts and follower information. The target receiving unit inputs related targets based on the analysis results. For example, it can display targets related to topics that the user frequently mentions on social media. It can also suggest related targets based on the activity of the user's friends on social media. Furthermore, it can analyze the content of the user's social media posts and suggest related targets. In this way, it is possible to provide related targets by analyzing the user's social media activity.

[0092] The target receiving unit can customize the input method by reflecting the user's past feedback when inputting a target. The target receiving unit, for example, reflects the user's past feedback. For example, it can suggest the optimal input method based on feedback provided by the user in the past. The target receiving unit customizes the input method based on the past feedback. For example, it can provide a specific input method with priority based on the user's past feedback. It can also analyze the user's feedback and customize the input method. In this way, it is possible to provide the optimal input method by reflecting the user's past feedback.

[0093] The generation unit can estimate the user's emotions and adjust the expression method of the generated idea based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions. For example, the generation unit can estimate the user's emotions using facial expression recognition or voice analysis. The generation unit adjusts the expression method of the generated idea based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an idea that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate an idea that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an idea that adds visually stimulating effects. This allows the user to provide more appropriate ideas by adjusting the expression method of the idea according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The generation unit can optimize the generation algorithm based on past data and trend analysis when generating ideas. The generation unit, for example, optimizes the generation algorithm based on past data. For example, it can analyze past ideas and trend data to optimize the generation algorithm. The generation unit can also optimize the generation algorithm based on trend analysis. For example, it can perform time series analysis and market research to optimize the generation algorithm in line with current market trends. Furthermore, the generation unit can optimize the generation algorithm based on past data and trend analysis. For example, it can extract patterns of successful ideas in the past and optimize the generation algorithm. In this way, by optimizing the generation algorithm based on past data and trend analysis, it is possible to provide more accurate ideas.

[0095] The generation unit can improve the accuracy of idea generation by taking into account the target's attribute information when generating ideas. The generation unit generates ideas by taking into account the target's attribute information, for example. For example, it can generate ideas tailored to the target's age group. It can also generate related ideas based on the target's occupation. It can also generate optimal ideas based on the target's interests and concerns. In this way, by taking into account the target's attribute information, it is possible to provide more accurate ideas.

[0096] When generating ideas, the generation unit can improve the accuracy of generation by referring to the user's past idea generation history. The generation unit, for example, generates ideas by referring to the user's past idea generation history. For example, it can generate similar ideas based on ideas previously generated by the user. It can also extract patterns of successful ideas from the user's past idea generation history and optimize the generation algorithm. Furthermore, it can analyze the user's past idea generation history and build an algorithm to avoid unsuccessful ideas. In this way, by referring to the user's past idea generation history, it is possible to provide more accurate ideas.

[0097] The generation unit can estimate the user's emotion and adjust the length of the generated idea based on the estimated user emotion. The generation unit, for example, estimates the user's emotion. For example, the generation unit can estimate the user's emotion using facial expression recognition or voice analysis. The generation unit adjusts the length of the generated idea based on the estimated user emotion. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point idea. If the user is relaxed, the generation unit can generate a longer idea with detailed explanations. Furthermore, if the user is excited, the generation unit can generate an idea with visually stimulating effects. This allows for more appropriate ideas to be provided by adjusting the length of the idea according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The generation unit can generate ideas taking into account the geographical distribution of the targets. For example, the generation unit generates ideas taking into account the geographical distribution of the targets. For example, if the targets are concentrated in a specific region, it can generate ideas related to that region. Also, if the targets are distributed across multiple regions, it can generate ideas suitable for each region. Furthermore, it can analyze the geographical distribution of the targets and generate optimal ideas. In this way, it is possible to provide more appropriate ideas by taking into account the geographical distribution of the targets.

[0099] The generation unit can improve the accuracy of idea generation by referring to related literature when generating ideas. The generation unit, for example, generates ideas by referring to related literature. For example, it can refer to related academic papers to generate ideas that incorporate the latest research results. It can also refer to related patent documents to generate ideas that utilize existing technologies. It can also refer to related industry reports to generate ideas based on market trends. In this way, by referring to related literature, it is possible to provide more accurate ideas.

[0100] The generation unit can generate ideas taking market value into consideration. The generation unit generates ideas taking market value into consideration, for example. For example, it can generate ideas with high market value as a priority. It can also eliminate ideas with low market value and generate ideas efficiently. Furthermore, it can evaluate market value and generate optimal ideas. In this way, by taking market value into consideration, it is possible to provide ideas with higher value.

[0101] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. The evaluation unit, for example, estimates the user's emotions. For example, the user's emotions can be estimated using facial expression recognition or voice analysis. The evaluation unit adjusts the evaluation criteria based on the estimated user's emotions. For example, if the user is relaxed, detailed evaluation criteria can be provided. If the user is in a hurry, concise evaluation criteria can be provided. Furthermore, if the user is excited, visually stimulating evaluation criteria can be provided. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between ideas during evaluation. The evaluation unit, for example, analyzes the relevance between ideas. For example, it can highly evaluate ideas that complement each other. The evaluation unit can also analyze the competitive relationships between ideas. For example, it can appropriately evaluate competing ideas. Furthermore, the evaluation unit can also evaluate the overall balance by taking into account the interrelationships between ideas. In this way, by taking into account the interrelationships between ideas, it is possible to perform a more accurate evaluation.

[0103] The evaluation unit can make an evaluation taking into consideration attribute information of the idea submitter when making the evaluation. The evaluation unit makes an evaluation taking into consideration attribute information of the idea submitter, for example. For example, the evaluation unit can take into consideration the submitter's expertise and highly evaluate specialized ideas. It can also take into consideration the submitter's experience and highly evaluate ideas based on experience. It can also take into consideration the submitter's past performance and highly evaluate ideas based on performance. In this way, by taking into consideration attribute information of the idea submitter, a more appropriate evaluation can be made.

[0104] The evaluation unit can weight the evaluation based on the frequency of idea submission during evaluation. The evaluation unit weights the evaluation based on, for example, the frequency of idea submission. For example, the evaluation can be weighted by placing more emphasis on ideas that are submitted more frequently. The evaluation can also be weighted by placing less emphasis on ideas that are submitted less frequently. Furthermore, a balanced evaluation can be performed by taking the frequency of submission into consideration. Thus, by weighting the evaluation based on the frequency of idea submission, a more balanced evaluation can be performed.

[0105] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated user's emotions. The evaluation unit, for example, estimates the user's emotions. For example, the evaluation unit can estimate the user's emotions using facial expression recognition or voice analysis. The evaluation unit adjusts the order in which the evaluation results are displayed based on the estimated user's emotions. For example, if the user is relaxed, detailed evaluation results can be displayed in an orderly manner. Also, if the user is in a hurry, evaluation results that highlight the main points can be displayed preferentially. Furthermore, if the user is excited, visually stimulating evaluation results can be displayed. In this way, by adjusting the order in which the evaluation results are displayed according to the user's emotions, more appropriate evaluation results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] The evaluation unit can perform evaluation taking into consideration the geographical distribution of ideas when performing evaluation. The evaluation unit performs evaluation taking into consideration, for example, the geographical distribution of ideas. For example, ideas that are geographically widespread can be given a high rating. Also, ideas that are geographically limited can be given a low rating. Furthermore, a balanced evaluation can be performed by taking into consideration the geographical distribution of ideas. In this way, a more balanced evaluation can be performed by taking into consideration the geographical distribution of ideas.

[0107] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the idea during the evaluation. The evaluation unit, for example, performs the evaluation by referring to related literature. For example, the evaluation unit can refer to related academic papers to perform an evaluation that incorporates the latest research results. The evaluation unit can also refer to related patent documents to perform an evaluation that utilizes existing technology. Furthermore, the evaluation unit can refer to related industry reports to perform an evaluation based on market trends. In this way, by referring to related literature, a more accurate evaluation can be performed.

[0108] The evaluation unit can perform evaluation taking into consideration the market value of the idea when performing the evaluation. The evaluation unit performs evaluation taking into consideration, for example, market value. For example, it can prioritize evaluation of ideas with high market value. It can also exclude ideas with low market value and perform evaluation efficiently. Furthermore, it can evaluate market value and select the most suitable idea. In this way, by taking market value into consideration, it is possible to provide ideas with higher value.

[0109] The display unit can estimate the user's emotions and adjust the display method of the rankings based on the estimated user's emotions. The display unit, for example, estimates the user's emotions. For example, the display unit can estimate the user's emotions using facial expression recognition or voice analysis. The display unit adjusts the display method of the rankings based on the estimated user's emotions. For example, if the user is relaxed, a detailed ranking can be displayed. If the user is in a hurry, a ranking that focuses on the main points can be displayed. Furthermore, if the user is excited, a visually stimulating ranking can be displayed. In this way, by adjusting the display method of the rankings according to the user's emotions, a more appropriate ranking display can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The display unit can optimize the display algorithm by referring to past ranking data when displaying rankings. The display unit, for example, optimizes the display algorithm by referring to past ranking data. For example, the display unit can extract the most successful display pattern based on the past ranking data and optimize the display algorithm. It can also analyze past ranking data and provide a display method that matches the user's preferences. It can also refer to past ranking data and select the optimal display method. In this way, by referring to past ranking data, it is possible to provide a more appropriate ranking display.

[0111] The display unit can customize the display content by taking into account the user's attribute information when displaying the rankings. The display unit customizes the ranking display by taking into account the user's attribute information, for example. For example, it can provide a ranking display tailored to the user's age group. It can also provide a related ranking display based on the user's occupation. It can also provide an optimal ranking display based on the user's interests and concerns. In this way, it is possible to provide a more appropriate ranking display by taking into account the user's attribute information.

[0112] The display unit can improve the display method by reflecting the user's past feedback when displaying the rankings. The display unit, for example, reflects the user's past feedback. For example, it can suggest an optimal display method based on feedback provided by the user in the past. The display unit improves the display method based on the past feedback. For example, it can provide a specific display method with priority based on the user's past feedback. It can also analyze the user's feedback and customize the display method. In this way, it is possible to provide a more appropriate ranking display by reflecting the user's past feedback.

[0113] The display unit can estimate the user's emotions and determine the ranking priority based on the estimated user's emotions. The display unit, for example, estimates the user's emotions. For example, the user's emotions can be estimated using facial expression recognition or voice analysis. The display unit determines the ranking priority based on the estimated user's emotions. For example, if the user is relaxed, detailed rankings can be displayed preferentially. Also, if the user is in a hurry, rankings that highlight the main points can be displayed preferentially. Furthermore, if the user is excited, visually stimulating rankings can be displayed preferentially. In this way, by determining the ranking priority according to the user's emotions, a more appropriate ranking display can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] When displaying rankings, the display unit can select the optimal display method taking into account the user's geographical location information. The display unit selects the optimal display method taking into account, for example, the user's geographical location information. For example, if the user is in a specific area, rankings related to that area can be displayed preferentially. Also, if the user is traveling, rankings related to the travel destination can be displayed preferentially. Furthermore, if the user is at home, rankings that can be performed at home can be displayed preferentially. In this way, by taking into account the user's geographical location information, a more appropriate ranking display can be provided.

[0115] When displaying rankings, the display unit can analyze the user's social media activity and customize the display content. The display unit, for example, analyzes the user's social media activity. For example, the display unit can perform analysis based on the content of posts and follower information. The display unit customizes the display content based on the analysis results. For example, the display unit can display rankings related to topics that the user frequently mentions on social media. It can also suggest related rankings based on the activity of the user's friends on social media. It can also analyze the content of the user's social media posts and suggest related rankings. In this way, it is possible to provide a more appropriate ranking display by analyzing the user's social media activity.

[0116] When displaying rankings, the display unit can select the optimal display method by taking into consideration the user's device information. The display unit selects the optimal display method by taking into consideration, for example, the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Also, if the user is using a tablet, it can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, it can provide a simple and highly visible display method. In this way, by taking into consideration the user's device information, it is possible to provide a more appropriate ranking display. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, target reception unit, generation unit, evaluation unit, and display unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using a touch panel 38A or a microphone 38B of the smart device 14. The target reception unit processes target information input by the user via the control unit 46A of the smart device 14. The generation unit generates ideas using a generation AI by the specific processing unit 290 of the data processing device 12. The evaluation unit evaluates the ideas generated by the specific processing unit 290 of the data processing device 12. The display unit displays the evaluation results in a ranking format using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, target reception unit, generation unit, evaluation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the smart glasses 214. The target reception unit processes target information input by the user via the control unit 46A of the smart glasses 214. The generation unit generates ideas using a generation AI via the specific processing unit 290 of the data processing device 12. The evaluation unit evaluates the ideas generated by the specific processing unit 290 of the data processing device 12. The display unit displays the evaluation results in a ranking format using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, target reception unit, generation unit, evaluation unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the headset type terminal 314. The target reception unit processes target information input by the user via the control unit 46A of the headset type terminal 314. The generation unit generates ideas using a generation AI via the specific processing unit 290 of the data processing device 12. The evaluation unit evaluates the ideas generated by the specific processing unit 290 of the data processing device 12. The display unit displays the evaluation results in a ranking format using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, target reception unit, generation unit, evaluation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The target reception unit processes target information input by the user via the control unit 46A of the robot 414. The generation unit generates ideas using a generation AI by the specific processing unit 290 of the data processing device 12. The evaluation unit evaluates the ideas generated by the specific processing unit 290 of the data processing device 12. The display unit displays the evaluation results in a ranking format using the display of the robot 414.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The reception unit can analyze the user's past idea generation history and understand trends in the ideas the user has generated in the past. For example, the user's preferences and areas of expertise can be identified based on the categories and evaluation results of the ideas the user has generated in the past. This allows the reception unit to suggest the most suitable idea generation category for the user. The reception unit can also analyze the success rate of ideas the user has generated in the past and extract patterns of successful ideas. This can provide support for the user to generate ideas that are more likely to be successful. Furthermore, it can analyze the factors that caused the failure of ideas the user has generated in the past and provide advice to avoid similar failures. This makes it possible to utilize the user's past idea generation history to support more effective idea generation.

[0119] The generation unit can estimate the user's emotions and adjust the theme of the generated ideas based on the estimated user emotions. For example, if the user is relaxed, it can generate ideas with themes that are easy to tackle in a relaxed state. Also, if the user is excited, it can generate ideas with creative and challenging themes. Furthermore, if the user is stressed, it can generate ideas with themes related to relaxation and mental health to reduce stress. By adjusting the theme of ideas according to the user's emotions, it is possible to provide more appropriate ideas. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] The evaluation unit can estimate the user's emotions and customize the evaluation feedback based on the estimated user emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise and to-the-point feedback can be provided. Furthermore, if the user is excited, visually stimulating feedback can be provided. In this way, by customizing the evaluation feedback according to the user's emotions, more appropriate feedback can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0121] The display unit can estimate the user's emotions and adjust the display format of the rankings based on the estimated user emotions. For example, if the user is relaxed, a detailed ranking can be displayed. If the user is in a hurry, a concise ranking that focuses on the main points can be displayed. Furthermore, if the user is excited, a visually stimulating ranking can be displayed. In this way, by adjusting the display format of the rankings according to the user's emotions, a more appropriate ranking display can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0122] The reception unit can estimate the user's emotions and customize the input method for the idea generation category based on the estimated user's emotions. For example, if the user is relaxed, voice input can be provided preferentially. Also, if the user is in a hurry, text input can be provided preferentially. Furthermore, if the user is excited, a visually stimulating input method can be provided. In this way, by customizing the input method for the idea generation category according to the user's emotions, a more appropriate input method can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0123] The generation unit can increase the variety of ideas generated based on the user's past idea generation history. For example, it can analyze the patterns of ideas generated by the user in the past and generate ideas from different perspectives. It can also extract the success factors of ideas generated by the user in the past and generate ideas with similar success factors. It can also analyze the failure factors of ideas generated by the user in the past and generate ideas to avoid failure. This makes it possible to provide a wider variety of ideas by utilizing the user's past idea generation history.

[0124] When evaluating ideas, the evaluation unit can customize the evaluation criteria by referring to the user's past evaluation history. For example, it can extract the characteristics of ideas that the user has previously rated highly and highly rate ideas with similar characteristics. It can also analyze the characteristics of ideas that the user has previously rated low and highly rate ideas with similar characteristics. Furthermore, it can preferentially apply specific evaluation criteria based on the user's past evaluation history. This allows for more appropriate evaluation by referring to the user's past evaluation history.

[0125] The display unit can customize the ranking display method based on the user's past ranking display history. For example, it can provide a display method that the user has used favorably in the past with priority. It can also provide a more appropriate display method by excluding display methods that the user has avoided in the past. Furthermore, it can extract specific display patterns from the user's past ranking display history and suggest the optimal display method. In this way, it is possible to provide a more appropriate ranking display by utilizing the user's past ranking display history.

[0126] The receiving unit can support input of idea generation categories based on the user's current project or area of ​​interest. For example, categories related to the project the user is currently working on can be preferentially displayed. Also, related categories can be filtered and displayed based on the user's area of ​​interest. Furthermore, related categories can be suggested based on the user's past project history. This makes it possible to provide more appropriate categories based on the user's current project or area of ​​interest.

[0127] When generating ideas, the generation unit can take into account the latest trends in the relevant industry. For example, it can generate ideas that incorporate the latest technological trends. It can also generate ideas that reflect the market trends in the industry. It can also analyze the trends of competitors and generate ideas to counter them. This makes it possible to provide more competitive ideas by taking into account the latest trends in the relevant industry.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The reception unit receives an input of an idea generation category from the user. For example, the user can input categories such as "new smartphone apps" or "environmentally friendly products." Step 2: The target receiving unit receives an input of an idea generation target from the user. For example, the user can input targets such as "engineers," "marketers," and "consumers." Step 3: The generation unit uses the generation AI to generate ideas based on the information input by the reception unit and the target reception unit. For example, the generation AI can generate ideas based on past data and trend analysis. Step 4: The evaluation unit evaluates the ideas generated by the generation unit. For example, evaluation items such as "innovativeness," "feasibility," and "marketability" can be set for each idea and used to evaluate it. Step 5: The display unit displays the evaluation results obtained by the evaluation unit in a ranking format. For example, it is possible to set who the evaluation is valuable to and display the results in a ranking format based on the setting.

[0130] 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.

[0131] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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, to avoid confusion and 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.

[0200] 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.

[0201] [Explanation of symbols]

[0202] 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 reception section for inputting idea generation categories; a target receiving unit for inputting an idea generation target; a generation unit that generates ideas based on the information input by the reception unit and the target reception unit; an evaluation unit that evaluates the ideas generated by the generation unit; a display unit that displays the evaluation results obtained by the evaluation unit in a ranking format; Equipped with A system characterized by:

2. The generation unit Generate ideas based on historical data or trend analysis 2. The system of claim 1.

3. The evaluation unit Each idea is evaluated based on criteria such as innovation, feasibility, and marketability.

2. The system of claim 1.

4. The display unit Set who the evaluation is valuable to and display the results in a ranking format based on your settings.

2. The system of claim 1.

5. The reception unit The system estimates the user's emotions and adjusts the timing of inputting idea generation categories based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past input history and select the category input method 2. The system of claim 1.

7. The reception unit Filter idea generation categories based on your current projects and interests as you enter them 2. The system of claim 1.

8. The reception unit When entering idea generation categories, select the optimal input method according to the user's input method.

2. The system of claim 1.

9. The reception unit Estimate the user's emotions and prioritize the categories to be input based on the estimated user emotions.

2. The system of claim 1.

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