System

Abstract

An object of a system according to an embodiment is to develop an initial idea of a user from various angles and provide a new inspiration.SOLUTION: A system includes an initial idea input part, an expansion part, and an output generation part. The initial idea input unit inputs an initial idea provided by a user. The expansion unit expands the initial idea input by the initial idea input unit in various ways. The output generation unit generates a plurality of outputs based on the idea expanded by the expansion unit.SELECTED DRAWING: Figure 1

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 technology has had the problem of making it difficult to develop a user's initial idea from multiple angles and gain new inspiration.

[0005] The system according to the embodiment aims to develop a user's initial idea in a multifaceted way and provide new inspiration. [Means for solving the problem]

[0006] The system according to the embodiment includes an initial idea input unit, an expansion unit, and an output generation unit. The initial idea input unit inputs an initial idea provided by a user. The expansion unit expands the initial idea input by the initial idea input unit in multiple ways. The output generation unit generates multiple outputs based on the idea expanded by the expansion unit. [Effects of the Invention]

[0007] The system according to the embodiment can develop the initial idea of ​​the user in a multifaceted way and provide new inspiration. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AIdea Generator system according to an embodiment of the present invention is a system that receives an initial idea provided by a user as input, and with the help of a generating AI, develops the initial idea from multiple angles to propose new ideas. This allows the AIdea Generator system to develop the user's initial idea from multiple angles and propose new ideas.

[0029] The AIdea Generator system according to the embodiment includes an initial idea input unit, an expansion unit, and an output generation unit. The initial idea input unit inputs an initial idea provided by a user. For example, a user may input an idea such as "I want to develop a new eco-friendly product." The initial idea input unit can accept initial ideas in various formats, such as text, images, and audio. The expansion unit expands the initial idea input by the initial idea input unit in multiple ways. For example, the generation AI combines knowledge from different industries and fields to propose new perspectives and approaches. For example, for an idea for an eco-friendly product, the generation AI proposes various directions, such as "products that use renewable energy," "products made from recycled materials," and "highly energy-efficient products." The output generation unit generates multiple outputs based on the idea expanded by the expansion unit. For example, for an idea for an eco-friendly product, the generation AI makes specific suggestions, such as "smartphones with solar panels," "furniture made from recycled plastic," and "highly energy-efficient home appliances." This allows the AIdea Generator system to expand the user's initial idea in multiple ways and propose new ideas.

[0030] The initial idea input unit can refer to the user's past idea history and provide advice based on past successes and failures. For example, in the initial idea input unit, the generation AI refers to the past idea history for an initial idea entered by the user and provides advice based on past successes and failures. For example, if there are similarities with ideas that have been successful in the past, it is determined that the idea has a high probability of success. The past idea history is referenced based on ideas stored in a database and past project records. Examples of successes and failures are evaluated based on project results, user feedback, etc. This makes it possible to provide advice based on the past idea history.

[0031] The initial idea input unit can search relevant patent databases and provide feedback to avoid duplication with existing technologies and ideas. For example, in the initial idea input unit, the generative AI searches patent databases for an initial idea entered by a user and provides feedback to avoid duplication with existing technologies and ideas. For example, if similar patents exist, the system notifies the user of this information. Patent databases are searched based on patent office databases, commercial patent databases, etc. Feedback to avoid duplication is provided based on lists of similar patents and duplication risk assessments, etc. This makes it possible to provide feedback to avoid duplication with existing technologies and ideas.

[0032] The initial idea input unit can accept input in different languages, enabling idea development from an international perspective. The initial idea input unit, for example, allows users to input initial ideas in different languages, and the generation AI analyzes the ideas and develops them from an international perspective. For example, multilingual support such as English, French, and Chinese is realized. Different languages ​​are accepted based on multilingual support such as English, Chinese, and Spanish. The international perspective is developed based on cross-cultural understanding and trend analysis of the international market. This allows input in different languages ​​to be accepted, enabling idea development from an international perspective.

[0033] The initial idea input unit can also accept image and audio data, allowing ideas to be developed based on multimodal information. For example, when a user inputs an initial idea, the initial idea input unit allows them to upload image and audio data along with the initial idea, and the generative AI develops the idea based on that information. For example, it analyzes prototype images and explanatory audio. Image and audio data can be accepted in formats such as JPEG, PNG, MP3, and WAV. Multimodal information is developed based on an integrated analysis of text, images, and audio. This allows ideas to be developed based on image and audio data.

[0034] The development part can refer to related academic papers and research data and propose ideas based on scientific evidence. For example, when the generative AI develops ideas from multiple angles, the development part automatically collects related academic papers and research data and proposes ideas based on scientific evidence. For example, it can propose a new approach based on the latest research results. Academic papers come in various types, such as scientific papers, technical papers, and review papers, and can be referenced using database searches, etc. Research data comes in various types, such as experimental data, survey data, and statistical data, and can be referenced using database searches, etc. This makes it possible to propose ideas based on scientific evidence.

[0035] The development section can analyze market data and propose ideas that take economic effectiveness into consideration. For example, when the generative AI is deployed in a multifaceted manner, the development section analyzes market data and proposes ideas that take economic effectiveness into consideration. For example, ideas are evaluated based on the demand and competitive situation in a specific market. Market data comes in various types, such as market research reports, sales data, and consumer behavior data, and analysis methods include database searches and statistical analysis. Economic effectiveness is evaluated based on criteria such as ROI (return on investment) and cost-benefit analysis. This makes it possible to propose ideas that take economic effectiveness into consideration.

[0036] The development section incorporates knowledge from different cultural spheres and regions, and can propose ideas from a global perspective. For example, when the generative AI develops in a multifaceted manner, the development section incorporates knowledge from different cultural spheres and regions, and proposes ideas from a global perspective. For example, it generates ideas that take into account the cultures and customs of each country. Cultural spheres include Asian cultural spheres and Western cultural spheres, and characteristics are evaluated based on the customs and values ​​of each cultural sphere. Regions include urban areas, rural areas, and specific countries or regions, and characteristics are evaluated based on the economic situation and social environment of each region. This allows ideas to be proposed from a global perspective.

[0037] The development section can consider different time axes and propose ideas from a time-series perspective. For example, when the generative AI develops in a multifaceted manner, the development section considers future predictions and past trends and proposes ideas from a time-series perspective. For example, it predicts future trends based on past data. The time axis can range from trends over the past 10 years to predictions for the next 5 years, and methods of consideration include time series analysis and trend analysis. Future predictions are made based on methods such as scenario planning and predictive models. Past trends are predicted based on methods such as time series analysis and trend analysis. This makes it possible to propose ideas from a time-series perspective.

[0038] The output generation unit can evaluate technical feasibility and propose highly feasible outputs. For example, when the generation AI generates multiple outputs, the output generation unit evaluates technical feasibility and proposes highly feasible outputs. For example, it evaluates whether a specific technology is feasible. Technical feasibility is evaluated based on technical constraints, implementation difficulty, etc. This makes it possible to evaluate technical feasibility and propose highly feasible outputs.

[0039] The output generation unit can evaluate social impact and propose socially beneficial outputs. For example, when the generation AI generates multiple outputs, the output generation unit evaluates the social impact and proposes socially beneficial outputs. For example, it evaluates the impact that a specific output has on society. The social impact is evaluated based on social benefits, environmental impact, etc. This makes it possible to propose socially beneficial outputs.

[0040] The output generation unit incorporates knowledge from different industries and applications to discover new market needs. For example, when the generative AI generates multiple outputs, the output generation unit incorporates knowledge from different industries and applications to discover new market needs. For example, it generates ideas that combine the technology field with the consumer market. Different industries and applications include the medical industry, manufacturing, and education, and the characteristics are evaluated based on the needs and trends of each industry and application. Market needs are evaluated based on market research and consumer insights. This allows new market needs to be discovered.

[0041] The output generation unit can automatically generate prototypes that users can actually try out. For example, when the generation AI generates multiple outputs, the output generation unit automatically generates prototypes that users can actually try out. For example, it can automatically generate 3D models of products or demos of services. Prototypes come in various types, such as physical models or digital simulations, and are generated using methods such as 3D printing or simulation software. This makes it possible to automatically generate prototypes that users can actually try out.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The AIdea Generator system can also be equipped with a history reference section that references the user's past idea history and provides advice based on past successes and failures. For example, if an idea has similarities to successful ideas submitted by the user in the past, the system will determine that the idea has a high probability of success and recommend that the user take that direction. Also, if an idea has similarities to past failures, the system can notify the user of the risks and suggest areas for improvement. This allows the user to utilize their past experience to generate better ideas.

[0044] The AIdea Generator system can also include a patent search component that searches relevant patent databases and provides feedback to help users avoid overlaps with existing technologies and ideas. For example, the system searches patent databases for an initial idea entered by a user, and if similar patents exist, notifies the user. This allows users to avoid overlaps with existing technologies and ideas and ensures the uniqueness of their new ideas.

[0045] The AIdea Generator system can also be equipped with a multilingual section that accepts input in different languages ​​and enables idea development from an international perspective. For example, users can input initial ideas in different languages, such as English, French, and Chinese, and the generation AI will analyze the ideas and develop them from an international perspective. This allows ideas to be generated that take into account the needs of different cultures and markets.

[0046] The initial idea input section can also accept image and audio data, allowing ideas to be developed based on multimodal information. For example, if a user uploads images of a prototype or explanatory audio, the generative AI will analyze that information and propose more specific ideas. This makes it possible to develop ideas from multiple angles, utilizing not only text but also visual and audio information.

[0047] The development part can refer to relevant academic papers and research data to propose ideas based on scientific evidence. For example, it can propose a new approach based on the latest research results. This allows for the generation of reliable ideas with scientific backing. Academic papers and research data are automatically collected through database searches, and the generation AI analyzes them and uses them to develop ideas.

[0048] The output generation unit can automatically generate prototypes that users can try out. For example, it can automatically generate 3D models of products or demos of services. This allows users to try out the generated ideas and get concrete feedback. Prototypes can be provided in the form of physical models or digital simulations.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The initial idea input unit inputs an initial idea provided by the user. For example, the user can input an idea such as "I want to develop a new eco-friendly product." The initial idea input unit can also accept initial ideas in various formats, such as text, image, and audio. Step 2: The development section develops the initial ideas input by the initial idea input section from multiple angles. For example, the generative AI combines knowledge from different industries and fields to propose new perspectives and approaches. For example, for an idea for an eco-friendly product, the generative AI will propose various directions, such as "products that use renewable energy," "products made from recycled materials," and "highly energy-efficient products." Step 3: The output generation unit generates multiple outputs based on the ideas developed by the expansion unit. For example, in response to an idea for an eco-friendly product, the generation AI will make specific suggestions such as "a smartphone with a solar panel," "furniture made from recycled plastic," or "energy-efficient home appliances."

[0051] (Example 2) The AIdea Generator system according to an embodiment of the present invention is a system that receives an initial idea provided by a user as input, and with the help of a generating AI, develops the initial idea from multiple angles to propose new ideas. This allows the AIdea Generator system to develop the user's initial idea from multiple angles and propose new ideas.

[0052] The AIdea Generator system according to the embodiment includes an initial idea input unit, an expansion unit, and an output generation unit. The initial idea input unit inputs an initial idea provided by a user. For example, a user may input an idea such as "I want to develop a new eco-friendly product." The initial idea input unit can accept initial ideas in various formats, such as text, images, and audio. The expansion unit expands the initial idea input by the initial idea input unit in multiple ways. For example, the generation AI combines knowledge from different industries and fields to propose new perspectives and approaches. For example, for an idea for an eco-friendly product, the generation AI proposes various directions, such as "products that use renewable energy," "products made from recycled materials," and "highly energy-efficient products." The output generation unit generates multiple outputs based on the idea expanded by the expansion unit. For example, for an idea for an eco-friendly product, the generation AI makes specific suggestions, such as "smartphones with solar panels," "furniture made from recycled plastic," and "highly energy-efficient home appliances." This allows the AIdea Generator system to expand the user's initial idea in multiple ways and propose new ideas.

[0053] The initial idea input unit performs sentiment analysis on the initial idea and can evaluate the potential value of the idea based on the intensity and type of sentiment. For example, the initial idea input unit uses a generative AI to perform sentiment analysis on the initial idea entered by the user and quantify the intensity and type of sentiment. For example, if the user's sentiment toward an idea is positive, the idea is evaluated as having high potential value. Sentiment analysis is performed using text sentiment scoring and sentiment classification algorithms. The intensity and type of sentiment are evaluated based on classifications and scoring methods such as positive, negative, and neutral. This allows the value of an idea to be evaluated based on the user's sentiment.

[0054] The initial idea input unit can refer to the user's past idea history and provide advice based on past successes and failures. For example, in the initial idea input unit, the generation AI refers to the past idea history for an initial idea entered by the user and provides advice based on past successes and failures. For example, if there are similarities with ideas that have been successful in the past, it is determined that the idea has a high probability of success. The past idea history is referenced based on ideas stored in a database and past project records. Examples of successes and failures are evaluated based on project results, user feedback, etc. This makes it possible to provide advice based on the past idea history.

[0055] The initial idea input unit can search relevant patent databases and provide feedback to avoid duplication with existing technologies and ideas. For example, in the initial idea input unit, the generative AI searches patent databases for an initial idea entered by a user and provides feedback to avoid duplication with existing technologies and ideas. For example, if similar patents exist, the system notifies the user of this information. Patent databases are searched based on patent office databases, commercial patent databases, etc. Feedback to avoid duplication is provided based on lists of similar patents and duplication risk assessments, etc. This makes it possible to provide feedback to avoid duplication with existing technologies and ideas.

[0056] The initial idea input unit can accept input in different languages, enabling idea development from an international perspective. The initial idea input unit, for example, allows users to input initial ideas in different languages, and the generation AI analyzes the ideas and develops them from an international perspective. For example, multilingual support such as English, French, and Chinese is realized. Different languages ​​are accepted based on multilingual support such as English, Chinese, and Spanish. The international perspective is developed based on cross-cultural understanding and trend analysis of the international market. This allows input in different languages ​​to be accepted, enabling idea development from an international perspective.

[0057] The initial idea input unit can also accept image and audio data, allowing ideas to be developed based on multimodal information. For example, when a user inputs an initial idea, the initial idea input unit allows them to upload image and audio data along with the initial idea, and the generative AI develops the idea based on that information. For example, it analyzes prototype images and explanatory audio. Image and audio data can be accepted in formats such as JPEG, PNG, MP3, and WAV. Multimodal information is developed based on an integrated analysis of text, images, and audio. This allows ideas to be developed based on image and audio data.

[0058] The initial idea input unit uses an emotion estimation function to estimate the user's emotions in real time when they input their initial idea, and can make suggestions that elicit positive emotions. For example, when a user inputs an initial idea, the initial idea input unit uses the emotion estimation function to analyze the user's emotions in real time and make suggestions that elicit positive emotions. For example, if the user has negative emotions, an encouraging message is displayed. The emotion estimation function uses methods and algorithms such as facial expression recognition, voice analysis, and text analysis. Suggestions that elicit positive emotions are made based on encouraging messages and the presentation of success stories. This allows the user's emotions to be estimated in real time and suggestions that elicit positive emotions to be made.

[0059] The development unit can use the emotion estimation function to analyze the user's emotional reactions and prioritize emotionally positive developments. For example, when the generation AI develops from multiple angles, the development unit uses the emotion estimation function to analyze the user's emotional reactions in real time and prioritizes developments that have a high positive emotional response. For example, if the user is excited, that direction is reinforced. Emotional reactions are evaluated based on changes in facial expressions, tone of voice, and text emotion scores. Emotionally positive developments are prioritized based on factors such as increased user satisfaction and increased positive feedback. This allows positive developments to be prioritized based on the user's emotional reactions.

[0060] The development part can refer to related academic papers and research data and propose ideas based on scientific evidence. For example, when the generative AI develops ideas from multiple angles, the development part automatically collects related academic papers and research data and proposes ideas based on scientific evidence. For example, it can propose a new approach based on the latest research results. Academic papers come in various types, such as scientific papers, technical papers, and review papers, and can be referenced using database searches, etc. Research data comes in various types, such as experimental data, survey data, and statistical data, and can be referenced using database searches, etc. This makes it possible to propose ideas based on scientific evidence.

[0061] The development section can analyze market data and propose ideas that take economic effectiveness into consideration. For example, when the generative AI is deployed in a multifaceted manner, the development section analyzes market data and proposes ideas that take economic effectiveness into consideration. For example, ideas are evaluated based on the demand and competitive situation in a specific market. Market data comes in various types, such as market research reports, sales data, and consumer behavior data, and analysis methods include database searches and statistical analysis. Economic effectiveness is evaluated based on criteria such as ROI (return on investment) and cost-benefit analysis. This makes it possible to propose ideas that take economic effectiveness into consideration.

[0062] The development section incorporates knowledge from different cultural spheres and regions, and can propose ideas from a global perspective. For example, when the generative AI develops in a multifaceted manner, the development section incorporates knowledge from different cultural spheres and regions, and proposes ideas from a global perspective. For example, it generates ideas that take into account the cultures and customs of each country. Cultural spheres include Asian cultural spheres and Western cultural spheres, and characteristics are evaluated based on the customs and values ​​of each cultural sphere. Regions include urban areas, rural areas, and specific countries or regions, and characteristics are evaluated based on the economic situation and social environment of each region. This allows ideas to be proposed from a global perspective.

[0063] The development section can consider different time axes and propose ideas from a time-series perspective. For example, when the generative AI develops in a multifaceted manner, the development section considers future predictions and past trends and proposes ideas from a time-series perspective. For example, it predicts future trends based on past data. The time axis can range from trends over the past 10 years to predictions for the next 5 years, and methods of consideration include time series analysis and trend analysis. Future predictions are made based on methods such as scenario planning and predictive models. Past trends are predicted based on methods such as time series analysis and trend analysis. This makes it possible to propose ideas from a time-series perspective.

[0064] The development unit uses the emotion estimation function to monitor the user's emotional reactions in real time and continuously search for the optimal development. For example, when the generation AI performs multifaceted developments, the development unit uses the emotion estimation function to monitor the user's emotional reactions in real time and prioritizes developments that result in a greater number of positive emotional reactions. For example, if the user is excited, that direction is reinforced. Methods for real-time monitoring include sensor technology and real-time data analysis. The optimal development is evaluated based on criteria such as user satisfaction and success rate. This allows the development unit to monitor the user's emotional reactions in real time and continuously search for the optimal development.

[0065] The output generation unit can use the emotion estimation function to analyze the user's emotional response and prioritize emotionally positive output. For example, when the generation AI generates multiple outputs, the output generation unit can use the emotion estimation function to analyze the user's emotional response in real time and prioritize outputs with a higher number of positive emotional responses. For example, if the user is excited, that output is reinforced. Emotionally positive outputs are prioritized based on factors such as increased user satisfaction and increased positive feedback. This allows positive outputs to be prioritized based on the user's emotional response.

[0066] The output generation unit can evaluate technical feasibility and propose highly feasible outputs. For example, when the generation AI generates multiple outputs, the output generation unit evaluates technical feasibility and proposes highly feasible outputs. For example, it evaluates whether a specific technology is feasible. Technical feasibility is evaluated based on technical constraints, implementation difficulty, etc. This makes it possible to evaluate technical feasibility and propose highly feasible outputs.

[0067] The output generation unit can evaluate social impact and propose socially beneficial outputs. For example, when the generation AI generates multiple outputs, the output generation unit evaluates the social impact and proposes socially beneficial outputs. For example, it evaluates the impact that a specific output has on society. The social impact is evaluated based on social benefits, environmental impact, etc. This makes it possible to propose socially beneficial outputs.

[0068] The output generation unit incorporates knowledge from different industries and applications to discover new market needs. For example, when the generative AI generates multiple outputs, the output generation unit incorporates knowledge from different industries and applications to discover new market needs. For example, it generates ideas that combine the technology field with the consumer market. Different industries and applications include the medical industry, manufacturing, and education, and the characteristics are evaluated based on the needs and trends of each industry and application. Market needs are evaluated based on market research and consumer insights. This allows new market needs to be discovered.

[0069] The output generation unit can automatically generate prototypes that users can actually try out. For example, when the generation AI generates multiple outputs, the output generation unit automatically generates prototypes that users can actually try out. For example, it can automatically generate 3D models of products or demos of services. Prototypes come in various types, such as physical models or digital simulations, and are generated using methods such as 3D printing or simulation software. This makes it possible to automatically generate prototypes that users can actually try out.

[0070] The output generation unit uses the emotion estimation function to monitor the user's emotional responses in real time and continuously search for the optimal output. For example, when the generation AI generates multiple outputs, the output generation unit uses the emotion estimation function to monitor the user's emotional responses in real time and prioritizes outputs with a higher number of positive emotional responses. For example, if the user is excited, that output is reinforced. The optimal output is evaluated based on criteria such as user satisfaction and success rate. This allows the output generation unit to monitor the user's emotional responses in real time and continuously search for the optimal output.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The AIdea Generator system can also be equipped with a history reference section that references the user's past idea history and provides advice based on past successes and failures. For example, if an idea has similarities to successful ideas submitted by the user in the past, the system will determine that the idea has a high probability of success and recommend that the user take that direction. Also, if an idea has similarities to past failures, the system can notify the user of the risks and suggest areas for improvement. This allows the user to utilize their past experience to generate better ideas.

[0073] The initial idea input unit performs sentiment analysis on the initial idea entered by the user and evaluates the potential value of the idea based on the intensity and type of sentiment. For example, if a user enters the idea "I want to develop a new eco-friendly product," and the user's sentiment toward the idea is positive, the potential value of the idea is evaluated as high. Sentiment analysis is performed using text sentiment scoring and sentiment classification algorithms. This allows the value of the idea to be evaluated based on the user's sentiment.

[0074] The AIdea Generator system can also include a patent search component that searches relevant patent databases and provides feedback to help users avoid overlaps with existing technologies and ideas. For example, the system searches patent databases for an initial idea entered by a user, and if similar patents exist, notifies the user. This allows users to avoid overlaps with existing technologies and ideas and ensures the uniqueness of their new ideas.

[0075] The development unit can use the emotion estimation function to analyze the user's emotional response and prioritize emotionally positive development. For example, if the user is excited about the idea of ​​an eco-friendly product, the development unit can make suggestions to reinforce that direction. Also, if the user is expressing negative emotions, the development unit can suggest alternatives to alleviate those emotions. This allows the development unit to optimally develop ideas based on the user's emotions.

[0076] The AIdea Generator system can also be equipped with a multilingual section that accepts input in different languages ​​and enables idea development from an international perspective. For example, users can input initial ideas in different languages, such as English, French, and Chinese, and the generation AI will analyze the ideas and develop them from an international perspective. This allows ideas to be generated that take into account the needs of different cultures and markets.

[0077] The initial idea input section can also accept image and audio data, allowing ideas to be developed based on multimodal information. For example, if a user uploads images of a prototype or explanatory audio, the generative AI will analyze that information and propose more specific ideas. This makes it possible to develop ideas from multiple angles, utilizing not only text but also visual and audio information.

[0078] The initial idea input unit uses the emotion estimation function to estimate the user's emotions in real time when inputting the initial idea, and can make suggestions that will elicit positive emotions. For example, if the user has negative emotions, an encouraging message can be displayed. Also, if the user is expressing positive emotions, success stories and positive feedback can be provided to further enhance those emotions. This makes it possible to estimate the user's emotions in real time and make suggestions that will elicit positive emotions.

[0079] The development part can refer to relevant academic papers and research data to propose ideas based on scientific evidence. For example, it can propose a new approach based on the latest research results. This allows for the generation of reliable ideas with scientific backing. Academic papers and research data are automatically collected through database searches, and the generation AI analyzes them and uses them to develop ideas.

[0080] The output generation unit can use the emotion estimation function to analyze the user's emotional response and prioritize emotionally positive outputs. For example, if the user expresses positive emotions toward the generated output, the output generation unit can reinforce the output. Also, if the user expresses negative emotions, the output generation unit can suggest alternatives to alleviate the emotions. This allows the optimal output to be generated based on the user's emotions.

[0081] The output generation unit can automatically generate prototypes that users can try out. For example, it can automatically generate 3D models of products or demos of services. This allows users to try out the generated ideas and get concrete feedback. Prototypes can be provided in the form of physical models or digital simulations.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The initial idea input unit inputs an initial idea provided by the user. For example, the user can input an idea such as "I want to develop a new eco-friendly product." The initial idea input unit can also accept initial ideas in various formats, such as text, image, and audio. Step 2: The development section develops the initial ideas input by the initial idea input section from multiple angles. For example, the generative AI combines knowledge from different industries and fields to propose new perspectives and approaches. For example, for an idea for an eco-friendly product, the generative AI will propose various directions, such as "products that use renewable energy," "products made from recycled materials," and "highly energy-efficient products." Step 3: The output generation unit generates multiple outputs based on the ideas developed by the expansion unit. For example, in response to an idea for an eco-friendly product, the generation AI will make specific suggestions such as "a smartphone with a solar panel," "furniture made from recycled plastic," or "energy-efficient home appliances."

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] 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. [Explanation of symbols]

[0151] 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. an initial idea input unit for inputting an initial idea provided by a user; a development unit that develops the initial idea input by the initial idea input unit in a multifaceted manner; an output generation unit that generates a plurality of outputs based on the idea developed by the development unit; A system characterized by:

2. The initial idea input unit Accepts input in different languages, enabling the development of ideas from an international perspective 2. The system of claim 1.

3. The expansion section Refer to relevant academic papers and research data to propose ideas based on scientific evidence 2. The system of claim 1.

4. The output generation unit Incorporating knowledge from different industries and applications to discover new market needs 2. The system of claim 1.

5. The initial idea input unit Sentiment analysis is performed on the initial ideas to assess their potential value based on the intensity and type of emotion.

2. The system of claim 1.

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