system

Abstract

The system according to this embodiment aims to identify synergies between different industries and generate and implement new ideas. [Solution] The system according to this embodiment comprises a synergy identification unit, an idea generation unit, and a collaboration unit. The synergy identification unit identifies synergies. The idea generation unit generates new ideas based on the synergies identified by the synergy identification unit. The collaboration unit implements the ideas generated by the idea generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the process of identifying synergies between different industries and generating new ideas is not efficiently carried out.

[0005] The system according to the embodiment aims to identify synergies between different industries and generate and execute new ideas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a synergy identification unit, an idea generation unit, and a collaboration unit. The synergy identification unit identifies synergies. The idea generation unit generates new ideas based on the synergies identified by the synergy identification unit. The collaboration unit implements the ideas generated by the idea generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify synergies between different industries and generate and implement new ideas. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Generative AI for Cross-Industry Innovation Hub, according to an embodiment of the present invention, is an innovative platform that leverages advanced AI algorithms to facilitate unprecedented collaboration and innovation among SoftBank Group's diverse portfolio companies. This platform functions as a virtual innovation lab, automatically identifying synergies, proposing new solutions, and fostering groundbreaking innovation by combining expertise and technologies from various industries. For example, the Generative AI for Cross-Industry Innovation Hub analyzes data from all companies within the group to discover unexpected collaboration opportunities. For instance, it can identify opportunities for a telecommunications company and a robotics company to jointly develop a new type of communication device. Furthermore, the Generative AI for Cross-Industry Innovation Hub features an automated idea generation engine that combines knowledge from different industries to generate new product and service concepts. For example, it can propose new fintech services utilizing e-commerce data. In addition, the Generative AI for Cross-Industry Innovation Hub provides an AI-enhanced environment for teams from different companies to collaborate in a virtual space. Teams can seamlessly share ideas and resources while collaborating in this virtual environment. Furthermore, Generative AI for Cross-Industry Innovation Hub features market analysis capabilities that predict the potential success and impact of proposed innovations. For example, it can forecast market demand for new health technologies developed by combining the expertise of biotechnology and AI companies. Finally, Generative AI for Cross-Industry Innovation Hub includes a prototyping simulator that enables rapid testing and iteration of ideas in a virtual environment. Teams can simulate the performance of new products before investing in physical prototypes.The uniqueness of this platform lies in its leverage of advanced AI tailored to SoftBank Group's unique and diverse portfolio in idea generation, outcome prediction, and collaboration facilitation. This enables Generative AI for Cross-Industry Innovation Hub to transform SoftBank Group into an unparalleled innovation powerhouse, capable of rapidly identifying and capitalizing on cross-industry opportunities, and setting a new standard for how large, diverse technology conglomerates innovate in the AI ​​era. As a result, Generative AI for Cross-Industry Innovation Hub can foster collaboration and innovation among SoftBank Group's diverse portfolio companies, improve group-wide R&D efficiency by 30%, and generate 5-10 key cross-industry innovations annually.

[0029] The Generative AI for Cross-Industry Innovation Hub according to this embodiment comprises a synergy identification unit, an idea generation unit, and a collaboration unit. The synergy identification unit identifies synergies. For example, the synergy identification unit analyzes data within a group and discovers unexpected collaboration opportunities. For example, the synergy identification unit can identify an opportunity for a telecommunications company and a robotics company to jointly develop a new type of communication device. The synergy identification unit can also identify synergies using AI. For example, the synergy identification unit can use an AI model to take data within a group as input and output synergies. The idea generation unit generates new ideas based on the synergies identified by the synergy identification unit. For example, the idea generation unit can combine knowledge from different industries to generate new product or service concepts. For example, the idea generation unit can propose a new fintech service that utilizes e-commerce data. The idea generation unit can also generate new ideas using AI. For example, the idea generation unit can use an AI model to take knowledge from different industries as input and output new ideas. The collaboration unit implements the ideas generated by the idea generation unit. The Collaboration Department provides an environment for teams from different companies to collaborate in a virtual space. For example, the Collaboration Department allows for seamless sharing of ideas and resources in a virtual space. The Collaboration Department can also execute collaborations using AI. For example, the Collaboration Department can use an AI model to take teams from different companies as input and output a method of collaboration. As a result, the Generative AI for Cross-Industry Innovation Hub according to this embodiment can efficiently identify synergies, generate ideas, and execute collaborations.

[0030] The Synergy Identification Department identifies synergies. For example, it analyzes data within the group to discover unexpected collaboration opportunities. Specifically, the Synergy Identification Department collects business data, project history, technology patent information, and market trend data from each company and analyzes this data comprehensively. The analysis uses natural language processing (NLP) and machine learning algorithms to extract relationships and patterns between the data. For example, by analyzing the technology patent data of a telecommunications company and the project history of a robotics company, it may find the possibility of the two companies jointly developing a new communication device. The Synergy Identification Department can also identify synergies using AI. Specifically, it performs network analysis using deep learning as an AI model, taking data within the group as input and outputting synergies. For example, the AI ​​model analyzes each company's technology stack and market needs and recommends the most suitable collaboration partners. Furthermore, the Synergy Identification Department updates data in real time, enabling it to always identify synergies based on the latest information. This allows the Synergy Identification Department to quickly and accurately discover new collaboration opportunities between companies and support the promotion of innovation.

[0031] The Idea Generation Unit generates new ideas based on synergies identified by the Synergy Identification Unit. For example, the Idea Generation Unit generates new product and service concepts by combining knowledge from different industries. Specifically, it databases expertise and technical information from various industries and creates new ideas by combining this data. For instance, it can propose a new online payment system by combining e-commerce data and fintech technology. The Idea Generation Unit can also generate new ideas using AI. Specifically, it uses a generative AI model, taking knowledge from different industries as input and outputting new ideas. For example, the generative AI uses natural language processing technology to analyze technical documents and market reports from various industries and generates new product and service concepts based on this information. Furthermore, the Idea Generation Unit can collect user feedback and evaluate and improve the generated ideas. This allows the Idea Generation Unit to constantly provide innovative ideas that respond to the latest market needs and technological trends, thereby improving the company's competitiveness.

[0032] The Collaboration Department implements the ideas generated by the Idea Generation Department. For example, the Collaboration Department provides an environment for teams from different companies to collaborate in a virtual space. Specifically, it provides virtual meeting systems and collaborative work platforms, enabling teams from different companies to communicate in real time and advance projects. For instance, the virtual meeting system allows for idea sharing and discussion using video conferencing and chat functions. The collaborative work platform provides collaborative document editing and project management tools to support efficient work progress. Furthermore, the Collaboration Department can also utilize AI for collaboration. Specifically, it uses AI models, taking teams from different companies as input, to output the optimal collaboration method. For example, the AI ​​model analyzes each team's skill set and project progress to propose the optimal task allocation and schedule. This allows the Collaboration Department to facilitate smooth collaboration between different companies and increase the success rate of projects. Additionally, the Collaboration Department can monitor project progress in real time and provide adjustments and support as needed. This enables the Collaboration Department to effectively support inter-company collaboration and promote innovation.

[0033] The market analysis department can conduct market analysis. For example, the market analysis department can evaluate the market suitability of a proposed idea. The market analysis department can also conduct market analysis using AI. For example, the market analysis department can use an AI model to take a proposed idea as input and output market suitability. In this way, the market analysis department can evaluate the market suitability of a proposed idea by conducting market analysis.

[0034] The prototyping unit can perform virtual prototyping. For example, the prototyping unit can quickly evaluate the feasibility of an idea. The prototyping unit can also perform virtual prototyping using AI. For example, the prototyping unit can use an AI model to take an idea as input and output a virtual prototype. This allows the prototyping unit to quickly evaluate the feasibility of an idea through virtual prototyping.

[0035] The Synergy Identification Unit can analyze data within the group and discover unexpected collaboration opportunities. For example, the Synergy Identification Unit can analyze data within the group and discover unexpected collaboration opportunities. For example, the Synergy Identification Unit can identify opportunities for a telecommunications company and a robotics company to jointly develop a new type of communication device. The Synergy Identification Unit can also analyze data within the group using AI. For example, the Synergy Identification Unit can use an AI model to take data within the group as input and output unexpected collaboration opportunities. In this way, the Synergy Identification Unit can discover unexpected collaboration opportunities by analyzing data within the group.

[0036] The idea generation unit can generate new product and service concepts by combining knowledge from different industries. For example, the idea generation unit can propose new fintech services that utilize e-commerce data. Furthermore, the idea generation unit can combine knowledge from different industries using AI. For example, the idea generation unit can use an AI model to take knowledge from different industries as input and output new product and service concepts. Thus, the idea generation unit can generate new product and service concepts by combining knowledge from different industries.

[0037] The Collaboration Department can provide an environment for teams from different companies to collaborate in a virtual space. For example, the Collaboration Department can provide an environment for teams from different companies to collaborate in a virtual space, enabling seamless sharing of ideas and resources. The Collaboration Department can also provide an environment for teams from different companies to collaborate using AI. For example, the Collaboration Department can use an AI model to take teams from different companies as input and output an environment for collaboration. This enables efficient collaboration by providing an environment for teams from different companies to collaborate in a virtual space.

[0038] The Synergy Identification Unit can analyze data within the group in real time to improve the accuracy of synergy identification. For example, the Synergy Identification Unit can collect the latest project data from each company in real time and utilize it for synergy identification. The Synergy Identification Unit can also update the skill sets of employees in each company in real time and propose optimal collaborations. Furthermore, the Synergy Identification Unit can analyze market trend data from each company in real time to improve the accuracy of synergy identification. In this way, the Synergy Identification Unit improves the accuracy of synergy identification by analyzing data within the group in real time. Some or all of the above processes in the Synergy Identification Unit may be performed using AI, for example, or not. For example, the Synergy Identification Unit can input the data collected in real time into AI and have the AI ​​perform the improvement of synergy identification accuracy.

[0039] The synergy identification unit can improve the accuracy of synergy identification by referring to past success stories. For example, the synergy identification unit can create a database of past successful collaboration cases and refer to it when identifying synergies. The synergy identification unit can also analyze lessons learned from past success stories and reflect them in the synergy identification algorithm. Furthermore, the synergy identification unit can analyze patterns in past success stories and identify similar synergies. In this way, the synergy identification unit improves the accuracy of synergy identification by referring to past success stories. Some or all of the above processes in the synergy identification unit may be performed using AI, for example, or not using AI. For example, the synergy identification unit can input past success story data into AI and have the AI ​​perform the improvement of synergy identification accuracy.

[0040] The synergy identification unit can identify broader synergies by referencing data from outside the group during the synergy identification process. For example, the synergy identification unit can collect publicly available data from companies outside the group and utilize it for synergy identification. It can also refer to industry reports from outside the group to improve the accuracy of synergy identification. Furthermore, the synergy identification unit can analyze academic papers from outside the group to discover new synergies. This allows the synergy identification unit to identify broader synergies by referencing data from outside the group. Some or all of the above processes in the synergy identification unit may be performed using AI, for example, or not. For example, the synergy identification unit can input data from outside the group into an AI and have the AI ​​perform the identification of broader synergies.

[0041] The synergy identification unit can improve the accuracy of synergy identification by referencing trend data from different industries. For example, the synergy identification unit can collect the latest trend data from different industries and utilize it for synergy identification. The synergy identification unit can also analyze success stories from different industries and reflect them in its synergy identification algorithm. Furthermore, the synergy identification unit can monitor market trends from different industries in real time to improve the accuracy of synergy identification. Thus, the synergy identification unit improves the accuracy of synergy identification by referencing trend data from different industries. Some or all of the above-described processes in the synergy identification unit may be performed using AI, for example, or not. For example, the synergy identification unit can input trend data from different industries into an AI and have the AI ​​perform the accuracy improvement.

[0042] The idea generation unit can improve the accuracy of idea generation by referring to past idea data. For example, the idea generation unit can create a database of past successful ideas and refer to it during idea generation. Furthermore, the idea generation unit can analyze lessons learned from past idea data and reflect them in the idea generation algorithm. In addition, the idea generation unit can analyze patterns in past idea data and generate similar ideas. Thus, the idea generation unit improves the accuracy of idea generation by referring to past idea data. Some or all of the above processes in the idea generation unit may be performed using AI, for example, or without AI. For example, the idea generation unit can input past idea data into AI and have the AI ​​perform the task of improving generation accuracy.

[0043] The idea generation unit can generate new ideas by referencing successful case studies from different industries. For example, the idea generation unit can create a database of successful case studies from different industries and refer to it during idea generation. Furthermore, the idea generation unit can analyze lessons learned from successful case studies from different industries and incorporate them into its idea generation algorithm. In addition, the idea generation unit can analyze patterns in successful case studies from different industries and generate new ideas. Thus, the idea generation unit can generate new ideas by referencing successful case studies from different industries. Some or all of the above processes in the idea generation unit may be performed using AI, for example, or without AI. For example, the idea generation unit can input data on successful case studies from different industries into an AI and have the AI ​​generate new ideas.

[0044] The idea generation unit can generate diverse ideas by referencing data from different cultural spheres during the idea generation process. For example, the idea generation unit can create a database of successful case studies from different cultural spheres and refer to it during idea generation. It can also analyze market trends in different cultural spheres to generate diverse ideas. Furthermore, it can reference consumer behavior data from different cultural spheres to generate new ideas. Thus, the idea generation unit can generate diverse ideas by referencing data from different cultural spheres. Some or all of the above-described processes in the idea generation unit may be performed using AI, for example, or without AI. For example, the idea generation unit can input data from different cultural spheres into an AI and have the AI ​​generate diverse ideas.

[0045] The idea generation unit can generate new technical ideas by referencing data from different technical fields during the idea generation process. For example, the idea generation unit can create a database of successful cases from different technical fields and refer to it during idea generation. It can also analyze the latest research from different technical fields to generate new technical ideas. Furthermore, the idea generation unit can reference patent data from different technical fields to generate new technical ideas. Thus, the idea generation unit can generate new technical ideas by referencing data from different technical fields. Some or all of the above-described processes in the idea generation unit may be performed using AI, for example, or without AI. For example, the idea generation unit can input data from different technical fields into an AI and have the AI ​​generate new technical ideas.

[0046] The collaboration department can create optimal team compositions by referring to past collaboration data during collaboration. For example, the collaboration department can create a database of past successful collaboration cases and refer to it when forming teams. Furthermore, the collaboration department can analyze lessons learned from past collaboration data to create optimal team compositions. In addition, the collaboration department can analyze patterns in past collaboration data and create similar team compositions. This allows the collaboration department to create optimal team compositions by referring to past collaboration data. Some or all of the above processes in the collaboration department may be performed using AI, for example, or without AI. For example, the collaboration department can input past collaboration data into AI and have the AI ​​perform the optimal team composition.

[0047] The Collaboration Department can add a function to automatically match experts from different industries during collaboration. For example, the Collaboration Department can build a database of experts from different industries and automatically match them during collaboration. The Collaboration Department can also analyze the skill sets of experts from different industries to make the best match. Furthermore, the Collaboration Department can refer to the past project data of experts from different industries to make the best match. This enables optimal collaboration by automatically matching experts from different industries. Some or all of the above processes in the Collaboration Department may be performed using AI, for example, or not. For example, the Collaboration Department can input expert data from different industries into an AI and have the AI ​​perform the best match.

[0048] The Collaboration Department can add functionality to connect teams from different regions in a virtual space during collaboration. For example, the Collaboration Department can connect teams from different regions in a virtual meeting room and collaborate in real time. The Collaboration Department can also provide a virtual whiteboard function that allows teams from different regions to work together. Furthermore, the Collaboration Department can provide a virtual file sharing function that allows teams from different regions to share information. In this way, the Collaboration Department enables global collaboration by connecting teams from different regions in a virtual space. Some or all of the above processes in the Collaboration Department may be performed using AI, for example, or not. For example, the Collaboration Department can input data from teams from different regions into an AI and have the AI ​​perform the function of connecting them in a virtual space.

[0049] The Collaboration Department can facilitate international collaboration by adding translation capabilities for different languages ​​during collaboration. For example, the Collaboration Department can provide a translation chat function that allows teams speaking different languages ​​to communicate in real time. It can also provide a function to automatically translate documents in different languages. Furthermore, the Collaboration Department can provide a function to translate video conferences in different languages ​​in real time. Thus, by adding translation capabilities for different languages, the Collaboration Department facilitates international collaboration. Some or all of the above processes in the Collaboration Department may be performed using AI, for example, or not. For example, the Collaboration Department can input data in different languages ​​into an AI and have the AI ​​perform the translation function.

[0050] The market analysis department can improve the accuracy of its analysis by referring to historical market data during market analysis. For example, the market analysis department can create a database of historical market data and refer to it during market analysis. Furthermore, the market analysis department can analyze lessons learned from historical market data and incorporate them into its market analysis algorithms. In addition, the market analysis department can analyze patterns in historical market data and predict similar market trends. Thus, the market analysis department improves the accuracy of its analysis by referring to historical market data. Some or all of the above processes in the market analysis department may be performed using AI, for example, or not. For example, the market analysis department can input historical market data into AI and have the AI ​​perform the task of improving the accuracy of its analysis.

[0051] The market analysis department can conduct global market analysis by referencing market data from different regions. For example, the market analysis department can collect market data from different regions and conduct global market analysis. Furthermore, the market analysis department can analyze market trends in different regions and propose global market strategies. In addition, the market analysis department can conduct global market analysis by referencing consumer behavior data from different regions. This enables the market analysis department to conduct global market analysis by referencing market data from different regions. Some or all of the above processes in the market analysis department may be performed using AI, for example, or not. For example, the market analysis department can input market data from different regions into an AI and have the AI ​​perform global market analysis.

[0052] The prototyping unit can improve accuracy by referring to past prototype data during the prototyping process. For example, the prototyping unit can create a database of past prototype data and refer to it during prototyping. Furthermore, the prototyping unit can analyze lessons learned from past prototype data and incorporate them into the prototyping algorithm. In addition, the prototyping unit can analyze patterns in past prototype data and generate similar prototypes. This allows the prototyping unit to improve accuracy by referring to past prototype data. Some or all of the above processes in the prototyping unit may be performed using AI, for example, or without AI. For example, the prototyping unit can input past prototype data into AI and have the AI ​​perform the accuracy improvement.

[0053] The prototyping unit can generate diverse prototypes by referencing data from different technical fields during the prototyping process. For example, the prototyping unit can create a database of successful cases from different technical fields and refer to it during prototyping. It can also analyze the latest research in different technical fields and generate diverse prototypes. Furthermore, the prototyping unit can reference patent data from different technical fields to generate diverse prototypes. Thus, the prototyping unit can generate diverse prototypes by referencing data from different technical fields. Some or all of the above-described processes in the prototyping unit may be performed using AI, for example, or without AI. For example, the prototyping unit can input data from different technical fields into an AI and have the AI ​​generate diverse prototypes.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The Synergy Identification Department can analyze data within the group in real time to improve the accuracy of synergy identification. For example, the Synergy Identification Department can collect the latest project data from each company in real time and utilize it for synergy identification. Furthermore, the Synergy Identification Department can update the skill sets of employees in each company in real time and propose optimal collaborations. In addition, the Synergy Identification Department can analyze market trend data from each company in real time to improve the accuracy of synergy identification. In this way, the Synergy Identification Department improves the accuracy of synergy identification by analyzing data within the group in real time.

[0056] The idea generation unit can improve the accuracy of idea generation by referring to past idea data. For example, the idea generation unit can create a database of past successful ideas and refer to it during idea generation. Furthermore, the idea generation unit can analyze lessons learned from past idea data and incorporate them into the idea generation algorithm. In addition, the idea generation unit can analyze patterns in past idea data and generate similar ideas. In this way, the idea generation unit improves the accuracy of idea generation by referring to past idea data.

[0057] The Collaboration Department can add a feature that automatically matches experts from different industries during collaboration. For example, the Collaboration Department can build a database of experts from different industries and automatically match them during collaboration. Furthermore, the Collaboration Department can analyze the skill sets of experts from different industries to achieve optimal matching. In addition, the Collaboration Department can refer to the past project data of experts from different industries to achieve optimal matching. This enables optimal collaboration by automatically matching experts from different industries.

[0058] The market analysis department can conduct global market analysis by referencing market data from different regions. For example, it can collect market data from different regions and conduct global market analysis. Furthermore, the market analysis department can analyze market trends in different regions and propose global market strategies. In addition, the market analysis department can conduct global market analysis by referencing consumer behavior data from different regions. This enables the market analysis department to conduct global market analysis by referencing market data from different regions.

[0059] The prototyping unit can generate diverse prototypes by referencing data from different technical fields during the prototyping process. For example, the prototyping unit can create a database of successful cases in different technical fields and refer to it during prototyping. Furthermore, the prototyping unit can analyze the latest research in different technical fields and generate diverse prototypes. In addition, the prototyping unit can reference patent data from different technical fields to generate diverse prototypes. Thus, the prototyping unit can generate diverse prototypes by referencing data from different technical fields.

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

[0061] Step 1: The synergy identification unit identifies synergies. For example, it analyzes data within the group to discover unexpected collaboration opportunities. The synergy identification unit can identify opportunities for a telecommunications company and a robotics company to jointly develop new types of communication devices. It can also use AI to identify synergies. For example, an AI model can be used as input for data within the group and output synergies. Step 2: The Idea Generation Unit generates new ideas based on the synergies identified by the Synergy Identification Unit. For example, it can combine knowledge from different industries to generate new product or service concepts. The Idea Generation Unit can also propose new fintech services utilizing e-commerce data. Furthermore, it can generate new ideas using AI. For example, an AI model can be used to take knowledge from different industries as input and output new ideas. Step 3: The Collaboration Unit implements the ideas generated by the Idea Generation Unit. For example, it provides an environment for teams from different companies to collaborate in a virtual space. The Collaboration Unit enables seamless sharing of ideas and resources in the virtual space. It can also perform collaboration using AI. For example, an AI model can be used to take teams from different companies as input and output a method for collaboration.

[0062] (Example of form 2) The Generative AI for Cross-Industry Innovation Hub, according to an embodiment of the present invention, is an innovative platform that leverages advanced AI algorithms to facilitate unprecedented collaboration and innovation among SoftBank Group's diverse portfolio companies. This platform functions as a virtual innovation lab, automatically identifying synergies, proposing new solutions, and fostering groundbreaking innovation by combining expertise and technologies from various industries. For example, the Generative AI for Cross-Industry Innovation Hub analyzes data from all companies within the group to discover unexpected collaboration opportunities. For instance, it can identify opportunities for a telecommunications company and a robotics company to jointly develop a new type of communication device. Furthermore, the Generative AI for Cross-Industry Innovation Hub features an automated idea generation engine that combines knowledge from different industries to generate new product and service concepts. For example, it can propose new fintech services utilizing e-commerce data. In addition, the Generative AI for Cross-Industry Innovation Hub provides an AI-enhanced environment for teams from different companies to collaborate in a virtual space. Teams can seamlessly share ideas and resources while collaborating in this virtual environment. Furthermore, Generative AI for Cross-Industry Innovation Hub features market analysis capabilities that predict the potential success and impact of proposed innovations. For example, it can forecast market demand for new health technologies developed by combining the expertise of biotechnology and AI companies. Finally, Generative AI for Cross-Industry Innovation Hub includes a prototyping simulator that enables rapid testing and iteration of ideas in a virtual environment. Teams can simulate the performance of new products before investing in physical prototypes.The uniqueness of this platform lies in its leverage of advanced AI tailored to SoftBank Group's unique and diverse portfolio in idea generation, outcome prediction, and collaboration facilitation. This enables Generative AI for Cross-Industry Innovation Hub to transform SoftBank Group into an unparalleled innovation powerhouse, capable of rapidly identifying and capitalizing on cross-industry opportunities, and setting a new standard for how large, diverse technology conglomerates innovate in the AI ​​era. As a result, Generative AI for Cross-Industry Innovation Hub can foster collaboration and innovation among SoftBank Group's diverse portfolio companies, improve group-wide R&D efficiency by 30%, and generate 5-10 key cross-industry innovations annually.

[0063] The Generative AI for Cross-Industry Innovation Hub according to this embodiment comprises a synergy identification unit, an idea generation unit, and a collaboration unit. The synergy identification unit identifies synergies. For example, the synergy identification unit analyzes data within a group and discovers unexpected collaboration opportunities. For example, the synergy identification unit can identify an opportunity for a telecommunications company and a robotics company to jointly develop a new type of communication device. The synergy identification unit can also identify synergies using AI. For example, the synergy identification unit can use an AI model to take data within a group as input and output synergies. The idea generation unit generates new ideas based on the synergies identified by the synergy identification unit. For example, the idea generation unit can combine knowledge from different industries to generate new product or service concepts. For example, the idea generation unit can propose a new fintech service that utilizes e-commerce data. The idea generation unit can also generate new ideas using AI. For example, the idea generation unit can use an AI model to take knowledge from different industries as input and output new ideas. The collaboration unit implements the ideas generated by the idea generation unit. The Collaboration Department provides an environment for teams from different companies to collaborate in a virtual space. For example, the Collaboration Department allows for seamless sharing of ideas and resources in a virtual space. The Collaboration Department can also execute collaborations using AI. For example, the Collaboration Department can use an AI model to take teams from different companies as input and output a method of collaboration. As a result, the Generative AI for Cross-Industry Innovation Hub according to this embodiment can efficiently identify synergies, generate ideas, and execute collaborations.

[0064] The Synergy Identification Department identifies synergies. For example, it analyzes data within the group to discover unexpected collaboration opportunities. Specifically, the Synergy Identification Department collects business data, project history, technology patent information, and market trend data from each company and analyzes this data comprehensively. The analysis uses natural language processing (NLP) and machine learning algorithms to extract relationships and patterns between the data. For example, by analyzing the technology patent data of a telecommunications company and the project history of a robotics company, it may find the possibility of the two companies jointly developing a new communication device. The Synergy Identification Department can also identify synergies using AI. Specifically, it performs network analysis using deep learning as an AI model, taking data within the group as input and outputting synergies. For example, the AI ​​model analyzes each company's technology stack and market needs and recommends the most suitable collaboration partners. Furthermore, the Synergy Identification Department updates data in real time, enabling it to always identify synergies based on the latest information. This allows the Synergy Identification Department to quickly and accurately discover new collaboration opportunities between companies and support the promotion of innovation.

[0065] The Idea Generation Unit generates new ideas based on synergies identified by the Synergy Identification Unit. For example, the Idea Generation Unit generates new product and service concepts by combining knowledge from different industries. Specifically, it databases expertise and technical information from various industries and creates new ideas by combining this data. For instance, it can propose a new online payment system by combining e-commerce data and fintech technology. The Idea Generation Unit can also generate new ideas using AI. Specifically, it uses a generative AI model, taking knowledge from different industries as input and outputting new ideas. For example, the generative AI uses natural language processing technology to analyze technical documents and market reports from various industries and generates new product and service concepts based on this information. Furthermore, the Idea Generation Unit can collect user feedback and evaluate and improve the generated ideas. This allows the Idea Generation Unit to constantly provide innovative ideas that respond to the latest market needs and technological trends, thereby improving the company's competitiveness.

[0066] The Collaboration Department implements the ideas generated by the Idea Generation Department. For example, the Collaboration Department provides an environment for teams from different companies to collaborate in a virtual space. Specifically, it provides virtual meeting systems and collaborative work platforms, enabling teams from different companies to communicate in real time and advance projects. For instance, the virtual meeting system allows for idea sharing and discussion using video conferencing and chat functions. The collaborative work platform provides collaborative document editing and project management tools to support efficient work progress. Furthermore, the Collaboration Department can also utilize AI for collaboration. Specifically, it uses AI models, taking teams from different companies as input, to output the optimal collaboration method. For example, the AI ​​model analyzes each team's skill set and project progress to propose the optimal task allocation and schedule. This allows the Collaboration Department to facilitate smooth collaboration between different companies and increase the success rate of projects. Additionally, the Collaboration Department can monitor project progress in real time and provide adjustments and support as needed. This enables the Collaboration Department to effectively support inter-company collaboration and promote innovation.

[0067] The market analysis department can conduct market analysis. For example, the market analysis department can evaluate the market suitability of a proposed idea. The market analysis department can also conduct market analysis using AI. For example, the market analysis department can use an AI model to take a proposed idea as input and output market suitability. In this way, the market analysis department can evaluate the market suitability of a proposed idea by conducting market analysis.

[0068] The prototyping unit can perform virtual prototyping. For example, the prototyping unit can quickly evaluate the feasibility of an idea. The prototyping unit can also perform virtual prototyping using AI. For example, the prototyping unit can use an AI model to take an idea as input and output a virtual prototype. This allows the prototyping unit to quickly evaluate the feasibility of an idea through virtual prototyping.

[0069] The Synergy Identification Unit can analyze data within the group and discover unexpected collaboration opportunities. For example, the Synergy Identification Unit can analyze data within the group and discover unexpected collaboration opportunities. For example, the Synergy Identification Unit can identify opportunities for a telecommunications company and a robotics company to jointly develop a new type of communication device. The Synergy Identification Unit can also analyze data within the group using AI. For example, the Synergy Identification Unit can use an AI model to take data within the group as input and output unexpected collaboration opportunities. In this way, the Synergy Identification Unit can discover unexpected collaboration opportunities by analyzing data within the group.

[0070] The idea generation unit can generate new product and service concepts by combining knowledge from different industries. For example, the idea generation unit can propose new fintech services that utilize e-commerce data. Furthermore, the idea generation unit can combine knowledge from different industries using AI. For example, the idea generation unit can use an AI model to take knowledge from different industries as input and output new product and service concepts. Thus, the idea generation unit can generate new product and service concepts by combining knowledge from different industries.

[0071] The Collaboration Department can provide an environment for teams from different companies to collaborate in a virtual space. For example, the Collaboration Department can provide an environment for teams from different companies to collaborate in a virtual space, enabling seamless sharing of ideas and resources. The Collaboration Department can also provide an environment for teams from different companies to collaborate using AI. For example, the Collaboration Department can use an AI model to take teams from different companies as input and output an environment for collaboration. This enables efficient collaboration by providing an environment for teams from different companies to collaborate in a virtual space.

[0072] The synergy identification unit can estimate the user's emotions and adjust the priority of synergy identification based on the estimated user emotions. For example, if the user is excited, the synergy identification unit can increase the priority of synergy identification and make suggestions quickly. Conversely, if the user is stressed, the synergy identification unit can lower the priority of synergy identification and provide a relaxing environment. Furthermore, if the user is relaxed, the synergy identification unit can set the priority of synergy identification to a medium level and make balanced suggestions. In this way, the synergy identification unit can identify more appropriate synergies by adjusting the priority of synergy identification based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the synergy identification unit may be performed using AI, or not. For example, the synergy identification unit can input user emotion data into an AI and have the AI ​​prioritize synergy identification based on emotions.

[0073] The Synergy Identification Unit can analyze data within the group in real time to improve the accuracy of synergy identification. For example, the Synergy Identification Unit can collect the latest project data from each company in real time and utilize it for synergy identification. The Synergy Identification Unit can also update the skill sets of employees in each company in real time and propose optimal collaborations. Furthermore, the Synergy Identification Unit can analyze market trend data from each company in real time to improve the accuracy of synergy identification. In this way, the Synergy Identification Unit improves the accuracy of synergy identification by analyzing data within the group in real time. Some or all of the above processes in the Synergy Identification Unit may be performed using AI, for example, or not. For example, the Synergy Identification Unit can input the data collected in real time into AI and have the AI ​​perform the improvement of synergy identification accuracy.

[0074] The synergy identification unit can improve the accuracy of synergy identification by referring to past success stories. For example, the synergy identification unit can create a database of past successful collaboration cases and refer to it when identifying synergies. The synergy identification unit can also analyze lessons learned from past success stories and reflect them in the synergy identification algorithm. Furthermore, the synergy identification unit can analyze patterns in past success stories and identify similar synergies. In this way, the synergy identification unit improves the accuracy of synergy identification by referring to past success stories. Some or all of the above processes in the synergy identification unit may be performed using AI, for example, or not using AI. For example, the synergy identification unit can input past success story data into AI and have the AI ​​perform the improvement of synergy identification accuracy.

[0075] The synergy identification unit can estimate the user's emotions and adjust the display method of synergy identification based on the estimated user emotions. For example, if the user is tense, the synergy identification unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a concise display method. This allows the synergy identification unit to provide a more appropriate display by adjusting the display method of synergy identification based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the synergy identification unit may be performed using AI, or not. For example, the synergy identification unit can input user emotion data into an AI and have the AI ​​perform the emotion-based adjustment of the display method.

[0076] The synergy identification unit can identify broader synergies by referencing data from outside the group during the synergy identification process. For example, the synergy identification unit can collect publicly available data from companies outside the group and utilize it for synergy identification. It can also refer to industry reports from outside the group to improve the accuracy of synergy identification. Furthermore, the synergy identification unit can analyze academic papers from outside the group to discover new synergies. This allows the synergy identification unit to identify broader synergies by referencing data from outside the group. Some or all of the above processes in the synergy identification unit may be performed using AI, for example, or not. For example, the synergy identification unit can input data from outside the group into an AI and have the AI ​​perform the identification of broader synergies.

[0077] The synergy identification unit can improve the accuracy of synergy identification by referencing trend data from different industries. For example, the synergy identification unit can collect the latest trend data from different industries and utilize it for synergy identification. The synergy identification unit can also analyze success stories from different industries and reflect them in its synergy identification algorithm. Furthermore, the synergy identification unit can monitor market trends from different industries in real time to improve the accuracy of synergy identification. Thus, the synergy identification unit improves the accuracy of synergy identification by referencing trend data from different industries. Some or all of the above-described processes in the synergy identification unit may be performed using AI, for example, or not. For example, the synergy identification unit can input trend data from different industries into an AI and have the AI ​​perform the accuracy improvement.

[0078] The idea generation unit can estimate the user's emotions and adjust the type of ideas it generates based on those emotions. For example, if the user is relaxed, the idea generation unit can generate creative and free-flowing ideas. If the user is in a hurry, it can also generate practical and quickly actionable ideas. Furthermore, if the user is excited, it can generate challenging and innovative ideas. This allows the idea generation unit to generate more appropriate ideas by adjusting the type of ideas it generates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the idea generation unit may be performed using AI, or not. For example, the idea generation unit can input user emotion data into an AI and have the AI ​​adjust the type of ideas based on those emotions.

[0079] The idea generation unit can improve the accuracy of idea generation by referring to past idea data. For example, the idea generation unit can create a database of past successful ideas and refer to it during idea generation. Furthermore, the idea generation unit can analyze lessons learned from past idea data and reflect them in the idea generation algorithm. In addition, the idea generation unit can analyze patterns in past idea data and generate similar ideas. Thus, the idea generation unit improves the accuracy of idea generation by referring to past idea data. Some or all of the above processes in the idea generation unit may be performed using AI, for example, or without AI. For example, the idea generation unit can input past idea data into AI and have the AI ​​perform the task of improving generation accuracy.

[0080] The idea generation unit can generate new ideas by referencing successful case studies from different industries. For example, the idea generation unit can create a database of successful case studies from different industries and refer to it during idea generation. Furthermore, the idea generation unit can analyze lessons learned from successful case studies from different industries and incorporate them into its idea generation algorithm. In addition, the idea generation unit can analyze patterns in successful case studies from different industries and generate new ideas. Thus, the idea generation unit can generate new ideas by referencing successful case studies from different industries. Some or all of the above processes in the idea generation unit may be performed using AI, for example, or without AI. For example, the idea generation unit can input data on successful case studies from different industries into an AI and have the AI ​​generate new ideas.

[0081] The idea generation unit can estimate the user's emotions and determine the priority of ideas to generate based on those emotions. For example, if the user is excited, the idea generation unit may prioritize challenging and innovative ideas. If the user is relaxed, it may also prioritize creative and free-flowing ideas. Furthermore, if the user is in a hurry, it may prioritize practical and quickly actionable ideas. In this way, the idea generation unit can prioritize more appropriate ideas by determining the priority of ideas to generate based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the idea generation unit may be performed using AI or not. For example, the idea generation unit can input user emotion data into an AI and have the AI ​​perform the determination of emotion-based idea prioritization.

[0082] The idea generation unit can generate diverse ideas by referencing data from different cultural spheres during the idea generation process. For example, the idea generation unit can create a database of successful case studies from different cultural spheres and refer to it during idea generation. It can also analyze market trends in different cultural spheres to generate diverse ideas. Furthermore, it can reference consumer behavior data from different cultural spheres to generate new ideas. Thus, the idea generation unit can generate diverse ideas by referencing data from different cultural spheres. Some or all of the above-described processes in the idea generation unit may be performed using AI, for example, or without AI. For example, the idea generation unit can input data from different cultural spheres into an AI and have the AI ​​generate diverse ideas.

[0083] The idea generation unit can generate new technical ideas by referencing data from different technical fields during the idea generation process. For example, the idea generation unit can create a database of successful cases from different technical fields and refer to it during idea generation. It can also analyze the latest research from different technical fields to generate new technical ideas. Furthermore, the idea generation unit can reference patent data from different technical fields to generate new technical ideas. Thus, the idea generation unit can generate new technical ideas by referencing data from different technical fields. Some or all of the above-described processes in the idea generation unit may be performed using AI, for example, or without AI. For example, the idea generation unit can input data from different technical fields into an AI and have the AI ​​generate new technical ideas.

[0084] The collaboration unit can estimate the user's emotions and adjust the collaboration method based on the estimated emotions. For example, if the user is nervous, the collaboration unit can provide a simple and highly visible collaboration method. If the user is relaxed, the collaboration unit can also provide a collaboration method that includes detailed information. Furthermore, if the user is in a hurry, the collaboration unit can provide a concise collaboration method. In this way, the collaboration unit enables more appropriate collaboration by adjusting the collaboration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input user emotion data into AI and have the AI ​​perform emotion-based adjustments to the collaboration method.

[0085] The collaboration department can create optimal team compositions by referring to past collaboration data during collaboration. For example, the collaboration department can create a database of past successful collaboration cases and refer to it when forming teams. Furthermore, the collaboration department can analyze lessons learned from past collaboration data to create optimal team compositions. In addition, the collaboration department can analyze patterns in past collaboration data and create similar team compositions. This allows the collaboration department to create optimal team compositions by referring to past collaboration data. Some or all of the above processes in the collaboration department may be performed using AI, for example, or without AI. For example, the collaboration department can input past collaboration data into AI and have the AI ​​perform the optimal team composition.

[0086] The Collaboration Department can add a function to automatically match experts from different industries during collaboration. For example, the Collaboration Department can build a database of experts from different industries and automatically match them during collaboration. The Collaboration Department can also analyze the skill sets of experts from different industries to make the best match. Furthermore, the Collaboration Department can refer to the past project data of experts from different industries to make the best match. This enables optimal collaboration by automatically matching experts from different industries. Some or all of the above processes in the Collaboration Department may be performed using AI, for example, or not. For example, the Collaboration Department can input expert data from different industries into an AI and have the AI ​​perform the best match.

[0087] The collaboration unit can estimate the user's emotions and determine collaboration priorities based on those emotions. For example, if the user is excited, the collaboration unit might prioritize challenging and innovative collaborations. If the user is relaxed, it might prioritize creative and free-flowing collaborations. Furthermore, if the user is in a hurry, it might prioritize practical and quickly actionable collaborations. This allows the collaboration unit to enable more appropriate collaborations by prioritizing collaborations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input user emotion data into an AI and have the AI ​​perform emotion-based collaboration prioritization.

[0088] The Collaboration Department can add functionality to connect teams from different regions in a virtual space during collaboration. For example, the Collaboration Department can connect teams from different regions in a virtual meeting room and collaborate in real time. The Collaboration Department can also provide a virtual whiteboard function that allows teams from different regions to work together. Furthermore, the Collaboration Department can provide a virtual file sharing function that allows teams from different regions to share information. In this way, the Collaboration Department enables global collaboration by connecting teams from different regions in a virtual space. Some or all of the above processes in the Collaboration Department may be performed using AI, for example, or not. For example, the Collaboration Department can input data from teams from different regions into an AI and have the AI ​​perform the function of connecting them in a virtual space.

[0089] The Collaboration Department can facilitate international collaboration by adding translation capabilities for different languages ​​during collaboration. For example, the Collaboration Department can provide a translation chat function that allows teams speaking different languages ​​to communicate in real time. It can also provide a function to automatically translate documents in different languages. Furthermore, the Collaboration Department can provide a function to translate video conferences in different languages ​​in real time. Thus, by adding translation capabilities for different languages, the Collaboration Department facilitates international collaboration. Some or all of the above processes in the Collaboration Department may be performed using AI, for example, or not. For example, the Collaboration Department can input data in different languages ​​into an AI and have the AI ​​perform the translation function.

[0090] The market analysis department can estimate user emotions and adjust its market analysis methods based on those emotions. For example, if a user is stressed, the market analysis department can provide a simple and easy-to-understand market analysis report. If a user is relaxed, the market analysis department can provide a detailed market analysis report. Furthermore, if a user is in a hurry, the market analysis department can provide a concise market analysis report. This allows the market analysis department to perform more appropriate market analysis by adjusting its methods based on user emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the market analysis department may be performed using AI or not. For example, the market analysis department can input user emotion data into an AI and have the AI ​​perform emotion-based adjustments to its market analysis methods.

[0091] The market analysis department can improve the accuracy of its analysis by referring to historical market data during market analysis. For example, the market analysis department can create a database of historical market data and refer to it during market analysis. Furthermore, the market analysis department can analyze lessons learned from historical market data and incorporate them into its market analysis algorithms. In addition, the market analysis department can analyze patterns in historical market data and predict similar market trends. Thus, the market analysis department improves the accuracy of its analysis by referring to historical market data. Some or all of the above processes in the market analysis department may be performed using AI, for example, or not. For example, the market analysis department can input historical market data into AI and have the AI ​​perform the task of improving the accuracy of its analysis.

[0092] The market analysis department can estimate user emotions and prioritize market analysis based on those estimated emotions. For example, if a user is excited, the market analysis department might prioritize challenging and innovative market analysis. If a user is relaxed, it might prioritize creative and free-flowing market analysis. Furthermore, if a user is in a hurry, it might prioritize practical and quickly actionable market analysis. This allows the market analysis department to conduct more appropriate market analysis by prioritizing it based on user emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the market analysis department may be performed using AI or not. For example, the market analysis department could input user emotion data into an AI and have the AI ​​perform emotion-based market analysis prioritization.

[0093] The market analysis department can conduct global market analysis by referencing market data from different regions. For example, the market analysis department can collect market data from different regions and conduct global market analysis. Furthermore, the market analysis department can analyze market trends in different regions and propose global market strategies. In addition, the market analysis department can conduct global market analysis by referencing consumer behavior data from different regions. This enables the market analysis department to conduct global market analysis by referencing market data from different regions. Some or all of the above processes in the market analysis department may be performed using AI, for example, or not. For example, the market analysis department can input market data from different regions into an AI and have the AI ​​perform global market analysis.

[0094] The prototyping unit can estimate the user's emotions and adjust the prototyping method based on the estimated emotions. For example, if the user is nervous, the prototyping unit can provide a simple and highly visual prototyping method. It can also provide a detailed prototyping method if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a concise prototyping method. This allows the prototyping unit to perform more appropriate prototyping by adjusting the prototyping method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prototyping unit may be performed using AI or not. For example, the prototyping unit can input user emotion data into an AI and have the AI ​​adjust the prototyping method based on the emotions.

[0095] The prototyping unit can improve accuracy by referring to past prototype data during the prototyping process. For example, the prototyping unit can create a database of past prototype data and refer to it during prototyping. Furthermore, the prototyping unit can analyze lessons learned from past prototype data and incorporate them into the prototyping algorithm. In addition, the prototyping unit can analyze patterns in past prototype data and generate similar prototypes. This allows the prototyping unit to improve accuracy by referring to past prototype data. Some or all of the above processes in the prototyping unit may be performed using AI, for example, or without AI. For example, the prototyping unit can input past prototype data into AI and have the AI ​​perform the accuracy improvement.

[0096] The prototyping unit can estimate the user's emotions and determine prototyping priorities based on those estimated emotions. For example, if the user is excited, the prototyping unit may prioritize challenging and innovative prototyping. If the user is relaxed, the prototyping unit may also prioritize creative and free-form prototyping. Furthermore, if the user is in a hurry, the prototyping unit may prioritize practical and quickly executable prototyping. This allows the prototyping unit to perform more appropriate prototyping by determining prototyping priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prototyping unit may be performed using AI, for example, or not using AI. For example, the prototyping unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of prototyping based on those emotions.

[0097] The prototyping unit can generate diverse prototypes by referencing data from different technical fields during the prototyping process. For example, the prototyping unit can create a database of successful cases from different technical fields and refer to it during prototyping. It can also analyze the latest research in different technical fields and generate diverse prototypes. Furthermore, the prototyping unit can reference patent data from different technical fields to generate diverse prototypes. Thus, the prototyping unit can generate diverse prototypes by referencing data from different technical fields. Some or all of the above-described processes in the prototyping unit may be performed using AI, for example, or without AI. For example, the prototyping unit can input data from different technical fields into an AI and have the AI ​​generate diverse prototypes.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The synergy identification unit can estimate the user's emotions and adjust the priority of synergy identification based on those emotions. For example, if the user is excited, the synergy identification unit will increase the priority of synergy identification and provide suggestions quickly. Conversely, if the user is stressed, the synergy identification unit can lower the priority of synergy identification and provide a relaxing environment. Furthermore, if the user is relaxed, the synergy identification unit can set the priority of synergy identification to a medium level and provide balanced suggestions. In this way, the synergy identification unit can identify more appropriate synergies by adjusting the priority of synergy identification based on the user's emotions.

[0100] The idea generation unit can estimate the user's emotions and adjust the type of ideas it generates based on those emotions. For example, if the user is relaxed, the idea generation unit can generate creative and free-flowing ideas. If the user is in a hurry, it can also generate practical and quickly actionable ideas. Furthermore, if the user is excited, it can generate challenging and innovative ideas. In this way, the idea generation unit can generate more appropriate ideas by adjusting the type of ideas it generates based on the user's emotions.

[0101] The collaboration unit can estimate the user's emotions and adjust the collaboration method based on those emotions. For example, if the user is feeling stressed, the collaboration unit can provide a simple and highly visible collaboration method. If the user is relaxed, it can provide a collaboration method that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a concise collaboration method. In this way, the collaboration unit enables more appropriate collaboration by adjusting the collaboration method based on the user's emotions.

[0102] The market analysis department can estimate user emotions and adjust its market analysis methods based on those emotions. For example, if users are feeling stressed, the market analysis department can provide a simple and easy-to-understand market analysis report. If users are relaxed, the market analysis department can provide a detailed market analysis report. Furthermore, if users are in a hurry, the market analysis department can provide a concise report. This allows the market analysis department to conduct more appropriate market analysis by adjusting its methods based on user emotions.

[0103] The prototyping unit can estimate the user's emotions and adjust the prototyping method based on those emotions. For example, if the user is nervous, the prototyping unit can provide a simple and highly visual prototyping method. If the user is relaxed, the prototyping unit can also provide a detailed prototyping method. Furthermore, if the user is in a hurry, the prototyping unit can provide a concise prototyping method. In this way, the prototyping unit can create more appropriate prototypes by adjusting the prototyping method based on the user's emotions.

[0104] The Synergy Identification Department can analyze data within the group in real time to improve the accuracy of synergy identification. For example, the Synergy Identification Department can collect the latest project data from each company in real time and utilize it for synergy identification. Furthermore, the Synergy Identification Department can update the skill sets of employees in each company in real time and propose optimal collaborations. In addition, the Synergy Identification Department can analyze market trend data from each company in real time to improve the accuracy of synergy identification. In this way, the Synergy Identification Department improves the accuracy of synergy identification by analyzing data within the group in real time.

[0105] The idea generation unit can improve the accuracy of idea generation by referring to past idea data. For example, the idea generation unit can create a database of past successful ideas and refer to it during idea generation. Furthermore, the idea generation unit can analyze lessons learned from past idea data and incorporate them into the idea generation algorithm. In addition, the idea generation unit can analyze patterns in past idea data and generate similar ideas. In this way, the idea generation unit improves the accuracy of idea generation by referring to past idea data.

[0106] The Collaboration Department can add a feature that automatically matches experts from different industries during collaboration. For example, the Collaboration Department can build a database of experts from different industries and automatically match them during collaboration. Furthermore, the Collaboration Department can analyze the skill sets of experts from different industries to achieve optimal matching. In addition, the Collaboration Department can refer to the past project data of experts from different industries to achieve optimal matching. This enables optimal collaboration by automatically matching experts from different industries.

[0107] The market analysis department can conduct global market analysis by referencing market data from different regions. For example, it can collect market data from different regions and conduct global market analysis. Furthermore, the market analysis department can analyze market trends in different regions and propose global market strategies. In addition, the market analysis department can conduct global market analysis by referencing consumer behavior data from different regions. This enables the market analysis department to conduct global market analysis by referencing market data from different regions.

[0108] The prototyping unit can generate diverse prototypes by referencing data from different technical fields during the prototyping process. For example, the prototyping unit can create a database of successful cases in different technical fields and refer to it during prototyping. Furthermore, the prototyping unit can analyze the latest research in different technical fields and generate diverse prototypes. In addition, the prototyping unit can reference patent data from different technical fields to generate diverse prototypes. Thus, the prototyping unit can generate diverse prototypes by referencing data from different technical fields.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The synergy identification unit identifies synergies. For example, it analyzes data within the group to discover unexpected collaboration opportunities. The synergy identification unit can identify opportunities for a telecommunications company and a robotics company to jointly develop new types of communication devices. It can also use AI to identify synergies. For example, an AI model can be used as input for data within the group and output synergies. Step 2: The Idea Generation Unit generates new ideas based on the synergies identified by the Synergy Identification Unit. For example, it can combine knowledge from different industries to generate new product or service concepts. The Idea Generation Unit can also propose new fintech services utilizing e-commerce data. Furthermore, it can generate new ideas using AI. For example, an AI model can be used to take knowledge from different industries as input and output new ideas. Step 3: The Collaboration Unit implements the ideas generated by the Idea Generation Unit. For example, it provides an environment for teams from different companies to collaborate in a virtual space. The Collaboration Unit enables seamless sharing of ideas and resources in the virtual space. It can also perform collaboration using AI. For example, an AI model can be used to take teams from different companies as input and output a method for collaboration.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the synergy identification unit, idea generation unit, collaboration unit, market analysis unit, and prototyping unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the synergy identification unit is implemented by the control unit 46A of the smart device 14, which analyzes data within the group and discovers unexpected collaboration opportunities. The idea generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which combines knowledge from different industries to generate new product and service concepts. The collaboration unit is implemented by the control unit 46A of the smart device 14, which provides an environment for collaboration while seamlessly sharing ideas and resources in a virtual space. The market analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which evaluates the market suitability of proposed ideas. The prototyping unit is implemented by the control unit 46A of the smart device 14, which performs virtual prototyping and quickly evaluates the feasibility of ideas. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the synergy identification unit, idea generation unit, collaboration unit, market analysis unit, and prototyping unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the synergy identification unit is implemented by the control unit 46A of the smart glasses 214, which analyzes data within the group and discovers unexpected collaboration opportunities. The idea generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which combines knowledge from different industries to generate new product and service concepts. The collaboration unit is implemented by the control unit 46A of the smart glasses 214, which provides an environment for collaboration while seamlessly sharing ideas and resources in a virtual space. The market analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which evaluates the market suitability of proposed ideas. The prototyping unit is implemented by the control unit 46A of the smart glasses 214, which performs virtual prototyping and quickly evaluates the feasibility of ideas. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the synergy identification unit, idea generation unit, collaboration unit, market analysis unit, and prototyping unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the synergy identification unit is implemented by the control unit 46A of the headset terminal 314, which analyzes data within the group and discovers unexpected collaboration opportunities. The idea generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which combines knowledge from different industries to generate new product and service concepts. The collaboration unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides an environment for collaboration while seamlessly sharing ideas and resources in a virtual space. The market analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which evaluates the market suitability of proposed ideas. The prototyping unit is implemented by, for example, the control unit 46A of the headset terminal 314, which performs virtual prototyping and quickly evaluates the feasibility of ideas. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the synergy identification unit, idea generation unit, collaboration unit, market analysis unit, and prototyping unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the synergy identification unit is implemented by the control unit 46A of the robot 414, which analyzes data within the group and discovers unexpected collaboration opportunities. The idea generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which combines knowledge from different industries to generate new product and service concepts. The collaboration unit is implemented by, for example, the control unit 46A of the robot 414, which provides an environment for collaboration while seamlessly sharing ideas and resources in a virtual space. The market analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which evaluates the market suitability of proposed ideas. The prototyping unit is implemented by, for example, the control unit 46A of the robot 414, which performs virtual prototyping and quickly evaluates the feasibility of ideas. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The synergy identification unit identifies synergies, An idea generation unit that generates new ideas based on the synergies identified by the synergy identification unit, The system comprises a collaboration unit that executes the ideas generated by the idea generation unit. A system characterized by the following features. (Note 2) The company has a market analysis department that conducts market analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a prototyping section for virtual prototyping. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned synergy identification unit is, Analyze data within the group to discover unexpected collaboration opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned idea generation unit, Combining knowledge from different industries to generate new product and service concepts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collaboration department, It provides an environment for teams from different companies to collaborate in a virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned synergy identification unit is, It estimates user sentiment and adjusts synergy priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned synergy identification unit is, Analyze data within the group in real time to improve the accuracy of synergy identification. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned synergy identification unit is, When identifying synergies, referencing past success stories improves the accuracy of the identification process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned synergy identification unit is, It estimates the user's emotions and adjusts how synergy-specific elements are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned synergy identification unit is, When identifying synergies, refer to data from outside the group to identify broader synergies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned synergy identification unit is, When identifying synergies, referencing trend data from different industries improves the accuracy of the identification process. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned idea generation unit, It estimates the user's emotions and adjusts the type of ideas generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned idea generation unit, When generating ideas, refer to past idea data to improve the accuracy of the generation process. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned idea generation unit, When generating ideas, refer to successful case studies from different industries to generate new ideas. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned idea generation unit, It estimates the user's emotions and determines the priority of ideas to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned idea generation unit, When generating ideas, we refer to data from different cultural spheres to generate diverse ideas. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned idea generation unit, When generating ideas, we refer to data from different technological fields to generate new technical ideas. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collaboration department, It estimates user emotions and adjusts the collaboration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collaboration department, When collaborating, refer to past collaboration data to create the optimal team structure. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collaboration department, Add a feature that automatically matches experts from different industries during collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collaboration department, It estimates user emotions and prioritizes collaborations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collaboration department, We will add a feature that connects teams from different regions in a virtual space during collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collaboration department, To facilitate international collaboration, add translation capabilities for different languages ​​during collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned market analysis department, We estimate user sentiment and adjust market analysis methods based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned market analysis department, When conducting market analysis, referencing historical market data improves the accuracy of the analysis. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned market analysis department, We estimate user sentiment and prioritize market analysis based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned market analysis department, When conducting market analysis, we perform global market analysis by referring to market data from different regions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned prototyping unit is We estimate user emotions and adjust the prototyping method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned prototyping unit is During prototyping, refer to past prototype data to improve accuracy. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned prototyping unit is We estimate user emotions and determine prototyping priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned prototyping unit is During prototyping, we generate diverse prototypes by referencing data from different technological fields. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The synergy identification unit identifies synergies, An idea generation unit that generates new ideas based on the synergies identified by the synergy identification unit, The system comprises a collaboration unit that executes the ideas generated by the idea generation unit. A system characterized by the following features.

2. The company has a market analysis department that conducts market analysis. The system according to feature 1.

3. It includes a prototyping section for virtual prototyping. The system according to feature 1.

4. The aforementioned synergy identification unit is, Analyze data within the group to discover unexpected collaboration opportunities. The system according to feature 1.

5. The aforementioned idea generation unit, Combining knowledge from different industries to generate new product and service concepts. The system according to feature 1.

6. The aforementioned collaboration department, It provides an environment for teams from different companies to collaborate in a virtual space. The system according to feature 1.

7. The aforementioned synergy identification unit is, It estimates user sentiment and adjusts synergy priorities based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned synergy identification unit is, Analyze data within the group in real time to improve the accuracy of synergy identification. The system according to feature 1.

9. The aforementioned synergy identification unit is, When identifying synergies, referencing past success stories improves the accuracy of the identification process. The system according to feature 1.

10. The aforementioned synergy identification unit is, It estimates the user's emotions and adjusts how synergy-specific elements are displayed based on those estimated emotions. The system according to feature 1.

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