system

Abstract

The system according to the embodiment aims to combine information from different fields to generate new insights and ideas. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a synthesis unit, a proposal unit, and a sharing unit. The reception unit inputs a user's fields of interest and information sources. The analysis unit analyzes the information input by the reception unit. The synthesis unit performs synthesis based on the information analyzed by the analysis unit. The proposal unit proposes new insights and ideas obtained by the synthesis unit. The sharing unit shares the insights and ideas proposed by the proposal unit with other users.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of making it difficult to effectively combine information from different fields to generate new insights and ideas.

[0005] The system according to the embodiment aims to combine information from different fields to generate new insights and ideas. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a synthesis unit, a proposal unit, and a sharing unit. The reception unit inputs fields of interest and information sources of a user. The analysis unit analyzes the information input by the reception unit. The synthesis unit performs synthesis based on the information analyzed by the analysis unit. The proposal unit proposes new insights and ideas obtained by the synthesis unit. The sharing unit shares the insights and ideas proposed by the proposal unit with other users. [Effects of the Invention]

[0007] The system according to the embodiment can combine information from different fields to generate new insights and ideas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The knowledge synthesis platform of an embodiment of the present invention utilizes an LLM to provide tools for citing and synthesizing knowledge from multiple disparate fields and information sources to generate new insights and ideas. This system allows users to input their fields of interest and information sources, and the LLM analyzes the input information and cites related knowledge to perform synthesis. For example, the system combines information on cooking and medicine to analyze the effects of specific ingredients on mental health and propose dietary suggestions for improving mental health. In this way, users can combine knowledge from different fields to gain new insights and ideas. Based on the information entered by the user, the LLM automatically searches for and synthesizes related knowledge. For example, if a user enters the keywords "cooking" and "medical care," the LLM searches for information on cooking and medicine and combines them to provide new insights. This allows users to efficiently utilize knowledge from different fields. Furthermore, the platform also provides a function for sharing the insights and ideas gained by users. For example, if a user obtains a suggestion for improving mental health through diet, the user can share that information with other users. This allows users to share knowledge with each other and generate further new insights and ideas. By crossing over information from different fields, this platform helps discover new perspectives and innovative ideas. For example, by combining cooking and medical information, it can propose dietary suggestions for improving mental health. In this way, users can gain a deeper understanding of existing knowledge and gain new insights. This allows the knowledge synthesis platform to efficiently utilize knowledge from different fields and gain new insights and ideas.

[0029] A knowledge synthesis platform according to an embodiment includes a receiving unit, an analysis unit, a synthesis unit, a proposal unit, and a sharing unit. The receiving unit inputs fields of interest and information sources of information that a user has. Examples of fields of interest and information sources of information that a user has include, but are not limited to, science and technology, business, and entertainment. The receiving unit, for example, receives keywords input by the user and transmits them to the analysis unit. The analysis unit analyzes the information input by the receiving unit. The analysis may be performed using, for example, text analysis, data mining, statistical analysis, or other methods, but is not limited to these examples. The analysis unit, for example, searches a database for information related to the input keywords and transmits the information to the synthesis unit. The synthesis unit performs synthesis based on the information analyzed by the analysis unit. The synthesis may be performed based on, for example, an information integration method or a type of algorithm, but is not limited to these examples. The synthesis unit, for example, combines information from different fields to generate new insights and ideas. The proposal unit proposes the new insights and ideas obtained by the synthesis unit. The suggestions include, but are not limited to, suggestions for innovative technologies or business models. The suggestion unit, for example, presents the generated insights and ideas to the user. The sharing unit shares the insights and ideas suggested by the suggestion unit with other users. Sharing can be performed, for example, via email, social media, cloud storage, or other methods, but is not limited to these examples. The sharing unit, for example, provides an interface for users to share their obtained insights and ideas with other users. This allows the knowledge synthesis platform according to the embodiment to enable users to efficiently utilize knowledge in different fields and obtain new insights and ideas. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit may input keywords entered by the user into AI and cause the AI ​​to perform a process of searching for related information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input information related to the input keywords into AI and cause the AI ​​to analyze the information.Some or all of the above-described processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit may input analyzed information to AI and cause the AI ​​to synthesize the information. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit may input generated insights and ideas to AI and cause the AI ​​to generate proposals. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit may input insights and ideas obtained by a user to AI and cause the AI ​​to execute a sharing method.

[0030] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display fields and information sources that the user has frequently input in the past as candidates. The reception unit can also, for example, prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also, for example, predict and suggest fields and information sources that will be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0031] The reception unit can acquire the user's current interests and trends in real time and automatically present appropriate input candidates. The reception unit can, for example, suggest related fields and information sources based on topics in which the user is currently interested. The reception unit can also, for example, present fields and information sources that the user is likely to be interested in based on real-time trend information. The reception unit can also, for example, analyze the user's social media activity and suggest fields and information sources related to the user's current interests. This makes it possible to present optimal input candidates based on the user's current interests and trends. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input real-time trend data to AI and have the AI ​​present appropriate input candidates.

[0032] The reception unit can prioritize presenting highly relevant fields and information sources in consideration of the user's geographical location information. For example, the reception unit can prioritize displaying information sources related to a region based on the user's current location. The reception unit can also, for example, analyze the user's past location information and suggest highly relevant fields and information sources. For example, if the user is in a specific region, the reception unit can present the latest information related to that region. This makes it possible to present optimal fields and information sources based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data into AI and cause the AI ​​to present highly relevant information sources.

[0033] The reception unit can analyze the user's social media activity and automatically suggest related fields and information sources. The reception unit can suggest related fields and information sources based on, for example, topics frequently mentioned by the user on social media. The reception unit can also analyze, for example, topics of interest to the user's social media followers and present related fields and information sources. The reception unit can also analyze, for example, the content of the user's social media posts and suggest fields and information sources related to current interests. This makes it possible to suggest optimal fields and information sources based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI and have the AI ​​suggest related fields and information sources.

[0034] The analysis unit can evaluate the reliability of the input information and prioritize analyzing highly reliable information. The analysis unit can, for example, evaluate the source of the input information and prioritize analyzing highly reliable information. The analysis unit can also, for example, cross-check the content of the input information and select highly reliable information. The analysis unit can also, for example, analyze the past usage history of the input information and prioritize analyzing highly reliable information. This prioritizes analyzing highly reliable information, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can have AI perform a reliability evaluation of the input information and select highly reliable information.

[0035] The analysis unit can combine different analysis methods to obtain more accurate analysis results. The analysis unit can combine, for example, text analysis and image analysis to obtain more accurate analysis results. The analysis unit can also combine, for example, machine learning and rule-based analysis methods to obtain more accurate analysis results. The analysis unit can also combine, for example, different data sources to obtain more accurate analysis results. In this way, the accuracy of the analysis results is improved by combining different analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can obtain more accurate analysis results by having AI perform a combination of text analysis and image analysis.

[0036] The analysis unit can apply different analysis algorithms depending on the category of the input information. For example, the analysis unit can apply a natural language processing algorithm to text information. The analysis unit can also apply an image recognition algorithm to image information. The analysis unit can also apply a voice recognition algorithm to voice information. This allows the optimal analysis algorithm to be applied depending on the category of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input text information to AI and have the AI ​​apply a natural language processing algorithm.

[0037] The analysis unit can determine the priority of analysis based on the submission time of the input information. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can also postpone information that was submitted earlier, for example. The analysis unit can also dynamically adjust the priority of analysis based on the submission time, for example. This makes it possible to determine the optimal priority of analysis based on the submission time of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data into AI and have the AI ​​determine the priority of analysis.

[0038] The synthesis unit can improve the accuracy of the synthesis by taking into account interrelationships when combining information from different fields. For example, when combining cooking and medical information, the synthesis unit can consider the nutritional value and health benefits of ingredients. Furthermore, for example, when combining technology and education information, the synthesis unit can also consider the effects of educational technology. Furthermore, for example, when combining environmental and economic information, the synthesis unit can also consider elements of sustainable economic growth. In this way, by taking interrelationships into account when combining information from different fields, the accuracy of the synthesis is improved. Some or all of the above-mentioned processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input information from different fields into AI and have the AI ​​perform synthesis that takes interrelationships into account.

[0039] During the synthesis process, the synthesis unit can obtain new insights by referring to the user's past knowledge and experience. The synthesis unit can obtain new insights, for example, based on knowledge the user learned in the past. The synthesis unit can also improve the accuracy of the synthesis by referring to the user's past experience, for example. The synthesis unit can also generate new ideas, for example, based on insights the user gained in the past. In this way, new insights can be obtained by referring to the user's past knowledge and experience. Some or all of the above-described processing in the synthesis unit may be performed using, or without, AI, for example. For example, the synthesis unit can input the user's past knowledge and experience data into AI and cause the AI ​​to generate new insights.

[0040] The synthesis unit can perform synthesis taking geographical distribution into account when combining information from different fields. For example, when combining cooking and medical information, the synthesis unit can consider the availability of ingredients in each region. Furthermore, for example, when combining environmental and economic information, the synthesis unit can also consider the economic situation in each region. Furthermore, for example, when combining technology and education information, the synthesis unit can also consider the educational environment in each region. In this way, by taking geographical distribution into account when combining information from different fields, the accuracy of the synthesis is improved. Some or all of the above-mentioned processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input information from different fields into AI and have the AI ​​perform synthesis taking geographical distribution into account.

[0041] The synthesis unit can improve the accuracy of the synthesis by referring to related literature. For example, when combining cooking and medical information, the synthesis unit can refer to related academic papers. Furthermore, when combining environmental and economic information, the synthesis unit can also refer to related research reports. Furthermore, when combining technology and education information, the synthesis unit can also refer to related books. In this way, by referring to related literature, the accuracy of the synthesis is improved. Some or all of the above-mentioned processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input related literature data into AI and have the AI ​​improve the accuracy of the synthesis.

[0042] The suggestion unit can adjust the level of detail of the proposal based on the importance of the insight or idea when making a proposal. For example, the suggestion unit provides a detailed explanation for an insight or idea with a high importance. The suggestion unit can also provide a concise explanation for an insight or idea with a low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal based on the importance, for example. This makes it possible to provide an optimal level of detail of the proposal based on the importance of the insight or idea. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input importance data of the insight or idea into AI and cause the AI ​​to adjust the level of detail of the proposal.

[0043] When making a suggestion, the suggestion unit can select an appropriate suggestion method by referring to the user's past responses. For example, the suggestion unit can preferentially use suggestion methods to which the user has previously responded favorably. The suggestion unit can also avoid suggestion methods to which the user has previously responded negatively. The suggestion unit can also dynamically select an optimal suggestion method by analyzing the user's past responses. This makes it possible to provide an optimal suggestion method based on the user's past responses. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past response data into AI and have the AI ​​select an appropriate suggestion method.

[0044] When making a suggestion, the suggestion unit can make an appropriate suggestion by taking into account the user's geographical location information. For example, the suggestion unit can preferentially display local suggestion based on the user's current location. The suggestion unit can also analyze the user's past location information and make highly relevant suggestions. For example, if the user is in a specific area, the suggestion unit can present the latest suggestions related to that area. This makes it possible to provide optimal suggestions based on the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location data into AI and cause the AI ​​to generate appropriate suggestions.

[0045] When making a suggestion, the suggestion unit can analyze the user's social media activity and make relevant suggestions. The suggestion unit can make relevant suggestions based on, for example, topics frequently mentioned by the user on social media. The suggestion unit can also analyze, for example, topics of interest to the user's social media followers and make relevant suggestions. The suggestion unit can also analyze, for example, the content of the user's social media posts and make suggestions related to current interests. This makes it possible to provide optimal suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input the user's social media data into AI and cause the AI ​​to generate relevant suggestions.

[0046] When sharing, the sharing unit can select an appropriate sharing method by referring to the user's past sharing history. For example, the sharing unit can preferentially use sharing methods to which the user has previously responded favorably. The sharing unit can also avoid sharing methods to which the user has previously responded negatively. The sharing unit can also dynamically select an optimal sharing method by analyzing the user's past sharing history. This makes it possible to provide an optimal sharing method based on the user's past sharing history. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input the user's past sharing history data into AI and have the AI ​​select an appropriate sharing method.

[0047] The sharing unit can adjust the level of detail of the sharing based on the importance of the information to be shared when sharing. For example, the sharing unit provides a detailed explanation for information with a high level of importance. The sharing unit can also provide a concise explanation for information with a low level of importance. The sharing unit can also dynamically adjust the level of detail of the sharing based on the importance, for example. This makes it possible to provide an optimal level of detail of the sharing based on the importance of the information to be shared. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input importance data of the information to be shared into AI and have the AI ​​adjust the level of detail of the sharing.

[0048] When sharing, the sharing unit can select an appropriate sharing method taking into account the user's geographical location information. For example, the sharing unit prioritizes sharing information related to a region based on the user's current location. The sharing unit can also analyze the user's past location information and share highly relevant information. For example, if the user is in a specific region, the sharing unit can share the latest information related to that region. This makes it possible to provide an optimal sharing method based on the user's geographical location information. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's geographical location data into AI and have the AI ​​select an appropriate sharing method.

[0049] At the time of sharing, the sharing unit can analyze the user's social media activity and share relevant information. The sharing unit can share relevant information based on, for example, topics frequently mentioned by the user on social media. The sharing unit can also analyze, for example, topics of interest to the user's social media followers and share relevant information. The sharing unit can also analyze, for example, the content of the user's social media posts and share information related to their current interests. This makes it possible to share optimal information based on the user's social media activity. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input the user's social media data into AI and have the AI ​​share the relevant information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can suggest new related fields and information sources based on the user's past search history. For example, if the user has frequently searched for information on "cooking" and "health" in the past, the reception unit can suggest information sources related to "nutrition" and "fitness." If the user has searched for information on "technology" and "education" in the past, the reception unit can suggest information sources related to "edtech" and "online learning." Furthermore, if the user has searched for information on "environment" and "economy" in the past, the reception unit can suggest information sources related to "sustainable development" and "green economy." This makes it possible to efficiently suggest new fields and information sources based on the user's past search history.

[0052] The analysis unit can dynamically adjust the parameters of the analysis algorithm based on the user's past analysis results. For example, if the user has previously preferred detailed analysis results, the analysis unit can adjust the parameters of the algorithm to perform a more detailed analysis. Also, if the user has previously requested quick analysis results, the analysis unit can adjust the parameters of the algorithm to perform a quick analysis. Furthermore, if the user has previously preferred analysis results that are easy to understand visually, the analysis unit can adjust the parameters of the algorithm to provide analysis results that are easy to understand visually. In this way, it is possible to provide optimal parameters of the analysis algorithm based on the user's past analysis results.

[0053] The analysis unit can obtain the user's current interests and trends in real time and dynamically adjust the parameters of the analysis algorithm. For example, if the user is currently interested in "health," the analysis unit can prioritize analyzing health-related information. Also, if the user is currently interested in "technology," the analysis unit can prioritize analyzing technology-related information. Furthermore, if the user is currently interested in "environment," the analysis unit can prioritize analyzing environment-related information. This makes it possible to provide optimal analysis algorithm parameters based on the user's current interests and trends.

[0054] The synthesis unit can take time factors into account when combining information from different fields. For example, when combining cooking and medical information, it can take into account the seasonality and shelf life of ingredients. The synthesis unit can also take technological advances and changes in educational curricula into account when combining technology and education information. Furthermore, the synthesis unit can take into account economic cycles and fluctuations in environmental policy when combining environmental and economic information. This allows for the accuracy of synthesis to be improved by taking time factors into account when combining information from different fields.

[0055] The synthesis unit can take cultural factors into account when combining information from different fields. For example, when combining cooking and medical information, it can take into account regional food culture and medical practices. The synthesis unit can also take into account educational systems and technology acceptance when combining technology and education information. Furthermore, the synthesis unit can take into account regional environmental awareness and economic policies when combining environmental and economic information. In this way, taking cultural factors into account when combining information from different fields improves the accuracy of synthesis.

[0056] The suggestion unit can dynamically adjust the content of suggestions based on the user's past responses. For example, it can provide preferentially suggestions to which the user has responded favorably in the past. It can also avoid suggestions to which the user has responded negatively in the past. Furthermore, it can analyze the user's past responses and dynamically select optimal suggestions. This makes it possible to provide optimal suggestions based on the user's past responses.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit inputs the user's fields of interest and information sources. The reception unit receives the keywords entered by the user and sends them to the analysis unit. These fields include, for example, science and technology, business, and entertainment. Step 2: The analysis unit analyzes the information input by the reception unit. The analysis is performed using methods such as text analysis, data mining, and statistical analysis. The analysis unit searches the database for information related to the input keywords and sends that information to the synthesis unit. Step 3: The synthesis unit performs synthesis based on the information analyzed by the analysis unit. Synthesis is performed based on the method of information integration and the type of algorithm. For example, information from different fields can be combined to generate new insights and ideas. Step 4: The proposal section proposes new insights and ideas obtained by the synthesis section. The proposals may include proposals for innovative technologies or business models. The proposal section presents the generated insights and ideas to the user. Step 5: The sharing unit shares the insights and ideas suggested by the suggestion unit with other users. Sharing can be done via email, social media, cloud storage, etc. The sharing unit provides an interface for users to share their insights and ideas with other users.

[0059] (Example 2) The knowledge synthesis platform of an embodiment of the present invention utilizes an LLM to provide tools for citing and synthesizing knowledge from multiple disparate fields and information sources to generate new insights and ideas. This system allows users to input their fields of interest and information sources, and the LLM analyzes the input information and cites related knowledge to perform synthesis. For example, the system combines information on cooking and medicine to analyze the effects of specific ingredients on mental health and propose dietary suggestions for improving mental health. In this way, users can combine knowledge from different fields to gain new insights and ideas. Based on the information entered by the user, the LLM automatically searches for and synthesizes related knowledge. For example, if a user enters the keywords "cooking" and "medical care," the LLM searches for information on cooking and medicine and combines them to provide new insights. This allows users to efficiently utilize knowledge from different fields. Furthermore, the platform also provides a function for sharing the insights and ideas gained by users. For example, if a user obtains a suggestion for improving mental health through diet, the user can share that information with other users. This allows users to share knowledge with each other and generate further new insights and ideas. By crossing over information from different fields, this platform helps discover new perspectives and innovative ideas. For example, by combining cooking and medical information, it can propose dietary suggestions for improving mental health. In this way, users can gain a deeper understanding of existing knowledge and gain new insights. This allows the knowledge synthesis platform to efficiently utilize knowledge from different fields and gain new insights and ideas.

[0060] A knowledge synthesis platform according to an embodiment includes a receiving unit, an analysis unit, a synthesis unit, a proposal unit, and a sharing unit. The receiving unit inputs fields of interest and information sources of information that a user has. Examples of fields of interest and information sources of information that a user has include, but are not limited to, science and technology, business, and entertainment. The receiving unit, for example, receives keywords input by the user and transmits them to the analysis unit. The analysis unit analyzes the information input by the receiving unit. The analysis may be performed using, for example, text analysis, data mining, statistical analysis, or other methods, but is not limited to these examples. The analysis unit, for example, searches a database for information related to the input keywords and transmits the information to the synthesis unit. The synthesis unit performs synthesis based on the information analyzed by the analysis unit. The synthesis may be performed based on, for example, an information integration method or a type of algorithm, but is not limited to these examples. The synthesis unit, for example, combines information from different fields to generate new insights and ideas. The proposal unit proposes the new insights and ideas obtained by the synthesis unit. The suggestions include, but are not limited to, suggestions for innovative technologies or business models. The suggestion unit, for example, presents the generated insights and ideas to the user. The sharing unit shares the insights and ideas suggested by the suggestion unit with other users. Sharing can be performed, for example, via email, social media, cloud storage, or other methods, but is not limited to these examples. The sharing unit, for example, provides an interface for users to share their obtained insights and ideas with other users. This allows the knowledge synthesis platform according to the embodiment to enable users to efficiently utilize knowledge in different fields and obtain new insights and ideas. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit may input keywords entered by the user into AI and cause the AI ​​to perform a process of searching for related information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input information related to the input keywords into AI and cause the AI ​​to analyze the information.Some or all of the above-described processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit may input analyzed information to AI and cause the AI ​​to synthesize the information. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit may input generated insights and ideas to AI and cause the AI ​​to generate proposals. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit may input insights and ideas obtained by a user to AI and cause the AI ​​to execute a sharing method.

[0061] The reception unit estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple and intuitive interface and minimizes input steps. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input areas of interest or information sources. This allows for an optimal input interface to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using an AI, or without an AI. For example, the reception unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0062] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display fields and information sources that the user has frequently input in the past as candidates. The reception unit can also, for example, prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also, for example, predict and suggest fields and information sources that will be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into AI and have the AI ​​suggest the optimal input method.

[0063] The reception unit can acquire the user's current interests and trends in real time and automatically present appropriate input candidates. The reception unit can, for example, suggest related fields and information sources based on topics in which the user is currently interested. The reception unit can also, for example, present fields and information sources that the user is likely to be interested in based on real-time trend information. The reception unit can also, for example, analyze the user's social media activity and suggest fields and information sources related to the user's current interests. This makes it possible to present optimal input candidates based on the user's current interests and trends. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input real-time trend data to AI and have the AI ​​present appropriate input candidates.

[0064] The reception unit can estimate the user's emotions and determine the priority of inputs based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize displaying the most important input items and postpone other items. Furthermore, for example, if the user is relaxed, the reception unit can display all input items evenly, allowing the user to freely select. Furthermore, for example, if the user is in a hurry, the reception unit can prioritize displaying items that can be entered most quickly. This allows optimal input priorities to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0065] The reception unit can prioritize presenting highly relevant fields and information sources in consideration of the user's geographical location information. For example, the reception unit can prioritize displaying information sources related to a region based on the user's current location. The reception unit can also, for example, analyze the user's past location information and suggest highly relevant fields and information sources. For example, if the user is in a specific region, the reception unit can present the latest information related to that region. This makes it possible to present optimal fields and information sources based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data into AI and cause the AI ​​to present highly relevant information sources.

[0066] The reception unit can analyze the user's social media activity and automatically suggest related fields and information sources. The reception unit can suggest related fields and information sources based on, for example, topics frequently mentioned by the user on social media. The reception unit can also analyze, for example, topics of interest to the user's social media followers and present related fields and information sources. The reception unit can also analyze, for example, the content of the user's social media posts and suggest fields and information sources related to current interests. This makes it possible to suggest optimal fields and information sources based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI and have the AI ​​suggest related fields and information sources.

[0067] The analysis unit can estimate the user's emotions and dynamically adjust the parameters of the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can adjust the parameters of the algorithm to perform a detailed analysis. For example, if the user is in a hurry, the analysis unit can also adjust the parameters of the algorithm to perform a quick analysis. For example, if the user is excited, the analysis unit can also adjust the parameters of the algorithm to provide a visually stimulating analysis result. This allows the optimal parameters of the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the parameters of the analysis algorithm.

[0068] The analysis unit can evaluate the reliability of the input information and prioritize analyzing highly reliable information. The analysis unit can, for example, evaluate the source of the input information and prioritize analyzing highly reliable information. The analysis unit can also, for example, cross-check the content of the input information and select highly reliable information. The analysis unit can also, for example, analyze the past usage history of the input information and prioritize analyzing highly reliable information. This prioritizes analyzing highly reliable information, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can have AI perform a reliability evaluation of the input information and select highly reliable information.

[0069] The analysis unit can combine different analysis methods to obtain more accurate analysis results. The analysis unit can combine, for example, text analysis and image analysis to obtain more accurate analysis results. The analysis unit can also combine, for example, machine learning and rule-based analysis methods to obtain more accurate analysis results. The analysis unit can also combine, for example, different data sources to obtain more accurate analysis results. In this way, the accuracy of the analysis results is improved by combining different analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can obtain more accurate analysis results by having AI perform a combination of text analysis and image analysis.

[0070] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, for example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, for example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the optimal display method of the analysis results to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the display method of the analysis results.

[0071] The analysis unit can apply different analysis algorithms depending on the category of the input information. For example, the analysis unit can apply a natural language processing algorithm to text information. The analysis unit can also apply an image recognition algorithm to image information. The analysis unit can also apply a voice recognition algorithm to voice information. This allows the optimal analysis algorithm to be applied depending on the category of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input text information to AI and have the AI ​​apply a natural language processing algorithm.

[0072] The analysis unit can determine the priority of analysis based on the submission time of the input information. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can also postpone information that was submitted earlier, for example. The analysis unit can also dynamically adjust the priority of analysis based on the submission time, for example. This makes it possible to determine the optimal priority of analysis based on the submission time of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data into AI and have the AI ​​determine the priority of analysis.

[0073] The synthesis unit can estimate the user's emotion and dynamically change the synthesis method based on the estimated user's emotion. For example, if the user is relaxed, the synthesis unit can adjust the method to perform detailed synthesis. Furthermore, for example, if the user is in a hurry, the synthesis unit can adjust the method to perform quick synthesis. Furthermore, for example, if the user is excited, the synthesis unit can adjust the method to provide a visually stimulating synthesis result. This allows the optimal synthesis method to be provided depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the synthesis unit can be performed using, for example, an AI. For example, the synthesis unit can input the user's emotion data into an AI and have the AI ​​adjust the synthesis method.

[0074] The synthesis unit can improve the accuracy of the synthesis by taking into account interrelationships when combining information from different fields. For example, when combining cooking and medical information, the synthesis unit can consider the nutritional value and health benefits of ingredients. Furthermore, for example, when combining technology and education information, the synthesis unit can also consider the effects of educational technology. Furthermore, for example, when combining environmental and economic information, the synthesis unit can also consider elements of sustainable economic growth. In this way, by taking interrelationships into account when combining information from different fields, the accuracy of the synthesis is improved. Some or all of the above-mentioned processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input information from different fields into AI and have the AI ​​perform synthesis that takes interrelationships into account.

[0075] During the synthesis process, the synthesis unit can obtain new insights by referring to the user's past knowledge and experience. The synthesis unit can obtain new insights, for example, based on knowledge the user learned in the past. The synthesis unit can also improve the accuracy of the synthesis by referring to the user's past experience, for example. The synthesis unit can also generate new ideas, for example, based on insights the user gained in the past. In this way, new insights can be obtained by referring to the user's past knowledge and experience. Some or all of the above-described processing in the synthesis unit may be performed using, or without, AI, for example. For example, the synthesis unit can input the user's past knowledge and experience data into AI and cause the AI ​​to generate new insights.

[0076] The synthesis unit can estimate the user's emotion and adjust the order in which the synthesis results are displayed based on the estimated user's emotion. For example, if the user is nervous, the synthesis unit can display the most important result first. Furthermore, for example, if the user is relaxed, the synthesis unit can display all results evenly. Furthermore, for example, if the user is in a hurry, the synthesis unit can display results that can be quickly understood first. This allows the synthesis results to be displayed in an optimal order depending on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the synthesis unit can be performed using, for example, an AI, or without an AI. For example, the synthesis unit can input the user's emotion data into an AI and have the AI ​​adjust the display order of the synthesis results.

[0077] The synthesis unit can perform synthesis taking geographical distribution into account when combining information from different fields. For example, when combining cooking and medical information, the synthesis unit can consider the availability of ingredients in each region. Furthermore, for example, when combining environmental and economic information, the synthesis unit can also consider the economic situation in each region. Furthermore, for example, when combining technology and education information, the synthesis unit can also consider the educational environment in each region. In this way, by taking geographical distribution into account when combining information from different fields, the accuracy of the synthesis is improved. Some or all of the above-mentioned processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input information from different fields into AI and have the AI ​​perform synthesis taking geographical distribution into account.

[0078] The synthesis unit can improve the accuracy of the synthesis by referring to related literature. For example, when combining cooking and medical information, the synthesis unit can refer to related academic papers. Furthermore, when combining environmental and economic information, the synthesis unit can also refer to related research reports. Furthermore, when combining technology and education information, the synthesis unit can also refer to related books. In this way, by referring to related literature, the accuracy of the synthesis is improved. Some or all of the above-mentioned processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input related literature data into AI and have the AI ​​improve the accuracy of the synthesis.

[0079] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide a simple and intuitive way of expression. For example, if the user is relaxed, the suggestion unit can provide a way of expression that includes detailed information. For example, if the user is in a hurry, the suggestion unit can provide a way of expression that focuses on the main points. This allows the optimal way of expression to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into an AI and have the AI ​​adjust the way the suggestion is expressed.

[0080] The suggestion unit can adjust the level of detail of the proposal based on the importance of the insight or idea when making a proposal. For example, the suggestion unit provides a detailed explanation for an insight or idea with a high importance. The suggestion unit can also provide a concise explanation for an insight or idea with a low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal based on the importance, for example. This makes it possible to provide an optimal level of detail of the proposal based on the importance of the insight or idea. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input importance data of the insight or idea into AI and cause the AI ​​to adjust the level of detail of the proposal.

[0081] When making a suggestion, the suggestion unit can select an appropriate suggestion method by referring to the user's past responses. For example, the suggestion unit can preferentially use suggestion methods to which the user has previously responded favorably. The suggestion unit can also avoid suggestion methods to which the user has previously responded negatively. The suggestion unit can also dynamically select an optimal suggestion method by analyzing the user's past responses. This makes it possible to provide an optimal suggestion method based on the user's past responses. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past response data into AI and have the AI ​​select an appropriate suggestion method.

[0082] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can prioritize displaying the most important suggestions. Furthermore, for example, if the user is relaxed, the suggestion unit can equally display all suggestions. Furthermore, for example, if the user is in a hurry, the suggestion unit can prioritize displaying suggestions that can be quickly understood. This allows optimal suggestion prioritization to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into an AI and have the AI ​​determine the priority of suggestions.

[0083] When making a suggestion, the suggestion unit can make an appropriate suggestion by taking into account the user's geographical location information. For example, the suggestion unit can preferentially display local suggestion based on the user's current location. The suggestion unit can also analyze the user's past location information and make highly relevant suggestions. For example, if the user is in a specific area, the suggestion unit can present the latest suggestions related to that area. This makes it possible to provide optimal suggestions based on the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location data into AI and cause the AI ​​to generate appropriate suggestions.

[0084] When making a suggestion, the suggestion unit can analyze the user's social media activity and make relevant suggestions. The suggestion unit can make relevant suggestions based on, for example, topics frequently mentioned by the user on social media. The suggestion unit can also analyze, for example, topics of interest to the user's social media followers and make relevant suggestions. The suggestion unit can also analyze, for example, the content of the user's social media posts and make suggestions related to current interests. This makes it possible to provide optimal suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input the user's social media data into AI and cause the AI ​​to generate relevant suggestions.

[0085] The sharing unit can estimate the user's emotions and adjust the sharing method based on the estimated user's emotions. For example, if the user is nervous, the sharing unit can provide a simple and intuitive sharing method. For example, if the user is relaxed, the sharing unit can provide a sharing method that includes detailed information. For example, if the user is in a hurry, the sharing unit can provide a quick sharing method. This makes it possible to provide the optimal sharing method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sharing unit can be performed using an AI, for example, or without an AI. For example, the sharing unit can input the user's emotion data into an AI and have the AI ​​adjust the sharing method.

[0086] When sharing, the sharing unit can select an appropriate sharing method by referring to the user's past sharing history. For example, the sharing unit can preferentially use sharing methods to which the user has previously responded favorably. The sharing unit can also avoid sharing methods to which the user has previously responded negatively. The sharing unit can also dynamically select an optimal sharing method by analyzing the user's past sharing history. This makes it possible to provide an optimal sharing method based on the user's past sharing history. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input the user's past sharing history data into AI and have the AI ​​select an appropriate sharing method.

[0087] The sharing unit can adjust the level of detail of the sharing based on the importance of the information to be shared when sharing. For example, the sharing unit provides a detailed explanation for information with a high level of importance. The sharing unit can also provide a concise explanation for information with a low level of importance. The sharing unit can also dynamically adjust the level of detail of the sharing based on the importance, for example. This makes it possible to provide an optimal level of detail of the sharing based on the importance of the information to be shared. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input importance data of the information to be shared into AI and have the AI ​​adjust the level of detail of the sharing.

[0088] The sharing unit can estimate the user's emotions and determine sharing priorities based on the estimated user emotions. For example, if the user is nervous, the sharing unit prioritizes sharing of the most important information. Furthermore, for example, if the user is relaxed, the sharing unit can equally share all information. Furthermore, for example, if the user is in a hurry, the sharing unit can prioritize sharing of information that can be quickly understood. This allows optimal sharing priorities to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit can input the user's emotion data into an AI and have the AI ​​determine the sharing priorities.

[0089] When sharing, the sharing unit can select an appropriate sharing method taking into account the user's geographical location information. For example, the sharing unit prioritizes sharing information related to a region based on the user's current location. The sharing unit can also analyze the user's past location information and share highly relevant information. For example, if the user is in a specific region, the sharing unit can share the latest information related to that region. This makes it possible to provide an optimal sharing method based on the user's geographical location information. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's geographical location data into AI and have the AI ​​select an appropriate sharing method.

[0090] At the time of sharing, the sharing unit can analyze the user's social media activity and share relevant information. The sharing unit can share relevant information based on, for example, topics frequently mentioned by the user on social media. The sharing unit can also analyze, for example, topics of interest to the user's social media followers and share relevant information. The sharing unit can also analyze, for example, the content of the user's social media posts and share information related to their current interests. This makes it possible to share optimal information based on the user's social media activity. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input the user's social media data into AI and have the AI ​​share the relevant information. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, synthesis unit, proposal unit, and sharing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives keywords entered by the user and transmits them to the analysis unit. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and searches the database 24 for information related to the entered keywords and transmits the information to the synthesis unit. The synthesis unit is realized by the specific processing unit 290 of the data processing device 12 and performs synthesis based on the analyzed information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and presents the generated insights and ideas to the user. The sharing unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to share their obtained insights and ideas with other users. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, synthesis unit, suggestion unit, and sharing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives keywords entered by the user and transmits them to the analysis unit. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and searches the database 24 for information related to the entered keywords and transmits the information to the synthesis unit. The synthesis unit is realized by the specific processing unit 290 of the data processing device 12 and performs synthesis based on the analyzed information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and presents the generated insights and ideas to the user. The sharing unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to share the insights and ideas they have obtained with other users. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, synthesis unit, proposal unit, and sharing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives keywords entered by the user and transmits them to the analysis unit. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and searches the database 24 for information related to the entered keywords and transmits the information to the synthesis unit. The synthesis unit is realized by the specific processing unit 290 of the data processing device 12 and performs synthesis based on the analyzed information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and presents the generated insights and ideas to the user. The sharing unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for the user to share the insights and ideas they have obtained with other users. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, synthesis unit, proposal unit, and sharing unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives keywords entered by the user and transmits them to the analysis unit. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and searches the database 24 for information related to the entered keywords and transmits the information to the synthesis unit. The synthesis unit is realized by the specific processing unit 290 of the data processing device 12 and performs synthesis based on the analyzed information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and presents the generated insights and ideas to the user. The sharing unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to share the insights and ideas they have obtained with other users.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can suggest new related fields and information sources based on the user's past search history. For example, if the user has frequently searched for information on "cooking" and "health" in the past, the reception unit can suggest information sources related to "nutrition" and "fitness." If the user has searched for information on "technology" and "education" in the past, the reception unit can suggest information sources related to "edtech" and "online learning." Furthermore, if the user has searched for information on "environment" and "economy" in the past, the reception unit can suggest information sources related to "sustainable development" and "green economy." This makes it possible to efficiently suggest new fields and information sources based on the user's past search history.

[0093] The reception unit can estimate the user's emotions and adjust the timing of input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of input and wait until the user is relaxed. Also, if the user is relaxed, the reception unit can advance the timing of input and encourage the user to input while they are concentrating. Furthermore, if the user is in a hurry, the reception unit can optimize the timing of input and complete the input quickly. This makes it possible to provide the optimal timing of input according to the user's emotions.

[0094] The analysis unit can dynamically adjust the parameters of the analysis algorithm based on the user's past analysis results. For example, if the user has previously preferred detailed analysis results, the analysis unit can adjust the parameters of the algorithm to perform a more detailed analysis. Also, if the user has previously requested quick analysis results, the analysis unit can adjust the parameters of the algorithm to perform a quick analysis. Furthermore, if the user has previously preferred analysis results that are easy to understand visually, the analysis unit can adjust the parameters of the algorithm to provide analysis results that are easy to understand visually. In this way, it is possible to provide optimal parameters of the analysis algorithm based on the user's past analysis results.

[0095] The analysis unit can obtain the user's current interests and trends in real time and dynamically adjust the parameters of the analysis algorithm. For example, if the user is currently interested in "health," the analysis unit can prioritize analyzing health-related information. Also, if the user is currently interested in "technology," the analysis unit can prioritize analyzing technology-related information. Furthermore, if the user is currently interested in "environment," the analysis unit can prioritize analyzing environment-related information. This makes it possible to provide optimal analysis algorithm parameters based on the user's current interests and trends.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, it is possible to provide the optimal display method of the analysis results according to the user's emotions.

[0097] The synthesis unit can take time factors into account when combining information from different fields. For example, when combining cooking and medical information, it can take into account the seasonality and shelf life of ingredients. The synthesis unit can also take technological advances and changes in educational curricula into account when combining technology and education information. Furthermore, the synthesis unit can take into account economic cycles and fluctuations in environmental policy when combining environmental and economic information. This allows for the accuracy of synthesis to be improved by taking time factors into account when combining information from different fields.

[0098] The synthesis unit can estimate the user's emotion and dynamically change the synthesis method based on the estimated user's emotion. For example, if the user is relaxed, the method can be adjusted to perform detailed synthesis. If the user is in a hurry, the method can be adjusted to perform quick synthesis. Furthermore, if the user is excited, the method can be adjusted to provide a visually stimulating synthesis result. In this way, it is possible to provide an optimal synthesis method according to the user's emotion.

[0099] The synthesis unit can take cultural factors into account when combining information from different fields. For example, when combining cooking and medical information, it can take into account regional food culture and medical practices. The synthesis unit can also take into account educational systems and technology acceptance when combining technology and education information. Furthermore, the synthesis unit can take into account regional environmental awareness and economic policies when combining environmental and economic information. In this way, taking cultural factors into account when combining information from different fields improves the accuracy of synthesis.

[0100] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive way of expression can be provided. If the user is relaxed, a way of expression including detailed information can be provided. Furthermore, if the user is in a hurry, a way of expression that focuses on the main points can be provided. In this way, the optimal way of suggestion expression can be provided according to the user's emotions.

[0101] The suggestion unit can dynamically adjust the content of suggestions based on the user's past responses. For example, it can provide preferentially suggestions to which the user has responded favorably in the past. It can also avoid suggestions to which the user has responded negatively in the past. Furthermore, it can analyze the user's past responses and dynamically select optimal suggestions. This makes it possible to provide optimal suggestions based on the user's past responses.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit inputs the user's fields of interest and information sources. The reception unit receives the keywords entered by the user and sends them to the analysis unit. These fields include, for example, science and technology, business, and entertainment. Step 2: The analysis unit analyzes the information input by the reception unit. The analysis is performed using methods such as text analysis, data mining, and statistical analysis. The analysis unit searches the database for information related to the input keywords and sends that information to the synthesis unit. Step 3: The synthesis unit performs synthesis based on the information analyzed by the analysis unit. Synthesis is performed based on the method of information integration and the type of algorithm. For example, information from different fields can be combined to generate new insights and ideas. Step 4: The proposal section proposes new insights and ideas obtained by the synthesis section. The proposals may include proposals for innovative technologies or business models. The proposal section presents the generated insights and ideas to the user. Step 5: The sharing unit shares the insights and ideas suggested by the suggestion unit with other users. Sharing can be done via email, social media, cloud storage, etc. The sharing unit provides an interface for users to share their insights and ideas with other users.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section where a user inputs areas of interest and information sources; an analysis unit that analyzes the information input by the reception unit; a synthesis unit that performs synthesis based on the information analyzed by the analysis unit; a proposal unit that proposes new insights and ideas obtained by the synthesis unit; a sharing unit that shares the insights and ideas suggested by the suggestion unit with other users. A system characterized by:

2. The reception unit Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

4. The reception unit Obtaining the user's current interests and trends in real time and automatically presenting appropriate input suggestions 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Prioritize relevant topics and sources based on the user's geographic location 2. The system of claim 1.

7. The reception unit Analyzes users' social media activity and automatically suggests related topics and sources 2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and dynamically adjust the parameters of the analysis algorithm based on the estimated user emotions.

2. The system of claim 1.

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