System

Abstract

An object of a system according to an embodiment is to analyze business relationship data and a networking history of a user and propose an optimal business connection.SOLUTION: A system includes an input unit, an analysis unit, and a proposal unit. The input unit inputs business relationship data or networking history of a user. The analysis unit analyzes the data input by the input unit. The proposal unit proposes a business connection based on an analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not yet effectively utilized a user's business relationship data and networking history to propose optimal business connections, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's business relationship data and networking history and propose optimal business connections. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a proposal unit. The input unit inputs business relationship data or networking history of a user. The analysis unit analyzes the data input by the input unit. The proposal unit proposes business connections based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a user's business relationship data and networking history to suggest optimal business connections. [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) A business connection suggestion system according to an embodiment of the present invention analyzes a user's business relationship data and networking history to suggest optimal business connections. In the business connection suggestion system, a user inputs the business relationship data and networking history, and a generation AI analyzes the data to predict and suggest optimal business connections. For example, the business connection suggestion system analyzes data such as contact history from past business meetings and events, email exchanges, and connections on social networking services. Based on this data, the generation AI suggests business connections that are beneficial to the user. This allows business professionals to effectively build business connections and promote economic success. For example, in the business connection suggestion system, a user inputs business relationship data and networking history. For example, the business connection suggestion system inputs a list of participants from past business meetings, information about people with whom the user exchanged business cards, and email exchange history. This information is input into the generation AI. The business connection suggestion system then uses the generation AI to analyze the input data. The generation AI performs a detailed analysis of the user's business relationship data and networking history to predict business connections that will be beneficial to the user. For example, the system identifies people from past contacts who are likely to be useful to the user's current business. Next, the business connection recommendation system uses the generation AI to recommend optimal business connections to the user based on the analysis results. For example, it may recommend people with extensive experience in a specific industry or people with common business goals. This allows the user to effectively build business connections. This allows the business connection recommendation system to help business professionals effectively build business connections and promote economic success. The business connection recommendation system analyzes the user's business relationship data and networking history to recommend optimal business connections. For example, it can help users find new business partners or strengthen existing business relationships.It also streamlines networking at business events and meetings, saving time and effort.

[0029] A business connection proposal system according to an embodiment includes an input unit, an analysis unit, and a proposal unit. The input unit inputs a user's business relationship data and networking history. The user's business relationship data includes, but is not limited to, transaction history, contract information, and customer information. The networking history includes, but is not limited to, events attended, people contacted, and business cards exchanged. The input unit inputs, for example, a list of participants in past business meetings, information about people with whom the user exchanged business cards, and email exchange history. The analysis unit uses a generation AI to analyze the data input by the input unit. The analysis is performed using, for example, data mining, statistical analysis, machine learning algorithms, and other methods, but is not limited to these examples. For example, the generation AI analyzes contact history from past business meetings and events. The generation AI can also analyze email exchanges. The generation AI can also analyze connections on social networking services. For example, the generation AI identifies people from past contacts who are likely to be useful to the current business. The suggestion unit uses the generation AI to suggest optimal business connections based on the analysis results obtained by the analysis unit. The suggestions include, but are not limited to, people with extensive experience in a specific industry or people with common business goals. For example, the generation AI suggests people with extensive experience in a specific industry. The generation AI can also suggest people with common business goals. This allows the business connection suggestion system according to the embodiment to analyze a user's business relationship data and networking history and suggest optimal business connections. For example, business professionals can effectively build business connections.

[0030] The analysis unit can analyze contact history from past business meetings or events. Examples of business meetings include, but are not limited to, conferences, business negotiations, and presentations. Examples of events include, but are not limited to, exhibitions, seminars, and networking events. The analysis unit, for example, analyzes the attendee list of past business meetings. The analysis unit can also analyze contact history from past events. For example, the analysis unit analyzes information about people who came into contact at past business meetings or events to identify business connections that are beneficial to the user. In this way, analyzing contact history from past business meetings and events can identify business connections that are beneficial to the user.

[0031] The analysis unit can analyze email exchanges. Email exchanges include, but are not limited to, business emails, follow-up emails, newsletters, and the like. For example, the analysis unit analyzes business emails that the user has sent and received in the past. The analysis unit can also analyze follow-up emails. For example, the analysis unit analyzes the content of emails that the user has sent and received in the past to identify business connections that are beneficial to the user. In this way, by analyzing email exchanges, it is possible to identify business connections that are beneficial to the user.

[0032] The analysis unit can analyze connections on a social networking service. Examples of social networking services include, but are not limited to, LinkedIn (registered trademark), Facebook (registered trademark), and Twitter (registered trademark). The analysis unit, for example, analyzes information about people with whom the user has come into contact on the social networking service. The analysis unit can also analyze interactions on the social networking service. For example, the analysis unit analyzes information about people with whom the user has come into contact on the social networking service to identify business connections that are beneficial to the user. In this way, analyzing connections on the social networking service can identify business connections that are beneficial to the user.

[0033] The suggestion unit can suggest people with experience in a specific industry. Examples of specific industries include, but are not limited to, the IT industry, manufacturing, and finance. For example, the suggestion unit can suggest people with extensive experience in the IT industry. The suggestion unit can also suggest people with extensive experience in the manufacturing industry. For example, the suggestion unit can suggest people with extensive experience in a specific industry related to the user's business. This makes it possible to provide the user with useful business connections by suggesting people with extensive experience in a specific industry.

[0034] The suggestion unit can suggest people who share common business goals. Common business goals include, but are not limited to, sales growth, new market development, and technological innovation. For example, the suggestion unit can suggest people who aim to increase sales. The suggestion unit can also suggest people who aim to develop new markets. For example, the suggestion unit can suggest people who share common business goals with the user. This makes it possible to provide a business connection that is beneficial to the user by suggesting people who share common business goals.

[0035] The input unit can analyze the user's past business-related data input history and select the optimal input method. Optimal input methods include, but are not limited to, voice input, text input, and image input. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the input unit can analyze the format of data previously input by the user and suggest an optimal input format. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI and have the generation AI select the optimal input method.

[0036] When inputting business-related data, the input unit can filter the data based on the user's current project or areas of interest. Examples of filtering include, but are not limited to, keyword filtering and category filtering. For example, the input unit preferentially inputs only data related to the user's ongoing project. The input unit can also filter and input highly relevant data based on the user's areas of interest. For example, the input unit selects input data based on areas in which the user has previously shown interest. This allows data to be filtered based on the user's current project or areas of interest, thereby preferentially inputting highly relevant data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data related to the user's projects and areas of interest to a generation AI and have the generation AI perform the filtering.

[0037] When inputting business-related data, the input unit can select the optimal input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can prioritize voice input. Also, if the user prefers text input, the input unit can prioritize text input. For example, if the user prefers image input, the input unit can prioritize image input. This allows efficient data input by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the user's input method to a generation AI and have the generation AI select the optimal input means.

[0038] When inputting business-related data, the input unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location-based services. For example, when the user is in a specific area, the input unit prioritizes inputting business data related to that area. Furthermore, when the user is traveling, the input unit can also prioritize inputting data related to the user's current location. For example, when the user is in a specific city, the input unit prioritizes inputting business data related to that city. This allows highly relevant data to be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant data.

[0039] When inputting business-related data, the input unit can analyze the user's social media activity and input relevant data. Social media activity includes, but is not limited to, for example, the content of posts, the number of followers, and the engagement rate. For example, the input unit prioritizes inputting data related to locations where the user has checked in on social media. The input unit can also analyze the content of the user's social media posts and input relevant business data. For example, the input unit inputs relevant business data based on the activities of the user's friends on social media. This allows the user's social media activity to be analyzed and highly relevant data to be input preferentially. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the user's social media activity to the generation AI and cause the generation AI to select relevant data.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting business-related data. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The input unit can suggest an optimal input method based on, for example, feedback previously provided by the user. The input unit can also customize the input interface based on the user's past feedback. For example, the input unit improves the input method to resolve problems previously pointed out by the user. This allows the optimal input method to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the business-related data. Examples of importance include, but are not limited to, business impact, urgency, and priority. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the business-related data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the importance of the business-related data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the business-related data. Examples of categories include, but are not limited to, industry categories, product categories, and service categories. For example, the analysis unit can apply an industry-specific analysis algorithm to industry-specific data. The analysis unit can also apply a region-specific analysis algorithm to region-specific data. For example, the analysis unit can apply a company-specific analysis algorithm to company-specific data. This allows for applying different analysis algorithms depending on the category of the business-related data, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of a business-related data category into the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data sets, analysis reports, and feedback. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also set parameters for improving the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described 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 data of the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the submission time of the business-related data. The submission time includes, but is not limited to, for example, the submission date, submission time, and deadline. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone data that was submitted recently. For example, the analysis unit can appropriately prioritize data that was submitted recently. In this way, by determining the analysis priority based on the submission time of the business-related data, analysis can be performed efficiently. 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 data on the submission time of the business-related data to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the business-related data. Examples of relevance include, but are not limited to, common keywords, related topics, and correlations. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit appropriately prioritizes data with medium relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the business-related data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the relevance of the business-related data into the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, the analysis unit uses a lot of technical terms when the user has technical expertise. The analysis unit can also avoid technical terms when the user does not have technical expertise. For example, the analysis unit uses technical terms appropriately according to the user's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terms.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the business connection. Examples of importance include, but are not limited to, business impact, urgency, and priority. For example, the suggestion unit makes a detailed proposal for a business connection with high importance. The suggestion unit can also make a concise proposal for a business connection with low importance. For example, the suggestion unit makes a proposal with an appropriate level of detail for a business connection with medium importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the business connection. 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 data on the importance of the business connection to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the business connection. Examples of categories include, but are not limited to, industry categories, product categories, and service categories. For example, the proposal unit can apply an industry-specific proposal algorithm to business connections by industry. The proposal unit can also apply a region-specific proposal algorithm to business connections by region. For example, the proposal unit can apply a company-specific proposal algorithm to business connections by company. This allows for applying different proposal algorithms depending on the category of the business connection, thereby providing more accurate proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data of the business connection category into the generation AI and cause the generation AI to apply the proposal algorithm.

[0049] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. Past suggestion results include, but are not limited to, past suggestion reports, feedback, and success stories. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also set parameters for improving the accuracy of suggestions based on the user's past suggestion results. For example, the suggestion unit improves the accuracy of suggestions by referring to the user's past suggestion results. In this way, the accuracy of suggestions can be improved by referring to the user's past suggestion results. 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 data of the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0050] The suggestion unit can determine the priority of proposals based on the submission time of the business connections. The submission time includes, but is not limited to, for example, the submission date, submission time, and deadline. For example, the suggestion unit can prioritize the most recently submitted business connections. The suggestion unit can also postpone business connections that have been submitted recently. For example, the suggestion unit can moderately prioritize business connections that have been submitted recently. This allows proposals to be made efficiently by determining the priority of proposals based on the submission time of the business connections. 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 data on the submission time of business connections into the generation AI and have the generation AI determine the priority of proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the business connections. Examples of relevance include, but are not limited to, common keywords, related topics, and correlations. For example, the suggestion unit prioritizes suggesting highly relevant business connections. The suggestion unit can also postpone suggesting less relevant business connections. For example, the suggestion unit moderately prioritizes business connections with medium relevance. This allows suggestions to be made efficiently by adjusting the order of suggestions based on the relevance of the business connections. 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 data on the relevance of business connections into a generation AI and cause the generation AI to adjust the order of suggestions.

[0052] The suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, the suggestion unit uses a lot of technical terminology when the user has specialized knowledge. The suggestion unit can also avoid technical terminology when the user does not have specialized knowledge. For example, the suggestion unit uses technical terminology appropriately depending on the user's level of expertise. This allows for more understandable proposals to be provided by adjusting the use of technical terminology in the proposal depending on the user's level of expertise. 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 data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When analyzing the user's business relationship data, the analysis unit can take into account the user's past business performance data. For example, the analysis unit can analyze data on projects or transactions that the user has been successful in the past and identify business connections with similar success patterns. The analysis unit can also analyze data on projects or transactions that the user has failed in the past and suggest business connections that will help the user avoid similar failures. Furthermore, the analysis unit can predict future business performance based on the user's past business performance data and suggest optimal business connections based on that prediction. In this way, by taking the user's past business performance data into consideration, it is possible to suggest business connections with greater accuracy.

[0055] When inputting the user's business-related data, the input unit can prioritize the input data in consideration of the user's current business goals. For example, if the user's goal is to develop new markets, data related to new markets can be input preferentially. Also, if the user's goal is technological innovation, data related to technological innovation can be input preferentially. Furthermore, if the user's goal is to increase sales, data related to sales expansion can be input preferentially. This allows for more effective input of business-related data by taking the user's current business goals into consideration.

[0056] When analyzing the user's business relationship data, the suggestion unit can take into account the success rate of the user's past business relationships. For example, the suggestion unit can analyze data on business relationships in which the user has been successful in the past and identify business connections with similar success patterns. The suggestion unit can also analyze data on business relationships in which the user has failed in the past and suggest business connections that will help the user avoid similar failures. Furthermore, the suggestion unit can predict the success rate of future business relationships based on the success rate of the user's past business relationships and suggest optimal business connections based on that prediction. In this way, by taking into account the success rate of the user's past business relationships, it is possible to suggest business connections with greater accuracy.

[0057] The analysis unit can take into account the geographic distribution of the user's business relationships when analyzing the user's business relationship data. For example, if the user has many business relationships in a specific region, data related to that region can be analyzed preferentially. Also, if the user is trying to build business relationships in a new region, data related to that region can be analyzed preferentially. Furthermore, based on the geographic distribution of the user's business relationships, the analysis unit can predict the geographic distribution of future business relationships and suggest optimal business connections based on that prediction. This allows for more effective data analysis by taking into account the geographic distribution of the user's business relationships.

[0058] When inputting the user's business relationship data, the input unit can prioritize the input data by taking into account the importance of the user's business relationships. For example, if the user has an important business relationship, data related to that relationship can be input preferentially. It can also prioritize data related to a new business relationship the user is trying to establish. Furthermore, it can predict the importance of future business relationships based on the importance of the user's business relationships and input optimal business relationship data based on that prediction. This allows for more effective data input by taking into account the importance of the user's business relationships.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The input unit inputs the user's business relationship data and networking history. The user's business relationship data includes transaction history, contract information, customer information, etc., while the networking history includes events attended, people contacted, and business cards exchanged. For example, the user inputs a list of participants in past business meetings, information about people with whom they exchanged business cards, and a history of email correspondence. Step 2: The analysis unit uses the generation AI to analyze the data entered by the input unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI analyzes contact history from past business meetings and events, email correspondence, and connections on social networking services to identify people who are likely to be useful to the current business. Step 3: The suggestion unit uses the generative AI to suggest optimal business connections based on the analysis results obtained by the analysis unit. Suggestions include people with extensive experience in a specific industry or people who share common business goals. This allows users to effectively build business connections.

[0061] (Example 2) A business connection suggestion system according to an embodiment of the present invention analyzes a user's business relationship data and networking history to suggest optimal business connections. In the business connection suggestion system, a user inputs the business relationship data and networking history, and a generation AI analyzes the data to predict and suggest optimal business connections. For example, the business connection suggestion system analyzes data such as contact history from past business meetings and events, email exchanges, and connections on social networking services. Based on this data, the generation AI suggests business connections that are beneficial to the user. This allows business professionals to effectively build business connections and promote economic success. For example, in the business connection suggestion system, a user inputs business relationship data and networking history. For example, the business connection suggestion system inputs a list of participants from past business meetings, information about people with whom the user exchanged business cards, and email exchange history. This information is input into the generation AI. The business connection suggestion system then uses the generation AI to analyze the input data. The generation AI performs a detailed analysis of the user's business relationship data and networking history to predict business connections that will be beneficial to the user. For example, the system identifies people from past contacts who are likely to be useful to the user's current business. Next, the business connection recommendation system uses the generation AI to recommend optimal business connections to the user based on the analysis results. For example, it may recommend people with extensive experience in a specific industry or people with common business goals. This allows the user to effectively build business connections. This allows the business connection recommendation system to help business professionals effectively build business connections and promote economic success. The business connection recommendation system analyzes the user's business relationship data and networking history to recommend optimal business connections. For example, it can help users find new business partners or strengthen existing business relationships.It also streamlines networking at business events and meetings, saving time and effort.

[0062] A business connection proposal system according to an embodiment includes an input unit, an analysis unit, and a proposal unit. The input unit inputs a user's business relationship data and networking history. The user's business relationship data includes, but is not limited to, transaction history, contract information, and customer information. The networking history includes, but is not limited to, events attended, people contacted, and business cards exchanged. The input unit inputs, for example, a list of participants in past business meetings, information about people with whom the user exchanged business cards, and email exchange history. The analysis unit uses a generation AI to analyze the data input by the input unit. The analysis is performed using, for example, data mining, statistical analysis, machine learning algorithms, and other methods, but is not limited to these examples. For example, the generation AI analyzes contact history from past business meetings and events. The generation AI can also analyze email exchanges. The generation AI can also analyze connections on social networking services. For example, the generation AI identifies people from past contacts who are likely to be useful to the current business. The suggestion unit uses the generation AI to suggest optimal business connections based on the analysis results obtained by the analysis unit. The suggestions include, but are not limited to, people with extensive experience in a specific industry or people with common business goals. For example, the generation AI suggests people with extensive experience in a specific industry. The generation AI can also suggest people with common business goals. This allows the business connection suggestion system according to the embodiment to analyze a user's business relationship data and networking history and suggest optimal business connections. For example, business professionals can effectively build business connections.

[0063] The analysis unit can analyze contact history from past business meetings or events. Examples of business meetings include, but are not limited to, conferences, business negotiations, and presentations. Examples of events include, but are not limited to, exhibitions, seminars, and networking events. The analysis unit, for example, analyzes the attendee list of past business meetings. The analysis unit can also analyze contact history from past events. For example, the analysis unit analyzes information about people who came into contact at past business meetings or events to identify business connections that are beneficial to the user. In this way, analyzing contact history from past business meetings and events can identify business connections that are beneficial to the user.

[0064] The analysis unit can analyze email exchanges. Email exchanges include, but are not limited to, business emails, follow-up emails, newsletters, and the like. For example, the analysis unit analyzes business emails that the user has sent and received in the past. The analysis unit can also analyze follow-up emails. For example, the analysis unit analyzes the content of emails that the user has sent and received in the past to identify business connections that are beneficial to the user. In this way, by analyzing email exchanges, it is possible to identify business connections that are beneficial to the user.

[0065] The analysis unit can analyze connections on social networking services. Social networking services include, but are not limited to, LinkedIn, Facebook, Twitter, and the like. The analysis unit, for example, analyzes information about people with whom the user has come into contact on the social networking service. The analysis unit can also analyze interactions on the social networking service. For example, the analysis unit analyzes information about people with whom the user has come into contact on the social networking service to identify business connections that are beneficial to the user. In this way, analyzing connections on the social networking service can identify business connections that are beneficial to the user.

[0066] The suggestion unit can suggest people with experience in a specific industry. Examples of specific industries include, but are not limited to, the IT industry, manufacturing, and finance. For example, the suggestion unit can suggest people with extensive experience in the IT industry. The suggestion unit can also suggest people with extensive experience in the manufacturing industry. For example, the suggestion unit can suggest people with extensive experience in a specific industry related to the user's business. This makes it possible to provide the user with useful business connections by suggesting people with extensive experience in a specific industry.

[0067] The suggestion unit can suggest people who share common business goals. Common business goals include, but are not limited to, sales growth, new market development, and technological innovation. For example, the suggestion unit can suggest people who aim to increase sales. The suggestion unit can also suggest people who aim to develop new markets. For example, the suggestion unit can suggest people who share common business goals with the user. This makes it possible to provide a business connection that is beneficial to the user by suggesting people who share common business goals.

[0068] The input unit can estimate a user's emotions and adjust the timing of inputting business-related data based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, and the like. For example, if the user is stressed, the input unit can delay the input timing to allow the user to input in a relaxed state. Furthermore, if the user is relaxed, the input unit can prompt the user to input data immediately, thereby efficiently collecting data. For example, if the user is in a hurry, the input unit can speed up the input timing to allow the user to input data quickly. This allows for efficient data collection by adjusting the input timing 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 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 input unit may be performed using, for example, an AI. For example, the input unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] The input unit can analyze the user's past business-related data input history and select the optimal input method. Optimal input methods include, but are not limited to, voice input, text input, and image input. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the input unit can analyze the format of data previously input by the user and suggest an optimal input format. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI and have the generation AI select the optimal input method.

[0070] When inputting business-related data, the input unit can filter the data based on the user's current project or areas of interest. Examples of filtering include, but are not limited to, keyword filtering and category filtering. For example, the input unit preferentially inputs only data related to the user's ongoing project. The input unit can also filter and input highly relevant data based on the user's areas of interest. For example, the input unit selects input data based on areas in which the user has previously shown interest. This allows data to be filtered based on the user's current project or areas of interest, thereby preferentially inputting highly relevant data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data related to the user's projects and areas of interest to a generation AI and have the generation AI perform the filtering.

[0071] When inputting business-related data, the input unit can select the optimal input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can prioritize voice input. Also, if the user prefers text input, the input unit can prioritize text input. For example, if the user prefers image input, the input unit can prioritize image input. This allows efficient data input by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the user's input method to a generation AI and have the generation AI select the optimal input means.

[0072] The input unit can estimate the user's emotions and determine the priority of business-related data to be input based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is stressed, the input unit postpones less important data and prioritizes input of more important data. The input unit can also input all data equally when the user is relaxed. For example, when the user is in a hurry, the input unit prioritizes input of the most important data. Thus, by determining the priority of data according to the user's emotions, important data can be prioritized and input. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the input unit may be performed using AI, or without AI. For example, the input unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0073] When inputting business-related data, the input unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location-based services. For example, when the user is in a specific area, the input unit prioritizes inputting business data related to that area. Furthermore, when the user is traveling, the input unit can also prioritize inputting data related to the user's current location. For example, when the user is in a specific city, the input unit prioritizes inputting business data related to that city. This allows highly relevant data to be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant data.

[0074] When inputting business-related data, the input unit can analyze the user's social media activity and input relevant data. Social media activity includes, but is not limited to, for example, the content of posts, the number of followers, and the engagement rate. For example, the input unit prioritizes inputting data related to locations where the user has checked in on social media. The input unit can also analyze the content of the user's social media posts and input relevant business data. For example, the input unit inputs relevant business data based on the activities of the user's friends on social media. This allows the user's social media activity to be analyzed and highly relevant data to be input preferentially. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the user's social media activity to the generation AI and cause the generation AI to select relevant data.

[0075] The input unit can customize the input method by reflecting the user's past feedback when inputting business-related data. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The input unit can suggest an optimal input method based on, for example, feedback previously provided by the user. The input unit can also customize the input interface based on the user's past feedback. For example, the input unit improves the input method to resolve problems previously pointed out by the user. This allows the optimal input method to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0076] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, when the user is stressed, the analysis unit provides visually easy-to-understand analysis results. By adjusting the way the analysis is presented based on the user's emotion, more understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the business-related data. Examples of importance include, but are not limited to, business impact, urgency, and priority. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the business-related data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the importance of the business-related data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the business-related data. Examples of categories include, but are not limited to, industry categories, product categories, and service categories. For example, the analysis unit can apply an industry-specific analysis algorithm to industry-specific data. The analysis unit can also apply a region-specific analysis algorithm to region-specific data. For example, the analysis unit can apply a company-specific analysis algorithm to company-specific data. This allows for applying different analysis algorithms depending on the category of the business-related data, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of a business-related data category into the generation AI and cause the generation AI to apply the analysis algorithm.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past data sets, analysis reports, and feedback. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also set parameters for improving the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described 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 data of the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. For example, when the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotion, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0081] The analysis unit can determine the analysis priority based on the submission time of the business-related data. The submission time includes, but is not limited to, for example, the submission date, submission time, and deadline. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone data that was submitted recently. For example, the analysis unit can appropriately prioritize data that was submitted recently. In this way, by determining the analysis priority based on the submission time of the business-related data, analysis can be performed efficiently. 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 data on the submission time of the business-related data to the generation AI and have the generation AI determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the business-related data. Examples of relevance include, but are not limited to, common keywords, related topics, and correlations. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit appropriately prioritizes data with medium relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the business-related data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the relevance of the business-related data into the generation AI and cause the generation AI to adjust the order of analysis.

[0083] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, the analysis unit uses a lot of technical terms when the user has technical expertise. The analysis unit can also avoid technical terms when the user does not have technical expertise. For example, the analysis unit uses technical terms appropriately according to the user's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terms.

[0084] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, when the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. For example, when the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. This allows the suggestion to be more easily understood by adjusting the way the suggestion is expressed based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The suggestion unit can adjust the level of detail of the proposal based on the importance of the business connection. Examples of importance include, but are not limited to, business impact, urgency, and priority. For example, the suggestion unit makes a detailed proposal for a business connection with high importance. The suggestion unit can also make a concise proposal for a business connection with low importance. For example, the suggestion unit makes a proposal with an appropriate level of detail for a business connection with medium importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the business connection. 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 data on the importance of the business connection to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0086] The proposal unit can apply different proposal algorithms depending on the category of the business connection. Examples of categories include, but are not limited to, industry categories, product categories, and service categories. For example, the proposal unit can apply an industry-specific proposal algorithm to business connections by industry. The proposal unit can also apply a region-specific proposal algorithm to business connections by region. For example, the proposal unit can apply a company-specific proposal algorithm to business connections by company. This allows for applying different proposal algorithms depending on the category of the business connection, thereby providing more accurate proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data of the business connection category into the generation AI and cause the generation AI to apply the proposal algorithm.

[0087] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. Past suggestion results include, but are not limited to, past suggestion reports, feedback, and success stories. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also set parameters for improving the accuracy of suggestions based on the user's past suggestion results. For example, the suggestion unit improves the accuracy of suggestions by referring to the user's past suggestion results. In this way, the accuracy of suggestions can be improved by referring to the user's past suggestion results. 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 data of the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0088] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, when the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. For example, when the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. This allows the length of the suggestion to be adjusted according to the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The suggestion unit can determine the priority of proposals based on the submission time of the business connections. The submission time includes, but is not limited to, for example, the submission date, submission time, and deadline. For example, the suggestion unit can prioritize the most recently submitted business connections. The suggestion unit can also postpone business connections that have been submitted recently. For example, the suggestion unit can moderately prioritize business connections that have been submitted recently. This allows proposals to be made efficiently by determining the priority of proposals based on the submission time of the business connections. 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 data on the submission time of business connections into the generation AI and have the generation AI determine the priority of proposals.

[0090] The suggestion unit can adjust the order of suggestions based on the relevance of the business connections. Examples of relevance include, but are not limited to, common keywords, related topics, and correlations. For example, the suggestion unit prioritizes suggesting highly relevant business connections. The suggestion unit can also postpone suggesting less relevant business connections. For example, the suggestion unit moderately prioritizes business connections with medium relevance. This allows suggestions to be made efficiently by adjusting the order of suggestions based on the relevance of the business connections. 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 data on the relevance of business connections into a generation AI and cause the generation AI to adjust the order of suggestions.

[0091] The suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, the suggestion unit uses a lot of technical terminology when the user has specialized knowledge. The suggestion unit can also avoid technical terminology when the user does not have specialized knowledge. For example, the suggestion unit uses technical terminology appropriately depending on the user's level of expertise. This allows for more understandable proposals to be provided by adjusting the use of technical terminology in the proposal depending on the user's level of expertise. 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 data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit of the business connection proposal system can input a user's business-related data and networking history using the reception device 38 or microphone 38B of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input data using a generation AI. For example, the proposal unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and proposes optimal business connections based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit of the business connection suggestion system can input the user's business relationship data and networking history using the microphone 238 or camera 42 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input data using a generative AI. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and suggests optimal business connections based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and proposal unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit of the business connection proposal system can input the user's business relationship data and networking history using the microphone 238 or camera 42 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI. For example, the proposal unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and proposes optimal business connections based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit of the business connection proposal system can input the user's business relationship data and networking history using the microphone 238 or camera 42 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generative AI. For example, the proposal unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and proposes optimal business connections based on the analysis results.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] When analyzing the user's business relationship data, the analysis unit can take into account the user's past business performance data. For example, the analysis unit can analyze data on projects or transactions that the user has been successful in the past and identify business connections with similar success patterns. The analysis unit can also analyze data on projects or transactions that the user has failed in the past and suggest business connections that will help the user avoid similar failures. Furthermore, the analysis unit can predict future business performance based on the user's past business performance data and suggest optimal business connections based on that prediction. In this way, by taking the user's past business performance data into consideration, it is possible to suggest business connections with greater accuracy.

[0094] The suggestion unit can estimate the user's emotions and adjust the timing of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the timing of the suggestions can be delayed so that the user can receive the suggestions in a relaxed state. Also, if the user is relaxed, the suggestions can be made immediately, allowing for efficient building of business connections. Furthermore, if the user is in a hurry, the timing of the suggestions can be advanced so that business connections can be proposed quickly. In this way, by adjusting the timing of the suggestions according to the user's emotions, more effective business connection suggestions can be made.

[0095] When inputting the user's business-related data, the input unit can prioritize the input data in consideration of the user's current business goals. For example, if the user's goal is to develop new markets, data related to new markets can be input preferentially. Also, if the user's goal is technological innovation, data related to technological innovation can be input preferentially. Furthermore, if the user's goal is to increase sales, data related to sales expansion can be input preferentially. This allows for more effective input of business-related data by taking the user's current business goals into consideration.

[0096] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is relaxed, it can prioritize analyzing data with high importance. Also, if the user is feeling stressed, it can postpone analyzing data with low importance and prioritize analyzing data with high importance. Furthermore, if the user is in a hurry, it can prioritize analyzing the most important data. In this way, by determining the analysis priority according to the user's emotions, more effective data analysis is possible.

[0097] When analyzing the user's business relationship data, the suggestion unit can take into account the success rate of the user's past business relationships. For example, the suggestion unit can analyze data on business relationships in which the user has been successful in the past and identify business connections with similar success patterns. The suggestion unit can also analyze data on business relationships in which the user has failed in the past and suggest business connections that will help the user avoid similar failures. Furthermore, the suggestion unit can predict the success rate of future business relationships based on the success rate of the user's past business relationships and suggest optimal business connections based on that prediction. In this way, by taking into account the success rate of the user's past business relationships, it is possible to suggest business connections with greater accuracy.

[0098] The input unit can estimate the user's emotion and customize the input interface based on the estimated user's emotion. For example, if the user is relaxed, a detailed input interface can be provided. If the user is stressed, a simple input interface can be provided. Furthermore, if the user is in a hurry, an interface that allows for quick input can be provided. In this way, customizing the input interface according to the user's emotion enables more efficient data input.

[0099] The analysis unit can take into account the geographic distribution of the user's business relationships when analyzing the user's business relationship data. For example, if the user has many business relationships in a specific region, data related to that region can be analyzed preferentially. Also, if the user is trying to build business relationships in a new region, data related to that region can be analyzed preferentially. Furthermore, based on the geographic distribution of the user's business relationships, the analysis unit can predict the geographic distribution of future business relationships and suggest optimal business connections based on that prediction. This allows for more effective data analysis by taking into account the geographic distribution of the user's business relationships.

[0100] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is stressed, concise suggestions can be provided. Furthermore, if the user is in a hurry, suggestions that focus on the main points can be provided. In this way, by adjusting the content of the suggestions according to the user's emotions, it becomes possible to provide suggestions that are easier to understand.

[0101] When inputting the user's business relationship data, the input unit can prioritize the input data by taking into account the importance of the user's business relationships. For example, if the user has an important business relationship, data related to that relationship can be input preferentially. It can also prioritize data related to a new business relationship the user is trying to establish. Furthermore, it can predict the importance of future business relationships based on the importance of the user's business relationships and input optimal business relationship data based on that prediction. This allows for more effective data input by taking into account the importance of the user's business relationships.

[0102] The suggestion unit can estimate the user's emotions and adjust the format of the suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide suggestions in a detailed report format. If the user is stressed, the suggestion unit can provide suggestions in a visually easy-to-understand graph or chart format. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions in a concise format with bullet points. In this way, by adjusting the format of the suggestions according to the user's emotions, it is possible to provide suggestions that are easier to understand.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The input unit inputs the user's business relationship data and networking history. The user's business relationship data includes transaction history, contract information, customer information, etc., while the networking history includes events attended, people contacted, and business cards exchanged. For example, the user inputs a list of participants in past business meetings, information about people with whom they exchanged business cards, and a history of email correspondence. Step 2: The analysis unit uses the generation AI to analyze the data entered by the input unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI analyzes contact history from past business meetings and events, email correspondence, and connections on social networking services to identify people who are likely to be useful to the current business. Step 3: The suggestion unit uses the generative AI to suggest optimal business connections based on the analysis results obtained by the analysis unit. Suggestions include people with extensive experience in a specific industry or people who share common business goals. This allows users to effectively build business connections.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting the user's business relationship data or networking history; an analysis unit that analyzes the data input by the input unit; a proposal unit that proposes a business connection based on the analysis result obtained by the analysis unit. A system characterized by:

2. The analysis unit Analyze contact history from past business meetings or events The system of claim 1 .

3. The analysis unit Analyze email exchanges The system of claim 1 .

4. The analysis unit Analyzing connections on social networking services The system of claim 1 .

5. The proposal unit Suggest people with experience in a specific industry The system of claim 1 .

6. The proposal unit Suggest people with common business goals The system of claim 1 .

7. The input unit To estimate a user's emotion and adjust the timing of inputting business-related data based on the estimated user's emotion. The system of claim 1 .

8. The input unit Analyze the user's past business data entry history and select the optimal entry method The system of claim 1 .

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