system

Abstract

The system according to this embodiment aims to efficiently manage users' networks and discover new business opportunities. [Solution] The system according to the embodiment comprises a network mapping unit, a relationship management unit, an opportunity recommendation unit, and an evaluation unit. The network mapping unit visually maps the user's network of contacts. The relationship management unit manages the communication history with important contacts based on the data mapped by the network mapping unit. The opportunity recommendation unit proposes new business opportunities and collaborations based on the data managed by the relationship management unit. The evaluation unit quantitatively evaluates the quality and influence of the network based on the data proposed by the opportunity recommendation unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently manage a user's network and discover new business opportunities. ]

[0005] The system according to the embodiment aims to efficiently manage a user's network and discover new business opportunities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a network mapping unit, a relationship management unit, an opportunity recommendation unit, and an evaluation unit. The network mapping unit visually maps the user's network of contacts. The relationship management unit manages the communication history with important contacts based on the data mapped by the network mapping unit. The opportunity recommendation unit proposes new business opportunities and collaborations based on the data managed by the relationship management unit. The evaluation unit quantitatively evaluates the quality and influence of the network based on the data proposed by the opportunity recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage a user's network of contacts and discover new business opportunities. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The social capital management system according to an embodiment of the present invention is an AI-driven platform for founders and investors to efficiently manage and grow their social capital. This social capital management system supports the optimization of networking, the strengthening of relationship building, and the discovery of investment opportunities. Users can visualize their network and identify important contacts and potential business partners. For example, the network mapping unit visually maps the user's network and identifies important contacts. Next, the relationship management unit manages the communication history with important contacts and encourages regular follow-up. Furthermore, the opportunity recommendation unit suggests new business opportunities and collaborations based on the user's interests and past investment history. Finally, the evaluation unit quantitatively evaluates the quality and influence of the user's network and provides indicators for growth. These functions are realized by utilizing generative AI. Specifically, this includes an NLP unit for natural language processing, a machine learning unit for learning user behavior patterns, and a data analysis unit for collecting and analyzing data. As a result, the social capital management system enables founders and investors to efficiently manage and grow their social capital.

[0029] The social capital management system according to this embodiment comprises a network mapping unit, a relationship management unit, an opportunity recommendation unit, and an evaluation unit. The network mapping unit visually maps the user's network. The network mapping unit can visually map the user's network in the form of, for example, a graph, a heat map, or a network diagram. The network mapping unit can also collect the user's network data and identify important contacts. For example, the network mapping unit analyzes the user's network data and identifies important contacts based on frequent communication and business importance. Furthermore, the network mapping unit can update the user's network in real time to reflect the latest information. The relationship management unit manages the communication history with important contacts based on the data mapped by the network mapping unit. The relationship management unit can manage communication history such as emails, phone calls, and meeting records. The relationship management unit can also set reminders to periodically follow up on the communication history with important contacts. Furthermore, the relationship management unit can also suggest actions to maintain and strengthen relationships with important contacts for the user. The Opportunity Recommendation Department proposes new business opportunities and collaborations based on data managed by the Relationship Management Department. For example, the Opportunity Recommendation Department can propose new business opportunities based on the user's interests and past investment history. It can also propose optimal collaborations based on the user's business situation and areas of interest. Furthermore, the Opportunity Recommendation Department can analyze the user's network data to identify potential business partners. The Evaluation Department quantitatively evaluates the quality and influence of the network based on the data proposed by the Opportunity Recommendation Department. For example, the Evaluation Department can evaluate the quality and influence of the network based on criteria such as the number of contacts, the importance of contacts, and the scope of influence. The Evaluation Department can also quantitatively evaluate the growth of the user's network and provide growth indicators.Furthermore, the evaluation unit can analyze the user's network data and identify areas for network enhancement. This allows the social capital management system according to the embodiment to efficiently manage and grow the user's network of contacts.

[0030] The network mapping unit visually maps a user's network of contacts. Specifically, it can visually map a user's network in the form of graphs, heatmaps, and network diagrams. This allows users to grasp the breadth of their network and important connections at a glance. For example, in a graph, each connection is displayed as a node, and the edges between nodes indicate relationships. In a heatmap, the intensity of the color changes according to the importance and frequency of the connection, visually highlighting important connections. In a network diagram, the relationships between connections are arranged in a way that is intuitively understandable. Furthermore, the network mapping unit can also collect user network data and identify important connections. For example, it can analyze a user's email and social media data to identify important connections based on frequent communication and business importance. This makes it easier for users to understand which connections are important for their business. In addition, the network mapping unit can update the user's network in real time, reflecting the latest information. This allows users to always make decisions based on the latest network information. For example, if a new connection is added or the relationship with an existing connection changes, the network mapping unit immediately updates the information and notifies the user. This allows users to strategically manage their network based on the latest information at all times.

[0031] The Relationship Management Department manages communication history with important contacts based on data mapped by the Network Mapping Department. Specifically, the Relationship Management Department can centrally manage communication history such as emails, phone calls, and meeting records. This allows users to easily refer to past communication history and understand their relationships with important contacts. For example, the Relationship Management Department can display in chronological order what kind of emails a user exchanged with a particular contact or what kind of meetings they held. The Relationship Management Department can also set reminders to periodically follow up on communication history with important contacts. This makes it easier for users to maintain relationships with important contacts. For example, the Relationship Management Department can send a reminder to the user after a certain period of time has passed, notifying them of the need for follow-up. Furthermore, the Relationship Management Department can also suggest actions to maintain and strengthen relationships with important contacts for the user. For example, the Relationship Management Department can suggest setting up regular meetings or inviting users to events of shared interests to strengthen relationships with specific contacts. This allows users to strategically strengthen their relationships with important contacts.

[0032] The Opportunity Recommendation Department proposes new business opportunities and collaborations based on data managed by the Relationship Management Department. Specifically, the Opportunity Recommendation Department can propose new business opportunities based on the user's interests and past investment history. For example, it can identify new business opportunities related to projects the user has previously invested in or areas of interest, and notify the user. The Opportunity Recommendation Department can also propose optimal collaborations based on the user's business situation and areas of interest. For example, if a user is looking for a new partner in a particular field, the Opportunity Recommendation Department can identify a suitable partner in that field and propose collaboration. Furthermore, the Opportunity Recommendation Department can analyze the user's network data to identify potential business partners. For example, it can identify contacts within the user's network that share common interests and goals, and propose collaboration with those contacts. This allows users to efficiently discover and strategically utilize new business opportunities.

[0033] The evaluation department quantitatively assesses the quality and impact of the network based on data proposed by the opportunity recommendation department. Specifically, the evaluation department can assess the quality and impact of the network based on criteria such as the number of touchpoints, the importance of touchpoints, and the scope of influence. For example, the evaluation department scores the number of touchpoints within a user's network and the influence of those touchpoints, and provides this information to the user. The evaluation department can also quantitatively assess the growth of a user's network and provide growth indicators. For example, the evaluation department evaluates the addition of new touchpoints and the strengthening of relationships with existing touchpoints within a certain period, and provides feedback on the results to the user. Furthermore, the evaluation department can analyze the user's network data and identify areas for network improvement. For example, the evaluation department identifies weaknesses and areas for improvement within a user's network and proposes specific action plans based on these. This allows users to efficiently strengthen their networks and contribute to business success.

[0034] The system includes an NLP (Natural Language Processing) unit that performs natural language processing. The NLP unit can analyze user input using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the NLP unit can collect user input data and understand the user's intent based on the analysis results. For example, the NLP unit can analyze user input text and extract important keywords and phrases. In addition, the NLP unit can generate appropriate responses based on user input data. This allows for efficient analysis of user input through natural language processing.

[0035] It is equipped with a machine learning unit that learns user behavior patterns. The machine learning unit learns user behavior patterns. For example, the machine learning unit can collect data such as past behavior history, frequency and timing of behavior, and learn user behavior patterns. The machine learning unit can also analyze user behavior data and identify behavior patterns. For example, the machine learning unit can extract specific behavior patterns based on the user's past behavior data. Furthermore, the machine learning unit can make appropriate suggestions to the user based on the learned behavior patterns. In this way, learning user behavior patterns makes it possible to make more appropriate suggestions.

[0036] The system includes a data analysis department that collects and analyzes data. The data analysis department can collect and analyze data such as user network data, behavioral data, and communication history. Furthermore, based on the collected data, the data analysis department can evaluate the quality and influence of the user's network. For example, it can analyze user network data to evaluate the number and importance of contacts. Additionally, it can identify behavioral patterns based on user behavioral data. This allows for the evaluation of the quality and influence of the user's network through data collection and analysis.

[0037] The Relationship Management Department can manage communication history with important touchpoints for users and encourage regular follow-up. For example, the Relationship Management Department manages communication history such as emails, phone calls, and meeting records with important touchpoints. It can also set reminders for regular follow-up. For instance, it can suggest follow-up timings based on communication history with important touchpoints. Furthermore, the Relationship Management Department can suggest actions to users to maintain and strengthen relationships with important touchpoints. This allows for the maintenance and strengthening of relationships with important touchpoints.

[0038] The opportunity recommendation unit can suggest new business opportunities and collaborations based on the user's interests and past investment history. For example, it can suggest new business opportunities based on the user's interests and past investment history. It can also suggest optimal collaborations based on the user's business situation and areas of interest. For instance, it can analyze the user's network data to identify potential business partners. Furthermore, it can prioritize business opportunities based on the user's interests and past investment history. This allows it to provide users with beneficial business opportunities.

[0039] The network mapping unit can analyze the user's past network construction history and select the optimal mapping method. For example, the network mapping unit can prioritize displaying contacts that the user has frequently interacted with in the past. It can also automatically highlight important contacts from the user's past network construction history. Furthermore, the network mapping unit can analyze the user's past network construction history and propose the most effective mapping method. This allows for more efficient network management for the user by providing the optimal mapping method based on past history.

[0040] The network mapping unit can filter network data based on the user's current business situation and areas of interest. For example, it can prioritize displaying highly relevant contacts based on the user's current business situation. It can also filter relevant contacts based on the user's areas of interest. Furthermore, it can suggest optimal contacts considering the user's business situation and areas of interest. This allows the system to provide optimal contacts tailored to the user's business situation and areas of interest.

[0041] The network mapping unit can prioritize mapping highly relevant touchpoints by considering the user's geographical location information during network mapping. For example, the network mapping unit can prioritize displaying touchpoints close to the user's current location. Furthermore, the network mapping unit can filter highly relevant touchpoints based on the user's geographical location information. In addition, the network mapping unit can suggest optimal touchpoints considering the user's geographical location information. This allows the system to provide optimal touchpoints based on the user's geographical location.

[0042] The network mapping unit can analyze a user's social media activity during network mapping and map relevant touchpoints. For example, it can prioritize displaying highly relevant touchpoints based on the user's social media activity. Furthermore, the network mapping unit can analyze the user's social media activity and automatically highlight important touchpoints. In addition, it can suggest optimal touchpoints considering the user's social media activity. This allows for the provision of optimal touchpoints based on the user's social media activity.

[0043] The Relationship Management Department can analyze past interactions at key points of contact and select the optimal follow-up method when managing communication history. For example, the Relationship Management Department can propose the optimal follow-up method based on past interactions with key points of contact. Furthermore, the Relationship Management Department can analyze past interactions at key points of contact and select effective follow-up methods. In addition, the Relationship Management Department can propose the optimal follow-up timing, taking into account past interactions with key points of contact. This allows for strengthening relationships by providing optimal follow-up methods based on past interactions.

[0044] The Relationship Management Department can adjust the timing of follow-ups based on the user's current business situation when managing communication history. For example, the Relationship Management Department can suggest the optimal follow-up timing based on the user's current business situation. Furthermore, the Relationship Management Department can adjust the timing of follow-ups considering the user's business situation. In addition, the Relationship Management Department can select an effective follow-up method based on the user's current business situation. This allows for the provision of optimal follow-up timing tailored to the user's business situation.

[0045] The Relationship Management Department can prioritize managing highly relevant contacts when managing communication history, taking into account the user's geographical location. For example, it can prioritize contacts close to the user's current location. Furthermore, the Relationship Management Department can filter highly relevant contacts based on the user's geographical location. In addition, the Relationship Management Department can suggest optimal contacts, taking the user's geographical location into consideration. This allows for the provision of optimal contacts based on the user's geographical location.

[0046] The Relationship Management Department can analyze users' social media activity and manage relevant touchpoints when managing communication history. For example, it can prioritize and manage highly relevant touchpoints based on users' social media activity. Furthermore, the Relationship Management Department can analyze users' social media activity and automatically highlight important touchpoints. In addition, it can suggest optimal touchpoints considering users' social media activity. This allows for the provision of optimal touchpoints based on users' social media activity.

[0047] The opportunity recommendation unit can analyze the user's past investment history and select the optimal proposal method when proposing business opportunities. For example, the opportunity recommendation unit can propose the most suitable business opportunities based on the user's past investment history. It can also analyze the user's past investment history and select the most effective proposal method. Furthermore, the opportunity recommendation unit can consider the user's past investment history and propose the optimal timing for the proposal. In this way, by providing the optimal proposal method based on past investment history, it can provide users with beneficial business opportunities.

[0048] The opportunity recommendation unit can adjust the timing of business opportunity proposals based on the user's current business situation. For example, it can suggest the optimal timing for proposals based on the user's current business situation. Furthermore, the opportunity recommendation unit can also adjust the timing of proposals considering the user's business situation. In addition, it can select an effective proposal method based on the user's current business situation. This allows the system to provide the optimal timing for proposals tailored to the user's business situation.

[0049] The opportunity recommendation unit can prioritize suggesting highly relevant business opportunities by considering the user's geographical location when proposing business opportunities. For example, it can prioritize suggesting business opportunities close to the user's current location. Furthermore, the opportunity recommendation unit can filter highly relevant business opportunities based on the user's geographical location. In addition, the opportunity recommendation unit can suggest the most suitable business opportunities by considering the user's geographical location. This allows for the provision of optimal business opportunities based on the user's geographical location.

[0050] The opportunity recommendation unit can analyze a user's social media activity and suggest relevant opportunities when proposing business opportunities. For example, it can prioritize suggesting highly relevant business opportunities based on the user's social media activity. Furthermore, the opportunity recommendation unit can analyze the user's social media activity and automatically highlight important business opportunities. It can also suggest the most suitable business opportunities, taking the user's social media activity into consideration. This allows the system to provide optimal business opportunities based on the user's social media activity.

[0051] The evaluation unit can analyze the user's past network construction history and select the optimal evaluation method when assessing the quality and impact of a network. For example, the evaluation unit can propose the optimal evaluation method based on the user's past network construction history. Furthermore, the evaluation unit can analyze the user's past network construction history and select an effective evaluation method. In addition, the evaluation unit can propose the optimal evaluation timing, taking into account the user's past network construction history. This allows for an accurate assessment of the user's network quality and impact by providing the optimal evaluation method based on past network construction history.

[0052] The evaluation unit can adjust the timing of the evaluation based on the user's current business situation when assessing the quality and impact of the network. For example, the evaluation unit can propose the optimal evaluation timing based on the user's current business situation. Furthermore, the evaluation unit can also adjust the evaluation timing considering the user's business situation. In addition, the evaluation unit can select an effective evaluation method based on the user's current business situation. This allows for the provision of the optimal evaluation timing tailored to the user's business situation.

[0053] The evaluation unit can prioritize evaluating highly relevant networks by considering the user's geographical location when assessing network quality and impact. For example, the evaluation unit can prioritize evaluating networks close to the user's current location. Furthermore, the evaluation unit can filter highly relevant networks based on the user's geographical location. In addition, the evaluation unit can propose the optimal network considering the user's geographical location. This allows for the provision of the most suitable network based on the user's geographical location.

[0054] The evaluation unit can analyze users' social media activity and evaluate relevant networks when assessing the quality and influence of a network. For example, the evaluation unit can prioritize evaluating highly relevant networks based on users' social media activity. Furthermore, the evaluation unit can analyze users' social media activity and automatically highlight important networks. In addition, the evaluation unit can suggest the optimal network, taking into account users' social media activity. This allows for the provision of the most suitable network based on users' social media activity.

[0055] The NLP (Neuro-Language Processing) unit can analyze the user's past communication history during natural language processing and select the optimal processing method. For example, the NLP unit can propose the optimal processing method based on the user's past communication history. Furthermore, the NLP unit can analyze the user's past communication history and select the most effective processing method. In addition, the NLP unit can propose the optimal processing timing, taking into account the user's past communication history. This allows for efficient analysis of user input by providing the optimal processing method based on past communication history.

[0056] The NLP (Neuro-Language Processing) unit can adjust the timing of processing based on the user's current business situation during natural language processing. For example, the NLP unit can suggest the optimal processing timing based on the user's current business situation. Furthermore, the NLP unit can adjust the processing timing considering the user's business situation. In addition, the NLP unit can select the most effective processing method based on the user's current business situation. This allows the NLP unit to provide the optimal processing timing tailored to the user's business situation.

[0057] The NLP (Neuro-Language Processing) unit can prioritize processing highly relevant data by considering the user's geographical location during natural language processing. For example, the NLP unit prioritizes processing data close to the user's current location. Furthermore, the NLP unit can filter highly relevant data based on the user's geographical location. In addition, the NLP unit can suggest optimal data considering the user's geographical location. This allows the system to provide optimal data based on the user's geographical location.

[0058] The NLP (Neuro-Language Processing) unit can analyze a user's social media activity and process relevant data during natural language processing. For example, the NLP unit prioritizes processing highly relevant data based on the user's social media activity. It can also analyze the user's social media activity and automatically highlight important data. Furthermore, the NLP unit can suggest optimal data considering the user's social media activity. This allows for the provision of optimal data based on the user's social media activity.

[0059] The machine learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the machine learning unit can propose the optimal learning algorithm based on past learning data. It can also analyze past learning data and select an effective learning algorithm. Furthermore, the machine learning unit can propose the optimal learning timing, taking past learning data into consideration. This improves learning efficiency by providing the optimal learning algorithm based on past learning data.

[0060] The machine learning unit can adjust the timing of learning based on the user's current business situation. For example, the machine learning unit can suggest the optimal learning timing based on the user's current business situation. It can also adjust the learning timing considering the user's business situation. Furthermore, the machine learning unit can select an effective learning method based on the user's current business situation. This allows for the provision of optimal learning timing tailored to the user's business situation.

[0061] The machine learning unit can weight training data based on user behavior patterns during training. For example, the machine learning unit can propose the optimal training data weighting based on user behavior patterns. Furthermore, the machine learning unit can analyze user behavior patterns and select effective training data weighting. It can also propose the optimal training data weighting considering user behavior patterns. This allows for the provision of optimal training data weighting based on user behavior patterns.

[0062] The machine learning unit can analyze users' social media activity during training and select relevant training data. For example, it can prioritize selecting highly relevant training data based on users' social media activity. The machine learning unit can also analyze users' social media activity and automatically highlight important training data. Furthermore, it can suggest optimal training data considering users' social media activity. This allows for the provision of optimal training data based on users' social media activity.

[0063] The data analysis department can analyze a user's past data history and select the optimal analysis method during data analysis. For example, the data analysis department can propose the optimal analysis method based on the user's past data history. Furthermore, the data analysis department can analyze a user's past data history and select an effective analysis method. In addition, the data analysis department can propose the optimal timing for analysis, taking into account the user's past data history. This improves the efficiency of data analysis by providing the optimal analysis method based on past data history.

[0064] The data analysis department can adjust the timing of data analysis based on the user's current business situation. For example, the data analysis department can suggest the optimal timing for analysis based on the user's current business situation. Furthermore, the data analysis department can adjust the timing of analysis considering the user's business situation. In addition, the data analysis department can select an effective analysis method based on the user's current business situation. This allows the department to provide the optimal timing for analysis tailored to the user's business situation.

[0065] The data analysis department can prioritize the analysis of highly relevant data by considering the user's geographical location during data analysis. For example, the data analysis department can prioritize the analysis of data close to the user's current location. Furthermore, the data analysis department can filter highly relevant data based on the user's geographical location. In addition, the data analysis department can suggest optimal data considering the user's geographical location. This allows the department to provide optimal data based on the user's geographical location.

[0066] The Data Analysis Department can analyze users' social media activity and analyze relevant data during data analysis. For example, the Data Analysis Department can prioritize the analysis of highly relevant data based on users' social media activity. Furthermore, the Data Analysis Department can analyze users' social media activity and automatically highlight important data. In addition, the Data Analysis Department can suggest optimal data considering users' social media activity. This allows for the provision of optimal data based on users' social media activity.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] A social capital management system can suggest nearby business events and networking opportunities, taking into account the user's geographical location. For example, if a user is traveling for business, it will prioritize suggesting business events held in that area. It can also suggest events held near the user's home or office. Furthermore, it can suggest the most suitable events based on the user's travel range. This allows for the provision of optimal networking opportunities based on the user's geographical location.

[0069] A social capital management system can analyze a user's social media activity and suggest relevant business opportunities and networking events. For example, if a user frequently posts about a particular industry, it can suggest events related to that industry. It can also suggest business opportunities related to keywords if a user frequently uses them. Furthermore, it can suggest the most suitable networking events based on the user's social media activity. This allows for the provision of optimal business opportunities tailored to the user's social media presence.

[0070] A social capital management system can analyze a user's past networking event participation history and suggest the most suitable events. For example, it can suggest similar events based on the type and scale of events the user has previously attended. It can also suggest relevant events based on the user's past successful business opportunities. Furthermore, it can analyze the user's past participation history and suggest the optimal timing for events. This allows the system to provide the most suitable networking events based on past history.

[0071] A social capital management system can suggest optimal networking strategies based on the user's business situation. For example, if a user starts a new project, it can suggest business partners related to that project. If a user is raising funds, it can also suggest networking opportunities with investors. Furthermore, it can suggest optimal networking events based on the user's business situation. This allows the system to provide the most suitable networking strategy tailored to the user's business circumstances.

[0072] A social capital management system can learn user behavior patterns and suggest optimal networking strategies. For example, it can suggest similar events based on the types and times of events a user frequently attends. It can also analyze user behavior patterns and suggest the optimal timing for networking. Furthermore, it can suggest the most suitable business partners based on user behavior patterns. This allows for the provision of optimal networking strategies tailored to user behavior patterns.

[0073] A social capital management system can analyze a user's past investment history and suggest optimal investment opportunities. For example, it can suggest similar investment opportunities based on the type and scale of successful investments the user has made in the past. It can also analyze a user's past investment history and suggest the optimal timing for investment. Furthermore, it can suggest the most suitable investment partner based on the user's past investment history. In this way, it can provide optimal investment opportunities based on past investment history.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The network mapping unit visually maps the user's network. For example, the user's network can be visually mapped in the form of graphs, heatmaps, network diagrams, etc. The network mapping unit also collects the user's network data and identifies important contacts based on frequent communication and business importance. Furthermore, the network mapping unit can update the user's network in real time to reflect the latest information. Step 2: The Relationship Management Department manages communication history with key contacts based on the data mapped by the Network Mapping Department. For example, it can manage communication history such as emails, phone calls, and meeting records, and set reminders for regular follow-up. Furthermore, it can propose actions to maintain and strengthen relationships with key contacts. Step 3: The Opportunity Recommendation Department proposes new business opportunities and collaborations based on data managed by the Relationship Management Department. For example, it can propose new business opportunities based on the user's interests and past investment history, and propose optimal collaborations based on the user's business situation and areas of interest. Furthermore, it can analyze the user's network data to identify potential business partners. Step 4: The evaluation unit quantitatively assesses the quality and impact of the network based on the data proposed by the opportunity recommendation unit. For example, it evaluates the quality and impact of the network based on criteria such as the number of touchpoints, the importance of touchpoints, and the scope of influence, and quantitatively assesses the growth of the user's network. Furthermore, it can also identify areas for network enhancement.

[0076] (Example of form 2) The social capital management system according to an embodiment of the present invention is an AI-driven platform for founders and investors to efficiently manage and grow their social capital. This social capital management system supports the optimization of networking, the strengthening of relationship building, and the discovery of investment opportunities. Users can visualize their network and identify important contacts and potential business partners. For example, the network mapping unit visually maps the user's network and identifies important contacts. Next, the relationship management unit manages the communication history with important contacts and encourages regular follow-up. Furthermore, the opportunity recommendation unit suggests new business opportunities and collaborations based on the user's interests and past investment history. Finally, the evaluation unit quantitatively evaluates the quality and influence of the user's network and provides indicators for growth. These functions are realized by utilizing generative AI. Specifically, this includes an NLP unit for natural language processing, a machine learning unit for learning user behavior patterns, and a data analysis unit for collecting and analyzing data. As a result, the social capital management system enables founders and investors to efficiently manage and grow their social capital.

[0077] The social capital management system according to this embodiment comprises a network mapping unit, a relationship management unit, an opportunity recommendation unit, and an evaluation unit. The network mapping unit visually maps the user's network. The network mapping unit can visually map the user's network in the form of, for example, a graph, a heat map, or a network diagram. The network mapping unit can also collect the user's network data and identify important contacts. For example, the network mapping unit analyzes the user's network data and identifies important contacts based on frequent communication and business importance. Furthermore, the network mapping unit can update the user's network in real time to reflect the latest information. The relationship management unit manages the communication history with important contacts based on the data mapped by the network mapping unit. The relationship management unit can manage communication history such as emails, phone calls, and meeting records. The relationship management unit can also set reminders to periodically follow up on the communication history with important contacts. Furthermore, the relationship management unit can also suggest actions to maintain and strengthen relationships with important contacts for the user. The Opportunity Recommendation Department proposes new business opportunities and collaborations based on data managed by the Relationship Management Department. For example, the Opportunity Recommendation Department can propose new business opportunities based on the user's interests and past investment history. It can also propose optimal collaborations based on the user's business situation and areas of interest. Furthermore, the Opportunity Recommendation Department can analyze the user's network data to identify potential business partners. The Evaluation Department quantitatively evaluates the quality and influence of the network based on the data proposed by the Opportunity Recommendation Department. For example, the Evaluation Department can evaluate the quality and influence of the network based on criteria such as the number of contacts, the importance of contacts, and the scope of influence. The Evaluation Department can also quantitatively evaluate the growth of the user's network and provide growth indicators.Furthermore, the evaluation unit can analyze the user's network data and identify areas for network enhancement. This allows the social capital management system according to the embodiment to efficiently manage and grow the user's network of contacts.

[0078] The network mapping unit visually maps a user's network of contacts. Specifically, it can visually map a user's network in the form of graphs, heatmaps, and network diagrams. This allows users to grasp the breadth of their network and important connections at a glance. For example, in a graph, each connection is displayed as a node, and the edges between nodes indicate relationships. In a heatmap, the intensity of the color changes according to the importance and frequency of the connection, visually highlighting important connections. In a network diagram, the relationships between connections are arranged in a way that is intuitively understandable. Furthermore, the network mapping unit can also collect user network data and identify important connections. For example, it can analyze a user's email and social media data to identify important connections based on frequent communication and business importance. This makes it easier for users to understand which connections are important for their business. In addition, the network mapping unit can update the user's network in real time, reflecting the latest information. This allows users to always make decisions based on the latest network information. For example, if a new connection is added or the relationship with an existing connection changes, the network mapping unit immediately updates the information and notifies the user. This allows users to strategically manage their network based on the latest information at all times.

[0079] The Relationship Management Department manages communication history with important contacts based on data mapped by the Network Mapping Department. Specifically, the Relationship Management Department can centrally manage communication history such as emails, phone calls, and meeting records. This allows users to easily refer to past communication history and understand their relationships with important contacts. For example, the Relationship Management Department can display in chronological order what kind of emails a user exchanged with a particular contact or what kind of meetings they held. The Relationship Management Department can also set reminders to periodically follow up on communication history with important contacts. This makes it easier for users to maintain relationships with important contacts. For example, the Relationship Management Department can send a reminder to the user after a certain period of time has passed, notifying them of the need for follow-up. Furthermore, the Relationship Management Department can also suggest actions to maintain and strengthen relationships with important contacts for the user. For example, the Relationship Management Department can suggest setting up regular meetings or inviting users to events of shared interests to strengthen relationships with specific contacts. This allows users to strategically strengthen their relationships with important contacts.

[0080] The Opportunity Recommendation Department proposes new business opportunities and collaborations based on data managed by the Relationship Management Department. Specifically, the Opportunity Recommendation Department can propose new business opportunities based on the user's interests and past investment history. For example, it can identify new business opportunities related to projects the user has previously invested in or areas of interest, and notify the user. The Opportunity Recommendation Department can also propose optimal collaborations based on the user's business situation and areas of interest. For example, if a user is looking for a new partner in a particular field, the Opportunity Recommendation Department can identify a suitable partner in that field and propose collaboration. Furthermore, the Opportunity Recommendation Department can analyze the user's network data to identify potential business partners. For example, it can identify contacts within the user's network that share common interests and goals, and propose collaboration with those contacts. This allows users to efficiently discover and strategically utilize new business opportunities.

[0081] The evaluation department quantitatively assesses the quality and impact of the network based on data proposed by the opportunity recommendation department. Specifically, the evaluation department can assess the quality and impact of the network based on criteria such as the number of touchpoints, the importance of touchpoints, and the scope of influence. For example, the evaluation department scores the number of touchpoints within a user's network and the influence of those touchpoints, and provides this information to the user. The evaluation department can also quantitatively assess the growth of a user's network and provide growth indicators. For example, the evaluation department evaluates the addition of new touchpoints and the strengthening of relationships with existing touchpoints within a certain period, and provides feedback on the results to the user. Furthermore, the evaluation department can analyze the user's network data and identify areas for network improvement. For example, the evaluation department identifies weaknesses and areas for improvement within a user's network and proposes specific action plans based on these. This allows users to efficiently strengthen their networks and contribute to business success.

[0082] The system includes an NLP (Natural Language Processing) unit that performs natural language processing. The NLP unit can analyze user input using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the NLP unit can collect user input data and understand the user's intent based on the analysis results. For example, the NLP unit can analyze user input text and extract important keywords and phrases. In addition, the NLP unit can generate appropriate responses based on user input data. This allows for efficient analysis of user input through natural language processing.

[0083] It is equipped with a machine learning unit that learns user behavior patterns. The machine learning unit learns user behavior patterns. For example, the machine learning unit can collect data such as past behavior history, frequency and timing of behavior, and learn user behavior patterns. The machine learning unit can also analyze user behavior data and identify behavior patterns. For example, the machine learning unit can extract specific behavior patterns based on the user's past behavior data. Furthermore, the machine learning unit can make appropriate suggestions to the user based on the learned behavior patterns. In this way, learning user behavior patterns makes it possible to make more appropriate suggestions.

[0084] The system includes a data analysis department that collects and analyzes data. The data analysis department can collect and analyze data such as user network data, behavioral data, and communication history. Furthermore, based on the collected data, the data analysis department can evaluate the quality and influence of the user's network. For example, it can analyze user network data to evaluate the number and importance of contacts. Additionally, it can identify behavioral patterns based on user behavioral data. This allows for the evaluation of the quality and influence of the user's network through data collection and analysis.

[0085] The Relationship Management Department can manage communication history with important touchpoints for users and encourage regular follow-up. For example, the Relationship Management Department manages communication history such as emails, phone calls, and meeting records with important touchpoints. It can also set reminders for regular follow-up. For instance, it can suggest follow-up timings based on communication history with important touchpoints. Furthermore, the Relationship Management Department can suggest actions to users to maintain and strengthen relationships with important touchpoints. This allows for the maintenance and strengthening of relationships with important touchpoints.

[0086] The opportunity recommendation unit can suggest new business opportunities and collaborations based on the user's interests and past investment history. For example, it can suggest new business opportunities based on the user's interests and past investment history. It can also suggest optimal collaborations based on the user's business situation and areas of interest. For instance, it can analyze the user's network data to identify potential business partners. Furthermore, it can prioritize business opportunities based on the user's interests and past investment history. This allows it to provide users with beneficial business opportunities.

[0087] The network mapping unit can estimate the user's emotions and adjust the display method of the network mapping based on the estimated emotions. For example, if the user is stressed, the network mapping unit can provide a simple and visually less burdensome display method. If the user is relaxed, the network mapping unit can also provide a display method that includes detailed information. Furthermore, if the user is excited, the network mapping unit can provide a display method with visually stimulating effects. This reduces visual burden by providing a display method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The network mapping unit can analyze the user's past network construction history and select the optimal mapping method. For example, the network mapping unit can prioritize displaying contacts that the user has frequently interacted with in the past. It can also automatically highlight important contacts from the user's past network construction history. Furthermore, the network mapping unit can analyze the user's past network construction history and propose the most effective mapping method. This allows for more efficient network management for the user by providing the optimal mapping method based on past history.

[0089] The network mapping unit can filter network data based on the user's current business situation and areas of interest. For example, it can prioritize displaying highly relevant contacts based on the user's current business situation. It can also filter relevant contacts based on the user's areas of interest. Furthermore, it can suggest optimal contacts considering the user's business situation and areas of interest. This allows the system to provide optimal contacts tailored to the user's business situation and areas of interest.

[0090] The network mapping unit can estimate the user's emotions and determine the priority of touchpoints to map based on the estimated emotions. For example, if the user is stressed, the network mapping unit will prioritize displaying important touchpoints. It can also prioritize displaying touchpoints containing detailed information if the user is relaxed. Furthermore, if the user is excited, it can prioritize displaying visually stimulating touchpoints. This enables more effective network management by providing touchpoint priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The network mapping unit can prioritize mapping highly relevant touchpoints by considering the user's geographical location information during network mapping. For example, the network mapping unit can prioritize displaying touchpoints close to the user's current location. Furthermore, the network mapping unit can filter highly relevant touchpoints based on the user's geographical location information. In addition, the network mapping unit can suggest optimal touchpoints considering the user's geographical location information. This allows the system to provide optimal touchpoints based on the user's geographical location.

[0092] The network mapping unit can analyze a user's social media activity during network mapping and map relevant touchpoints. For example, it can prioritize displaying highly relevant touchpoints based on the user's social media activity. Furthermore, the network mapping unit can analyze the user's social media activity and automatically highlight important touchpoints. In addition, it can suggest optimal touchpoints considering the user's social media activity. This allows for the provision of optimal touchpoints based on the user's social media activity.

[0093] The Relationship Management Unit can estimate a user's emotions and adjust how the communication history is displayed based on those emotions. For example, if a user is stressed, the Relationship Management Unit can provide a simple and visually less burdensome display method. If a user is relaxed, the Relationship Management Unit can also provide a display method that includes detailed information. Furthermore, if a user is excited, the Relationship Management Unit can provide a display method with visually stimulating effects. This reduces visual burden by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The Relationship Management Department can analyze past interactions at key points of contact and select the optimal follow-up method when managing communication history. For example, the Relationship Management Department can propose the optimal follow-up method based on past interactions with key points of contact. Furthermore, the Relationship Management Department can analyze past interactions at key points of contact and select effective follow-up methods. In addition, the Relationship Management Department can propose the optimal follow-up timing, taking into account past interactions with key points of contact. This allows for strengthening relationships by providing optimal follow-up methods based on past interactions.

[0095] The Relationship Management Department can adjust the timing of follow-ups based on the user's current business situation when managing communication history. For example, the Relationship Management Department can suggest the optimal follow-up timing based on the user's current business situation. Furthermore, the Relationship Management Department can adjust the timing of follow-ups considering the user's business situation. In addition, the Relationship Management Department can select an effective follow-up method based on the user's current business situation. This allows for the provision of optimal follow-up timing tailored to the user's business situation.

[0096] The relationship management unit can estimate the user's emotions and determine the priority of follow-up based on those emotions. For example, if the user is stressed, the relationship management unit will prioritize following up on important touchpoints. Similarly, if the user is relaxed, the relationship management unit can prioritize following up on touchpoints containing detailed information. Furthermore, if the user is excited, the relationship management unit can prioritize following up on visually stimulating touchpoints. This enables more effective relationship management by providing follow-up priorities tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The Relationship Management Department can prioritize managing highly relevant contacts when managing communication history, taking into account the user's geographical location. For example, it can prioritize contacts close to the user's current location. Furthermore, the Relationship Management Department can filter highly relevant contacts based on the user's geographical location. In addition, the Relationship Management Department can suggest optimal contacts, taking the user's geographical location into consideration. This allows for the provision of optimal contacts based on the user's geographical location.

[0098] The Relationship Management Department can analyze users' social media activity and manage relevant touchpoints when managing communication history. For example, it can prioritize and manage highly relevant touchpoints based on users' social media activity. Furthermore, the Relationship Management Department can analyze users' social media activity and automatically highlight important touchpoints. In addition, it can suggest optimal touchpoints considering users' social media activity. This allows for the provision of optimal touchpoints based on users' social media activity.

[0099] The opportunity recommendation unit can estimate the user's emotions and adjust how business opportunities are presented based on those emotions. For example, if the user is stressed, the opportunity recommendation unit can provide simple and visually less burdensome suggestions. If the user is relaxed, it can also provide suggestions that include more detailed information. Furthermore, if the user is excited, it can provide suggestions with visually stimulating effects. This reduces visual burden by providing suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The opportunity recommendation unit can analyze the user's past investment history and select the optimal proposal method when proposing business opportunities. For example, the opportunity recommendation unit can propose the most suitable business opportunities based on the user's past investment history. It can also analyze the user's past investment history and select the most effective proposal method. Furthermore, the opportunity recommendation unit can consider the user's past investment history and propose the optimal timing for the proposal. In this way, by providing the optimal proposal method based on past investment history, it can provide users with beneficial business opportunities.

[0101] The opportunity recommendation unit can adjust the timing of business opportunity proposals based on the user's current business situation. For example, it can suggest the optimal timing for proposals based on the user's current business situation. Furthermore, the opportunity recommendation unit can also adjust the timing of proposals considering the user's business situation. In addition, it can select an effective proposal method based on the user's current business situation. This allows the system to provide the optimal timing for proposals tailored to the user's business situation.

[0102] The opportunity recommendation unit can estimate the user's emotions and determine the priority of business opportunities to suggest based on those emotions. For example, if the user is feeling stressed, the opportunity recommendation unit will prioritize suggesting important business opportunities. If the user is relaxed, the opportunity recommendation unit can also prioritize suggesting business opportunities that include detailed information. Furthermore, if the user is excited, the opportunity recommendation unit can prioritize suggesting visually stimulating business opportunities. This allows for more effective suggestions by providing priority suggestions for business opportunities tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The opportunity recommendation unit can prioritize suggesting highly relevant business opportunities by considering the user's geographical location when proposing business opportunities. For example, it can prioritize suggesting business opportunities close to the user's current location. Furthermore, the opportunity recommendation unit can filter highly relevant business opportunities based on the user's geographical location. In addition, the opportunity recommendation unit can suggest the most suitable business opportunities by considering the user's geographical location. This allows for the provision of optimal business opportunities based on the user's geographical location.

[0104] The opportunity recommendation unit can analyze a user's social media activity and suggest relevant opportunities when proposing business opportunities. For example, it can prioritize suggesting highly relevant business opportunities based on the user's social media activity. Furthermore, the opportunity recommendation unit can analyze the user's social media activity and automatically highlight important business opportunities. It can also suggest the most suitable business opportunities, taking the user's social media activity into consideration. This allows the system to provide optimal business opportunities based on the user's social media activity.

[0105] The evaluation unit can estimate the user's emotions and adjust the evaluation method for network quality and influence based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can provide a simple and visually less burdensome evaluation method. If the user is relaxed, the evaluation unit can also provide an evaluation method that includes detailed information. Furthermore, if the user is excited, the evaluation unit can provide an evaluation method with visually stimulating effects. This reduces the visual burden by providing an evaluation method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The evaluation unit can analyze the user's past network construction history and select the optimal evaluation method when assessing the quality and impact of a network. For example, the evaluation unit can propose the optimal evaluation method based on the user's past network construction history. Furthermore, the evaluation unit can analyze the user's past network construction history and select an effective evaluation method. In addition, the evaluation unit can propose the optimal evaluation timing, taking into account the user's past network construction history. This allows for an accurate assessment of the user's network quality and impact by providing the optimal evaluation method based on past network construction history.

[0107] The evaluation unit can adjust the timing of the evaluation based on the user's current business situation when assessing the quality and impact of the network. For example, the evaluation unit can propose the optimal evaluation timing based on the user's current business situation. Furthermore, the evaluation unit can also adjust the evaluation timing considering the user's business situation. In addition, the evaluation unit can select an effective evaluation method based on the user's current business situation. This allows for the provision of the optimal evaluation timing tailored to the user's business situation.

[0108] The evaluation unit can estimate the user's emotions and determine the priority of networks to evaluate based on the estimated emotions. For example, if the user is stressed, the evaluation unit will prioritize evaluating important networks. If the user is relaxed, the evaluation unit may also prioritize evaluating networks containing detailed information. Furthermore, if the user is excited, the evaluation unit may prioritize evaluating visually stimulating networks. This allows for more effective evaluation by providing network priorities tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The evaluation unit can prioritize evaluating highly relevant networks by considering the user's geographical location when assessing network quality and impact. For example, the evaluation unit can prioritize evaluating networks close to the user's current location. Furthermore, the evaluation unit can filter highly relevant networks based on the user's geographical location. In addition, the evaluation unit can propose the optimal network considering the user's geographical location. This allows for the provision of the most suitable network based on the user's geographical location.

[0110] The evaluation unit can analyze users' social media activity and evaluate relevant networks when assessing the quality and influence of a network. For example, the evaluation unit can prioritize evaluating highly relevant networks based on users' social media activity. Furthermore, the evaluation unit can analyze users' social media activity and automatically highlight important networks. In addition, the evaluation unit can suggest the optimal network, taking into account users' social media activity. This allows for the provision of the most suitable network based on users' social media activity.

[0111] The NLP (Neuro-Language Processing) unit can estimate the user's emotions and adjust the natural language processing algorithm based on those emotions. For example, if the user is stressed, the NLP unit can provide a simple and visually less burdensome algorithm. If the user is relaxed, the NLP unit can also provide an algorithm that includes detailed information. Furthermore, if the user is excited, the NLP unit can provide an algorithm with visually stimulating effects. This reduces the visual burden by providing an algorithm that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The NLP (Neuro-Language Processing) unit can analyze the user's past communication history during natural language processing and select the optimal processing method. For example, the NLP unit can propose the optimal processing method based on the user's past communication history. Furthermore, the NLP unit can analyze the user's past communication history and select the most effective processing method. In addition, the NLP unit can propose the optimal processing timing, taking into account the user's past communication history. This allows for efficient analysis of user input by providing the optimal processing method based on past communication history.

[0113] The NLP (Neuro-Language Processing) unit can adjust the timing of processing based on the user's current business situation during natural language processing. For example, the NLP unit can suggest the optimal processing timing based on the user's current business situation. Furthermore, the NLP unit can adjust the processing timing considering the user's business situation. In addition, the NLP unit can select the most effective processing method based on the user's current business situation. This allows the NLP unit to provide the optimal processing timing tailored to the user's business situation.

[0114] The NLP (Neuro-Language Processing) unit can estimate the user's emotions and determine the priority of natural language processing based on the estimated emotions. For example, if the user is stressed, the NLP unit will prioritize important communications. If the user is relaxed, the NLP unit can also prioritize communications containing detailed information. Furthermore, if the user is excited, the NLP unit can prioritize visually stimulating communications. This allows for more effective natural language processing by providing priorities tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The NLP (Neuro-Language Processing) unit can prioritize processing highly relevant data by considering the user's geographical location during natural language processing. For example, the NLP unit prioritizes processing data close to the user's current location. Furthermore, the NLP unit can filter highly relevant data based on the user's geographical location. In addition, the NLP unit can suggest optimal data considering the user's geographical location. This allows the system to provide optimal data based on the user's geographical location.

[0116] The NLP (Neuro-Language Processing) unit can analyze a user's social media activity and process relevant data during natural language processing. For example, the NLP unit prioritizes processing highly relevant data based on the user's social media activity. It can also analyze the user's social media activity and automatically highlight important data. Furthermore, the NLP unit can suggest optimal data considering the user's social media activity. This allows for the provision of optimal data based on the user's social media activity.

[0117] The machine learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the machine learning unit can provide simple, visually less burdensome training data. If the user is relaxed, the machine learning unit can also provide training data containing detailed information. Furthermore, if the user is excited, the machine learning unit can provide training data with visually stimulating effects. This reduces the visual burden by providing training data tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The machine learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the machine learning unit can propose the optimal learning algorithm based on past learning data. It can also analyze past learning data and select an effective learning algorithm. Furthermore, the machine learning unit can propose the optimal learning timing, taking past learning data into consideration. This improves learning efficiency by providing the optimal learning algorithm based on past learning data.

[0119] The machine learning unit can adjust the timing of learning based on the user's current business situation. For example, the machine learning unit can suggest the optimal learning timing based on the user's current business situation. It can also adjust the learning timing considering the user's business situation. Furthermore, the machine learning unit can select an effective learning method based on the user's current business situation. This allows for the provision of optimal learning timing tailored to the user's business situation.

[0120] The machine learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the machine learning unit can reduce the learning frequency. Conversely, if the user is relaxed, the machine learning unit can increase the learning frequency. Furthermore, if the user is excited, the machine learning unit can adjust the learning frequency. This improves learning efficiency by providing a learning frequency that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The machine learning unit can weight training data based on user behavior patterns during training. For example, the machine learning unit can propose the optimal training data weighting based on user behavior patterns. Furthermore, the machine learning unit can analyze user behavior patterns and select effective training data weighting. It can also propose the optimal training data weighting considering user behavior patterns. This allows for the provision of optimal training data weighting based on user behavior patterns.

[0122] The machine learning unit can analyze users' social media activity during training and select relevant training data. For example, it can prioritize selecting highly relevant training data based on users' social media activity. The machine learning unit can also analyze users' social media activity and automatically highlight important training data. Furthermore, it can suggest optimal training data considering users' social media activity. This allows for the provision of optimal training data based on users' social media activity.

[0123] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the data analysis unit can provide a simple and visually less burdensome data analysis method. If the user is relaxed, the data analysis unit can also provide a data analysis method that includes detailed information. Furthermore, if the user is excited, the data analysis unit can provide a data analysis method with visually stimulating effects. This reduces the visual burden by providing a data analysis method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The data analysis department can analyze a user's past data history and select the optimal analysis method during data analysis. For example, the data analysis department can propose the optimal analysis method based on the user's past data history. Furthermore, the data analysis department can analyze a user's past data history and select an effective analysis method. In addition, the data analysis department can propose the optimal timing for analysis, taking into account the user's past data history. This improves the efficiency of data analysis by providing the optimal analysis method based on past data history.

[0125] The data analysis department can adjust the timing of data analysis based on the user's current business situation. For example, the data analysis department can suggest the optimal timing for analysis based on the user's current business situation. Furthermore, the data analysis department can adjust the timing of analysis considering the user's business situation. In addition, the data analysis department can select an effective analysis method based on the user's current business situation. This allows the department to provide the optimal timing for analysis tailored to the user's business situation.

[0126] The data analysis unit can estimate the user's emotions and prioritize data analysis based on those emotions. For example, if the user is stressed, the data analysis unit will prioritize analyzing important data. If the user is relaxed, the data analysis unit can also prioritize analyzing data containing detailed information. Furthermore, if the user is excited, the data analysis unit can prioritize analyzing visually stimulating data. This allows for more effective data analysis by providing data analysis priorities tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The data analysis department can prioritize the analysis of highly relevant data by considering the user's geographical location during data analysis. For example, the data analysis department can prioritize the analysis of data close to the user's current location. Furthermore, the data analysis department can filter highly relevant data based on the user's geographical location. In addition, the data analysis department can suggest optimal data considering the user's geographical location. This allows the department to provide optimal data based on the user's geographical location.

[0128] The Data Analysis Department can analyze users' social media activity and analyze relevant data during data analysis. For example, the Data Analysis Department can prioritize the analysis of highly relevant data based on users' social media activity. Furthermore, the Data Analysis Department can analyze users' social media activity and automatically highlight important data. In addition, the Data Analysis Department can suggest optimal data considering users' social media activity. This allows for the provision of optimal data based on users' social media activity.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] A social capital management system can estimate a user's emotions and suggest networking events based on those emotions. For example, if a user is stressed, it can suggest a relaxing, casual event. If a user is excited, it can suggest a high-profile event with many business opportunities. Furthermore, if a user is relaxed, it can suggest a small-group event where deeper relationships can be built. This allows the system to provide the optimal networking event tailored to the user's emotions.

[0131] A social capital management system can suggest nearby business events and networking opportunities, taking into account the user's geographical location. For example, if a user is traveling for business, it will prioritize suggesting business events held in that area. It can also suggest events held near the user's home or office. Furthermore, it can suggest the most suitable events based on the user's travel range. This allows for the provision of optimal networking opportunities based on the user's geographical location.

[0132] A social capital management system can analyze a user's social media activity and suggest relevant business opportunities and networking events. For example, if a user frequently posts about a particular industry, it can suggest events related to that industry. It can also suggest business opportunities related to keywords if a user frequently uses them. Furthermore, it can suggest the most suitable networking events based on the user's social media activity. This allows for the provision of optimal business opportunities tailored to the user's social media presence.

[0133] A social capital management system can analyze a user's past networking event participation history and suggest the most suitable events. For example, it can suggest similar events based on the type and scale of events the user has previously attended. It can also suggest relevant events based on the user's past successful business opportunities. Furthermore, it can analyze the user's past participation history and suggest the optimal timing for events. This allows the system to provide the most suitable networking events based on past history.

[0134] A social capital management system can estimate a user's emotions and suggest communication methods with business partners based on those estimates. For example, if a user is stressed, it can suggest simple and low-burden communication methods. If the user is relaxed, it can suggest communication methods that include detailed information. Furthermore, if the user is excited, it can suggest visually stimulating communication methods. This allows for the provision of optimal communication methods tailored to the user's emotions.

[0135] A social capital management system can suggest optimal networking strategies based on the user's business situation. For example, if a user starts a new project, it can suggest business partners related to that project. If a user is raising funds, it can also suggest networking opportunities with investors. Furthermore, it can suggest optimal networking events based on the user's business situation. This allows the system to provide the most suitable networking strategy tailored to the user's business circumstances.

[0136] A social capital management system can estimate a user's emotions and adjust the timing of networking based on those emotions. For example, if a user is feeling stressed, it can suggest networking at a time when they can relax. If the user is relaxed, it can suggest a time for more active networking. Furthermore, if the user is excited, it can suggest a time for immediate networking. This allows the system to provide the optimal networking timing according to the user's emotions.

[0137] A social capital management system can learn user behavior patterns and suggest optimal networking strategies. For example, it can suggest similar events based on the types and times of events a user frequently attends. It can also analyze user behavior patterns and suggest the optimal timing for networking. Furthermore, it can suggest the most suitable business partners based on user behavior patterns. This allows for the provision of optimal networking strategies tailored to user behavior patterns.

[0138] A social capital management system can estimate a user's emotions and prioritize networking based on those emotions. For example, if a user is stressed, it can prioritize following up on important interactions. If a user is relaxed, it can prioritize following up on interactions containing detailed information. Furthermore, if a user is excited, it can prioritize following up on visually stimulating interactions. This allows for the provision of optimal networking priorities tailored to the user's emotions.

[0139] A social capital management system can analyze a user's past investment history and suggest optimal investment opportunities. For example, it can suggest similar investment opportunities based on the type and scale of successful investments the user has made in the past. It can also analyze a user's past investment history and suggest the optimal timing for investment. Furthermore, it can suggest the most suitable investment partner based on the user's past investment history. In this way, it can provide optimal investment opportunities based on past investment history.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The network mapping unit visually maps the user's network. For example, the user's network can be visually mapped in the form of graphs, heatmaps, network diagrams, etc. The network mapping unit also collects the user's network data and identifies important contacts based on frequent communication and business importance. Furthermore, the network mapping unit can update the user's network in real time to reflect the latest information. Step 2: The Relationship Management Department manages communication history with key contacts based on the data mapped by the Network Mapping Department. For example, it can manage communication history such as emails, phone calls, and meeting records, and set reminders for regular follow-up. Furthermore, it can propose actions to maintain and strengthen relationships with key contacts. Step 3: The Opportunity Recommendation Department proposes new business opportunities and collaborations based on data managed by the Relationship Management Department. For example, it can propose new business opportunities based on the user's interests and past investment history, and propose optimal collaborations based on the user's business situation and areas of interest. Furthermore, it can analyze the user's network data to identify potential business partners. Step 4: The evaluation unit quantitatively assesses the quality and impact of the network based on the data proposed by the opportunity recommendation unit. For example, it evaluates the quality and impact of the network based on criteria such as the number of touchpoints, the importance of touchpoints, and the scope of influence, and quantitatively assesses the growth of the user's network. Furthermore, it can also identify areas for network enhancement.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0144] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the network mapping unit, relationship management unit, opportunity recommendation unit, evaluation unit, NLP unit, machine learning unit, and data analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the network mapping unit visually maps the user's network and identifies important contacts using the control unit 46A of the smart device 14. The relationship management unit manages communication history using the control unit 46A of the smart device 14 and encourages regular follow-up. The opportunity recommendation unit proposes new business opportunities and collaborations using the specific processing unit 290 of the data processing unit 12. The evaluation unit quantitatively evaluates the quality and influence of the network using the specific processing unit 290 of the data processing unit 12. The NLP unit performs natural language processing using the specific processing unit 290 of the data processing unit 12. The machine learning unit learns the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The data analysis unit collects and analyzes data using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the network mapping unit, relationship management unit, opportunity recommendation unit, evaluation unit, NLP unit, machine learning unit, and data analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the network mapping unit visually maps the user's network and identifies important contacts using the control unit 46A of the smart glasses 214. The relationship management unit manages communication history using the control unit 46A of the smart glasses 214 and encourages regular follow-up. The opportunity recommendation unit proposes new business opportunities and collaborations using the specific processing unit 290 of the data processing unit 12. The evaluation unit quantitatively evaluates the quality and influence of the network using the specific processing unit 290 of the data processing unit 12. The NLP unit performs natural language processing using the specific processing unit 290 of the data processing unit 12. The machine learning unit learns the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The data analysis unit collects and analyzes data using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the network mapping unit, relationship management unit, opportunity recommendation unit, evaluation unit, NLP unit, machine learning unit, and data analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the network mapping unit visually maps the user's network of contacts and identifies important contacts using the control unit 46A of the headset terminal 314. The relationship management unit manages communication history using the control unit 46A of the headset terminal 314 and encourages regular follow-up. The opportunity recommendation unit proposes new business opportunities and collaborations using the specific processing unit 290 of the data processing unit 12. The evaluation unit quantitatively evaluates the quality and influence of the network using the specific processing unit 290 of the data processing unit 12. The NLP unit performs natural language processing using the specific processing unit 290 of the data processing unit 12. The machine learning unit learns the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The data analysis unit collects and analyzes data using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0180] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0181] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0182] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0184] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0185] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0186] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0187] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0188] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0189] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0190] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0191] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0192] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0193] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0194] Each of the multiple elements described above, including the network mapping unit, relationship management unit, opportunity recommendation unit, evaluation unit, NLP unit, machine learning unit, and data analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the network mapping unit visually maps the user's network of contacts and identifies important contacts using the control unit 46A of the robot 414. The relationship management unit manages communication history using the control unit 46A of the robot 414 and encourages regular follow-up. The opportunity recommendation unit proposes new business opportunities and collaborations using the specific processing unit 290 of the data processing unit 12. The evaluation unit quantitatively evaluates the quality and influence of the network using the specific processing unit 290 of the data processing unit 12. The NLP unit performs natural language processing using the specific processing unit 290 of the data processing unit 12. The machine learning unit learns the user's behavior patterns using the specific processing unit 290 of the data processing unit 12. The data analysis unit collects and analyzes data using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0195] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0196] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0197] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0198] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0199] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0200] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0201] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0202] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0203] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0204] 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.

[0205] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0206] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0207] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0208] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0209] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0210] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0211] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0212] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0213] (Note 1) A network mapping unit that visually maps the user's network of contacts, A relationship management unit manages the communication history with important contacts based on the data mapped by the network mapping unit, Based on the data managed by the aforementioned Relationship Management Department, the Opportunity Recommendation Department proposes new business opportunities and collaborations. The system includes an evaluation unit that quantitatively evaluates the quality and influence of the network based on the data proposed by the aforementioned opportunity recommendation unit. A system characterized by the following features. (Note 2) It includes an NLP (Natural Language Processing) unit for natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a machine learning unit that learns user behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a data analysis department that collects and analyzes data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Relationship Management Department, Manage communication history with important touchpoints for users and encourage regular follow-up. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned opportunity recommendation unit is Based on users' interests and past investment history, we propose new business opportunities and collaborations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned network mapping unit It estimates the user's emotions and adjusts how the network mapping is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned network mapping unit Analyze the user's past network construction history and select the optimal mapping method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned network mapping unit During network mapping, filtering is performed based on the user's current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned network mapping unit It estimates the user's emotions and determines the priority of touchpoints to map based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned network mapping unit During network mapping, the system prioritizes mapping highly relevant touchpoints by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned network mapping unit During network mapping, the system analyzes users' social media activity and maps relevant touchpoints. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned Relationship Management Department, It estimates the user's emotions and adjusts how the communication history is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned Relationship Management Department, When managing communication history, analyze past interactions at important points of contact and select the most appropriate follow-up method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned Relationship Management Department, When managing communication history, adjust the timing of follow-ups based on the user's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned Relationship Management Department, The system estimates the user's emotions and determines the priority of follow-up based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned Relationship Management Department, When managing communication history, the system prioritizes managing highly relevant contacts by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Relationship Management Department, When managing communication history, analyze users' social media activity and manage relevant touchpoints. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned opportunity recommendation unit is It estimates user sentiment and adjusts how business opportunities are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned opportunity recommendation unit is When proposing business opportunities, analyze the user's past investment history and select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned opportunity recommendation unit is When proposing business opportunities, adjust the timing of the proposal based on the user's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned opportunity recommendation unit is It estimates user emotions and prioritizes business opportunities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned opportunity recommendation unit is When proposing business opportunities, the system prioritizes suggesting highly relevant opportunities by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned opportunity recommendation unit is When proposing business opportunities, analyze users' social media activity and suggest relevant opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, It estimates user sentiment and adjusts the evaluation method for network quality and influence based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, When evaluating the quality and impact of a network, the system analyzes the user's past network construction history and selects the most appropriate evaluation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, When evaluating network quality and impact, the timing of the evaluation is adjusted based on the user's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, This process estimates user emotions and determines network priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit, When evaluating the quality and impact of a network, the system prioritizes evaluating networks that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The evaluation unit, When evaluating the quality and influence of a network, we analyze users' social media activity and evaluate the relevant networks. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned NLP unit is It estimates the user's emotions and adjusts the natural language processing algorithm based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned NLP unit is During natural language processing, the system analyzes the user's past communication history and selects the optimal processing method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned NLP unit is During natural language processing, the timing of processing is adjusted based on the user's current business situation. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned NLP unit is It estimates the user's emotions and determines the priority of natural language processing based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned NLP unit is When processing natural language, the system prioritizes processing highly relevant data by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned NLP unit is When processing natural language, analyze the user's social media activity and process the relevant data. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned machine learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned machine learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned machine learning unit, During the learning process, the timing of the learning process is adjusted based on the user's current business situation. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned machine learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned machine learning unit, During training, the training data is weighted based on user behavior patterns. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned machine learning unit, During training, the system analyzes users' social media activity and selects relevant training data. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned data analysis unit, We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned data analysis unit, During data analysis, the system analyzes the user's past data history and selects the most suitable analysis method. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned data analysis unit, When analyzing data, adjust the timing of the analysis based on the user's current business situation. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned data analysis unit, We estimate user sentiment and prioritize data analysis based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned data analysis unit, When analyzing data, the system prioritizes analyzing highly relevant data by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned data analysis unit, During data analysis, we analyze users' social media activity and analyze relevant data. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A network mapping unit that visually maps the user's network of contacts, A relationship management unit manages the communication history with important contacts based on the data mapped by the network mapping unit, Based on the data managed by the aforementioned Relationship Management Department, the Opportunity Recommendation Department proposes new business opportunities and collaborations. The system includes an evaluation unit that quantitatively evaluates the quality and influence of the network based on the data proposed by the aforementioned opportunity recommendation unit. A system characterized by the following features.

2. It includes an NLP (Natural Language Processing) unit for natural language processing. The system according to feature 1.

3. It includes a machine learning unit that learns user behavior patterns. The system according to feature 1.

4. It has a data analysis department that collects and analyzes data. The system according to feature 1.

5. The aforementioned Relationship Management Department, Manage communication history with important touchpoints for users and encourage regular follow-up. The system according to feature 1.

6. The aforementioned opportunity recommendation unit is Based on users' interests and past investment history, we propose new business opportunities and collaborations. The system according to feature 1.

7. The aforementioned network mapping unit It estimates the user's emotions and adjusts how the network mapping is displayed based on the estimated user emotions. The system according to feature 1.

8. The aforementioned network mapping unit Analyze the user's past network construction history and select the optimal mapping method. The system according to feature 1.

9. The aforementioned network mapping unit During network mapping, filtering is performed based on the user's current business situation and areas of interest. The system according to feature 1.

10. The aforementioned network mapping unit It estimates the user's emotions and determines the priority of touchpoints to map based on the estimated user emotions. The system according to feature 1.

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