system

Abstract

The system according to this embodiment aims to generate and execute an optimal promotion strategy based on past communication data and interests. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a strategy generation unit, and an execution unit. The collection unit collects past communication data and interests. The analysis unit analyzes the data collected by the collection unit. The strategy generation unit generates an optimal promotion strategy based on the analysis results obtained by the analysis unit. The execution unit executes the promotion strategy generated by the strategy generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 conventional technology, an optimal promotion strategy has not been sufficiently generated based on past communication data and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and execute an optimal promotion strategy based on past communication data and interests.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a strategy generation unit, and an execution unit. The data collection unit collects past communication data and interests. The analysis unit analyzes the data collected by the data collection unit. The strategy generation unit generates an optimal promotion strategy based on the analysis results obtained by the analysis unit. The execution unit executes the promotion strategy generated by the strategy generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate and execute an optimal promotion strategy based on past communication data and interests. [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 manages communication between a plurality of 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 networking enhancement system according to an embodiment of the present invention is a system that uses generative AI to enhance networking between employees and alumni within the community and designs campaigns that encourage contributions to the company. The networking enhancement system uses generative AI to analyze past communication data and interests and automatically generate an optimal promotion strategy. This promotion strategy aims to enhance networking between employees and alumni and encourage contributions to the company. First, the generative AI analyzes past communication data and interests. In this process, data such as emails, chats, and meeting records exchanged between employees and alumni are collected and analyzed by the generative AI. For example, communication data related to a specific project and data on the interests of employees and alumni are collected. This allows the generative AI to understand the interests and communication patterns of employees and alumni. Next, the generative AI automatically generates an optimal promotion strategy based on the analysis results. Based on the collected data, the generative AI designs a promotion strategy tailored to the interests of employees and alumni. For example, it proposes events to connect employees and alumni who are interested in a specific project, or campaigns based on common interests. In this way, the generating AI automatically generates promotional strategies that enhance networking among employees and alumni and encourage their contributions to the company. This mechanism strengthens networking among employees and alumni and encourages their contributions to the company. For example, by participating in events and campaigns suggested by the generating AI, employees and alumni can share their interests and collaborate on projects. Furthermore, it is expected that the promotional strategies suggested by the generating AI will stimulate communication among employees and alumni, leading to improved productivity throughout the company. Thus, the networking enhancement system can automatically generate promotional strategies that strengthen networking among employees and alumni and encourage their contributions to the company.

[0029] The networking enhancement system according to this embodiment comprises a collection unit, an analysis unit, a strategy generation unit, and an execution unit. The collection unit collects past communication data and interests. For example, the collection unit collects data such as emails, chats, and meeting records exchanged between employees and alumni. For example, the collection unit can collect the content of emails as text data. The collection unit can also analyze chat logs and extract topics of interest. Furthermore, the collection unit can collect meeting records as audio data and convert them into text data using speech recognition technology. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes collected email data to understand the interests and communication patterns of employees and alumni. For example, the analysis unit uses natural language processing technology to analyze the content of emails and extract topics of interest. The analysis unit can also analyze chat logs and identify frequently discussed themes. Furthermore, the analysis unit can analyze meeting records to understand the communication patterns of employees and alumni. The Strategy Generation Unit generates the optimal promotion strategy based on the analysis results obtained by the Analysis Unit. For example, the Strategy Generation Unit uses generational AI to design promotion strategies tailored to the interests of employees and alumni. For example, the Strategy Generation Unit proposes events to connect employees and alumni interested in specific projects. It can also propose campaigns based on shared interests. Furthermore, the Strategy Generation Unit can automatically generate promotion strategies based on the interests of employees and alumni using generational AI. The Execution Unit executes the promotion strategies generated by the Strategy Generation Unit. For example, the Execution Unit executes events and campaigns based on the generated promotion strategies. For example, the Execution Unit can hold online events to enhance networking between employees and alumni. It can also hold offline events to promote interaction between employees and alumni. Furthermore, the Execution Unit can execute email campaigns based on the generated promotion strategies.As a result, the networking enhancement system according to this embodiment can automatically generate promotional strategies that enhance networking among employees and alumni and encourage their contributions to the company.

[0030] The data collection unit collects past communication data and interests. Specifically, it collects data such as emails, chats, and meeting records exchanged between employees and alumni. For example, it can collect email content as text data. Email content includes information such as sender, recipient, date and time of sending, subject, and body, and this data is centrally managed. Furthermore, the data collection unit can also analyze chat logs and extract topics of interest. Chat logs include information such as speaker, content of message, and date and time of message, and by analyzing this data, frequently discussed themes and topics of interest can be identified. In addition, the data collection unit can collect meeting records as audio data and convert them into text data using speech recognition technology. Speech recognition technology can transcribe the statements of meeting participants in real time and organize the content of each speaker's statements. This allows the data collection unit to efficiently collect diverse forms of communication data and provide them to the analysis unit. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, it's possible to configure the system to prioritize the collection of data related to specific projects, or to collect records of important meetings with high accuracy. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it analyzes collected email data to understand the interests and communication patterns of employees and alumni. The analysis department uses natural language processing technology to analyze the content of emails and extract topics of interest. For example, by extracting specific keywords and phrases from the email body and analyzing their frequency and relevance, it is possible to identify the interests of employees and alumni. The analysis department can also analyze chat logs to identify frequently discussed topics. Text mining technology is used to analyze chat logs to understand patterns and trends in the content of conversations. Furthermore, the analysis department can analyze meeting records to understand the communication patterns of employees and alumni. From meeting records, it is possible to analyze the content, frequency, and timing of each speaker's statements to identify the flow of communication and important topics. In this way, the analysis department can quickly and accurately analyze the collected data and understand the interests and communication patterns of employees and alumni. Furthermore, the analysis department can also utilize historical data and statistical information to analyze long-term trends and patterns. For example, past communication data can be used to predict fluctuations in interest during specific periods or events, which can then be used to develop future promotional strategies. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term trend analysis, improving the overall reliability and effectiveness of the system.

[0032] The Strategy Generation Department generates optimal promotional strategies based on the analysis results obtained by the Analysis Department. Specifically, it uses a generative AI to design promotional strategies tailored to the interests of employees and alumni. Based on the data provided by the Analysis Department, the generative AI automatically generates the optimal promotional strategy, taking into account the interests and communication patterns of employees and alumni. For example, it can propose events to connect employees and alumni interested in a particular project. The generative AI can analyze past event data and participant feedback to propose the most effective event format and content. The Strategy Generation Department can also propose campaigns based on common interests. For example, it can propose webinars or workshops targeting employees and alumni interested in a particular technology or industry. Furthermore, the Strategy Generation Department can use the generative AI to automatically generate promotional strategies based on the interests of employees and alumni. The generative AI has an algorithm that learns from the effectiveness of past promotional strategies and proposes the optimal strategy. This allows the Strategy Generation Department to quickly and effectively generate promotional strategies and strengthen networking between employees and alumni. In addition, the Strategy Generation Department can continuously monitor the effectiveness of the generated promotional strategies and modify them as needed. This allows the strategy generation unit to consistently provide highly accurate promotional strategies based on the latest information, maximizing the overall effectiveness of the system.

[0033] The execution team implements the promotional strategies generated by the strategy generation team. Specifically, they execute events and campaigns based on the generated strategies. For example, they might hold online events to enhance networking between employees and alumni. Online events can take the form of webinars or virtual meetups, and participants can join via the internet. The execution team is responsible for planning and running the events, collecting participant feedback, and incorporating it into future events. The execution team can also hold offline events to promote interaction between employees and alumni. Offline events can take the form of seminars, workshops, or networking parties, allowing participants to interact face-to-face. Furthermore, the execution team can also run email campaigns based on the generated promotional strategies. Email campaigns provide employees and alumni with information related to specific interests or projects, encouraging participation. The execution team optimizes the content and timing of emails to ensure effective communication. This allows the execution team to quickly and effectively implement the generated promotional strategies and enhance networking between employees and alumni. In addition, the execution team can continuously monitor the effectiveness of the implemented promotional strategies and modify them as needed. This allows the execution unit to provide highly accurate promotional strategies based on the latest information at all times, maximizing the overall effectiveness of the system.

[0034] The data collection unit can collect data such as emails, chats, and meeting records exchanged between employees and alumni. For example, the data collection unit can collect email content as text data. For example, the data collection unit can retrieve email data from a mail server and save it as text data. The data collection unit can also analyze chat logs and extract areas of interest. For example, the data collection unit can retrieve log data from chat applications and save it as text data. Furthermore, the data collection unit can collect meeting records as audio data and convert it into text data using speech recognition technology. For example, the data collection unit can record meeting audio data and convert it into text data using speech recognition technology. This allows for the collection of communication data between employees and alumni, enabling the understanding of interests and communication patterns. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input email data obtained from a mail server into a generating AI and have the generating AI perform the analysis of the email data.

[0035] The analysis unit can analyze collected data to understand the interests and communication patterns of employees and alumni. For example, the analysis unit can analyze collected email data to understand the interests and communication patterns of employees and alumni. For example, the analysis unit can use natural language processing technology to analyze the content of emails and extract topics of interest. The analysis unit can also analyze chat logs to identify frequently discussed topics. For example, the analysis unit can analyze the text data of chat logs and extract frequently discussed keywords. Furthermore, the analysis unit can analyze meeting records to understand the communication patterns of employees and alumni. For example, the analysis unit can analyze meeting audio data to analyze the frequency and content of statements. In this way, by analyzing the collected data, the interests and communication patterns of employees and alumni can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected email data into a generating AI and have the generating AI perform the analysis of the email data.

[0036] The Strategy Generation Unit can design promotional strategies tailored to the interests of employees and alumni. For example, the Strategy Generation Unit uses generative AI to design promotional strategies tailored to the interests of employees and alumni. For example, the Strategy Generation Unit proposes events to connect employees and alumni interested in a particular project. For example, the Strategy Generation Unit proposes online events targeting employees and alumni interested in a particular project. The Strategy Generation Unit can also propose campaigns based on common interests. For example, the Strategy Generation Unit proposes email campaigns based on common interests. Furthermore, the Strategy Generation Unit can use generative AI to automatically generate promotional strategies based on the interests of employees and alumni. For example, the Generative AI automatically generates promotional strategies tailored to the interests of employees and alumni based on collected data. This enhances networking by designing promotional strategies tailored to the interests of employees and alumni. Some or all of the above processes in the Strategy Generation Unit may be performed using, for example, generative AI, or without generative AI. For example, the strategy generation unit can input collected data into a generation AI and have the generation AI design a promotional strategy.

[0037] The execution unit can carry out events and campaigns based on the generated promotional strategy. For example, the execution unit can carry out online events based on the generated promotional strategy. For example, the execution unit can host webinars to enhance networking between employees and alumni. The execution unit can also host offline events to promote interaction between employees and alumni. For example, the execution unit can host workshops to provide opportunities for employees and alumni to interact directly. Furthermore, the execution unit can also carry out email campaigns based on the generated promotional strategy. For example, the execution unit can send emails providing information about a particular project to employees and alumni who are interested in that project. This enhances networking between employees and alumni by carrying out events and campaigns based on the generated promotional strategy. Some or all of the above processes in the execution unit may be carried out using AI, for example, or not. For example, the execution unit can input the generated promotional strategy into a generating AI and have the generating AI carry out the events and campaigns.

[0038] The Strategy Generation Department can propose events to connect employees interested in a particular project with alumni. For example, the Strategy Generation Department could propose an online event targeting employees and alumni interested in a particular project. For example, the Strategy Generation Department could host a webinar to provide an opportunity for employees and alumni interested in a particular project to share information. The Strategy Generation Department can also propose an offline event. For example, the Strategy Generation Department could host a workshop to provide an opportunity for employees and alumni interested in a particular project to interact directly. Furthermore, the Strategy Generation Department can propose an email campaign to connect employees and alumni interested in a particular project. For example, the Strategy Generation Department could send emails providing information about a particular project to attract the interest of employees and alumni. This facilitates the progress of the project by proposing events to connect employees and alumni interested in a particular project. Some or all of the above processes in the Strategy Generation Department may be performed using, for example, generative AI, or not using generative AI. For example, the strategy generation unit can input collected data into a generation AI and have the generation AI execute event proposals.

[0039] The Strategy Generation Unit can propose campaigns based on common interests. For example, it can propose online campaigns based on common interests. For example, it can propose online discussions targeting employees and alumni interested in a particular technological field. The Strategy Generation Unit can also propose offline campaigns based on common interests. For example, it can propose workshops targeting employees and alumni interested in a particular business strategy. Furthermore, the Strategy Generation Unit can propose email campaigns based on common interests. For example, it can send emails providing information on a particular social issue to attract the attention of employees and alumni. This strengthens networking among employees and alumni by proposing campaigns based on common interests. Some or all of the above processes in the Strategy Generation Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Strategy Generation Unit can input collected data into a generative AI and have the generative AI execute campaign proposals.

[0040] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can prioritize data collection methods that have been successful in the past. For example, the data collection unit can analyze past data collection history and identify successful methods. The data collection unit can also eliminate failed methods from past data collection history. For example, the data collection unit can analyze past data collection history, identify and eliminate failed methods. Furthermore, the data collection unit can propose new collection methods based on past data collection history. For example, the data collection unit can analyze past data collection history and propose new collection methods. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter data based on specific projects or areas of interest during data collection. For example, the data collection unit can collect only data related to a specific project. For example, the data collection unit can filter data based on keywords related to a specific project. The data collection unit can also exclude unnecessary data based on areas of interest. For example, the data collection unit can filter data that is not related to an area of ​​interest. Furthermore, the data collection unit can adjust the data it collects according to the progress of a project. For example, the data collection unit can prioritize the collection of necessary data according to the progress of a project. This allows for the collection of highly relevant data by filtering data based on specific projects or areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input keywords related to a specific project into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will acquire the user's geographical location information and collect data related to that region. The data collection unit can also collect data related to the user's destination if the user is on the move. For example, the data collection unit will acquire the geographical location information of the user's destination and collect related data. Furthermore, if the user is staying in a specific location for an extended period, the data collection unit can also collect data related to that location. For example, the data collection unit will acquire the geographical location information of the user's current location and collect related data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect the relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant data. The data collection unit can also analyze the activities of a user's social media followers and friends and collect relevant data. For example, the data collection unit can analyze the content of posts by a user's followers and friends and collect relevant data. Furthermore, the data collection unit can collect data based on topics that the user has shown interest in on social media. For example, the data collection unit can analyze posts that a user has "liked" or commented on and collect relevant data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the content of a user's social media posts into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit evaluates the importance of the data and performs a simplified analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit evaluates the importance of the data and prioritizes the analysis of important data. This allows for a detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a speech recognition algorithm to audio data. The analysis unit can also apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data into a generating AI and have the generating AI execute a natural language processing algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may, for example, evaluate the data collection timing and prioritize the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. The analysis unit may, for example, evaluate the data collection timing and postpone the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection timing. The analysis unit may, for example, evaluate the data collection timing and adjust the priority of analysis according to the collection timing. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may evaluate the relevance of the data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit may evaluate the relevance of the data and postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit may evaluate the relevance of the data and adjust the order of analysis according to the relevance. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The strategy generation unit can adjust the level of detail of a strategy based on the importance of the promotion when generating a strategy. For example, the strategy generation unit can provide a detailed strategy for important promotions. For example, the strategy generation unit can evaluate the importance of promotions and provide a detailed strategy for important promotions. The strategy generation unit can also provide a simplified strategy for less important promotions. For example, the strategy generation unit can evaluate the importance of promotions and provide a simplified strategy for less important promotions. Furthermore, the strategy generation unit can determine the priority of strategies according to the importance of the promotions. For example, the strategy generation unit can evaluate the importance of promotions and prioritize the strategizing of important promotions. This allows for the provision of detailed strategies for important promotions by adjusting the level of detail of the strategy based on the importance of the promotions. Some or all of the above processing in the strategy generation unit may be performed using a generation AI, or not. For example, the strategy generation unit can input the importance of promotions into the generation AI and have the generation AI perform the adjustment of the level of detail of the strategy.

[0049] The strategy generation unit can apply different strategy algorithms depending on the promotion category when generating strategies. For example, the strategy generation unit applies an event management algorithm to event promotions. For example, the strategy generation unit uses an event management algorithm when strategizing event promotions. The strategy generation unit can also apply a social media analysis algorithm to social media promotions. For example, the strategy generation unit uses a social media analysis algorithm when strategizing social media promotions. Furthermore, the strategy generation unit can apply an email marketing algorithm to email promotions. For example, the strategy generation unit uses an email marketing algorithm when strategizing email promotions. By applying different strategy algorithms depending on the promotion category, a more appropriate strategy can be provided. Some or all of the above processing in the strategy generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the strategy generation unit can input the promotion category into the generation AI and have the generation AI execute the application of the strategy algorithm.

[0050] The strategy generation unit can determine the priority of strategies based on the timing of promotions when generating strategies. For example, the strategy generation unit can prioritize strategizing the most recent promotions. For example, the strategy generation unit can evaluate the timing of promotions and prioritize strategizing the most recent promotions. The strategy generation unit can also postpone promotions in the distant future. For example, the strategy generation unit can evaluate the timing of promotions and postpone promotions in the distant future. Furthermore, the strategy generation unit can adjust the priority of strategies according to the timing of promotions. For example, the strategy generation unit can evaluate the timing of promotions and adjust the priority of strategies according to the timing. This allows for prioritizing the most recent promotions by determining the priority of strategies based on the timing of promotions. Some or all of the above processing in the strategy generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the strategy generation unit can input the timing of promotions into the generation AI and have the generation AI determine the priority of strategies.

[0051] The strategy generation unit can adjust the order of strategies based on the relevance of promotions when generating strategies. For example, the strategy generation unit can prioritize the strategizing of highly relevant promotions. For example, the strategy generation unit can evaluate the relevance of promotions and prioritize the strategizing of highly relevant promotions. The strategy generation unit can also postpone promotions with low relevance. For example, the strategy generation unit can evaluate the relevance of promotions and postpone promotions with low relevance. Furthermore, the strategy generation unit can adjust the order of strategies according to the relevance of promotions. For example, the strategy generation unit can evaluate the relevance of promotions and adjust the order of strategies according to the relevance. In this way, by adjusting the order of strategies based on the relevance of promotions, highly relevant promotions can be prioritized for strategizing. Some or all of the above processing in the strategy generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the strategy generation unit can input the relevance of promotions into a generation AI and have the generation AI perform the adjustment of the strategy order.

[0052] The execution unit can select the optimal execution method by referring to the history of past events and campaigns during execution. For example, the execution unit can prioritize the selection of execution methods for past successful events and campaigns. For example, the execution unit can analyze the history of past events and campaigns to identify successful methods. The execution unit can also eliminate failed methods from the history of past events and campaigns. For example, the execution unit can analyze the history of past events and campaigns to identify and eliminate failed methods. Furthermore, the execution unit can propose new execution methods based on the history of past events and campaigns. For example, the execution unit can analyze the history of past events and campaigns to propose new execution methods. This allows the optimal execution method to be selected by referring to the history of past events and campaigns. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the history of past events and campaigns into a generating AI and have the generating AI select the optimal execution method.

[0053] The execution unit can customize the means of execution based on specific projects or areas of interest during execution. For example, the execution unit can carry out events or campaigns related to a specific project. For example, the execution unit can host events related to a specific project to support the project's progress. The execution unit can also customize the content of events and campaigns based on areas of interest. For example, the execution unit can host events for employees and alumni interested in a specific technological field. Furthermore, the execution unit can adjust the means of execution according to the project's progress. For example, the execution unit can carry out necessary events or campaigns according to the project's progress. This allows for more appropriate execution by customizing the means of execution based on specific projects or areas of interest. Some or all of the above processes in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input information related to a specific project into a generating AI and have the generating AI customize the means of execution.

[0054] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, if the user is in a specific region, the execution unit will prioritize executing events and campaigns related to that region. For example, the execution unit will acquire the user's geographical location information and execute events and campaigns related to that region. The execution unit can also execute events and campaigns related to the user's destination if the user is on the move. For example, the execution unit will acquire the geographical location information of the user's destination and execute related events and campaigns. Furthermore, if the user is staying in a specific location for an extended period, the execution unit can execute events and campaigns related to that location. For example, the execution unit will acquire the geographical location information of the user's current location and execute related events and campaigns. This allows the execution unit to select the optimal execution method by considering the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal execution method.

[0055] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can propose relevant events and campaigns based on information shared by the user on social media. For example, the execution unit can analyze the content of the user's social media posts and propose relevant events and campaigns. The execution unit can also analyze the activities of the user's social media followers and friends and propose relevant events and campaigns. For example, the execution unit can analyze the content of posts by the user's followers and friends and propose relevant events and campaigns. Furthermore, the execution unit can propose events and campaigns based on topics the user has shown interest in on social media. For example, the execution unit can analyze the content of posts that the user has "liked" or commented on and propose relevant events and campaigns. In this way, relevant events and campaigns can be proposed by analyzing the user's social media activity. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the content of the user's social media posts into a generating AI and have the generating AI execute suggestions for relevant events and campaigns.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The enhanced networking system can further tailor promotional strategies to users' geographical locations. For example, it can suggest events held in a specific region to users in that area. It can also provide promotions relevant to the user's destination region if the user is on the move. Furthermore, if a user is staying in a particular location for an extended period, it can prioritize providing information and events relevant to that location. This allows for more relevant networking by providing promotional strategies based on users' geographical locations.

[0058] The networking enhancement system can further analyze users' social media activity and generate relevant promotional strategies. For example, it can suggest events related to topics users are interested in based on information they share on social media. It can also analyze the activity of users' followers and friends and design campaigns based on shared interests. Furthermore, it can analyze the content of posts that users "like" or comment on and provide relevant promotions. This enables more effective networking by providing promotional strategies based on users' social media activity.

[0059] The networking enhancement system can further adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on important data, and simplified analysis on less important data. Furthermore, it can determine the priority of the analysis based on the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the data's importance.

[0060] The networking enhancement system can further apply different analysis algorithms depending on the data category during analysis. For example, natural language processing algorithms can be applied to text data. Image recognition algorithms can be applied to image data. Furthermore, speech recognition algorithms can be applied to audio data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided.

[0061] The enhanced networking system can further prioritize analysis based on the data collection date. For example, it can prioritize the analysis of the most recent data, while delaying older data. Furthermore, it can adjust the analysis priority according to the data collection date. This allows for prioritizing the analysis of the most recent data based on the data collection date.

[0062] The networking enhancement system can further adjust the order of analysis based on the relevance of the data. For example, it can prioritize the analysis of highly relevant data, while delaying the analysis of less relevant data. Furthermore, it can adjust the order of analysis based on the relevance of the data, thereby prioritizing the analysis of highly relevant data.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects past communication data and interests. For example, it collects data such as emails, chats, and meeting records exchanged between employees and alumni. The data collection unit can collect email content as text data, analyze chat logs to extract interests, collect meeting records as audio data, and convert them into text data using speech recognition technology. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes collected email data to understand the interests and communication patterns of employees and alumni. The analysis unit can use natural language processing technology to analyze the content of emails and extract topics of interest, analyze chat logs to identify frequently discussed themes, and analyze meeting records to understand communication patterns. Step 3: The strategy generation unit generates the optimal promotion strategy based on the analysis results obtained by the analysis unit. For example, it can use generation AI to design promotion strategies tailored to the interests of employees and alumni, and propose events to connect employees and alumni interested in specific projects, or campaigns based on common interests. Step 4: The execution unit executes the promotional strategy generated by the strategy generation unit. For example, it can execute events and campaigns based on the generated promotional strategy, hold online and offline events to enhance networking among employees and alumni, and run email campaigns.

[0065] (Example of form 2) The networking enhancement system according to an embodiment of the present invention is a system that uses generative AI to enhance networking between employees and alumni within the community and designs campaigns that encourage contributions to the company. The networking enhancement system uses generative AI to analyze past communication data and interests and automatically generate an optimal promotion strategy. This promotion strategy aims to enhance networking between employees and alumni and encourage contributions to the company. First, the generative AI analyzes past communication data and interests. In this process, data such as emails, chats, and meeting records exchanged between employees and alumni are collected and analyzed by the generative AI. For example, communication data related to a specific project and data on the interests of employees and alumni are collected. This allows the generative AI to understand the interests and communication patterns of employees and alumni. Next, the generative AI automatically generates an optimal promotion strategy based on the analysis results. Based on the collected data, the generative AI designs a promotion strategy tailored to the interests of employees and alumni. For example, it proposes events to connect employees and alumni who are interested in a specific project, or campaigns based on common interests. In this way, the generating AI automatically generates promotional strategies that enhance networking among employees and alumni and encourage their contributions to the company. This mechanism strengthens networking among employees and alumni and encourages their contributions to the company. For example, by participating in events and campaigns suggested by the generating AI, employees and alumni can share their interests and collaborate on projects. Furthermore, it is expected that the promotional strategies suggested by the generating AI will stimulate communication among employees and alumni, leading to improved productivity throughout the company. Thus, the networking enhancement system can automatically generate promotional strategies that strengthen networking among employees and alumni and encourage their contributions to the company.

[0066] The networking enhancement system according to this embodiment comprises a collection unit, an analysis unit, a strategy generation unit, and an execution unit. The collection unit collects past communication data and interests. For example, the collection unit collects data such as emails, chats, and meeting records exchanged between employees and alumni. For example, the collection unit can collect the content of emails as text data. The collection unit can also analyze chat logs and extract topics of interest. Furthermore, the collection unit can collect meeting records as audio data and convert them into text data using speech recognition technology. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes collected email data to understand the interests and communication patterns of employees and alumni. For example, the analysis unit uses natural language processing technology to analyze the content of emails and extract topics of interest. The analysis unit can also analyze chat logs and identify frequently discussed themes. Furthermore, the analysis unit can analyze meeting records to understand the communication patterns of employees and alumni. The Strategy Generation Unit generates the optimal promotion strategy based on the analysis results obtained by the Analysis Unit. For example, the Strategy Generation Unit uses generational AI to design promotion strategies tailored to the interests of employees and alumni. For example, the Strategy Generation Unit proposes events to connect employees and alumni interested in specific projects. It can also propose campaigns based on shared interests. Furthermore, the Strategy Generation Unit can automatically generate promotion strategies based on the interests of employees and alumni using generational AI. The Execution Unit executes the promotion strategies generated by the Strategy Generation Unit. For example, the Execution Unit executes events and campaigns based on the generated promotion strategies. For example, the Execution Unit can hold online events to enhance networking between employees and alumni. It can also hold offline events to promote interaction between employees and alumni. Furthermore, the Execution Unit can execute email campaigns based on the generated promotion strategies.As a result, the networking enhancement system according to this embodiment can automatically generate promotional strategies that enhance networking among employees and alumni and encourage their contributions to the company.

[0067] The data collection unit collects past communication data and interests. Specifically, it collects data such as emails, chats, and meeting records exchanged between employees and alumni. For example, it can collect email content as text data. Email content includes information such as sender, recipient, date and time of sending, subject, and body, and this data is centrally managed. Furthermore, the data collection unit can also analyze chat logs and extract topics of interest. Chat logs include information such as speaker, content of message, and date and time of message, and by analyzing this data, frequently discussed themes and topics of interest can be identified. In addition, the data collection unit can collect meeting records as audio data and convert them into text data using speech recognition technology. Speech recognition technology can transcribe the statements of meeting participants in real time and organize the content of each speaker's statements. This allows the data collection unit to efficiently collect diverse forms of communication data and provide them to the analysis unit. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, it's possible to configure the system to prioritize the collection of data related to specific projects, or to collect records of important meetings with high accuracy. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis department analyzes the data collected by the data collection department. Specifically, it analyzes collected email data to understand the interests and communication patterns of employees and alumni. The analysis department uses natural language processing technology to analyze the content of emails and extract topics of interest. For example, by extracting specific keywords and phrases from the email body and analyzing their frequency and relevance, it is possible to identify the interests of employees and alumni. The analysis department can also analyze chat logs to identify frequently discussed topics. Text mining technology is used to analyze chat logs to understand patterns and trends in the content of conversations. Furthermore, the analysis department can analyze meeting records to understand the communication patterns of employees and alumni. From meeting records, it is possible to analyze the content, frequency, and timing of each speaker's statements to identify the flow of communication and important topics. In this way, the analysis department can quickly and accurately analyze the collected data and understand the interests and communication patterns of employees and alumni. Furthermore, the analysis department can also utilize historical data and statistical information to analyze long-term trends and patterns. For example, past communication data can be used to predict fluctuations in interest during specific periods or events, which can then be used to develop future promotional strategies. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term trend analysis, improving the overall reliability and effectiveness of the system.

[0069] The Strategy Generation Department generates optimal promotional strategies based on the analysis results obtained by the Analysis Department. Specifically, it uses a generative AI to design promotional strategies tailored to the interests of employees and alumni. Based on the data provided by the Analysis Department, the generative AI automatically generates the optimal promotional strategy, taking into account the interests and communication patterns of employees and alumni. For example, it can propose events to connect employees and alumni interested in a particular project. The generative AI can analyze past event data and participant feedback to propose the most effective event format and content. The Strategy Generation Department can also propose campaigns based on common interests. For example, it can propose webinars or workshops targeting employees and alumni interested in a particular technology or industry. Furthermore, the Strategy Generation Department can use the generative AI to automatically generate promotional strategies based on the interests of employees and alumni. The generative AI has an algorithm that learns from the effectiveness of past promotional strategies and proposes the optimal strategy. This allows the Strategy Generation Department to quickly and effectively generate promotional strategies and strengthen networking between employees and alumni. In addition, the Strategy Generation Department can continuously monitor the effectiveness of the generated promotional strategies and modify them as needed. This allows the strategy generation unit to consistently provide highly accurate promotional strategies based on the latest information, maximizing the overall effectiveness of the system.

[0070] The execution team implements the promotional strategies generated by the strategy generation team. Specifically, they execute events and campaigns based on the generated strategies. For example, they might hold online events to enhance networking between employees and alumni. Online events can take the form of webinars or virtual meetups, and participants can join via the internet. The execution team is responsible for planning and running the events, collecting participant feedback, and incorporating it into future events. The execution team can also hold offline events to promote interaction between employees and alumni. Offline events can take the form of seminars, workshops, or networking parties, allowing participants to interact face-to-face. Furthermore, the execution team can also run email campaigns based on the generated promotional strategies. Email campaigns provide employees and alumni with information related to specific interests or projects, encouraging participation. The execution team optimizes the content and timing of emails to ensure effective communication. This allows the execution team to quickly and effectively implement the generated promotional strategies and enhance networking between employees and alumni. In addition, the execution team can continuously monitor the effectiveness of the implemented promotional strategies and modify them as needed. This allows the execution unit to provide highly accurate promotional strategies based on the latest information at all times, maximizing the overall effectiveness of the system.

[0071] The data collection unit can collect data such as emails, chats, and meeting records exchanged between employees and alumni. For example, the data collection unit can collect email content as text data. For example, the data collection unit can retrieve email data from a mail server and save it as text data. The data collection unit can also analyze chat logs and extract areas of interest. For example, the data collection unit can retrieve log data from chat applications and save it as text data. Furthermore, the data collection unit can collect meeting records as audio data and convert it into text data using speech recognition technology. For example, the data collection unit can record meeting audio data and convert it into text data using speech recognition technology. This allows for the collection of communication data between employees and alumni, enabling the understanding of interests and communication patterns. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input email data obtained from a mail server into a generating AI and have the generating AI perform the analysis of the email data.

[0072] The analysis unit can analyze collected data to understand the interests and communication patterns of employees and alumni. For example, the analysis unit can analyze collected email data to understand the interests and communication patterns of employees and alumni. For example, the analysis unit can use natural language processing technology to analyze the content of emails and extract topics of interest. The analysis unit can also analyze chat logs to identify frequently discussed topics. For example, the analysis unit can analyze the text data of chat logs and extract frequently discussed keywords. Furthermore, the analysis unit can analyze meeting records to understand the communication patterns of employees and alumni. For example, the analysis unit can analyze meeting audio data to analyze the frequency and content of statements. In this way, by analyzing the collected data, the interests and communication patterns of employees and alumni can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected email data into a generating AI and have the generating AI perform the analysis of the email data.

[0073] The Strategy Generation Unit can design promotional strategies tailored to the interests of employees and alumni. For example, the Strategy Generation Unit uses generative AI to design promotional strategies tailored to the interests of employees and alumni. For example, the Strategy Generation Unit proposes events to connect employees and alumni interested in a particular project. For example, the Strategy Generation Unit proposes online events targeting employees and alumni interested in a particular project. The Strategy Generation Unit can also propose campaigns based on common interests. For example, the Strategy Generation Unit proposes email campaigns based on common interests. Furthermore, the Strategy Generation Unit can use generative AI to automatically generate promotional strategies based on the interests of employees and alumni. For example, the Generative AI automatically generates promotional strategies tailored to the interests of employees and alumni based on collected data. This enhances networking by designing promotional strategies tailored to the interests of employees and alumni. Some or all of the above processes in the Strategy Generation Unit may be performed using, for example, generative AI, or without generative AI. For example, the strategy generation unit can input collected data into a generation AI and have the generation AI design a promotional strategy.

[0074] The execution unit can carry out events and campaigns based on the generated promotional strategy. For example, the execution unit can carry out online events based on the generated promotional strategy. For example, the execution unit can host webinars to enhance networking between employees and alumni. The execution unit can also host offline events to promote interaction between employees and alumni. For example, the execution unit can host workshops to provide opportunities for employees and alumni to interact directly. Furthermore, the execution unit can also carry out email campaigns based on the generated promotional strategy. For example, the execution unit can send emails providing information about a particular project to employees and alumni who are interested in that project. This enhances networking between employees and alumni by carrying out events and campaigns based on the generated promotional strategy. Some or all of the above processes in the execution unit may be carried out using AI, for example, or not. For example, the execution unit can input the generated promotional strategy into a generating AI and have the generating AI carry out the events and campaigns.

[0075] The Strategy Generation Department can propose events to connect employees interested in a particular project with alumni. For example, the Strategy Generation Department could propose an online event targeting employees and alumni interested in a particular project. For example, the Strategy Generation Department could host a webinar to provide an opportunity for employees and alumni interested in a particular project to share information. The Strategy Generation Department can also propose an offline event. For example, the Strategy Generation Department could host a workshop to provide an opportunity for employees and alumni interested in a particular project to interact directly. Furthermore, the Strategy Generation Department can propose an email campaign to connect employees and alumni interested in a particular project. For example, the Strategy Generation Department could send emails providing information about a particular project to attract the interest of employees and alumni. This facilitates the progress of the project by proposing events to connect employees and alumni interested in a particular project. Some or all of the above processes in the Strategy Generation Department may be performed using, for example, generative AI, or not using generative AI. For example, the strategy generation unit can input collected data into a generation AI and have the generation AI execute event proposals.

[0076] The Strategy Generation Unit can propose campaigns based on common interests. For example, it can propose online campaigns based on common interests. For example, it can propose online discussions targeting employees and alumni interested in a particular technological field. The Strategy Generation Unit can also propose offline campaigns based on common interests. For example, it can propose workshops targeting employees and alumni interested in a particular business strategy. Furthermore, the Strategy Generation Unit can propose email campaigns based on common interests. For example, it can send emails providing information on a particular social issue to attract the attention of employees and alumni. This strengthens networking among employees and alumni by proposing campaigns based on common interests. Some or all of the above processes in the Strategy Generation Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Strategy Generation Unit can input collected data into a generative AI and have the generative AI execute campaign proposals.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may temporarily delay data collection. For example, the data collection unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit may calculate an emotion score based on changes in facial expressions and adjust the timing of data collection. The data collection unit can also actively collect data when the user is relaxed. For example, the data collection unit may record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit may analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of data collection. Furthermore, the data collection unit can postpone data collection if the user is busy. For example, the data collection unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit may calculate an emotion score based on fluctuations in heart rate and adjust the timing of data collection. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can prioritize data collection methods that have been successful in the past. For example, the data collection unit can analyze past data collection history and identify successful methods. The data collection unit can also eliminate failed methods from past data collection history. For example, the data collection unit can analyze past data collection history, identify and eliminate failed methods. Furthermore, the data collection unit can propose new collection methods based on past data collection history. For example, the data collection unit can analyze past data collection history and propose new collection methods. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0079] The data collection unit can filter data based on specific projects or areas of interest during data collection. For example, the data collection unit can collect only data related to a specific project. For example, the data collection unit can filter data based on keywords related to a specific project. The data collection unit can also exclude unnecessary data based on areas of interest. For example, the data collection unit can filter data that is not related to an area of ​​interest. Furthermore, the data collection unit can adjust the data it collects according to the progress of a project. For example, the data collection unit can prioritize the collection of necessary data according to the progress of a project. This allows for the collection of highly relevant data by filtering data based on specific projects or areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input keywords related to a specific project into a generating AI and have the generating AI perform data filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting important data. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions and determine the priority of data to collect. The data collection unit can also collect detailed data if the user is relaxed. For example, the data collection unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of data to collect. Furthermore, if the user is stressed, the data collection unit can also prioritize collecting simple data. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of data to collect. This allows for the priority of data collection based on the user's emotions, enabling the collection of more important data first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will acquire the user's geographical location information and collect data related to that region. The data collection unit can also collect data related to the user's destination if the user is on the move. For example, the data collection unit will acquire the geographical location information of the user's destination and collect related data. Furthermore, if the user is staying in a specific location for an extended period, the data collection unit can also collect data related to that location. For example, the data collection unit will acquire the geographical location information of the user's current location and collect related data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect the relevant data.

[0082] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant data. The data collection unit can also analyze the activities of a user's social media followers and friends and collect relevant data. For example, the data collection unit can analyze the content of posts by a user's followers and friends and collect relevant data. Furthermore, the data collection unit can collect data based on topics that the user has shown interest in on social media. For example, the data collection unit can analyze posts that a user has "liked" or commented on and collect relevant data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the content of a user's social media posts into a generating AI and have the generating AI perform the collection of relevant data.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the presentation of the analysis. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, the analysis unit can record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the presentation of the analysis. In addition, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the presentation of the analysis. This allows for the provision of more appropriate analysis results by adjusting the expression method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit evaluates the importance of the data and performs a simplified analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit evaluates the importance of the data and prioritizes the analysis of important data. This allows for a detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a speech recognition algorithm to audio data. The analysis unit can also apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data into a generating AI and have the generating AI execute a natural language processing algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the length of the analysis. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit can record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the analysis. In addition, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the length of the analysis. This allows for the provision of more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may, for example, evaluate the data collection timing and prioritize the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. The analysis unit may, for example, evaluate the data collection timing and postpone the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection timing. The analysis unit may, for example, evaluate the data collection timing and adjust the priority of analysis according to the collection timing. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may evaluate the relevance of the data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit may evaluate the relevance of the data and postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit may evaluate the relevance of the data and adjust the order of analysis according to the relevance. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0089] The strategy generation unit can estimate the user's emotions and adjust the way the strategy is presented based on the estimated emotions. For example, if the user is relaxed, the strategy generation unit can provide a detailed strategy. For example, the strategy generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the strategy generation unit can calculate an emotion score based on changes in facial expressions and adjust the way the strategy is presented. Furthermore, if the user is in a hurry, the strategy generation unit can provide a concise strategy that gets straight to the point. For example, the strategy generation unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the strategy generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the way the strategy is presented. In addition, if the user is excited, the strategy generation unit can provide a visually stimulating strategy. For example, the strategy generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The strategy generation unit calculates an emotion score based on, for example, heart rate fluctuations and adjusts the way the strategy is presented. This allows for the provision of more appropriate strategies by adjusting the presentation of the strategy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the strategy generation unit may be performed using AI, or not using AI. For example, the strategy generation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0090] The strategy generation unit can adjust the level of detail of a strategy based on the importance of the promotion when generating a strategy. For example, the strategy generation unit can provide a detailed strategy for important promotions. For example, the strategy generation unit can evaluate the importance of promotions and provide a detailed strategy for important promotions. The strategy generation unit can also provide a simplified strategy for less important promotions. For example, the strategy generation unit can evaluate the importance of promotions and provide a simplified strategy for less important promotions. Furthermore, the strategy generation unit can determine the priority of strategies according to the importance of the promotions. For example, the strategy generation unit can evaluate the importance of promotions and prioritize the strategizing of important promotions. This allows for the provision of detailed strategies for important promotions by adjusting the level of detail of the strategy based on the importance of the promotions. Some or all of the above processing in the strategy generation unit may be performed using a generation AI, or not. For example, the strategy generation unit can input the importance of promotions into the generation AI and have the generation AI perform the adjustment of the level of detail of the strategy.

[0091] The strategy generation unit can apply different strategy algorithms depending on the promotion category when generating strategies. For example, the strategy generation unit applies an event management algorithm to event promotions. For example, the strategy generation unit uses an event management algorithm when strategizing event promotions. The strategy generation unit can also apply a social media analysis algorithm to social media promotions. For example, the strategy generation unit uses a social media analysis algorithm when strategizing social media promotions. Furthermore, the strategy generation unit can apply an email marketing algorithm to email promotions. For example, the strategy generation unit uses an email marketing algorithm when strategizing email promotions. By applying different strategy algorithms depending on the promotion category, a more appropriate strategy can be provided. Some or all of the above processing in the strategy generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the strategy generation unit can input the promotion category into the generation AI and have the generation AI execute the application of the strategy algorithm.

[0092] The strategy generation unit can estimate the user's emotions and adjust the length of the strategy based on the estimated emotions. For example, if the user is in a hurry, the strategy generation unit can provide a short, concise strategy. For example, the strategy generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the strategy generation unit can calculate an emotion score based on changes in facial expressions and adjust the length of the strategy. The strategy generation unit can also provide a detailed strategy if the user is relaxed. For example, the strategy generation unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the strategy generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the strategy. Furthermore, if the user is excited, the strategy generation unit can provide a visually stimulating strategy. For example, the strategy generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the strategy generation unit can calculate an emotion score based on fluctuations in heart rate and adjust the length of the strategy. This allows for the provision of more appropriate strategies by adjusting the length of the strategy 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy generation unit may be performed using AI, or not using AI. For example, the strategy generation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The strategy generation unit can determine the priority of strategies based on the timing of promotions when generating strategies. For example, the strategy generation unit can prioritize strategizing the most recent promotions. For example, the strategy generation unit can evaluate the timing of promotions and prioritize strategizing the most recent promotions. The strategy generation unit can also postpone promotions in the distant future. For example, the strategy generation unit can evaluate the timing of promotions and postpone promotions in the distant future. Furthermore, the strategy generation unit can adjust the priority of strategies according to the timing of promotions. For example, the strategy generation unit can evaluate the timing of promotions and adjust the priority of strategies according to the timing. This allows for prioritizing the most recent promotions by determining the priority of strategies based on the timing of promotions. Some or all of the above processing in the strategy generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the strategy generation unit can input the timing of promotions into the generation AI and have the generation AI determine the priority of strategies.

[0094] The strategy generation unit can adjust the order of strategies based on the relevance of promotions when generating strategies. For example, the strategy generation unit can prioritize the strategizing of highly relevant promotions. For example, the strategy generation unit can evaluate the relevance of promotions and prioritize the strategizing of highly relevant promotions. The strategy generation unit can also postpone promotions with low relevance. For example, the strategy generation unit can evaluate the relevance of promotions and postpone promotions with low relevance. Furthermore, the strategy generation unit can adjust the order of strategies according to the relevance of promotions. For example, the strategy generation unit can evaluate the relevance of promotions and adjust the order of strategies according to the relevance. In this way, by adjusting the order of strategies based on the relevance of promotions, highly relevant promotions can be prioritized for strategizing. Some or all of the above processing in the strategy generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the strategy generation unit can input the relevance of promotions into a generation AI and have the generation AI perform the adjustment of the strategy order.

[0095] The execution unit can estimate the user's emotions and adjust how events and campaigns are executed based on the estimated emotions. For example, if the user is relaxed, the execution unit can provide detailed events and campaigns. For example, the execution unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the execution unit can calculate an emotion score based on changes in facial expressions and adjust how events and campaigns are executed. Furthermore, if the user is in a hurry, the execution unit can provide concise events and campaigns that get straight to the point. For example, the execution unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the execution unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust how events and campaigns are executed. In addition, if the user is excited, the execution unit can provide visually stimulating events and campaigns. For example, the execution unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The execution unit calculates an emotion score based on, for example, heart rate fluctuations and adjusts how events and campaigns are executed. This allows for more appropriate execution by adjusting how events and campaigns are executed 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, or not using AI. For example, the execution unit can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0096] The execution unit can select the optimal execution method by referring to the history of past events and campaigns during execution. For example, the execution unit can prioritize the selection of execution methods for past successful events and campaigns. For example, the execution unit can analyze the history of past events and campaigns to identify successful methods. The execution unit can also eliminate failed methods from the history of past events and campaigns. For example, the execution unit can analyze the history of past events and campaigns to identify and eliminate failed methods. Furthermore, the execution unit can propose new execution methods based on the history of past events and campaigns. For example, the execution unit can analyze the history of past events and campaigns to propose new execution methods. This allows the optimal execution method to be selected by referring to the history of past events and campaigns. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the history of past events and campaigns into a generating AI and have the generating AI select the optimal execution method.

[0097] The execution unit can customize the means of execution based on specific projects or areas of interest during execution. For example, the execution unit can carry out events or campaigns related to a specific project. For example, the execution unit can host events related to a specific project to support the project's progress. The execution unit can also customize the content of events and campaigns based on areas of interest. For example, the execution unit can host events for employees and alumni interested in a specific technological field. Furthermore, the execution unit can adjust the means of execution according to the project's progress. For example, the execution unit can carry out necessary events or campaigns according to the project's progress. This allows for more appropriate execution by customizing the means of execution based on specific projects or areas of interest. Some or all of the above processes in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input information related to a specific project into a generating AI and have the generating AI customize the means of execution.

[0098] The execution unit can estimate the user's emotions and determine the priority of events and campaigns based on the estimated emotions. For example, if the user is excited, the execution unit will prioritize important events and campaigns. For example, the execution unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the execution unit can calculate an emotion score based on changes in facial expressions and determine the priority of events and campaigns. Furthermore, if the user is relaxed, the execution unit can also execute more detailed events and campaigns. For example, the execution unit can record the user's voice and estimate their emotions using voice analysis technology. For example, the execution unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of events and campaigns. In addition, if the user is stressed, the execution unit can prioritize simpler events and campaigns. For example, the execution unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the execution unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of events and campaigns. This allows for prioritizing events and campaigns based on user emotions, enabling the execution of more important events and campaigns first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0099] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, if the user is in a specific region, the execution unit will prioritize executing events and campaigns related to that region. For example, the execution unit will acquire the user's geographical location information and execute events and campaigns related to that region. The execution unit can also execute events and campaigns related to the user's destination if the user is on the move. For example, the execution unit will acquire the geographical location information of the user's destination and execute related events and campaigns. Furthermore, if the user is staying in a specific location for an extended period, the execution unit can execute events and campaigns related to that location. For example, the execution unit will acquire the geographical location information of the user's current location and execute related events and campaigns. This allows the execution unit to select the optimal execution method by considering the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal execution method.

[0100] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can propose relevant events and campaigns based on information shared by the user on social media. For example, the execution unit can analyze the content of the user's social media posts and propose relevant events and campaigns. The execution unit can also analyze the activities of the user's social media followers and friends and propose relevant events and campaigns. For example, the execution unit can analyze the content of posts by the user's followers and friends and propose relevant events and campaigns. Furthermore, the execution unit can propose events and campaigns based on topics the user has shown interest in on social media. For example, the execution unit can analyze the content of posts that the user has "liked" or commented on and propose relevant events and campaigns. In this way, relevant events and campaigns can be proposed by analyzing the user's social media activity. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the content of the user's social media posts into a generating AI and have the generating AI execute suggestions for relevant events and campaigns.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The networking enhancement system can further estimate user emotions and adjust promotional strategies based on those estimates. For example, if a user is feeling stressed, the emotion estimation unit can suggest events that help them relax. If a user is excited, it can design campaigns that encourage active participation. Furthermore, if a user is feeling down, it can generate promotions that offer encouragement and support. This allows for more effective networking by providing promotional strategies tailored to user emotions.

[0103] The enhanced networking system can further tailor promotional strategies to users' geographical locations. For example, it can suggest events held in a specific region to users in that area. It can also provide promotions relevant to the user's destination region if the user is on the move. Furthermore, if a user is staying in a particular location for an extended period, it can prioritize providing information and events relevant to that location. This allows for more relevant networking by providing promotional strategies based on users' geographical locations.

[0104] The networking enhancement system can further analyze users' social media activity and generate relevant promotional strategies. For example, it can suggest events related to topics users are interested in based on information they share on social media. It can also analyze the activity of users' followers and friends and design campaigns based on shared interests. Furthermore, it can analyze the content of posts that users "like" or comment on and provide relevant promotions. This enables more effective networking by providing promotional strategies based on users' social media activity.

[0105] The enhanced networking system can further estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if a user is stressed, data collection can be temporarily delayed. Conversely, if a user is relaxed, data collection can be actively pursued. Furthermore, if a user is busy, data collection can be postponed. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions.

[0106] The networking enhancement system can further estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is excited, important data can be prioritized for collection. If the user is relaxed, detailed data can be collected. Furthermore, if the user is stressed, simple data can be prioritized for collection. By prioritizing the data to be collected according to the user's emotions, more important data can be collected first.

[0107] The networking enhancement system can further estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it can provide more appropriate analysis results.

[0108] The networking enhancement system can further adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on important data, and simplified analysis on less important data. Furthermore, it can determine the priority of the analysis based on the importance of the data. This allows for detailed analysis of important data by adjusting the level of detail based on the data's importance.

[0109] The networking enhancement system can further apply different analysis algorithms depending on the data category during analysis. For example, natural language processing algorithms can be applied to text data. Image recognition algorithms can be applied to image data. Furthermore, speech recognition algorithms can be applied to audio data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided.

[0110] The enhanced networking system can further prioritize analysis based on the data collection date. For example, it can prioritize the analysis of the most recent data, while delaying older data. Furthermore, it can adjust the analysis priority according to the data collection date. This allows for prioritizing the analysis of the most recent data based on the data collection date.

[0111] The networking enhancement system can further adjust the order of analysis based on the relevance of the data. For example, it can prioritize the analysis of highly relevant data, while delaying the analysis of less relevant data. Furthermore, it can adjust the order of analysis based on the relevance of the data, thereby prioritizing the analysis of highly relevant data.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects past communication data and interests. For example, it collects data such as emails, chats, and meeting records exchanged between employees and alumni. The data collection unit can collect email content as text data, analyze chat logs to extract interests, collect meeting records as audio data, and convert them into text data using speech recognition technology. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes collected email data to understand the interests and communication patterns of employees and alumni. The analysis unit can use natural language processing technology to analyze the content of emails and extract topics of interest, analyze chat logs to identify frequently discussed themes, and analyze meeting records to understand communication patterns. Step 3: The strategy generation unit generates the optimal promotion strategy based on the analysis results obtained by the analysis unit. For example, it can use generation AI to design promotion strategies tailored to the interests of employees and alumni, and propose events to connect employees and alumni interested in specific projects, or campaigns based on common interests. Step 4: The execution unit executes the promotional strategy generated by the strategy generation unit. For example, it can execute events and campaigns based on the generated promotional strategy, hold online and offline events to enhance networking among employees and alumni, and run email campaigns.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, strategy generation unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The strategy generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a promotion strategy based on the analysis results. The execution unit is implemented in the specific processing unit 46A of the smart device 14 and executes the generated promotion strategy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, strategy generation unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The strategy generation unit is implemented, for example, in the specific processing unit 290 of the data processing device 12 and generates a promotion strategy based on the analysis results. The execution unit is implemented, for example, in the control unit 46A of the smart glasses 214 and executes the generated promotion strategy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, strategy generation unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The strategy generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a promotion strategy based on the analysis results. The execution unit is implemented in the specific processing unit 46A of the headset terminal 314 and executes the generated promotion strategy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the data collection unit, analysis unit, strategy generation unit, and execution unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The strategy generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a promotion strategy based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the robot 414 and executes the generated promotion strategy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) The collection department collects past communication data and interests, An analysis unit analyzes the data collected by the aforementioned collection unit, A strategy generation unit generates an optimal promotion strategy based on the analysis results obtained by the analysis unit, The system comprises an execution unit that executes the promotion strategy generated by the strategy generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as emails, chats, and meeting records exchanged between employees and alumni. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to understand the interests and communication patterns of employees and alumni. The system described in Appendix 1, characterized by the features described herein. (Note 4) The strategy generation unit, Design a promotional strategy that aligns with the interests of employees and alumni. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, Execute events and campaigns based on the generated promotional strategy. The system described in Appendix 1, characterized by the features described herein. (Note 6) The strategy generation unit, Propose an event to connect employees interested in a specific project with alumni. The system described in Appendix 1, characterized by the features described herein. (Note 7) The strategy generation unit, Propose a campaign based on common interests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filter it based on specific projects or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The strategy generation unit, It estimates user sentiment and adjusts the way strategies are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The strategy generation unit, When generating a strategy, adjust the level of detail based on the importance of the promotion. The system described in Appendix 1, characterized by the features described herein. (Note 22) The strategy generation unit, When generating strategies, different strategy algorithms are applied depending on the promotion category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The strategy generation unit, The system estimates the user's emotions and adjusts the length of the strategy based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The strategy generation unit, When generating a strategy, prioritize strategies based on the timing of promotional activities. The system described in Appendix 1, characterized by the features described herein. (Note 25) The strategy generation unit, When generating strategies, adjust the order of strategies based on the relevance of the promotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, It estimates user sentiment and adjusts how events and campaigns are executed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, During execution, the system will refer to the history of past events and campaigns to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, At runtime, customize the execution method based on specific projects or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, It estimates user sentiment and prioritizes events and campaigns based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, During execution, the system selects the optimal execution method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects past communication data and interests, An analysis unit analyzes the data collected by the aforementioned collection unit, A strategy generation unit generates an optimal promotion strategy based on the analysis results obtained by the analysis unit, The system comprises an execution unit that executes the promotion strategy generated by the strategy generation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as emails, chats, and meeting records exchanged between employees and alumni. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to understand the interests and communication patterns of employees and alumni. The system according to feature 1.

4. The strategy generation unit, Design a promotional strategy that aligns with the interests of employees and alumni. The system according to feature 1.

5. The execution unit is, Execute events and campaigns based on the generated promotional strategy. The system according to feature 1.

6. The strategy generation unit, Propose an event to connect employees interested in a specific project with alumni. The system according to feature 1.

7. The strategy generation unit, Propose a campaign based on common interests. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filter it based on specific projects or areas of interest. The system according to feature 1.

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