system

Abstract

The system according to this embodiment aims to automate the initial handling of telephone calls and to respond appropriately to specific requirements. [Solution] The system according to the embodiment comprises a reception unit, a summarization unit, a decision unit, and a rejection unit. The reception unit receives the initial call. The summarization unit summarizes the requirements received by the reception unit. The decision unit decides whether to connect the call based on the requirements summarized by the summarization unit. The rejection unit rejects the call if the requirements are specific.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to automate the primary reception of a call and it is difficult to appropriately respond to specific requirements.

[0005] The system according to the embodiment aims to automate the primary reception of a call and appropriately respond to specific requirements.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a summarization unit, a determination unit, and a rejection unit. The reception unit performs the primary reception of a call. The summarization unit summarizes the requirements received by the reception unit. The determination unit determines whether to connect a call based on the requirements summarized by the summarization unit. The rejection unit rejects if it is a specific requirement. [Effects of the Invention]

[0007] The system according to this embodiment can automate the initial handling of telephone calls and respond appropriately to specific requirements. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An agent service according to an embodiment of the present invention is a system in which an AI takes the initial call, summarizes the request, communicates it to the user, and decides whether to connect the call. When a call comes in, the AI ​​takes the initial call and listens to the request. Next, the AI ​​summarizes the request and communicates it to the user. Based on the summary, the user decides whether to connect the call. If it is the phone number of an acquaintance, the AI ​​can connect the call directly. Also, if the request is for a specific purpose (for example, something that seems like a sales call or a scam), the AI ​​can refuse it. This allows users to shut out unnecessary calls and only receive necessary calls. For example, when a call comes in, the AI ​​asks the user, "Mr. / Ms. A is calling about XX. Shall I transfer the call?" The user listens to the request and decides whether to transfer it. If the request is for a sales call or something that seems like a scam, the AI ​​can refuse it. This allows users to receive only necessary calls without being bothered by unnecessary calls. Furthermore, the AI ​​can incorporate the interfaces of phone and messaging apps, and for example, it can exchange messages using a messaging app. This makes communication via messaging apps, as well as phone calls, more efficient. This system can address phone vulnerabilities and improve the user's phone experience. For example, even if a phone number is leaked or there are many suspicious calls, the AI ​​can handle initial calls, blocking unwanted calls and only forwarding necessary ones. Furthermore, the AI ​​can summarize and communicate the requirements, making it easy for users to decide whether or not to connect a call. This agent service is a groundbreaking system that not only solves phone vulnerabilities but also improves the user's phone experience. For instance, reducing unwanted calls such as sales calls and scams will increase user satisfaction and enhance the brand value of the telecommunications carrier. It can also contribute to improving business efficiency when implemented on corporate landlines and business terminals. This allows the agent service to efficiently handle initial call reception, summarization, decision-making, and rejection.

[0029] The agent service according to this embodiment comprises a reception unit, a summarization unit, a decision unit, and a rejection unit. The reception unit performs initial reception of calls. For example, when a call comes in, the reception unit performs initial reception and listens to the caller's request. The summarization unit summarizes the request and conveys it to the user. For example, the summarization unit summarizes the request and conveys it to the user. The decision unit decides whether to connect the call based on the summary. For example, the decision unit decides whether to connect the call based on the summary. The rejection unit rejects calls if they meet certain requirements. For example, the rejection unit rejects calls if they meet certain requirements. This allows the agent service to efficiently perform the initial reception, summarization, decision, and rejection processes for calls.

[0030] The reception department handles initial telephone inquiries. Specifically, it automatically answers incoming calls and listens to the caller's request. The reception department uses speech recognition technology to convert what the caller says into text data, accurately understanding the request. For example, as the caller speaks their inquiry, the reception department analyzes the content in real time and extracts important keywords and phrases. This allows the reception department to quickly and accurately understand the caller's request and provide the information necessary for the next step. Furthermore, the reception department has a function to save the caller's voice data for later review. This allows for accurate recording of the caller's request and reconfirmation as needed. In addition to listening to the caller's request, the reception department can also verify and authenticate the caller's identity. For example, it can match the caller's phone number and voice pattern to confirm whether the caller is a legitimate user. This allows the reception department to efficiently handle initial telephone inquiries while ensuring security.

[0031] The summarization unit summarizes requirements and communicates them to the user. Specifically, it analyzes the caller's requirements received from the reception unit, extracts key points, and creates a summary. The summarization unit uses natural language processing technology to understand what the caller is saying and grasp the essence of the requirements. For example, if the caller makes multiple inquiries, the summarization unit organizes them and summarizes them concisely. When creating a summary, the summarization unit also considers the caller's intent and background information, providing information in a way that is easy for the user to understand. After creating the summary, the summarization unit communicates its contents to the user. Based on the summarized information, the user can quickly decide on the next course of action. To improve the accuracy of the summaries, the summarization unit refers to past data and cases to continuously improve the quality of the summaries. In addition, when communicating the content of the summary to the user, the summarization unit can provide information in a natural voice using speech synthesis technology. As a result, the summarization unit can quickly and accurately communicate requirements to the user and support efficient responses.

[0032] The decision-making unit determines whether to connect the call based on the summary. Specifically, it analyzes the summary information received from the summarization unit to determine whether the caller's request should be handled by the appropriate person. The decision-making unit makes the decision based on pre-set rules and conditions. For example, if the caller's request relates to a specific department or person, the decision-making unit will decide to connect the call based on that information. The decision-making unit can use AI to analyze the summary information and automatically determine the optimal response. For example, if the caller's request is urgent, the decision-making unit will immediately decide to connect the call, enabling a rapid response. Also, if the caller's request relates to multiple people, the decision-making unit will select the most suitable person and decide to connect the call. This allows the decision-making unit to respond appropriately to the caller's request quickly. Furthermore, when deciding whether to connect a call, the decision-making unit can refer to past data and cases to improve the accuracy of its decision. This allows the decision-making unit to provide the optimal response to the caller's request and improve the efficiency and quality of agent services.

[0033] The rejection unit will reject requests if they meet certain requirements. Specifically, based on the summary information received from the decision unit, it will reject requests if the caller's requirements are unmet or if they meet certain conditions. The rejection unit decides whether to reject a caller's request based on pre-set rules and conditions. For example, if the caller's requirements are outside the scope of the service or cannot be met during a specific time slot, the rejection unit will reject the request based on that information. The rejection unit can clearly communicate the reason for the rejection to the caller and propose alternative solutions or other methods of handling the request. For example, it can provide the caller with available times or other means of contact. When rejecting a request, the rejection unit takes into consideration the caller's feelings and circumstances, and responds politely and appropriately. This allows for a smooth rejection process without causing offense to the caller. Furthermore, the rejection unit has a function to record the information after a rejection has been made, allowing for later review. This allows for the management of the rejection history and re-review as needed. The rejection function can respond appropriately to the caller's requirements, thereby improving the efficiency and quality of agent services.

[0034] The direct connection section can connect directly to the phone number of an acquaintance. For example, the direct connection section can quickly connect directly to the phone number of an acquaintance. It is necessary to clarify the specific definition and criteria for what constitutes an acquaintance's phone number. For example, how will one be determined to be an acquaintance? This will enable quick and direct connections to the phone number of an acquaintance.

[0035] The interface unit can incorporate interfaces from phone and messaging apps. For example, the interface unit can incorporate interfaces from phone and messaging apps. It is necessary to clarify the specific types of interfaces and implementation methods. For example, what kind of interfaces will be incorporated? This will streamline communication not only through phone calls but also through messaging apps.

[0036] The reception department handles initial phone calls and gathers information about the caller's needs. For example, the reception department handles initial phone calls and gathers information about the caller's needs. It is necessary to clearly define the specific methods and criteria for initial handling. For example, what information should be collected, and how should the caller respond? This will enable the reception department to gather information about the caller's needs when they receive a call.

[0037] The summary section can summarize the requirements and communicate them to the user. For example, the summary section summarizes the requirements and communicates them to the user. The specific methods and criteria for summarization need to be clearly defined, such as the importance of the information being summarized and the format of the summary. This allows the requirements to be summarized and communicated to the user.

[0038] The decision-making unit can decide whether or not to connect the call based on the summary. For example, the decision-making unit will decide whether or not to connect the call based on the summary. It is necessary to clarify the specific criteria and methods for making this decision. For example, what requirements will determine whether to connect or decline the call? This will allow the decision-making unit to decide whether or not to connect the call based on the summary.

[0039] The refusal section allows for refusal under specific conditions. For example, the refusal section will refuse if certain conditions are not met. It is necessary to clearly define the specific content and criteria of these conditions. For example, what kinds of conditions apply? This allows for refusal under specific conditions.

[0040] The reception desk can analyze a user's past call history and select the optimal primary call handling method. For example, the reception desk can analyze patterns of calls the user has frequently received in the past and propose the optimal primary call handling method. For example, the reception desk can prioritize primary calls the user has received in the past during specific time periods. For example, the reception desk can prioritize primary calls the user has received in the past from specific individuals. It is necessary to clarify the specific criteria and methods for determining the optimal primary call handling method. For example, what criteria will be used to determine what is optimal? This will allow the reception desk to analyze a user's past call history and select the optimal primary call handling method.

[0041] The reception desk can filter incoming calls based on the user's current situation and areas of interest. For example, if a user is in a meeting, the AI ​​can take the call, summarize the request, and notify the user later. The reception desk can prioritize incoming calls related to a user's specific areas of interest. For example, if a user is busy, the AI ​​can filter out less important calls. The specific content and criteria for "current situation" need to be clearly defined, such as what information is used to determine the situation. The specific content and criteria for "areas of interest" also need to be clearly defined, such as what information is used to determine areas of interest. This allows for filtering calls based on the user's current situation and areas of interest.

[0042] The reception desk can prioritize incoming calls based on the user's geographical location, taking this information into account. For example, if the user is in a specific region, the reception desk will prioritize incoming calls related to that region. If the user is traveling, the reception desk will prioritize incoming calls related to their travel destination. If the user is at home, the reception desk will prioritize incoming calls related to their home. It is necessary to clarify the specific content and criteria of geographical location information, such as what information is used to determine the location. This will allow the reception desk to prioritize incoming calls based on the user's geographical location.

[0043] The reception desk can analyze a user's social media activity when initially receiving a call and prioritize relevant calls. For example, if a user posts about a specific topic on social media, the reception desk will prioritize calls related to that topic. For example, if a user participates in a specific event on social media, the reception desk will prioritize calls related to that event. For example, if a user belongs to a specific group on social media, the reception desk will prioritize calls related to that group. It is necessary to clarify the specific content and criteria of social media activity. For example, what information will be used to judge activity. This will allow the reception desk to prioritize relevant calls based on the user's social media activity.

[0044] The summarization section can adjust the level of detail in the summary based on the importance of the requirements during summary generation. For example, the summarization section provides a detailed summary for high-importance requirements. For example, the summarization section provides a concise summary for low-importance requirements. For example, the summarization section provides a summary with a moderate level of detail for requirements of moderate importance. It is necessary to clarify the specific criteria and methods for determining the importance of requirements. For example, what criteria will be used to judge importance? This will allow the level of detail in the summary to be adjusted based on the importance of the requirements.

[0045] The summarization unit can apply different summarization algorithms depending on the category of the requirement when generating summaries. For example, the summarization unit applies a business-specific summarization algorithm for business-related requirements. For example, the summarization unit applies a personal-specific summarization algorithm for personal requirements. For example, the summarization unit applies an urgent-specific summarization algorithm for urgent requirements. It is necessary to clarify the specific content and criteria of the requirement categories. For example, what criteria will be used to determine the category? This will allow different summarization algorithms to be applied depending on the category of the requirement.

[0046] The summarization unit can prioritize summaries based on when the requirements were submitted. For example, it will prioritize summarizing recently submitted requirements. For example, it will postpone summarizing older requirements. For example, it will generate summaries with a moderate priority for requirements that were submitted at a moderate time. It is necessary to clarify the specific criteria and methods for determining submission timing. For example, what criteria will be used to judge the timing? This will allow the summarization unit to prioritize summaries based on when the requirements were submitted.

[0047] The summarization unit can adjust the order of summaries based on the relevance of the requirements during the summarization process. For example, the summarization unit will prioritize summarizing highly relevant requirements. For example, it will postpone summarizing less relevant requirements. For example, it will generate summaries in an appropriate order for requirements of moderate relevance. It is necessary to clarify the specific criteria and methods for determining relevance. For example, what criteria will be used to judge relevance? This will allow the order of summaries to be adjusted based on the relevance of the requirements.

[0048] The decision-making unit can improve the accuracy of its decisions by considering the interrelationships of requirements during the decision-making process. For example, if a requirement is related to other requirements, the decision-making unit will consider that relationship when making a decision. For example, if a requirement is in conflict with other requirements, the decision-making unit will consider that conflict when making a decision. For example, if a requirement is complementary to other requirements, the decision-making unit will consider that complementarity when making a decision. It is necessary to clarify the specific criteria and methods for determining interrelationships. For example, what criteria will be used to judge the relationship? By considering the interrelationships of requirements, the accuracy of decisions can be improved.

[0049] The decision-making department can consider the attribute information of the requirements submitter when making a decision. For example, if the submitter is an important customer, the decision-making department will prioritize the requirements. For example, if the submitter is a new customer, the decision-making department will carefully consider the requirements. For example, if the submitter is an internal staff member, the decision-making department will quickly consider the requirements. It is necessary to clarify the specific content and criteria of attribute information. For example, what information will be used to determine attributes. By considering the attribute information of the requirements submitter when making a decision, more appropriate decisions can be made.

[0050] The decision-making unit can consider the geographical distribution of requirements when making a decision. For example, if the requirements are concentrated in a particular region, the decision-making unit will prioritize the requirements of that region. For example, if the requirements are distributed over a wide area, the decision-making unit will prioritize each region when making a decision. For example, if the requirements are biased towards a particular region, the decision-making unit will consider that bias when making a decision. It is necessary to clarify the specific content and criteria of geographical distribution. For example, what kind of information will be used to determine the distribution. By considering the geographical distribution of requirements when making a decision, more appropriate decisions can be made.

[0051] The decision-making unit can improve the accuracy of its decision by referring to relevant literature on the requirements during the decision-making process. For example, the decision-making unit may refer to relevant literature on the requirements and make a decision considering its content. For example, the decision-making unit may refer to past cases related to the requirements and make a decision based on those cases. For example, the decision-making unit may refer to the latest research findings related to the requirements and make a decision based on those findings. It is necessary to clarify the specific content and criteria of the relevant literature. For example, what kind of literature should be referred to. This will allow the accuracy of the decision to be improved by referring to relevant literature on the requirements.

[0052] The rejection unit can optimize its rejection algorithm by referring to past rejection data when making a rejection. For example, the rejection unit can analyze data on previously rejected requirements and optimize its rejection algorithm. For example, the rejection unit can evaluate the success rate of previously rejected requirements and improve its rejection algorithm. For example, the rejection unit can learn patterns of previously rejected requirements and optimize its rejection algorithm. It is necessary to clarify the specific content and criteria of the past rejection data. For example, what kind of data will be referenced. By doing so, more appropriate rejections can be made by optimizing the rejection algorithm by referring to past rejection data.

[0053] The rejection function can apply different rejection methods depending on the category of the requirement. For example, it might apply a polite rejection method to sales-related requirements, a strict rejection method to requirements that appear to be fraudulent, and a standard rejection method to general requirements. It is necessary to clarify the specific content and criteria of the requirement categories, for example, what criteria will be used to determine the category. By applying different rejection methods to each requirement category, more appropriate rejections can be made.

[0054] The rejection function can adjust its rejection criteria based on when the requirement was submitted. For example, it might apply strict rejection criteria to recently submitted requirements, lenient rejection criteria to older requirements, and moderate rejection criteria to requirements submitted at a moderate time. It is necessary to clarify the specific criteria and methods for determining the submission timing, such as what criteria will be used to judge the timing. By adjusting the rejection criteria based on when the requirement was submitted, more appropriate rejections can be made.

[0055] The rejection function can adjust its rejection criteria by referring to relevant market data for the requirements. For example, it can refer to market data to strictly reject sales-related requirements. For example, it can refer to market data to strictly reject requirements that appear to be fraudulent. For example, it can refer to market data to reject general requirements in a standard manner. It is necessary to clarify the specific content and criteria of the relevant market data. For example, what kind of data will be referred to. This will allow for more appropriate rejections by adjusting the rejection criteria by referring to relevant market data for the requirements.

[0056] The direct connection unit can select the optimal connection method by referring to the user's past connection history when a direct connection is made. For example, the direct connection unit may prioritize direct connections to parties the user has frequently connected with in the past. For example, the direct connection unit may prioritize direct connections to parties the user has connected with in the past during specific time periods. For example, the direct connection unit may prioritize selecting the method the user has used to connect with specific parties in the past. It is necessary to clarify the specific content and criteria of the past connection history. For example, what kind of data will be referenced. By doing so, more appropriate connections can be made by selecting the optimal connection method by referring to the user's past connection history.

[0057] The direct connection unit can customize the connection method based on the user's current situation when a direct connection is established. For example, if the user is in a meeting, the direct connection unit will connect via message. If the user is on the move, the direct connection unit will connect via voice call. If the user is at home, the direct connection unit will connect via video call. It is necessary to clarify the specific content and criteria of the current situation. For example, what information will be used to determine the situation. By customizing the connection method based on the user's current situation, a more appropriate connection can be established.

[0058] The direct connection unit can select the optimal connection method by considering the user's geographical location information during a direct connection. For example, if the user is in a specific region, the direct connection unit will select a connection method related to that region. For example, if the user is traveling, the direct connection unit will select a connection method related to the travel destination. For example, if the user is at home, the direct connection unit will select a connection method related to home. It is necessary to clarify the specific content and criteria of the geographical location information. For example, what information is used to determine the location. By doing so, a more appropriate connection can be made by selecting the optimal connection method by considering the user's geographical location information.

[0059] The direct connection unit can analyze a user's social media activity and suggest connection methods during a direct connection. For example, if a user posts about a specific topic on social media, the direct connection unit will suggest connection methods related to that topic. For example, if a user participates in a specific event on social media, the direct connection unit will suggest connection methods related to that event. For example, if a user belongs to a specific group on social media, the direct connection unit will suggest connection methods related to that group. It is necessary to clarify the specific content and criteria of social media activity. For example, what information will be used to judge the activity. By analyzing the user's social media activity and suggesting connection methods, more appropriate connections can be made.

[0060] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize providing display methods that the user has frequently used in the past. For example, the interface unit can prioritize providing display methods that the user has used when performing a specific operation in the past. For example, the interface unit can analyze the user's past operation history and propose the optimal display method. It is necessary to clarify the specific content and criteria of the past operation history. For example, what kind of data will be referenced. By doing so, a more appropriate display can be provided by selecting the optimal display method by referring to the user's past operation history.

[0061] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a larger screen. For example, if the user is using a smartwatch, the interface unit provides a concise and highly visible display method. It is necessary to clarify the specific content and criteria of the device information. For example, what information is used to determine the device. By doing so, a more appropriate display can be achieved by selecting the optimal display method considering the user's device information.

[0062] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a larger screen. For example, if the user is using a smartwatch, the interface unit provides a concise and highly visible display method. It is necessary to clarify the specific content and criteria of the device information. For example, what information is used to determine the device. By doing so, a more appropriate display can be achieved by selecting the optimal display method considering the user's device information.

[0063] The interface unit can analyze the user's social media activity and select the optimal display method when displaying the interface. For example, if the user posts about a specific topic on social media, the interface unit will provide a display method related to that topic. For example, if the user participates in a specific event on social media, the interface unit will provide a display method related to that event. For example, if the user belongs to a specific group on social media, the interface unit will provide a display method related to that group. It is necessary to clarify the specific content and criteria of social media activity. For example, what information will be used to judge the activity. By doing so, it will be possible to provide more appropriate displays by analyzing the user's social media activity and selecting the optimal display method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The reception desk can analyze a user's past call history and select the optimal first-line answering method. For example, it can analyze patterns of calls a user has frequently received in the past and suggest the most suitable first-line answering method. It can also prioritize first-line answering calls a user has received in the past during specific time periods. Furthermore, it can prioritize first-line answering calls a user has received in the past from specific individuals. It is necessary to clarify the specific criteria and methods for determining the optimal first-line answering method. For example, what criteria will be used to determine what is optimal? This will enable the system to analyze a user's past call history and select the most suitable first-line answering method.

[0066] The reception desk can filter incoming calls based on the user's current situation and areas of interest. For example, if a user is in a meeting, the AI ​​can take the call, summarize the request, and notify the user later. It can also prioritize incoming calls related to the user's specific areas of interest. Furthermore, if a user is busy, the AI ​​can filter out less important calls. It is necessary to clearly define the specific content and criteria for "current situation," such as what information is used to determine the situation. Similarly, it is necessary to clearly define the specific content and criteria for "areas of interest," such as what information is used to determine areas of interest. This will enable the filtering of calls based on the user's current situation and areas of interest.

[0067] The summarization section can adjust the level of detail in the summary based on the importance of the requirements during generation. For example, a detailed summary can be provided for high-importance requirements, a concise summary for low-importance requirements, and a moderately detailed summary for requirements of moderate importance. It is necessary to clarify the specific criteria and methods for determining the importance of requirements. For example, what criteria will be used to judge importance? This will allow the level of detail in the summary to be adjusted based on the importance of the requirements.

[0068] The summarization section can apply different summarization algorithms depending on the category of the requirement during summary generation. For example, a business-specific summarization algorithm can be applied to business-related requirements. Similarly, a personal-specific summarization algorithm can be applied to personal requirements. Furthermore, an urgent-specific summarization algorithm can be applied to urgent requirements. It is necessary to clearly define the specific content and criteria for requirement categories, such as what criteria will be used to determine the category. This allows for the application of different summarization algorithms depending on the requirement category.

[0069] The decision-making unit can improve the accuracy of its decisions by considering the interrelationships of requirements. For example, if a requirement is related to other requirements, the decision can be made considering that relationship. Also, if a requirement conflicts with other requirements, the decision can be made considering that conflict. Furthermore, if a requirement is complementary to other requirements, the decision can be made considering that complementarity. It is necessary to clarify the specific criteria and methods for determining interrelationships. For example, what criteria will be used to judge the relationship? By considering the interrelationships of requirements, the accuracy of decisions can be improved.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk handles initial phone calls. For example, when a call comes in, they handle the initial response and listen to the caller's request. Step 2: The summarization unit summarizes the requirements received by the reception unit and communicates them to the user. For example, it summarizes the requirements and communicates them to the user. Step 3: The decision unit decides whether to connect the call based on the requirements summarized by the summarization unit. For example, it decides whether to connect the call based on the summary. Step 4: The refusal section should decline if the requirements are specific. For example, decline if the requirements are specific.

[0072] (Example of form 2) An agent service according to an embodiment of the present invention is a system in which an AI takes the initial call, summarizes the request, communicates it to the user, and decides whether to connect the call. When a call comes in, the AI ​​takes the initial call and listens to the request. Next, the AI ​​summarizes the request and communicates it to the user. Based on the summary, the user decides whether to connect the call. If it is the phone number of an acquaintance, the AI ​​can connect the call directly. Also, if the request is for a specific purpose (for example, something that seems like a sales call or a scam), the AI ​​can refuse it. This allows users to shut out unnecessary calls and only receive necessary calls. For example, when a call comes in, the AI ​​asks the user, "Mr. / Ms. A is calling about XX. Shall I transfer the call?" The user listens to the request and decides whether to transfer it. If the request is for a sales call or something that seems like a scam, the AI ​​can refuse it. This allows users to receive only necessary calls without being bothered by unnecessary calls. Furthermore, the AI ​​can incorporate the interfaces of phone and messaging apps, and for example, it can exchange messages using a messaging app. This makes communication via messaging apps, as well as phone calls, more efficient. This system can address phone vulnerabilities and improve the user's phone experience. For example, even if a phone number is leaked or there are many suspicious calls, the AI ​​can handle initial calls, blocking unwanted calls and only forwarding necessary ones. Furthermore, the AI ​​can summarize and communicate the requirements, making it easy for users to decide whether or not to connect a call. This agent service is a groundbreaking system that not only solves phone vulnerabilities but also improves the user's phone experience. For instance, reducing unwanted calls such as sales calls and scams will increase user satisfaction and enhance the brand value of the telecommunications carrier. It can also contribute to improving business efficiency when implemented on corporate landlines and business terminals. This allows the agent service to efficiently handle initial call reception, summarization, decision-making, and rejection.

[0073] The agent service according to this embodiment comprises a reception unit, a summarization unit, a decision unit, and a rejection unit. The reception unit performs initial reception of calls. For example, when a call comes in, the reception unit performs initial reception and listens to the caller's request. The summarization unit summarizes the request and conveys it to the user. For example, the summarization unit summarizes the request and conveys it to the user. The decision unit decides whether to connect the call based on the summary. For example, the decision unit decides whether to connect the call based on the summary. The rejection unit rejects calls if they meet certain requirements. For example, the rejection unit rejects calls if they meet certain requirements. This allows the agent service to efficiently perform the initial reception, summarization, decision, and rejection processes for calls.

[0074] The reception department handles initial telephone inquiries. Specifically, it automatically answers incoming calls and listens to the caller's request. The reception department uses speech recognition technology to convert what the caller says into text data, accurately understanding the request. For example, as the caller speaks their inquiry, the reception department analyzes the content in real time and extracts important keywords and phrases. This allows the reception department to quickly and accurately understand the caller's request and provide the information necessary for the next step. Furthermore, the reception department has a function to save the caller's voice data for later review. This allows for accurate recording of the caller's request and reconfirmation as needed. In addition to listening to the caller's request, the reception department can also verify and authenticate the caller's identity. For example, it can match the caller's phone number and voice pattern to confirm whether the caller is a legitimate user. This allows the reception department to efficiently handle initial telephone inquiries while ensuring security.

[0075] The summarization unit summarizes requirements and communicates them to the user. Specifically, it analyzes the caller's requirements received from the reception unit, extracts key points, and creates a summary. The summarization unit uses natural language processing technology to understand what the caller is saying and grasp the essence of the requirements. For example, if the caller makes multiple inquiries, the summarization unit organizes them and summarizes them concisely. When creating a summary, the summarization unit also considers the caller's intent and background information, providing information in a way that is easy for the user to understand. After creating the summary, the summarization unit communicates its contents to the user. Based on the summarized information, the user can quickly decide on the next course of action. To improve the accuracy of the summaries, the summarization unit refers to past data and cases to continuously improve the quality of the summaries. In addition, when communicating the content of the summary to the user, the summarization unit can provide information in a natural voice using speech synthesis technology. As a result, the summarization unit can quickly and accurately communicate requirements to the user and support efficient responses.

[0076] The decision-making unit determines whether to connect the call based on the summary. Specifically, it analyzes the summary information received from the summarization unit to determine whether the caller's request should be handled by the appropriate person. The decision-making unit makes the decision based on pre-set rules and conditions. For example, if the caller's request relates to a specific department or person, the decision-making unit will decide to connect the call based on that information. The decision-making unit can use AI to analyze the summary information and automatically determine the optimal response. For example, if the caller's request is urgent, the decision-making unit will immediately decide to connect the call, enabling a rapid response. Also, if the caller's request relates to multiple people, the decision-making unit will select the most suitable person and decide to connect the call. This allows the decision-making unit to respond appropriately to the caller's request quickly. Furthermore, when deciding whether to connect a call, the decision-making unit can refer to past data and cases to improve the accuracy of its decision. This allows the decision-making unit to provide the optimal response to the caller's request and improve the efficiency and quality of agent services.

[0077] The rejection unit will reject requests if they meet certain requirements. Specifically, based on the summary information received from the decision unit, it will reject requests if the caller's requirements are unmet or if they meet certain conditions. The rejection unit decides whether to reject a caller's request based on pre-set rules and conditions. For example, if the caller's requirements are outside the scope of the service or cannot be met during a specific time slot, the rejection unit will reject the request based on that information. The rejection unit can clearly communicate the reason for the rejection to the caller and propose alternative solutions or other methods of handling the request. For example, it can provide the caller with available times or other means of contact. When rejecting a request, the rejection unit takes into consideration the caller's feelings and circumstances, and responds politely and appropriately. This allows for a smooth rejection process without causing offense to the caller. Furthermore, the rejection unit has a function to record the information after a rejection has been made, allowing for later review. This allows for the management of the rejection history and re-review as needed. The rejection function can respond appropriately to the caller's requirements, thereby improving the efficiency and quality of agent services.

[0078] The direct connection section can connect directly to the phone number of an acquaintance. For example, the direct connection section can quickly connect directly to the phone number of an acquaintance. It is necessary to clarify the specific definition and criteria for what constitutes an acquaintance's phone number. For example, how will one be determined to be an acquaintance? This will enable quick and direct connections to the phone number of an acquaintance.

[0079] The interface unit can incorporate interfaces from phone and messaging apps. For example, the interface unit can incorporate interfaces from phone and messaging apps. It is necessary to clarify the specific types of interfaces and implementation methods. For example, what kind of interfaces will be incorporated? This will streamline communication not only through phone calls but also through messaging apps.

[0080] The reception department handles initial phone calls and gathers information about the caller's needs. For example, the reception department handles initial phone calls and gathers information about the caller's needs. It is necessary to clearly define the specific methods and criteria for initial handling. For example, what information should be collected, and how should the caller respond? This will enable the reception department to gather information about the caller's needs when they receive a call.

[0081] The summary section can summarize the requirements and communicate them to the user. For example, the summary section summarizes the requirements and communicates them to the user. The specific methods and criteria for summarization need to be clearly defined, such as the importance of the information being summarized and the format of the summary. This allows the requirements to be summarized and communicated to the user.

[0082] The decision-making unit can decide whether or not to connect the call based on the summary. For example, the decision-making unit will decide whether or not to connect the call based on the summary. It is necessary to clarify the specific criteria and methods for making this decision. For example, what requirements will determine whether to connect or decline the call? This will allow the decision-making unit to decide whether or not to connect the call based on the summary.

[0083] The refusal section allows for refusal under specific conditions. For example, the refusal section will refuse if certain conditions are not met. It is necessary to clearly define the specific content and criteria of these conditions. For example, what kinds of conditions apply? This allows for refusal under specific conditions.

[0084] The reception desk can estimate the user's emotions and adjust the timing of the initial call answering based on the estimated emotions. For example, if the user is stressed, the AI ​​will delay the initial call answering and wait until the user calms down. If the user is relaxed, the AI ​​will answer the call immediately. If the user is in a hurry, the AI ​​will answer the call quickly. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used and how will emotions be determined. This will allow the timing of the initial call answering to be adjusted according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The reception desk can analyze a user's past call history and select the optimal primary call handling method. For example, the reception desk can analyze patterns of calls the user has frequently received in the past and propose the optimal primary call handling method. For example, the reception desk can prioritize primary calls the user has received in the past during specific time periods. For example, the reception desk can prioritize primary calls the user has received in the past from specific individuals. It is necessary to clarify the specific criteria and methods for determining the optimal primary call handling method. For example, what criteria will be used to determine what is optimal? This will allow the reception desk to analyze a user's past call history and select the optimal primary call handling method.

[0086] The reception desk can filter incoming calls based on the user's current situation and areas of interest. For example, if a user is in a meeting, the AI ​​can take the call, summarize the request, and notify the user later. The reception desk can prioritize incoming calls related to a user's specific areas of interest. For example, if a user is busy, the AI ​​can filter out less important calls. The specific content and criteria for "current situation" need to be clearly defined, such as what information is used to determine the situation. The specific content and criteria for "areas of interest" also need to be clearly defined, such as what information is used to determine areas of interest. This allows for filtering calls based on the user's current situation and areas of interest.

[0087] The reception desk can estimate the user's emotions and determine the priority of incoming calls based on those emotions. For example, if the user is stressed, the reception desk will prioritize only high-priority calls. If the user is relaxed, the reception desk will prioritize all calls equally. If the user is in a hurry, the reception desk will prioritize urgent calls. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used and how will emotions be determined. This will enable the system to prioritize calls according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception desk can prioritize incoming calls based on the user's geographical location, taking this information into account. For example, if the user is in a specific region, the reception desk will prioritize incoming calls related to that region. If the user is traveling, the reception desk will prioritize incoming calls related to their travel destination. If the user is at home, the reception desk will prioritize incoming calls related to their home. It is necessary to clarify the specific content and criteria of geographical location information, such as what information is used to determine the location. This will allow the reception desk to prioritize incoming calls based on the user's geographical location.

[0089] The reception desk can analyze a user's social media activity when initially receiving a call and prioritize relevant calls. For example, if a user posts about a specific topic on social media, the reception desk will prioritize calls related to that topic. For example, if a user participates in a specific event on social media, the reception desk will prioritize calls related to that event. For example, if a user belongs to a specific group on social media, the reception desk will prioritize calls related to that group. It is necessary to clarify the specific content and criteria of social media activity. For example, what information will be used to judge activity. This will allow the reception desk to prioritize relevant calls based on the user's social media activity.

[0090] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is tense, the summarization unit will provide a simple and clear summary. If the user is relaxed, the summarization unit will provide a detailed summary. If the user is in a hurry, the summarization unit will provide a concise summary that gets to the point. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be determined. This will allow the way the summary is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The summarization section can adjust the level of detail in the summary based on the importance of the requirements during summary generation. For example, the summarization section provides a detailed summary for high-importance requirements. For example, the summarization section provides a concise summary for low-importance requirements. For example, the summarization section provides a summary with a moderate level of detail for requirements of moderate importance. It is necessary to clarify the specific criteria and methods for determining the importance of requirements. For example, what criteria will be used to judge importance? This will allow the level of detail in the summary to be adjusted based on the importance of the requirements.

[0092] The summarization unit can apply different summarization algorithms depending on the category of the requirement when generating summaries. For example, the summarization unit applies a business-specific summarization algorithm for business-related requirements. For example, the summarization unit applies a personal-specific summarization algorithm for personal requirements. For example, the summarization unit applies an urgent-specific summarization algorithm for urgent requirements. It is necessary to clarify the specific content and criteria of the requirement categories. For example, what criteria will be used to determine the category? This will allow different summarization algorithms to be applied depending on the category of the requirement.

[0093] The summarization section can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is tense, the summarization section will provide a short, to-the-point summary. If the user is relaxed, the summarization section will provide a detailed summary. If the user is in a hurry, the summarization section will provide a concise summary. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be determined. This will allow the length of the summary to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The summarization unit can prioritize summaries based on when the requirements were submitted. For example, it will prioritize summarizing recently submitted requirements. For example, it will postpone summarizing older requirements. For example, it will generate summaries with a moderate priority for requirements that were submitted at a moderate time. It is necessary to clarify the specific criteria and methods for determining submission timing. For example, what criteria will be used to judge the timing? This will allow the summarization unit to prioritize summaries based on when the requirements were submitted.

[0095] The summarization unit can adjust the order of summaries based on the relevance of the requirements during the summarization process. For example, the summarization unit will prioritize summarizing highly relevant requirements. For example, it will postpone summarizing less relevant requirements. For example, it will generate summaries in an appropriate order for requirements of moderate relevance. It is necessary to clarify the specific criteria and methods for determining relevance. For example, what criteria will be used to judge relevance? This will allow the order of summaries to be adjusted based on the relevance of the requirements.

[0096] The decision-making unit can estimate the user's emotions and adjust the criteria for connecting a call based on the estimated emotions. For example, if the user is stressed, the decision-making unit will only connect high-priority calls. If the user is relaxed, the decision-making unit will connect all calls. If the user is in a hurry, the decision-making unit will only connect urgent calls. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used and how will emotions be determined. This will allow the decision-making criteria for connecting a call to be adjusted according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The decision-making unit can improve the accuracy of its decisions by considering the interrelationships of requirements during the decision-making process. For example, if a requirement is related to other requirements, the decision-making unit will consider that relationship when making a decision. For example, if a requirement is in conflict with other requirements, the decision-making unit will consider that conflict when making a decision. For example, if a requirement is complementary to other requirements, the decision-making unit will consider that complementarity when making a decision. It is necessary to clarify the specific criteria and methods for determining interrelationships. For example, what criteria will be used to judge the relationship? By considering the interrelationships of requirements, the accuracy of decisions can be improved.

[0098] The decision-making department can consider the attribute information of the requirements submitter when making a decision. For example, if the submitter is an important customer, the decision-making department will prioritize the requirements. For example, if the submitter is a new customer, the decision-making department will carefully consider the requirements. For example, if the submitter is an internal staff member, the decision-making department will quickly consider the requirements. It is necessary to clarify the specific content and criteria of attribute information. For example, what information will be used to determine attributes. By considering the attribute information of the requirements submitter when making a decision, more appropriate decisions can be made.

[0099] The decision-making unit can estimate the user's emotions and determine the priority of whether to connect a call based on the estimated emotions. For example, if the user is stressed, the decision-making unit will prioritize connecting only high-priority calls. If the user is relaxed, the decision-making unit will prioritize connecting all calls equally. If the user is in a hurry, the decision-making unit will prioritize connecting urgent calls. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used and how will emotions be judged. This will enable the decision-making unit to determine the priority of whether to connect a call according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The decision-making unit can consider the geographical distribution of requirements when making a decision. For example, if the requirements are concentrated in a particular region, the decision-making unit will prioritize the requirements of that region. For example, if the requirements are distributed over a wide area, the decision-making unit will prioritize each region when making a decision. For example, if the requirements are biased towards a particular region, the decision-making unit will consider that bias when making a decision. It is necessary to clarify the specific content and criteria of geographical distribution. For example, what kind of information will be used to determine the distribution. By considering the geographical distribution of requirements when making a decision, more appropriate decisions can be made.

[0101] The decision-making unit can improve the accuracy of its decision by referring to relevant literature on the requirements during the decision-making process. For example, the decision-making unit may refer to relevant literature on the requirements and make a decision considering its content. For example, the decision-making unit may refer to past cases related to the requirements and make a decision based on those cases. For example, the decision-making unit may refer to the latest research findings related to the requirements and make a decision based on those findings. It is necessary to clarify the specific content and criteria of the relevant literature. For example, what kind of literature should be referred to. This will allow the accuracy of the decision to be improved by referring to relevant literature on the requirements.

[0102] The rejection unit can estimate the user's emotions and adjust the rejection criteria based on the estimated emotions. For example, if the user is stressed, the rejection unit will tighten the rejection criteria. For example, if the user is relaxed, the rejection unit will loosen the rejection criteria. For example, if the user is in a hurry, the rejection unit will quickly apply rejection criteria. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used and how will emotions be determined. This will allow for more appropriate rejections by adjusting the rejection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The rejection unit can optimize its rejection algorithm by referring to past rejection data when making a rejection. For example, the rejection unit can analyze data on previously rejected requirements and optimize its rejection algorithm. For example, the rejection unit can evaluate the success rate of previously rejected requirements and improve its rejection algorithm. For example, the rejection unit can learn patterns of previously rejected requirements and optimize its rejection algorithm. It is necessary to clarify the specific content and criteria of the past rejection data. For example, what kind of data will be referenced. By doing so, more appropriate rejections can be made by optimizing the rejection algorithm by referring to past rejection data.

[0104] The rejection function can apply different rejection methods depending on the category of the requirement. For example, it might apply a polite rejection method to sales-related requirements, a strict rejection method to requirements that appear to be fraudulent, and a standard rejection method to general requirements. It is necessary to clarify the specific content and criteria of the requirement categories, for example, what criteria will be used to determine the category. By applying different rejection methods to each requirement category, more appropriate rejections can be made.

[0105] The rejection function can estimate the user's emotions and determine the priority of rejections based on those emotions. For example, if the user is stressed, the rejection function will prioritize rejecting less important requests. If the user is relaxed, the rejection function will reject all requests equally. If the user is in a hurry, the rejection function will prioritize rejecting less urgent requests. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged. This will allow for more appropriate rejections by determining the priority of rejections according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The rejection function can adjust its rejection criteria based on when the requirement was submitted. For example, it might apply strict rejection criteria to recently submitted requirements, lenient rejection criteria to older requirements, and moderate rejection criteria to requirements submitted at a moderate time. It is necessary to clarify the specific criteria and methods for determining the submission timing, such as what criteria will be used to judge the timing. By adjusting the rejection criteria based on when the requirement was submitted, more appropriate rejections can be made.

[0107] The rejection function can adjust its rejection criteria by referring to relevant market data for the requirements. For example, it can refer to market data to strictly reject sales-related requirements. For example, it can refer to market data to strictly reject requirements that appear to be fraudulent. For example, it can refer to market data to reject general requirements in a standard manner. It is necessary to clarify the specific content and criteria of the relevant market data. For example, what kind of data will be referred to. This will allow for more appropriate rejections by adjusting the rejection criteria by referring to relevant market data for the requirements.

[0108] The direct connection unit can estimate the user's emotions and adjust the timing of the direct connection based on the estimated emotions. For example, if the user is stressed, the direct connection unit will delay the direct connection. For example, if the user is relaxed, the direct connection unit will immediately connect. For example, if the user is in a hurry, the direct connection unit will quickly connect. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged. This will allow for more appropriate connections by adjusting the timing of the direct connection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The direct connection unit can select the optimal connection method by referring to the user's past connection history when a direct connection is made. For example, the direct connection unit may prioritize direct connections to parties the user has frequently connected with in the past. For example, the direct connection unit may prioritize direct connections to parties the user has connected with in the past during specific time periods. For example, the direct connection unit may prioritize selecting the method the user has used to connect with specific parties in the past. It is necessary to clarify the specific content and criteria of the past connection history. For example, what kind of data will be referenced. By doing so, more appropriate connections can be made by selecting the optimal connection method by referring to the user's past connection history.

[0110] The direct connection unit can customize the connection method based on the user's current situation when a direct connection is established. For example, if the user is in a meeting, the direct connection unit will connect via message. If the user is on the move, the direct connection unit will connect via voice call. If the user is at home, the direct connection unit will connect via video call. It is necessary to clarify the specific content and criteria of the current situation. For example, what information will be used to determine the situation. By customizing the connection method based on the user's current situation, a more appropriate connection can be established.

[0111] The direct connection unit can estimate the user's emotions and determine the priority of direct connections based on the estimated emotions. For example, if the user is stressed, the direct connection unit will prioritize only high-priority connections. If the user is relaxed, the direct connection unit will prioritize all connections equally. If the user is in a hurry, the direct connection unit will prioritize urgent connections. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged. This will allow for more appropriate connections by determining the priority of direct connections according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The direct connection unit can select the optimal connection method by considering the user's geographical location information during a direct connection. For example, if the user is in a specific region, the direct connection unit will select a connection method related to that region. For example, if the user is traveling, the direct connection unit will select a connection method related to the travel destination. For example, if the user is at home, the direct connection unit will select a connection method related to home. It is necessary to clarify the specific content and criteria of the geographical location information. For example, what information is used to determine the location. By doing so, a more appropriate connection can be made by selecting the optimal connection method by considering the user's geographical location information.

[0113] The direct connection unit can analyze a user's social media activity and suggest connection methods during a direct connection. For example, if a user posts about a specific topic on social media, the direct connection unit will suggest connection methods related to that topic. For example, if a user participates in a specific event on social media, the direct connection unit will suggest connection methods related to that event. For example, if a user belongs to a specific group on social media, the direct connection unit will suggest connection methods related to that group. It is necessary to clarify the specific content and criteria of social media activity. For example, what information will be used to judge the activity. By analyzing the user's social media activity and suggesting connection methods, more appropriate connections can be made.

[0114] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is tense, the interface unit will provide an interface with calm colors. For example, if the user is enjoying themselves, the interface unit will provide an interface with bright colors. For example, if the user is tired, the interface unit will provide a simple and highly visible interface. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged. This will allow for a more appropriate display by adjusting the interface display method according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize providing display methods that the user has frequently used in the past. For example, the interface unit can prioritize providing display methods that the user has used when performing a specific operation in the past. For example, the interface unit can analyze the user's past operation history and propose the optimal display method. It is necessary to clarify the specific content and criteria of the past operation history. For example, what kind of data will be referenced. By doing so, a more appropriate display can be provided by selecting the optimal display method by referring to the user's past operation history.

[0116] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a larger screen. For example, if the user is using a smartwatch, the interface unit provides a concise and highly visible display method. It is necessary to clarify the specific content and criteria of the device information. For example, what information is used to determine the device. By doing so, a more appropriate display can be achieved by selecting the optimal display method considering the user's device information.

[0117] The interface unit can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. For example, if the user is nervous, the interface unit provides simple and intuitive operation procedures. For example, if the user is enjoying themselves, the interface unit provides customizable operation procedures. For example, if the user is tired, the interface unit provides minimal operation procedures. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data to use and how to determine emotions. This will allow for more appropriate operation by adjusting the interface's operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a larger screen. For example, if the user is using a smartwatch, the interface unit provides a concise and highly visible display method. It is necessary to clarify the specific content and criteria of the device information. For example, what information is used to determine the device. By doing so, a more appropriate display can be achieved by selecting the optimal display method considering the user's device information.

[0119] The interface unit can analyze the user's social media activity and select the optimal display method when displaying the interface. For example, if the user posts about a specific topic on social media, the interface unit will provide a display method related to that topic. For example, if the user participates in a specific event on social media, the interface unit will provide a display method related to that event. For example, if the user belongs to a specific group on social media, the interface unit will provide a display method related to that group. It is necessary to clarify the specific content and criteria of social media activity. For example, what information will be used to judge the activity. By doing so, it will be possible to provide more appropriate displays by analyzing the user's social media activity and selecting the optimal display method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The reception desk can estimate the user's emotions and adjust the timing of the initial call answering based on those emotions. For example, if the user is stressed, the AI ​​can delay the initial call answering and wait until the user calms down. If the user is relaxed, the AI ​​can answer the call immediately. Furthermore, if the user is in a hurry, the AI ​​can answer the call quickly. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged? This will allow the timing of the initial call answering to be adjusted according to the user's emotions.

[0122] The reception desk can analyze a user's past call history and select the optimal first-line answering method. For example, it can analyze patterns of calls a user has frequently received in the past and suggest the most suitable first-line answering method. It can also prioritize first-line answering calls a user has received in the past during specific time periods. Furthermore, it can prioritize first-line answering calls a user has received in the past from specific individuals. It is necessary to clarify the specific criteria and methods for determining the optimal first-line answering method. For example, what criteria will be used to determine what is optimal? This will enable the system to analyze a user's past call history and select the most suitable first-line answering method.

[0123] The reception desk can filter incoming calls based on the user's current situation and areas of interest. For example, if a user is in a meeting, the AI ​​can take the call, summarize the request, and notify the user later. It can also prioritize incoming calls related to the user's specific areas of interest. Furthermore, if a user is busy, the AI ​​can filter out less important calls. It is necessary to clearly define the specific content and criteria for "current situation," such as what information is used to determine the situation. Similarly, it is necessary to clearly define the specific content and criteria for "areas of interest," such as what information is used to determine areas of interest. This will enable the filtering of calls based on the user's current situation and areas of interest.

[0124] The reception desk can estimate the user's emotions and prioritize which calls to answer based on those estimates. For example, if the user is stressed, only high-priority calls can be prioritized. If the user is relaxed, all calls can be answered equally. Furthermore, if the user is in a hurry, urgent calls can be prioritized. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged. This will enable the system to prioritize calls according to the user's emotions.

[0125] The summarization section can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is stressed, a simple and clear summary can be provided. If the user is relaxed, a detailed summary can be provided. Furthermore, if the user is in a hurry, a concise summary that gets straight to the point can be provided. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged? This will allow the summary to be presented in accordance with the user's emotions.

[0126] The summarization section can adjust the level of detail in the summary based on the importance of the requirements during generation. For example, a detailed summary can be provided for high-importance requirements, a concise summary for low-importance requirements, and a moderately detailed summary for requirements of moderate importance. It is necessary to clarify the specific criteria and methods for determining the importance of requirements. For example, what criteria will be used to judge importance? This will allow the level of detail in the summary to be adjusted based on the importance of the requirements.

[0127] The summarization section can apply different summarization algorithms depending on the category of the requirement during summary generation. For example, a business-specific summarization algorithm can be applied to business-related requirements. Similarly, a personal-specific summarization algorithm can be applied to personal requirements. Furthermore, an urgent-specific summarization algorithm can be applied to urgent requirements. It is necessary to clearly define the specific content and criteria for requirement categories, such as what criteria will be used to determine the category. This allows for the application of different summarization algorithms depending on the requirement category.

[0128] The decision-making unit can estimate the user's emotions and adjust the criteria for connecting a call based on those emotions. For example, if the user is stressed, only high-priority calls can be connected. If the user is relaxed, all calls can be connected. Furthermore, if the user is in a hurry, only urgent calls can be connected. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged. This will allow the decision-making criteria for connecting a call to be adjusted according to the user's emotions.

[0129] The decision-making unit can improve the accuracy of its decisions by considering the interrelationships of requirements. For example, if a requirement is related to other requirements, the decision can be made considering that relationship. Also, if a requirement conflicts with other requirements, the decision can be made considering that conflict. Furthermore, if a requirement is complementary to other requirements, the decision can be made considering that complementarity. It is necessary to clarify the specific criteria and methods for determining interrelationships. For example, what criteria will be used to judge the relationship? By considering the interrelationships of requirements, the accuracy of decisions can be improved.

[0130] The rejection function can estimate the user's emotions and adjust the rejection criteria based on those emotions. For example, if the user is stressed, the AI ​​can tighten the rejection criteria. Conversely, if the user is relaxed, the AI ​​can loosen the rejection criteria. Furthermore, if the user is in a hurry, the AI ​​can quickly apply rejection criteria. It is necessary to clarify the specific methods and criteria for estimating emotions. For example, what data will be used, and how will emotions be judged? By doing so, more appropriate rejections can be made by adjusting the rejection criteria according to the user's emotions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The reception desk handles initial phone calls. For example, when a call comes in, they handle the initial response and listen to the caller's request. Step 2: The summarization unit summarizes the requirements received by the reception unit and communicates them to the user. For example, it summarizes the requirements and communicates them to the user. Step 3: The decision unit decides whether to connect the call based on the requirements summarized by the summarization unit. For example, it decides whether to connect the call based on the summary. Step 4: The refusal section should decline if the requirements are specific. For example, decline if the requirements are specific.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, summarization unit, decision unit, rejection unit, direct connection unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which receives the call and listens to the requirements. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12, which summarizes the requirements and communicates them to the user. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12, which decides whether to connect the call based on the summary. The rejection unit is implemented by the specific processing unit 290 of the data processing unit 12, which rejects the call if the requirements are specific. The direct connection unit is implemented by the control unit 46A of the smart device 14, which can connect directly if the phone number belongs to an acquaintance. The interface unit is implemented by the control unit 46A of the smart device 14, which can incorporate the interfaces of telephone and messaging applications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, summarization unit, decision unit, rejection unit, direct connection unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which takes the initial call and listens to the requirements. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12, which summarizes the requirements and conveys them to the user. The decision unit is implemented by the identification processing unit 290 of the data processing unit 12, which decides whether to connect the call based on the summary. The rejection unit is implemented by the identification processing unit 290 of the data processing unit 12, which rejects calls if the requirements are specific. The direct connection unit is implemented by the control unit 46A of the smart glasses 214, which can directly connect if the phone number belongs to an acquaintance. The interface unit is implemented by the control unit 46A of the smart glasses 214, which can incorporate the interfaces of telephones and messaging applications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reception unit, summarization unit, decision unit, rejection unit, direct connection unit, and interface unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which receives the call and listens to the requirements. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12, which summarizes the requirements and conveys them to the user. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12, which decides whether to connect the call based on the summary. The rejection unit is implemented by the specific processing unit 290 of the data processing unit 12, which rejects the call if the requirements are specific. The direct connection unit is implemented by the control unit 46A of the headset terminal 314, which can connect directly if the phone number belongs to an acquaintance. The interface unit is implemented by the control unit 46A of the headset terminal 314, which can incorporate the interfaces of telephones and messaging applications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the reception unit, summarization unit, decision unit, rejection unit, direct connection unit, and interface unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which receives the call initially and listens to the requirements. The summarization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which summarizes the requirements and conveys them to the user. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which decides whether to connect the call based on the summary. The rejection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which rejects calls if the requirements are specific. The direct connection unit is implemented, for example, by the control unit 46A of the robot 414, which can directly connect if the phone number belongs to an acquaintance. The interface unit is implemented, for example, by the control unit 46A of the robot 414, which can incorporate interfaces for telephones and messaging applications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) The reception desk handles initial telephone inquiries, A summarization unit that summarizes the requirements received by the reception unit, A decision unit that determines whether to connect a call based on the requirements summarized by the summarization unit, It includes a rejection section that rejects requests under specific conditions. A system characterized by the following features. (Note 2) It has a direct connection port that allows you to connect directly to the phone number of someone you know. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an interface section that incorporates the interfaces of phone and messaging apps. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When a call comes in, you answer it first and listen to the caller's request. The system described in Appendix 1, characterized by the features described herein. (Note 5) The summary section above is, Summarize the requirements and communicate them to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned determination unit, We will decide whether to connect the call based on the summary. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned rejection section is, I will refuse if the requirements are specific. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of the initial call response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past call history to select the optimal initial response method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When an initial call is received, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of initial calls based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When answering a call, the system prioritizes calls that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When answering a call, the system analyzes the user's social media activity and selects relevant calls. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the requirements. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of requirements. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, When generating summaries, prioritize the summaries based on when the requirements were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary section above is, When generating summaries, adjust the order of summaries based on the relevance of the requirements. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned determination unit, We estimate the user's emotions and adjust the criteria for deciding whether to connect a call based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned determination unit, When making a decision, consider the interrelationships of the requirements to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned determination unit, When making a decision, the attribute information of the person submitting the requirements will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned determination unit, It estimates the user's emotions and determines the priority of whether or not to connect the call based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned determination unit, When making a decision, the geographical distribution of the requirements should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned determination unit, When making a decision, refer to relevant literature on the requirements to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned rejection section is, We estimate the user's emotions and adjust the rejection criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned rejection section is, When declining a request, the rejection algorithm is optimized by referring to past rejection data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned rejection section is, When declining, apply different refusal methods depending on the category of the requirement. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned rejection section is, It estimates the user's emotions and determines the priority of rejections based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned rejection section is, When declining an application, adjust the criteria for refusal based on when the requirements were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned rejection section is, When declining a request, we adjust the criteria for refusal by referring to relevant market data for the requirements. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned direct connection portion is It estimates the user's emotions and adjusts the timing of direct connections based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned direct connection portion is When connecting directly, the system selects the optimal connection method by referring to the user's past connection history. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned direct connection portion is When connecting directly, the connection method is customized based on the user's current status. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned direct connection portion is It estimates the user's emotions and determines the priority of direct connections based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned direct connection portion is When connecting directly, the system selects the optimal connection method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned direct connection portion is When connecting directly, the system analyzes the user's social media activity and suggests connection methods. The system described in Appendix 2, characterized by the features described herein. (Note 38) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 3, characterized by the features described herein. (Note 40) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 41) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 43) The interface unit is When displaying the interface, the system analyzes the user's social media activity to select the optimal display method. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0205] 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 reception desk handles initial telephone inquiries, A summarization unit that summarizes the requirements received by the reception unit, A decision unit that determines whether to connect a call based on the requirements summarized by the summarization unit, It includes a rejection section that rejects requests under specific conditions. A system characterized by the following features.

2. It has a direct connection port that allows you to connect directly to the phone number of someone you know. The system according to feature 1.

3. It features an interface section that incorporates the interfaces of phone and messaging apps. The system according to feature 1.

4. The aforementioned reception unit is When a call comes in, you answer it first and listen to the caller's request. The system according to feature 1.

5. The summary section above is, Summarize the requirements and communicate them to the user. The system according to feature 1.

6. The aforementioned determination unit, We will decide whether to connect the call based on the summary. The system according to feature 1.

7. The aforementioned rejection section is, I will refuse if the requirements are specific. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of the initial call response based on those emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze the user's past call history to select the optimal initial response method. The system according to feature 1.

10. The aforementioned reception unit is When an initial call is received, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

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