system

Abstract

The system according to this embodiment aims to efficiently gather customer requests and personal information during reception work at a city hall. [Solution] The system according to the embodiment comprises a collection unit, a hearing unit, a preference hearing unit, and a provision unit. The collection unit temporarily receives the customer's request. The hearing unit hears the customer's personal information based on the information collected by the collection unit. The preference hearing unit hears the customer's preferences based on the information heard by the hearing unit. The provision unit transmits the information heard by the preference hearing unit as digital data to the receptionist.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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, in the reception work of the city hall, the hearing of customers' requests and personal information is not efficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently hear customers' requests and personal information in the reception work of the city hall.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a hearing unit, a preference hearing unit, and a provision unit. The collection unit temporarily receives customer requests. The hearing unit hears the customer's personal information based on the information collected by the collection unit. The preference hearing unit hears the customer's preferences based on the information heard by the hearing unit. The provision unit transmits the information heard by the preference hearing unit as digital data to the receptionist. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently gather customer requests and personal information during reception work at city halls. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The city hall reception robot system according to an embodiment of the present invention is a city hall reception robot that utilizes an AI agent. This city hall reception robot system consists of the following steps. First, the city hall reception robot system temporarily receives the customer's request. Next, the city hall reception robot system interviews the customer about their current personal information. Furthermore, the city hall reception robot system interviews the customer about their wishes. This makes human interaction smoother and reduces customer service time. For example, the city hall reception robot system temporarily receives the customer's request. At this time, the city hall reception robot system collects information to understand the customer's request and take appropriate action. For example, if the customer says, "I want to change my address," the city hall reception robot system records that request and proceeds to the next step. Next, the city hall reception robot system interviews the customer about their current personal information. The city hall reception robot system collects basic information such as the customer's name, address, and contact information. For example, if the customer says, "My name is Yamada Taro, my address is Shinjuku-ku, Tokyo, and my contact number is 090-1234-5678," the city hall reception robot system records that information. Furthermore, the city hall reception robot system listens to customers' requests. The city hall reception robot system specifically asks what services customers want. For example, if a customer says, "I want to change my resident registration to a new address," the city hall reception robot system records that request and proceeds to the next step. This system makes human service smoother and reduces customer service time. By having the city hall reception robot system collect customer requests and personal information in advance, human receptionists can respond quickly based on that information. For example, based on the information collected by the city hall reception robot system, receptionists can prepare the necessary documents and provide them to the customer, making the process smoother. In addition, the city hall reception robot system can handle multiple customers simultaneously, thus alleviating the problem of staff shortages. For example, by having the city hall reception robot system temporarily receive requests from multiple customers and listen to their personal information and requests, the burden on receptionists can be reduced.In this way, a city hall reception robot system utilizing AI agents can receive customer requests temporarily, gather personal information and preferences, thereby streamlining human service and reducing customer interaction time. Furthermore, by handling multiple customers simultaneously, it can alleviate the problem of labor shortages. As a result, the city hall reception robot system efficiently collects customer requests, gathers personal information and preferences, and provides this information to reception staff, leading to smoother service and reduced customer interaction time.

[0029] The city hall reception robot system according to this embodiment comprises a collection unit, a hearing unit, a request hearing unit, and a provision unit. The collection unit temporarily receives the customer's request. For example, if the customer says, "I would like to change my address," the collection unit records that request and proceeds to the next step. The collection unit can also record if the customer says, "I would like to renew my passport," and proceed to the next step. The collection unit can also record if the customer says, "I would like to have a resident registration certificate issued," and proceed to the next step. The hearing unit hears the customer's personal information based on the information collected by the collection unit. For example, if the customer says, "My name is Taro Yamada, my address is Shinjuku-ku, Tokyo, and my contact number is 090-1234-5678," the hearing unit records that information. The hearing unit can also record if the customer says, "My name is Hanako Sato, my address is Osaka-shi, Osaka Prefecture, and my contact number is 080-9876-5432." The Hearing Department can record information such as, for example, if a customer says, "My name is Ichiro Suzuki, my address is Nagoya City, Aichi Prefecture, and my contact number is 070-1122-3344." The Desired Services Hearing Department then hears the customer's wishes based on the information gathered by the Hearing Department. For example, if a customer says, "I would like to move my resident registration to a new address," the Desired Services Hearing Department records that wish and proceeds to the next step. For example, if a customer says, "I would like to renew my passport," the Desired Services Hearing Department can record that wish and proceed to the next step. For example, if a customer says, "I would like to have a resident registration certificate issued," the Desired Services Hearing Department can record that wish and proceed to the next step. The Provision Department transmits the information gathered by the Desired Services Hearing Department as digital data to the reception staff. For example, the Provision Department transmits the gathered information as digital data to the reception staff, who then prepare the necessary documents. The service department can, for example, transmit the information gathered during the interview as digital data to the receptionist, allowing the receptionist to respond quickly.As a result, the city hall reception robot system according to this embodiment can efficiently collect customer requests, interview them about their personal information and preferences, and provide this information to reception staff, thereby streamlining service and reducing customer interaction time.

[0030] The data collection unit initially receives customer requests. For example, if a customer says, "I want to change my address," the data collection unit records the request and proceeds to the next step. The data collection unit can also record if a customer says, "I want to renew my passport," and proceed to the next step. The data collection unit can also record if a customer says, "I would like to request a certificate of residence," and proceed to the next step. The data collection unit uses speech recognition technology to accurately recognize customer statements and records the requests as text data. The speech recognition technology has a noise-canceling function, which removes ambient noise and can clearly recognize the customer's voice. The data collection unit supports multiple languages ​​and can accurately record requests from customers who speak foreign languages. The data collection unit categorizes requests according to their content and prepares them for the next step. For example, a request for a change of address is categorized as "Change of Address," a request for a passport renewal as "Passport Renewal," and a request for a certificate of residence as "Certificate of Residence Issuance." Once the data collection unit has finished recording the requests, it provides the customer with instructions on how to proceed to the next step. For example, they might announce, "We will now ask for your personal information, so please wait a moment." This allows the data collection department to efficiently gather customer requests and smoothly proceed to the next step.

[0031] The interviewing department interviews customers to gather personal information based on the information collected by the data collection department. For example, if a customer says, "My name is Taro Yamada, my address is Shinjuku-ku, Tokyo, and my phone number is 090-1234-5678," the interviewing department will record that information. The interviewing department can also record if a customer says, "My name is Hanako Sato, my address is Osaka-shi, Osaka Prefecture, and my phone number is 080-9876-5432." The interviewing department can also record if a customer says, "My name is Ichiro Suzuki, my address is Nagoya-shi, Aichi Prefecture, and my phone number is 070-1122-3344." The interviewing department uses speech recognition technology to accurately recognize the customer's statements and records the personal information as text data. The speech recognition technology uses a highly accurate speech analysis algorithm that can accurately transcribe the customer's statements into text. The interviewing department has a function to ask customers for confirmation to prevent errors in entering personal information. For example, the system will ask a question such as, "Is your name Taro Yamada?" and confirm the information if the customer answers "Yes." Once the customer has finished entering their personal information, the interviewing department will provide instructions to move on to the next step. For example, they might say, "We will now ask you about the procedure you wish to proceed with, so please wait a moment." This allows the interviewing department to accurately collect the customer's personal information and proceed smoothly to the next step.

[0032] The Request Hearing Department will hear the customer's wishes based on the information gathered by the Hearing Department. For example, if the customer says, "I want to move my residence registration to a new address," the Request Hearing Department will record that wish and proceed to the next step. The Request Hearing Department can also record if the customer says, "I want to renew my passport," and proceed to the next step. For example, if the customer says, "I would like to have a residence certificate issued," the Request Hearing Department can also record that wish and proceed to the next step. The Request Hearing Department uses speech recognition technology to accurately recognize the customer's statements and record their wishes as text data. The speech recognition technology is used in combination with natural language processing technology, allowing it to accurately understand the intent of the customer's statements. The Request Hearing Department prepares to take appropriate action according to the content of the wishes. For example, a request to move residence registration is classified into the "Residence Registration Movement" category, a request to renew passport is classified into the "Passport Renewal" category, and a request to have a residence certificate issued into the "Residence Certificate Issuance" category. Once the preferences gathering department has finished recording the customer's preferences, it provides instructions to move on to the next step. For example, it might say, "We will now send your information to the receptionist, so please wait a moment." This allows the preferences gathering department to efficiently collect customer preferences and move smoothly to the next step.

[0033] The service provider transmits the information gathered by the request hearing service as digital data to the reception staff. For example, the service provider transmits the gathered information as digital data to the reception staff, who then prepare the necessary documents. The service provider also transmits the gathered information as digital data to the reception staff, who then respond quickly. The service provider also transmits the gathered information as digital data to the reception staff, who then respond efficiently. The service provider implements security measures when transmitting digital data, using encryption technology to prevent information leakage and tampering. Once the transmission of digital data is complete, the service provider notifies the reception staff so that they can respond quickly. For example, they might notify the reception staff that "a customer wants to change their resident registration to a new address," allowing the reception staff to prepare the necessary documents. Once the transmission of digital data is complete, the service provider provides the customer with instructions to proceed to the next step. For example, they might say, "A reception staff member will assist you, please wait a moment." This allows the service provider to provide the gathered information to the reception staff quickly and accurately, enabling them to respond efficiently. Furthermore, the data delivery unit has a function to record the transmission history of digital data, allowing for later review. This enables the data delivery unit to understand the status of information transmission and revise its response as needed.

[0034] The data collection unit can analyze the user's past request history and select the optimal collection method. For example, the data collection unit can prioritize collecting requests based on requests that the user has frequently submitted in the past. For example, the data collection unit can also find specific patterns in the user's past request history and collect requests based on those patterns. For example, the data collection unit can analyze the user's past request history and select the most efficient collection method. This allows for the selection of the optimal collection method and efficient collection of requests by analyzing past request history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past request history data into a generating AI and have the generating AI select the optimal collection method.

[0035] The collection unit can filter requests based on the user's current situation and areas of interest when collecting them. For example, if the user inputs their current situation, the collection unit will prioritize collecting requests relevant to that situation. The collection unit can also analyze the user's areas of interest and collect requests related to those areas. The collection unit can also filter requests based on the user's current situation and areas of interest to collect the most relevant requests. This allows for the collection of highly relevant requests by filtering requests based on the user's current situation and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current situation data into a generating AI and have the generating AI perform the request filtering.

[0036] The collection unit can prioritize collecting highly relevant requests by considering the user's geographical location information when collecting requests. For example, if the user is in a specific region, the collection unit will prioritize collecting requests related to that region. The collection unit can also collect the most relevant requests based on the user's geographical location information. For example, if the user is on the move, the collection unit can update the user's current location information in real time and collect relevant requests. This allows for the priority collection of highly relevant requests by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the request collection.

[0037] The data collection unit can analyze the user's social media activity and collect relevant requests when collecting requests. For example, the data collection unit can analyze the user's social media posts and collect requests based on their content. For example, the data collection unit can identify areas of interest from the user's social media activity and collect relevant requests. For example, the data collection unit can collect relevant requests by referring to the activities of the user's social media followers and friends. In this way, relevant requests can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the request collection.

[0038] The interviewing unit can adjust the level of detail in the interview based on the importance of the personal information being interviewed. For example, when collecting important personal information, the interviewing unit will ask detailed questions. For example, when collecting general personal information, the interviewing unit may ask concise questions. The interviewing unit can also adjust the level of detail of the questions according to the importance of the personal information. This allows for efficient information collection by adjusting the level of detail of the interview according to the importance of the personal information. Some or all of the above-described processes in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input personal information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the interview.

[0039] The interviewing unit can apply different interviewing algorithms depending on the category of personal information during the interview. For example, when collecting basic information such as name and address, the interviewing unit applies a simple algorithm. When collecting important information such as contact information and emergency contact information, the interviewing unit can also apply a detailed algorithm. The interviewing unit can also select the optimal algorithm depending on the category of personal information. This allows for efficient information collection by applying the optimal interviewing algorithm according to the category of personal information. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input personal information category data into a generating AI and have the generating AI execute the application of the interviewing algorithm.

[0040] The interviewing department can determine the priority of interviews based on when the personal information was submitted. For example, the interviewing department might prioritize interviews with recently submitted personal information. For example, the interviewing department might postpone interviews with older personal information. For example, the interviewing department could determine the optimal priority based on the submission date. This allows for efficient information collection by prioritizing interviews based on when the personal information was submitted. Some or all of the above-described processes in the interviewing department may be performed using AI, for example, or without AI. For example, the interviewing department could input personal information submission date data into a generating AI and have the generating AI determine the priority of interviews.

[0041] The interviewing unit can adjust the order of interviews based on the relevance of the personal information. For example, the interviewing unit may prioritize interviewing important personal information. For example, the interviewing unit may postpone interviewing less relevant personal information. For example, the interviewing unit may determine the optimal order based on the relevance of the personal information. This allows for efficient information collection by adjusting the order of interviews based on the relevance of the personal information. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the relevance data of personal information into a generating AI and have the generating AI perform the adjustment of the interview order.

[0042] The preference gathering unit can adjust the level of detail in the interview based on the importance of the desired content. For example, when gathering important preferences, the preference gathering unit can ask detailed questions. For example, when gathering general preferences, the preference gathering unit can ask concise questions. The preference gathering unit can also adjust the level of detail of the questions according to the importance of the desired content. This allows for efficient information collection by adjusting the level of detail of the interview according to the importance of the desired content. Some or all of the above processing in the preference gathering unit may be performed using AI, for example, or without AI. For example, the preference gathering unit can input importance data of the desired content into a generating AI and have the generating AI perform the adjustment of the level of detail of the interview.

[0043] The preference gathering unit can apply different gathering algorithms depending on the category of the desired content during the preference gathering process. For example, when collecting basic preferences such as a change of address, the preference gathering unit applies a simple algorithm. For example, when collecting preferences related to a specific service, the preference gathering unit can also apply a detailed algorithm. The preference gathering unit can also select the optimal algorithm depending on the category of the desired content. This allows for efficient information collection by applying the optimal gathering algorithm according to the category of the desired content. Some or all of the above-described processes in the preference gathering unit may be performed using AI, for example, or without AI. For example, the preference gathering unit can input the category data of desired content into a generating AI and have the generating AI execute the application of the gathering algorithm.

[0044] The preference hearing unit can determine the priority of hearings based on when the preferences were submitted. For example, the preference hearing unit may prioritize hearings on recently submitted preferences. For example, the preference hearing unit may also postpone hearings on older preferences. For example, the preference hearing unit may determine the optimal priority based on the submission date. This allows for efficient information collection by determining the priority of hearings based on the submission date of the preferences. Some or all of the above processing in the preference hearing unit may be performed using AI, for example, or without AI. For example, the preference hearing unit can input data on the submission date of preferences into a generating AI and have the generating AI determine the priority of hearings.

[0045] The preference hearing unit can adjust the order of interviews based on the relevance of the requested content during the preference hearing process. For example, the preference hearing unit can prioritize interviewing important requests. For example, the preference hearing unit can postpone interviewing less relevant requests. For example, the preference hearing unit can determine the optimal order based on the relevance of the requested content. This allows for efficient information collection by adjusting the order of interviews based on the relevance of the requested content. Some or all of the above-described processes in the preference hearing unit may be performed using AI, for example, or without AI. For example, the preference hearing unit can input the relevance data of the requested content into a generating AI and have the generating AI perform the adjustment of the interview order.

[0046] The information delivery unit can adjust the level of detail provided based on the importance of the information gathered during the delivery process. For example, when providing important information, the delivery unit can provide a display method that includes detailed explanations. For example, when providing general information, the delivery unit can also provide a concise display method. The delivery unit can also select the optimal level of detail according to the importance of the information gathered during the delivery process. This allows for efficient information delivery by adjusting the level of detail according to the importance of the information gathered during the delivery process. Some or all of the above-described processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input importance data of the gathered information into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0047] The information delivery unit can apply different delivery algorithms depending on the category of information gathered during delivery. For example, when providing basic information, the delivery unit can apply a simple algorithm. For example, when providing important information, the delivery unit can also apply a detailed algorithm. The delivery unit can also select the optimal algorithm depending on the category of information gathered. This allows for efficient information delivery by applying the optimal delivery algorithm according to the category of information gathered. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category data of the gathered information into a generating AI and have the generating AI execute the application of the delivery algorithm.

[0048] The information delivery unit can adjust the order of delivery based on the submission timing of the information gathered during the delivery process. For example, the delivery unit can prioritize the delivery of information gathered recently. For example, the delivery unit can postpone the delivery of information that was submitted a long time ago. For example, the delivery unit can determine the optimal order based on the submission timing. This allows for efficient information delivery by adjusting the order of delivery based on the submission timing of the information gathered during the delivery process. Some or all of the above-described processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the submission timing data of the gathered information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0049] The information delivery unit can adjust the order of delivery based on the relevance of the information gathered during the delivery process. For example, the delivery unit may prioritize the delivery of important information. For example, the delivery unit may postpone the delivery of less relevant information. For example, the delivery unit may determine the optimal order based on the relevance of the information gathered during the delivery process. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information gathered during the delivery process. Some or all of the above-described processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance data of the gathered information into a generating AI and have the generating AI perform the adjustment of the delivery order.

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

[0051] The data collection unit can analyze a user's past request history and select the optimal collection method. For example, it can prioritize collecting requests based on those that a user has frequently submitted in the past. It can also identify specific patterns in a user's past request history and collect requests based on those patterns. Furthermore, it can analyze a user's past request history and select the most efficient collection method. In this way, by analyzing past request history, the optimal collection method can be selected, and requests can be collected efficiently.

[0052] The data collection unit can filter requests based on the user's current situation and areas of interest. For example, if a user enters their current situation, the unit can prioritize collecting requests relevant to that situation. It can also analyze the user's areas of interest and collect requests related to those areas. Furthermore, it can filter requests based on the user's current situation and areas of interest to collect the most relevant requests. This allows for the collection of highly relevant requests by filtering requests based on the user's current situation and areas of interest.

[0053] The data collection unit can prioritize collecting highly relevant requests by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize collecting requests related to that region. It can also collect the most relevant requests based on the user's geographical location. Furthermore, if a user is on the move, it can update their current location in real time and collect relevant requests. This allows for the priority collection of highly relevant requests by considering the user's geographical location.

[0054] The data collection unit can analyze a user's social media activity and collect relevant requests when gathering requests. For example, it can analyze a user's social media posts and collect requests based on their content. It can also identify areas of interest from a user's social media activity and collect relevant requests. Furthermore, it can collect relevant requests by referring to the activities of the user's social media followers and friends. In this way, relevant requests can be collected by analyzing a user's social media activity.

[0055] The interviewing department can adjust the level of detail in interviews based on the importance of the personal information being interviewed. For example, when collecting important personal information, the interviewing department can ask detailed questions. Conversely, when collecting general personal information, the interviewing department can ask concise questions. Furthermore, the interviewing department can adjust the level of detail of the questions according to the importance of the personal information. This allows for efficient information collection by adjusting the level of detail in interviews according to the importance of the personal information.

[0056] The interviewing unit can apply different interviewing algorithms depending on the category of personal information being collected. For example, when collecting basic information such as name and address, the interviewing unit can apply a simple algorithm. However, when collecting important information such as contact details and emergency contact information, the interviewing unit can apply a more detailed algorithm. Furthermore, the interviewing unit can select the most suitable algorithm based on the category of personal information. This allows for efficient information collection by applying the most appropriate interviewing algorithm according to the category of personal information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection department temporarily receives the customer's request. For example, if the customer says, "I want to change my address," the request is recorded and the department proceeds to the next step. Similarly, if the customer says, "I want to renew my passport" or "I would like to request a certificate of residence," the department records the request and proceeds to the next step. Step 2: The interviewing department interviews customers to obtain their personal information based on the information collected by the data collection department. For example, if a customer says, "My name is Taro Yamada, my address is Shinjuku-ku, Tokyo, and my phone number is 090-1234-5678," this information is recorded. Similarly, if a customer says, "My name is Hanako Sato, my address is Osaka-shi, Osaka Prefecture, and my phone number is 080-9876-5432," or "My name is Ichiro Suzuki, my address is Nagoya-shi, Aichi Prefecture, and my phone number is 070-1122-3344," this information is also recorded. Step 3: The Request Hearing Department will hear the customer's requests based on the information gathered by the Hearing Department. For example, if the customer says, "I want to move my resident registration to my new address," this request will be recorded and the process will proceed to the next step. Similarly, if the customer says, "I want to renew my passport" or "I would like to have a resident registration issued," this request will also be recorded and the process will proceed to the next step. Step 4: The service department transmits the information gathered by the request hearing department as digital data to the receptionist. For example, the information gathered is transmitted as digital data to the receptionist, who then prepares the necessary documents. This allows the receptionist to respond quickly and efficiently.

[0059] (Example of form 2) The city hall reception robot system according to an embodiment of the present invention is a city hall reception robot that utilizes an AI agent. This city hall reception robot system consists of the following steps. First, the city hall reception robot system temporarily receives the customer's request. Next, the city hall reception robot system interviews the customer about their current personal information. Furthermore, the city hall reception robot system interviews the customer about their wishes. This makes human interaction smoother and reduces customer service time. For example, the city hall reception robot system temporarily receives the customer's request. At this time, the city hall reception robot system collects information to understand the customer's request and take appropriate action. For example, if the customer says, "I want to change my address," the city hall reception robot system records that request and proceeds to the next step. Next, the city hall reception robot system interviews the customer about their current personal information. The city hall reception robot system collects basic information such as the customer's name, address, and contact information. For example, if the customer says, "My name is Yamada Taro, my address is Shinjuku-ku, Tokyo, and my contact number is 090-1234-5678," the city hall reception robot system records that information. Furthermore, the city hall reception robot system listens to customers' requests. The city hall reception robot system specifically asks what services customers want. For example, if a customer says, "I want to change my resident registration to a new address," the city hall reception robot system records that request and proceeds to the next step. This system makes human service smoother and reduces customer service time. By having the city hall reception robot system collect customer requests and personal information in advance, human receptionists can respond quickly based on that information. For example, based on the information collected by the city hall reception robot system, receptionists can prepare the necessary documents and provide them to the customer, making the process smoother. In addition, the city hall reception robot system can handle multiple customers simultaneously, thus alleviating the problem of staff shortages. For example, by having the city hall reception robot system temporarily receive requests from multiple customers and listen to their personal information and requests, the burden on receptionists can be reduced.In this way, a city hall reception robot system utilizing AI agents can receive customer requests temporarily, gather personal information and preferences, thereby streamlining human service and reducing customer interaction time. Furthermore, by handling multiple customers simultaneously, it can alleviate the problem of labor shortages. As a result, the city hall reception robot system efficiently collects customer requests, gathers personal information and preferences, and provides this information to reception staff, leading to smoother service and reduced customer interaction time.

[0060] The city hall reception robot system according to this embodiment comprises a collection unit, a hearing unit, a request hearing unit, and a provision unit. The collection unit temporarily receives the customer's request. For example, if the customer says, "I would like to change my address," the collection unit records that request and proceeds to the next step. The collection unit can also record if the customer says, "I would like to renew my passport," and proceed to the next step. The collection unit can also record if the customer says, "I would like to have a resident registration certificate issued," and proceed to the next step. The hearing unit hears the customer's personal information based on the information collected by the collection unit. For example, if the customer says, "My name is Taro Yamada, my address is Shinjuku-ku, Tokyo, and my contact number is 090-1234-5678," the hearing unit records that information. The hearing unit can also record if the customer says, "My name is Hanako Sato, my address is Osaka-shi, Osaka Prefecture, and my contact number is 080-9876-5432." The Hearing Department can record information such as, for example, if a customer says, "My name is Ichiro Suzuki, my address is Nagoya City, Aichi Prefecture, and my contact number is 070-1122-3344." The Desired Services Hearing Department then hears the customer's wishes based on the information gathered by the Hearing Department. For example, if a customer says, "I would like to move my resident registration to a new address," the Desired Services Hearing Department records that wish and proceeds to the next step. For example, if a customer says, "I would like to renew my passport," the Desired Services Hearing Department can record that wish and proceed to the next step. For example, if a customer says, "I would like to have a resident registration certificate issued," the Desired Services Hearing Department can record that wish and proceed to the next step. The Provision Department transmits the information gathered by the Desired Services Hearing Department as digital data to the reception staff. For example, the Provision Department transmits the gathered information as digital data to the reception staff, who then prepare the necessary documents. The service department can, for example, transmit the information gathered during the interview as digital data to the receptionist, allowing the receptionist to respond quickly.As a result, the city hall reception robot system according to this embodiment can efficiently collect customer requests, interview them about their personal information and preferences, and provide this information to reception staff, thereby streamlining service and reducing customer interaction time.

[0061] The data collection unit initially receives customer requests. For example, if a customer says, "I want to change my address," the data collection unit records the request and proceeds to the next step. The data collection unit can also record if a customer says, "I want to renew my passport," and proceed to the next step. The data collection unit can also record if a customer says, "I would like to request a certificate of residence," and proceed to the next step. The data collection unit uses speech recognition technology to accurately recognize customer statements and records the requests as text data. The speech recognition technology has a noise-canceling function, which removes ambient noise and can clearly recognize the customer's voice. The data collection unit supports multiple languages ​​and can accurately record requests from customers who speak foreign languages. The data collection unit categorizes requests according to their content and prepares them for the next step. For example, a request for a change of address is categorized as "Change of Address," a request for a passport renewal as "Passport Renewal," and a request for a certificate of residence as "Certificate of Residence Issuance." Once the data collection unit has finished recording the requests, it provides the customer with instructions on how to proceed to the next step. For example, they might announce, "We will now ask for your personal information, so please wait a moment." This allows the data collection department to efficiently gather customer requests and smoothly proceed to the next step.

[0062] The interviewing department interviews customers to gather personal information based on the information collected by the data collection department. For example, if a customer says, "My name is Taro Yamada, my address is Shinjuku-ku, Tokyo, and my phone number is 090-1234-5678," the interviewing department will record that information. The interviewing department can also record if a customer says, "My name is Hanako Sato, my address is Osaka-shi, Osaka Prefecture, and my phone number is 080-9876-5432." The interviewing department can also record if a customer says, "My name is Ichiro Suzuki, my address is Nagoya-shi, Aichi Prefecture, and my phone number is 070-1122-3344." The interviewing department uses speech recognition technology to accurately recognize the customer's statements and records the personal information as text data. The speech recognition technology uses a highly accurate speech analysis algorithm that can accurately transcribe the customer's statements into text. The interviewing department has a function to ask customers for confirmation to prevent errors in entering personal information. For example, the system will ask a question such as, "Is your name Taro Yamada?" and confirm the information if the customer answers "Yes." Once the customer has finished entering their personal information, the interviewing department will provide instructions to move on to the next step. For example, they might say, "We will now ask you about the procedure you wish to proceed with, so please wait a moment." This allows the interviewing department to accurately collect the customer's personal information and proceed smoothly to the next step.

[0063] The Request Hearing Department will hear the customer's wishes based on the information gathered by the Hearing Department. For example, if the customer says, "I want to move my residence registration to a new address," the Request Hearing Department will record that wish and proceed to the next step. The Request Hearing Department can also record if the customer says, "I want to renew my passport," and proceed to the next step. For example, if the customer says, "I would like to have a residence certificate issued," the Request Hearing Department can also record that wish and proceed to the next step. The Request Hearing Department uses speech recognition technology to accurately recognize the customer's statements and record their wishes as text data. The speech recognition technology is used in combination with natural language processing technology, allowing it to accurately understand the intent of the customer's statements. The Request Hearing Department prepares to take appropriate action according to the content of the wishes. For example, a request to move residence registration is classified into the "Residence Registration Movement" category, a request to renew passport is classified into the "Passport Renewal" category, and a request to have a residence certificate issued into the "Residence Certificate Issuance" category. Once the preferences gathering department has finished recording the customer's preferences, it provides instructions to move on to the next step. For example, it might say, "We will now send your information to the receptionist, so please wait a moment." This allows the preferences gathering department to efficiently collect customer preferences and move smoothly to the next step.

[0064] The service provider transmits the information gathered by the request hearing service as digital data to the reception staff. For example, the service provider transmits the gathered information as digital data to the reception staff, who then prepare the necessary documents. The service provider also transmits the gathered information as digital data to the reception staff, who then respond quickly. The service provider also transmits the gathered information as digital data to the reception staff, who then respond efficiently. The service provider implements security measures when transmitting digital data, using encryption technology to prevent information leakage and tampering. Once the transmission of digital data is complete, the service provider notifies the reception staff so that they can respond quickly. For example, they might notify the reception staff that "a customer wants to change their resident registration to a new address," allowing the reception staff to prepare the necessary documents. Once the transmission of digital data is complete, the service provider provides the customer with instructions to proceed to the next step. For example, they might say, "A reception staff member will assist you, please wait a moment." This allows the service provider to provide the gathered information to the reception staff quickly and accurately, enabling them to respond efficiently. Furthermore, the data delivery unit has a function to record the transmission history of digital data, allowing for later review. This enables the data delivery unit to understand the status of information transmission and revise its response as needed.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of request collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect requests quickly to reduce the user's burden. For example, if the user is relaxed, the data collection unit can take more time to collect detailed requests. For example, if the user is in a hurry, the data collection unit can simplify request collection and quickly move on to the next step. This reduces the user's burden by adjusting the timing of request collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0066] The data collection unit can analyze the user's past request history and select the optimal collection method. For example, the data collection unit can prioritize collecting requests based on requests that the user has frequently submitted in the past. For example, the data collection unit can also find specific patterns in the user's past request history and collect requests based on those patterns. For example, the data collection unit can analyze the user's past request history and select the most efficient collection method. This allows for the selection of the optimal collection method and efficient collection of requests by analyzing past request history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past request history data into a generating AI and have the generating AI select the optimal collection method.

[0067] The collection unit can filter requests based on the user's current situation and areas of interest when collecting them. For example, if the user inputs their current situation, the collection unit will prioritize collecting requests relevant to that situation. The collection unit can also analyze the user's areas of interest and collect requests related to those areas. The collection unit can also filter requests based on the user's current situation and areas of interest to collect the most relevant requests. This allows for the collection of highly relevant requests by filtering requests based on the user's current situation and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current situation data into a generating AI and have the generating AI perform the request filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of requests to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important requests. For example, if the user is relaxed, the data collection unit may take more time to collect detailed requests. For example, if the user is in a hurry, the data collection unit may prioritize collecting requests that can be collected quickly. This allows for the priority collection of important requests by determining the priority of requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI determine the priority of requests.

[0069] The collection unit can prioritize collecting highly relevant requests by considering the user's geographical location information when collecting requests. For example, if the user is in a specific region, the collection unit will prioritize collecting requests related to that region. The collection unit can also collect the most relevant requests based on the user's geographical location information. For example, if the user is on the move, the collection unit can update the user's current location information in real time and collect relevant requests. This allows for the priority collection of highly relevant requests by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the request collection.

[0070] The data collection unit can analyze the user's social media activity and collect relevant requests when collecting requests. For example, the data collection unit can analyze the user's social media posts and collect requests based on their content. For example, the data collection unit can identify areas of interest from the user's social media activity and collect relevant requests. For example, the data collection unit can collect relevant requests by referring to the activities of the user's social media followers and friends. In this way, relevant requests can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the request collection.

[0071] The interviewing unit can estimate the user's emotions and adjust the interview's presentation based on the estimated emotions. For example, if the user is nervous, the interviewing unit can ask questions in a calm tone. If the user is relaxed, the interviewing unit can also ask questions in a friendly tone. If the user is in a hurry, the interviewing unit can also ask questions quickly and concisely. By adjusting the interview's presentation according to the user's emotions, the user can relax and provide information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit may be performed using AI, or not using AI. For example, the interviewing unit can input the user's facial expression data into the generative AI and have the generative AI adjust the interview's presentation.

[0072] The interviewing unit can adjust the level of detail in the interview based on the importance of the personal information being interviewed. For example, when collecting important personal information, the interviewing unit will ask detailed questions. For example, when collecting general personal information, the interviewing unit may ask concise questions. The interviewing unit can also adjust the level of detail of the questions according to the importance of the personal information. This allows for efficient information collection by adjusting the level of detail of the interview according to the importance of the personal information. Some or all of the above-described processes in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input personal information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the interview.

[0073] The interviewing unit can apply different interviewing algorithms depending on the category of personal information during the interview. For example, when collecting basic information such as name and address, the interviewing unit applies a simple algorithm. When collecting important information such as contact information and emergency contact information, the interviewing unit can also apply a detailed algorithm. The interviewing unit can also select the optimal algorithm depending on the category of personal information. This allows for efficient information collection by applying the optimal interviewing algorithm according to the category of personal information. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input personal information category data into a generating AI and have the generating AI execute the application of the interviewing algorithm.

[0074] The interview unit can estimate the user's emotions and adjust the length of the interview based on the estimated emotions. For example, if the user is nervous, the interview unit can finish the questions quickly. For example, if the user is relaxed, the interview unit can take more time to ask detailed questions. For example, if the user is in a hurry, the interview unit can ask questions quickly. This reduces the user's burden by adjusting the length of the interview according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using AI, for example, or without AI. For example, the interview unit can input the user's facial expression data into the generative AI and have the generative AI adjust the length of the interview.

[0075] The interviewing department can determine the priority of interviews based on when the personal information was submitted. For example, the interviewing department might prioritize interviews with recently submitted personal information. For example, the interviewing department might postpone interviews with older personal information. For example, the interviewing department could determine the optimal priority based on the submission date. This allows for efficient information collection by prioritizing interviews based on when the personal information was submitted. Some or all of the above-described processes in the interviewing department may be performed using AI, for example, or without AI. For example, the interviewing department could input personal information submission date data into a generating AI and have the generating AI determine the priority of interviews.

[0076] The interviewing unit can adjust the order of interviews based on the relevance of the personal information. For example, the interviewing unit may prioritize interviewing important personal information. For example, the interviewing unit may postpone interviewing less relevant personal information. For example, the interviewing unit may determine the optimal order based on the relevance of the personal information. This allows for efficient information collection by adjusting the order of interviews based on the relevance of the personal information. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can input the relevance data of personal information into a generating AI and have the generating AI perform the adjustment of the interview order.

[0077] The preference-gathering unit can estimate the user's emotions and adjust the preferred interview method based on the estimated emotions. For example, if the user is nervous, the preference-gathering unit can ask questions in a calm tone. If the user is relaxed, the preference-gathering unit can also ask questions in a friendly tone. If the user is in a hurry, the preference-gathering unit can also ask questions quickly and concisely. By adjusting the preferred interview method according to the user's emotions, the user can relax and provide information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the preference-gathering unit may be performed using AI or not. For example, the preference-gathering unit can input the user's facial expression data into the generative AI and have the generative AI adjust the preferred interview method.

[0078] The preference gathering unit can adjust the level of detail in the interview based on the importance of the desired content. For example, when gathering important preferences, the preference gathering unit can ask detailed questions. For example, when gathering general preferences, the preference gathering unit can ask concise questions. The preference gathering unit can also adjust the level of detail of the questions according to the importance of the desired content. This allows for efficient information collection by adjusting the level of detail of the interview according to the importance of the desired content. Some or all of the above processing in the preference gathering unit may be performed using AI, for example, or without AI. For example, the preference gathering unit can input importance data of the desired content into a generating AI and have the generating AI perform the adjustment of the level of detail of the interview.

[0079] The preference gathering unit can apply different gathering algorithms depending on the category of the desired content during the preference gathering process. For example, when collecting basic preferences such as a change of address, the preference gathering unit applies a simple algorithm. For example, when collecting preferences related to a specific service, the preference gathering unit can also apply a detailed algorithm. The preference gathering unit can also select the optimal algorithm depending on the category of the desired content. This allows for efficient information collection by applying the optimal gathering algorithm according to the category of the desired content. Some or all of the above-described processes in the preference gathering unit may be performed using AI, for example, or without AI. For example, the preference gathering unit can input the category data of desired content into a generating AI and have the generating AI execute the application of the gathering algorithm.

[0080] The preference hearing unit can estimate the user's emotions and adjust the length of the preference hearing based on the estimated emotions. For example, if the user is nervous, the preference hearing unit can finish the questions quickly. For example, if the user is relaxed, the preference hearing unit can take more time to ask detailed questions. For example, if the user is in a hurry, the preference hearing unit can ask questions quickly. In this way, the burden on the user can be reduced by adjusting the length of the preference hearing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the preference hearing unit may be performed using AI, for example, or without AI. For example, the preference hearing unit can input the user's facial expression data into the generative AI and have the generative AI adjust the length of the preference hearing.

[0081] The preference hearing unit can determine the priority of hearings based on when the preferences were submitted. For example, the preference hearing unit may prioritize hearings on recently submitted preferences. For example, the preference hearing unit may also postpone hearings on older preferences. For example, the preference hearing unit may determine the optimal priority based on the submission date. This allows for efficient information collection by determining the priority of hearings based on the submission date of the preferences. Some or all of the above processing in the preference hearing unit may be performed using AI, for example, or without AI. For example, the preference hearing unit can input data on the submission date of preferences into a generating AI and have the generating AI determine the priority of hearings.

[0082] The preference hearing unit can adjust the order of interviews based on the relevance of the requested content during the preference hearing process. For example, the preference hearing unit can prioritize interviewing important requests. For example, the preference hearing unit can postpone interviewing less relevant requests. For example, the preference hearing unit can determine the optimal order based on the relevance of the requested content. This allows for efficient information collection by adjusting the order of interviews based on the relevance of the requested content. Some or all of the above-described processes in the preference hearing unit may be performed using AI, for example, or without AI. For example, the preference hearing unit can input the relevance data of the requested content into a generating AI and have the generating AI perform the adjustment of the interview order.

[0083] The information provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is nervous, the provider can provide a simple and highly visible display method. For example, if the user is relaxed, the provider can also provide a display method that includes detailed information. For example, if the user is in a hurry, the provider can also provide a display method that gets straight to the point. By adjusting how the information is displayed according to the user's emotions, the information becomes easier for the user to understand. 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. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user facial expression data into the generative AI and have the generative AI adjust how the information is displayed.

[0084] The information delivery unit can adjust the level of detail provided based on the importance of the information gathered during the delivery process. For example, when providing important information, the delivery unit can provide a display method that includes detailed explanations. For example, when providing general information, the delivery unit can also provide a concise display method. The delivery unit can also select the optimal level of detail according to the importance of the information gathered during the delivery process. This allows for efficient information delivery by adjusting the level of detail according to the importance of the information gathered during the delivery process. Some or all of the above-described processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input importance data of the gathered information into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0085] The information delivery unit can apply different delivery algorithms depending on the category of information gathered during delivery. For example, when providing basic information, the delivery unit can apply a simple algorithm. For example, when providing important information, the delivery unit can also apply a detailed algorithm. The delivery unit can also select the optimal algorithm depending on the category of information gathered. This allows for efficient information delivery by applying the optimal delivery algorithm according to the category of information gathered. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category data of the gathered information into a generating AI and have the generating AI execute the application of the delivery algorithm.

[0086] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is tense, the information provider will prioritize providing important information. For example, if the user is relaxed, the information provider may take more time to provide detailed information. For example, if the user is in a hurry, the information provider may prioritize providing information that can be delivered quickly. In this way, by prioritizing information according to the user's emotions, important information can be provided preferentially. 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. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform the determination of information prioritization.

[0087] The information delivery unit can adjust the order of delivery based on the submission timing of the information gathered during the delivery process. For example, the delivery unit can prioritize the delivery of information gathered recently. For example, the delivery unit can postpone the delivery of information that was submitted a long time ago. For example, the delivery unit can determine the optimal order based on the submission timing. This allows for efficient information delivery by adjusting the order of delivery based on the submission timing of the information gathered during the delivery process. Some or all of the above-described processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the submission timing data of the gathered information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0088] The information delivery unit can adjust the order of delivery based on the relevance of the information gathered during the delivery process. For example, the delivery unit may prioritize the delivery of important information. For example, the delivery unit may postpone the delivery of less relevant information. For example, the delivery unit may determine the optimal order based on the relevance of the information gathered during the delivery process. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information gathered during the delivery process. Some or all of the above-described processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance data of the gathered information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of request collection based on those emotions. For example, if the user is stressed, the data collection unit can collect requests quickly to reduce the user's burden. If the user is relaxed, the unit can take more time to collect detailed requests. Furthermore, if the user is in a hurry, the unit can simplify the request collection process and quickly move on to the next step. In this way, by adjusting the timing of request collection according to the user's emotions, the user's burden can be reduced.

[0091] The data collection unit can analyze a user's past request history and select the optimal collection method. For example, it can prioritize collecting requests based on those that a user has frequently submitted in the past. It can also identify specific patterns in a user's past request history and collect requests based on those patterns. Furthermore, it can analyze a user's past request history and select the most efficient collection method. In this way, by analyzing past request history, the optimal collection method can be selected, and requests can be collected efficiently.

[0092] The data collection unit can filter requests based on the user's current situation and areas of interest. For example, if a user enters their current situation, the unit can prioritize collecting requests relevant to that situation. It can also analyze the user's areas of interest and collect requests related to those areas. Furthermore, it can filter requests based on the user's current situation and areas of interest to collect the most relevant requests. This allows for the collection of highly relevant requests by filtering requests based on the user's current situation and areas of interest.

[0093] The data collection unit can estimate the user's emotions and prioritize the requests to be collected based on those emotions. For example, if the user is stressed, the data collection unit can prioritize collecting important requests. If the user is relaxed, the unit can take more time to collect detailed requests. Furthermore, if the user is in a hurry, the unit can prioritize collecting requests that can be collected quickly. In this way, by prioritizing requests according to the user's emotions, important requests can be collected first.

[0094] The data collection unit can prioritize collecting highly relevant requests by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize collecting requests related to that region. It can also collect the most relevant requests based on the user's geographical location. Furthermore, if a user is on the move, it can update their current location in real time and collect relevant requests. This allows for the priority collection of highly relevant requests by considering the user's geographical location.

[0095] The data collection unit can analyze a user's social media activity and collect relevant requests when gathering requests. For example, it can analyze a user's social media posts and collect requests based on their content. It can also identify areas of interest from a user's social media activity and collect relevant requests. Furthermore, it can collect relevant requests by referring to the activities of the user's social media followers and friends. In this way, relevant requests can be collected by analyzing a user's social media activity.

[0096] The interview function can estimate the user's emotions and adjust the interview's tone based on that estimation. For example, if the user is nervous, the interview function can ask questions in a calm tone. If the user is relaxed, the interview function can ask questions in a friendly tone. Furthermore, if the user is in a hurry, the interview function can ask questions quickly and concisely. By adjusting the interview's tone according to the user's emotions, the system can help the user relax and provide information.

[0097] The interviewing department can adjust the level of detail in interviews based on the importance of the personal information being interviewed. For example, when collecting important personal information, the interviewing department can ask detailed questions. Conversely, when collecting general personal information, the interviewing department can ask concise questions. Furthermore, the interviewing department can adjust the level of detail of the questions according to the importance of the personal information. This allows for efficient information collection by adjusting the level of detail in interviews according to the importance of the personal information.

[0098] The interviewing unit can apply different interviewing algorithms depending on the category of personal information being collected. For example, when collecting basic information such as name and address, the interviewing unit can apply a simple algorithm. However, when collecting important information such as contact details and emergency contact information, the interviewing unit can apply a more detailed algorithm. Furthermore, the interviewing unit can select the most suitable algorithm based on the category of personal information. This allows for efficient information collection by applying the most appropriate interviewing algorithm according to the category of personal information.

[0099] The interview function can estimate the user's emotions and adjust the length of the interview based on those estimates. For example, if the user is nervous, the interview function can complete the questions quickly. Conversely, if the user is relaxed, the interview function can take more time to ask detailed questions. Furthermore, if the user is in a hurry, the interview function can ask questions quickly. By adjusting the length of the interview according to the user's emotions, the burden on the user can be reduced.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The collection department temporarily receives the customer's request. For example, if the customer says, "I want to change my address," the request is recorded and the department proceeds to the next step. Similarly, if the customer says, "I want to renew my passport" or "I would like to request a certificate of residence," the department records the request and proceeds to the next step. Step 2: The interviewing department interviews customers to obtain their personal information based on the information collected by the data collection department. For example, if a customer says, "My name is Taro Yamada, my address is Shinjuku-ku, Tokyo, and my phone number is 090-1234-5678," this information is recorded. Similarly, if a customer says, "My name is Hanako Sato, my address is Osaka-shi, Osaka Prefecture, and my phone number is 080-9876-5432," or "My name is Ichiro Suzuki, my address is Nagoya-shi, Aichi Prefecture, and my phone number is 070-1122-3344," this information is also recorded. Step 3: The Request Hearing Department will hear the customer's requests based on the information gathered by the Hearing Department. For example, if the customer says, "I want to move my resident registration to my new address," this request will be recorded and the process will proceed to the next step. Similarly, if the customer says, "I want to renew my passport" or "I would like to have a resident registration issued," this request will also be recorded and the process will proceed to the next step. Step 4: The service department transmits the information gathered by the request hearing department as digital data to the receptionist. For example, the information gathered is transmitted as digital data to the receptionist, who then prepares the necessary documents. This allows the receptionist to respond quickly and efficiently.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the collection unit, hearing unit, preference hearing unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer requests using the camera 42 and microphone 38B of the smart device 14 and processes the information with the control unit 46A. The hearing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hears the customer's personal information. The preference hearing unit is implemented by, for example, the control unit 46A of the smart device 14 and hears the customer's preferences. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the heard information to the receptionist. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the collection unit, hearing unit, preference hearing unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects customer requests using the camera 42 and microphone 238 of the smart glasses 214 and processes the information with the control unit 46A. The hearing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and hears the customer's personal information. The preference hearing unit is implemented, for example, by the control unit 46A of the smart glasses 214 and hears the customer's preferences. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides the heard information to the receptionist. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the collection unit, hearing unit, preference hearing unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer requests using the camera 42 and microphone 238 of the headset terminal 314 and processes the information with the control unit 46A. The hearing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hears the customer's personal information. The preference hearing unit is implemented by, for example, the control unit 46A of the headset terminal 314 and hears the customer's preferences. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the heard information to the receptionist. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, hearing unit, preference hearing unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer requests using the camera 42 and microphone 238 of the robot 414 and processes the information with the control unit 46A. The hearing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hears the customer's personal information. The preference hearing unit is implemented by, for example, the control unit 46A of the robot 414 and hears the customer's preferences. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the heard information to the receptionist. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) The collection department temporarily receives customer requests, Based on the information collected by the aforementioned collection unit, there is an interview unit that interviews customers to obtain their personal information, A desire-gathering unit that gathers information from customers based on the information gathered by the aforementioned hearing unit, The system includes a provisioning unit that transmits the information gathered by the aforementioned preference hearing unit as digital data to the receptionist. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of request collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past request history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting requests, filter them based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and determines the priority of requests to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting requests, the system prioritizes collecting requests 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 7) The aforementioned collection unit is When collecting requests, we analyze users' social media activity and gather relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned hearing section is, The system estimates the user's emotions and adjusts the way the interview is conducted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned hearing section is, During the interview, the level of detail in the interview will be adjusted based on the importance of the personal information being asked. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned hearing section is, During the interview process, different interview algorithms are applied depending on the category of personal information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned hearing section is, The system estimates the user's emotions and adjusts the length of the interview based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned hearing section is, During the interview process, we will prioritize interviews based on when personal information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned hearing section is, During the interview process, the order of the interviews will be adjusted based on the relevance of the personal information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned preference hearing section is, It estimates the user's emotions and adjusts the desired interview method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned preference hearing section is, During the initial consultation to understand your preferences, we will adjust the level of detail in the consultation based on the importance of your desired content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned preference hearing section is, During the initial consultation to understand your needs, we apply different consultation algorithms depending on the category of your requests. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned preference hearing section is, It estimates the user's emotions and adjusts the length of the desired response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned preference hearing section is, During the initial consultation to understand your preferences, we will prioritize the consultations based on when you submitted your preferences. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned preference hearing section is, During the initial consultation to understand your needs, we will adjust the order of the questions based on the relevance of the requested information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the information, we adjust the level of detail based on the importance of the information gathered during the interview. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the information, different provision algorithms are applied depending on the category of information gathered during the interview. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the information, the order of provision will be adjusted based on when the information gathered during the interview was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the information, the order of delivery will be adjusted based on the relevance of the information gathered during the interview. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department temporarily receives customer requests, Based on the information collected by the aforementioned collection unit, there is an interview unit that interviews customers to obtain their personal information, A desire-gathering unit that gathers information from customers based on the information gathered by the aforementioned hearing unit, The system includes a provisioning unit that transmits the information gathered by the aforementioned preference hearing unit as digital data to the receptionist. A system characterized by the following features.

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

3. The aforementioned collection unit is Analyze the user's past request history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting requests, filter them based on the user's current situation and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and determines the priority of requests to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting requests, the system prioritizes collecting requests that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting requests, we analyze users' social media activity and gather relevant requests. The system according to feature 1.

8. The aforementioned hearing section is, The system estimates the user's emotions and adjusts the way the interview is conducted based on those estimated emotions. The system according to feature 1.

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