system

Abstract

The system according to the embodiment aims to automate API cooperation between different services. [Solution] A system according to an embodiment includes a collection unit, an interpretation unit, a linking unit, and an error handling unit. The collection unit collects API information. The interpretation unit interprets the API information collected by the collection unit. The linking unit performs API linking based on the information interpreted by the interpretation unit. The error handling unit performs error handling.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, API integration between different services was often done manually, which was inefficient.

[0005] The system according to the embodiment aims to automate API cooperation between different services. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an interpretation unit, a linking unit, and an error handling unit. The collection unit collects API information. The interpretation unit interprets the API information collected by the collection unit. The linking unit performs API linking based on the information interpreted by the interpretation unit. The error handling unit performs error handling. [Effects of the Invention]

[0007] The system according to the embodiment can automate API cooperation between different services. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) An API integration system according to an embodiment of the present invention provides a function for connecting existing services. This API integration system automatically interprets the APIs of each service and functions as a gateway for API integration. This gateway not only exchanges data but also integrates with a variety of services, such as translation services and map guidance services. For example, the API integration system collects API information from each service. For example, it collects API information from translation services and map guidance services. This information is input into the gateway. Next, the API integration system automatically interprets the collected API information. The API integration system analyzes the API specifications of each service and determines how to integrate. For example, it analyzes how to translate specific text using the API of a translation service. Then, the API integration system performs API integration. The API integration system exchanges data using the APIs of each service. For example, it can send text entered by a user to a translation service and receive the translation results. Furthermore, the API integration system can integrate with a variety of services. For example, it can integrate with a map guidance service to provide route guidance from the user's current location to their destination. It can also integrate with other services to provide various information to the user. This mechanism makes it possible to easily link existing services together, enabling the provision of a variety of services to users. For example, by linking a translation service with a map guidance service, it is possible to provide map guidance in a foreign language. Furthermore, by linking with other services, it is possible to provide more convenient services to users. In this way, the API integration system can efficiently link existing services together and provide a variety of services.

[0029] The API integration system according to the embodiment includes a collection unit, an interpretation unit, an integration unit, and an error handling unit. The collection unit collects API information of each service. For example, the collection unit can collect API information of a translation service and API information of a map guidance service. The collection unit can collect API information using, for example, periodic polling or an event-driven collection method. The interpretation unit interprets the API information collected by the collection unit. For example, the interpretation unit can convert data formats and analyze metadata. The interpretation unit analyzes the collected API information and determines how to integrate. For example, the interpretation unit can analyze how to translate specific text using the API of a translation service. The integration unit performs API integration based on the information interpreted by the interpretation unit. For example, the integration unit can exchange data using the API of each service. For example, the integration unit can send text entered by a user to a translation service and receive the translation result. The error handling unit handles errors that occur during API integration. For example, the error handling unit can implement a retry mechanism and record an error log. As a result, the API integration system according to the embodiment can efficiently collect, interpret, integrate, and handle errors in API information.

[0030] The API integration system includes a translation unit that integrates with a translation service. The translation unit integrates with the translation service. For example, the translation unit can translate specific text using the API of the translation service. The translation unit can integrate with translation services such as Google (registered trademark) Translate and Microsoft (registered trademark) Translator. This allows the translation unit to send text entered by a user to the translation service and receive the translation results. This enables integration with the translation service.

[0031] The API linkage system includes a guidance unit that links with a map guidance service. The guidance unit links with the map guidance service. For example, the guidance unit can use the API of the map guidance service to provide route guidance from the user's current location to the destination. The guidance unit can link with map guidance services such as Google Maps and Apple Maps. This allows the guidance unit to provide route guidance from the user's current location to the destination. This enables linkage with the map guidance service.

[0032] The collection unit can collect API information of the translation service. The collection unit collects the API information of the translation service. For example, the collection unit can collect API information such as an endpoint URL and an authentication method of the translation service. The collection unit can collect the API information by using, for example, periodic polling or an event-driven collection method. In this way, the collection unit can collect the API information of the translation service.

[0033] The collection unit can collect API information of the map guidance service. The collection unit collects the API information of the map guidance service. For example, the collection unit can collect API information such as an endpoint URL and an authentication method of the map guidance service. The collection unit can collect the API information by using, for example, periodic polling or an event-driven collection method. In this way, the collection unit can collect the API information of the map guidance service.

[0034] The interpretation unit can analyze the collected API information and determine how to link. The interpretation unit can analyze the collected API information and determine how to link. For example, the interpretation unit can convert data formats and analyze metadata. For example, the interpretation unit can analyze how to translate specific text using the API of a translation service. This allows the interpretation unit to analyze the collected API information and determine how to link.

[0035] The collaboration unit can exchange data using the API of each service. The collaboration unit exchanges data using the API of each service. For example, the collaboration unit can send text entered by a user to a translation service and receive the translation results. The collaboration unit can perform data synchronization and transaction management, for example. This allows the collaboration unit to exchange data using the API of each service.

[0036] The error handling unit can process errors that occur during API integration. The error handling unit processes errors that occur during API integration. For example, the error handling unit can implement a retry mechanism or record an error log. The error handling unit can process errors such as network errors and authentication errors. This allows the error handling unit to process errors that occur during API integration.

[0037] The collection unit can monitor the update frequency of the API information of each service and automatically collect the latest API information. The collection unit monitors the update frequency of the API information of each service and automatically collects the latest API information. For example, the collection unit can periodically check the update frequency of the API information of each service and automatically collect the latest information. The collection unit can also prioritize collecting information for services whose API information is updated frequently. Furthermore, the collection unit can periodically collect information for services whose update frequency is low. This allows the collection unit to automatically collect the latest API information.

[0038] When collecting API information, the collection unit can evaluate the reliability and security level of each service and select the API information to be collected. When collecting API information, the collection unit evaluates the reliability and security level of each service and select the API information to be collected. For example, the collection unit can evaluate the reliability of each service and preferentially collect API information of highly reliable services. The collection unit can also evaluate the security level of each service and collect API information of services with high security. Furthermore, the collection unit can not collect API information of services with low reliability or security. This allows the collection unit to evaluate reliability and security level and select API information.

[0039] When collecting API information, the collection unit can prioritize collecting highly relevant API information by taking into account the user's geographical location information. When collecting API information, the collection unit prioritizes collecting highly relevant API information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting API information of nearby services based on the user's current location. The collection unit can also prioritize collecting API information related to the user's destination. Furthermore, the collection unit can refer to the user's movement history to collect highly relevant API information. This allows the collection unit to prioritize collecting highly relevant API information by taking into account the user's geographical location information.

[0040] When collecting API information, the collection unit can analyze the user's social media activities and collect related API information. When collecting API information, the collection unit can analyze the user's social media activities and collect related API information. For example, the collection unit can analyze the content of the user's posts on social media and collect related API information. The collection unit can also collect related API information by referring to the activities of the user's followers and friends. Furthermore, the collection unit can analyze the user's interests on social media and collect related API information. In this way, the collection unit can analyze the user's social media activities and collect related API information.

[0041] During interpretation, the interpretation unit can improve the accuracy of interpretation by referring to the change history of the API specifications of each service. During interpretation, the interpretation unit can improve the accuracy of interpretation by referring to the change history of the API specifications of each service. For example, the interpretation unit can periodically check the change history of the API specifications of each service to improve the accuracy of interpretation. Furthermore, the interpretation unit can adjust the interpretation method as appropriate based on the change history. Furthermore, the interpretation unit can respond to the latest API specifications by referring to the change history. This allows the interpretation unit to improve the accuracy of interpretation by referring to the change history of the API specifications of each service.

[0042] The interpretation unit can perform interpretation taking into consideration the frequency of use and success rate of the API of each service when interpreting. The interpretation unit can perform interpretation taking into consideration the frequency of use and success rate of the API of each service when interpreting. For example, the interpretation unit can give priority to interpreting APIs that are used frequently. Also, the interpretation unit can give priority to interpreting APIs that have a high success rate. Furthermore, the interpretation unit can carefully interpret APIs that have a low frequency of use or a low success rate. This allows the interpretation unit to perform interpretation taking into consideration the frequency of use and success rate of the API of each service.

[0043] The interpretation unit can perform interpretation taking into account the geographic distribution of the API of each service during interpretation. The interpretation unit can perform interpretation taking into account the geographic distribution of the API of each service during interpretation. For example, the interpretation unit can analyze the geographic distribution of the API of each service to improve the accuracy of the interpretation. Furthermore, the interpretation unit can select an optimal interpretation method based on the geographic distribution. Furthermore, the interpretation unit can adjust the interpretation result taking into account the geographic distribution. This allows the interpretation unit to perform interpretation taking into account the geographic distribution of the API of each service.

[0044] The interpretation unit can improve the accuracy of the interpretation by referring to related literature for the API of each service during interpretation. The interpretation unit can improve the accuracy of the interpretation by referring to related literature for the API of each service during interpretation. For example, the interpretation unit can improve the accuracy of the interpretation by referring to related literature for the API of each service. The interpretation unit can also adjust the interpretation method as appropriate based on the related literature. Furthermore, the interpretation unit can respond to the latest API specifications by referring to related literature. This allows the interpretation unit to improve the accuracy of the interpretation by referring to related literature for the API of each service.

[0045] The integration unit monitors the API response time of each service during integration and selects the optimal integration method. The integration unit monitors the API response time of each service during integration and selects the optimal integration method. For example, the integration unit can periodically check the API response time of each service and select the optimal integration method. The integration unit can also prioritize integration with services with short response times. Furthermore, the integration unit can adjust the integration method for services with long response times. This allows the integration unit to monitor the API response time of each service and select the optimal integration method.

[0046] When linking, the integration unit can take into consideration the API usage limits and fee structure of each service. When linking, the integration unit can take into consideration the API usage limits and fee structure of each service. For example, the integration unit can check the API usage limits of each service and adjust the integration method. Furthermore, the integration unit can select a cost-effective integration method by taking into consideration the API fee structure of each service. Furthermore, the integration unit can provide the optimal integration method based on the usage limits and fee structure. This allows the integration unit to take into consideration the API usage limits and fee structure of each service when linking.

[0047] The collaboration unit can perform collaboration taking into account the geographic distribution of the APIs of each service when performing collaboration. The collaboration unit can perform collaboration taking into account the geographic distribution of the APIs of each service when performing collaboration. For example, the collaboration unit can analyze the geographic distribution of the APIs of each service and select an optimal collaboration method. The collaboration unit can also adjust the collaboration method based on the geographic distribution. Furthermore, the collaboration unit can adjust the collaboration results taking into account the geographic distribution. This allows the collaboration unit to perform collaboration taking into account the geographic distribution of the APIs of each service.

[0048] The coordination unit can improve the accuracy of coordination by referring to related literature for the API of each service during coordination. The coordination unit can improve the accuracy of coordination by referring to related literature for the API of each service during coordination. For example, the coordination unit can improve the accuracy of coordination by referring to related literature for the API of each service. The coordination unit can also adjust the coordination method as appropriate based on the related literature. Furthermore, the coordination unit can support the latest API specifications by referring to related literature. This allows the coordination unit to improve the accuracy of coordination by referring to related literature for the API of each service.

[0049] When handling an error, the error handling unit can select the optimal error handling method by referring to past error data. When handling an error, the error handling unit selects the optimal error handling method by referring to past error data. For example, the error handling unit can analyze past error data and select the optimal error handling method. The error handling unit can also adjust the error handling method appropriately based on the error data. Furthermore, the error handling unit can prevent the recurrence of an error by referring to past error data. This allows the error handling unit to select the optimal error handling method by referring to past error data.

[0050] The error handling unit can monitor the frequency of errors in the API of each service when handling errors and take preventive measures against errors. The error handling unit can monitor the frequency of errors in the API of each service when handling errors and take preventive measures against errors. For example, the error handling unit can periodically check the frequency of errors in the API of each service and take preventive measures against errors. The error handling unit can also strengthen preventive measures for services with a high frequency of errors. Furthermore, the error handling unit can adjust the error handling method based on the frequency of errors. In this way, the error handling unit can monitor the frequency of errors in the API of each service and take preventive measures against errors.

[0051] The error handling unit can handle errors taking into account the geographic distribution of the APIs of each service when handling errors. The error handling unit can handle errors taking into account the geographic distribution of the APIs of each service when handling errors. For example, the error handling unit can analyze the geographic distribution of the APIs of each service and improve the accuracy of error handling. The error handling unit can also select an optimal error handling method based on the geographic distribution. Furthermore, the error handling unit can adjust the error handling result by taking into account the geographic distribution. This allows the error handling unit to handle errors taking into account the geographic distribution of the APIs of each service.

[0052] The error handling unit can improve the accuracy of error handling by referring to related literature for the API of each service when handling an error. The error handling unit can improve the accuracy of error handling by referring to related literature for the API of each service when handling an error. For example, the error handling unit can improve the accuracy of error handling by referring to related literature for the API of each service. The error handling unit can also adjust the error handling method as appropriate based on the related literature. Furthermore, the error handling unit can support the latest API specifications by referring to the related literature. This allows the error handling unit to improve the accuracy of error handling by referring to related literature for the API of each service.

[0053] The translation unit can improve the accuracy of the translation by taking into account the context and technical terminology of the target object during translation. The translation unit can improve the accuracy of the translation by taking into account the context and technical terminology of the target object during translation. For example, the translation unit can analyze the context of the target object and provide an appropriate translation. The translation unit can also provide an accurate translation by taking into account the technical terminology. Furthermore, the translation unit can improve the accuracy of the translation based on the context and technical terminology. This allows the translation unit to improve the accuracy of the translation by taking into account the context and technical terminology of the target object.

[0054] The translation unit can perform translation taking into consideration the cultural background of the language to be translated when translating. The translation unit can perform translation taking into consideration the cultural background of the language to be translated when translating. For example, the translation unit can analyze the cultural background of the language to be translated and provide an appropriate translation. Furthermore, the translation unit can provide a natural translation by taking the cultural background into consideration. Furthermore, the translation unit can improve the accuracy of the translation based on the cultural background. This allows the translation unit to perform translation taking into consideration the cultural background of the language to be translated.

[0055] The translation unit can perform translation taking into account the geographical distribution of the translation target when translating. The translation unit can perform translation taking into account the geographical distribution of the translation target when translating. For example, the translation unit can analyze the geographical distribution of the translation target and provide an appropriate translation. The translation unit can also select an optimal translation method based on the geographical distribution. Furthermore, the translation unit can adjust the translation result taking into account the geographical distribution. This allows the translation unit to perform translation taking into account the geographical distribution of the translation target.

[0056] The translation unit can improve the accuracy of the translation by referring to related literature of the translation target during translation. The translation unit can improve the accuracy of the translation by referring to related literature of the translation target during translation. For example, the translation unit can improve the accuracy of the translation by referring to related literature of the translation target. The translation unit can also adjust the translation method appropriately based on the related literature. Furthermore, the translation unit can respond to the latest translation technology by referring to related literature. This allows the translation unit to improve the accuracy of the translation by referring to related literature of the translation target.

[0057] When providing guidance, the guidance unit can provide guidance on the optimal route taking into consideration information about the user's current location and destination. When providing guidance, the guidance unit provides guidance on the optimal route taking into consideration information about the user's current location and destination. For example, the guidance unit can provide guidance on the optimal route based on the user's current location. Furthermore, the guidance unit can provide guidance on the optimal route taking into consideration information about the destination. Furthermore, the guidance unit can provide the optimal route based on information about the current location and destination. This allows the guidance unit to provide guidance on the optimal route taking into consideration information about the user's current location and destination.

[0058] The guidance unit can provide guidance taking into consideration traffic conditions and weather information when providing guidance. The guidance unit can provide guidance taking into consideration traffic conditions and weather information when providing guidance. For example, the guidance unit can analyze traffic conditions and provide guidance on the optimal route. The guidance unit can also provide guidance on the optimal route taking into consideration weather information. Furthermore, the guidance unit can provide optimal guidance based on traffic conditions and weather information. This allows the guidance unit to provide guidance taking into consideration traffic conditions and weather information.

[0059] The guidance unit can select the optimal guidance method taking into consideration the user's geographical location information when providing guidance. The guidance unit selects the optimal guidance method taking into consideration the user's geographical location information when providing guidance. For example, the guidance unit can select the optimal guidance method based on the user's current location. The guidance unit can also select the optimal guidance method taking into consideration destination information. Furthermore, the guidance unit can provide the optimal guidance method based on the current location and destination information. This allows the guidance unit to select the optimal guidance method taking into consideration the user's geographical location information.

[0060] The guidance unit can provide optimal guidance by referring to the user's past movement history when providing guidance. The guidance unit can provide optimal guidance by referring to the user's past movement history when providing guidance. For example, the guidance unit can analyze the user's past movement history and provide optimal guidance. Furthermore, the guidance unit can provide optimal route guidance based on the past movement history. Furthermore, the guidance unit can provide optimal guidance methods based on the user's movement history. This allows the guidance unit to provide optimal guidance by referring to the user's past movement history.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] When collecting API information for each service, the API integration system can prioritize collecting highly relevant API information by taking into account the user's geographical location information. For example, if the user is in a specific area, API information for services related to that area can be prioritized. Also, if the user is traveling, API information related to the destination can be prioritized. Furthermore, the user's movement history can be used as a reference to collect highly relevant API information. This allows the API integration system to prioritize collecting highly relevant API information by taking into account the user's geographical location information.

[0063] The API integration system can monitor the update frequency of API information for each service and automatically collect the latest API information. For example, the system can periodically check the update frequency of API information for each service and automatically collect the latest information. It can also prioritize collection of information for services with frequently updated API information. Furthermore, it can periodically collect information for services with less frequent updates. This allows the API integration system to automatically collect the latest API information.

[0064] The API integration system can perform interpretation taking into account the frequency of use and success rate of the API of each service. For example, it can give priority to interpretation to APIs that are used frequently. It can also give priority to interpretation to APIs that have a high success rate. Furthermore, it can carefully interpret APIs that are used less frequently or have a low success rate. This allows the API integration system to perform interpretation taking into account the frequency of use and success rate of the API of each service.

[0065] The API integration system can perform interpretation taking into account the geographic distribution of the APIs of each service. For example, it can analyze the geographic distribution of the APIs of each service and improve the accuracy of the interpretation. It can also select the optimal interpretation method based on the geographic distribution. Furthermore, it can adjust the interpretation results taking into account the geographic distribution. This allows the API integration system to perform interpretation taking into account the geographic distribution of the APIs of each service.

[0066] The API integration system can improve the accuracy of integration by referring to literature related to the API of each service. For example, the accuracy of integration can be improved by referring to literature related to the API of each service. The integration method can also be adjusted appropriately based on the literature. Furthermore, the latest API specifications can be supported by referring to the literature. In this way, the API integration system can improve the accuracy of integration by referring to literature related to the API of each service.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects API information for each service. For example, the collection unit can collect API information for a translation service, API information for a map guidance service, etc. The collection unit can collect API information using, for example, periodic polling or an event-driven collection method. Step 2: The interpretation unit interprets the API information collected by the collection unit. For example, the interpretation unit can convert data formats or analyze metadata. The interpretation unit analyzes the collected API information and determines how to link them. For example, the interpretation unit can analyze how to translate a specific text using the API of a translation service. Step 3: The linking unit performs API linking based on the information interpreted by the interpretation unit. For example, the linking unit can exchange data using the API of each service. For example, the linking unit can send text entered by the user to a translation service and receive the translation results. Step 4: The error handling unit processes errors that occur during API integration. For example, the error handling unit can implement a retry mechanism or record an error log.

[0069] (Example 2) An API integration system according to an embodiment of the present invention provides a function for connecting existing services. This API integration system automatically interprets the APIs of each service and functions as a gateway for API integration. This gateway not only exchanges data but also integrates with a variety of services, such as translation services and map guidance services. For example, the API integration system collects API information from each service. For example, it collects API information from translation services and map guidance services. This information is input into the gateway. Next, the API integration system automatically interprets the collected API information. The API integration system analyzes the API specifications of each service and determines how to integrate. For example, it analyzes how to translate specific text using the API of a translation service. Then, the API integration system performs API integration. The API integration system exchanges data using the APIs of each service. For example, it can send text entered by a user to a translation service and receive the translation results. Furthermore, the API integration system can integrate with a variety of services. For example, it can integrate with a map guidance service to provide route guidance from the user's current location to their destination. It can also integrate with other services to provide various information to the user. This mechanism makes it possible to easily link existing services together, enabling the provision of a variety of services to users. For example, by linking a translation service with a map guidance service, it is possible to provide map guidance in a foreign language. Furthermore, by linking with other services, it is possible to provide more convenient services to users. In this way, the API integration system can efficiently link existing services together and provide a variety of services.

[0070] The API integration system according to the embodiment includes a collection unit, an interpretation unit, an integration unit, and an error handling unit. The collection unit collects API information of each service. For example, the collection unit can collect API information of a translation service and API information of a map guidance service. The collection unit can collect API information using, for example, periodic polling or an event-driven collection method. The interpretation unit interprets the API information collected by the collection unit. For example, the interpretation unit can convert data formats and analyze metadata. The interpretation unit analyzes the collected API information and determines how to integrate. For example, the interpretation unit can analyze how to translate specific text using the API of a translation service. The integration unit performs API integration based on the information interpreted by the interpretation unit. For example, the integration unit can exchange data using the API of each service. For example, the integration unit can send text entered by a user to a translation service and receive the translation result. The error handling unit handles errors that occur during API integration. For example, the error handling unit can implement a retry mechanism and record an error log. As a result, the API integration system according to the embodiment can efficiently collect, interpret, integrate, and handle errors in API information.

[0071] The API integration system includes a translation unit that integrates with a translation service. The translation unit integrates with the translation service. For example, the translation unit can translate specific text using the API of the translation service. The translation unit can integrate with translation services such as Google Translate and Microsoft Translator. This allows the translation unit to send text entered by a user to the translation service and receive the translation results. This enables integration with the translation service.

[0072] The API linkage system includes a guidance unit that links with a map guidance service. The guidance unit links with the map guidance service. For example, the guidance unit can use the API of the map guidance service to provide route guidance from the user's current location to the destination. The guidance unit can link with map guidance services such as Google Maps and Apple Maps. This allows the guidance unit to provide route guidance from the user's current location to the destination. This enables linkage with the map guidance service.

[0073] The collection unit can collect API information of the translation service. The collection unit collects the API information of the translation service. For example, the collection unit can collect API information such as an endpoint URL and an authentication method of the translation service. The collection unit can collect the API information by using, for example, periodic polling or an event-driven collection method. In this way, the collection unit can collect the API information of the translation service.

[0074] The collection unit can collect API information of the map guidance service. The collection unit collects the API information of the map guidance service. For example, the collection unit can collect API information such as an endpoint URL and an authentication method of the map guidance service. The collection unit can collect the API information by using, for example, periodic polling or an event-driven collection method. In this way, the collection unit can collect the API information of the map guidance service.

[0075] The interpretation unit can analyze the collected API information and determine how to link. The interpretation unit can analyze the collected API information and determine how to link. For example, the interpretation unit can convert data formats and analyze metadata. For example, the interpretation unit can analyze how to translate specific text using the API of a translation service. This allows the interpretation unit to analyze the collected API information and determine how to link.

[0076] The collaboration unit can exchange data using the API of each service. The collaboration unit exchanges data using the API of each service. For example, the collaboration unit can send text entered by a user to a translation service and receive the translation results. The collaboration unit can perform data synchronization and transaction management, for example. This allows the collaboration unit to exchange data using the API of each service.

[0077] The error handling unit can process errors that occur during API integration. The error handling unit processes errors that occur during API integration. For example, the error handling unit can implement a retry mechanism or record an error log. The error handling unit can process errors such as network errors and authentication errors. This allows the error handling unit to process errors that occur during API integration.

[0078] The collection unit can estimate the user's emotions and adjust the timing of collecting API information based on the estimated user's emotions. The collection unit can estimate the user's emotions and adjust the timing of collecting API information based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to reduce the burden. Also, if the user is relaxed, the collection unit can advance the collection timing to efficiently acquire information. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to quickly acquire information. This allows the collection unit to adjust the timing of collecting API information based on the user's emotions.

[0079] The collection unit can monitor the update frequency of the API information of each service and automatically collect the latest API information. The collection unit monitors the update frequency of the API information of each service and automatically collects the latest API information. For example, the collection unit can periodically check the update frequency of the API information of each service and automatically collect the latest information. The collection unit can also prioritize collecting information for services whose API information is updated frequently. Furthermore, the collection unit can periodically collect information for services whose update frequency is low. This allows the collection unit to automatically collect the latest API information.

[0080] When collecting API information, the collection unit can evaluate the reliability and security level of each service and select the API information to be collected. When collecting API information, the collection unit evaluates the reliability and security level of each service and select the API information to be collected. For example, the collection unit can evaluate the reliability of each service and preferentially collect API information of highly reliable services. The collection unit can also evaluate the security level of each service and collect API information of services with high security. Furthermore, the collection unit can not collect API information of services with low reliability or security. This allows the collection unit to evaluate reliability and security level and select API information.

[0081] The collection unit can estimate the user's emotions and determine the priority of API information to be collected based on the estimated user's emotions. The collection unit can estimate the user's emotions and determine the priority of API information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can postpone collection of less important API information. Furthermore, when the user is relaxed, the collection unit can collect all API information equally. Furthermore, when the user is in a hurry, the collection unit can preferentially collect more important API information. This allows the collection unit to determine the priority of API information to be collected based on the user's emotions.

[0082] When collecting API information, the collection unit can prioritize collecting highly relevant API information by taking into account the user's geographical location information. When collecting API information, the collection unit prioritizes collecting highly relevant API information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting API information of nearby services based on the user's current location. The collection unit can also prioritize collecting API information related to the user's destination. Furthermore, the collection unit can refer to the user's movement history to collect highly relevant API information. This allows the collection unit to prioritize collecting highly relevant API information by taking into account the user's geographical location information.

[0083] When collecting API information, the collection unit can analyze the user's social media activities and collect related API information. When collecting API information, the collection unit can analyze the user's social media activities and collect related API information. For example, the collection unit can analyze the content of the user's posts on social media and collect related API information. The collection unit can also collect related API information by referring to the activities of the user's followers and friends. Furthermore, the collection unit can analyze the user's interests on social media and collect related API information. In this way, the collection unit can analyze the user's social media activities and collect related API information.

[0084] The interpretation unit can estimate the user's emotions and adjust the interpretation method of the API information based on the estimated user's emotions. The interpretation unit can estimate the user's emotions and adjust the interpretation method of the API information based on the estimated user's emotions. For example, the interpretation unit can provide a simple interpretation method when the user is stressed. Furthermore, the interpretation unit can provide a detailed interpretation method when the user is relaxed. Furthermore, the interpretation unit can provide a quick interpretation when the user is in a hurry. This allows the interpretation unit to adjust the interpretation method of the API information based on the user's emotions.

[0085] During interpretation, the interpretation unit can improve the accuracy of interpretation by referring to the change history of the API specifications of each service. During interpretation, the interpretation unit can improve the accuracy of interpretation by referring to the change history of the API specifications of each service. For example, the interpretation unit can periodically check the change history of the API specifications of each service to improve the accuracy of interpretation. Furthermore, the interpretation unit can adjust the interpretation method as appropriate based on the change history. Furthermore, the interpretation unit can respond to the latest API specifications by referring to the change history. This allows the interpretation unit to improve the accuracy of interpretation by referring to the change history of the API specifications of each service.

[0086] The interpretation unit can perform interpretation taking into consideration the frequency of use and success rate of the API of each service when interpreting. The interpretation unit can perform interpretation taking into consideration the frequency of use and success rate of the API of each service when interpreting. For example, the interpretation unit can give priority to interpreting APIs that are used frequently. Also, the interpretation unit can give priority to interpreting APIs that have a high success rate. Furthermore, the interpretation unit can carefully interpret APIs that have a low frequency of use or a low success rate. This allows the interpretation unit to perform interpretation taking into consideration the frequency of use and success rate of the API of each service.

[0087] The interpretation unit can estimate the user's emotion and adjust the display method of the interpretation result based on the estimated user's emotion. The interpretation unit can estimate the user's emotion and adjust the display method of the interpretation result based on the estimated user's emotion. For example, if the user is feeling stressed, the interpretation unit can provide a simple, highly visible display method. If the user is relaxed, the interpretation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the interpretation unit can provide a display method that focuses on the main points. This allows the interpretation unit to adjust the display method of the interpretation result based on the user's emotion.

[0088] The interpretation unit can perform interpretation taking into account the geographic distribution of the API of each service during interpretation. The interpretation unit can perform interpretation taking into account the geographic distribution of the API of each service during interpretation. For example, the interpretation unit can analyze the geographic distribution of the API of each service to improve the accuracy of the interpretation. Furthermore, the interpretation unit can select an optimal interpretation method based on the geographic distribution. Furthermore, the interpretation unit can adjust the interpretation result taking into account the geographic distribution. This allows the interpretation unit to perform interpretation taking into account the geographic distribution of the API of each service.

[0089] The interpretation unit can improve the accuracy of the interpretation by referring to related literature for the API of each service during interpretation. The interpretation unit can improve the accuracy of the interpretation by referring to related literature for the API of each service during interpretation. For example, the interpretation unit can improve the accuracy of the interpretation by referring to related literature for the API of each service. The interpretation unit can also adjust the interpretation method as appropriate based on the related literature. Furthermore, the interpretation unit can respond to the latest API specifications by referring to related literature. This allows the interpretation unit to improve the accuracy of the interpretation by referring to related literature for the API of each service.

[0090] The collaboration unit can estimate the user's emotions and adjust the API collaboration method based on the estimated user's emotions. The collaboration unit can estimate the user's emotions and adjust the API collaboration method based on the estimated user's emotions. For example, the collaboration unit can provide a simple collaboration method when the user is feeling stressed. Furthermore, the collaboration unit can provide a detailed collaboration method when the user is relaxed. Furthermore, the collaboration unit can quickly perform collaboration when the user is in a hurry. This allows the collaboration unit to adjust the API collaboration method based on the user's emotions.

[0091] The integration unit monitors the API response time of each service during integration and selects the optimal integration method. The integration unit monitors the API response time of each service during integration and selects the optimal integration method. For example, the integration unit can periodically check the API response time of each service and select the optimal integration method. The integration unit can also prioritize integration with services with short response times. Furthermore, the integration unit can adjust the integration method for services with long response times. This allows the integration unit to monitor the API response time of each service and select the optimal integration method.

[0092] When linking, the integration unit can take into consideration the API usage limits and fee structure of each service. When linking, the integration unit can take into consideration the API usage limits and fee structure of each service. For example, the integration unit can check the API usage limits of each service and adjust the integration method. Furthermore, the integration unit can select a cost-effective integration method by taking into consideration the API fee structure of each service. Furthermore, the integration unit can provide the optimal integration method based on the usage limits and fee structure. This allows the integration unit to take into consideration the API usage limits and fee structure of each service when linking.

[0093] The linking unit can estimate the user's emotion and adjust the display method of the linking result based on the estimated user's emotion. The linking unit can estimate the user's emotion and adjust the display method of the linking result based on the estimated user's emotion. For example, if the user is feeling stressed, the linking unit can provide a simple, highly visible display method. If the user is relaxed, the linking unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the linking unit can provide a display method that focuses on the main points. This allows the linking unit to adjust the display method of the linking result based on the user's emotion.

[0094] The collaboration unit can perform collaboration taking into account the geographic distribution of the APIs of each service when performing collaboration. The collaboration unit can perform collaboration taking into account the geographic distribution of the APIs of each service when performing collaboration. For example, the collaboration unit can analyze the geographic distribution of the APIs of each service and select an optimal collaboration method. The collaboration unit can also adjust the collaboration method based on the geographic distribution. Furthermore, the collaboration unit can adjust the collaboration results taking into account the geographic distribution. This allows the collaboration unit to perform collaboration taking into account the geographic distribution of the APIs of each service.

[0095] The coordination unit can improve the accuracy of coordination by referring to related literature for the API of each service during coordination. The coordination unit can improve the accuracy of coordination by referring to related literature for the API of each service during coordination. For example, the coordination unit can improve the accuracy of coordination by referring to related literature for the API of each service. The coordination unit can also adjust the coordination method as appropriate based on the related literature. Furthermore, the coordination unit can support the latest API specifications by referring to related literature. This allows the coordination unit to improve the accuracy of coordination by referring to related literature for the API of each service.

[0096] The error handling unit can estimate a user's emotion and adjust an error handling method based on the estimated user's emotion. The error handling unit can estimate a user's emotion and adjust an error handling method based on the estimated user's emotion. For example, the error handling unit can provide a simple and quick error handling method when the user is stressed. Furthermore, the error handling unit can provide a detailed error handling method when the user is relaxed. Furthermore, the error handling unit can perform quick error handling when the user is in a hurry. Thus, the error handling unit can adjust the error handling method based on the user's emotion.

[0097] When handling an error, the error handling unit can select the optimal error handling method by referring to past error data. When handling an error, the error handling unit selects the optimal error handling method by referring to past error data. For example, the error handling unit can analyze past error data and select the optimal error handling method. The error handling unit can also adjust the error handling method appropriately based on the error data. Furthermore, the error handling unit can prevent the recurrence of an error by referring to past error data. This allows the error handling unit to select the optimal error handling method by referring to past error data.

[0098] The error handling unit can monitor the frequency of errors in the API of each service when handling errors and take preventive measures against errors. The error handling unit can monitor the frequency of errors in the API of each service when handling errors and take preventive measures against errors. For example, the error handling unit can periodically check the frequency of errors in the API of each service and take preventive measures against errors. The error handling unit can also strengthen preventive measures for services with a high frequency of errors. Furthermore, the error handling unit can adjust the error handling method based on the frequency of errors. In this way, the error handling unit can monitor the frequency of errors in the API of each service and take preventive measures against errors.

[0099] The error handling unit can estimate the user's emotion and adjust the display method of the error handling result based on the estimated user's emotion. The error handling unit can estimate the user's emotion and adjust the display method of the error handling result based on the estimated user's emotion. For example, if the user is feeling stressed, the error handling unit can provide a simple, highly visible display method. If the user is relaxed, the error handling unit can provide a display method including detailed information. If the user is in a hurry, the error handling unit can provide a display method that focuses on the main points. This allows the error handling unit to adjust the display method of the error handling result based on the user's emotion.

[0100] The error handling unit can handle errors taking into account the geographic distribution of the APIs of each service when handling errors. The error handling unit can handle errors taking into account the geographic distribution of the APIs of each service when handling errors. For example, the error handling unit can analyze the geographic distribution of the APIs of each service and improve the accuracy of error handling. The error handling unit can also select an optimal error handling method based on the geographic distribution. Furthermore, the error handling unit can adjust the error handling result by taking into account the geographic distribution. This allows the error handling unit to handle errors taking into account the geographic distribution of the APIs of each service.

[0101] The error handling unit can improve the accuracy of error handling by referring to related literature for the API of each service when handling an error. The error handling unit can improve the accuracy of error handling by referring to related literature for the API of each service when handling an error. For example, the error handling unit can improve the accuracy of error handling by referring to related literature for the API of each service. The error handling unit can also adjust the error handling method as appropriate based on the related literature. Furthermore, the error handling unit can support the latest API specifications by referring to the related literature. This allows the error handling unit to improve the accuracy of error handling by referring to related literature for the API of each service.

[0102] The translation unit can estimate the user's emotion and adjust the translation expression method based on the estimated user's emotion. The translation unit can estimate the user's emotion and adjust the translation expression method based on the estimated user's emotion. For example, if the user is feeling stressed, the translation unit can provide a simple and easy-to-understand translation. If the user is relaxed, the translation unit can provide a detailed translation. Furthermore, if the user is in a hurry, the translation unit can quickly perform a translation. This allows the translation unit to adjust the translation expression method based on the user's emotion.

[0103] The translation unit can improve the accuracy of the translation by taking into account the context and technical terminology of the target object during translation. The translation unit can improve the accuracy of the translation by taking into account the context and technical terminology of the target object during translation. For example, the translation unit can analyze the context of the target object and provide an appropriate translation. The translation unit can also provide an accurate translation by taking into account the technical terminology. Furthermore, the translation unit can improve the accuracy of the translation based on the context and technical terminology. This allows the translation unit to improve the accuracy of the translation by taking into account the context and technical terminology of the target object.

[0104] The translation unit can perform translation taking into consideration the cultural background of the language to be translated when translating. The translation unit can perform translation taking into consideration the cultural background of the language to be translated when translating. For example, the translation unit can analyze the cultural background of the language to be translated and provide an appropriate translation. Furthermore, the translation unit can provide a natural translation by taking the cultural background into consideration. Furthermore, the translation unit can improve the accuracy of the translation based on the cultural background. This allows the translation unit to perform translation taking into consideration the cultural background of the language to be translated.

[0105] The translation unit can estimate the user's emotions and adjust the display method of the translation result based on the estimated user's emotions. The translation unit can estimate the user's emotions and adjust the display method of the translation result based on the estimated user's emotions. For example, if the user is feeling stressed, the translation unit can provide a simple, highly visible display method. If the user is relaxed, the translation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the translation unit can provide a display method that focuses on the main points. This allows the translation unit to adjust the display method of the translation result based on the user's emotions.

[0106] The translation unit can perform translation taking into account the geographical distribution of the translation target when translating. The translation unit can perform translation taking into account the geographical distribution of the translation target when translating. For example, the translation unit can analyze the geographical distribution of the translation target and provide an appropriate translation. The translation unit can also select an optimal translation method based on the geographical distribution. Furthermore, the translation unit can adjust the translation result taking into account the geographical distribution. This allows the translation unit to perform translation taking into account the geographical distribution of the translation target.

[0107] The translation unit can improve the accuracy of the translation by referring to related literature of the translation target during translation. The translation unit can improve the accuracy of the translation by referring to related literature of the translation target during translation. For example, the translation unit can improve the accuracy of the translation by referring to related literature of the translation target. The translation unit can also adjust the translation method appropriately based on the related literature. Furthermore, the translation unit can respond to the latest translation technology by referring to related literature. This allows the translation unit to improve the accuracy of the translation by referring to related literature of the translation target.

[0108] The guidance unit can estimate the user's emotion and adjust the way in which guidance is presented based on the estimated user's emotion. The guidance unit can estimate the user's emotion and adjust the way in which guidance is presented based on the estimated user's emotion. For example, if the user is feeling stressed, the guidance unit can provide simple and easy-to-understand guidance. Furthermore, if the user is relaxed, the guidance unit can provide detailed guidance. Furthermore, if the user is in a hurry, the guidance unit can provide quick guidance. This allows the guidance unit to adjust the way in which guidance is presented based on the user's emotion.

[0109] When providing guidance, the guidance unit can provide guidance on the optimal route taking into consideration information about the user's current location and destination. When providing guidance, the guidance unit provides guidance on the optimal route taking into consideration information about the user's current location and destination. For example, the guidance unit can provide guidance on the optimal route based on the user's current location. Furthermore, the guidance unit can provide guidance on the optimal route taking into consideration information about the destination. Furthermore, the guidance unit can provide the optimal route based on information about the current location and destination. This allows the guidance unit to provide guidance on the optimal route taking into consideration information about the user's current location and destination.

[0110] The guidance unit can provide guidance taking into consideration traffic conditions and weather information when providing guidance. The guidance unit can provide guidance taking into consideration traffic conditions and weather information when providing guidance. For example, the guidance unit can analyze traffic conditions and provide guidance on the optimal route. The guidance unit can also provide guidance on the optimal route taking into consideration weather information. Furthermore, the guidance unit can provide optimal guidance based on traffic conditions and weather information. This allows the guidance unit to provide guidance taking into consideration traffic conditions and weather information.

[0111] The guidance unit can estimate the user's emotions and adjust the display method of the guidance result based on the estimated user's emotions. The guidance unit can estimate the user's emotions and adjust the display method of the guidance result based on the estimated user's emotions. For example, if the user is feeling stressed, the guidance unit can provide a simple, highly visible display method. If the user is relaxed, the guidance unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the guidance unit can provide a display method that focuses on the main points. This allows the guidance unit to adjust the display method of the guidance result based on the user's emotions.

[0112] The guidance unit can select the optimal guidance method taking into consideration the user's geographical location information when providing guidance. The guidance unit selects the optimal guidance method taking into consideration the user's geographical location information when providing guidance. For example, the guidance unit can select the optimal guidance method based on the user's current location. The guidance unit can also select the optimal guidance method taking into consideration destination information. Furthermore, the guidance unit can provide the optimal guidance method based on the current location and destination information. This allows the guidance unit to select the optimal guidance method taking into consideration the user's geographical location information.

[0113] The guidance unit can provide optimal guidance by referring to the user's past movement history when providing guidance. The guidance unit can provide optimal guidance by referring to the user's past movement history when providing guidance. For example, the guidance unit can analyze the user's past movement history and provide optimal guidance. Furthermore, the guidance unit can provide optimal route guidance based on the past movement history. Furthermore, the guidance unit can provide optimal guidance methods based on the user's movement history. This allows the guidance unit to provide optimal guidance by referring to the user's past movement history. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, interpretation unit, linking unit, error handling unit, translation unit, and guidance unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The interpretation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The linking unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The error handling unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The translation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The guidance unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, interpretation unit, linking unit, error handling unit, translation unit, and guidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The interpretation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The linking unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The error handling unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The translation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The guidance unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, interpretation unit, linking unit, error handling unit, translation unit, and guidance unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The interpretation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The linking unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The error handling unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The translation unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The guidance unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, interpretation unit, linking unit, error handling unit, translation unit, and guidance unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The interpretation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The linking unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The error handling unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The translation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The guidance unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The API integration system can estimate the user's emotions and dynamically adjust the priority of API integration based on the estimated emotions. For example, if the user is feeling stressed, the system can prioritize API integrations with high importance, reducing the user's burden. Also, if the user is relaxed, all API integrations can be processed equally. Furthermore, if the user is in a hurry, API integrations that require quick processing can be prioritized. This allows the API integration system to dynamically adjust the priority of API integrations based on the user's emotions.

[0116] When collecting API information for each service, the API integration system can prioritize collecting highly relevant API information by taking into account the user's geographical location information. For example, if the user is in a specific area, API information for services related to that area can be prioritized. Also, if the user is traveling, API information related to the destination can be prioritized. Furthermore, the user's movement history can be used as a reference to collect highly relevant API information. This allows the API integration system to prioritize collecting highly relevant API information by taking into account the user's geographical location information.

[0117] The API integration system can estimate the user's emotions and adjust the API integration method based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a simple integration method to reduce the user's burden. If the user is relaxed, the system can provide a detailed integration method. Furthermore, if the user is in a hurry, the system can quickly perform integration. This allows the API integration system to adjust the API integration method based on the user's emotions.

[0118] The API integration system can monitor the update frequency of API information for each service and automatically collect the latest API information. For example, the system can periodically check the update frequency of API information for each service and automatically collect the latest information. It can also prioritize collection of information for services with frequently updated API information. Furthermore, it can periodically collect information for services with less frequent updates. This allows the API integration system to automatically collect the latest API information.

[0119] The API integration system can estimate the user's emotions and adjust the timing of API information collection based on the estimated emotions. For example, if the user is feeling stressed, the collection timing can be delayed to reduce the burden. Also, if the user is relaxed, the collection timing can be advanced to efficiently acquire information. Furthermore, if the user is in a hurry, the collection timing can be optimized to quickly acquire information. This allows the API integration system to adjust the timing of API information collection based on the user's emotions.

[0120] The API integration system can perform interpretation taking into account the frequency of use and success rate of the API of each service. For example, it can give priority to interpretation to APIs that are used frequently. It can also give priority to interpretation to APIs that have a high success rate. Furthermore, it can carefully interpret APIs that are used less frequently or have a low success rate. This allows the API integration system to perform interpretation taking into account the frequency of use and success rate of the API of each service.

[0121] The API integration system can estimate the user's emotions and adjust the display method of the interpretation results based on the estimated emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the API integration system to adjust the display method of the interpretation results based on the user's emotions.

[0122] The API integration system can perform interpretation taking into account the geographic distribution of the APIs of each service. For example, it can analyze the geographic distribution of the APIs of each service and improve the accuracy of the interpretation. It can also select the optimal interpretation method based on the geographic distribution. Furthermore, it can adjust the interpretation results taking into account the geographic distribution. This allows the API integration system to perform interpretation taking into account the geographic distribution of the APIs of each service.

[0123] The API integration system can estimate the user's emotions and adjust the error handling method based on the estimated emotions. For example, if the user is feeling stressed, a simple and quick error handling method can be provided. If the user is relaxed, a detailed error handling method can be provided. Furthermore, if the user is in a hurry, error handling can be performed quickly. This allows the API integration system to adjust the error handling method based on the user's emotions.

[0124] The API integration system can improve the accuracy of integration by referring to literature related to the API of each service. For example, the accuracy of integration can be improved by referring to literature related to the API of each service. The integration method can also be adjusted appropriately based on the literature. Furthermore, the latest API specifications can be supported by referring to the literature. In this way, the API integration system can improve the accuracy of integration by referring to literature related to the API of each service.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The collection unit collects API information for each service. For example, the collection unit can collect API information for a translation service, API information for a map guidance service, etc. The collection unit can collect API information using, for example, periodic polling or an event-driven collection method. Step 2: The interpretation unit interprets the API information collected by the collection unit. For example, the interpretation unit can convert data formats or analyze metadata. The interpretation unit analyzes the collected API information and determines how to link them. For example, the interpretation unit can analyze how to translate a specific text using the API of a translation service. Step 3: The linking unit performs API linking based on the information interpreted by the interpretation unit. For example, the linking unit can exchange data using the API of each service. For example, the linking unit can send text entered by the user to a translation service and receive the translation results. Step 4: The error handling unit processes errors that occur during API integration. For example, the error handling unit can implement a retry mechanism or record an error log.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0129] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0171] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0182] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0183] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0189] 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.

[0190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0191] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0192] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0195] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0196] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0197] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects API information; an interpretation unit that interprets the API information collected by the collection unit; a linking unit that performs API linking based on the information interpreted by the interpretation unit; An error handling unit that handles errors. A system characterized by:

2. Equipped with a translation department that cooperates with translation services The system of claim 1 .

3. Equipped with a guidance section that links with map guidance services The system of claim 1 .

4. The collecting unit Collect translation service API information The system of claim 1 .

5. The collecting unit Collect API information for map guidance services The system of claim 1 .

6. The interpretation unit Analyze the collected API information and determine how to integrate The system of claim 1 .

7. The linking unit is Exchange data using the API of each service The system of claim 1 .

8. The error handling unit Handling errors that occur during API integration The system of claim 1 .

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