system

Abstract

The system according to this embodiment aims to concisely summarize corporate policies and provide users with useful information. [Solution] The system according to the embodiment comprises a reception unit, a summarization unit, and a notification unit. The reception unit receives input from the user. The summarization unit searches the database for the information received by the reception unit and summarizes the company policy. The notification unit provides notifications based on the information summarized by the summarization unit, in conjunction with location information.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=�4]] In the conventional technology, users may not be willing to read long and difficult corporate policies and may miss useful information.

[0005] The system according to the embodiment aims to concisely summarize corporate policies and provide useful information to users.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a summarization unit, and a notification unit. The reception unit receives an input from a user. The summarization unit searches for information received by the reception unit in a database and summarizes corporate policies. The notification unit performs a notification in cooperation with location information based on the information summarized by the summarization unit. [Effects of the Invention]

[0007] The system according to this embodiment can concisely summarize corporate policies and provide users with useful information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The corporate policy-focused AI chatbot system according to an embodiment of the present invention solves the problem of users overlooking "special offers" because they are unwilling to read long and difficult corporate policies. In this system, when a user enters a store name or service name, the AI ​​chatbot searches the database for that information and provides a summary of the relevant corporate policy. Furthermore, by linking with location information, users can receive notifications of relevant offers when they are in a specific location. For example, if a user enters "Starbucks," the AI ​​chatbot will provide information about "One More Coffee." Next, by linking with location information, users can receive notifications of relevant offers when they are in a specific location. For example, if a user is at an airport, they can receive a notification about compensation in case of flight delays. This system allows users to easily obtain special offers without the hassle of reading long corporate policies. In addition, by linking with location information, users can receive useful information in real time, improving user convenience. Thus, the corporate policy-focused AI chatbot system allows users to easily understand corporate policies and receive special offers in real time by linking with location information.

[0029] The corporate policy-specific AI chatbot system according to this embodiment comprises a reception unit, a summarization unit, and a notification unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone and voice recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera and image recognition technology for receiving image input. The summarization unit searches a database for the information received by the reception unit and summarizes the corporate policy. Summarization is performed, for example, based on the length of the text and the importance of the information to be summarized, but is not limited to such examples. The summarization unit may, for example, use natural language processing technology to extract the key points of the corporate policy and summarize them concisely. The summarization unit may also use generative AI to generate a summary of the corporate policy. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The notification unit provides notifications based on information summarized by the summarization unit, in conjunction with location information. These notifications may, for example, provide relevant information when the user is in a specific location, but are not limited to such examples. The notification unit obtains the user's location information using, for example, GPS data or Wi-Fi location information. The notification unit may also include a protection unit to safeguard user privacy. This protection unit may, for example, encrypt data or control access. As a result, the corporate policy-focused AI chatbot system according to this embodiment allows users to easily understand corporate policies and receive timely information about special offers in conjunction with their location.

[0030] The reception unit may be equipped with a processing unit that performs natural language processing. The processing unit analyzes user input using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the processing unit can use morphological analysis to divide user input into words and identify the part of speech of each word. The processing unit can also use grammatical analysis to analyze the grammatical structure of user input and identify sentence components such as subjects and predicates. Furthermore, the processing unit can use semantic analysis to analyze the meaning of user input and understand the intent of the input. For example, if a user inputs "Tell me about Starbucks' One More Coffee," the processing unit can use morphological analysis to extract the words "Starbucks," "One More Coffee," and "Tell me," use grammatical analysis to analyze the sentence structure of "Tell me about Starbucks' One More Coffee," and use semantic analysis to understand the intent of "Provide information about Starbucks' One More Coffee." This allows for a more accurate understanding of user input and appropriate responses through natural language processing.

[0031] The summary section may include an update section that updates the database. The update section updates the database using methods such as real-time updates or periodic updates. For example, the update section updates the database in real time when there are changes in corporate policy. The update section can also periodically check the database and make updates as needed. For example, the update section updates the database daily, weekly, or monthly. Furthermore, the update section can also update the database based on user feedback. For example, if a user provides feedback that "this information is outdated," the update section updates that information to the latest version. This ensures that the database is updated to provide the most up-to-date corporate policy information.

[0032] The notification unit may include a protection unit to safeguard user privacy. The protection unit safeguards user privacy through methods such as data encryption and access control. For example, the protection unit may encrypt user data to prevent access by third parties. It can also implement access control, ensuring that only specific users can access the data. Furthermore, the protection unit may provide user privacy settings, allowing users to manage their own privacy. For example, the protection unit may allow users to configure how and what kind of notifications they receive. This enables notifications to be delivered while protecting user privacy.

[0033] The summary section can instantly reflect updates to corporate policies. For example, if there is a change in corporate policy, the summary section updates its database and reflects it in the summary within seconds. For example, when a change in corporate policy is notified, the summary section updates its database in real time and reflects it in the summary. In addition, if corporate policy changes occur regularly, the summary section can periodically check the database and update the summary as needed. For example, the summary section can update the database and reflect it in the summary at frequencies such as daily, weekly, or monthly. This allows for the provision of the latest corporate policy information in real time.

[0034] The notification section can allow users to select the information they want to receive. For example, the notification section can allow users to select the information they want to receive through methods such as setting notification categories and priorities. For instance, the notification section can allow users to select categories of information they want to receive. Users can choose from categories such as discount information, compensation details, and event information. The notification section can also allow users to set notification priorities. For example, users can set a higher priority for notifications if they want to receive important information first. This ensures that users receive only the information they need.

[0035] The reception desk can analyze a user's past input history and select the most suitable reception method. For example, the reception desk can analyze a user's past input history using data mining or machine learning algorithms. For instance, it can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. It can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, it can suggest similar input methods based on content the user has previously entered. For example, if the reception desk frequently used voice input in the past, it will prioritize suggesting voice input. Similarly, if the reception desk used text input during specific time periods in the past, it can suggest text input during those times. This allows the reception desk to provide the most suitable reception method based on the user's past input history.

[0036] The input system can filter input based on the user's current areas of interest. For example, the input system can identify the user's current areas of interest based on their search history and social media activity. For instance, it can prioritize displaying relevant information based on keywords the user has previously searched for. The input system can also filter out irrelevant information based on the user's current areas of interest. Furthermore, the input system can prioritize receiving information related to topics the user is interested in. For example, if the user has frequently searched for "discount information" in the past, the input system will prioritize displaying information related to discounts. Similarly, if the user is currently interested in "travel," the input system can prioritize receiving travel-related information. This allows the system to prioritize receiving highly relevant information based on the user's areas of interest.

[0037] The reception desk can prioritize receiving highly relevant information based on the user's geographical location information during input. The reception desk obtains the user's geographical location information through methods such as GPS data and location-based filtering. For example, the reception desk prioritizes receiving information related to the user's current location. It can also prioritize receiving information about nearby stores and services based on the user's geographical location. Furthermore, if the user is in a specific region, the reception desk can prioritize receiving information related to that region. For example, it can prioritize receiving discount and event information related to the user's current location. It can also prioritize receiving compensation and service information related to that region if the user is in a specific region. This allows the reception desk to prioritize receiving highly relevant information based on the user's geographical location information.

[0038] The reception desk can analyze a user's social media activity and receive relevant information when data is submitted. For example, the reception desk analyzes user social media activity through methods such as analyzing post content and follower analysis. For instance, it prioritizes receiving relevant information based on what the user has shared on social media. It can also receive information related to topics the user is interested in, based on their social media activity. Furthermore, it can prioritize receiving information related to accounts the user follows on social media. For example, if the user has shared "discount information" on social media, the reception desk will prioritize receiving information related to discounts. Similarly, if the user has shown interest in "travel" on social media, the reception desk can prioritize receiving travel-related information. This allows the reception desk to prioritize receiving relevant information based on the user's social media activity.

[0039] The summarization function can adjust the level of detail in a summary based on the importance of the corporate policy during summary generation. For example, it can adjust the level of detail in the summary, such as providing detailed or concise explanations, depending on the importance of the corporate policy. For instance, it can provide detailed summaries for important corporate policies and concise summaries for less important ones. Furthermore, it can adjust the length of the summary depending on the importance of the corporate policy. For example, it can provide longer summaries for important corporate policies and shorter summaries for less important ones. This ensures that summaries are provided with the appropriate level of detail according to the importance of the corporate policy.

[0040] The summarization unit can apply different summarization algorithms depending on the category of the corporate policy during summary generation. For example, the summarization unit can apply different summarization algorithms, such as abstract summarization or extractive summarization, depending on the category of the corporate policy. For instance, the summarization unit might apply a detailed summarization algorithm to corporate policies regarding compensation details. It could also apply a concise summarization algorithm to corporate policies regarding discount information. Furthermore, the summarization unit can select the most appropriate summarization algorithm depending on the category of the corporate policy. For example, the summarization unit might select an abstract summarization algorithm or an extractive summarization algorithm depending on the category of the corporate policy. This ensures that the most appropriate summarization algorithm is applied depending on the category of the corporate policy.

[0041] The summarization function can prioritize summaries based on when the company policy was updated. For example, the summarization function prioritizes summaries for recently updated company policies. It can also postpone summaries for older company policies. Furthermore, the summarization function can adjust the priority of summaries based on when the company policy was updated. For example, it can prioritize summaries based on importance and urgency based on when the company policy was updated. This allows for the provision of summaries with appropriate priority based on when the company policy was updated.

[0042] The summarization function can adjust the order of summaries based on the relevance of corporate policies during summary generation. For example, the summarization function can adjust the order of summaries according to the relevance of corporate policies. For instance, it can prioritize summarizing corporate policies that the user is interested in. It can also postpone summarizing less relevant corporate policies. Furthermore, the summarization function can adjust the order of summaries according to the relevance of corporate policies. For example, it can provide summaries in order of relevance or importance, according to the relevance of corporate policies. This ensures that summaries are provided in an appropriate order according to the relevance of corporate policies.

[0043] The notification unit can select the appropriate notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can analyze the user's past notification history and select the optimal notification method. For instance, the notification unit can prioritize providing notification methods that the user has preferred to receive in the past. The notification unit can also suggest the most suitable notification method for a specific time period based on the user's past notification history. Furthermore, the notification unit can suggest new notification methods while avoiding those that the user has ignored in the past. For example, if the notification unit has preferred to receive email notifications in the past, it will prioritize providing email notifications. Also, if the notification unit has ignored push notifications in the past, it can avoid push notifications and suggest other notification methods. This allows the notification unit to provide the optimal notification method based on the user's past notification history.

[0044] The notification unit can customize notification content based on the user's current situation. For example, it can customize notification content based on the user's current situation and areas of interest. For instance, if the user is in a specific location, the notification unit will notify them of information related to that location. It can also notify the user of information related to a specific time period if the user is in that time period. Furthermore, the notification unit can provide optimal notification content based on the user's current activity. For example, it can notify the user of discount information or event information related to their current location. It can also notify the user of compensation information or service information related to a specific time period if the user is in that time period. This allows the system to provide notifications with appropriate content tailored to the user's current situation.

[0045] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location. The notification unit obtains the user's geographical location through methods such as GPS data or location-based filtering. For example, the notification unit can notify the user of information relevant to their current location. It can also notify the user of information about nearby stores and services based on their geographical location. Furthermore, if the user is in a specific region, the notification unit can notify the user of information relevant to that region. For example, it can notify the user of discount information or event information relevant to their current location. It can also notify the user of compensation and service information relevant to that region if they are in a specific region. This allows the system to provide the most appropriate notification method based on the user's geographical location.

[0046] The notification unit can analyze the user's social media activity and adjust the content of the notification when it is sent. The notification unit analyzes the user's social media activity by methods such as analyzing the content of posts and analyzing followers. For example, the notification unit will notify relevant information based on the information the user has shared on social media. The notification unit can also notify information related to topics the user is interested in based on their social media activity. Furthermore, the notification unit can notify information related to accounts the user follows on social media. For example, if the notification unit has shared "discount information" on social media, it will notify information related to discount information. Also, if the notification unit has shown interest in "travel" on social media, it can notify information related to travel. This allows the notification unit to provide appropriate content based on the user's social media activity.

[0047] The processing unit can select the optimal processing method by referring to the user's past input history during natural language processing. For example, the processing unit can analyze the user's past input history and select the optimal processing method. For example, the processing unit can select the optimal processing method based on the vocabulary the user has used in the past. The processing unit can also extract specific patterns from the user's past input history and propose the optimal processing method. Furthermore, the processing unit can select a similar processing method by referring to content the user has entered in the past. For example, the processing unit can select the optimal processing method based on the vocabulary the user has used in the past. The processing unit can also extract specific patterns from the user's past input history and propose the optimal processing method. This allows the system to provide the optimal processing method based on the user's past input history.

[0048] The processing unit can select the optimal processing method when processing natural language, taking into account the user's geographical location. The processing unit obtains the user's geographical location through methods such as GPS data or location-based filtering. For example, the processing unit prioritizes processing information related to the user's current location. It can also prioritize processing information about nearby stores and services based on the user's geographical location. Furthermore, if the user is in a specific region, the processing unit can prioritize processing information related to that region. For example, it prioritizes processing discount and event information related to the user's current location. It can also prioritize processing compensation and service information related to that region if the user is in a specific region. This allows the processing unit to provide the optimal processing method based on the user's geographical location.

[0049] The update unit can determine the priority of database updates based on the importance of corporate policies. For example, the update unit can determine the priority of updates according to the importance of corporate policies. For instance, it can prioritize updating the database for important corporate policies. It can also postpone updating the database for less important corporate policies. Furthermore, the update unit can adjust the priority of updates according to the importance of corporate policies. For example, it can determine the priority of updates based on importance and urgency according to the importance of corporate policies. This allows the database to be updated with an appropriate priority according to the importance of corporate policies.

[0050] The update unit can adjust the update order based on the relevance of corporate policies when updating the database. For example, the update unit can adjust the update order according to the relevance of corporate policies. For instance, it can prioritize updating the database for corporate policies that users are interested in. It can also postpone updating the database for less relevant corporate policies. Furthermore, the update unit can adjust the update order according to the relevance of corporate policies. For example, it can update the database in order of relevance or importance according to the relevance of corporate policies. This ensures that the database is updated in an appropriate order according to the relevance of corporate policies.

[0051] The protection unit can select the optimal protection method by referring to the user's past behavioral history when protecting privacy. For example, the protection unit can analyze the user's past behavioral history and select the optimal protection method. For example, the protection unit can propose the optimal protection method based on the privacy protection settings the user has previously selected. The protection unit can also extract specific patterns from the user's past behavioral history and select the optimal protection method. Furthermore, the protection unit can propose similar protection methods by referring to privacy protection methods the user has used in the past. For example, the protection unit can propose the optimal protection method based on the privacy protection settings the user has previously selected. The protection unit can also extract specific patterns from the user's past behavioral history and select the optimal protection method. This allows the system to provide the optimal privacy protection method based on the user's past behavioral history.

[0052] The protection unit can select the optimal protection method when protecting privacy, taking into account the user's geographical location information. The protection unit obtains the user's geographical location information, for example, through methods such as GPS data or location-based filtering. For example, the protection unit provides privacy protection related to the user's current location. It can also provide privacy protection related to nearby stores and services based on the user's geographical location information. Furthermore, if the user is in a specific region, the protection unit can provide privacy protection related to that region. For example, the protection unit provides privacy protection related to the user's current location. It can also provide privacy protection related to that region if the user is in a specific region. This allows the protection unit to provide the optimal privacy protection method based on the user's geographical location information.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can search for relevant past user reviews based on user input and provide them to the summarization desk. For example, if a user asks a question about a specific service, the reception desk can search the database for past user reviews about that service and provide them to the summarization desk. The summarization desk can then summarize these reviews and provide them to the user. This allows users to refer to the opinions and evaluations of other users, enabling them to make better decisions. The reception desk can also search for relevant FAQs based on user input and provide them to the summarization desk. This allows users to quickly obtain the information they need.

[0055] The summary section can provide relevant legal information in addition to summarizing corporate policies. For example, it can search a database for laws and regulations related to corporate policies and include them in the summary. This allows users to deepen their understanding of corporate policies and grasp the legal aspects. The summary section can also provide a history of changes to corporate policies. For example, it can compare past and current versions of corporate policies and include the changes in the summary. This allows users to quickly grasp changes to corporate policies.

[0056] The notification unit can integrate with the user's schedule information to deliver notifications at the appropriate time. For example, it can connect with the user's calendar app to notify them of relevant information before and after important meetings or appointments. This ensures that users don't miss important information and receive it at the right time. Furthermore, the notification unit can integrate with the user's activity information to adjust notifications according to the user's situation, such as during exercise or rest. This enables flexible notifications tailored to the user's lifestyle.

[0057] The reception unit can search for relevant video content based on user input and provide it to the summarization unit. For example, if a user asks a question about a specific product, the reception unit can search its database for video reviews and tutorials related to that product and provide them to the summarization unit. The summarization unit can then summarize this video content and provide it to the user. This allows the user to gain a deeper understanding through visual information. The reception unit can also search for relevant image content based on user input and provide it to the summarization unit. This allows the user to gain more multifaceted information through visual information.

[0058] The summary section can provide relevant statistical information in addition to summarizing the company policy. For example, the summary section can retrieve market research data and statistics related to the company policy from a database and include them in the summary. This allows users to gain a deeper understanding of the background and impact of the company policy. The summary section can also provide data on the results of implementing the company policy. For example, the summary section can retrieve the results and impacts after the implementation of the company policy from a database and include them in the summary. This allows users to evaluate the effectiveness of the company policy.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception area receives input from the user. User input includes text input, voice input, and image input. The reception area is equipped with a keyboard or touchscreen for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The summarization unit searches the database for the information received by the reception unit and summarizes the company policy. The summarization is performed based on the length of the text and the importance of the information to be summarized. The summarization unit uses natural language processing technology and generative AI (e.g., text generation AI and multimodal generation AI) to extract the key points of the company policy and summarize them concisely. Step 3: The notification unit uses the information summarized by the summarization unit to send notifications in conjunction with location information. The notifications will inform the user of relevant deals when they are in a specific location. The notification unit may also include a protection unit to obtain the user's location information using GPS data or Wi-Fi location information and to protect the user's privacy.

[0061] (Example of form 2) The corporate policy-focused AI chatbot system according to an embodiment of the present invention solves the problem of users overlooking "special offers" because they are unwilling to read long and difficult corporate policies. In this system, when a user enters a store name or service name, the AI ​​chatbot searches the database for that information and provides a summary of the relevant corporate policy. Furthermore, by linking with location information, users can receive notifications of relevant offers when they are in a specific location. For example, if a user enters "Starbucks," the AI ​​chatbot will provide information about "One More Coffee." Next, by linking with location information, users can receive notifications of relevant offers when they are in a specific location. For example, if a user is at an airport, they can receive a notification about compensation in case of flight delays. This system allows users to easily obtain special offers without the hassle of reading long corporate policies. In addition, by linking with location information, users can receive useful information in real time, improving user convenience. Thus, the corporate policy-focused AI chatbot system allows users to easily understand corporate policies and receive special offers in real time by linking with location information.

[0062] The corporate policy-specific AI chatbot system according to this embodiment comprises a reception unit, a summarization unit, and a notification unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone and voice recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera and image recognition technology for receiving image input. The summarization unit searches a database for the information received by the reception unit and summarizes the corporate policy. Summarization is performed, for example, based on the length of the text and the importance of the information to be summarized, but is not limited to such examples. The summarization unit may, for example, use natural language processing technology to extract the key points of the corporate policy and summarize them concisely. The summarization unit may also use generative AI to generate a summary of the corporate policy. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The notification unit provides notifications based on information summarized by the summarization unit, in conjunction with location information. These notifications may, for example, provide relevant information when the user is in a specific location, but are not limited to such examples. The notification unit obtains the user's location information using, for example, GPS data or Wi-Fi location information. The notification unit may also include a protection unit to safeguard user privacy. This protection unit may, for example, encrypt data or control access. As a result, the corporate policy-focused AI chatbot system according to this embodiment allows users to easily understand corporate policies and receive timely information about special offers in conjunction with their location.

[0063] The reception unit may be equipped with a processing unit that performs natural language processing. The processing unit analyzes user input using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the processing unit can use morphological analysis to divide user input into words and identify the part of speech of each word. The processing unit can also use grammatical analysis to analyze the grammatical structure of user input and identify sentence components such as subjects and predicates. Furthermore, the processing unit can use semantic analysis to analyze the meaning of user input and understand the intent of the input. For example, if a user inputs "Tell me about Starbucks' One More Coffee," the processing unit can use morphological analysis to extract the words "Starbucks," "One More Coffee," and "Tell me," use grammatical analysis to analyze the sentence structure of "Tell me about Starbucks' One More Coffee," and use semantic analysis to understand the intent of "Provide information about Starbucks' One More Coffee." This allows for a more accurate understanding of user input and appropriate responses through natural language processing.

[0064] The summary section may include an update section that updates the database. The update section updates the database using methods such as real-time updates or periodic updates. For example, the update section updates the database in real time when there are changes in corporate policy. The update section can also periodically check the database and make updates as needed. For example, the update section updates the database daily, weekly, or monthly. Furthermore, the update section can also update the database based on user feedback. For example, if a user provides feedback that "this information is outdated," the update section updates that information to the latest version. This ensures that the database is updated to provide the most up-to-date corporate policy information.

[0065] The notification unit may include a protection unit to safeguard user privacy. The protection unit safeguards user privacy through methods such as data encryption and access control. For example, the protection unit may encrypt user data to prevent access by third parties. It can also implement access control, ensuring that only specific users can access the data. Furthermore, the protection unit may provide user privacy settings, allowing users to manage their own privacy. For example, the protection unit may allow users to configure how and what kind of notifications they receive. This enables notifications to be delivered while protecting user privacy.

[0066] The summary section can instantly reflect updates to corporate policies. For example, if there is a change in corporate policy, the summary section updates its database and reflects it in the summary within seconds. For example, when a change in corporate policy is notified, the summary section updates its database in real time and reflects it in the summary. In addition, if corporate policy changes occur regularly, the summary section can periodically check the database and update the summary as needed. For example, the summary section can update the database and reflect it in the summary at frequencies such as daily, weekly, or monthly. This allows for the provision of the latest corporate policy information in real time.

[0067] The notification section can allow users to select the information they want to receive. For example, the notification section can allow users to select the information they want to receive through methods such as setting notification categories and priorities. For instance, the notification section can allow users to select categories of information they want to receive. Users can choose from categories such as discount information, compensation details, and event information. The notification section can also allow users to set notification priorities. For example, users can set a higher priority for notifications if they want to receive important information first. This ensures that users receive only the information they need.

[0068] The reception system can estimate the user's emotions and adjust the timing of input acceptance based on those emotions. The reception system estimates user emotions using methods such as facial recognition, voice analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use voice analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the reception system can delay input acceptance to allow them to relax. Conversely, if the user is relaxed, it can accept input immediately to provide smooth operation. Furthermore, if the user is in a hurry, the reception system can speed up input acceptance for a quicker response. This allows for input acceptance at an appropriate time according to the user's emotions.

[0069] The reception desk can analyze a user's past input history and select the most suitable reception method. For example, the reception desk can analyze a user's past input history using data mining or machine learning algorithms. For instance, it can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. It can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, it can suggest similar input methods based on content the user has previously entered. For example, if the reception desk frequently used voice input in the past, it will prioritize suggesting voice input. Similarly, if the reception desk used text input during specific time periods in the past, it can suggest text input during those times. This allows the reception desk to provide the most suitable reception method based on the user's past input history.

[0070] The input system can filter input based on the user's current areas of interest. For example, the input system can identify the user's current areas of interest based on their search history and social media activity. For instance, it can prioritize displaying relevant information based on keywords the user has previously searched for. The input system can also filter out irrelevant information based on the user's current areas of interest. Furthermore, the input system can prioritize receiving information related to topics the user is interested in. For example, if the user has frequently searched for "discount information" in the past, the input system will prioritize displaying information related to discounts. Similarly, if the user is currently interested in "travel," the input system can prioritize receiving travel-related information. This allows the system to prioritize receiving highly relevant information based on the user's areas of interest.

[0071] The reception desk can estimate the user's emotions and prioritize the information it receives based on those emotions. The reception desk estimates user emotions using methods such as facial recognition, voice analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use voice analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the reception desk will prioritize receiving important information. If the user is relaxed, it can prioritize receiving detailed information. Furthermore, if the user is in a hurry, it can prioritize receiving information that can be handled quickly. This allows the reception desk to prioritize important information according to the user's emotions.

[0072] The reception desk can prioritize receiving highly relevant information based on the user's geographical location information during input. The reception desk obtains the user's geographical location information through methods such as GPS data and location-based filtering. For example, the reception desk prioritizes receiving information related to the user's current location. It can also prioritize receiving information about nearby stores and services based on the user's geographical location. Furthermore, if the user is in a specific region, the reception desk can prioritize receiving information related to that region. For example, it can prioritize receiving discount and event information related to the user's current location. It can also prioritize receiving compensation and service information related to that region if the user is in a specific region. This allows the reception desk to prioritize receiving highly relevant information based on the user's geographical location information.

[0073] The reception desk can analyze a user's social media activity and receive relevant information when data is submitted. For example, the reception desk analyzes user social media activity through methods such as analyzing post content and follower analysis. For instance, it prioritizes receiving relevant information based on what the user has shared on social media. It can also receive information related to topics the user is interested in, based on their social media activity. Furthermore, it can prioritize receiving information related to accounts the user follows on social media. For example, if the user has shared "discount information" on social media, the reception desk will prioritize receiving information related to discounts. Similarly, if the user has shown interest in "travel" on social media, the reception desk can prioritize receiving travel-related information. This allows the reception desk to prioritize receiving relevant information based on the user's social media activity.

[0074] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. The summarization unit estimates user emotions using methods such as facial recognition, speech analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use speech analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the summarization unit will use concise and easy-to-understand language. If the user is relaxed, it can use language that includes detailed explanations. Furthermore, if the user is in a hurry, it can use concise and to-the-point language. This allows the summarization unit to provide a summary using appropriate language that reflects the user's emotions.

[0075] The summarization function can adjust the level of detail in a summary based on the importance of the corporate policy during summary generation. For example, it can adjust the level of detail in the summary, such as providing detailed or concise explanations, depending on the importance of the corporate policy. For instance, it can provide detailed summaries for important corporate policies and concise summaries for less important ones. Furthermore, it can adjust the length of the summary depending on the importance of the corporate policy. For example, it can provide longer summaries for important corporate policies and shorter summaries for less important ones. This ensures that summaries are provided with the appropriate level of detail according to the importance of the corporate policy.

[0076] The summarization unit can apply different summarization algorithms depending on the category of the corporate policy during summary generation. For example, the summarization unit can apply different summarization algorithms, such as abstract summarization or extractive summarization, depending on the category of the corporate policy. For instance, the summarization unit might apply a detailed summarization algorithm to corporate policies regarding compensation details. It could also apply a concise summarization algorithm to corporate policies regarding discount information. Furthermore, the summarization unit can select the most appropriate summarization algorithm depending on the category of the corporate policy. For example, the summarization unit might select an abstract summarization algorithm or an extractive summarization algorithm depending on the category of the corporate policy. This ensures that the most appropriate summarization algorithm is applied depending on the category of the corporate policy.

[0077] The summarization function can estimate the user's emotions and adjust the length of the summary based on those emotions. The summarization function estimates user emotions using methods such as facial recognition, speech analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use speech analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the summarization function provides a short, concise summary. If the user is relaxed, it can provide a longer summary with more detailed explanations. Furthermore, if the user is in a hurry, it can provide a concise and quickly understandable summary. This allows for the provision of summaries of appropriate length based on the user's emotions.

[0078] The summarization function can prioritize summaries based on when the company policy was updated. For example, the summarization function prioritizes summaries for recently updated company policies. It can also postpone summaries for older company policies. Furthermore, the summarization function can adjust the priority of summaries based on when the company policy was updated. For example, it can prioritize summaries based on importance and urgency based on when the company policy was updated. This allows for the provision of summaries with appropriate priority based on when the company policy was updated.

[0079] The summarization function can adjust the order of summaries based on the relevance of corporate policies during summary generation. For example, the summarization function can adjust the order of summaries according to the relevance of corporate policies. For instance, it can prioritize summarizing corporate policies that the user is interested in. It can also postpone summarizing less relevant corporate policies. Furthermore, the summarization function can adjust the order of summaries according to the relevance of corporate policies. For example, it can provide summaries in order of relevance or importance, according to the relevance of corporate policies. This ensures that summaries are provided in an appropriate order according to the relevance of corporate policies.

[0080] The notification unit can estimate the user's emotions and adjust the way notifications are displayed based on those estimated emotions. The notification unit estimates user emotions using methods such as facial recognition, voice analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use voice analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the notification unit provides a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display method. This allows notifications to be delivered in an appropriate display method according to the user's emotions.

[0081] The notification unit can select the appropriate notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can analyze the user's past notification history and select the optimal notification method. For instance, the notification unit can prioritize providing notification methods that the user has preferred to receive in the past. The notification unit can also suggest the most suitable notification method for a specific time period based on the user's past notification history. Furthermore, the notification unit can suggest new notification methods while avoiding those that the user has ignored in the past. For example, if the notification unit has preferred to receive email notifications in the past, it will prioritize providing email notifications. Also, if the notification unit has ignored push notifications in the past, it can avoid push notifications and suggest other notification methods. This allows the notification unit to provide the optimal notification method based on the user's past notification history.

[0082] The notification unit can customize notification content based on the user's current situation. For example, it can customize notification content based on the user's current situation and areas of interest. For instance, if the user is in a specific location, the notification unit will notify them of information related to that location. It can also notify the user of information related to a specific time period if the user is in that time period. Furthermore, the notification unit can provide optimal notification content based on the user's current activity. For example, it can notify the user of discount information or event information related to their current location. It can also notify the user of compensation information or service information related to a specific time period if the user is in that time period. This allows the system to provide notifications with appropriate content tailored to the user's current situation.

[0083] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. The notification unit estimates user emotions using methods such as facial recognition, voice analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use voice analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the notification unit will prioritize important notifications. If the user is relaxed, it can prioritize detailed notifications. Furthermore, if the user is in a hurry, it can prioritize notifications that require a quick response. This allows important notifications to be prioritized according to the user's emotions.

[0084] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location. The notification unit obtains the user's geographical location through methods such as GPS data or location-based filtering. For example, the notification unit can notify the user of information relevant to their current location. It can also notify the user of information about nearby stores and services based on their geographical location. Furthermore, if the user is in a specific region, the notification unit can notify the user of information relevant to that region. For example, it can notify the user of discount information or event information relevant to their current location. It can also notify the user of compensation and service information relevant to that region if they are in a specific region. This allows the system to provide the most appropriate notification method based on the user's geographical location.

[0085] The notification unit can analyze the user's social media activity and adjust the content of the notification when it is sent. The notification unit analyzes the user's social media activity by methods such as analyzing the content of posts and analyzing followers. For example, the notification unit will notify relevant information based on the information the user has shared on social media. The notification unit can also notify information related to topics the user is interested in based on their social media activity. Furthermore, the notification unit can notify information related to accounts the user follows on social media. For example, if the notification unit has shared "discount information" on social media, it will notify information related to discount information. Also, if the notification unit has shown interest in "travel" on social media, it can notify information related to travel. This allows the notification unit to provide appropriate content based on the user's social media activity.

[0086] The processing unit can estimate the user's emotions and adjust the natural language processing algorithm based on the estimated emotions. The processing unit estimates the user's emotions using methods such as facial recognition, speech analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use speech analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the processing unit uses a concise and easy-to-understand algorithm. If the user is relaxed, it can use an algorithm that includes detailed explanations. Furthermore, if the user is in a hurry, it can use an algorithm that can respond quickly. This allows for natural language processing with an appropriate algorithm tailored to the user's emotions.

[0087] The processing unit can select the optimal processing method by referring to the user's past input history during natural language processing. For example, the processing unit can analyze the user's past input history and select the optimal processing method. For example, the processing unit can select the optimal processing method based on the vocabulary the user has used in the past. The processing unit can also extract specific patterns from the user's past input history and propose the optimal processing method. Furthermore, the processing unit can select a similar processing method by referring to content the user has entered in the past. For example, the processing unit can select the optimal processing method based on the vocabulary the user has used in the past. The processing unit can also extract specific patterns from the user's past input history and propose the optimal processing method. This allows the system to provide the optimal processing method based on the user's past input history.

[0088] The processing unit can estimate the user's emotions and determine the priority of natural language processing based on the estimated emotions. The processing unit estimates the user's emotions using methods such as facial recognition, speech analysis, and text analysis. For example, the processing unit can use facial recognition to estimate emotions from the user's facial expressions. It can also use speech analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the processing unit will prioritize processing important information. If the user is relaxed, the processing unit can also prioritize processing detailed information. Furthermore, if the user is in a hurry, the processing unit can prioritize processing information that can be responded to quickly. This allows natural language processing to be performed with appropriate priorities according to the user's emotions.

[0089] The processing unit can select the optimal processing method when processing natural language, taking into account the user's geographical location. The processing unit obtains the user's geographical location through methods such as GPS data or location-based filtering. For example, the processing unit prioritizes processing information related to the user's current location. It can also prioritize processing information about nearby stores and services based on the user's geographical location. Furthermore, if the user is in a specific region, the processing unit can prioritize processing information related to that region. For example, it prioritizes processing discount and event information related to the user's current location. It can also prioritize processing compensation and service information related to that region if the user is in a specific region. This allows the processing unit to provide the optimal processing method based on the user's geographical location.

[0090] The update unit can estimate the user's emotions and adjust the database update frequency based on the estimated emotions. The update unit estimates the user's emotions using methods such as facial recognition, speech analysis, and text analysis. For example, the update unit can estimate emotions from the user's facial expressions using facial recognition. It can also estimate emotions from the tone and speed of the user's voice using speech analysis. Furthermore, it can estimate emotions from the user's input using text analysis. For example, if the user is stressed, the update unit can lower the update frequency to avoid information overload. Conversely, if the user is relaxed, the update unit can increase the update frequency to provide the latest information. Furthermore, if the user is in a hurry, the update unit can quickly update only the necessary information. This allows the database to be updated at an appropriate frequency according to the user's emotions.

[0091] The update unit can determine the priority of database updates based on the importance of corporate policies. For example, the update unit can determine the priority of updates according to the importance of corporate policies. For instance, it can prioritize updating the database for important corporate policies. It can also postpone updating the database for less important corporate policies. Furthermore, the update unit can adjust the priority of updates according to the importance of corporate policies. For example, it can determine the priority of updates based on importance and urgency according to the importance of corporate policies. This allows the database to be updated with an appropriate priority according to the importance of corporate policies.

[0092] The update unit can estimate the user's emotions and adjust the database updates based on those emotions. The update unit estimates user emotions using methods such as facial recognition, speech analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use speech analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the update unit will update only the most important information. If the user is relaxed, it can update detailed information. Furthermore, if the user is in a hurry, it can quickly update only the necessary information. This allows the database to be updated with appropriate content that reflects the user's emotions.

[0093] The update unit can adjust the update order based on the relevance of corporate policies when updating the database. For example, the update unit can adjust the update order according to the relevance of corporate policies. For instance, it can prioritize updating the database for corporate policies that users are interested in. It can also postpone updating the database for less relevant corporate policies. Furthermore, the update unit can adjust the update order according to the relevance of corporate policies. For example, it can update the database in order of relevance or importance according to the relevance of corporate policies. This ensures that the database is updated in an appropriate order according to the relevance of corporate policies.

[0094] The protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated emotions. The protection unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, the protection unit can use facial recognition to estimate emotions from the user's facial expressions. It can also use voice analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is feeling stressed, the protection unit can set a higher level of privacy protection. Conversely, if the user is relaxed, the protection unit can set the level of privacy protection to standard. Furthermore, if the user is in a hurry, the protection unit can provide a privacy protection method that can be responded to quickly. This allows for privacy protection in an appropriate manner according to the user's emotions.

[0095] The protection unit can select the optimal protection method by referring to the user's past behavioral history when protecting privacy. For example, the protection unit can analyze the user's past behavioral history and select the optimal protection method. For example, the protection unit can propose the optimal protection method based on the privacy protection settings the user has previously selected. The protection unit can also extract specific patterns from the user's past behavioral history and select the optimal protection method. Furthermore, the protection unit can propose similar protection methods by referring to privacy protection methods the user has used in the past. For example, the protection unit can propose the optimal protection method based on the privacy protection settings the user has previously selected. The protection unit can also extract specific patterns from the user's past behavioral history and select the optimal protection method. This allows the system to provide the optimal privacy protection method based on the user's past behavioral history.

[0096] The protection unit can estimate the user's emotions and determine the priority of privacy protection based on those estimated emotions. The protection unit estimates user emotions using methods such as facial recognition, voice analysis, and text analysis. For example, it can use facial recognition to estimate emotions from the user's facial expressions. It can also use voice analysis to estimate emotions from the tone and speed of the user's voice. Furthermore, it can use text analysis to estimate emotions from the user's input. For example, if the user is stressed, the protection unit will prioritize providing important privacy protection. If the user is relaxed, it can provide more detailed privacy protection. Furthermore, if the user is in a hurry, it can provide privacy protection that can be addressed quickly. This allows for privacy protection to be prioritized appropriately according to the user's emotions.

[0097] The protection unit can select the optimal protection method when protecting privacy, taking into account the user's geographical location information. The protection unit obtains the user's geographical location information, for example, through methods such as GPS data or location-based filtering. For example, the protection unit provides privacy protection related to the user's current location. It can also provide privacy protection related to nearby stores and services based on the user's geographical location information. Furthermore, if the user is in a specific region, the protection unit can provide privacy protection related to that region. For example, the protection unit provides privacy protection related to the user's current location. It can also provide privacy protection related to that region if the user is in a specific region. This allows the protection unit to provide the optimal privacy protection method based on the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, summarization unit, notification unit, and protection unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives input from the user using the touchscreen or microphone 38B of the smart device 14. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and summarizes corporate policies using natural language processing technology or generative AI. The notification unit is implemented, for example, by the control unit 46A of the smart device 14, and acquires the user's location information using GPS data or Wi-Fi location information and notifies them of relevant deals. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and performs data encryption and access control. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, summarization unit, notification unit, and protection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice input from the user using the microphone 238 of the smart glasses 214. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and summarizes corporate policies using natural language processing technology and generative AI. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214, and acquires the user's location information using GPS data and Wi-Fi location information, and notifies the user of relevant deals. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and performs data encryption and access control. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, summarization unit, notification unit, and protection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice input from the user using the microphone 238 of the headset terminal 314. The summarization unit is implemented in the specific processing unit 290 of the data processing unit 12 and summarizes corporate policies using natural language processing technology and generative AI. The notification unit is implemented in the control unit 46A of the headset terminal 314 and acquires the user's location information using GPS data and Wi-Fi location information and notifies them of relevant deals. The protection unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs data encryption and access control. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, summarization unit, notification unit, and protection unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input from the user using the microphone 238 of the robot 414. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and summarizes corporate policies using natural language processing technology and generative AI. The notification unit is implemented, for example, by the control unit 46A of the robot 414, and acquires the user's location information using GPS data and Wi-Fi location information, and notifies them of relevant deals. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and performs data encryption and access control.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can search for relevant past user reviews based on user input and provide them to the summarization desk. For example, if a user asks a question about a specific service, the reception desk can search the database for past user reviews about that service and provide them to the summarization desk. The summarization desk can then summarize these reviews and provide them to the user. This allows users to refer to the opinions and evaluations of other users, enabling them to make better decisions. The reception desk can also search for relevant FAQs based on user input and provide them to the summarization desk. This allows users to quickly obtain the information they need.

[0100] The summary section can provide relevant legal information in addition to summarizing corporate policies. For example, it can search a database for laws and regulations related to corporate policies and include them in the summary. This allows users to deepen their understanding of corporate policies and grasp the legal aspects. The summary section can also provide a history of changes to corporate policies. For example, it can compare past and current versions of corporate policies and include the changes in the summary. This allows users to quickly grasp changes to corporate policies.

[0101] The notification unit can integrate with the user's schedule information to deliver notifications at the appropriate time. For example, it can connect with the user's calendar app to notify them of relevant information before and after important meetings or appointments. This ensures that users don't miss important information and receive it at the right time. Furthermore, the notification unit can integrate with the user's activity information to adjust notifications according to the user's situation, such as during exercise or rest. This enables flexible notifications tailored to the user's lifestyle.

[0102] The reception unit can search for relevant video content based on user input and provide it to the summarization unit. For example, if a user asks a question about a specific product, the reception unit can search its database for video reviews and tutorials related to that product and provide them to the summarization unit. The summarization unit can then summarize this video content and provide it to the user. This allows the user to gain a deeper understanding through visual information. The reception unit can also search for relevant image content based on user input and provide it to the summarization unit. This allows the user to gain more multifaceted information through visual information.

[0103] The summary section can provide relevant statistical information in addition to summarizing the company policy. For example, the summary section can retrieve market research data and statistics related to the company policy from a database and include them in the summary. This allows users to gain a deeper understanding of the background and impact of the company policy. The summary section can also provide data on the results of implementing the company policy. For example, the summary section can retrieve the results and impacts after the implementation of the company policy from a database and include them in the summary. This allows users to evaluate the effectiveness of the company policy.

[0104] The reception desk can estimate the user's emotions and suggest input methods based on those estimates. For example, if the reception desk is stressed, it can reduce the burden of input by providing simple options. If the user is relaxed, it can also suggest more detailed input methods. Furthermore, if the user is in a hurry, it can suggest voice input to enable a quick response. This allows the system to provide the optimal input method tailored to the user's emotions.

[0105] The summary section can estimate the user's emotions and adjust the visual representation of the summary based on those emotions. For example, if the user is stressed, the summary section can use simple, highly visible graphics. If the user is relaxed, it can also provide a detailed infographic. Furthermore, if the user is in a hurry, it can provide a concise, to-the-point visual. This allows the summary to be delivered with an appropriate visual representation that matches the user's emotions.

[0106] The notification unit can estimate the user's emotions and adjust the voice alert of the notification based on those emotions. For example, if the user is feeling stressed, the notification unit will use a gentle voice alert. It can also provide detailed voice guidance if the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can provide a short, clear voice alert. This allows notifications to be delivered with appropriate voice alerts tailored to the user's emotions.

[0107] The reception desk can estimate the user's emotions and provide feedback on the input based on those emotions. For example, if the reception desk is feeling stressed, it can display an encouraging message. It can also provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, it can provide quick feedback. This allows for the provision of appropriate feedback tailored to the user's emotions.

[0108] The summary section can estimate the user's emotions and adjust the summary's language style based on that estimation. For example, if the user is stressed, the summary section will use a concise and easy-to-understand language style. Conversely, if the user is relaxed, it can provide a detailed and polite language style. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point language style. This allows the summary to be delivered in an appropriate language style tailored to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception area receives input from the user. User input includes text input, voice input, and image input. The reception area is equipped with a keyboard or touchscreen for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The summarization unit searches the database for the information received by the reception unit and summarizes the company policy. The summarization is performed based on the length of the text and the importance of the information to be summarized. The summarization unit uses natural language processing technology and generative AI (e.g., text generation AI and multimodal generation AI) to extract the key points of the company policy and summarize them concisely. Step 3: The notification unit uses the information summarized by the summarization unit to send notifications in conjunction with location information. The notifications will inform the user of relevant deals when they are in a specific location. The notification unit may also include a protection unit to obtain the user's location information using GPS data or Wi-Fi location information and to protect the user's privacy.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, The information received by the aforementioned reception department is searched in a database and summarized by a summarization department, The system includes a notification unit that provides notifications in conjunction with location information based on the information summarized by the summarization unit. A system characterized by the following features.

2. The aforementioned reception unit is It includes a processing unit for natural language processing. The system according to feature 1.

3. The summary section above is, It includes an update unit that performs database updates. The system according to feature 1.

4. The aforementioned notification unit, It features a protective section to safeguard user privacy. The system according to feature 1.

5. The summary section above is, Immediately reflect updates to company policies. The system according to feature 1.

6. The aforementioned notification unit, Allow users to select the information they want to receive. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the appropriate reception method. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest. The system according to feature 1.

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