Audio distribution management method

Abstract

To provide an automatic answering system that is realized in a form that is like having a conversation with a real person based on artificial intelligence, and that quickly and conveniently processes inquiries, reservations, delivery orders, etc. while realizing more natural conversations with users. [Solution] The method includes a device acquiring text information, generating voice data using a TTS engine, and automatically broadcasting the voice data by transmitting it to a speaker terminal at a specified time or interval.

Description

[Technical Field]

[0001] The present invention relates to an audio distribution management method. [Background technology]

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Patent Document 1 discloses a technique for playing back pre-recorded audio files at designated times in an in-store broadcasting system. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2024-140917 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the inventors have recognized that at least the above embodiment has a drawback in that it does not have a method for automatically generating optimal audio broadcast content according to the facility and time period and for integrated management at multiple locations. [Means for solving the problem]

[0006] At least one aspect of the present disclosure provides a method for manufacturing a semiconductor device, comprising: 1. A method for managing audio distribution, comprising: obtaining text information; requesting a TTS engine to generate speech data from the text information; transmitting the generated voice data to a speaker terminal at a specified time or interval; The present invention provides a method having the following structure: [Effects of the Invention]

[0007] This configuration has the advantage of being able to automatically generate and distribute audio broadcasts according to facilities and time periods.

[0008] These and other aspects, features, and advantages of the present disclosure will become apparent from the following detailed written description of the preferred embodiments and aspects taken in conjunction with the following drawings, variations and modifications of which may be made without departing from the spirit and scope of the novel concepts of the present disclosure. Aspects of one embodiment of the present disclosure may be combined with or substituted for one or more aspects of another embodiment of the present disclosure, unless inconsistent. DETAILED DESCRIPTION OF THE INVENTION

[0009] The following disclosure provides many different embodiments and examples for implementing different features of the presented subject matter. To simplify the disclosure, specific examples of components and arrangements are disclosed below. Of course, these are merely examples and are not intended to be limiting. For example, a structure in which a first feature is covered by or in contact with a subsequently disclosed second feature may include an embodiment in which the first and second features are formed in direct contact, as well as an embodiment in which an additional feature is formed between the first and second features to prevent direct contact between the first and second features. Furthermore, the disclosure may repeat reference numbers and / or letters in various examples. Such repetition is for the purposes of brevity and clarity and does not, in itself, require a relationship between the various embodiments and / or configurations described. Furthermore, when a first element is described as being "coupled" or "coupled" to a second element, such description includes embodiments in which the first and second elements are directly coupled or coupled to each other, as well as embodiments in which the first and second elements are indirectly coupled or coupled to each other with one or more other intervening elements therebetween.

[0010] As used herein, the phrase "at least one" encompasses all exemplified variations. For example, the phrase "at least one of A, B, and C" is equivalent to "A, B, and C, and combinations thereof," encompassing all possible variations of A, B, C, A+B, A+C, B+C, and A+B+C.

[0011] In this disclosure, disclosures using an electronic operator or computer may include embodiments of a method, a recording medium, an apparatus, or a program. As used herein, the statement "A is B" may be replaced with "A includes B" unless there is a contradiction or unless otherwise stated in this specification.

[0012] The operating method used in at least one or more embodiments can take the following embodiments: The following description will be made with reference to JP6456303 (the following reference begins), which clearly explains at least one or more embodiments.

[0013] As used herein, the term "computer" generally includes, as known in the art, a processor; memory; at least one information storage / retrieval device, such as a hard drive, disk drive, or flash drive or memory stick, or other non-transitory computer-readable medium or non-transitory storage device; at least one input device, such as a keyboard, mouse, point-and-touch device, touch screen, or microphone; and a display structure, such as a well-known computer screen. In addition, a computer may include one or more network connections, such as wired or wireless connections. As known in the art, such a computer or computer system may include more or less of the above, including, for example, but not limited to, tablet computers and smart devices, as well as other electronic media and devices.

[0014] As used herein, the term "cloud" or "cloud computing" refers to a centralized and virtualized computing facility in which all computing resources are shared. Application systems and subsystems can no longer be referred to as specific machines because they are all in the "cloud."

[0015] As used herein, the term "distributed internet services system" refers to a distributed internet services platform that transforms internet applications to run in various computing environments. The DIS system distributes internet applications, including content, data, and logic, to any number and type of devices across appropriate scopes and networks via component distributed servers / asset distributed servers. Through the DIS, internet applications can be hosted and centrally managed, with services based on each user's needs, and cached and executed locally on the user's device or nearby locations while maintaining their integrity. Web-enabled computing devices can be upgraded with DIS software to become DIS-enabled, enjoying and running distributed internet services. The distributed internet services system is more fully described in any one of the following patent families: U.S. Patent Nos. 7,136,857, 7,150,015, 7,181,731, 7,209,921, 7,430,610, 7,685,183, 7,685,577, 7,752,214, 8,326,883, 8,386,525, 8,443,035, 8,458,142, 8,458,222, 8,473,468, 8,527,545, and 8,650,226, and U.S. Patent Publication Nos. 20120005205, and 20130091252, all of which, like the present invention, are commonly owned by OP40 Holdings, Inc., and all of which are incorporated by reference. (End of quote)

[0016] The operating method used in at least one embodiment can be implemented in the following manner using a conventional Internet system that does not use a distributed Internet. Reference is made to JP7113047 (the following reference begins), which clearly explains at least one embodiment.

[0017] Embodiments including those specifically disclosed in this specification can provide an automated response system that is based on artificial intelligence and is implemented in a manner that resembles a real conversation with a human, thereby enabling more natural conversations with users and quickly and conveniently handling inquiries, reservations, delivery orders, etc.

[0018] The electronic devices 110, 120, 130, and 140 may be fixed or mobile terminals implemented by computer systems. Examples of the electronic devices 110, 120, 130, and 140 include AI speakers, smartphones, mobile phones, navigation systems, PCs, laptops, digital broadcasting terminals, PDAs, PMPs, tablets, game consoles, wearable devices, IoT devices, VR devices, and AR devices. While FIG. 1 illustrates an AI speaker as the electronic device 110, in an embodiment of the present invention, the electronic device 110 may represent one of a variety of physical computer systems capable of communicating with other electronic devices 120, 130, and 140 and / or servers 150 and 160 via a network 170 using a substantially wireless or wired communication method.

[0019] The communication method is not limited, and may include not only a communication method using a communication network (for example, a mobile communication network, a wired Internet, a wireless Internet, a broadcast network, a satellite network, etc.) that can be included in network 170, but also short-range wireless communication between devices. For example, network 170 may include any one or more of networks such as PAN, LAN, CAN, MAN, WAN, BBN, and the Internet. Furthermore, network 170 may include any one or more of network topologies including, but not limited to, a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree or hierarchical network, etc.

[0020] The servers 150 and 160 may each be realized by one or more computer devices that communicate with the plurality of electronic devices 110, 120, 130, and 140 via the network 170 and provide instructions, codes, files, content, services, etc. For example, the server 150 may be a system that provides a first service to the plurality of electronic devices 110, 120, 130, and 140 connected via the network 170, and the server 160 may be a system that provides a second service to the plurality of electronic devices 110, 120, 130, and 140 connected via the network 170. As a more specific example, the server 150 may provide a service (such as an audio distribution service, for example) targeted by an application, which is a computer program installed and executed in the plurality of electronic devices 110, 120, 130, and 140, as a first service to the plurality of electronic devices 110, 120, 130, and 140. As another example, the server 160 may provide, as a second service, a service of distributing files for installing and executing the above-mentioned application to the multiple electronic devices 110, 120, 130, and 140.

[0021] 2 is a block diagram illustrating the internal configuration of an electronic device and a server according to an embodiment of the present invention. In FIG. 2, the internal configuration of electronic device 110 and the internal configuration of server 150 are described as examples of electronic devices. Furthermore, other electronic devices 120, 130, 140 and server 160 may also have the same or similar internal configuration as electronic device 110 or server 150 described above.

[0022] The electronic device 110 and the server 150 may include memories 211 and 221, processors 212 and 222, communication modules 213 and 223, and input / output interfaces 214 and 224. The memories 211 and 221 may be non-transitory computer-readable recording media and may include non-transitory mass storage devices such as RAM, ROM, disk drives, SSDs, flash memory, etc. Here, non-transitory mass storage devices such as ROM, SSDs, flash memory, and disk drives may be included in the electronic device 110 and the server 150 as separate non-transitory storage devices distinct from the memories 211 and 221. The memories 211 and 221 may also store an operating system and at least one program code (for example, code for a browser installed and executed on the electronic device 110, or code for an application installed on the electronic device 110 to provide a particular service). Such software components may be loaded from a computer-readable recording medium separate from the memories 211 and 221. Such other computer-readable recording media may include computer-readable recording media such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc. In other embodiments, software components may be loaded into memory 211, 221 through communication modules 213, 223 that are not computer-readable recording media. For example, at least one program may be loaded into memory 211, 221 based on a computer program (such as the above-mentioned application) being installed by a file provided over network 170 by a developer or a file distribution system that distributes application installation files (such as the above-mentioned server 160, for example).

[0023] The processors 212, 222 may be configured to process computer program instructions by performing basic arithmetic, logic, and input / output operations. The instructions may be provided to the processors 212, 222 by the memories 211, 221 or the communication modules 213, 223. For example, the processors 212, 222 may be configured to execute instructions received according to program code stored in a storage device such as the memories 211, 221.

[0024] The communication modules 213 and 223 may provide a function for the electronic device 110 and the server 150 to communicate with each other via the network 170, or may provide a function for the electronic device 110 and / or the server 150 to communicate with other electronic devices (for example, the electronic device 120) or other servers (for example, the server 160). For example, a request generated by the processor 212 of the electronic device 110 in accordance with program code recorded in a recording device such as the memory 211 may be transmitted to the server 150 via the network 170 under the control of the communication module 213. Conversely, a control signal, instruction, content, file, etc. provided under the control of the processor 222 of the server 150 may be received by the electronic device 110 via the communication module 213 of the electronic device 110 via the communication module 223 and the network 170. For example, control signals, instructions, content, files, etc. from the server 150 received through the communication module 213 may be transmitted to the processor 212 or memory 211, and the content, files, etc. may be recorded on a recording medium (the non-transitory recording device described above) that the electronic device 110 may further include.

[0025] The input / output interface 214 may be a means for interfacing with the input / output device 215. For example, the input device may include a keyboard, a mouse, a microphone, a camera, etc., and the output device may include a display, a speaker, a haptic feedback device, etc. As another example, the input / output interface 214 may be a means for interfacing with a device that integrates input and output functions into one, such as a touchscreen. The input / output device 215 may be configured as a single device together with the electronic device 110. Furthermore, the input / output interface 224 of the server 150 may be a means for interfacing with an input or output device (not shown) that may be connected to or included in the server 150. As a more specific example, when the processor 212 of the electronic device 110 processes instructions of a computer program loaded in the memory 211, a service screen or content configured using data provided by the server 150 or the electronic device 120 may be displayed on a display via the input / output interface 214.

[0026] In other embodiments, the electronic device 110 and the server 150 may include more components than those shown in FIG. 2 . However, it is not necessary to explicitly illustrate most of the conventional components. For example, the electronic device 110 may be implemented to include at least some of the input / output devices 215 described above, and may further include other components such as a transceiver, a camera, various sensors, a database, etc. As a more specific example, if the electronic device 110 is an AI speaker, the electronic device 110 may be implemented to further include various components typically included in AI speakers, such as various sensors, a camera module, various physical buttons, buttons using a touch panel, input / output ports, and a vibrator for vibration. (End of quote)

[0027]

[0023] The machine will now be described. According to at least one embodiment, the distribution management device comprises a control unit, RAM, a storage unit, a graphics processing unit, a communication interface, and an interface unit, all of which are connected by an internal bus.

[0028] According to at least one embodiment, the control unit is composed of a CPU and a ROM. The control unit executes programs stored in the storage unit and controls the distribution management device. The RAM is the work area of ​​the control unit. The storage unit is a memory area for saving programs and data. The control unit reads and processes the programs and data from the RAM. The control unit processes the programs and data loaded into the RAM and outputs drawing commands to the graphics processing unit.

[0029] According to at least one embodiment, the graphics processing unit is connected to a display unit. The display unit has a display screen. When the control unit outputs a drawing command to the graphics processing unit, the graphics processing unit outputs a video signal for displaying an image on the display screen. Here, the display unit may be a touch panel equipped with a touch sensor. The touch panel of the display unit functions as an input unit.

[0030] According to at least one embodiment, the communication interface can be connected to a communication network wirelessly or via a wire, and can transmit and receive data to and from a server device via the communication network. Data received via the communication interface is loaded into RAM, and processed by the control unit. An external memory (e.g., an SD card) is connected to the interface unit.

[0031] According to at least one embodiment, the distribution management device is not particularly limited as long as it is a computer device having a display screen and an input unit. Examples of the distribution management device include conventional mobile phones, tablet devices, smartphones, and desktop or laptop personal computers. It may also be configured as VR goggles, i.e., a screen (or two display panels, one for each eye) attached to a frame (or headset) strapped or attached to the head. The distribution management device has an audio output unit.

[0032] According to at least one embodiment, the distribution management device is communicatively connected to the server device via a communication network, and is capable of transmitting or receiving information via the communication network.

[0033] According to at least one embodiment, the server device includes at least a control unit, a RAM, a storage unit, and a communication interface, which are connected to each other by an internal bus.

[0034] According to at least one embodiment, the control unit is composed of a CPU and a ROM, executes a program stored in the storage unit, and controls the server device. The control unit also has an internal timer for measuring time. The RAM is the work area of ​​the control unit. The storage unit is a memory area for saving programs and data. The control unit reads the program and data from the RAM and performs program execution processing based on information received from the distribution management device, etc.

[0035] This section explains AI. According to at least one embodiment, artificial intelligence includes machine learning, deep learning, generative AI, large-scale language models, LLMs, foundational models, and generative AI. Generative AI uses transformers and multiple mechanisms called attention. It employs self-supervised learning and extract prediction. In this case, the AI ​​can guess the next word. Given a sentence, it guesses the next word based on the sentence up to that point. A large number of supervised learning problems are created. These techniques result in an AI that can guess the next word. Generative AI can predict grammatical structure, topic connections, and the type of sentence a person with a certain writing style is likely to write. Furthermore, simply by guessing the next sentence, generative AI can learn the underlying structure, causal relationships, and knowledge. Generative AI is scalable, and the larger the number of parameters, the higher its accuracy. In conventional statistics and machine learning, overfitting can occur if the model parameters are too large compared to the data sample size. In LLM, the higher the number of parameters, the higher its accuracy. One generative AI has 175 billion parameters and is overlaid with supervised learning to ensure smooth dialogue. They are instructed not to say anything strange. They write reviews and act as call center operators.

[0036] According to at least one embodiment, a large-scale language model is a machine learning natural language processing model built non-comprehensively using large datasets and deep learning techniques. It is typically adapted to various natural language processing tasks, such as text classification and generation, sentiment analysis, text summarization, and question answering, using a technique called "fine-tuning," which trains the model on a specific task. According to at least one embodiment, self-supervised learning is similar to intrinsic human intelligence. When humans behave, they constantly predict the next event and the next input. In the process, they learn the structure of the external world. Predicting the next word is considered intrinsic intelligence and is similar to what the cerebral cortex does. According to at least one embodiment, a large-scale language model memorizes all input information but generalizes only to the extent necessary to predict the next word. It does not attempt to generalize all information from the beginning. A large-scale language model requires capacity to memorize information. This also requires parameters. According to at least one embodiment, a large-scale language model includes eight models with 175 billion parameters or 220 billion parameters.

[0037] According to at least one embodiment, we represent videos and images as a collection of visual patches, which are small units of data similar to text tokens in LLMs. Patches effectively represent models of visual data and serve as a highly scalable and effective representation for training generative models on a wide variety of videos and images. We convert videos into patches by first compressing the video into a low-dimensional latent space and then decomposing the representation into spatiotemporal patches.

[0038] According to at least one embodiment, a video compression network is a network that reduces the dimensionality of visual data, taking raw video as input and outputting a temporally and spatially compressed latent representation. An AI is trained on this compressed latent space and then generates video within this compressed latent space.

[0039] According to at least one embodiment, Spacetime Latent Patches extracts a set of spacetime patches that act as Transformer tokens given a compressed input video. The patch-based representation allows Sora to be trained on videos and images of various resolutions, lengths, and aspect ratios, and controls the size of the generated video by placing randomly initialized patches on an appropriately sized grid during inference.

[0040] According to at least one embodiment, the AI ​​is a diffusion model, trained to predict the original "clean" patch when fed a noisy patch (and conditioning information such as a text prompt). The AI ​​is a diffusion transformer, which exhibits remarkable scaling properties in a variety of domains, including language modeling, computer vision, and image generation. Diffusion transformers are also effective as video generation models. The AI ​​significantly improves sample quality as the training computational effort increases.

[0041] According to at least one embodiment, the AI ​​applies caption regeneration techniques to train a highly descriptive caption model and then use it to generate text captions for all videos in a training set. Training highly descriptive captions improves the fidelity of the text as well as the overall quality of the generated videos. GPT is leveraged to convert short user prompts into long, detailed captions that are sent to the model. This allows the AI ​​to generate high-quality videos that accurately follow the user prompts.

[0042] According to at least one embodiment, AI can perform vectorization in natural language processing according to the following process. First, a preprocessing step is performed on the given text. This preprocessing step involves removing unnecessary words, such as JavaScript code and HTML tags, from the text. These codes are used for displaying information on the Internet and are therefore not generally used in natural language processing. Next, the text is divided into words using morphological analysis. Morphological analysis is the process of classifying natural language sentences written in text into the smallest meaningful linguistic units. Morphological analysis tools available include "MeCab," "JUMAN," and "JANOME." Normalization involves unifying words with the same meaning, such as spelling variations, into a single word. Stop words are words that are not processed because they cannot be used in natural language processing. Examples of stop words include particles and auxiliary verbs, which have no meaning on their own. When calculating vectors, these words may be removed, leaving only meaningful words. Vectorization may also be performed without removing these stop words. Vectorization is the process of converting words, which are strings of characters, into vectors. Vectorization converts word data into numerical data. Converting words into vectors is done using methods called bag of words or distributed representations. Bag of words is a method of vectorizing a sentence using the number of words that appear in a given sentence. It focuses on how often a word appears in a sentence, and does not take into account the order of the words or sentences. Distributed representation is a vectorization method that focuses on the meaning of words. By vectorizing the meaning of words, it is possible to assign vectors that are similar to words with similar meanings or usages, and the relationships between words can also be expressed as vectors. Representation as vectors makes it possible to add and subtract word meanings from each other. In applied processing, natural language converted into numerical data can be used as input for machine learning. Specifically, vectorized natural language is input into a classifier to classify the sentences.Tools used here include "TensorFlow," "scikit-learn," and "PyTorch."

[0043] A step of acquiring text information is disclosed. In at least one embodiment, the device can be implemented as any one or more machines or computers described herein. A fixed terminal or a mobile terminal acquires text information from a user. As an example, the fixed terminal is a management terminal provided in a facility, and includes the machines, computers, and tablet terminals described herein. The management terminal is installed in facilities such as department stores, supermarkets, art galleries, museums, aquariums, zoos, live music venues, event halls, schools, public facilities, companies, and factories. The fixed terminal requests the user to enter the broadcast content in text. The request may be made by displaying an input form on the display of the fixed terminal or by issuing a voice notification from the fixed terminal. The user enters the broadcast content in text into the fixed terminal. The fixed terminal acquires the user's text information. In another embodiment, the mobile terminal may include an administrator terminal, a smartphone or tablet terminal owned by the administrator, or any such terminal with an application installed. The administrator launches the application on the mobile terminal from any location within the facility. The application is pre-installed on the mobile terminal. Alternatively, a display is displayed within the facility that describes how to install the application to be installed. The administrator launches the application. Based on the instructions of the application, the mobile terminal requests the administrator to input the broadcast content in text. The request may be made by displaying an input form on the display of the mobile terminal, or by issuing a voice message to that effect from the mobile terminal. The administrator inputs the broadcast content in text into the mobile terminal. The mobile terminal acquires the administrator's text information.

[0044] A step of requesting a TTS engine to generate voice data from text information is disclosed. In at least one embodiment, a facility operator stores one or more narrator voice model information in the device. The narrator voice model information includes information such as linguistic information, acoustic parameters, and voice quality features related to a speaker model for voice synthesis. For example, the information includes multiple narrator voice models, such as Akari 1, Akari 2, Akari 3, and Akari 10, linguistic information such as voice pitch, speaking rate, and emotional expression parameters corresponding to those models, and acoustic and voice quality features corresponding to that linguistic information. Each piece of information can be stored with associated information or concepts. For example, for the model Akari 1, information associated with the associations or concepts of female, young, cheerful, and energetic is stored. As described below, this information may be used for voice generation by the TTS engine.

[0045] The device requests the TTS engine to generate voice data from text information. In at least one embodiment, the operation of the TTS engine can take any of the above-described embodiments. For example, the TTS engine resides in one or more of a distributed internet service system, a fixed terminal, a mobile terminal, an application, and a server. The device provides text information acquired by the terminal to the TTS engine. The TTS engine acquires stored narrator voice model information. The TTS engine performs phonological analysis on the text information. Morphological analysis can be used on the text information. However, prosodic analysis may be more suitable for generating voice that includes emotional expressions. The TTS engine also acquires acoustic parameters for the selected narrator voice model. The TTS engine combines the phonological information of the text information with the acoustic parameters of the narrator voice model to synthesize a voice waveform. In another embodiment, the TTS engine performs deep learning-based voice synthesis on the text information. Through these tasks, the TTS engine generates natural-looking voice data from the text information. If appropriate voice data cannot be generated, the TTS engine can generate status information indicating that the voice data cannot be generated. For example, if an administrator enters the text "We are having a special sale today," the TTS engine will generate voice data using the narrator voice model "Akari 1," but other narrator voice models such as "Akari 2" and "Akari 3" can also be selected.

[0046] A step of transmitting the generated voice data to a speaker terminal is disclosed. In at least one embodiment, a computer or terminal serving as a speaker terminal is provided at a location in each area of ​​the facility or at a location where visitors gather. The terminal may be a fixed terminal or a mobile terminal. In the case of a mobile terminal, it may be a portable terminal. The device transmits the voice data generated by the TTS engine or the voice data with the highest quality to the speaker terminal. The speaker terminal plays the received voice data. The playback method involves outputting the voice data from an audio output device of the speaker terminal. After the voice data is output, visual information and text information related to the voice can be output along with it.

[0047] This disclosure discloses a step of acquiring confirmed voice data confirming delivery from the generated voice data. The device transmits voice data generated by the TTS engine or multiple voice data in order of highest quality to an administrator terminal. The administrator terminal presents this information to the administrator and receives an instruction to deliver the voice data. The presentation method may involve playing the voice data from the administrator terminal's speaker or displaying the waveform of the voice data on a display. The instruction to deliver may involve displaying a button to confirm delivery on the administrator terminal's display, and determining that pressing the button indicates an instruction to deliver. Alternatively, the administrator terminal acquires voice information from the administrator. The device requests AI to perform natural language processing on the voice information. The AI ​​may vectorize the voice information and confirm that the vector indicating delivery or the intention or emotion is similar to vector information, thereby determining that the instruction to deliver is correct. The device acquires the voice data for which the instruction to deliver has been received as confirmed voice data. Acquiring this information may involve generating new information as confirmed voice data or adding status information indicating confirmation to the voice data. As an example, the administrator terminal presents voice data such as "voice by Akari 1," "voice by Akari 2," and "voice by Akari 3," and the device acquires the voice data selected by the administrator as the final voice data.

[0048] A step of transmitting the confirmed audio data to a speaker terminal is disclosed. In at least one embodiment, the device transmits the confirmed audio data to the speaker terminal. The speaker terminal plays the received confirmed audio data. The playback method may include outputting information from an audio output device of the speaker terminal or outputting the confirmed audio data via an in-house public address system. After the confirmed audio data is output, visual information or text information related to the audio may be output.

[0049] A step of requesting an AI to generate text information based on context information is disclosed. In at least one embodiment, a facility operator stores one or more pieces of context information in a device. The context information includes facility information, time information, weather information, event information, etc. As an example, the context information is linguistic information about a department store, such as business hours, floor layout, product categories, ongoing events, and patterns of busy times. The context information also includes images and videos corresponding to the linguistic information. Each piece of information can be stored with associated information or concept-evoking information. As an example, information about morning opening hours can be stored with information that evokes associations or concepts such as greetings, liveliness, and freshness. This information may be used for AI analysis, as described below.

[0050] The device requests the AI ​​to generate text information based on the context information. In at least one embodiment, the device provides the AI ​​with context information acquired on the terminal. The AI ​​acquires stored context information. The AI ​​vectorizes the context information. Bag of Words can be used for the context information. However, distributed representations may be more suitable for analyzing the status of a facility. The AI ​​also vectorizes the template information. The AI ​​compares the vectors of the context information with those of the template information, identifies template information with vectors close to those of the context information, and determines the order of their similarity. In another embodiment, the AI ​​performs various natural language processing tasks, such as text classification, sentiment analysis, and text summarization, on the context information. Based on these tasks, the AI ​​generates appropriate text information from the context information. If appropriate text information cannot be generated, the AI ​​can generate status information indicating that it will not be generated. For example, if the context information indicates that the facility is a "department store" and the time is "9:50 AM," the AI ​​would generate text information such as "We will be opening soon. We look forward to seeing you today."

[0051] A step of transmitting audio data corresponding to a plurality of areas to a speaker terminal in each area is disclosed. In at least one embodiment, an apparatus transmits the audio data corresponding to a plurality of areas to a speaker terminal in each area. Each speaker terminal plays the received audio data in its respective area. The playback method includes outputting information from an audio output device of each speaker terminal. After outputting the audio data for each area, information related to that area can be output along with the audio data.

[0052] This document discloses a step for recording the usage history of audio data. The device records the distribution history of the audio data. Recorded information includes the distribution time, distribution area, narrator voice model used, text content, playback time, and number of plays. The administrator terminal presents this information to the administrator so that the administrator can check the usage history. The method for presenting the information is to display the usage history on the administrator terminal display or output it as a report. The method for checking the usage history includes displaying a dashboard on the administrator terminal display and visualizing it with graphs or charts. The device saves the usage history data as data for analysis. Saving can mean generating new information as usage history data or adding additional information to existing data. As an example, the administrator terminal presents the usage history such as "Light 1: used 100 times" and "Light 2: used 50 times," and the device records the usage history data.

[0053] A step of calculating a remuneration based on a usage history is disclosed. In at least one embodiment, the device calculates the remuneration based on the usage history. A usage cost is set for each narrator voice model, and the remuneration amount is calculated by multiplying the number of uses by the cost. The remuneration calculation method may be monthly, quarterly, or annual. The calculated remuneration is paid to the rights holder of the narrator voice.

[0054] A step of specifying a delivery time or interval for the audio data is disclosed. In at least one embodiment, the facility operator sets a delivery schedule for the audio data in the device. The schedule can be set by specifying a time (e.g., 9:00 AM, 3:00 PM, and 7:00 PM every day) or an interval (e.g., every 15 minutes, every 30 minutes, or every hour). The device delivers the audio data according to the set schedule. Schedule management can be done using AWS EventBridge (registered trademark), a Cron job, or a dedicated scheduler.

[0055] The device determines whether the distribution time has arrived according to the schedule. To make this determination, the device uses an internal timer to monitor the current time and compares it with the set distribution time. When the distribution time arrives, the device automatically transmits the audio data to the speaker terminal. If an interval is specified, the device calculates the elapsed time from the previous distribution time and performs the next distribution when the set interval is reached.

[0056] A step of storing business-specific template information is disclosed. In at least one embodiment, the device stores business-specific template information. The template information is a template for broadcast messages specialized for each business. Templates for department stores and supermarkets include opening announcements, closing announcements, time sale announcements, floor guides, etc. Templates for art galleries and museums include exhibit guides, entrance precautions, closing notices, etc. Templates for aquariums and zoos include show start announcements, feeding time announcements, animal introductions, etc.

[0057] The device identifies the facility's business type and automatically selects a corresponding template. Administrators can then choose from the selected templates and customize them as needed. The templates have variable sections that allow for dynamic insertion of date, time, product name, event name, etc.

[0058] This disclosure discloses a method for analyzing broadcast effectiveness using AI. In at least one embodiment, a device requests an AI to analyze broadcast effectiveness. The AI ​​receives broadcast history data, facility sales data, visitor count data, and other data as input. The AI ​​analyzes correlations between these data and determines which broadcasts were effective at which times.

[0059] For example, if sales in a particular department increase after a sale announcement is broadcast, the AI ​​will learn this correlation. The AI ​​will use machine learning algorithms to recognize patterns and optimize future distribution strategies. The analysis results will be provided to managers as recommendations.

[0060] A step of proposing an optimal broadcast time is disclosed. In at least one embodiment, the device proposes the optimal broadcast time based on the analysis results of the AI. The AI ​​comprehensively judges the past broadcast effectiveness, facility congestion patterns, event schedules, etc.

[0061] The suggested delivery times are those that are most likely to maximize effectiveness. For example, optimal timings for restaurant floor guides during lunchtime, evening sales information, and final announcements before closing time are suggested. Administrators can choose to accept the suggestions or adjust them manually.

[0062] Creating a custom voice model is disclosed. In at least one embodiment, a user can create a custom voice model by registering their own voice. The user reads and records a specified text (e.g., approximately 50 standard phrases). The recording is uploaded in WAV or MP3 format.

[0063] The device extracts acoustic features from the uploaded audio data, including fundamental frequency, spectral envelope, and phonetic duration. Based on these features, it uses a deep learning model to generate a new narrator voice model. The resulting custom model can then be used by the TTS engine.

[0064] IoT speaker integration is disclosed. In at least one embodiment, the device integrates with various IoT speakers, including Amazon Echo®, Google Home®, Apple HomePod®, and custom Raspberry Pi®-based speakers.

[0065] The devices connect through each IoT speaker's API. For Amazon Echo, it uses the Alexa Skills Kit, for Google Home it uses Actions on Google, and for Apple HomePod it uses the HomeKit API. Audio data is sent in a format optimized for each platform. When multiple IoT speakers are played in sync, the devices use a time synchronization protocol to adjust the timing.

[0066] Cloud sync and backup are disclosed. In at least one embodiment, the device provides cloud-based data sync functionality. All settings, schedules, and audio data are stored in cloud storage. Cloud services used include AWS S3®, Google Cloud Storage®, or Azure Blob Storage®.

[0067] Synchronization is performed in real time whenever changes are made. When offline, changes are saved to a local cache and synchronized when the system returns online. Backups are automatically performed daily, weekly, and monthly. Administrators can restore from backups at any point in time.

[0068] Security and access control are disclosed. In at least one embodiment, the device implements defense-in-depth security. All communications are encrypted with SSL / TLS 1.3. Data at rest is encrypted with AES-256-GCM.

[0069] Role-based access control (RBAC) is used for access control. Multiple permission levels can be set, including administrator, operator, and viewer. Multi-factor authentication (MFA) is mandatory for authentication. MFA can be achieved via SMS, an authentication app, or biometric authentication. Audit logs record all access and operations and are used to detect unauthorized access.

[0070] External system integration via API integration is disclosed. In at least one embodiment, the device provides API integration with external systems. The provided APIs are both RESTful APIs and GraphQL APIs. Through the APIs, functions such as voice generation, schedule management, and distribution control can be used externally.

[0071] External systems that can be linked include POS systems, inventory management systems, customer relationship management (CRM), event management systems, weather information services, and traffic information services. For example, by linking with a POS system, it is possible to dynamically generate broadcast content based on sales data. By linking with a weather information service, it is possible to automatically change broadcast content according to the weather.

[0072] Fault response and failover are disclosed. In at least one embodiment, the device has a redundant configuration to ensure high availability. It consists of two systems: a main system (primary) and a secondary system (secondary). If a fault occurs in the main system, it automatically switches over to the secondary system.

[0073] Failover is monitored by a health check function. The health check is performed every 5 seconds, and if it fails three times in a row, failover will be performed. In the event of a network failure, a fallback function will be activated, which will play audio data from the local cache. The local cache stores the last 7 days' worth of audio data.

[0074] A mobile application is disclosed. In at least one embodiment, the functionality of the device is also provided as a mobile application. Supported platforms are iOS® 13 and later and Android® 10 and later. The application is developed using React Native®.

[0075] Key features of the mobile application include audio preview, schedule management, remote broadcast control, push notifications, and offline mode. Push notifications are used for important broadcast schedules, system alerts, maintenance notices, etc. In offline mode, basic functionality is available and automatically syncs when the device is back online.

[0076] Usage analysis and reporting capabilities are disclosed. In at least one embodiment, the device provides detailed usage analysis capabilities. Analysis items include number of streams, play time, frequency of use by narrator, stream patterns by time of day, and stream statistics by area.

[0077] The analysis results are visualized on a dashboard. Graph formats include line graphs, bar graphs, pie charts, and heat maps. Reports can be exported in formats such as PDF, Excel, and CSV. Scheduled reports are automatically generated daily, weekly, and monthly and sent to a specified email address.

[0078] Multilingual support is disclosed. In at least one embodiment, the device supports speech generation in multiple languages, including Japanese, English, Chinese (Simplified and Traditional), Korean, Spanish, and French. Narrator voice models are provided for each language.

[0079] A TTS engine optimized for each language is used. For example, VOICEVOX (registered trademark) is used for Japanese, Amazon Polly (registered trademark) for English, and Microsoft Azure Speech (registered trademark) for Chinese. A translation function is also provided, allowing text created in one language to be automatically translated into other languages. Google Translate API (registered trademark) or DeepL API (registered trademark) is used for translation.

[0080] An emergency broadcast function is disclosed. In at least one embodiment, a device is provided with an emergency broadcast function. An emergency broadcast is broadcast with the highest priority, interrupting the normal schedule. Triggers for an emergency broadcast include manual activation, an alert from an external system, or sensor detection.

[0081] Emergency broadcasts include fire warnings, earthquake warnings, evacuation instructions, security alerts, and medical emergencies. Emergency broadcasts are simultaneously streamed to all speaker devices. The volume is automatically set to maximum. Emergency broadcast history is stored for five years in accordance with compliance requirements.

[0082] Fee calculation and billing management are disclosed. In at least one embodiment, the device provides fee calculation and billing management functionality. Fee structures include fixed monthly plans, pay-as-you-go plans, and hybrid plans. Pay-as-you-go plans are calculated based on the number of audio streams, delivery time, data usage, etc.

[0083] Invoices are automatically generated monthly. Payment methods include credit card, bank transfer, and direct debit. A detailed usage report is attached to the invoice. A fee alert function notifies you when a set amount is reached.

[0084] An outline of the above-described embodiment will be disclosed below.

[0085] 1. A method for managing audio distribution, comprising: obtaining text information; requesting a TTS engine to generate speech data from the text information; transmitting the generated voice data to a speaker terminal at a specified time or interval; A method of having

[0086] According to the present disclosure, at least, there is the convenience of being able to automatically generate and distribute audio broadcasts according to the facility and time period, and further, there is industrial applicability and an advantage that the facility operator can broadcast audio at an appropriate time without any human intervention.

[0087] 1. A method for managing audio distribution, comprising: obtaining context information including facility information and time information; Requesting the AI ​​to generate text information based on the context information; requesting a TTS engine to generate speech data from the text information; transmitting the generated voice data to a speaker terminal at a specified time or interval; A method of having

[0088] This disclosure provides at least the convenience of automatically generating broadcast content optimized for a facility or situation using AI, and also has industrial applicability and advantages in that it allows facility operators to reduce the work of creating broadcast content.

[0089] 1. A method for managing audio distribution, comprising: acquiring facility information including a plurality of area information; obtaining text information corresponding to each area; requesting a TTS engine to generate speech data from each piece of text information; transmitting the generated voice data to a speaker terminal in a corresponding area; A method of having

[0090] According to the present disclosure, at least, there is the convenience of being able to simultaneously manage different broadcasts in multiple areas in a large-scale facility. Furthermore, there is industrial applicability and an advantage in that facility operators can provide information optimized for each area.

[0091] 1. A method for managing audio distribution, comprising: obtaining text information; requesting a TTS engine to generate speech data using a model selected from a plurality of narrator voice models; Recording a usage history of the voice data; calculating a reward based on the usage history; A method of having

[0092] The present disclosure provides at least the convenience of being able to appropriately manage remuneration according to the amount of use of the narrator's voice. Furthermore, the audio provider has industrial applicability and advantages in that usage records are automatically managed and remuneration is calculated.

[0093] Any of the above audio distribution management methods, Requesting the AI ​​to analyze the distribution history of the voice data; proposing an optimal delivery time based on the analysis results; Further, the method

[0094] According to the present disclosure, at least, there is the convenience of being able to propose optimal broadcast timing by learning from past distribution effects. Furthermore, there is industrial applicability and an advantage that facility operators can effectively communicate information.

[0095] Any of the above audio distribution management methods, storing template information for each business type; generating text information from a template according to the type of business of the facility; Further, the method

[0096] The present disclosure provides at least the convenience of being able to easily create broadcast content specialized for a particular industry, and further has industrial applicability and advantages in that facility operators can carry out appropriate broadcasting without specialized knowledge.

[0097] In at least one embodiment, in the "step of acquiring confirmation voice data confirming the distribution from the generated voice data," the "method of receiving an instruction to distribute" by the device includes the following forms: The facility operator defines motion information related to one or more physical motions in the device and stores it as specific motion information. The motion information includes information such as linguistic information, images, and videos accompanying the physical motion. For example, for the physical motion of "tapping the screen twice," the information includes the linguistic information and information such as images or videos showing examples of the motion. Physical motions include all gestures such as swiping, pinching, and long presses. The specific motion information is information that the facility operator registers in the device from the motion information. The administrator terminal acquires the administrator's motion information. One method of acquisition is to detect the administrator's motion using a touch sensor on the administrator terminal.

[0098] The device requests the AI ​​to determine whether the movement information is similar to the specific movement information. The AI ​​represents the patterns of the acquired movement information as small data units similar to the text tokens of an LLM. The AI ​​is a network that reduces the dimension of the movement patterns for the movement information, receiving raw movement data as input and outputting a latent representation that is temporally and spatially compressed. The AI ​​also outputs a latent representation for the specific movement information in a similar manner. The AI ​​compares the latent representation of the movement information with the latent representation of the specific movement information to determine whether there is a similarity. For this determination, the facility operator can store in the device a similarity threshold for determining similarity. If the AI ​​determines that the movement information is similar to the specific movement information, it can create data or status information indicating the similarity.

[0099] The device acquires the voice data that it has been instructed to distribute as confirmed voice data, provided that data or status information indicating similarity has been generated. Acquiring can mean generating new information as confirmed voice data, or adding status information indicating confirmation to the voice data. As an example, the device presents "voice by Akari 1" to the administrator as voice data. The administrator taps the screen twice. The device determines that the administrator's action information is specific action information, and acquires "voice by Akari 1" as confirmed voice data.

[0100] In at least one embodiment, the "step of selecting appropriate text information from template information for each industry" can employ the above-described embodiment as the "method of receiving a selection instruction" by the device. That is, the facility operator defines motion information related to one or more physical motions in the device and stores it as specific motion information. The manager terminal acquires the manager's motion information. The AI ​​determines whether the motion information is similar to the confirmed motion information. If data or status information indicating similarity is created, the device acquires the template information for which the selection instruction was received as confirmed template information. "Acquisition" can mean generating new information as confirmed template information or adding status information indicating confirmation to the template information. As an example, the device presents a "store opening announcement template" to the manager as template information. The manager double-tap the screen. The device determines that the manager's motion information is specific motion information and acquires the "store opening announcement template" as confirmed template information.

[0101] In at least one embodiment, a device or terminal for detecting operational information of the administrator may be provided separately from the administrator terminal or used in combination with the administrator terminal in the above-described embodiment.

[0102] In at least one embodiment, instead of the action information and specific action information in the above embodiment, language information and specific language information may be used as the information to be determined. That is, the facility operator defines language information corresponding to one or more passwords in the device and stores it as specific language information. The administrator terminal acquires the administrator's language information. Acquisition may be performed using a microphone on the device or terminal. The AI ​​determines whether the language information is similar to the specific language information using natural language processing. The device requests the AI ​​to make this determination. As already described, the natural language processing method may be one or more of the methods disclosed herein. When data or status information indicating that the language information is similar to the specific language information is created by the AI ​​or the device, the device acquires the voice data or template information instructed to be distributed as confirmed voice data or confirmed template information. Acquiring may involve generating new information as confirmed voice data or adding status information indicating confirmation to the voice data. As an example, the device presents "voice by Akari 2" to the administrator as voice data. The administrator speaks a password determined by the facility operator (for example, the facility's trademark, mark, brand name, nickname, or special call). The device determines that the administrator's language information is specific language information and acquires the "voice from Akari 2" as confirmed voice data.

[0103] An outline of the above-described embodiment will be disclosed below.

[0104] Any of the above audio distribution management methods, The step of acquiring confirmation voice data confirming the distribution includes: storing, by the device, specific motion information relating to one or more physical motions; Requesting the AI ​​to determine whether the motion information is similar to specific motion information; Further includes:

[0105] Any of the above audio distribution management methods, The step of acquiring confirmation voice data confirming the distribution includes: When the AI ​​determines that the motion information is similar to the specific motion information, the voice data is determined to be final voice data; Further includes:

[0106] According to the present disclosure, at least the administrator can confirm distribution by gesture, which is easier than the conventional technique of clicking a button, and thus has industrial applicability and advantages in that it improves the convenience of using the device or system.

[0107] The invention according to the present disclosure may have at least one of the above-described effects.

[0108] The basic configuration of the voice analysis and personal adjustment functions is disclosed. In at least one embodiment, the device acquires reference voice data and performs detailed analysis of its voice characteristics. The reference voice data is a voice sample recorded by a facility manager, store owner, or specific speaker, and is saved in a common audio format such as WAV, MP3, or AAC. The device requests the AI ​​to analyze the speaking rate (number of characters or syllables per minute), pitch (fundamental frequency F0), and intonation pattern (pattern of tone change). The speaking rate analysis calculates the accurate speaking rate from the actual speaking time excluding silent intervals, and quantifies individual characteristics based on a standard speaking rate of 300-400 characters per minute. The pitch analysis extracts the fundamental frequency using cepstrum analysis or the YIN algorithm and compares it with the average for men (100-150 Hz) and women (200-250 Hz) to identify individual characteristics.

[0109] This paper describes an analysis of intonation patterns. In at least one embodiment, the device learns intonation change patterns for each type of sentence (declarative, interrogative, and exclamatory). For Japanese, factors such as rising or falling intonation at the end of a sentence and the location of the accent nucleus are important. The device automatically generates annotations based on the Tones and Break Indices (ToBI) system to identify prosodic boundaries and accent phrase divisions. When multiple reference speech data are available, the device uses the Dynamic Time Warping (DTW) algorithm to align speech patterns on the time axis and separate common features and individual differences. The mean calculation employs trimmed mean and Winsorized mean to remove outliers, resulting in a more stable individual profile. The standard deviation calculation quantifies the stability and expressive range of a speaker's voice and is used to improve the naturalness of synthesized speech.

[0110] This document discloses an emotional state analysis function. In at least one embodiment, the device analyzes both acoustic and linguistic features to identify emotions from speech. Acoustic features include pitch variation coefficient, sound pressure level variation, spectral centroid, first and second formant frequencies, jitter, and shimmer. These features are efficiently calculated using speech processing libraries such as openSMILE and LibROSA. Emotion categories are expressed in two dimensions: arousal and valence, based on Russell's circumplex model of emotions. Joy is classified as high arousal and positive valence, while sadness is classified as low arousal and negative valence. Deep learning models (LSTM, Transformer) are used to track changes in emotions over time and predict appropriate emotional expressions based on the context. The accuracy of emotion recognition is verified using data labeled by multiple annotators.

[0111] A dialect and accent detection process is disclosed. In at least one embodiment, the device detects regional pronunciation patterns from speech and incorporates them into speech synthesis. Dialect detection involves identifying phonological changes (e.g., Kansai dialect's "nai" to "hen"), differences in accent type (Tokyo, Keihan, unaccented), and regional intonation differences. The device compares the results with a Japanese dialect corpus and a regional speech database to estimate the speaker's region of origin. The degree of accent is quantified by the acoustic distance from standard Japanese (Euclidean distance of Mel-frequency cepstrum coefficients). The detected dialect features are incorporated into the HMM and DNN models of a parametric speech synthesis system to generate natural-sounding speech with regional characteristics. However, official broadcasts also offer a standardization option, allowing the intensity of the dialect to be adjusted from 0 to 100%. In multilingual environments, American, British, and Australian accents of English are also handled similarly.

[0112] The present invention discloses a method for analyzing and incorporating breathing patterns. In at least one embodiment, the device detects natural breathing positions and incorporates them into the synthesized speech. Breathing detection involves comprehensively assessing sudden drops in sound pressure level, broadband noise components in the spectrum, and the length of silent intervals. Syntactic breathing positions (at the end of a sentence, at a punctuation mark) and physiological breathing positions (in the middle of a long phrase) are distinguished and different breathing sounds are inserted for each. The duration of breathing sounds is typically set to 200-500 ms, and the sound pressure is set to approximately -20 dB below the speech sound. Breathing intervals (average 3-5 seconds) are modeled based on individual lung capacity and speaking style. Changes in breathing patterns due to emotional states (short breathing when excited, deep breathing when calm) are also taken into account. End-to-end speech synthesis systems such as WaveNet and Tacotron automatically learn breathing patterns using training data containing breathing sounds.

[0113] Methods for removing personally identifiable information and anonymization are disclosed. In at least one embodiment, a device removes personally identifiable information from speech to protect privacy. Voiceprint anonymization combines pitch shifting (±20%), formant shifting (±15%), and speech rate conversion (±10%) to prevent the original speaker from being identified. However, to maintain the naturalness of the speech, the conversion parameters are limited to a perceptually sound range. Spectral envelope smoothing removes individual-specific vocal tract shape information while preserving phonological information. Differential privacy is applied, and Gaussian noise is added to speech features to make it difficult to identify individuals while maintaining statistical properties. Three anonymization levels (light, medium, and strong) are available to choose from depending on the application. Light is recommended for internal use, while strong is recommended for public use.

[0114] A multi-speaker voice feature blending function is disclosed. In at least one embodiment, the device combines the voice features of multiple speakers to generate new synthesized speech. The blending process extracts each speaker's voice feature vector (i-vector, x-vector) and generates a new feature vector by weighted averaging. The blending ratio can be adjusted from 0 to 100% using a slider UI, and multiple speakers can be mixed, such as "Speaker A: 30%, Speaker B: 50%, Speaker C: 20%." Voice morphing technology smoothly interpolates spectral features in the frequency domain to prevent unnatural sound quality degradation. For applications requiring gradual changes in the age or gender of the voice, it can generate voices that change continuously from a young woman to an elderly man. The naturalness of the blended results is verified using MOS (Mean Opinion Score), with a score of 3.5 or higher being the quality standard.

[0115] Dynamic adjustment based on time of day and crowding level is disclosed. In at least one embodiment, the device automatically adjusts voice parameters based on environmental conditions. During the morning hours (6:00-9:00 AM), the device automatically adjusts to a slightly higher pitch and clear pronunciation to promote alertness, during the daytime (12:00-15:00 PM) to a standard setting, and in the evening (18:00-21:00 PM) to a more subdued tone. Crowd levels are estimated from the power spectral density of environmental sounds captured by a microphone array, and clarity enhancement processing is applied: normal volume for noise levels below 60 dB, +3 dB amplification for noise levels above 70 dB, and +6 dB amplification for noise levels above 80 dB. The Lombard effect is mimicked, and speech rate is automatically reduced by 5-10% and consonants are emphasized in noisy environments. During late-night hours (after 10:00 PM), the device automatically attenuates the volume by -6 dB and suppresses low-frequency components to consider neighboring noise. These adjustments can be customized based on the facility's business patterns.

[0116] A basic architecture for voice style modulation is disclosed. In at least one embodiment, a device implements a multi-layer processing system that receives basic voice data as input and modulates it into a voice style corresponding to the facility type. The modulation process is performed at three levels: acoustic level (pitch, volume, speaking rate), prosodic level (intonation, rhythm, accent), and timbre level (formant, spectral tilt). Style templates for each facility type are defined in JSON format and managed as parameter sets. A department store style is quantified as "pitch_ratio: 1.1, speed_ratio: 0.9, formant_shift: 1.05, reverb_level: 0.3," and a supermarket style is quantified as "pitch_ratio: 1.15, speed_ratio: 1.1, formant_shift: 1.0, reverb_level: 0.1." Style conversion is performed using the WORLD speech analysis and synthesis system and the STRAIGHT algorithm while minimizing sound quality degradation.

[0117] Detailed specifications for facility-specific voice styles are disclosed. In at least one embodiment, the department store style creates a sophisticated and calm impression by raising the pitch by 5-10% from the reference pitch, slowing the speech rate by 10-15%, and softening and lowering the pitch at the end of sentences. A 5% increase in formant frequency achieves clearer, more refined sound quality. The supermarket style expresses liveliness and friendliness by increasing the pitch range by 20%, increasing the speech rate by 5-10%, and adopting a bouncy intonation with a slight rise at the end of sentences. The theme park style creates an exciting and fun atmosphere by expanding the pitch range by 30%, frequently using exclamation marks, and emphasizing clear consonants that are easy for children to understand. The art museum style, designed for a tranquil environment, reduces the volume by -3dB, slows the speech rate (-20%), and uses a calm, lower pitch. The effectiveness of each style is verified through A / B testing.

[0118] A background music mixing process is disclosed. In at least one embodiment, the device automatically adjusts the optimal balance between audio and background music. The ducking process gradually attenuates the volume of the background music 0.5 seconds before the audio begins, attenuates by -12 to -18 dB during audio, and restores the original volume over 0.5 seconds after audio ends. To avoid frequency masking, a notch filter is applied to the background music in the fundamental frequency band (100-300 Hz) and the first formant band (700-1000 Hz). Elegant classical music is mixed in a 7:3 audio:background music ratio in a department store, light pop music in an 8:2 ratio in a supermarket, and vibrant marches in a 6:4 ratio in a theme park. Sidechain compression prevents competition between audio and background music pressure levels and maintains intelligibility. In compliance with the loudness standard (ITU-R BS.1770), the integrated loudness value is normalized to -23 LUFS ±1 dB.

[0119] This document discloses the application of acoustic effects and spatial presentation. In at least one embodiment, the device performs effect processing appropriate for the acoustic characteristics of the facility. Reverb is calculated based on the facility's volume and reverberation time. A rich reverberation time of 1.5-2.0 seconds is added to a large department store space, a natural reverberation time of 0.8-1.2 seconds is added to a medium-sized supermarket space, and a short reverberation time of 0.3-0.5 seconds is added to a small store. Early reflections are generated with a delay of 5-50 ms based on the distance from the wall. A delay effect creates a sense of spaciousness by layering short delays of 10-30 ms. A chorus effect adds thickness and warmth to the sound and reduces mechanical impressions. Equalizer processing improves clarity by attenuating low frequencies (below 100 Hz) by -3 dB and enhances high frequencies (above 8 kHz) by +2 dB to enhance realism. These effects are processed in real time by a DSP chip.

[0120] This document describes a method for measuring and optimizing the acoustic characteristics of a facility. In at least one embodiment, the device automatically measures the acoustic characteristics of a facility at installation and determines optimal sound processing parameters. A measurement sweep signal (20 Hz-20 kHz) is played, and the reverberation time (RT60), initial delay time (ITDG), and intelligibility indices (D50, C80) are calculated from the impulse response picked up by a microphone. Acoustic impairments (echo, flutter echo, standing waves) are detected and corrected using an adaptive filter. Noise levels are measured by equivalent continuous acoustic pressure (LAeq) for each time period, and the volume is automatically adjusted to maintain a signal-to-noise ratio of 15 dB or higher. When directional speakers are used, beamforming technology is used to optimize sound delivery to specific areas. By linking with architectural acoustic simulation software (EASE, ODEON), the device also proposes optimal speaker placement. Measurement results are stored in the cloud and can be used for initial setup in similar facilities.

[0121] Layering and gradual transitions of audio styles are disclosed. In at least one embodiment, the device provides the ability to smoothly transition between multiple audio styles over time. Starting with a calm style during opening hours, the style gradually shifts to a more energetic style toward peak hours, and then returns to a more subdued style toward closing time. The transition is performed using linear interpolation or an S-curve to prevent abrupt changes. The excitement level gradually increases 30 minutes before the event begins, reaches a peak during the event, and gradually returns to normal mode after the event ends. Automatic adjustment based on season and weather selects bright tones for sunny days, muted tones for rainy days, and a more vibrant style for the end of the year. Up to five style layers can be layered, and the transparency of each layer can be adjusted (0-100%) to blend them. The morphing time can be set from 5 to 60 seconds.

[0122] This document discusses competitive differentiation and brand identity. In at least one embodiment, the device generates a unique audio style that differentiates a facility from its competitors. The competitive analysis function collects and analyzes the audio characteristics (sound pressure level, frequency response, speech rate) of surrounding facilities to create distinct acoustic spaces. When matching brand colors with audio, warm colors emphasize low frequencies, while cool colors emphasize high frequencies for a clearer feel. The rhythmic pattern of the company slogan is reflected in the audio, enhancing brand recognition. An audio logo is inserted at the beginning and end of the audio to achieve auditory branding. The custom style creation tool allows users to adjust parameters in real time on the GUI and listen to the resulting sound. The created style is unique enough to be registered as a trademark. Success stories are shared as industry best practices.

[0123] This document discloses methods for measuring effectiveness and optimizing styles. In at least one embodiment, the device quantitatively measures the effectiveness of voice styles and continuously optimizes them. The effectiveness of styles is evaluated through correlation analysis with customer length of stay, purchase rate, and repeat visit rate. Emotion analysis using a facial recognition camera quantifies changes in customers' facial expressions (smile rate, satisfaction) after listening to the voice. Customer reactions (utterances such as "easy to listen to" or "noisy") are collected through voice recognition and used as feedback. A / B testing applies different styles depending on the time of day or day of the week, and statistical significance (p-value < 0.05) is verified. Machine learning (random forest, gradient boosting) is used to find the optimal parameter combination. Time series analysis taking seasonal fluctuations and long-term trends into account predicts the optimal style in the future. The effects of improvements are visualized in monthly reports. Group C: Delivery timing support function (corresponding to claims 28-37)

[0124] This paper discloses a method for optimizing delivery times through text content analysis. In at least one embodiment, the device analyzes the semantic content of generated text and automatically suggests the optimal delivery time. Natural language processing is used to categorize text into categories such as product introductions, event announcements, warnings, and ambient music. Product introductions are delivered during peak sales times for the relevant product, 30 minutes before meals for food products, and peak store traffic times for clothing products. Event announcements are broadcast at three different intensity levels: 60 minutes, 30 minutes, and 10 minutes before the event begins. Time-limited sale information is broadcast at maximum volume just before the start, every 5 minutes while the sale is ongoing, and last-minute announcements are made 10 minutes before the end. Machine learning models (BERT, GPT) calculate the urgency score (0-100) of each text. Score 80 or above is delivered immediately, 50-79 within 10 minutes, and below 50 is included in the regular schedule. Scheduling is also performed taking into account weekday characteristics (quiet start on Mondays, busy weekends).

[0125] This document discloses methods for determining urgency and managing priorities. In at least one embodiment, the device determines urgency on multiple levels based on keywords and context within a sentence. Explicit urgency terms such as "urgent," "urgent," and "immediately" are classified as the highest priority, terms like "today only" and "limited time sale" are classified as high priority, terms like "limited time only" and "this week" are classified as medium priority, and regular announcements are classified as low priority. The priority score is calculated based on the frequency of occurrence of urgency terms (0-10 points), the time remaining until the deadline (0-10 points), the scope of impact (whole building / floor / sales area: 0-10 points), and the monetary impact (discount rate: 0-10 points). When multiple delivery requests conflict within the same time slot, they are queued in descending order of priority score, with low-priority requests automatically postponed to the next time slot. However, safety-related content takes priority over all other requests and is overridden for delivery. The priority threshold can be customized according to facility policies.

[0126] A time-sequential delivery plan for related text is disclosed. In at least one embodiment, the device delivers multiple semantically related texts in an effective order. Using a storytelling approach, a delivery plan is created with a three-stage structure: interest generation, detailed explanation, and action promotion. For example, in the case of a new product, the following is delivered every 10 minutes: "New, popular products have arrived" (interest generation), "Features of XX are..." (detailed explanation), and "Purchase at the special sales counter on the third floor" (action promotion). Co-occurrence analysis identifies related products and determines the delivery order to maximize cross-selling opportunities. Delivery intervals are set at 5, 15, and 60 minutes after the initial delivery based on the psychological memory retention curve (Ebbinghaus's forgetting curve). Long texts are divided into multiple short texts, and delivery is performed taking into account the listener's attention span (average 30 seconds). The delivery order can also be manually adjusted using drag and drop.

[0127] This paper describes a method for optimizing delivery times using historical data learning. In at least one embodiment, the device uses machine learning to predict the optimal delivery time based on past delivery performance data. The learning data includes delivery time, text content, weather, day of the week, number of customers, and impact on sales. Time series prediction models (LSTM, Prophet) are used to predict delivery effectiveness for each time period. For example, patterns such as "coffee promotions increase sales by 20% between 7:00 and 9:00 a.m. on weekdays, and by 15% between 2:00 and 3:00 p.m." Seasonal decomposition is used to extract daily, weekly, monthly, and annual periodic patterns. Outlier detection is used to isolate the effects of special events (typhoons, large-scale sales) and improve learning accuracy for normal patterns. Transfer learning accelerates learning by utilizing data from similar facilities. Prediction accuracy is evaluated using MAPE (Mean Absolute Percentage Error), with a target error of 15% or less. Learning results are visualized using heat maps.

[0128] Conflict avoidance and resource management are disclosed. In at least one embodiment, the device automatically adjusts conflicts between audio streams within the same time slot. Based on psychoacoustics, overlapping multiple audio streams within a 30-second period reduces listener comprehension by 40%, so the minimum interval is set to 30 seconds. Content similarity is calculated using cosine similarity, and content with a similarity of 0.8 or higher is separated by a minimum of 5 minutes. Conflicting content (price increase and price decrease, opening and closing) is prohibited from simultaneous streaming through logical checks. When speaker zones overlap, audio is streamed using time-division multiplexing (TDM) to prevent acoustic interference. When bandwidth is limited, the audio bitrate is dynamically adjusted (from 128 kbps to 64 kbps) to ensure multiple streams. When resource utilization exceeds 80%, low-priority streaming is automatically moved to the next time slot. These adjustments are performed by a real-time scheduler.

[0129] This document discloses dynamic adjustments based on external information. In at least one embodiment, the device dynamically adjusts delivery times in conjunction with external information such as weather, traffic, and events. The device obtains the probability of rain from a weather API, and if the probability is 70% or higher, triples the frequency of announcements about the umbrella section. It obtains delay information from a traffic information API, and if there is a delay of 30 minutes or more on major routes, postpones opening sale announcements by 30 minutes. It links with a local event calendar, and on days when there is a nearby sporting event, it synchronizes announcements about related products with the end of the game. It analyzes social media trends to immediately start delivering information about popular products. When a power outage or disaster forecast is received, it prioritizes the delivery of information about disaster prevention supplies. These externally triggered deliveries use priority slots that can interrupt the regular schedule. The strength of reflection is adjusted in three stages depending on the reliability of the external information.

[0130] This document discloses real-time monitoring of distribution effectiveness. In at least one embodiment, the device measures the effectiveness immediately after distribution in real time and dynamically adjusts the next distribution. It links with a POS system to track sales fluctuations within five minutes of audio distribution. A people flow sensor detects increases or decreases in the number of people moving to a specific sales area. Sentiment analysis is performed on customer responses (e.g., "Wow," "Let's go," etc.) picked up by a microphone array. If the effectiveness falls below a benchmark (a 5% sales increase), the next distribution is postponed or the content is automatically revised. A / B testing is performed in real time, and the more effective one is continued. The inertia effect is monitored for 30 minutes even after distribution is stopped to accurately measure the actual impact. If an abnormal response (sudden store departures, increased complaints) is detected, the distribution is immediately stopped and an alert is sent. These feedback loops continuously improve distribution effectiveness.

[0131] The interface and operability are disclosed. In at least one embodiment, the device provides an intuitive distribution schedule management interface. The timeline view displays 24 hours on the horizontal axis and distribution content as color-coded blocks. Distribution times can be adjusted in one-minute increments using drag and drop. The calendar view provides an overview of monthly distribution plans and allows for the setting of regular distribution patterns. The Gantt chart format allows for visual confirmation of temporal overlaps and gaps between multiple contents. The distribution simulation function allows for advance preview of the daily distribution flow with fast playback. Conflict warnings are displayed with red highlights, and automatic adjustment suggestions are presented in a pop-up. Distribution history can be viewed back in time with infinite scrolling, and successful patterns can be saved as templates. Emergency adjustments can also be made on the go using the mobile app. All change history is version-controlled. Group D: Image and flyer-based text generation function (corresponding to claims 38-47)

[0132] This document discloses a method for extracting product information using image analysis. In at least one embodiment, the device extracts product information from photographs and flyers with high accuracy. Image preprocessing involves normalizing the image resolution to 4K (3840 x 2160) and optimizing contrast using histogram equalization. OCR processing uses Tesseract OCR 5.0, optimized for Japanese, or the Google Cloud Vision API, achieving character recognition accuracy of over 95%. Price recognition uses currency symbols such as "\", "yen", and ","-" as anchors to extract the numerical portion using regular expressions. Product name recognition identifies key products based on font size and placement, and recognizes decorative and POP characters using a dedicated model. Object detection (YOLO v8, EfficientDet) identifies regions in product images and associates each product with corresponding text information. Barcode and QR Code (registered trademark) detection references a product master database from the JAN code to provide accurate product information.

[0133] This paper discloses a method for automatically generating broadcast text using AI. In at least one embodiment, the device generates compelling broadcast text from extracted information. Product categories (e.g., food, clothing, home appliances) are determined using image classification CNN, and category-specific text templates are applied. Product features (color, shape, size) are converted into natural language using image description models (CLIP, DALL-E). Price appeal is calculated based on comparisons with regular prices, discount rates, and competitive prices, and converted into expressions such as "Amazing 50% Off" and "1000 Yen Better Than Other Stores." Seasonality is determined using color temperature analysis and product attributes, and expressions such as "Perfect for Summer" and "Winter Essentials" are added. Emotional appeal expressions are selected using keywords such as "Bargain," "Trend," and "Reliable" depending on the target demographic (housewives, students, seniors). The generated text is automatically adjusted to a reading time of 30 seconds or less.

[0134] This document describes flyer layout analysis. In at least one embodiment, the device determines the importance of products based on the flyer's design layout. Layout analysis uses Connected Component Analysis to separate text blocks, image blocks, and decorative elements. An importance score (0-100) is calculated based on the product's display area, placement (golden ratio, rule of thirds), and decorative prominence. Heading levels are classified into three levels (main headline, subheadline, and main text) based on font size ratios. Color analysis estimates appeal axes, such as warm colors like red and yellow representing "good value" and cool colors like blue and green representing "fresh" and "eco-friendly." A gaze-tracking prediction model (saliency map) identifies the product that customers first notice, and then the text is organized starting with that product. Multi-page flyers are weighted by page importance (100% cover, 70% inside pages, 50% back pages).

[0135] Multimodal information integration is disclosed. In at least one embodiment, the device integrates images, text, and metadata to generate comprehensive sentences. Visual information from images (product texture, freshness, luxury) and linguistic information from text (origin, brand, features) are integrated in a multimodal embedding space. The level of focus on a product is estimated from the photographic quality (resolution, focus, exposure). When there are multiple images, the similarity between images is calculated using SIFT features or CNN to determine whether they are different angles or variations of the same product. New products and price changes are automatically detected from the differences between time-series images (last week's and this week's flyers), and phrases such as "New Arrival" and "Even Cheaper" are added. Events and seasons are estimated from metadata (photo date and time, location information, file name) to generate context-appropriate phrases.

[0136] A text editing interface is disclosed. In at least one embodiment, the device provides an interface for efficiently editing generated text. The WYSIWYG editor allows users to edit text while viewing it, intuitively setting font size, color, emphasis, and more. Automatically generated text is displayed in blue, while manually edited text is displayed in black, and change history is tracked. Thesaurus API integration allows users to click on a word to see alternatives, allowing for one-click replacement. Text evaluation scores (readability, persuasiveness, and emotional value) are displayed in real time, and suggestions for improvement are provided. The audio preview function allows users to instantly hear the text being edited and correct any unnatural parts. Users can insert frequently used expressions from a phrase library by dragging and dropping. Proofreading assistance automatically detects typos, spelling variations, and inappropriate expressions.

[0137] Multiple version generation and length adjustment are disclosed. In at least one embodiment, the device generates multiple versions of the same content with different lengths. A short version (10 seconds), a standard version (30 seconds), and a long version (60 seconds) are automatically generated and selected based on the broadcast time slot. A text compression algorithm removes redundant modifiers while retaining important information. Importance is determined by sentence importance scores using TF-IDF, Text Rank, and BERT. The short version contains only the product name and price, the standard version adds features, and the long version includes detailed descriptions and usage scenarios. The number of characters is adjusted based on the reading speed, with a standard of 5-6 characters per second. When multiple products are included, the number of products mentioned is adjusted based on priority. Variation generation creates 3-5 variations of the same content with different expressions to prevent boredom. Each generated version is automatically used by the broadcast scheduler.

[0138] This document describes update detection and difference management. In at least one embodiment, the device automatically detects updates to images and flyers and dynamically updates text. File monitoring detects the addition of new images to a specified folder. Image hashes (MD5, SHA-256) are used to eliminate duplicates of the same image. Difference detection algorithms (SSIM, perceptual hash) identify changes to images. Price changes are detected by calculating the numerical difference and automatically converted to expressions such as "¥100 price reduction" or "20% off." Out-of-stock items are detected by detecting the words "SOLD OUT" or "Sold Out" in the image and automatically halting distribution. New product additions are prioritized by detecting "NEW" or "New Release" marks and converting them into text. Regular update schedules (every day at 6:00 AM, every Monday) are set up, and batch processing is used for efficiency. Update history is version-managed in Git format and can be restored to any point in time.

[0139] This document discloses output and integration with external systems. In at least one embodiment, the device outputs generated audio data to a variety of external systems. In addition to output in standard audio formats (WAV, MP3, AAC, FLAC), it also supports streaming distribution (HLS, RTMP). Analog audio signals (XLR, RCA) or digital signals (AES / EBU, S / PDIF) are output to the in-house public address system. Audio over IP protocols such as Dante and AES67 are used for transmission over IP networks. Automatic uploading to cloud storage (AWS S3, Google Cloud Storage) enables sharing across multiple locations. Social media integration automatically posts videos with audio to Twitter, Instagram, and TikTok. Synchronizes with digital signage to display visual information linked to audio. Integrates with POS registers to play audio guidance for related products during checkout. These outputs are controlled via REST API and WebSocket. Group E: Lost child information template function (corresponding to claims 48-57)

[0140] The structure and management of lost child information templates are disclosed. In at least one embodiment, the device implements a structured template system dedicated to lost child guidance. The basic template is defined in XML format and includes required fields (age, gender, clothing) and optional fields (name, height, belongings). Age is entered ambiguously as "about x years old," gender as "boy / girl," and clothing as "jacket color and type, pants / skirt" in a hierarchical format. To protect privacy, names are used for internal management only and are not broadcast, with the standard expression being "Anyone who has any idea?" Templates are customized for each facility type (department store, theme park, event venue), each with different wording and information priority. Multilingual templates (Japanese, English, Chinese, Korean) are provided to accommodate lost foreign children. Input validation displays an error if inconsistent information (e.g., 3-year-old and 180 cm tall) is found.

[0141] Structured input of feature information and natural language conversion are disclosed. In at least one embodiment, a device efficiently inputs detailed feature information and converts it into natural-sounding sentences. Clothing input involves selecting color (20 colors), type (30 types, e.g., T-shirt, dress), and pattern (10 types, e.g., solid, striped) from a pull-down menu. Hairstyles are selected by combining length (short, medium, long), color (black, brown, gold, etc.), and style (straight, curly, etc.). Characteristic possessions are designated by icon selection, such as a backpack, hat, or stuffed animal. This structured data is converted into sentences such as "A boy with brown hair wearing a red T-shirt and blue pants" using natural language generation. A significance algorithm is used to mention the most distinguishing features (bright color, distinctive possessions) first. To avoid redundancy, overly general features (black hair, average height) are omitted.

[0142] This document discloses broadcast pattern control based on elapsed time. In at least one embodiment, the device dynamically adjusts broadcast frequency and content according to the time elapsed since the child became lost. In the initial stage (0-15 minutes), only basic information is broadcast every 5 minutes, waiting for a parent or guardian to independently find the child. In the intermediate stage (15-30 minutes), the frequency increases to 3-minute intervals and more detailed characteristic information is added. In the emergency stage (30-60 minutes), a building-wide broadcast is made every 2 minutes, with the word "urgent" added. In the prolonged stage (60 minutes or more), the broadcast continues every 5 minutes, including information on police cooperation. The tone of the voice also changes gradually, from a gentle announcement tone in the initial stage to a serious appeal tone in the emergency stage. During late nights and early mornings, the frequency is halved to minimize noise. The elapsed time is displayed as a countdown on the dashboard, visualizing the time until the next broadcast. Through historical analysis, the device learns the average time to discovery (typically 20-30 minutes) and automatically adjusts the optimal frequency.

[0143] This paper describes a method for automatically extracting features from photographs. In at least one embodiment, the device automatically extracts and documents visual features from photographs of lost children. Face recognition determines gender (with a confidence level of 90% or higher) and estimated age (with an accuracy of ±2 years). Clothing recognition uses semantic segmentation to separate clothing regions and color histogram analysis to identify dominant colors. Clothing classification CNN distinguishes categories such as T-shirts, hoodies, and dresses with 94% accuracy. Accessory detection detects whether a person is wearing glasses, a hat, or a mask. Hairstyle recognition estimates length and style using edge detection and texture analysis. However, to protect privacy, the system does not store the actual facial images; only the feature vectors are retained. Extracted features are displayed along with a confidence score, and low-confidence items prompt manual review. When multiple photos are available, the system checks for feature consistency and uses the clearest information.

[0144] This document discloses a method for managing the priority of multiple cases. In at least one embodiment, the device appropriately manages priorities when multiple children are lost at the same time. Priority is determined by comprehensively evaluating age (toddlers > elementary school students > junior high school students), elapsed time, and special circumstances (disabilities, chronic illnesses). Children under three years of age are given top priority and are generally searched every two minutes, children between four and six years of age are searched every three minutes, and children seven years of age and older are searched every five minutes. Broadcasts for multiple cases are distributed in a round-robin format, with up to two children being searched consecutively in one broadcast. Cases with similar urgency are grouped together and announced as "We are currently searching for ○ children" to improve efficiency. Cases that have been found are immediately removed from the queue, and a single completion announcement is broadcast, stating, "The boy, age ○, who was previously identified, has been safely rescued." The queue status is displayed in Gantt chart format on the management screen, and manual adjustments to the order are also possible.

[0145] Multilingual support and cultural considerations are disclosed. In at least one embodiment, the device generates a multilingual lost child guidance system that is compatible with internationalization. In addition to the four basic languages ​​(Japanese, English, Chinese, and Korean), additional languages ​​such as Spanish, Portuguese, and Thai can be added depending on the facility's location. Translation is performed using neural machine translation with a specialized terminology dictionary, and appropriate honorific expressions for children are selected. Cultural considerations include expressing age in both years and full years in Asian countries, and providing height in feet and inches in Western countries. Religious considerations include using appropriate expressions for specific clothing (hijab, turban). The timing of delivery for each language is automatically adjusted based on the facility's foreign population, with one or two languages ​​added after Japanese. Speech synthesis also uses native speaker models for each language to achieve natural pronunciation. In emergencies, pictograms and sound effects are used in combination to communicate information across language barriers.

[0146] Privacy and security information is disclosed. In at least one embodiment, the device strictly protects lost child information. Personally identifiable information (name, address, phone number) is stored in a database using AES-256 encryption and is accessible only to authorized staff. Broadcasts do not include any personally identifiable information and only provide visual characteristics. Parental contact information is displayed only after two-factor authentication, and access logs are recorded. Information is automatically deleted 24 hours after discovery, and only anonymized data is retained for statistical purposes. Image data is immediately deleted after feature extraction, and only irreversible hash values ​​are stored. Providing information to external parties (such as police) requires a formal request and administrator approval. In compliance with GDPR and COPPA, protecting the information of minors is a top priority. Access to the system is protected by VPN and SSL / TLS to prevent unauthorized access.

[0147] Statistical analysis and prevention suggestion functions are disclosed. In at least one embodiment, the device analyzes patterns of lost children and suggests preventative measures. Time-of-day analysis identifies peak times (2:00 PM to 4:00 PM) and locations (toy section, food court). Age-based analysis shows that 3-5 year olds account for 60% of the total, with a particularly high proportion of boys. Day-of-week analysis visualizes that the rate of lost children on weekends and holidays is three times higher than on weekdays. Heat maps identify areas where children are likely to get lost (near escalators, around large playground equipment) and suggest the deployment of additional staff. Machine learning predicts the risk of lost children based on crowd levels, events, and weather, encouraging advance vigilance. Preventive broadcasts, such as "Please keep an eye on your children," are automatically broadcast during high-risk times. Locations for distributing lost child stickers (with contact information) are optimized based on data. Monthly reports verify the effectiveness of improvements and implement the PDCA cycle.

[0148] A method for predicting event content from keywords is disclosed. In at least one embodiment, the device predicts comprehensive event content from event-related keywords. Semantic associations between keywords are calculated using word embedding techniques such as Word2Vec and BERT. Related elements such as "yukata, fireworks, food stalls, and Bon Odori" are probabilistically generated from "summer festival" and "Santa, presents, illuminations, and choir" from "Christmas." Knowledge graphs (ConceptNet, WikiData) are referenced to complement general event components. Based on the seasonal context, a "festival" in July is predicted to be a summer festival, while a "festival" in December is predicted to be a year-end sale. Statistics such as the number of visitors, popular content, and length of stay are extracted from past data on similar events. Based on facility characteristics, a department store event is specified as a promotional event, while a theme park event is specified as a show event. Ambiguous keywords are assigned probability scores, and multiple interpretations are presented in parallel. Prediction accuracy is continuously improved by comparing with performance data after the event.

[0149] Personalization based on visitor attributes is disclosed. In at least one embodiment, the device analyzes visitor attribute data and recommends optimal events. Age group, gender, and group composition are statistically estimated using visitor data collected from Wi-Fi access points and beacons. For families (with children), kids' workshops, character shows, and parent-child events are prioritized. For young people, social media-worthy experiential events, limited-edition collaboration products, and events featuring influencers are recommended. For seniors, health seminars, traditional craft demonstrations, and nostalgic music events are suggested. Preferences are learned from past participation history, and exercise-related events are recommended for sports enthusiasts and art-related events for culture lovers. Collaborative filtering is used to suggest events highly rated by visitors with similar attributes. The recommendation score is calculated by multiplying relevance by novelty and diversity, ensuring timeless recommendations.

[0150] This document discloses a method for measuring popularity in real time. In at least one embodiment, the device measures the popularity of an event in real time and dynamically adjusts broadcast content. An attendance counter tracks the number of participants in each event down to the minute. A wait-time sensor estimates popularity based on the length of the queue. An acoustic sensor measures the volume of cheers and applause to quantify the level of excitement. Social media analysis evaluates popularity based on the number of hashtag posts, likes, and shares. Stay-time analysis estimates satisfaction based on the percentage of people staying beyond the planned time. These indicators are combined to calculate a popularity score (0-100), and the top 20% of events are prioritized for broadcast as "very popular." Events that are rapidly gaining popularity are immediately announced as "hot topics right now." Conversely, events with low popularity are revised or promoted with a different appeal. A real-time dashboard visualizes the trending popularity and provides feedback to management.

[0151] This document discloses grouping and cross-promotion of related events. In at least one embodiment, the device groups semantically related events and provides guidance that creates synergy. Clustering analysis is used to automatically classify events into categories such as "Food," "Art," "Kids," and "Sports." Events within the same category are branded with a unified theme, such as "Food Festival" or "Art Week." Sequential Pattern Mining analyzes participant movement patterns to discover combinations of events that are likely to be consecutively attended. Based on correlations such as "70% of participants in Event A also attend Event B," it suggests set tickets or common stamp rallies. For events that are consecutive in time, guidance is provided such as "Starting at 3:00 PM." For events that are nearby in location, guidance is provided such as "At the neighboring venue," encouraging people to move around. Storytelling is generated to enhance the appeal of the entire group ("A whole day of fun filled with XX")

[0152] Optimal announcement timing for different time periods is disclosed. In at least one embodiment, the device determines the optimal announcement timing based on the nature and time of the event. Advance announcements begin three days in advance depending on the scale of the event, with concentrated announcements distributed the day before and the morning of the event. On the day of the event, announcements are distributed gradually, starting two hours before the event starts, at 60, 30, 10, and 5 minutes before. For events requiring advance registration, such as workshops, announcements are prioritized 30 and 10 minutes before registration begins. For meal-related events, announcements are provided one hour before mealtime using language that stimulates hunger. For children's events, announcements are distributed to avoid naptime (1:00-3:00 PM). Near the end (30 minutes remaining), announcements are made to encourage last-minute participation by announcing "ending soon." For weather-dependent events, the decision to hold the event is made two hours in advance based on the weather forecast. For repeating events, announcements are prioritized for the first event and lighter for subsequent events.

[0153] This document discloses a topicality analysis system that integrates with social media. In at least one embodiment, the system analyzes social media data to evaluate the topicality of an event. Using the Twitter API, the system collects posts containing event-related keywords and hashtags. Using sentiment analysis, the system calculates the rate of positive posts, and those with 80% or more are used for distribution as "highly popular." Using influencer detection, mentions from accounts with 10,000 or more followers are quoted as "popular with celebrities." Using image analysis, the system estimates crowd levels and excitement from Instagram photos. Using trend analysis, the system instantly distributes trending events as "currently trending on social media." Using word-of-mouth diffusion (retweet rate, share rate), the system predicts viral effects. If negative posts (such as "crowded," "boring," etc.) are detected, the system modifies or pauses the content of the distribution. Using the geographical information of the posts, the system prioritizes references from actual attendees. Using UGC (User Generated Content), the system incorporates attendee feedback into the distribution.

[0154] This document discloses dynamic adjustments based on advance reservation status. In at least one embodiment, the device adjusts recommendation strength according to the advance reservation fulfillment rate. It works with a reservation system to obtain the reservation rate for each event in real time. When the reservation rate is less than 30%, it actively announces "still available," while when it is 70% or more, it creates a sense of urgency by announcing "limited availability." When it reaches 100%, it announces "full capacity" and announces the next event. When cancellations occur, it immediately announces "X cancellation slots available." It works with a cancellation waiting list to notify registrants individually as soon as a space becomes available. Based on reservation patterns by time period, it strengthens re-opening during times when last-minute cancellations are most common (1 hour before the start). For events with available capacity, it encourages walk-ins by announcing same-day participation. For popular events, it simultaneously announces advance reservations for the next event to prevent opportunity loss. It measures the effectiveness of announcements based on changes in reservation rates over time and provides feedback to its distribution strategy.

[0155] Weather-adaptive event priority control is disclosed. In at least one embodiment, the device dynamically changes event priorities based on weather information. It obtains hourly weather forecasts, temperature, humidity, wind speed, and precipitation probability from a weather API. During sunny days, outdoor events, garden-related events, and sports events are prioritized and promoted as "perfect weather for going out." During rainy weather, indoor events, workshops, and screenings are promoted with the keyword "enjoyable even in the rain." On extremely hot days with temperatures above 30°C, priority is given to indoor air-conditioned events and water stations. During strong winds, advance notice is given of the possibility of cancellation of events using tents or decorations. In conjunction with pollen dispersion information, allergy-prevention events and indoor evacuation are recommended. When a sudden change in weather is forecast, early participation is encouraged. The optimal event mix is ​​suggested based on past weather-specific participation rate data. Seasonal expressions (e.g., spring-like, clear autumn) are automatically added.

[0156] High-precision human detection through sensor integration is disclosed. In at least one embodiment, the device integrates multiple types of sensors to detect approaching people with high accuracy. The infrared (PIR) sensor detects human body heat and has a detection range of 5-10 m and a response time of 0.5 seconds. The ultrasonic sensor measures distance using 20-200 kHz sound waves and calculates approach speed and direction. The image sensor (camera) identifies people using skeletal detection using OpenPose or YOLO and eliminates false positives. The millimeter-wave radar detects breathing and heartbeat through clothing and recognizes stationary people. These sensor data are fused using a Kalman filter or particle filter to improve location estimation accuracy to ±10 cm. Inconsistencies between sensors (unilateral detection) are resolved using weighted voting. Environmental adaptation automatically corrects false positives due to lighting changes and temperature fluctuations. In privacy mode, only silhouettes are detected and personal identification is not performed.

[0157] This paper discloses gradual voice control based on dwell time analysis. In at least one embodiment, the device gradually changes the voice content depending on the time a person dwells. When approaching (0-3 seconds), attention is attracted with a gentle "Welcome" greeting or sound effects. When initial interest (3-10 seconds) is detected, a brief introduction to the product category and features is provided. When continued interest (10-30 seconds) is achieved, detailed function explanations and pricing information are provided. When deep interest (30-60 seconds) is achieved, appealing information such as usage scenarios and customer testimonials is added. When a person dwells for a long time (60 seconds or more), a human response is encouraged, with "Our staff will explain." When abandonment is detected, the voice ends with "Thank you" or "Please come again." When multiple people are detected, the voice changes to a group-oriented expression. When children are detected, content for children is added. Dwell patterns are classified using machine learning and used to predict purchase probability.

[0158] This paper discloses a method for generating audio guidance based on movement direction detection. In at least one embodiment, the device detects a person's movement direction and generates appropriate audio guidance. Optical flow is used to calculate motion vectors between image frames and estimate movement direction and speed. For people moving from right to left in an aisle, the system guides them to their left, indicating relative position, such as "This item is on your left," and for people moving from left to right, the system guides them to their right. It delivers a welcome message to approaching people, a brief product name only to passing people, and a follow-up message to leaving people. It identifies people in a hurry based on their walking speed and switches to a shortened message. When it detects a slowdown that could lead to a stop, it begins providing detailed information. For people making a U-turn, it adds a thoughtful message, such as "Have you left anything behind?" Escalator users are guided to the upper or lower floor depending on their direction of movement. It learns major traffic flows from multiple movement trajectories and suggests effective placement.

[0159] This document discloses congestion-adaptive volume control. In at least one embodiment, the device automatically adjusts volume and delivery timing according to the surrounding congestion level. Image analysis is used to count the number of people within the field of view in real time and calculate the density (people / square meter). Acoustic analysis is used to measure environmental noise levels and ensure a required signal-to-noise ratio (SN ratio) of 15 dB or higher. When the congestion level is low (density less than 1 person / square meter), a personalized, intimate voice is used at normal volume. When the congestion level is medium (1-3 people / square meter), the volume is increased by +3 dB, and the content is changed to concise, easy-to-listen content. When the congestion level is high (3 people / square meter or more), the volume is amplified by +6 dB, and a short message is delivered that focuses only on important information. However, the maximum volume is limited to 85 dB to prevent adverse effects on hearing. When congestion is high, delivery is alternated between nearby speakers in a time-sharing manner to avoid interference from simultaneous speech. During late nights and early mornings, the volume is reduced regardless of the congestion level.

[0160] This paper discloses a method for selecting content by age group using facial recognition. In at least one embodiment, the device selects optimal content based on the age group estimated using facial recognition technology. Deep learning models (VGGFace, FaceNet) are used to estimate age from facial images with an accuracy of ±5 years. For children (ages 0-12), anime character voices, background music, and easy-to-understand expressions are used. For younger adults (ages 13-29), trending information, social media-related content, and casual speech are used. For middle-aged adults (ages 30-59), quality, functionality, and family-friendly content are emphasized. For older adults (ages 60 and over), slower speech rates, louder volume, and health-related information are provided. Combined with gender estimation, this allows for more granular targeting. However, given the uncertainty of estimation, extremely biased content is avoided. Facial recognition data is not used for personal identification; only statistical information is retained. An opt-out option is provided to accommodate those who do not wish to have their faces recognized.

[0161] This document discloses spatial design using the cooperation of multiple speakers. In at least one embodiment, the device creates a three-dimensional audio space by linking multiple sensor-equipped speakers. A master-slave configuration allows one master to oversee the entire system, preventing delays and overlaps. Audio is relayed sequentially from speaker 1 to speaker 3 along a person's path, providing natural guidance. 3D audio technology creates virtual movement and rotation of the sound source, increasing attention. Beamforming generates directional audio that can only be heard by specific individuals. Multiple languages ​​are simultaneously broadcast from different speakers, allowing users to distinguish between languages. During events, all speakers are synchronized for simultaneous broadcasting. A malfunctioning speaker is automatically detected, and adjacent speakers increase the volume to compensate. A mesh network enables communication between speakers without wired wiring. A placement simulator suggests optimal speaker placement.

[0162] This document discloses heat map analysis of sensor data. In at least one embodiment, the device aggregates sensor response data to generate a heat map. Detection frequency is represented by color intensity on a two-dimensional map with time (horizontal axis) and location (vertical axis). High-frequency areas are visualized in red, medium-frequency areas in yellow, and low-frequency areas in blue. 3D display also allows for understanding vertical distribution (differences between children and adults). Time-of-day heat maps analyze peak-time pedestrian flow patterns. Day-of-week comparisons clarify differences between weekdays and weekends. Traffic flow analysis identifies major movement routes and congestion points. Dead spaces (areas where people do not pass) are discovered from the heat map and layout improvements are proposed. A / B testing quantitatively evaluates the effectiveness of changes to speaker placement and audio content. Abnormal patterns (sudden crowd concentration or dispersion) are detected and used for safety management. Changes in the heat map are reported chronologically as a monthly report.

[0163] Power-saving control and false-detection learning are disclosed. In at least one embodiment, the device maintains detection accuracy while minimizing power consumption. A gradual startup approach allows for initial detection using a low-power PIR sensor, activating the camera only when confirmation is required. During unoccupied times, the device enters deep sleep mode, reducing power consumption to 5% of normal levels. Auxiliary power is provided by solar panels and energy harvesting. Machine learning is used to learn and filter false-detection patterns (air conditioning breeze, small animals, shadows). The spectral characteristics of environmental noise (passing cars, opening and closing doors) are learned and removed. Seasonal changes in detection characteristics are automatically corrected. The balance between false positives (false detections) and false negatives (missed detections) can be adjusted according to the application. Edge computing reduces cloud communication, achieving both response speed and power savings. Functions are gradually restricted based on the remaining battery level, maintaining minimal operation.

[0164] Language identification and automatic translation processing are disclosed. In at least one embodiment, the device automatically identifies the language of a source text and translates it into a target language in real time. Language identification uses n-gram analysis and deep learning (fastText) to identify over 100 languages ​​with 99% accuracy. The translation engine uses Transformer-based neural machine translation (Google Translate API, DeepL API, Microsoft Translator). Domain adaptation improves translation accuracy for facility-specific terms (product names, service names). Context-aware translation selects appropriate translations by referencing the surrounding text. Speech synthesis uses speech models of native speakers of each language to achieve natural intonation. Translation caching speeds up the translation of frequently used phrases and reduces latency to 50 ms or less. Quality scoring evaluates the reliability of the translation and suggests alternative expressions if the quality is low. Back-translation automatically verifies the validity of the translation.

[0165] This document discloses a method for analyzing the language distribution of facility users. In at least one embodiment, the device analyzes the language distribution of facility users and sets translation priorities according to demand. The device estimates users' language preferences based on the language settings of Wi-Fi-connected devices. It measures the distribution of languages ​​used within the facility using voice recognition. It links with tourism statistics by country to predict language demand according to seasons and events. It analyzes time-of-day analysis to discover patterns, such as Chinese being used more frequently in the morning, Korean in the afternoon, and English in the evening. It analyzes facility usage patterns of speakers of each language based on length of stay by language. It prioritizes translation of the top five languages ​​in demand, and translates others on request. It measures actual needs based on frequency of use of the language switch button. It links with the allocation of multilingual staff to optimally combine machine translation and human support. It analyzes the sales contribution of each language and prioritizes languages ​​with high business value.

[0166] Terminology dictionaries and cultural considerations are disclosed. In at least one embodiment, the device accurately translates facility-specific terminology and is culturally sensitive. Industry-specific dictionaries (e.g., retail, food and beverage, tourism) are included to supplement terminology not covered by general dictionaries. For proper nouns (brand names, product names), official spellings are prioritized, with transliterations and free translations provided. Unit conversions are automatically performed: meters to feet, Celsius to Fahrenheit, and yen to local currency. Date formats are adjusted for each region (year / month / day, month / day / year, day.month.year). Cultural taboos are detected and inappropriate language is automatically corrected. Religious considerations include suggesting alternatives such as pork to other meats and alcohol to soft drinks. Colors are selected based on cultural meanings (e.g., red: good luck in China, danger in the West). Honorific levels are adjusted by language to maintain appropriate politeness. Idioms and proverbs are replaced with local equivalents rather than literal translations.

[0167] A method for automatically assessing and improving translation quality is disclosed. In at least one embodiment, the device automatically assesses and continuously improves translation quality. Automatic assessment metrics, such as BLEU, METEOR, and BERTScore, quantify the fluency and appropriateness of translations. Deviations are measured against a gold standard for manual translation. Back-translation estimates quality based on the degree of match between source text, translation, and source text. A grammar checker verifies the grammatical accuracy of translations. Periodic sampling assessments by experts verify the validity of the automated assessment. Low-quality translations are flagged and queued for manual correction. Correction history is used to learn frequent translation error patterns and correct them using a rule-based approach. A user feedback button allows reporting of translation issues. A / B testing compares the effectiveness of different translation engines and settings. Continuous learning improves the accuracy of translations of facility-specific expressions.

[0168] Language-specific voice optimization is disclosed. In at least one embodiment, the device optimizes voice parameters based on the characteristics of each language. For Chinese (a tonal language), a wide pitch range and clear distinction between four tones are provided. For English, stress accents are emphasized and linking and reductions are reproduced naturally. For Japanese, pitch accents and metric rhythm are accurately controlled. For Korean, the distinction between aspirated and tense consonants is made clear and final consonants are properly handled. For Arabic, special consonants such as pharyngeal consonants are accurately pronounced and the reading direction is right-to-left. The speech rate is adjusted to 300 syllables per minute for syllabic languages ​​(Chinese) and 150 words per minute for stressed languages ​​(English). Natural intonation patterns for each language (e.g., rising intonation in questions) are reproduced. Number reading (four-digit group vs. three-digit group) is switched by language. Punctuation (periods, periods, and Arabic inverted question marks) is properly handled.

[0169] This document discloses simultaneous multilingual broadcasting in emergencies. In at least one embodiment, the device broadcasts alerts simultaneously in all languages ​​during an emergency. Depending on the level of urgency, life-critical information is broadcast simultaneously in all languages, while other information is broadcast sequentially in the primary language. International standard emergency sounds (sirens, beeps) are used language-independently. Pictograms and symbols (emergency exit, fire extinguisher symbols) are also used to overcome language barriers. Short sentences and simple structures are prioritized to minimize the risk of translation errors. Numbers and directional instructions are used in conjunction with visual displays to increase reliability. Preparing standard phrases in advance ensures accuracy of translation. Key points are conveyed in five seconds or less in each language, and a complete cycle in all languages ​​is completed within 30 seconds. Repeated broadcasts prevent missed messages. Emergency alerts are also broadcast simultaneously in text format via email to mobile phones. Multilingual staff are guided to the location and directed to human support.

[0170] This document describes translation learning and corpus construction. In at least one embodiment, the device learns from translation performance and builds a facility-specific corpus. Pairs of source and translated sentences are stored in a translation pair database. Frequently occurring phrases are extracted and prioritized as a translation memory. Sentence alignment is used to estimate translations from partially matching sentences. Correct translation patterns are learned from user correction history. Domain adaptation is used to fine-tune a translation model specific to the facility's industry. Translation data is shared with competing facilities to establish industry-standard expressions. Templates are created for standard expressions used in seasonal events (Christmas, New Year's). Terms for new products and services are added to the dictionary as needed. A collection of mistranslation examples is created to prevent the same mistakes from being repeated. Cross-lingual expression patterns are discovered from a multilingual corpus.

[0171] This document discloses sign language video generation and accessibility. In at least one embodiment, the device converts audio information into sign language video, ensuring accessibility for the hearing impaired. The sign language avatar system supports Japanese Sign Language, American Sign Language (ASL), and international sign language. Natural hand movements are generated from motion capture data. Facial expression recognition adds non-manual markers (facial expressions, mouth shapes) necessary for sign language. Fingerspelling conversion expresses proper nouns and new words. Addresses regional differences in sign language (differences between Kanto and Kansai). Synchronizes subtitles and sign language, allowing for use in conjunction with speech reading. Uses large, clear sign language expressions in emergencies. Enables basic information transmission even when a sign language interpreter is unavailable. Sign language videos are displayed on digital signage, making them visible from a distance. The speed of sign language expressions can be adjusted, making them easy to understand even for beginners.

[0172] A multifaceted collection of listener responses is disclosed. In at least one embodiment, the device collects listener responses in multiple ways. A facial recognition camera (1920x1080 resolution, 30 fps) recognizes seven basic emotions (happiness, sadness, anger, disgust, fear, surprise, and neutrality). Action Unit detection tracks subtle facial changes (eyebrow movements, changes in the corners of the mouth). An audio microphone array detects responses such as "Oh," "I see," laughter, and sighs. Speech recognition extracts impressions such as "interesting" and "noisy." Body motion sensors detect actions such as stopping, turning around, and taking out a smartphone. Biometric sensors (only for compatible device wearers) estimate emotions from heart rate variability and galvanic skin response. Eye tracking measures attention to audio. Connected to a smartphone app, active feedback (likes, shares) is collected. This multimodal data is time-synchronized and integrated.

[0173] This document discloses a deep analysis of emotional states using AI. In at least one embodiment, the device uses AI to analyze complex emotional states. A deep learning model (CNN-LSTM) tracks emotional transitions based on time-series changes in facial expressions. A VAD emotion model (Valence-Arousal-Dominance) represents emotions in three-dimensional space. Multimodal fusion achieves an 85% emotion recognition accuracy rate by integrating facial expressions, voice, and behavior. Context consideration adjusts emotional interpretation based on the facility and time of day. Crowd emotion analysis estimates the atmosphere of a group rather than an individual. An emotional contagion model predicts the impact of one person's emotions on those around them. Abnormal emotion detection immediately alerts users to extreme discomfort or anger. An impact score is calculated based on the duration and intensity of the emotion. The degree of emotional expression is normalized by region to account for cultural differences. To protect privacy, only statistical data is stored without identifying individuals.

[0174] This document discloses a correlation analysis with purchasing behavior. In at least one embodiment, the device performs a detailed analysis of the correlation between emotional responses and purchasing behavior. It links with POS data to track sales changes within 15 minutes of audio distribution. It uses RFID and beacons to track the movement of people listening to the audio on the sales floor. It uses basket analysis to calculate the purchase rate of products promoted in the audio. It calculates the correlation coefficient between emotional valence (positive / negative) and purchase probability. It quantifies the impact of high-arousal emotions (excitement, surprise) on impulse purchases. It measures the impact on long-term brand favorability based on correlation with repeat purchases. It separates and evaluates the cross-selling effect (increased sales of related products). It uses a time delay model to estimate the lag before the audio effect appears. It compares the effectiveness of emotional appeals by price range, demonstrating that the emotional impact is greater for more expensive products. It clarifies the return on investment of emotional marketing through ROI calculations.

[0175] This document discloses automated A / B testing and statistical validation. In at least one embodiment, the device automatically conducts scientifically rigorous A / B testing. Randomization is used to design experiments to eliminate biases in time of day and day of the week. Sample size calculation determines the minimum sample size required to ensure 80% statistical power. Bayesian statistics is used to sequentially update results and enable early decision-making. Multivariate testing evaluates the combined effects of voice, background music, and speaking rate. Interaction testing detects synergistic or counteracting effects between factors. False discovery rate control prevents the problem of multiple testing. Effect size (Cohen's d, odds ratio) evaluates practical significance. Confidence intervals quantify the uncertainty of the effect. Robust statistics are applied to eliminate the influence of outliers. Residual effects are tracked for two weeks after the experiment is completed to confirm their sustainability.

[0176] A real-time feedback loop is disclosed. In at least one embodiment, the device immediately detects negative reactions and automatically responds. If angry or displeased facial expressions persist for more than three seconds, the volume is automatically reduced by -3dB. If multiple people move away at the same time, the broadcast is paused. If negative words such as "too loud" or "stop" are detected using voice recognition, the broadcast is immediately stopped. If psychological reactance (strong rejection reaction) is detected, a 180-second cooldown period is imposed. Automatically switches to more gentle content from an alternative content library. A one-time apology message "We apologize for the inconvenience" is sent. An incident report is generated and an administrator is notified. Machine learning is used to learn patterns that trigger negative reactions. The effectiveness of improvement actions is tracked and the PDCA cycle is implemented. Serious incidents (customer complaints, social media flame wars) are handled by escalation.

[0177] Competitive comparison and differentiation analysis is disclosed. In at least one embodiment, the device performs a comparative analysis of the effectiveness of competitors' audio distribution. Mystery shoppers record and evaluate the audio distribution of competing facilities. Acoustic analysis quantitatively compares the volume, speech rate, and background music usage of competitors. Sentiment analysis evaluates which service is more popular. Social media analysis compares the reputation of each facility's audio distribution. Differentiating points (unique voice characters, special effects). Benchmarking indicators (comparison with industry averages) confirm relative positioning. Successful cases are analyzed, and good points are taken into consideration while maintaining uniqueness. Price surveys are used to compare the cost performance of audio distribution services. The quality of human resources (narrators, sound engineers) is compared and evaluated. Future competitiveness is predicted based on the level of technological innovation (use of AI, sensor accuracy).

[0178] Long-term effectiveness measurement and brand value assessment are disclosed. In at least one embodiment, the device tracks the long-term effectiveness of audio distribution. A brand recall survey measures the retention rate of audio listeners one week later. A repeat visit rate analysis evaluates the correlation between the audio experience and customer loyalty. A Net Promoter Score (NPS) quantifies the impact on recommendation intention. A brand association survey tracks changes in images evoked by audio. An emotional connection index measures brand attachment. Calculates contribution to Customer Lifetime Value (CLV). Word-of-mouth effects estimate the indirect impact on customer acquisition. Media exposure conversion converts the advertising value of audio distribution into monetary value. An employee satisfaction survey also evaluates internal branding effects. Quarterly periodic observations monitor the sustainability and decay of effects.

[0179] This document discloses ROI calculations and investment decision support. In at least one embodiment, the device precisely calculates the ROI of audio distribution. Direct revenue (increased sales) = post-distribution sales - baseline sales. Indirect revenue is calculated by converting increased average customer spending, extended stay time, and increased repeat visit rate into monetary terms. Cost items include initial system investment, monthly usage fees, content production costs, and operational labor costs. Break-even point analysis is used to calculate the payback period. Sensitivity analysis is used to identify factors with the greatest impact on sales. Risk-adjusted ROI is used to evaluate profitability taking uncertainty into account. Relative investment validity, including opportunity costs (compared to other measures), is verified. Long-term value is evaluated using the present value of future cash flows (NPV). Investment efficiency is compared with other projects using the internal rate of return (IRR). KPIs are displayed on a management dashboard to support decision-making.

[0180] This document discloses a voice element recording process. In at least one embodiment, a dedicated recording environment is created to capture the speaker's original voice data with high quality. The recording studio is a soundproof room with a background noise level of 30 dB or less, and sound-absorbing materials are appropriately placed to suppress reflected sound. Recording equipment includes a condenser microphone (frequency response 20 Hz-20 kHz), an audio interface (24-bit / 96 kHz compatible), a pop filter, and a microphone stand. The speaker maintains a distance of 15-30 cm from the microphone and speaks at a consistent volume. The recording script includes 200 phoneme-balanced sentences covering all Japanese phonemes, 30 sentences each expressing emotions (joy, sadness, anger, surprise, and fear), a 500-word accent-specific word list, and 100 facility-specific terminology. Multiple takes are performed and the best take is selected. Recording monitoring is performed to immediately detect clipping, noise contamination, and volume fluctuations, and re-recording is performed as necessary. The recorded audio data is saved in WAV format (uncompressed), and multiple backups are created.

[0181] This paper describes voice element preprocessing. In at least one embodiment, audio signal processing is performed on recorded original voice data. In the noise reduction process, stationary noise is removed using a spectral subtraction method, and non-stationary noise is suppressed using a Wiener filter. Volume normalization standardizes the RMS (root mean square) level of all audio files to -20 dBFS. A silence detection algorithm automatically removes silent sections before and after speech, extracting only the speech section. Sudden noises such as pop noises and clicks are removed using spectral restoration technology. Speech segmentation divides the audio into sentences, words, and phonemes, and labels each segment. Pitch extraction obtains the trajectory of the fundamental frequency to identify the speaker's pitch characteristics. Formant analysis measures the F1-F4 frequencies and quantifies phonemic features. These preprocessing steps create a high-quality dataset suitable for subsequent training of a speech synthesis model.

[0182] This paper describes a method for extracting speaker voice features. In at least one embodiment, speaker-specific voice feature parameters are extracted from preprocessed original voice data. Mel-frequency cepstral coefficients (MFCCs) are extracted in 40 dimensions to represent short-term spectral characteristics. Vocal tract characteristics are modeled using linear predictive coefficients (LPC). The mean, standard deviation, and fluctuation range of fundamental frequency (F0) are statistically analyzed to quantify the speaker's pitch characteristics. Speaking rate characteristics include phoneme duration, pause length, and speaking rate. Volume characteristics include average sound pressure level, dynamic range, and volume fluctuation pattern. Voice quality characteristics include spectral tilt, jitter (fine pitch fluctuations), and shimmer (fine amplitude fluctuations). Emotional expression characteristics include learning patterns of acoustic parameter changes for each emotional state. These feature parameters are represented as multidimensional vectors and saved as a voice profile that comprehensively represents the speaker's vocal individuality.

[0183] This paper discloses a voice model training process. In at least one embodiment, a deep learning-based speech synthesis model is constructed using extracted speech feature parameters. The training architecture employs a Transformer-based seq2seq model with an attention mechanism. The encoder converts text information into a latent representation, and the decoder generates acoustic features from the latent representation. Approximately 10 hours of text-speech pair data are used as training data. During the training process, parameters are updated to minimize the error from the target speech using supervised learning. A combination of L1 loss and L2 loss is used as a loss function, and the degree of match at the spectrogram level is evaluated. Dropout (with a probability of 0.1) and weight decay (with a coefficient of 0.0001) are applied as regularization techniques to prevent overfitting. A learning rate schedule is employed, gradually decaying the learning rate after a warm-up period. Convergence is determined by terminating the training if the loss on the validation data does not improve for five consecutive epochs.

[0184] A method for designing a virtual character's voice is disclosed. In at least one embodiment, voice parameters are designed to generate a fictional character's voice. Character settings include defining age (child, young, middle-aged, elderly), gender (male, female, neutral), and personality (cheerful, calm, energetic, cool). Basic voice parameters include pitch range (80-400 Hz), average pitch, speaking rate (slow-normal-fast), and volume (quiet-normal-loud). Voice quality adjustment parameters control clarity, nasality, breathiness, and creaky voice components. For example, a young female character might be configured with a higher pitch (250-350 Hz), a slightly faster speaking rate, clear pronunciation, and a lightly breathy voice. The intensity of emotional expression is also adjusted according to the character's personality, with a lively character displaying greater emotional fluctuations and a cool character displaying more restrained emotions. These parameters can be adjusted in real time, allowing the same character to express different voices depending on the situation.

[0185] A process for generating a virtual character voice is disclosed. In at least one embodiment, voice is synthesized based on designed character parameters. Voice conversion technology converts the base speaker's voice into a target character voice. Pitch shifting shifts the fundamental frequency to a target value. Formant shifting virtually changes the vocal tract length to represent differences in age and physique. Time warping adjusts the speaking rate while maintaining sound quality. Spectral transformation adjusts the brightness and softness of the voice. Vibrato addition adds expressiveness like a singing voice. Whispering voice conversion increases the breath component to achieve an intimate expression. These processes achieve a natural conversion using deep learning-based voice conversion models (CycleGAN, StarGAN-VC). The generated voice is then perceptually evaluated to confirm that it is both natural and unique.

[0186] This document discloses a method for recording and managing the voices of multiple speakers. In at least one embodiment, the voices of multiple speakers and characters are systematically recorded and managed. The speaker database integrates management of real speakers (professional narrators, voice actors, and facility staff) and virtual characters. Each speaker is assigned a unique ID, and profile information (name, age, gender, vocal characteristics, available languages, and recording date and time) is recorded. Recording session management manages multiple recordings of the same speaker in chronological order, tracking changes in the voice over time. Each speaker's voice clarity, stability, and expressiveness are quantified and recorded as quality evaluation indicators. Usage rights management sets usage conditions (expiry date, purpose, region) for each speaker. Version management maintains an update history of the voice model, allowing rollback to previous versions as needed. A function is implemented to suggest alternative speakers based on similarity analysis between speakers.

[0187] This document discloses character voice customization. In at least one embodiment, a feature is provided that allows users to customize their own character voices. The voice editor interface allows intuitive adjustment of parameters using sliders and dials. Preset templates include typical characters such as a "lively girl," a "refined middle-aged man," and a "kind mother." The voice mixing function blends the characteristics of multiple speakers to create a new voice. For example, the timbre of speaker A can be combined with the speaking style of speaker B. Effects processing adds special effects such as echo, reverb, and distortion. Age simulation allows the same character's voice to change from childhood to old age. Emotion presets allow users to customize expression patterns for joy, anger, sadness, and happiness. Created custom voices can be saved and shared on a project-by-project basis.

[0188] Hybrid voice generation is disclosed. In at least one embodiment, a hybrid voice is created by combining the recorded voice of a real speaker with a virtual generated voice. The natural voice of the real speaker is used as the base voice, with partial virtual processing applied. For example, the real speaker's voice is used for normal parts, and the pitch is raised only for emphasized parts to attract attention. Voice morphing technology smoothly changes the voice from the real speaker to a virtual character. Emotion amplification exaggerates the emotional expressions of the real speaker to create a more impressive voice. Voice rejuvenation processing makes the voice of an elderly speaker sound more youthful. Dialect conversion adds a regional accent to the voice of a standard speaker. These processes emphasize or modify only necessary features while maintaining the naturalness of the original voice. The quality of the generated hybrid voice is confirmed by listening to it in comparison with the original voice.

[0189] A method for building a voice library is disclosed. In at least one embodiment, a voice library is built by systematically organizing recorded and generated voices. The voices are organized by category, such as speaker, purpose, emotion, and length. Metadata includes the voice file name, speaker ID, recording date and time, duration, text content, emotion label, and quality score. A full-text search function enables fast search of relevant voices based on text content. A similar voice search function is implemented to search for acoustically similar voices. A tagging function classifies voices with tags such as "cheerful," "business," and "emergency." Usage history tracking records the frequency of use, location of use, and effectiveness measurement results for each voice. License management clarifies copyright, usage period, and usage restrictions. Periodic inventory encourages the deletion of unused voices and the re-recording of voices with degraded quality. Cloud storage integration enables efficient management of large voice libraries.

[0190] Real-time voice conversion is disclosed. In at least one embodiment, live input voice is converted to character voice in real time. Low-latency processing keeps the delay from input to output to 100 ms or less. Streaming processing executes the conversion process sequentially without waiting for the end of the voice. Adaptive noise reduction converts while removing environmental noise in real time. Speaker recognition distinguishes between multiple speakers and converts each into a different character. Emotion recognition determines the emotion of the input voice and outputs the corresponding emotional expression. Pitch correction corrects pitch discrepancies in real time. Volume normalization absorbs fluctuations in input volume and outputs at a constant level. Error handling implements recovery processing in the event of processing delays or voice interruptions. Buffer management achieves continuous voice output while absorbing network delays.

[0191] A store opening / closing management function for commercial facilities is disclosed. In at least one embodiment, the device automates staged audio distribution according to the commercial facility's business hours. The opening preparation stage includes an announcement for staff to begin preparations 60 minutes before opening, final confirmations 30 minutes before, confirmation of completion of opening preparations 10 minutes before, and background music and lighting adjustments 5 minutes before. At opening, a welcome message such as "Thank you for visiting XX Department Store today" is broadcast in a refined tone that matches the facility's brand image. The closing process includes staged announcements such as "We will be open until XX PM today" 60 minutes before closing, "We will be closing soon. Is there anything you forgot to buy?" 30 minutes before closing, "Thank you for visiting today. Please pay your bill" 10 minutes before closing, and "We have closed business for today. We look forward to seeing you again." Outside business hours, the volume is automatically attenuated by -20 dB, and broadcasts are suspended except for emergency announcements. It automatically responds to changes in business hours depending on the day of the week and the season, and can also flexibly accommodate special business hours during the New Year holidays and sales periods.

[0192] A system for gradual announcement of limited-time sales is disclosed. In at least one embodiment, the system implements gradual announcements to maximize the effectiveness of limited-time sales. Sale information, including product name, discount rate, start and end times, target sales area, and limited quantity information, is pre-registered as structured data. A 60-minute advance announcement builds anticipation by announcing, "Today, a limited-time sale will be held in the △△ sales area starting at ○○ o'clock." 30 minutes before the sale, specific product information is added, such as, "The limited-time sale starts in 30 minutes. Target products are up to 50% off ○○." Ten minutes before the sale, a sense of urgency is created, with the message, "The limited-time sale is coming soon! Limited quantities available, so please come early." Five minutes before the sale, a final announcement is made, "Five minutes until the start of the limited-time sale! We look forward to seeing you at the △△ sales area." During the sale, the system continues to announce, "The limited-time sale is currently underway," every 15 minutes, and similar gradual announcements are made 15 minutes before the end. The system dynamically adjusts by linking with sales data, providing additional announcements for popular products and changing the appeal points for unsuccessful products.

[0193] This paper discloses a method for optimizing delivery by time slot for each floor. In at least one embodiment, the device automatically generates an optimal delivery schedule based on each floor's product characteristics and customer behavior patterns. On the food floor, delivery focuses on morning market and fresh vegetables from 9:00 AM to 10:00 AM, lunch and prepared foods from 11:00 AM to 12:00 PM, snacks and sweets from 3:00 PM to 4:00 PM, and dinner ingredients and limited-time specials from 5:00 PM to 7:00 PM. On the clothing floor, office casual is promoted to office ladies visiting from 4:00 PM to 7:00 PM, and parent-child coordination is promoted to families visiting from 1:00 PM to 4:00 PM on weekends. On the cosmetics floor, time-saving makeup is promoted during the morning commute, touch-up services are offered during lunch breaks, and skin care is promoted in the evening. On the home appliance floor, new product demos are offered to accommodate an increase in male customers on weekends, and energy-efficient home appliances are promoted to housewives during the daytime on weekdays. Point-of-sale (POS) data analysis is used to learn sales by time slot over the past three months, and delivery is focused on time slots with high conversion rates. Taking seasonal fluctuations into consideration, cooling appliances are turned on in the morning in summer and heating appliances are turned on in the evening in winter.

[0194] This paper discloses congestion-based traffic flow control. In at least one embodiment, the device monitors the congestion status within the building in real time and provides voice guidance to optimize people flow. Using 3D cameras and laser sensors, the device measures the number of people in each area every minute and evaluates the congestion level on a five-point scale (empty / slightly crowded / crowded / very crowded / full). If the queue at the register exceeds five people, the device guides customers to an available register by saying, "Register XX on floor XX is available." If there are ten or more people waiting in front of the elevator, the device suggests an alternative, saying, "It's crowded. If you're in a hurry, please use the stairs." If one side of the escalator is crowded, the device encourages users to use both sides by saying, "Please use the right side as well." If the device detects a concentration in a specific sales area, the device encourages users to disperse by saying, "The XX sales area is very crowded. We also carry similar products in the △△ sales area." During peak hours on the food floor (6:00 PM - 7:00 PM), the device recommends mobile ordering by saying, "If you order in advance, we can deliver your order without waiting." Crowd-prediction AI predicts congestion 30 minutes in advance and provides advance dispersion guidance.

[0195] A weather-linked product promotion system is disclosed. In at least one embodiment, the device automatically promotes related products in conjunction with weather information. It obtains hourly forecast data (temperature, humidity, probability of precipitation, wind speed, and UV index) from the Japan Meteorological Agency API. If the probability of precipitation is 60% or higher, the system starts announcing three hours in advance, saying, "Rain is forecast for this afternoon. Umbrellas and raincoats are available near the first-floor entrance." If the temperature is forecast to be over 30°C, the system promotes heatstroke prevention products from the morning, saying, "Today is a midsummer day. We have cooling innerwear, sunscreen, and cooling products available." If the temperature is below 10°C, the system promotes cold weather products, saying, "We have cold weather gear, hot drinks, and hand warmers in a special corner." On days with high pollen counts, the system suggests allergy prevention measures, such as "Pollen masks, eye drops, and air purifiers at affordable prices." When a typhoon is approaching, the system encourages advance preparation, saying, "We have disaster preparedness supplies, preserved foods, and flashlights in stock." To combat poor health caused by changes in atmospheric pressure, health-related products such as headache medicine and energy drinks are also available. Products with high appeal are automatically selected through correlation analysis between past weather conditions and sales data.

[0196] A system for providing advanced notice of the start of an exhibition or show is disclosed. In at least one embodiment, the device provides advanced notices to effectively guide visitors to exhibitions or shows at cultural facilities. Event information, such as the name, venue, start and end times, capacity, fee, duration, and target age group, is pre-registered. A 30-minute advance notice is broadcast throughout the building, stating, "The △△ show will be held in the XX hall from XX o'clock. Those interested are requested to arrive early." A 15-minute advance notice includes specific availability information, such as, "15 minutes until the △△ show. The capacity is XX people, and △ people are currently waiting." A final notice is given 5 minutes before the show, stating, "The △△ show will begin shortly. If you arrive late, you may not be able to enter." The system works in conjunction with a crowd detection system to suggest alternative routes if the main traffic flow is congested, such as, "Entrance from the west exit is available," or "Using the stairs will provide a faster route than the elevator." After the show begins, the audio in the building is automatically attenuated by -10 dB to prevent acoustic interference with the performance. For popular shows, information about the next performance will also be provided at the same time to encourage people to spread out their attendance.

[0197] This document discloses information about facility etiquette and acoustic considerations. In at least one embodiment, the device provides timely guidance on etiquette rules specific to cultural facilities. At art museums, proximity sensors are used to transmit messages such as "Please refrain from using flash photography to protect the artworks" at the entrance to exhibition rooms and "Please do not touch the artworks" in sculpture areas. In museums' valuable materials rooms, a clear distinction is made between "Photography is prohibited. Please enjoy commemorative photos in areas where photography is permitted." At aquariums, consideration for the animals is encouraged with messages such as "Please do not strike the aquariums, as this will frighten the creatures." In library facilities, the volume is automatically reduced to 30% of normal volume, and a low-volume message is displayed stating, "Please cooperate in maintaining a quiet environment." In areas with restricted eating and drinking, the device provides guidance to alternative locations, such as "Please refrain from eating and drinking in this area. The rest area is on the XX floor." During times when many children are present (weekend mornings), the device automatically switches to more user-friendly messages such as "Please do not run" and "Please wait in line." Pictograms and multilingual audio are used in combination for foreign visitors.

[0198] This document discloses storytelling to promote visitor visits. In at least one embodiment, the device uses narrative audio to promote seasonal events and stamp rallies. During the stamp rally, the device announces the start of the adventure with a game-like message: "The adventure begins! The first stamp is in the dinosaur exhibition room." Upon reaching each checkpoint, the device presents the next goal, creating a sense of accomplishment with a message like, "You found it! The next hint is..." Depending on the user's progress, encouraging messages like, "You're halfway there!" and "Just two more to complete!" are delivered. Seasonal events are themed, such as "A journey through cherry blossom-related artworks" in spring, "A world of water for coolness" in summer, "Enjoying autumn art" in autumn, and "Warm artworks" in winter. Stories tailored to specific demographics are provided, such as an "Experience corner for families to enjoy with their children" and a "Romantic special nighttime exhibition" for couples. A system is also implemented that unlocks additional stories at specific locations through linkage with the AR app. For those who complete the rally, the device shares the joy of achievement with the user, saying, "Congratulations! Exchange for a souvenir at the XX counter."

[0199] A breeding event countdown system is disclosed. In at least one embodiment, the device effectively announces breeding events at zoos and aquariums. Since the popular content of animal feeding is "Penguin feeding time" during feeding time, the system creates excitement by announcing, "Penguin feeding time in 10 minutes!" The system provides a live broadcast-style atmosphere with announcements like, "The zookeepers started preparing five minutes ago," and "The penguins are getting excited three minutes ago." Highlights are announced with, "Starting soon! Watch them enthusiastically eat sardines." The zookeeper commentary highlights the event by highlighting their expertise with, "Today's commentator is Mr. / Ms. XX. He will provide a detailed explanation of penguin ecology." If an animal is unwell, the system offers an alternative, such as, "In consideration of Mr. / Ms. XX's health, today's feeding will be canceled. You can see him / her in good health on the live camera." During breeding and parenting seasons, the system provides special considerations, such as, "A baby has been born! Please watch the heartwarming mother and child." Based on crowd predictions, the system provides advance announcements such as, "This is a very popular event. We recommend reserving a good spot 15 minutes in advance."

[0200] Weather-adaptive outdoor event management is disclosed. In at least one embodiment, the device manages weather risks for outdoor events and provides appropriate guidance. In conjunction with weather radar, it monitors the approach of rainclouds to the venue in 10-minute increments based on a 1-km mesh precipitation forecast. The event decision criteria are set in stages: a warning is issued when wind speeds exceed 10 m / s, cancellation is considered when wind speeds exceed 15 m / s, and immediate cancellation when wind speeds exceed 20 m / s. Regarding rainfall, the event is proceeded with light rain (less than 1 mm / h), modified content when moderate rain (1-10 mm / h), and canceled when heavy rain (10 mm / h or more) is detected. Two hours before the event, the system issues a warning of uncertainty, stating, "Today's XX show will be held while monitoring the weather." A final decision is made 30 minutes before the event, clearly stating whether the event will proceed as scheduled, with some changes, or unfortunately canceled. In the event of cancellation, the system immediately suggests an alternative program, stating, "We have decided to cancel the event, prioritizing safety. Instead, you can enjoy XX indoors." It works in conjunction with a lightning detector, and if there is a risk of lightning, it will prioritize safety by telling people, "Please take shelter indoors as thunderclouds are approaching."

[0201] This document discloses a multilingual automated guidance system that utilizes a transportation information API. In at least one embodiment, the system obtains real-time operational information from various transportation systems and provides automated guidance in multiple languages. Information on delays, service suspensions, gate changes, and cancellations is obtained at one-minute intervals from railway company APIs, airline APIs, and bus operator APIs. Upon detecting an information update, voice generation begins within three seconds and delivery is completed within ten seconds. Information is inserted into the template "The [route name] is operating with a delay of approximately [delay time] due to [cause]" and simultaneously generated in Japanese, English, Chinese (Simplified and Traditional), and Korean. Based on impact assessment, delays of 30 minutes or more are delivered with top priority, delays of 10-30 minutes with high priority, and delays of less than 10 minutes with normal priority. Location-linked delivery limits guidance to the ticket gates, platforms, and waiting rooms of the relevant line, preventing information overload in unrelated areas. An alternative route suggestion function provides specific solutions, such as "If you are using Line XX, you can use alternative transportation to Line XX." When asked about the expected resumption of service, the report cited official announcements from the railway companies and avoided speculation.

[0202] This paper describes a traffic flow distribution system based on congestion prediction. In at least one embodiment, the system predicts congestion at transportation facilities and provides proactive dispersion guidance. It calculates real-time congestion levels based on automated ticket gate pass data, surveillance camera people counts, and IC card swipe counts. Using machine learning based on past data, it predicts congestion 30 minutes after an event with 85% accuracy based on the day of the week, time of day, weather, and event. During the morning rush hour (7:30-9:00), the system starts announcing "The peak of congestion is coming soon. If possible, please cooperate by staggering your commute." It compares the speed of people passing through each ticket gate and suggests specific distribution routes, such as "The South Gate is clear. Please cooperate in alleviating congestion at the North Gate." When the number of people waiting for an escalator exceeds 50, the system emphasizes the time benefit of using the stairs, saving three minutes. At baggage inspection areas, the system provides real-time guidance to the optimal lane, stating, "Lane 3 is the fastest." At the end of a large-scale event, the system manages gradual exits by announcing, "We are implementing a gradual exit process. Please follow the announcements and move slowly."

[0203] A lost and found information system is disclosed. In at least one embodiment, the device provides information about lost items efficiently and with privacy in mind. Lost and found information includes the item category (umbrella, wallet, cell phone, etc.), color, brand, location, and time of discovery. Personal information (name, contact information) is not included in the voice announcement, and only minimal characteristics are provided, such as "We have a black folding umbrella at platform number XX." For valuable items (wallets, cell phones, keys), details are withheld for security reasons, with announcements such as "We have your valuables in storage. If you have any information, please contact the XX counter." Regular announcements are sent three times a day, after the morning and evening rush hours (9:30, 19:30) and during the lunch break (12:30), with more frequent announcements during busy periods. For items stored for a long period of time (more than one week), the announcement encourages online confirmation, stating, "We have a large number of lost and found items. You can also check the lost and found center's website." For children's lost items, friendly language is used, such as, "We have your school bag and stuffed animal in storage."

[0204] This document discloses a system for preventing nuisance behavior and promoting good manners. In at least one embodiment, the device detects nuisance behavior and provides appropriate etiquette. Image recognition AI automatically detects escalator walking, cutting in line, sitting, and loud conversations. Acoustic sensors identify loud voices (over 70 dB), musical instrument playing, and speaker use. Within three seconds of detection, a pinpoint message is sent to the relevant area, stating, "Please stand and use the escalator." Frequency control ensures that the same message is sent five minutes apart to prevent discomfort from excessive warnings. The message escalates in stages, with the first message being a general announcement, the second message being more direct, such as "We request you to use the escalator," and the third message being linked to the dispatch of an attendant. Considering the time of day, the message is brief during rush hour, polite during the day, and lowered at night. Positive reinforcement is also provided to encourage good behavior, such as "Thank you for lining up to board the escalator" and "We appreciate your cooperation in maintaining good manners." Multilingual support is also available, incorporating emojis and pictograms for easy understanding by foreigners.

[0205] A step-by-step evacuation guidance system for disasters is disclosed. In at least one embodiment, the system implements step-by-step evacuation guidance to prevent panic when a disaster occurs. The system assesses threat levels on a five-level scale (Level 1: Caution, Level 2: Preparation Instructions, Level 3: Evacuation Begins, Level 4: Emergency Evacuation, Level 5: Crisis). In the initial stage, the system prioritizes personal safety by stating, "An earthquake has occurred. Please remain calm and hold on to the handrails." During the situation assessment stage, the system discourages hasty action by stating, "We are currently checking the safety of the building. Please wait where you are." At the start of evacuation, the system emphasizes maintaining order by stating, "Follow the instructions of the staff and begin evacuating calmly. Do not run." Route guidance is provided with specific and clear instructions, such as, "Please head to the east exit. The elevator is not in use." To prevent delays, the system performs a final confirmation, stating, "Those still inside the facility, please evacuate immediately." After evacuation is complete, the system guides users with instructions, such as, "The evacuation site is XX Square. Please cooperate with roll call."

[0206] A department-specific waiting time management system is disclosed. In at least one embodiment, the system efficiently manages waiting times at medical facilities and reduces patient anxiety. It works in conjunction with an electronic medical record system to calculate waiting times based on each department's reception number, current consultation number, and average consultation time. Based on standard consultation times for each department (e.g., 15 minutes for internal medicine, 20 minutes for surgery, and 10 minutes for pediatrics), the system also takes into account the characteristics of each doctor (e.g., careful but time-consuming). The waiting room displays a message every five minutes, quietly (usually at -10 dB), saying, "We are currently seeing patient number ○. It is estimated that patient number △ will arrive in approximately □ minutes." When there are five patients waiting, a message is displayed, saying, "Your turn will soon. Please prepare," encouraging mental preparation. When entering the examination room, the system announces, "Patient number ○, please enter examination room △," in a gentle, calm tone (speech rate 90%, pitch -5%). For patients who have been waiting for a long time, the system offers consideration by saying, "We apologize for the wait. If you are not feeling well, please let a staff member know." If the reservation time is significantly exceeded, an apology message will be automatically added.

[0207] This document discloses announcements based on infection control levels. In at least one embodiment, the device adjusts the intensity of preventive measures based on the infectious disease epidemic situation. The device coordinates with public health centers and the Ministry of Health, Labor and Welfare to assess the local epidemic level on a five-point scale. Level 1 (normal) provides basic hygiene guidance, such as "Please help prevent infection by washing your hands and gargling." Level 2 (caution) encourages enhanced prevention, such as "We recommend wearing a mask." Level 3 (alert) requests cooperation, such as "Please wear a mask. Temperature checks will be conducted at the entrance." Level 4 (high alert) clarifies entry restrictions, such as "Mask wearing is mandatory. Those with a temperature of 37.5 degrees or higher will not be admitted." Level 5 (emergency) requests behavioral restrictions, such as "To prevent the spread of infection, please refrain from visiting the clinic except for emergencies." By detecting crowding in waiting rooms, the device encourages dispersal with a message that "The waiting room is crowded. If possible, please wait in your car or outside." Disinfection timing also provides a sense of security, with the message "Common areas are disinfected every hour."

[0208] This document discloses an automated schedule guide for educational facilities. In at least one embodiment, the device automatically provides information about lectures and exams at the appropriate time. It connects with the academic affairs system to obtain timetables, classrooms, instructors, student numbers, and exam information. Ten minutes before a lecture, the device displays a notice in the hallway or lobby saying, "Lecture XX will begin soon in classroom XX." Five minutes before the lecture, the device reinforces the message, saying, "This is a subject where lateness is strictly prohibited. Please remain seated." Classroom changes are immediately broadcast to all relevant areas with an emergency message saying, "Today's lecture XX has been moved from classroom XX to classroom XX." Class cancellation information is provided with information such as, "Lecture XX at period XX is canceled. A make-up class will be held on XX / XX," along with an alternative date. During exams, the device provides instructions such as, "The exam begins 10 minutes ago. Please have your writing implements and student ID ready," and "Please turn off your cell phone," to prevent fraud. When using online classes in conjunction with online classes, the device clarifies the attendance format by stating, "Today's class will be a hybrid format. In-person participants should go to classroom XX." Library areas will automatically switch to silent mode.

[0209] A facility maintenance advance notification system is disclosed. In at least one embodiment, the device systematically notifies facility maintenance and inspection information. It works in conjunction with a facility management system to automatically obtain schedules for elevator, escalator, air conditioning, and cleaning inspections. Advance notice is given one week in advance, stating, "Regular elevator inspections will be conducted on XX / XX." The day before, specific impacts are announced, such as, "The east-side elevator will be unavailable from XX tomorrow to XX." On the day of the inspection, an alternative method is clearly stated, such as, "Inspection will begin at XX today. Please use the west-side elevator." At the start of the inspection, clear guidance is provided, such as, "Inspection work will now begin. We apologize for the inconvenience, but please use the stairs or the west-side elevator." Wheelchair users are given individualized attention, such as, "Barrier-free routes are available from the west side." Upon completion of the inspection, a notification is sent stating, "The inspection is complete. Normal access is resumed." Emergency maintenance is announced, along with the reason, such as, "Due to a facility malfunction, an emergency inspection will be conducted."

[0210] A medical emergency code response system is disclosed. In at least one embodiment, the system responds appropriately to emergencies in the medical field. It instantly identifies emergency codes such as Code Blue (cardiac arrest), Code Red (fire), and Code White (violence). In the medical staff area, the system clearly repeats the location twice: "Code Blue, 3rd floor East Ward. Code Blue, 3rd floor East Ward." It is not broadcast to the general patient area to prevent unnecessary anxiety. However, only when necessary is it provided with a roundabout message: "Medical staff is responding to the emergency. Please cooperate." It connects with medical equipment alarms, such as ventilators and electrocardiogram monitors, to notify the nurse's station, "The equipment alarm in room X is activated." Emergency requests to the pharmacy department are specifically directed as, "Request for emergency cart refill, 3rd floor nurse's station." Special silent settings are used in the operating room area, suppressing broadcasts except for the highest level of urgency during surgery. When transporting patients in a disaster, changes to the medical system are announced, such as, "A triage area will be set up at the main entrance."

[0211] This document discloses a system for making scheduled announcements to accommodate shift work in factories. In at least one embodiment, the system automates scheduled announcements to accommodate complex shift work systems. It manages various work patterns by area, including three-shift (early shift 6:00-14:00, middle shift 14:00-22:00, night shift 22:00-6:00), two-shift, and irregular shifts. Ten minutes before the start of work, the system provides step-by-step announcements, such as "Work will begin soon. Please check your work clothes and safety equipment," and five minutes before, "Please prepare for the morning meeting." At the scheduled time, the system motivates employees with an announcement of "Good morning. Please work safely today." At break time, the system clearly announces, "It's 10:00. Your 15-minute break will begin," and three minutes before the end, a warning of "Work will resume shortly." At lunchtime, the system divides employees into two groups, an early one and a late one, and distributes congestion by announcing, "Lunchtime for Group A. Lunchtime begins at 12:30." At the end of the shift, the system also encourages employees to clean up, saying, "Today's work is over. Please leave after completing 5S activities." In noisy areas (85dB or higher, such as press factories), the volume is automatically corrected by +10dB to ensure reliable transmission.

[0212] This document discloses a safety and health KY activity support system. In at least one embodiment, the device provides audio support for occupational safety and health activities. Before work begins each morning, the device encourages employees to practice KY (hazard prediction) activities, encouraging them to identify potential hazards for today's work. Priority themes are set for each day of the week, such as "Trip and Fall Prevention" on Monday, "Pinch and Entanglement Prevention" on Tuesday, and "Cut and Abrasion Prevention" on Wednesday. Specific check items are provided, such as "Are you wearing protective equipment correctly? Check your helmet, safety shoes, and safety glasses." At the beginning of each month, the overall goal is shared: "This month's safety goal: Thorough separation of forklifts and pedestrians." Near-miss incidents are shared horizontally, such as "Yesterday, there was a near-tip incident at Plant 3. Be careful of oil on the floor." When an accident occurs, an alarm sounds and then role-specific instructions are issued, such as "Emergency situation. An accident has occurred at Plant XX. A medical team will be dispatched to the scene. All others should stop work and wait." Raise awareness by saying, "Today marks XX days without an accident. Let's keep going at this pace and aim to break this record."

[0213] This document discloses warehouse picking deadline management. In at least one embodiment, the device manages work progress toward the shipping deadline via voice. It connects with a warehouse management system (WMS) to monitor shipping schedules, picking progress, and packaging status in real time. Sixty minutes before the deadline, the device shares the overall status with a message such as, "Please prioritize picking for the deadline at XX. The current progress rate is XX%." Thirty minutes before the deadline, the device provides area-specific progress information, such as, "30 minutes until the deadline. Area A is complete, but Area B needs to hurry." Ten minutes before the deadline, the device switches to emergency mode, requesting cooperation with a message such as, "10 minutes until the deadline! Unfinished work is concentrated in Area XX. Please help us." Five minutes before the deadline, the device encourages error prevention with a message such as, "Please make a final check. Please check inspection and packaging." Upon completion, the device shares a sense of accomplishment with a message such as, "All work for the deadline at XX has been completed. Thank you for your hard work." Express orders are handled with an interruption message, such as, "Please treat this as an express order with top priority." When a truck arrives, the device communicates closely with the user, saying, "The truck has arrived at Berth XX. Please prepare for loading."

[0214] An energy management and energy conservation encouragement system is disclosed. In at least one embodiment, the device monitors power usage and promotes energy-saving behavior. In conjunction with a demand monitoring system, the system calculates power usage every 30 minutes, the difference from the contracted power, and predicted maximum power. When the warning level (90% of the contracted power) is reached, the system requests an initial response, such as "Power usage is increasing. Please turn off unnecessary lighting and air conditioning." At the critical level (95%), the system issues an emergency response instruction, such as "We are at risk of exceeding the contracted power soon. Please turn off power except for production equipment." When an exceedance is predicted, the system issues quantitative instructions, such as "If this continues, we will exceed the contracted power. Please reduce the load at Factory No. XX by XX%." Specific time-of-day targets are provided, such as "2-3 PM is peak time. Please operate at a target XX kW or less." Monthly results are shared, such as "We have achieved XX% energy savings compared to last month." Temperature-linked reporting allows for seasonal responses, such as "Today is an extremely hot day. Please strictly set the air conditioning temperature to 28 degrees." Automatically generate records that comply with ISO50001 requirements.

[0215] A call center staffing optimization system is disclosed. In at least one embodiment, the system optimizes staffing according to the call center's call volume. It works in conjunction with a CTI (Computer Telephony Integration) system to monitor the number of incoming calls, waiting calls, average call time, and call abandonment rate in real time. When the number of waiting calls exceeds 10, an initial alert is issued, stating, "Incoming calls are increasing. Please remain seated if available." When the number exceeds 20, an alert is issued, stating, "Emergency support requested. Even those on breaks are requested to temporarily respond." A call abandonment rate of over 5% encourages efficiency by stating, "We apologize for keeping our customers waiting. Please cooperate in shortening call times." Conversely, when there are fewer calls, the system encourages appropriate breaks by stating, "Incoming calls are currently calm. Please take turns taking breaks." Skill-based allocation is also implemented, with the system assigning specialized responses, such as, "Incoming technical support calls are increasing. Those with the appropriate skills should be directed to window number 2." During shift changes, a message is sent, stating, "It's almost time for the shift change. Please confirm the handover details."

[0216] A MICE venue session management system is disclosed. In at least one embodiment, the system supports complex session management for international conferences and exhibitions. It works in conjunction with a conference management system to centrally manage each venue's capacity, current seating, speaker information, and language usage. Thirty minutes before a session, a multilingual announcement is made, such as, "Individual X's lecture will begin at 10:00 in Room A. Simultaneous interpretation will be available in Japanese, English, and Chinese." Fifteen minutes before a session, an alternative is offered, such as, "Approximately X seats remaining. If full, you can watch the live broadcast in Room B." Five minutes before a session, a reminder of proper etiquette is issued, such as, "The session will begin shortly. Please set your mobile phone to silent mode." Popular sessions are assigned with the message, "We have reached capacity. Please use the satellite venue in Room C." Time management is supported for transitions between sessions, such as, "There will be a 10-minute break until the next session. If you wish to move to another venue, please do so early." Networking time is promoted by a message, such as, "We are currently holding a coffee break in the lounge on the second floor." Exhibition booths encourage people to visit by displaying, "Today's featured exhibit is a demonstration of XX's AI technology."

[0217] This document discloses entrance and exit management for stadiums and theaters. In at least one embodiment, the device ensures smooth entry and exit at large-scale facilities. It works in conjunction with the ticket system to track the entrance gates, opening times, and travel times required to reach each seat. Sixty minutes before the doors open, basic information is provided, such as "Today's doors open at XX o'clock. Please enter through the designated gate." At the doors open, distributed entry is managed with instructions such as "The doors are now open. Please enter from Block A first." Entry rules are communicated, such as "Please cooperate with baggage inspection. Food and drink are prohibited." Ten minutes before the start of the show, a message is sent encouraging final preparations, such as "The show will begin soon. Please use the restroom now." At halftime, a message is sent encouraging dispersion, such as "There will be a 20-minute break. Concessions and restrooms are expected to be crowded." After the show, a regulated exit is implemented, with instructions such as "Thank you for coming today. Please exit first from Block A." Caution is also given for the journey home, with instructions such as "Public transportation will be congested. Please allow yourself plenty of time."

[0218] This document discloses congestion management for hotel breakfast venues. In at least one embodiment, the device predicts and manages congestion in a hotel's breakfast venue. The device obtains the number of guests and check-out times from the PMS (Property Management System) to predict breakfast demand. The night before, the device displays a message on the guest room TV saying, "Tomorrow's breakfast is expected to be busy between 7:00 and 8:00. The 6:00 and 9:00 a.m. times are relatively quiet." The morning of the day, the device provides real-time information such as, "The breakfast venue is currently XX% crowded. Wait times are approximately XX minutes." When the breakfast venue is crowded, the device offers an alternative option, saying, "We are very busy. Room service is also available." For group guests, the device provides a separate breakfast venue for groups. The device announces the last breakfast order 30 minutes before the last order, saying, "Last breakfast orders are at 9:30," and 15 minutes before, saying, "Last orders will be coming soon. Please come early." The device emphasizes freshness by displaying messages such as, "We are currently replenishing bread" and "Fruit has been added." For allergy-friendly options, the device provides personalized guidance, saying, "Please ask a staff member for allergy-friendly menu items."

[0219] Disclosed is support for banquet and party progress. In at least one embodiment, the device provides audio support for the smooth progress of a banquet. The device obtains the start time, food course, schedule, and special requests from a banquet reservation system. Thirty minutes before the start of the banquet, the device prompts staff to prepare by saying, "30 minutes before Mr. / Ms. XX's party. Please make a final check of the venue." At the start of reception, the device welcomes guests by saying, "We have opened reception for the XX party. Welcome drinks are available." Five minutes before the start of the banquet, the device prompts guests to take their seats by saying, "The banquet will begin shortly. Please be seated." Before the toast, the device attracts attention by saying, "We will now propose a toast." The device adjusts the timing of food service by saying, "Appetizers will be served" and "The main dish is ready." Before speeches, the device encourages silence by saying, "We apologize for interrupting during conversation. Please accept your speech." Thirty minutes before the end of the banquet, the device manages time by saying, "The party will finish at XX o'clock," and 10 minutes before the end, the device manages time by saying, "The party will close shortly." The information about the after-party will direct you to the bar on the second floor.

[0220] This document discloses weather risk management for outdoor events. In at least one embodiment, the device monitors weather risks for outdoor events and provides guidance with safety as its top priority. It works in conjunction with a lightning detection system, and if it detects lightning within a 10-kilometer radius, it immediately warns, "A thundercloud is approaching. Please take shelter indoors for safety." If wind speed measurements exceed 15 m / s, it instructs participants to evacuate, saying, "Due to strong winds, it is dangerous under tents. Please move inside." Heavy rain forecasts encourage advance precautions, stating, "Heavy rain is expected in 30 minutes. Please prepare rain gear." If temperatures exceed 35°C, it urges participants to take health precautions, stating, "A heatstroke alert has been issued. Please hydrate and rest in the shade." If an event is canceled, it clearly notifies participants, "Today's event has been canceled to ensure safety." Refund information provides specific procedures, such as, "Ticket refunds will be accepted at the XX counter or online until XX date." In the event of a postponement, it indicates future measures, such as, "The postponed date is scheduled for XX / XX. Details will be announced on the official website." During evacuations, we aim to maintain order by asking people to "remain calm and follow the instructions of our staff."

[0221] This document discloses the basic processing of multilingual automatic translation. In at least one embodiment, the device translates Japanese source text into languages ​​around the world with high accuracy. The original text announcement uses morphological analysis to analyze sentence structure and clarify subject, predicate, object, and modifier relationships. The country selection interface automatically selects the country's official language by clicking on a world map or selecting a country name from a pull-down menu. In multilingual countries, regional language selection is also possible, such as Switzerland (German, French, Italian, Romansh). The translation process uses neural machine translation (Transformer, BERT, GPT) to achieve natural translation that takes context into account. The speech synthesis uses a DNN model trained on speech corpora of native speakers of each language to accurately reproduce intonation, accent, and rhythm. The distribution scheduler has the function of simultaneously distributing all translated versions of the original text and controlling the language latency to within ±100 ms.

[0222] Support for 10 major languages ​​and regional variations is disclosed. In at least one embodiment, the device comprehensively supports the world's major languages ​​and their regional variations. The 10 basic languages ​​are English (1 billion people), Simplified Chinese (900 million people), Traditional Chinese (50 million people), Korean (70 million people), Spanish (500 million people), Portuguese (250 million people), French (280 million people), German (100 million people), Italian (60 million people), and Russian (250 million people), covering more than 70% of the world's population. For English, the device distinguishes between American English (color, center) and British English (colour, centre) spelling and vocabulary and uses them appropriately. For Chinese, the device correctly selects different expressions (calculator / computer) between simplified Chinese (mainland China) and traditional Chinese (Taiwan, Hong Kong). For Portuguese, the device considers differences between European and Brazilian personal pronouns (tu). Spanish also reflects the vocabulary differences (ordenador / computadora) between Spain and other Latin American countries.

[0223] This document discloses a method for improving translation accuracy through context analysis. In at least one embodiment, the device provides an appropriate translation by deeply understanding the intent and context of the original text. The context analysis engine references surrounding sentences, entire paragraphs, and even past distribution history to correctly interpret ambiguous words and expressions. For example, "It's fine" can be translated as "That's fine" (affirmative) or "No, thank you" (negative) depending on the context. Sentiment analysis identifies tones such as joy, gratitude, apology, and warning, and selects vocabulary to convey the same sentiment in the translated text. Specialty determination prioritizes medical terminology for medical use and legal terminology for legal use. The translation accuracy score is calculated on a scale of 0-100, weighted by grammatical accuracy (40%), semantic consistency (40%), and naturalness (20%). Translations with a score below 80 are displayed in yellow, and those below 60 are displayed in red, indicating a manual review is recommended. Low confidence levels are underlined, and alternative translations are presented in a tooltip.

[0224] Appropriate handling of proper nouns and numbers is disclosed. In at least one embodiment, the device appropriately distinguishes between elements that should not be translated and elements that should be converted. The proper noun dictionary includes brand names (Uniqlo → UNIQLO), product names (PlayStation → PlayStation), and facility names (Tokyo Skytree → Tokyo Skytree), and only transliterates them phonetically, not semantically. Date notation is automatically converted to US format (MM / DD / YYYY), European format (DD.MM.YYYY), and ISO format (YYYY-MM-DD). Time is displayed in either a 12-hour format (3:30 PM) or a 24-hour format (15:30). Numbers are separated by three digits (1,000) or four digits (10,000), and decimal points (.) or commas (,) are used. Currency conversion is performed at real-time rates, with both figures displayed side-by-side, such as "1,000 yen (approximately 9 dollars, 8 euros)." Units are displayed in both metric and imperial units, such as "temperature 30 degrees (86°F)" and "distance 1 km (0.6 miles)."

[0225] This document discloses honorific language levels and age-specific expression adjustments. In at least one embodiment, the device selects the appropriate honorific language level according to the sociolinguistic norms of each language. Japanese honorifics (respectful, humble, and polite language) correspond to formal expressions such as "Sir / Madam" and "Would you kindly" in English, a six-level honorific system in Korean, and honorific expressions such as "Yu" and "Qing" in Chinese. Depending on the type of facility, the most polite expression is selected for luxury hotels, while friendly expressions are selected for casual establishments. By age group, for children, "everyone" is translated to "everyone" and "friends" to be more friendly, while for elderly people, "seniors" is translated to "respected seniors" to show respect. Adjustments are also made based on time of day, selecting natural greetings such as "Good morning" in the morning and "Good evening" in the evening. Cultural considerations include adjustments based on language and culture, such as English-speaking countries, which avoid imperative forms and frequently use request forms such as "Please" and "Kindly," and German-speaking countries, which prefer more direct expressions.

[0226] This document discloses a method for optimizing reading time. In at least one embodiment, the device adjusts the length of the translated text to match the original text to unify delivery timing. The number of characters is adjusted during translation, taking into account the average information density of each language (100% Japanese, 95% Chinese, 130% English, and 145% German). Because English tends to have 1.3 times the number of characters as Japanese, redundant expressions are simplified (e.g., "at" → "at"; "can" → "can"). Reading speed is also optimized for each language, with the standard speeds being 300 characters per minute for Japanese, 150 words per minute for English, and 250 characters per minute for Chinese. If a 30-second original text is translated to 45 seconds, a summarization algorithm removes less important modifiers to adjust the time. Conversely, if the text is too short, an explanation is added to make it easier to understand (e.g., "Sale" → "Special Sale Event"). Prosody control adjusts the length of pauses to fine-tune the overall time. When parallel delivery is performed, pauses are inserted in other languages ​​to match the longest language, synchronizing the end timing.

[0227] A terminology dictionary and learning function are disclosed. In at least one embodiment, the device has a dictionary function and learning mechanism for accurately translating facility-specific terminology. The industry-specific terminology dictionary contains 100,000 words across 20 fields, including retail (POS, SKU, VMD), healthcare (MRI, ICU, triage), and manufacturing (JIT, SPC, OEE). The facility-specific dictionary allows pre-registration of menu names, service names, department names, etc. to ensure consistent translation. The frequency of term usage is analyzed, and frequently occurring terms are registered in the translation memory to speed up translation. The new word detection function generates a predicted translation from context when a term not in the dictionary is discovered and prompts the administrator for confirmation. The system learns more appropriate translations from user revision history and fine-tunes the translation model. Translation dictionaries can be shared among similar facilities to establish industry-standard translations. Version control allows tracking of term change history and the ability to revert to previous translations as needed.

[0228] This document describes back-translation verification and quality assurance. In at least one embodiment, the device automatically verifies the validity of translations through back-translation. The translated text is then translated back into the source language (back translation), and its semantic similarity with the source text is evaluated using BERTScore, BLEU, and METEOR. Similarity scores below 80% require further review, while scores below 60% recommend retranslation. Important information (price, date, time, location) is individually checked to ensure that numerical values ​​and proper nouns are correctly preserved. A grammar checker verifies the grammatical accuracy of the translation and detects unnatural word order and conjugations. The native check function uses a native speaker AI model for each language to rate naturalness on a scale of 0-100. Cross-lingual verification compares translation results from multiple interlingual languages ​​to ensure consistency. Important announcements (emergency, safety, financial) are flagged for final human review. A quality log is compiled to monitor changes in translation quality over time.

[0229] This document discloses a system for prioritizing translation of emergency information. In at least one embodiment, the device dynamically controls the priority of translation processing according to the level of urgency. Urgency is classified into five levels (urgent, urgent, important, normal, and low), with translations completed within 0.5 seconds for emergency information and within 2 seconds for urgent information. Speed ​​is prioritized over completeness for emergency information, and distribution begins once basic information (what, where, and how) is conveyed. Sentences containing safety-related keywords (evacuation, danger, caution, stop) are automatically prioritized. Pre-translated templates are used for standard emergency messages (evacuation, fire evacuation) for instantaneous distribution. In the event of a disaster, all normal translation processing is halted, and resources are focused on translating emergency information. To prevent damage caused by mistranslations, emergency information is verified using multiple translation engines, and translations with the highest match are used. Pictograms and warning sounds are also used to communicate information across language barriers.

[0230] The integrated use of multiple translation engines is disclosed. In at least one embodiment, the device uses multiple translation engines to select the optimal translation. Queries are sent simultaneously to five or more engines, including Google Translate, DeepL, Microsoft Translator, Baidu Translate, and Papago. Leveraging each engine's strengths (Google for multilingual support, DeepL for European languages, and Baidu for Chinese), the optimal engine is selected for each language pair. An ensemble approach integrates multiple translation results and adopts the most frequently occurring word-level translation. Confidence voting marks translations that are consistent across three or more engines as highly reliable. Cost optimization prioritizes the use of free tiers and only uses paid APIs when high quality is required. Engine response times are monitored, and fallback to a different engine is performed if latency is too high. A / B testing continuously evaluates the actual translation quality of each engine and dynamically adjusts the adoption ratio.

[0231] This document discloses control of sequential and parallel delivery modes. In at least one embodiment, the device selects the optimal multilingual delivery mode depending on the situation. In sequential mode, each language is played in its entirety before moving on to the next language in the order of Japanese → English → Chinese → Korean. A 0.5-second distinguishing pause is inserted between each language to clarify the language transition. The total delivery time is the sum of the languages, which is 4 languages ​​x 30 seconds = 2 minutes, but it has the advantage of ensuring that all languages ​​are delivered. In parallel mode, multiple speaker zones are used to simultaneously deliver Japanese in the east zone, English in the west zone, Chinese in the north zone, and Korean in the south zone. Directional and parametric speakers are used to minimize sound mixing. In emergencies, the device automatically switches to parallel mode, prioritizing immediate delivery of all languages. During busy times, sequential mode is used to deliver messages in order to prevent sound confusion. Flexible switching is possible depending on the time of day, such as sequential during the day and parallel at night.

[0232] Dynamic management of language priorities is disclosed. In at least one embodiment, the device optimizes delivery priorities by analyzing the language distribution of facility users. Real-time language demand is estimated based on the language settings of Wi-Fi-connected devices, the language selection in the app, and the number of visitors by country. For example, during times when there are many Chinese group visitors, the priority of Chinese is increased and delivery frequency is doubled. Analysis of weekday patterns adjusts the ratio to 60% Japanese, 30% English, and 10% other languages ​​on weekdays, and 40% Japanese, 30% English, 20% Chinese, and 10% Korean on weekends. During international events, languages ​​of participating countries are temporarily added and prioritized for delivery. Response rates by language (language selection in the app, language of inquiries) are measured to learn actual demand. Sales contribution analysis prioritizes language groups with high purchasing power. However, a minimum delivery frequency (at least once per hour) is guaranteed for minority languages ​​to maintain inclusiveness.

[0233] Language group management and batch distribution are disclosed. In at least one embodiment, the device groups similar languages ​​for efficient management. Languages ​​are grouped into Asian languages ​​(Japanese, Chinese, Korean), European languages ​​(English, German, French, Italian, Spanish), and Slavic languages ​​(Russian, Polish, Ukrainian). Classification by writing system (Chinese characters, Latin alphabet, Arabic characters) is also possible. Similar translation patterns are shared within a group to streamline processing. For example, European languages ​​have similar grammatical structures, so translation processing is performed in batches. Distribution schedules are also set for each group, such as "Asian languages: every hour, European languages: every hour, 30 minutes." A minimum of five minutes is maintained between groups to prevent interference between them. In an emergency, all groups are started simultaneously for parallel distribution. Languages ​​for major customer groups are distributed first based on group priority.

[0234] This document discloses a scheduling system that takes time differences into account for international facilities. In at least one embodiment, the system provides schedules that take time differences into account for international airports and global corporations. The system sets the time zone associated with each language (Japan JST, China CST, the United States EST / PST, and Europe CET) to enable guidance in local time. The system converts "The flight is scheduled to arrive at 3:00 PM local time" into the appropriate time display for each language. The automatic daylight saving time adjustment function recognizes DST periods in Europe and the United States and adjusts for a one-hour time difference. For facilities that operate 24 hours a day, the system sets active time periods for each language, limiting delivery times to, for example, 5:00 AM to midnight for Japanese, 24 hours for English, and 7:00 AM to 11:00 PM for Chinese. International conference call information takes into account the business hours of each country and displays multiple times, such as "9:00 AM in New York, 2:00 PM in London, and 11:00 PM in Tokyo." The repetition frequency is also set by language, adjusting according to demand, such as every 10 minutes for the primary language and every 30 minutes for the secondary language.

[0235] This document discloses language-specific acoustic optimization. In at least one embodiment, the device performs optimal speech processing based on the acoustic characteristics of each language. For Chinese (a tonal language), pitch information is important, so the low-frequency range (100-500 Hz) is emphasized by +3 dB. For English, which has many consonants, the high-frequency range (2-4 kHz) is emphasized by +2 dB to improve intelligibility. For Japanese, which is vowel-centered, the mid-range (500-2 kHz) is kept flat. For Arabic, which has pharyngeal sounds, special filtering is applied. In noisy environments, the Lombard effect is simulated for each language, widening the difference in tones for Chinese, lengthening consonants for English, and clarifying vowels for Japanese. Volume balance is adjusted by +2 dB for quiet languages ​​(Japanese) and -2 dB for loud languages ​​(Arabic) relative to the reference sound pressure level (70 dB). In reverberant environments, optimal speech speed adjustment is performed for each language, slowing English by 10% and Japanese by 5%.

[0236] A method for synchronizing multilingual streaming is disclosed. In at least one embodiment, the device precisely synchronizes the timing of multiple language streams. A master clock synchronizes the start times of all languages ​​to within ±50 ms. The streaming time for each language is pre-calculated and based on the longest language (usually German or Finnish). For shorter languages, the time is adjusted by extending inter-sentence pauses or adding additional explanations. During parallel streaming, the delay times of all speakers are measured and delay compensation is used to ensure simultaneous arrival. During network streaming, a buffering strategy maintains synchronization even with packet delays. Streaming progress is visualized with a progress bar, allowing the progress of each language to be seen at a glance. Anomaly detection alerts if a specific language is delayed and automatically triggers resynchronization. In recording mode, all languages ​​are saved as multiple audio tracks for later playback.

[0237] This paper discloses area-specific language distribution control. In at least one embodiment, the device optimizes the distribution language according to the characteristics of each area within the facility. The international terminal of an international airport distributes all four languages ​​(Japanese, English, Chinese, and Korean), while the domestic terminal is limited to two languages ​​(Japanese and English). The duty-free shopping area prioritizes Chinese, while the business lounge prioritizes English. In the restaurant area, the language is selected according to each store's cuisine genre (Chinese, Italian, or Japanese). Beacons and Wi-Fi positioning are used to track users' locations, and the distribution language dynamically switches as they move. The elevators are small, so only the two main languages ​​are distributed, while the large lobby distributes all languages, adjusting the language distribution according to the size of the space. In the international conference center, the language set of participating countries is pre-registered for each conference and automatically switched. The children's area focuses on easy-to-understand expressions in native languages, mixed with educational foreign language phrases. The staff area uses only native languages ​​for business communications.

[0238] A method for restricting language distribution by time period is disclosed. In at least one embodiment, the device appropriately restricts the languages ​​distributed depending on the time period. During late-night hours (11:00 PM - 5:00 AM), only two primary languages ​​(Japanese and English) are available, and the volume is attenuated by -6 dB to minimize noise. During early morning hours (5:00 AM - 7:00 AM), languages ​​are gradually added, transitioning to a full language distribution system by 7:00 AM. During lunchtime (11:30 AM - 1:30 PM), restaurant information is actively distributed in all languages. During the evening rush hour (5:00 PM - 7:00 PM), traffic information is prioritized and commercial information is suppressed. Due to the high number of tourists on weekends, multilingual support is strengthened more than usual. On holidays and event days, a special system is implemented in which all languages ​​are distributed 24 hours a day. During maintenance hours, distribution is kept to a minimum to avoid disrupting work. During energy-saving hours, distribution frequency is halved to reduce power consumption.

[0239] A visual display of the broadcast status is disclosed. In at least one embodiment, the device visually indicates the language currently being broadcast. A large flag icon of the language being broadcast is displayed on digital signage, flashing in sync with the audio. Subtitles are displayed, allowing the audio content to be confirmed in text. The next broadcast language and waiting time are displayed as a countdown, such as "Next: English in 30 seconds." A Gantt chart of the broadcast schedule by language is displayed on a timeline, visualizing the overall flow. A QR code (registered trademark) is displayed, and by scanning it with a smartphone, the user can receive streaming audio in the desired language. LED indicators color-code the language each speaker is broadcasting (Japanese: white, English: blue, Chinese: red, Korean: yellow). Sign language videos are also displayed synchronously for the hearing impaired. A scrolling display of the broadcast history allows users to check content they missed.

[0240] This document discloses language identification sounds and transition effects. In at least one embodiment, the device auditorily clarifies language transitions. At the start of each language, a unique identification sound (Japanese: koto sound, English: bell sound, Chinese: gong sound, Korean: gayageum sound) is played for 0.5 seconds. The identification sound is selected from culturally familiar musical instruments and set at a volume that is not unpleasant (-10 dB). When switching languages, a fade-out / fade-in effect is used to ensure a smooth transition. In an emergency, the identification sound is omitted and the main text begins immediately. The background music is also changed depending on the language, creating a cultural atmosphere by using Japanese-style background music for Japanese, pop music for English, and the sound of an erhu for Chinese. A longer silence (1 second) is inserted between language groups to clarify the transition. During continuous streaming, silence between languages ​​is minimized (0.2 seconds) to ensure efficient information transmission.

[0241] Adaptation of cultural expressions is disclosed. In at least one embodiment, the device automatically selects expressions that conform to the cultural norms of each language. Greetings are expressed using the most natural expressions in each culture, such as "Irasshaimase" (Japanese), "Welcome" (English), "Ahlan wa sahlan" (Ahlan wa sahlan) (Arabic), and "Namaste" (Hindi). Expressions of gratitude also reflect cultural nuances, such as "Thank you so much" (emotional) in American English, "Much obliged" (modest) in British English, and "Sore imasu" (humble) in Japanese. Due to cultural differences in apologizing, Japanese uses frequent "I'm sorry," English uses the minimal "We apologize," and Chinese uses expressions that emphasize explaining the reason. A taboo detection engine detects references to inappropriate numbers (4 in China, 9 in Japan), colors (white in China, black in Western countries), and animals (pig in Muslim countries, cow in Hindu countries) and replaces them with alternative expressions. To maintain religious neutrality, terms specific to specific religions are avoided and universal expressions are selected.

[0242] This document discloses voice personalization by language. In at least one embodiment, the device selects the optimal voice character for each language. The default settings for Japanese are a female voice (20s-30s, cheerful and polite), English allow for gender selection (from the perspective of gender equality), and Arabic allow for a male voice (cultural preference). Age settings are also adjusted, with a younger voice (emphasizing liveliness) for Chinese and a calmer, middle-aged voice (emphasizing trustworthiness) for German. Regional accents are selectable for English, including American, British, Australian, and Indian accents, and for Spanish, including Spanish, Mexican, and Argentine accents. In addition to standard Chinese, Cantonese and Mandarin Chinese are also offered. Voice quality adjustment allows customization based on the intended use, such as a firm voice for business establishments and a friendly voice for entertainment establishments. Emotional expression is also adjusted, with a rich accent for Latin-based languages ​​and a more restrained accent for Germanic languages.

[0243] Cultural prioritization of information is disclosed. In at least one embodiment, the device prioritizes delivery of culturally important information. Japanese culture adheres to a polite greeting and introduction followed by the main topic. German culture emphasizes efficiency and states the main points first. Arab culture emphasizes relationship-building language and avoids direct requests. Chinese culture emphasizes face-saving language and conveys negative content indirectly. American culture emphasizes positive and forward-looking language and emphasizes success and achievement. Information selection is also culturally dependent, with Japanese culture providing detailed explanations, American culture focusing on the main points, and Chinese culture emphasizing numbers and achievements. Time is also handled differently, with monochronic cultures (German, Japanese) providing precise time and polychronic cultures (Latin, Arabic) providing approximate time. Privacy considerations dictate minimal personal information for European cultures and inclusion of affiliation and title for Asian cultures.

[0244] Consideration of cultural meanings of colors and numbers is disclosed. In at least one embodiment, the device performs translations that take into account the cultural meanings of colors and numbers. "Red" means loss in the West but is auspicious in China, so the device translates it as "deficit" or "prosperous" depending on the context. Because white represents cleanliness and purity in Japan, death and misfortune in China, and purity and peace in the West, the device adjusts the representation of "white flowers" appropriately. Numbers such as 4 (sounds like "death" in Chinese), 9 (sounds like "suffering" in Japanese), 13 (sounds like "bad luck" in the West), and 666 (the number of the devil in Christianity) are avoided, and alternative representations are used in pricing and room numbers. Conversely, numbers 8 (auspicious in China) and 7 (lucky in the West) are actively used. Gesture representations, such as a thumbs-up (sounds like "good" in the West and an insult in the Middle East) and a palm-opening (sounds like an insult in Greece), are also detected and appropriately explained. Animal metaphors are also culturally appropriate, with dragons (sacred in Asia, evil in the West), pigs (unclean in Islam), and cows (sacred in Hinduism).

[0245] Disclose legal requirements and compliance information. In at least one embodiment, the device uses language that complies with the laws and regulations of each country. To comply with the EU General Data Protection Regulation (GDPR), the European language version includes an explicit consent statement regarding the handling of personal information. To comply with the US ADA, the English version uses language that is considerate of people with disabilities. In accordance with Japan's Act against Unjustifiable Premiums and Misleading Representations, superlative terms such as "best" and "number one in Japan" are not used unless there is objective evidence. In accordance with China's Advertising Law, absolute terms such as "national-grade," "top-grade," and "best" are prohibited. Pharmaceutical efficacy claims are regulated by the FDA (USA), EMA (Europe), and PMDA (Japan), and only include approved efficacy claims. For health foods, avoid pharmaceutical terms such as "treatment" and "prevention" and stick to terms such as "support" and "maintenance." For financial products, include risk descriptions in dosages and descriptions appropriate to each country's regulations. For tobacco and alcohol products, clearly state the age restrictions in each country (21 in the US, 20 in Japan, and 18 in Europe).

[0246] Cultural adaptations for holidays and anniversaries are disclosed. In at least one embodiment, the device delivers special messages corresponding to national holidays and anniversaries. Global holidays (New Year's, Christmas) are delivered in all languages, but the wording is culturally tailored ("Happy New Year" in the West, "Happy New Year" in China, "Happy New Year" in Japan). National holidays (Independence Day in the United States, National Day in China, Emperor's Birthday in Japan) are delivered in the relevant language only. Religious holidays (Ramadan, Diwali, Hanukkah) include appropriate greetings in the language of the majority of followers of that religion. Seasonal events are reversed between the northern and southern hemispheres, with December representing winter in Japan and summer in Australia. Anniversary sale announcements are also tailored to each country's commercial practices (Black Friday in the United States, Double Eleven in China, New Year's sale in Japan). War and disaster anniversaries use language that is sensitive to the sentiments of the relevant country and avoid inappropriate commercial use.

[0247] Cultural adjustment of emotional expression is disclosed. In at least one embodiment, the device optimizes the intensity of emotional expression depending on the culture. Mediterranean and Latin cultures (Italy, Spain, Brazil) are expressive, expressive with a strong intonation, and use a lot of laughter and exclamations. Nordic and Germanic cultures (Germany, Sweden, and the Netherlands) are restrained and state facts matter-of-factly. Asian cultures are neutral, Japan is reserved, Korea is somewhat emotional, and China is situation-dependent. Expressions of joy are also adjusted: American "Awesome!" or "Amazing!" (exaggerated), British "Quite good" or "Rather nice" (modest), and Japanese "Yoroshii desu ne" (indirect). Laughter is also expressed differently: "hahaha" (Western), "hahaha" (Chinese), "fufufu" (Japanese), and "jajaja" (Spanish). Expressions of sadness and sympathy are expressed by selecting appropriate words of comfort, taking into account cultural differences in views on life and death.

[0248] The device provides guidance on cultural differences in business practices. In at least one embodiment, the device appropriately guides users through cultural differences in business etiquette. The device explains that exchanging business cards is polite in Japan with both hands, while in China, the highest ranking person should be first, and in the West, it is concise. Regarding meeting procedures, the device explains that Germany is punctual and sticks to the agenda, while Japan emphasizes laying the groundwork and meetings are a place to confirm, and America emphasizes brainstorming. Regarding business negotiations, the device explains that Arabs spend time building relationships, China emphasizes dinners, Japan emphasizes long-term relationships of trust, and America emphasizes efficiency and ROI. Payment methods also reflect preferences: China prefers mobile payments (Alipay, WeChat Pay), Japan favors cash, and Europe favors card payments. Regarding dress codes, the device explains that Japan prefers dark suits, Silicon Valley prefers casual wear, and the Middle East prefers conservative attire. Regarding presentation styles, the device explains that America prefers passion and storytelling, Japan prefers data and humility, and Germany prefers logic and accuracy.

[0249] Disclose considerations for dietary restrictions. In at least one embodiment, the device appropriately guides users to religious and health dietary restrictions. For Muslims, the device clearly displays whether the product is Halal certified and whether it is pork- and alcohol-free, with "This is Halal certified" displayed in English. For Jews, the device informs users about kosher certification and the separation of meat and dairy. For Hindus, the device emphasizes beef-free and vegetarian options. For Buddhists, the device informs users about vegetarian cuisine and the five pungent vegetables (garlic, chives, etc.). For vegans and vegetarians, the device clearly displays plant-based and animal-free options in each language. Allergy information highlights the seven most important allergens (egg, milk, wheat, buckwheat, peanut, shrimp, and crab) in all languages. Special diets such as gluten-free and lactose-free are also explained in detail in languages ​​where demand is high. Cooking methods are also honestly communicated, including "fried in the same oil" and "possibility of contamination."

[0250] Humor and localization are disclosed. In at least one embodiment, the device uses culturally appropriate humor and wordplay. Puns are used actively in Japanese, moderately in English (using "puns"), and avoided in German. Jokes that require cultural context are explained and context is provided with a "This is a reference to..." caption. Buzzwords and memes are used actively in youth-oriented facilities, but are updated regularly to prevent them from becoming outdated. Slang is used sparingly in casual settings and avoided in formal settings. Self-deprecating humor is preferred in the UK, moderated in the US, and avoided in Asia. Irony and satire are perceived differently across cultures, so care is taken to avoid misunderstandings. Seasonal jokes (e.g., April Fool's Day) are used only within the context of that culture. The personalities of corporate characters and mascots are also tailored to the preferred types in each culture (Japan: cute, America: cool, Europe: sophisticated).

[0251] A multilingual audio guide for the visually impaired is disclosed. In at least one embodiment, the device provides detailed audio information in each language for the visually impaired. Spatial information is provided using a clock face to indicate direction ("Exit at 3 o'clock") and step counts ("Approximately 20 steps ahead"). Steps and obstacles are specifically guided, such as "Five more steps to reach three uphill steps, handrail on the right." Braille information is output to a braille display or embosser, conforming to the braille standard for each language (Japanese braille, Grade 2 English Braille, Chinese blind script). Audio speed is adjustable from 50-200%, and pitch can be changed by ±50% to accommodate individual hearing needs. Haptic feedback works in conjunction with a smartwatch or wearable device, providing direction via a vibration pattern (for example, turning right, two vibrations on the right). An audio beacon continuously emits a sound from the destination to assist with sound localization.

[0252] A multilingual sign language interpretation system is disclosed. In at least one embodiment, the device automatically generates sign language versions for each country to provide information to the hearing impaired. It supports major sign language systems, including American Sign Language (ASL), British Sign Language (BSL), Japanese Sign Language (JSL), and Chinese Sign Language (CSL). A 3D avatar accurately reproduces hand shapes, movements, and positions, including non-manual markers (facial expressions, mouth shape, and body position). Regional differences in sign language (differences between Japanese Sign Language in the Kanto and Kansai regions) are accommodated and selectable. Finger spelling is used to express proper nouns and new words, enabling the communication of words not in sign language. Synchronized display with subtitles allows for the combined use of speech reading and sign language, improving comprehension. The speed of sign language interpretation is adjustable within a ±30% range, catering to beginners and experts alike. Important information is displayed in large, slower sign language to ensure accurate communication. A recording function allows for the saving of sign language videos for later review.

[0253] A simple translation function for children is disclosed. In at least one embodiment, the device generates simple translations tailored to a child's comprehension level. Vocabulary limits are based on grade-level vocabulary (1,000 words for first grade, 6,000 words for sixth grade). Complex sentence structures are simplified, and compound and complex sentences are broken down into simpler ones. Technical terms are explained using analogies that children can understand (e.g., "bacteria are very small creatures" and "democracy is something everyone decides on"). Kanji characters are given furigana and English pronunciation symbols are added. Long explanations are organized using bullet points and numbering to make them easier to understand. Character voices and sound effects are used to attract attention. Quiz-style confirmation questions are inserted to check comprehension. A narrative structure makes the story easier to remember. Audio supplements are provided to illustrations and emojis to link them with visual information. Age-inappropriate content is automatically filtered or replaced with euphemisms.

[0254] A cultural commentary function for tourists is disclosed. In at least one embodiment, the device provides a wealth of cultural information for tourists. For historical buildings, detailed explanations are provided in various languages, including the construction date, architectural style, and historical significance. For traditional events, the device explains the origin, meaning, and etiquette of the event, and also provides guidance on how to participate. For food culture, the device introduces the history of the dish, the origin of the ingredients, cooking methods, and eating etiquette. For photo spots, the device provides tips on photography, such as "You can see Mt. Fuji beautifully from here" and "It's most beautiful at sunset." For souvenir information, the device includes purchase precautions, such as "Only available in this region," "Best before date x days," and "Must be refrigerated." Cultural taboos and etiquette (removing shoes, no photography, remaining quiet) are explained in advance to prevent trouble. Local legends and folktales are introduced to promote deeper cultural understanding. Shopping support is also provided, including explanations of exchange rates and taxes (consumption tax, duty-free).

[0255] This document discloses precision translation at the medical interpreter level. In at least one embodiment, the device provides the high-precision translation required in medical settings. Medical terminology is accurately translated in accordance with international standards such as ICD-10 and SNOMED CT. Symptom descriptions accurately convey the pain scale (0-10), nature (dull, sharp, painful), and location (anatomical name). Drug names are listed with both generic and trade names, and dosage and administration are clearly explained. Tests and treatments are explained comprehensively, including their risks and benefits. Informed consent forms are translated with completeness that meets legal requirements. Urgency triage (red: urgent, yellow: semi-urgent, green: non-urgent) is uniformly expressed in each language. Medical insurance and payment information is also provided appropriately in accordance with each country's system. To ensure privacy, volume and distribution area are limited to prevent other patients from hearing. Important points are confirmed multiple times to prevent medical accidents due to mistranslation.

[0256] A sports commentary-style translation system is disclosed. In at least one embodiment, the system generates commentary-style translations that convey the excitement of a sporting event in each language. Play-by-play commentary uses short sentences, such as "He's in front of the goal! He's shooting!" to create a sense of realism. During thrilling scenes, emotional expressions, such as "An incredible comeback!" and "Witness a historic moment!", are used. Language-specific commentary expressions (English: "He shoots, he scores!", Spanish: "Goooool!") are used. Sporting terms are localized to match local expressions (baseball: home run → home run). Player names are given in their local names (nicknames, registered names). Statistics (batting average, runs, time) are updated and reported. The meaning of crowd cheers and chants is also translated to convey the atmosphere of the venue. Highlights are depicted in slow, detailed, and detailed slow-motion style. Information on upcoming games and past match results is also included to maintain ongoing interest.

[0257] A poetic and literary translation style is disclosed. In at least one embodiment, the device provides artistic translations for museums and cultural institutions. Poetry translations preserve the meter (iambic pentameter) as much as possible to recreate the beauty of the original poem. Metaphors and allusions are replaced with culturally equivalent expressions (e.g., "snowy skin" to "porcelain skin" in the Western sense). Literary quotations are translated using the best translations in each language, with citations clearly indicated where necessary. Personification, onomatopoeia, and mimetic words are creatively translated according to the characteristics of each language. Classical expressions are translated using Shakespearean English or Chinese poetry to create a sense of the era. Modern poetry maintains the free verse format, and spaces and line breaks are treated as meaningful. Descriptions of artworks use appropriate artistic terms, such as "contrast of light and shadow" and "color harmony." Phonetically beautiful translations are selected, taking into account how they sound when read aloud. Seasonal words and seasonal elements are translated while explaining the cultural context.

[0258] This document describes the accurate translation of academic and technical documents. In at least one embodiment, the device performs accurate translations at the level of academic papers and technical documents. Technical terminology conforms to the standard glossaries of each field (IEEE, ISO, JIS) and is consistent. Mathematical and chemical formulas are formatted in LaTeX, accurately conveying the meaning of symbols. Citations are converted to the local citation format (APA, MLA, Chicago). References to figures and tables follow language-specific conventions, such as "Figure 1" and "Table 2." Abbreviations are spelled out in full and explained on first appearance, and an abbreviation glossary is created. Units are based on the SI system, with national units added as needed. Statistical terms (p-values, confidence intervals, standard deviations) are used with academic accuracy. Patent documents maintain the claim hierarchy to ensure legal validity. Technical specifications are translated in full, including tolerances and precision. Version information and revision history are also retained for traceability.

[0259] Marketing-optimized translation is disclosed. In at least one embodiment, the device generates marketing translations that maximize purchase intent. Taglines are creatively translated to resonate locally, prioritizing effectiveness over meaning. Emotional trigger words (limited, special, limited-time offer) are replaced with expressions that are effective in each culture. Social proof ("#1 in sales," "chosen by XX million people") is converted to local trust indicators. Calls to action are optimized for each culture (Japan: subtle, United States: direct, China: value-for-money). Price expressions are adapted to the customs of each country by using fractional amounts ($9.99, ¥980). Scarcity appeals ("XX units left," "limited time only") are adjusted in intensity depending on the culture. Brand stories are edited to fit local values ​​(Japan: craftsmanship, Europe: tradition, United States: innovation). Customer testimonials are adapted to fit typical local personas. Emphasis on warranties and after-sales service is adjusted to reflect expectations in each country.

[0260] This document discloses a method for delivering surround sound in AR / VR environments. In at least one embodiment, the device provides an immersive multilingual experience in augmented reality and virtual reality environments. 3D audio technology (Ambisonics, binaural recording) localizes the position of the sound source in three dimensions. Audio is delivered from different positions for each language, spatially separated, such as directly in front for Japanese, 30 degrees to the right for English, and 30 degrees to the left for Chinese. Head-related transfer functions (HRTFs) are used to generate optimal surround sound for each individual's ear shape. In AR environments, audio is anchored to real objects, with distance attenuation implemented, increasing the volume as the user approaches. In VR environments, the acoustic characteristics of virtual spaces (reverberation, reflections) are simulated to recreate a realistic sound field. Eye-tracking automatically selects the language based on the user's gaze. Head tracking rotates the sound field to follow head movement, providing a natural auditory experience. Haptic feedback is also used, along with vibration for important information. In multi-person environments, each user can share the same space with their own language settings.

[0261] This paper describes a method for generating a voice model from a recorded source voice. In at least one embodiment, the device records the voice of a professional narrator or facility staff member and extracts their speech features to construct a personalized voice model. The recording environment is a soundproof room or quiet environment, with a sampling rate of 48 kHz or higher and a quantization bit depth of 24 bits or higher. The recording content includes at least 100 phoneme-balanced sentences (a set of sentences containing all phonemes), 50 sentences containing emotional expressions, and approximately 30 sentences containing facility-specific terminology. The recorded speech undergoes preprocessing, including noise reduction, normalization, and removal of silent segments. Speech feature extraction involves analyzing Mel-Frequency Cepstral Coefficients (MFCCs), fundamental frequency (F0), spectral envelope, and phoneme duration. Deep learning models (WaveNet, Tacotron, and FastSpeech) are used to train an end-to-end model for generating speech from text. Transfer learning enables the construction of a high-quality voice model even with a small amount of recording data (approximately 30 minutes). Furthermore, speaker adaptation techniques such as Learning Hidden Unit Contributions (LHUC) and Model-Agnostic Meta-Learning (MAML) are applied, enabling adaptation to new speakers with just five minutes of audio data. A Transformer architecture with an enhanced attention mechanism is used for acoustic model training, maintaining stable sound quality even for long sentences. A CycleGAN-based voice conversion method is used to improve intelligibility while preserving speaker identity. During recording, phoneme boundaries are automatically annotated using a labeling tool, and manual correction increases accuracy to over 99%. Emotion labeling is performed through cross-validation with multiple annotators to ensure consistency in emotional expression. The audio recording protocol uses 3D audio recording compliant with ISO / IEC 23008-3 (MPEG-H 3D Audio), enabling spatial sound reproduction. Binaural recording technology uses a dummy head microphone to record surround sound tailored to the characteristics of human hearing. In speech segmentation, forced alignment automatically establishes temporal correspondence between text and speech, improving the quality of training data.The speaker diarization function extracts only specific speakers from audio of multiple speakers to generate clean training data. Audio quality is evaluated using objective metrics such as PESQ, STOI, and MOSnet to ensure correlation with human subjective evaluation. The real-time voice conversion pipeline simultaneously trains the voice model during recording, making it immediately usable after recording is complete.

[0262] The device discloses the integration of text input and auto-generation. In at least one embodiment, the device efficiently creates content by combining direct input from an administrator with AI-based auto-generation. The input interface provides a WYSIWYG editor, allowing users to visually check font, size, emphasis, and other settings while entering text. A predictive text function suggests the next word or sentence based on the characters being entered, improving input efficiency. The phrase library includes over 1,000 templates, categorized by categories such as greetings, announcements, and warnings. AI auto-generation uses a GPT-based language model to generate natural-looking sentences from keywords. Context recognition maintains consistency with the content being delivered and prevents abrupt changes. Hybrid mode enables collaborative writing, where an administrator enters the outline and the AI ​​adds details. Generated sentences are automatically evaluated based on metrics such as readability score, emotional valence, and persuasiveness. Version control allows editing history to be saved and previous versions can be reverted as needed. The prompt engineering function provides effective prompt templates and generates natural-looking promotional text from structured input such as "Product name: XX, Features: △△, Price: □□ yen." The style learning function learns the facility's unique writing style from past broadcasts and automatically generates text that matches the brand image. The diversity control parameter allows for the balance between creativity and consistency to be adjusted, with a temperature value of 0.3-0.9. The collaborative editing function allows multiple administrators to edit simultaneously, with changes reflected in real time. Supports various input methods, including voice input, handwriting input, and OCR input from images. The text mining function collects and analyzes customer feedback from social media and review sites and incorporates it into broadcast content. Utilizing sentiment dictionaries (J-LIWC, ML-Ask) quantitatively controls the emotional tone of generated text. Syntactic analysis (dependency analysis, morphological analysis) generates grammatically correct Japanese and automatically corrects unnatural expressions. Corpus linguistics techniques extract frequent patterns from large-scale text data and learn natural expressions. Text summarization technology (extractive and generative) automatically adjusts long sentences to the appropriate length and optimizes delivery time.It supports multiple languages ​​by automatically detecting the input language and switching to the appropriate language model for processing. Semantic Web technology is used to generate semantically accurate sentences from structured data (RDF, OWL).

[0263] This document discloses an optimization of a voice text-to-speech engine. In at least one embodiment, the device achieves natural, easy-to-listen voice reading. The voice synthesis engine employs neural Text-to-Speech (TTS) technology to generate natural, human-like speech. Prosody control applies appropriate intonation, stress, and pauses depending on the meaning of the sentence. The reading speed is adjustable from 50 to 500 characters per minute, based on a default of 300 characters per minute. Pitch is adjustable within a ±50% range to create a voice that matches the image of the facility. Five emotional expressions are selectable: joy, sadness, anger, surprise, and neutral, with automatic selection also possible based on the content of the sentence. A terminology dictionary accurately pronounces facility-specific pronunciations (company names, product names, place names). Numbers are pronounced using either positional readings (ichi, ju, hyaku) or monotonous readings (ichi, zero, zero) depending on the context. The length of spaces between punctuation marks is adjusted to create a rhythm that is easy to listen to. The system implements BERT-based prosody prediction as a prosody prediction model to predict optimal prosody patterns based on sentence structure. Vocal tract length normalization (VTLN) enables voice quality conversion based on age and gender, generating speech appropriate for the target demographic. A formant emphasis filter improves vowel clarity, making speech easier to understand even in noisy environments. Glottal pulse shape control allows for adjustment of vocal tension and softness. Corpus-based prosody generation selects and applies optimal prosody patterns from a large-scale speech database. A time warping algorithm (WSOLA) allows for speech rate adjustment without degradation in sound quality. Neural vocoders (WaveGlow, Parallel WaveGAN) enable fast, high-quality waveform generation, enabling real-time synthesis. Style transfer learning transcribes specific speaker and emotional styles into other speech, generating diverse expressions. Phoneme context-dependent modeling achieves natural speech connection by taking into account the surrounding phonetic context. Spectral smoothing technology removes unnatural spectral irregularities from the synthesized speech, improving perceived quality. The natural phase information is reconstructed from the spectrogram using a phase recovery algorithm (Griffin-Lim, RTISI-LA).It controls the fine structure of speech (jitter, shimmer) to reproduce human-like voice fluctuations. By integrating language models, it automatically reads appropriately according to the context and completes omitted particles.

[0264] Detailed functions of the scheduling engine are disclosed. In at least one embodiment, the device efficiently manages complex delivery schedules. A calendar view allows users to view schedules at annual, monthly, weekly, and daily levels. Regular delivery settings allow flexible recurrence patterns, such as daily, specific days of the week, specific days of the month, or business days only. Time specifications can be set in one-minute increments, with support for fine-tuning down to the second. Priority management controls delivery order using three levels: normal, important, and urgent, and prioritizes higher priority in the event of conflicts. Automatic delivery time optimization suggests optimal delivery times based on past effectiveness measurement data. Holiday calendar integration allows scheduling to take national holidays, local anniversaries, and facility-specific closing days into consideration. Pre-delivery notifications send administrators a five-minute reminder before a delivery, providing a final confirmation opportunity. Schedule conflict detection warns against duplicate deliveries at the same time or consecutive deliveries with too short intervals. Detailed schedule settings in cron job format allow users to define complex delivery patterns such as "* / 15 9-18 * * 1-5." Dependency management allows you to create a chain schedule that starts the next delivery only after a specific delivery is completed. The load balancing scheduler automatically adjusts delivery timing taking system load into account to prevent performance degradation. The schedule template function allows you to save and reuse frequently used schedule patterns. External calendar integration (Google Calendar, Outlook) automatically imports event information and reflects it in the delivery schedule. Delivery slot management limits the maximum number of deliveries per hour to prevent information overload. The resource reservation system reserves processing capacity for specific time periods in advance to ensure the quality of important deliveries. Schedule optimization algorithms (genetic algorithms, simulated annealing) automatically generate optimal schedules under complex constraints. Cross-time zone scheduling automatically takes time differences into account during global delivery, ensuring delivery at the optimal time for each region. Event-driven scheduling dynamically adjusts the schedule in response to external triggers (out of stock, weather changes).Schedule analysis reports visualize distribution density, effectiveness by time period, and patterns by day of the week, and suggest areas for improvement. Multi-tenant support allows you to independently manage schedules for multiple facilities and tenants, preventing mutual interference. Distribution capacity planning predicts future distribution demand and prepares the necessary resources in advance.

[0265] A voice quality management and improvement process is disclosed. In at least one embodiment, the device implements a mechanism for continuously monitoring and improving voice quality. Sound quality evaluation metrics include automatic measurement of signal-to-noise ratio (SNR), total harmonic distortion (THD), and frequency response flatness. A MOS (Mean Opinion Score) prediction model objectively evaluates subjective sound quality. A / B testing compares and verifies the effects of different voice parameters to find optimal settings. A listener feedback function collects evaluations such as "easy to hear" and "quiet." Acoustic environment adaptation automatically adjusts voice processing parameters based on reverberation time and background noise level. Compression artifact detection and correction minimizes degradation due to voice compression. A peak limiter and compressor maintain consistent volume levels and prevent sudden volume changes. Periodic voice sample audits enable early detection and addressing of long-term quality degradation. The PESQ (Perceptual Evaluation of Speech Quality) algorithm performs objective sound quality evaluation based on the ITU-T P.862 standard. Spectrogram analysis detects and automatically corrects distortions in formant structure and unwanted resonances. Loudness normalization (compliant with EBU R128) maintains an integrated loudness value of -23 LUFS ±1 dB to ensure listening comfort. Masking analysis based on psychoacoustic models removes imperceptible components and optimizes bitrate. Machine learning-based sound quality improvement includes DNN-based noise suppression, bandwidth expansion, and dereverberation. A real-time quality monitoring dashboard visualizes trends in sound quality indicators and immediately detects abnormalities. Acoustic fingerprinting technology ensures the uniqueness of delivered audio and detects unauthorized copying. Adaptive equalization measures the frequency characteristics of the playback environment and corrects them with an inverse filter. Binaural cue preservation maintains interaural time difference (ITD) and interaural level difference (ILD) to accurately reproduce spatial positioning. Improved accuracy of voice activity detection (VAD) enables accurate identification of speech and silence periods, enabling efficient voice processing.Crosstalk cancellation prevents sound leakage when using multiple speakers and improves intelligibility. Acoustic model adaptation learns the acoustic characteristics of the installation environment and automatically sets the optimal voice processing parameters. A continuous integration / delivery (CI / CD) pipeline automatically tests voice engine updates and deploys them quickly while ensuring quality.

[0266] This document discloses batch generation and transfer from a central server. In at least one embodiment, the device generates audio content centrally and efficiently distributes it to each device. The central server is equipped with a high-performance GPU, which processes multiple audio generation tasks in parallel, enabling simultaneous generation of content for more than 1,000 devices. Audio data is selected from MP3 (music-oriented), AAC (sound quality-oriented), Opus (low latency-oriented), and FLAC (uncompressed) formats depending on the application. For packaging, the audio itself, metadata (delivery time, priority, expiration date), and digital signature are compressed in ZIP format. The transfer protocol is automatically selected based on network conditions from TCP (reliability-oriented), UDP (speed-oriented), and HTTP (high-capacity). A CDN (Content Delivery Network) is used to minimize delivery delays to geographically distributed devices. Encrypted communication (TLS 1.3) prevents eavesdropping and tampering. Transfer scheduling allows for pre-delivery of large volumes of data during late-night hours when network load is low. Distributed object storage (MinIO, Ceph) efficiently manages petabytes of content and enables high-speed delivery through parallel access. Container orchestration (Kubernetes) automatically scales worker nodes according to load and dynamically adjusts processing capacity. Message queue systems (RabbitMQ, Kafka) process delivery tasks asynchronously and handle load spikes. GraphQL-based APIs efficiently fetch only the necessary data and prevent over-fetching. WebRTC data channels enable P2P delivery of real-time audio data. Edge caching places frequently accessed content close to the device, reducing delivery latency. Serverless architecture (AWS Lambda, Azure Functions) performs event-driven audio generation processing, maximizing cost efficiency. Blockchain technology prevents tampering with delivery history and ensures content authenticity. Quantum cryptography enables secure delivery that is resistant to future quantum computer decryption. 5G / 6G network slicing creates a virtual network dedicated to audio delivery and guarantees QoS.Device groupcasting efficiently distributes the same content to multiple devices, optimizing bandwidth usage. AI-based distribution route optimization dynamically selects the optimal route taking into account network topology and congestion. Digital twin technology builds a virtual model of the physical distribution network, enabling optimization through advance simulation.

[0267] Device-adaptive format optimization is disclosed. In at least one embodiment, the device selects the optimal audio format based on the characteristics of each device. A device profile database stores supported codecs, maximum bitrates, storage capacity, CPU performance, and speaker characteristics. High-quality 48kHz / 24-bit data is generated for high-end devices, while lightweight 16kHz / 16-bit data is generated for low-end devices. Variable bitrate (VBR) dynamically adjusts between 32-320kbps depending on the complexity of the audio. Automatic mono / stereo detection removes unnecessary stereo data for single-speaker devices. Audio codec fallback automatically converts incompatible formats to a compatible format when detected. Progressive download allows playback to begin before the audio is fully downloaded, reducing perceived latency. Device aging is taken into account, and volume is automatically boosted for older devices. Device fingerprinting learns the acoustic characteristics of the device and generates an inverse filter to compensate for individual differences. Adaptive bitrate (ABR) streaming gradually adjusts audio quality based on network bandwidth. Hardware acceleration detection uses high-efficiency codecs on devices capable of utilizing DSPs and GPUs. Rich content, including spatial audio effects, is delivered to devices that support multi-channel audio (5.1ch, 7.1ch). Acoustic impedance information is used to estimate the power required to drive speakers and adjust to the appropriate sound pressure level. Real-time transcoding instantly converts formats upon request from devices. Device group profiles allow common settings to be applied to devices of the same model at once, improving management efficiency. Device capability negotiation protocols automatically negotiate the optimal format upon connection, reducing setup effort. Audio watermarking technology embeds device-specific identification information into the audio, enabling distribution tracking. Adaptive quantization adjusts the quantization bitrate according to the noise level of the device's playback environment to optimize perceived quality. Device clustering groups devices with similar characteristics and efficiently performs optimization on a group-by-group basis.The profile learning function automatically learns the characteristics of new devices after they are first used, continuously improving the optimal settings. Codec performance benchmarks measure the decoding performance of each device and select formats that take CPU load into consideration. Acoustic model simulation virtually reproduces the speaker characteristics of each device, predicting and optimizing sound quality in advance.

[0268] Fully Enhanced Version: Differential updates and bandwidth optimization are disclosed. In at least one embodiment, the device minimizes the amount of data transferred to achieve efficient delivery. A difference detection algorithm extracts only the differences from the previously delivered content and transfers only the changed portions. Binary diff tools (bsdiff, xdelta) calculate differences at the binary level of audio files. Chunking divides large files into small blocks and transfers only the changed blocks. Deduplication ensures that identical data is transferred only once and restored on the device side. Bandwidth measurement dynamically detects available bandwidth and optimizes transfer rates. Congestion control automatically reduces transfer speeds during network congestion. Multipath transfer improves transfer speeds by simultaneously using multiple communication paths. A compression algorithm is selected based on the processing capabilities of the device, taking into account the trade-off between compression rate and CPU load. Rolling hashing (Rabin fingerprint) quickly detects similar portions of content and efficiently calculates differences. Delta compression encodes only the differences between consecutive frames, improving the compression rate of time-series data. Content-Dependent Chunking (CDC) determines the optimal division point based on the data content, improving the accuracy of difference detection. Network coding technology linearly combines multiple packets before transmitting them, improving packet loss tolerance. Traffic shaping smooths out bursty transfers and reduces the load on network equipment. Priority queuing prioritizes the transfer of urgent content and guarantees QoS. HTTP range requests enable partial downloads and resumption, improving transfer reliability. Predictive prefetching learns device usage patterns and transfers required content in advance. Entropy coding (arithmetic coding, range coding) achieves compression rates close to the information-theoretic limit. Segmented delivery divides large files into small segments, enabling parallel transfers for high-speed and error recovery. Adaptive Forward Error Correction (FEC) dynamically adjusts redundancy according to packet loss rates, optimizing the balance between efficiency and reliability.Dynamic Content Delivery Network (CDN) selection selects the optimal CDN node, taking into account geographic location, network conditions, and cost. P2P-assisted delivery allows nearby devices to share content, reducing server load and bandwidth usage. Machine learning-based compression learns content characteristics and automatically selects optimal compression parameters.

[0269] This document discloses error handling and redundancy. In at least one embodiment, the device builds a delivery system that is robust against transmission errors. Checksums (CRC32, MD5, SHA-256) reliably detect data corruption. Forward Error Correction (FEC) allows packet loss to be repaired on the receiving side. Automatic Repeat Request (ARQ) automatically sends a retransmission request when an error is detected. Timeout processing automatically retries up to three times if there is no response, and if that fails, records the error in an error log. Device health monitoring sends a heartbeat signal every 30 seconds and determines that a device that does not respond is abnormal. Redundant delivery simultaneously transmits important content via multiple routes, and the first one to arrive is adopted. A backup server automatically fails over if the primary server fails. Log aggregation centrally collects error logs from all devices and monitors the health of the entire system. Reed-Solomon coding implements powerful error correction capable of correcting up to n / 2 errors. Interleaving distributes burst errors and maximizes the effectiveness of FEC. Adaptive retransmission control dynamically adjusts the retransmission interval and number of retransmissions according to network conditions. Byzantine failure detection identifies malicious or malfunctioning devices and isolates them from the system. Checkpointing periodically saves the state during transmission, shortening recovery time in the event of a failure. Distributed transactions (two-phase commit) ensure atomicity of delivery to multiple devices. The circuit breaker pattern detects consecutive failures, preventing unnecessary retries and protecting system resources. Failure prediction models (machine learning) detect device anomalies in advance and prompt preventive maintenance. Chaos engineering intentionally injects failures to verify and improve the system's fault tolerance. Distributed consensus algorithms (Raft, Paxos) ensure data consistency across multiple servers. Error budget management defines an acceptable error rate and operates the system within SLOs. Mutation testing verifies the quality of error handling code and discovers potential vulnerabilities.Fault injection testing verifies operation under various failure scenarios. Self-healing systems automatically detect and repair common failures, minimizing human intervention. Anomaly detection AI (Isolation Forest, One-Class SVM) makes it possible to detect unknown abnormal patterns. Distributed tracing (Zipkin, Jaeger) visualizes error propagation in complex distributed systems and quickly identifies the root cause.

[0270] This document describes distributed processing and edge computing. In at least one embodiment, the device achieves load balancing by leveraging the computing power of edge devices. Lightweight processing, such as text normalization, simple speech synthesis, and cache management, is performed on the edge side. Heavyweight processing, such as complex natural language processing, high-quality speech synthesis, and large-scale data analysis, is handled on the cloud side. Dynamic offloading migrates processing to the cloud when edge CPU utilization exceeds 80%. Collaborative processing allows multiple edge devices to share processing and share results. Local caching stores frequently used voice data at the edge, reducing network communication. Edge AI inference executes lightweight models at the edge to ensure real-time performance. A fog computing layer performs processing between the edge and cloud, optimizing latency and bandwidth. An offline operating mode maintains basic functionality through local processing even when the network is disconnected. Containerization (Docker) creates an execution environment on edge devices that is identical to that of the cloud, enabling seamless processing migration. Distributed machine learning (Federated Learning) trains models on edge devices, improving performance while preserving privacy. Utilizing edge TPUs / NPUs accelerates inference processing using dedicated AI chips and reduces power consumption. Data locality optimization performs calculations close to the data required for processing, minimizing data movement. Workload forecasting predicts future processing loads and proactively allocates resources. Edge clustering logically groups multiple nearby edge devices to streamline collaborative processing. Event-driven architecture executes processing only when needed, reducing idle power consumption. Edge-native application design develops and deploys microservices optimized for edge environments. Compute Continuum creates a unified platform that can seamlessly move processing from the edge to the cloud. Edge intelligence gives edge devices the ability to learn and adapt, enabling autonomous optimization.Distributed stream processing (Apache Flink, Apache Storm) processes stream data generated at the edge in real time. Edge security utilizes a Trusted Execution Environment (TEE) to protect confidential data while processing it. Edge orchestration (KubeEdge, Azure IoT Edge) unifies the management and operation of large-scale edge device groups. Edge-to-edge communication enables direct collaboration between edge devices without going through the cloud, achieving low-latency processing. AI model compression technology (quantization, pruning, knowledge distillation) enables highly accurate inference even at the edge.

[0271] A multilingual parallel translation process is disclosed. In at least one embodiment, the device efficiently translates a single source text into multiple languages. A parallel processing architecture enables simultaneous translation into 10 languages, reducing processing time by 1 / 10 compared to serial processing. A translation engine pool utilizes multiple APIs, including Google, DeepL, Microsoft, and Baidu, in a load-balanced manner. Leveraging similarities between languages, the system improves translation efficiency for Romance languages ​​(French, Italian, Spanish, Portuguese) through a common intermediate representation. Indirect translation via a pivot language (English) improves translation quality for minor language pairs. Optimized sentence segmentation ensures translation is performed in appropriate units (average 20-30 words) that are neither too long nor too short. Batch processing reduces communication overhead by sending multiple sentences to the API. Translation queuing processes high-priority languages ​​sequentially, prioritizing delivery to important markets. GPU acceleration increases the inference speed of neural translation models by 10x. Zero-shot translation enables knowledge transfer from multilingual models, enabling translation even for language pairs for which no training data is available. Attention visualization visualizes which words the translation model focuses on and assists in verifying translation quality. Dynamic adjustment of beam search optimizes the balance between translation diversity and quality based on the content type. Cross-linguistic alignment of distributed representations represents the meaning of words and sentences in a language-independent space, improving translation accuracy. Parallel computation of translation metrics (BLEU, METEOR, BERTScore) speeds up quality assessment. Multi-task learning improves translation quality by simultaneously training auxiliary tasks such as part-of-speech tagging and named entity recognition. Custom tokenizers achieve optimal word segmentation based on the characteristics of each language. Meta-learning rapidly learns translation for new language pairs from small amounts of training data. Adversarial training simultaneously improves the naturalness and accuracy of translation. Reinforcement learning continuously improves translation strategies based on human evaluation feedback. Graph neural networks (GNNs) utilize structural information from sentences to improve translation accuracy.Multimodal translation enables translation that takes into account not only text but also image and audio context. Continual learning retains existing knowledge while learning from new translation data. Translation uncertainty estimation identifies low-confidence translations and prompts manual review. Domain adaptation enables highly accurate translation specialized for specific fields (medical, legal, technical).

[0272] Cultural adaptation and quality assurance are disclosed. In at least one embodiment, the device implements multi-layered quality control to ensure cultural appropriateness of translations. The cultural adaptation engine adjusts expressions based on factors such as color meaning (red: good luck in Chinese, warning in Western), number meaning (4: bad luck in Chinese, 13: bad luck in Western), and gesture meaning. A taboo detector automatically detects and displays warnings for religious, political, and sexual inappropriate language. Quality scoring is based on a 0-100 scale based on grammatical accuracy, fluency, and appropriateness. Back-translation verification translates the translation back into the source language to verify the retention of meaning. Native check AI evaluates the naturalness of each language and detects unnatural expressions. A terminology consistency checker ensures consistency by preventing the same term from being translated differently within a document. Sentiment analysis ensures that the sentiment polarity of the source and translated texts is consistent. The cultural ontology database systematically manages the values, customs, and taboos of each cultural sphere and utilizes them for automated checking. Context-dependent honorific conversion estimates the relationship between the speaker and listener and selects the appropriate level of honorific language. The metaphor conversion engine replaces culture-specific metaphors with expressions understandable in the target culture. Gender-neutral translation generates inclusive expressions that do not specify gender. The regional dialect database provides translations that take into account not only standard language but also regional expressions. Religious calendar integration selects expressions that take into account important religious days. The legal compliance checker ensures that each country's advertising and expression regulations are not violated. Cultural distance measurement quantifies differences between cultures based on Hofstede's cultural dimensions theory and determines the need for adaptation. Sociolinguistic analysis ensures appropriate language usage according to age, gender, and social class. Pragmatic analysis understands the difference between literal and contextual meanings and provides appropriate translation. The idiom and idiom database appropriately converts expressions that would not make sense if translated literally. Cultural context generation adds necessary background knowledge as supplementary explanations, and multicultural team verification ensures the appropriateness of translations by native speakers from each culture.Cultural sensitivity training allows AI models to learn cultural nuances and make appropriate decisions, balancing globalization and localization to ensure the right mix of universal and region-specific elements.

[0273] The present invention discloses a translation memory and knowledge base utilization method. In at least one embodiment, the device maximizes the use of past translation assets to improve quality and efficiency. The translation memory database stores over one million pairs of source and translated sentences and searches for reusable translations using fuzzy matching (similarity of 70% or higher). Segmentation manages translation units at the sentence, phrase, and word level, enabling partial reuse. Context-aware search selects the most appropriate translation memory, taking into account the surrounding context. High-quality translations are prioritized for reuse by storing them with a quality score. Domain classification builds translation memories for specific fields, such as medical, legal, and technical, to ensure expertise. Update history management tracks changes in terminology and expressions and applies the latest translation rules. Collaborative filtering references translation memories from similar projects to expand coverage. Automatic translation memory cleaning periodically removes outdated and inaccurate translations. Leverage analysis pre-calculates the applicability of translation memories to new text and predicts costs and time. The alignment tool automatically builds a translation memory from existing bilingual documents. Support for TMX format allows import / export using the industry-standard translation memory exchange format. The terminology extraction engine automatically detects frequently occurring technical terms and adds them to the glossary. Translation memory quality assessment uses machine learning to calculate the reliability of each entry and automatically flags low-quality entries. Cross-project cross-referencing ensures consistency by leveraging translation assets from related projects. Incremental learning automatically updates translation memories every time a new translation is approved, keeping them always up-to-date. Fast similarity calculations instantly search for the best candidates from millions of translation memories. Semantic search allows you to search semantically similar translation memories, not just superficial string matches. Translation memory versioning tracks changes over time and allows you to refer to previous versions as needed. Context memory preserves contextual information across the entire document, ensuring consistent translations. Hierarchical management of macro and micro languages ​​efficiently manages common expressions and technical terms.A translation memory sharing platform allows organizations to share best practices and improve translation quality across the industry. AI-powered translation memory augmentation learns new patterns from existing translations and automatically expands coverage. Quality propagation applies the characteristics of high-quality translations to other translations, improving overall quality. Translation memory security management ensures translations containing sensitive information are properly protected and access controlled.

[0274] A real-time speech translation process is disclosed. In at least one embodiment, the device translates live speech input with low latency and outputs speech. Streaming speech recognition minimizes latency by starting text conversion sequentially without waiting for the end of the utterance. Voice Activity Detection (VAD) identifies speech and silence segments and determines appropriate translation units. Incremental translation begins partial translation before the sentence is completed and updates it sequentially. A context buffer stores the context of the past 10 or so sentences and uses it to resolve pronouns and abbreviations. Low-latency mode achieves latency of less than 500 ms at the expense of translation quality. High-quality mode reviews and translates the entire sentence after completion, ensuring high accuracy with a latency of 2-3 seconds. Speaker adaptation learns the speaker's voice quality, speaking rate, and accent to improve recognition accuracy. Noise-robust processing ensures stable speech recognition even in noisy environments. Optimized endpoint detection accurately identifies sentence breaks and prevents unnatural segmentation. The language identification module automatically detects the speaker's language and switches to the appropriate recognition model. Code-switching support accurately processes speech containing multiple languages. Prosody-preserving translation reproduces the intonation and rhythm of the original speech in the translated speech. Real-time speaker separation separates simultaneous speech from multiple people and translates it individually. Echo cancellation eliminates audio feedback from the speaker to maintain recognition accuracy. Confidence estimation of partial recognition results identifies uncertain parts and complements them from context. Acoustic model adaptation learns the acoustic characteristics of the venue and improves recognition accuracy. Neural language model integration improves recognition accuracy by simultaneously considering acoustic and linguistic information. Cross-lingual speech recognition appropriately identifies and processes each language, even in multilingual environments. Emotion-preserving translation expresses the original speaker's emotions (joy, sadness, anger) in the translated speech. Real-time subtitling generation displays text simultaneously with speech translation, making it accessible to the hearing impaired. Audio segment caching speeds up the translation of frequently occurring phrases and reduces latency. Parallel decoding allows multiple hypotheses to be explored simultaneously, quickly determining the best translation.Adaptive beam search dynamically adjusts search width according to sentence complexity, optimizing the balance between accuracy and speed. Online training learns speaker characteristics during use, continuously improving recognition accuracy during a session.

[0275] This document discloses a method for dealing with time differences in scheduled distribution. In at least one embodiment, the device manages an optimal distribution schedule that takes into account time differences around the world. A time zone database (IANA Time Zone Database) accurately manages all time difference information worldwide. Automatic daylight saving time adjustment automatically reflects the start and end of DST (Daylight Saving Time). Local time priority mode automatically adjusts distribution to the same local time (e.g., 9:00 AM) in each region. Global simultaneous distribution mode distributes simultaneously worldwide at a specific UTC time. Business hours consideration ensures distribution during each country's business hours (9:00 AM to 5:00 PM in the United States, 9:00 AM to 6:00 AM in Japan). A holiday database considers national holidays and religious anniversaries in each country and avoids inappropriate timing. Optimal distribution time AI learns and suggests the optimal time for each region based on past distribution effectiveness data. Distribution confirmation reports are recorded in both local time and UTC for each region, facilitating global management. Automatic time difference change tracking instantly responds to time difference changes due to political decisions (such as the unification of time zones in Russia). The flight timetable format displays both "local arrival time" and "departure time" to prevent confusion. The distribution window setting defines the available time slots for distribution in each region (e.g., 6:00 AM - 10:00 PM) to prevent late-night distribution. Global event support allows for special schedules to be set for international events such as the Olympics and World Championships. Jet lag considerations allow for departure time information to be displayed for travelers arriving on international flights. Moon phase calendar integration supports lunar calendar-based events such as Ramadan in Islamic countries. Distribution order optimization ensures information is distributed sequentially from east to west, taking the International Date Line into account, ensuring freshness of information. The time difference simulator allows users to check in advance what time the distribution schedule will be in each region. The global distribution orchestrator manages distribution to multiple regions and enables scheduling that takes into account inter-regional dependencies. Geopolitical considerations allow for careful selection of distribution timing for politically sensitive regions. Regionally tailored distribution strategies are adopted, taking into account differences in cultural concepts of time (monochronic and polychronic cultures).By linking with a global content delivery network (CDN), content is delivered at the optimal time from edge servers in each region. Real-time regional event detection automatically adjusts or stops delivery to regions where natural disasters or political events occur. Multi-region failover ensures alternative delivery from other regions in the event of a system failure in a specific region. Global delivery analysis allows comparison of delivery effectiveness by region and sharing of best practices. International regulatory compliance automatically sets delivery times in accordance with each country's broadcasting and advertising regulations.

[0276] This document discloses basic control for repeat delivery. In at least one embodiment, the device provides a function for effectively repeating audio content. Repeat patterns can be selected from equal intervals (every hour, every 30 minutes, or every 15 minutes), irregular intervals (concentrated morning, afternoon, and evening delivery), and random intervals (normal distribution with an average of 30 minutes and a standard deviation of 10 minutes). The number of repetitions can be set to an infinite loop, a specified number (up to 10 times), or until a condition is met (sales target achieved, out of stock). After the first delivery, listener responses (stop-in rate, purchase conversion rate) are measured to establish a baseline effect. From the second delivery onwards, the difference in effect from the previous delivery is calculated and the progress of the effect is graphed. Based on memory consolidation theory, a spaced repetition method can also be selected, repeating at intervals of 5 minutes, 30 minutes, 2 hours, and the next day after the first delivery. Repeat delivery logs are recorded along with the delivery time, target area, estimated number of listeners, and effect indicators. The Ebbinghaus forgetting curve model is implemented to automatically calculate the optimal repetition timing based on memory retention. The Fibonacci sequence-based interval setting achieves a natural increase pattern of 1, 1, 2, 3, 5, 8, and 13 minutes. Circadian rhythm considerations concentrate broadcasts during times of heightened human attention (10:00, 15:00, and 19:00). Based on the Weber-Fechner law, stimulus intensity is logarithmically increased to maintain a consistent perceptual effect. Priming effect measurement evaluates the degree to which repetition solidifies in implicit memory. A Markov chain model probabilistically predicts the optimal timing for the next broadcast. Jitter is intentionally added to the broadcast interval to avoid a mechanical impression. Based on cognitive load theory, the repetition frequency is adjusted within a range that does not exceed information processing capacity. An adaptive repetition algorithm personalizes the repetition pattern based on individual listener responses. Psychological reactance avoidance sets a limit on the number of repetitions to prevent backlash due to excessive repetition. Semantic saturation prevention alters wording to prevent meaning loss due to repetition of the same words. Intermittent reinforcement schedules produce stronger learning effects through repetition at irregular intervals. Context-dependent memory promotes memory consolidation by repeating the same environmental conditions (time, place, background music).We take advantage of the spaced learning effect, which shows that dispersed repetition is more effective for long-term memory than concentrated learning. We also use metacognitive monitoring to design repetition so that listeners can be aware of their own memory status.

[0277] This paper discloses a method for generating variations to prevent listening fatigue. In at least one embodiment, the device maintains freshness by changing the expression of the same content. Paraphrase generation changes the expression while preserving meaning, such as changing "Sale on today" to "Great sale today" to "Sale is on now." Voice parameter randomization creates slight changes within the range of ±5% pitch, ±10% speech rate, and ±3dB volume to avoid complete repetition. Speaker switching rotates from male to female to child's voice, maintaining attention. BGM change changes the background music even with the same narration, changing the impression. Partial updating updates only variable parts such as the date, time, and inventory, while maintaining the fixed parts. Order shuffling randomly changes the presentation order of multiple pieces of information to prevent fixed priorities. Emotional expression changes depending on the time of day, such as energetic in the morning, calm in the afternoon, and gentle in the evening. Sound effects, such as chimes, bells, and applause, are added probabilistically to prevent monotony. The GPT-3-based paraphrase engine can generate over 100 different expressions for the same content. Homophones and similar words are used for phonological changes to create auditory variations. Dialect variations rotate from standard Japanese to Kansai dialect to Kyushu dialect to create a sense of familiarity. Tempo changes add musical variations from allegro (lively) to andante (walking) to largo (slow). Rhetorical transformation changes rhetorical techniques from simile to metaphor to metonymy to synecdoche. Stylistic transformation changes from honorific to casual to catchy. Dynamic sound effects are randomly applied, such as reverb, echo, and flanger. Mashup generation combines multiple pieces of content to create new variations. Generative adversarial networks (GANs) automatically generate new expression patterns from existing variations. Variational autoencoders (VAEs) explore a continuous expression space to achieve smooth variations. Style transfer transforms text into different writing styles (Shakespearean, haiku, rap) for a fresh feel. Creative AI generates unexpected combinations and expressions to add an element of surprise.Based on emotional engineering, emotional values ​​(cute, cool, elegant) are quantified to generate variations of the targeted impression. By applying music theory, techniques of harmonic progression, counterpoint, and variations are applied to audio distribution. By adding storytelling elements, the same information is presented with different narrative structures (introduction, development, turn, conclusion, jo-ha-kyu). Interactive elements allow variations to be switched in real time depending on the listener's reaction.

[0278] This document discloses methods for detecting and optimizing diminishing returns. In at least one embodiment, the device analyzes changes in effectiveness with repetition and determines the optimal distribution strategy. Using marginal effectiveness measurement, the effectiveness of the nth distribution is defined as E(n), and if E(n) - E(n-1) falls below a threshold, it is determined that effectiveness is diminishing. Wearout curve analysis identifies the point at which effectiveness peaks and then starts to decline. Statistical significance testing is used to verify that changes in effectiveness are not coincidental before modifying the strategy. Analysis by audience segment identifies optimal frequencies for each attribute, such as high frequency for new users and low frequency for repeat users. A content fatigue score is calculated based on the number of repetitions, elapsed time, and negative response rate, and is managed on a 100-point scale. If the fatigue score exceeds 80 points, it is recommended that the content be updated or paused. Machine learning models (random forest, XGBoost) predict the optimal number of repetitions based on past data. A refresh period is set, during which distribution is paused for a certain period before resuming, thereby restoring effectiveness. Bayesian optimization is used to identify the optimal distribution frequency while balancing exploration and exploitation. Time series analysis (ARIMA, Prophet) predicts long-term effectiveness, taking seasonality and trends into account. Sequential A / B testing detects significant differences early and quickly stops inefficient delivery patterns. Cohort analysis groups listeners by the time of first contact and tracks their individual response patterns. Survival analysis predicts the time until listeners "drop off" and determines the optimal delivery duration. Causal inference (propensity score matching) measures the true effect of repeated delivery, separating it from other factors. A recommender system individually suggests the optimal repeat pattern based on each listener's preferences. Reinforcement learning (Q-learning, DQN) continuously improves delivery strategies to maximize long-term effectiveness. Formulates as a multi-armed bandit problem to optimize the trade-off between exploration and exploitation. Deep reinforcement learning (A3C, PPO) learns the optimal repeat strategy in a complex state space. Transfer learning adapts successful patterns from other facilities to your own facility. Meta-optimization automatically adjusts the parameters of the optimization algorithm itself, improving convergence speed. Ensemble learning combines multiple predictive models to achieve more robust effect predictions.Explainable AI (XAI) presents the causes of diminishing returns in a way that humans can understand. Simulation optimization tests various strategies in a virtual environment to discover optimal solutions without risk. Dynamic programming determines a delivery schedule that maximizes long-term cumulative effects.

[0279] Fully Expanded Version: This document discloses the automation of routine business communications. In at least one embodiment, the device fully automates daily business communications. More than 50 standard templates are included, including opening and closing announcements, break times, cleaning times, and inspection work. Variable substitution automatically inserts variable parts such as "XX:XX," "XX sales floor," and "XX person in charge." Calendar integration automatically determines business days, holidays, and special business days and selects appropriate messages. Day-of-the-week settings allow for adding content specific to each day, such as a start-of-the-week message on Mondays and a weekend message on Fridays. Monthly templates automatically share goals at the beginning of the month and notify closing work at the end of the month. Annual events (anniversary, fiscal year-end, inventory) can also be automatically handled by pre-registering them. Weather integration automatically adds umbrella stand information during rainy weather and heatstroke warnings during extremely hot weather. Shift integration automatically includes the name of the person in charge in the audio ("Today's person in charge is XX"). Robotic Process Automation (RPA) integration automatically retrieves business data from core systems and reflects it in audio content. Natural language generation (NLG) automatically creates business reports from numerical data ("Today's number of visitors is XX, an increase of XX% from the previous day"). Anomaly detection algorithms detect unusual patterns (temporary closures, emergency maintenance) and generate special announcements. Workflow integration automatically sends notifications encouraging preparation for the next step based on work progress. KPI linkage dynamically generates encouraging or warning messages based on goal achievement rates. Multi-site synchronization simultaneously sends unified messages across chain stores. Labor law compliance checks ensure notifications comply with legal requirements regarding break times and working hours. Incident response automates initial notifications in the event of system failures or accidents, supporting rapid response. Process mining visualizes actual work flows and identifies the optimal timing for notifications. Predictive analysis predicts workload based on past patterns and sends notifications encouraging advance preparation. By linking with a chatbot, it automatically responds to inquiries from employees and delivers necessary information by voice. By integrating with a workflow management system, it automates approval processes and work completion notifications. By linking with IoT sensors, it monitors the status of equipment (temperature, humidity, operating status) and automatically notifies employees if an abnormality occurs.Blockchain technology records the delivery and confirmation of business communications in an unalterable form. AI assistant functionality provides work instructions and answers questions in a natural conversational format. Emotional labor support estimates employees' stress levels and delivers appropriate encouraging messages.

[0280] A method for ensuring the reliable transmission of emergency messages is disclosed. In at least one embodiment, the device repeatedly transmits emergency information to ensure its accuracy. Based on the emergency level determination, Level 1 (information provision) is transmitted three times, Level 2 (alert) is transmitted five times, and Level 3 (evacuation) is transmitted indefinitely until confirmation is received. An escalation method is used, increasing the volume stepwise: normal volume the first time, +3dB the second time, and +6dB the third time. Multimodal integration combines voice, siren, flashlight, and vibration to attract attention. Language rotation is used to transmit messages in Japanese, English, Chinese, and Korean in order to ensure that all messages are received. Verification system integration accepts confirmation via smartphone app or IC card, and stops repetition once the message is confirmed. Unconfirmed person tracking generates a list of unconfirmed individuals and encourages individual response. Progressive detailing is used, keeping the first message brief and adding detailed information from the second time onwards. Automatic release sends an "alert cleared" message three times and then terminates once the danger has passed. The warning sound is designed based on psychoacoustics, using the most audible frequency range, centered around the 1-3 kHz frequency band. The panic prevention algorithm sets a calming tone and appropriate intervals to avoid excessive anxiety. Location information integration ensures more frequent alerts for people closer to danger zones and less frequent alerts for those further away. Biometric monitoring (for those wearing compatible devices) detects elevated heart rates and adds individual messages encouraging calm. Crowd psychology simulation determines the optimal information delivery order to prevent a panic outbreak. Blockchain recording preserves immutable evidence of emergency message delivery and confirmation. Machine learning analysis of effectiveness allows for optimal delivery patterns to be learned from past emergency responses and continuously improved. Redundant communication routes ensure continued delivery via alternative means, such as satellite communication and LoRaWAN, even if the main line is cut off. Emergency scenario planning allows for advance preparation of delivery strategies for various disaster scenarios. Real-time crowd density estimation predicts congestion on evacuation routes and enables dispersed evacuation guidance. Drone integration provides three-dimensional information delivery, combining aerial situation assessment and audio delivery. Social media analysis is used to collect emergency information on social media and reflect it in official announcements.By cooperating with a multilingual emergency call center, we can respond to individual inquiries that cannot be handled through voice transmission. Based on the principles of psychological first aid (PFA), we provide information in a way that minimizes trauma. By integrating with business continuity plans (BCP), we can achieve step-by-step information distribution in line with the disaster recovery process. We provide information in accordance with international humanitarian law, ensuring humanitarian consideration during disasters.

[0281] The overall configuration of an audio distribution management device is disclosed. In at least one embodiment, the audio distribution management device of the present invention includes an input unit into which an administrator directly inputs text or a generation unit in which AI automatically generates the text; a speech synthesis unit that reads the text aloud using a voice model generated from pre-recorded original voice data of a speaker; and a distribution control unit that schedules and distributes the read-out voice at a specified time. The input unit and the generation unit can be selectively used, and the administrator can select direct input mode, AI generation mode, or a hybrid mode that combines the two. The speech synthesis unit converts any text into natural-sounding speech while preserving the speech characteristics of the original voice data. The distribution control unit automatically distributes the generated speech according to a set date and time, number of repetitions, and distribution interval. The entire device is implemented as a computer system equipped with a CPU, m...

Claims

1. (After correction) 1. A method for managing audio distribution, comprising: storing a text of the Japanese announcement; accepting an operation of a language switching button; automatically translating the text into the language selected by operating the language switching button; generating speech from the automatically translated text using speech synthesis technology; Steps to simultaneously broadcast emergency announcements in all languages, A step where only the selected language is delivered under normal circumstances, and switching the distribution mode with one touch.

2. 2. The audio distribution management method according to claim 1, The method, wherein the speech synthesis technique includes using a neural network-based speech synthesis model.

3. 3. The audio distribution management method according to claim 1, further comprising: The language switching button corresponds to at least four languages: Japanese, English, Chinese, and Korean; You can switch languages ​​with one touch. A way to visually indicate the currently selected language.

4. 3. The audio distribution management method according to claim 1, further comprising: The method, wherein generating the speech includes using a text-to-speech engine.

5. 2. The audio distribution management method according to claim 1, A step of pre-generating speech in multiple languages ​​from the same Japanese source text; storing the generated audio in a cache; and immediately playing a corresponding sound upon button operation.

6. 2. The audio distribution management method according to claim 1, The speech synthesis technology includes: using a deep learning speech generation model; Implementing a WaveNet, Tacotron, or FastSpeech-based architecture; generating natural-sounding speech including emotional expressions.

7. 2. The audio distribution management method according to claim 1, a step of executing translation processing and speech generation in parallel when switching languages; minimizing the waiting time for processing completion; and completing the process from switching to distribution within 3 seconds.

8. 2. The audio distribution management method according to claim 1, Steps to place a language preview button on the admin screen, making audio available for each language; and verifying quality before delivery.

9. 2. The audio distribution management method according to claim 1, a step of preferentially displaying a frequently used language; saving recently used languages ​​as a history; and registering frequently used language combinations as presets.

10. (After correction) An audio distribution management device, a memory unit for storing the text of the Japanese announcement; an operation interface having a language switching button; a translation engine that performs automatic translation into the language selected by operating the language switching button; a speech synthesis unit that generates speech from the automatically translated text using speech synthesis technology; The device is equipped with a distribution control unit that simultaneously distributes announcements in all languages ​​during emergency announcements and distributes only selected languages ​​during normal times.