System for latency-aware orchestration and performance optimization in artificial intelligence telephone communication

Abstract

A system for latency-aware orchestration and performance optimization in AI-driven telephone communication, consisting of: a speech capture unit configured to capture an analog audio signal from a telephone interface and convert the analog audio signal into a digital audio signal stream; a feature extraction unit that is operationally coupled with the speech acquisition unit and is configured to generate a feature representation of the digital audio signal stream through spectral decomposition, noise reduction, and temporal segmentation; an AI inference processor communicatively connected to the feature extraction unit, configured to run one or more AI models for automatic speech recognition, natural language understanding, and emotion recognition on the feature representation to generate intermediate results for inference; a latency orchestration controller coupled to the AI ​​inference processor, wherein the latency orchestration controller is configured to monitor latency across multiple processing stages, predict cumulative delay propagation using a hybrid latency estimation model, and orchestrate the execution scheduling of the AI ​​inference processor based on the predicted latency deviation; a performance optimization unit coupled with the latency orchestration controller and configured to dynamically adjust computational accuracy, inference batch size, and feature processing resolution based on latency thresholds and quality constraints set by the latency orchestration controller; and a transmission synchronization array configured to time-align the processed output generated by the AI ​​inference processor and transmit it to a remote communication node, with the transmission synchronization array maintaining deterministic time coordination between successive packets and the orchestrated inference results.

Description

Technical field of the invention

[0001] The present invention relates to real-time communication systems, and in particular to an AI-supported telephone communication system with latency-aware orchestration and a dynamic performance optimization mechanism. The invention is related to telecommunications engineering, distributed computing, and artificial intelligence optimization. Latency reduction, voice quality improvement, and adaptive call routing are dynamically controlled by means of an orchestrated multi-agent processing architecture integrated into hardware and software components. BACKGROUND OF THE INVENTION

[0002] Traditional telephony systems, including VoIP and AI-powered voice assistants, often suffer from latency issues due to unpredictable network fluctuations, packet congestion, jitter, and delays in the execution of distributed models. Existing systems attempt to reduce latency through static buffering, fixed jitter compensation, or quality-of-service prioritization layers. However, these mechanisms do not dynamically respond to changing runtime conditions such as variable network bandwidth, AI inference load, and bottlenecks in the distributed model pipeline.

[0003] Furthermore, AI-powered telephone systems with speech recognition, speaker verification, emotion detection, and automated call analysis are computationally intensive. In real-time call operations, even minor delays in data processing can lead to noticeable delays or interruptions, impacting the user experience. Existing architectures distribute model processing across cloud nodes but lack an adaptive orchestration layer capable of predicting latency propagation, redistributing workloads, and adjusting processing accuracy or model precision to ensure synchronous, real-time conversations.

[0004] Furthermore, current systems do not account for latency effects between the network and computing layers. The lack of a unified orchestration mechanism that jointly manages packet transmission, audio feature extraction, model execution priority, and adaptive compression leads to unpredictable round-trip times and inconsistent voice quality. Therefore, there is a need for a latency-aware orchestration system with an integrated performance optimization mechanism. This system must be able to continuously analyze latency trends, predict system bottlenecks, and dynamically optimize AI model execution and network resource allocation within an AI-driven telephony pipeline.

[0005] In modern telecommunications, artificial intelligence (AI) has begun to fundamentally change how real-time conversations, call analytics, and voice-based customer interactions are conducted. The integration of AI into telephone communication systems has introduced advanced features such as automatic speech recognition (ASR), voice biometrics, emotion recognition, and natural language processing (NLU). However, these advancements have come with increased computational and transmission complexity, resulting in significant latency and performance degradation during live voice communication. Traditional telephone systems were designed for deterministic packet transmission with predictable delays.The introduction of AI-driven processing pipelines, distributed cloud inference, and hybrid deployment models has fundamentally changed this predictability, making latency management a key technical challenge for maintaining conversation continuity and quality.

[0006] Latency in AI-powered telephony systems arises from several interconnected factors: network transmission latency, buffering and jitter compensation, model inference latency, synchronization issues, and resource planning overhead. In traditional VoIP or IP telephony, a round-trip latency exceeding 150 milliseconds disrupts the flow of conversation, resulting in overlapping speech, delayed responses, and perceptible delays. AI-powered systems, such as those using neural networks for speech recognition and dialogue management, introduce additional inference times on top of this transmission delay. When AI models are run on remote cloud servers, the cumulative delay from data serialization, model invocation, and response decoding further exacerbates latency, leading to user experiences that fall short of real-time interaction expectations.

[0007] Current latency reduction solutions primarily focus on network optimization rather than system control. Protocols like RTP (Real-Time Transport Protocol) and SRTP (Secure RTP) ensure reliable packet delivery with minimal delay, while QoS (Quality of Service) mechanisms prioritize voice packets in multi-tenant networks. However, these approaches only consider transmission latency and neglect computationally induced delays during inference or adaptive control between AI components. Jitter buffers and adaptive codecs also attempt to compensate for variations in packet arrival time, but they do not dynamically adjust model execution or computational scheduling based on latency predictions.As a result, although the audio stream can be stabilized, the end-to-end conversation latency continues to fluctuate unpredictably due to unoptimized AI model inference and inefficient synchronization.

[0008] In AI-based speech communication systems, the computational load from deep learning models such as Transformer or LSTM-based speech decoders is substantial. These models often require parallel GPU or TPU computations, typically performed in the cloud or on edge nodes. Transmitting raw or feature-compressed audio segments to these nodes introduces additional serialization delay, and the inference process itself can vary depending on GPU utilization, model complexity, and concurrent inference requests. Existing systems attempt to mitigate this through static resource allocation or pre-warmed models, but such mechanisms cannot adapt to the runtime variability of user traffic or network bandwidth.For example, cloud-based voice assistants and call analytics systems often experience temporary latency spikes when the length of the inference queue increases, leading to delayed responses or truncated speech synthesis.

[0009] Another limitation of existing systems lies in their lack of predictive control. Current communication architectures only employ reactive measures—such as increasing buffer size or reducing bit rate—when latency thresholds are exceeded. These reactive strategies are not predictive and do not account for the latency evolution caused by systemic feedback between the network and computing layers. The absence of predictive control models that can estimate future latencies based on system telemetry data results in a limited time horizon for latency control. This short-term approach does not prevent latency accumulation across multiple inference cycles, leading to a cumulative degradation of call responsiveness.

[0010] Although many AI phone systems incorporate adaptive bitrate control and codec switching, they rarely mention mechanisms for adjusting AI inference accuracy or computational precision to latency budgets. Deep learning models often operate at fixed precision levels (e.g., FP32 or FP16) and lack runtime flexibility to switch to lower-precision computations (e.g., INT8) during latency spikes. This static precision configuration leads to overprovisioning of computational resources and underutilization of potential latency savings through dynamic quantization. While some research on model compression and quantization has demonstrated latency reductions, these are typically performed offline prior to deployment and are not dynamically controlled during live communication.

[0011] From a systems integration perspective, orchestrating AI pipelines across distributed nodes presents a further challenge. Most cloud-based communication systems use asynchronous task queues or microservices that operate independently without global time synchronization. This leads to timing discrepancies between individual phases, particularly between feature extraction, model inference, and response generation. The lack of a unified orchestration controller to ensure consistent timing across all nodes results in unpredictable round-trip delays. For example, in AI-powered customer service applications where multiple models—such as speech recognition, sentiment analysis, and intention classification—run concurrently on separate servers, even minor synchronization errors can lead to timing incoherence between recognized speech and response generation.

[0012] Existing frameworks like Google Dialogflow, Amazon Connect, and Twilio Flex integrate AI-powered conversational capabilities, but rely heavily on cloud-centric architectures that inherently introduce latency variations. While these systems perform exceptionally well in natural language understanding, they lack device-level orchestration for local inference optimization and latency-aware scheduling. Their architectures prioritize accuracy and scalability over real-time capability. Consequently, these systems cannot guarantee the consistent sub-100ms latency essential for natural conversation flow in telephony environments.

[0013] Attempts have also been made to use edge computing to reduce latency by moving inference tasks closer to the end user. While this reduces network latency, it presents challenges related to load balancing, edge resource heterogeneity, and synchronization of distributed inference nodes. Existing edge AI systems typically use simple load balancers that distribute tasks based on static CPU / GPU availability rather than predicted latency results. Furthermore, they lack cross-layer orchestration mechanisms that can optimize network and inference layers together. Therefore, while edge computing can reduce network latency, it does not eliminate internal inference jitter, queue delays, or compute saturation effects.

[0014] Another class of existing systems focuses on end-to-end pipeline compression through model clipping and reduced feature dimensionality. While these methods shorten inference time, they do so at the cost of a significant decrease in accuracy. Furthermore, such static optimization techniques cannot dynamically respond to fluctuating runtime conditions such as temporary network congestion or abrupt user requests. In practical telephone communication scenarios, where voice quality varies due to background noise or channel interference, static compression can impair intelligibility and the recognition of emotional context. Therefore, an adaptive orchestration layer is needed that dynamically balances latency and accuracy through selective model clipping and reconfiguration.

[0015] Besides latency, real-time synchronization remains a key challenge for AI-powered telephony. Most systems use asynchronous, event-driven architectures where timestamp alignment between audio input, model output, and network transmission is only loosely coupled. This leads to synchronization drift between conversational input and AI-generated responses. Without precise timing, speech segments can be processed out of order, resulting in conversation incoherence or overlap. The lack of hardware-based synchronization mechanisms like the IEEE 1588 Precision Time Protocol (PTP) further exacerbates this problem, especially in distributed systems with multiple hops.

[0016] Furthermore, the lack of feedback-driven performance optimization prevents existing systems from achieving consistent optimization. While telemetry data such as packet delay, jitter, and CPU utilization are frequently logged, they are rarely incorporated into an adaptive control loop for continuous orchestration improvement. Instead, system optimization is performed manually and based on historical averages, thus failing to account for the inherent real-time variability of voice communication. Feedback loops, where present, primarily serve to assess voice quality rather than actively control latency.

[0017] Hardware limitations of traditional telephony devices hinder the implementation of advanced orchestration techniques. Most IP-based mobile phones and telephony servers are equipped with general-purpose processors that lack dedicated co-processors for AI inference or latency orchestration. Without hardware acceleration, latency optimization must rely solely on software scheduling, resulting in additional operating system overhead and non-deterministic context-switching delays.

[0018] Furthermore, thermal throttling and resource conflicts in shared systems can unpredictably affect inference time, so purely software-based latency reduction is insufficient.

[0019] In summary, the existing landscape of AI-powered telephony systems is characterized by fragmented and reactive latency management approaches. Network-layer solutions optimize packet transmission but neglect data processing delays. AI model optimizations improve inference speed but are static and context-independent. Edge computing reduces network latency but introduces new synchronization challenges. None of the existing systems offers an integrated, predictive, and latency-aware orchestration mechanism that encompasses the communication, inference, and hardware layers. This results in a persistent trade-off between responsiveness and conversational intelligence, as systems must choose between maintaining real-time interaction and leveraging advanced AI capabilities.

[0020] The need for a unified, latency-aware orchestration and performance optimization system is therefore urgent. Such a system must integrate hardware-based orchestration controllers, real-time synchronization circuits, and AI-powered latency prediction models capable of dynamically managing computing and network resources. It should operate as a closed system, continuously learning from runtime telemetry data and adjusting orchestration parameters in real time to ensure a consistent call latency of less than 100 ms. The absence of such an architecture in current solutions motivates the development of the present invention, which bridges the gap between deterministic real-time telecommunications and AI-powered intelligence through predictive, adaptive, and hardware-based latency orchestration. OBJECTS OF INVENTION

[0021] The main objective of the present invention is to provide a system and device for latency-aware orchestration and performance optimization in AI-driven telephone communication, which dynamically identifies, predicts and reduces latency propagation across network, inference and I / O layers.

[0022] Another objective of the invention is the development of a hardware-integrated orchestration controller capable of synchronizing speech recording, AI inference planning, and adaptive transmission control using multidimensional latency prediction models.

[0023] Another goal is to enable adaptive model accuracy adjustment, feature reduction, and selective pipeline compression based on latency budgets, thereby ensuring end-to-end real-time conversation integrity without loss of quality.

[0024] It is also an object for establishing a closed feedback structure for continuous latency learning and orchestration optimization based on network telemetry, inference protocols, and system-wide time synchronization markers. SUMMARY OF THE INVENTION

[0025] The invention describes a latency-aware orchestration and performance optimization system for AI-powered telephone communication. It comprises an integrated device architecture and an orchestration controller that monitors, predicts, and minimizes latency in the audio recording, processing, and transmission layers. The system utilizes distributed AI agents that work in conjunction with a hardware-based orchestration controller. This controller operates based on a hybrid latency prediction model. The orchestration layer dynamically distributes processing tasks, adjusts processing accuracy, reorders pipeline execution, and regulates packet transmission intervals to maintain target latency thresholds.

[0026] The system utilizes a performance optimization control loop that continuously analyzes real-time telemetry data, including packet timestamps, inference execution delay vectors, and CPU / GPU queue utilization data, to adjust operational parameters. The orchestration controller performs a probabilistic delay optimization procedure that estimates latency propagation across pipeline segments and reconfigures resource allocations accordingly. The system also includes a latency buffer synchronization unit and an AI model quantization controller that work together to ensure a real-time response with a round-trip latency of less than 100 ms.

[0027] The invention further comprises an embodiment of a physical device in which the orchestration control circuit, the clock synchronization bus and the dedicated AI inference coprocessor are housed in a communication interface unit that can be used as a smart telephony hub or integrated into IP-based mobile phones.

[0028] The present invention aims to provide a system and a device for latency-aware orchestration and performance optimization in AI-powered telephone communication. This system ensures the continuity of real-time conversations through intelligent monitoring, prediction, and mitigation of latency propagation across network and computing layers. The invention aims to establish a unified orchestration mechanism that dynamically harmonizes the operation of the subsystems for speech capture, feature extraction, AI inference, and data transmission to achieve a response latency of less than 100 milliseconds without compromising the quality or accuracy of the AI ​​processing.

[0029] A further objective of the invention is the development of a hybrid orchestration control architecture that integrates hardware synchronization circuits with software-based adaptive scheduling methods to achieve deterministic latency behavior even under variable network and load conditions. By directly integrating orchestration intelligence into the communication hardware, the dependence on purely cloud-based latency control is eliminated, thereby reducing jitter, synchronization drift, and round-trip delay during ongoing telephone calls. The system is designed to proactively manage latency rather than reactively compensate for it, ensuring that every computational and network event remains temporally synchronized within a closed control structure.

[0030] A further objective of the invention is the introduction of an intelligent performance optimization mechanism that continuously adjusts the execution parameters of the AI ​​model—such as computational accuracy, inference order, and pipeline truncation level—based on real-time latency predictions. The invention enables the dynamic adaptation of the AI ​​model's behavior—for example, switching between high-precision and low-latency inference modes—depending on the system's current latency budget. This adaptive optimization process ensures that the system can contextually balance inference accuracy and response speed, thus maintaining smooth conversation even under high load or limited network conditions.

[0031] Another key objective of the invention is to provide an integrated hardware device, the so-called Latency-Orchestrated Communication Hub (LOCH), capable of performing orchestration and synchronization functions at the physical layer. This device comprises dedicated inference coprocessors, timestamp alignment circuits, and orchestration logic controllers that enable sub-millisecond coordination between communication and AI processing elements. The hardware implementation aims to make AI-powered telephony scalable and deployable in both enterprise and consumer environments without relying on remote cloud nodes, thereby achieving deterministic latency control directly at the network edge.

[0032] A further objective of the invention is the establishment of a closed-loop latency learning mechanism that uses predictive analytics and reinforcement learning to model the dynamic behavior of the communication system over time. The invention provides that the orchestration controller learns latency patterns from historical telemetry data in order to predict future bottlenecks and proactively reallocate resources, rearrange inference tasks, or adjust network transmission parameters before latency exceedances occur. This predictive orchestration framework is designed for self-optimization and enables the system to improve its performance and latency consistency during continuous operation.

[0033] Another objective is the development of a communication orchestration system capable of operating in distributed and hybrid topologies—including on-premises, edge, and cloud environments—while ensuring consistent temporal coordination between all inference nodes. The invention aims to overcome the synchronization drift and timing uncertainty typically encountered in distributed AI telephone systems by introducing precisely clocked orchestration buses and timestamp propagation models that ensure the temporal coherence of all nodes involved in audio capture, processing, and response generation.

[0034] A further objective of the invention is the integration of a performance feedback system that measures latency deviations, jitter accumulation, and packet reordering delays in real time and uses these measurements to dynamically optimize the orchestration parameters. Through continuous monitoring and feedback, the system is intended to achieve self-calibrating performance, in which latency optimization is autonomously controlled in real time based on system metrics rather than by fixed configuration rules. This closed-loop control ensures consistent performance even with fluctuating network bandwidth or varying computational load.

[0035] A further objective of the invention is the integration of adaptive data transmission control, in which packet transmission intervals, buffer sizes, and compression levels are adjusted based on instantaneous latency feedback and predicted end-to-end delays. The system aims to ensure high perceived voice quality while simultaneously minimizing packet delay fluctuations. By combining AI-supported inference control with dynamic transmission control, the invention achieves holistic latency optimization across the entire telephony chain, instead of treating the network and computing layers as separate units.

[0036] Another objective is the development of a fault-tolerant orchestration model that ensures a smooth performance degradation in the event of temporary overloads or hardware failures. The invention aims to implement redundancy-aware orchestration protocols that can dynamically distribute inference tasks across the available processing units without any perceptible interruption of communication. This ensures uninterrupted communication even in the event of node failure or deterioration of the communication link.

[0037] A further objective of the invention is the introduction of time-synchronized orchestration between multi-agent AI components used for speech recognition, intention recognition, and emotion recognition. This ensures that each component processes time-aligned data segments. The invention aims to avoid asynchronies between models, which could otherwise lead to inconsistent or delayed conversation responses. This time-synchronized orchestration enables multiple AI functions to work together harmoniously within a uniform latency range, thus enabling natural and human-like AI-supported telephone interactions.

[0038] Another important goal is to improve the scalability and energy efficiency of AI-supported telephone communication systems by optimizing computational scheduling and data flow control while considering latency constraints. The invention aims to reduce redundant processing and idle cycles through just-in-time task distribution, thereby lowering the power consumption and heat generation of telephony servers and telephones with orchestration hardware.

[0039] Ultimately, an objective of the invention is to enable real-time interoperability between existing telephony standards and AI processing frameworks by embedding latency-aware orchestration interfaces that are compatible with standard VoIP protocols, RTP / SRTP data streams, and AI model APIs. By ensuring backward compatibility, the invention enables seamless integration into existing telephony infrastructures while simultaneously providing a transformative latency optimization layer that improves responsiveness, speech intelligibility, and conversational naturalness.

[0040] At its core, this invention aims to create a seamless, latency-optimized, and AI-powered communication ecosystem that combines deterministic hardware synchronization with intelligent software orchestration. The invention's architecture is not only designed for latency management but also proactively anticipates, adapts to, and minimizes latency at all levels of AI-driven telephony—from audio input and inference output to transmission feedback. This achieves a new standard for real-time talk performance and communication reliability. BRIEF DESCRIPTION OF THE IMAGE

[0041] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a system for latency-aware orchestration and performance optimization in AI-driven telephone communication.

[0042] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0043] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0044] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0045] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0046] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0048] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0049] Fig.Figure 1 shows a block diagram of a system for latency-aware orchestration and performance optimization in AI-powered telephone communication. The system 100 comprises: a speech capture unit (102) for capturing an analog audio signal from a telephone interface and converting it into a digital audio signal stream; a feature extraction unit (104) operationally connected to the speech capture unit, which generates a feature representation of the digital audio signal stream through spectral decomposition, noise reduction, and temporal segmentation; and an AI inference processor (106) communicatively connected to the feature extraction unit, which executes one or more AI models for automatic speech recognition, natural language understanding, and emotion recognition on the feature representation to generate intermediate results.A latency orchestration controller (108), coupled to the AI ​​inference processor, monitors latency across multiple processing stages, predicts cumulative delay propagation using a hybrid latency estimation model, and orchestrates the execution schedule of the AI ​​inference processor based on the predicted latency deviation. A performance optimization unit (110), also coupled to the latency orchestration controller, dynamically adjusts computational accuracy, inference batch size, and feature processing resolution based on latency thresholds and quality constraints set by the latency orchestration controller. A transmission synchronization array (112) synchronizes the processed output generated by the AI ​​inference processor and transmits it to a remote communication node.The transmission synchronization array ensures deterministic time coordination between successive packets and the orchestrated inference results.

[0050] Each of the aforementioned components is implemented in hardware to ensure deterministic behavior, low latency, and real-time synchronization within the communication system. The speech capture unit is implemented with an analog front-end circuit that includes dedicated microphones, anti-aliasing filters, and high-speed analog-to-digital converters on a printed circuit board substrate. The feature extraction unit is implemented by a hardware-accelerated digital signal processing (DSP) block that integrates FIR filters, FFT cores, and temporal framing logic in an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).The AI ​​inference processor is implemented as a dedicated neural processing unit (NPU) or graphics processing unit (GPU) on a hardware inference board and configured to perform matrix operations and tensor calculations in hardware using parallelized arithmetic pipelines. Latency control is implemented in a real-time hardware scheduler, which includes a programmable timing matrix, delay counters, and a clock range synchronization circuit. This scheduler controls task execution based on latency predictions. The performance optimization unit is implemented as a hardware control logic block connected to programmable voltage and frequency scaling circuits. This enables adaptive adjustment of processing accuracy and resource allocation based on measured system latency.The transmission synchronization unit is implemented as a hardware-based timing sequencer with high-precision oscillators, timestamp registers, and packet alignment buffers to ensure deterministic synchronization and transmission integrity across telecommunications channels.

[0051] In one embodiment, the latency orchestration controller (108) comprises a latency prediction processor configured to compute a latency distribution profile by analyzing the timestamp differences between successive processing events occurring in the feature extraction unit, the AI ​​inference processor, and the transfer synchronization array, and wherein the latency orchestration controller continuously updates a latency mapping table that defines intermediate delay values ​​for predictive orchestration.

[0052] In one embodiment, the latency orchestration controller (108) is further configured to perform a recursive optimization operation to adjust the task scheduling order within the AI ​​inference processor based on the latency mapping table. This selectively prioritizes inference tasks with lower computational complexity during periods of detected latency exceedances and restores full model operation once the latency deviation has fallen below a predefined threshold.

[0053] In one embodiment, the performance optimization unit (110) includes a circuit for adjusting the computational accuracy, which is configured to dynamically switch between floating-point and integer arithmetic processing states of the artificial intelligence inference processor depending on the current latency deviation, so that a reduction in accuracy occurs when the latency exceeds a predetermined upper limit, and a high-accuracy computation is restored when the latency is again within the target tolerance limits.

[0054] In one embodiment, the transmission synchronization array (112) comprises a deterministic communication bus and a time synchronization circuit configured to align the output transmission time of each processed packet with a reference clock signal distributed to the speech capture unit, feature extraction unit, and artificial intelligence inference processor, thereby ensuring synchronous operation and avoiding cumulative time drift during multi-stage processing.

[0055] In one embodiment, the latency orchestration controller (108) is further configured to generate a predictive latency profile by combining the measured inference delay, the network packet transfer delay, and the buffer queue delay, and to compute a latency correction instruction which is transmitted to the performance optimization unit for the dynamic redistribution of computational tasks to the available inference processors in real time.

[0056] In one embodiment, the performance optimization unit (110) is configured to perform an adaptive model reconfiguration process that truncates non-critical subgraphs of the artificial intelligence model when the latency exceeds the predefined tolerance limit, so that non-essential processing layers related to secondary attributes such as emotional tone or context weighting are temporarily bypassed without affecting the primary recognition accuracy.

[0057] In one embodiment, the transmission synchronization array (112) further comprises a timestamp processor configured to assign a global time identifier to each packet leaving the artificial intelligence inference processor, the timestamp processor ensuring a monotonic progression of the time identifiers so that the temporal order of the output packets is maintained across distributed network nodes.

[0058] In one embodiment, the latency orchestration controller (108) comprises a delay compensation processor configured to analyze historical latency deviation data and generate a predictive delay compensation factor that is applied as a time offset adjustment in the transmission synchronization array to ensure a uniform round-trip latency across bidirectional communication links.

[0059] In one embodiment, the feature extraction unit (104) comprises a dynamic segmentation circuit configured to change the size of the time window of the digital audio signal stream based on instantaneous latency feedback from the latency orchestration controller, so that as network latency increases, shorter segments are processed, thereby reducing inference delay without losing temporal continuity.

[0060] The detailed description of the present invention, entitled "System for Latency-Aware Orchestration and Performance Optimization in AI-Powered Telephone Communication," relates to an integrated hardware-software architecture that ensures real-time call latency through predictive orchestration, adaptive performance optimization, and deterministic synchronization. The system functions as a unified orchestration device that harmonizes speech capture, feature extraction, AI inference, and network transmission within a tightly coupled feedback control structure. The invention is particularly suitable for next-generation voice communication networks and AI-enabled telephony devices, where delays, jitter, and asynchronous execution can impair call flow and system reliability.

[0061] The system comprises a speech capture unit that captures analog speech signals from telephone hardware interfaces and digitizes them using high-resolution analog-to-digital conversion. The digitized audio stream is immediately processed by the feature extraction unit, which performs frame-by-frame segmentation and spectral decomposition. The technique used in this unit employs a multi-stage signal conditioning pipeline that first performs a noise reduction function based on spectral subtraction and adaptive Wiener filtering. Subsequently, temporal-spectral coefficients such as Mel-frequency-cepstral coefficients (MFCCs) and prosodic markers are generated. These features are organized into temporally aligned data frames, which serve as input for the AI ​​inference processor.

[0062] The AI ​​inference processor executes one or more deep learning models for real-time conversation analysis, including automatic speech recognition (ASR), natural language processing (NLU), speaker verification, and emotion recognition. Each of these models is divided into subgraphs or execution blocks to allow for detailed scheduling by the latency orchestration controller. The inference processor continuously communicates with the orchestration controller, which monitors queue length, processing completion times, and resource utilization. Based on this telemetry data, the orchestration controller predicts latency propagation using a hybrid latency estimation model. This model represents latency as a dynamic function of inference delay, transmission delay, and buffer delay.The orchestration controller uses a recursive prediction approach, where the expected latency (L_t) for a given inference cycle is calculated as a weighted sum of previously observed delays and filtered through an exponential decay weighting to weight current conditions.

[0063] As soon as a deviation in latency from the target value is detected, the latency orchestration controller triggers an orchestration response via its internal optimization routine. This routine performs a constrained scheduling operation that reorganizes the order and accuracy of tasks executed by the inference processor. The scheduling procedure uses a priority-based dispatching mechanism, where latency-prone tasks are temporarily relegated to lower priority or offloaded to secondary processors, while tasks of lower complexity are prioritized to ensure continuous speech output. The orchestration controller updates its scheduling strategy using a reinforcement learning model that evaluates the impact of previous orchestration decisions on overall latency performance.The model's reward function is designed to minimize overall system latency while maintaining speech intelligibility and model accuracy. This allows the controller to autonomously optimize its orchestration behavior over time.

[0064] The performance optimization unit acts as an adaptive control layer, regulating computational accuracy, inference accuracy, and feature processing resolution. It dynamically adjusts the computational accuracy of the inference processor by switching between high-precision and quantized arithmetic operations depending on the latency deviation. If a latency exceedance is predicted, the performance optimization unit instructs the inference processor to perform quantized inference (e.g., with reduced bit width) and to truncate non-critical subgraphs of the neural network, such as auxiliary sentiment classifiers or redundant context encoders. After latency normalization, full model execution with full accuracy is automatically restored.The control logic of this unit operates as a continuous optimization function that minimizes the difference between current latency and target latency threshold using a gradient-based adjustment rule derived from telemetry feedback.

[0065] The orchestration technique also includes adaptive feature truncation. The feature extraction unit, in communication with the latency orchestration controller, dynamically adjusts the size of the feature processing window. For example, in the event of network or compute overload, the segmentation window is shortened, resulting in smaller feature frames that reduce the inference load while maintaining perceived speech continuity. The orchestration controller predicts optimal segmentation intervals using a regression model trained on past latency patterns, thus ensuring an effective balance between data completeness and real-time capability.

[0066] The transmission synchronization array plays a crucial role in ensuring consistent timing throughout the system. It contains a deterministic communication bus and a precise time synchronization circuit that operates with a central reference clock shared by all subsystems. Each output packet generated by the inference processor is tagged with a global timing identifier by the array's integrated timestamp processor. This identifier ensures chronological order across distributed processing nodes, preventing packet shifts or timing drift. The array also includes a predictive buffer management unit that adjusts packet transmission intervals based on the predictive delay compensation instructions from the latency orchestration controller. By estimating downstream network delay, the buffer aligns outgoing packets to maintain a constant overall latency, even under varying transmission conditions.

[0067] The system's orchestration control loop operates as a closed-loop feedback mechanism. Real-time telemetry data, including inference time, queue length, and packet transmission delay, are continuously collected and sent to the latency orchestration controller. This controller processes the data to update the hybrid latency prediction model, which provides both instantaneous and trend-based estimates of expected latency. The controller then executes corrective actions in coordination with the performance optimization unit and the transmission synchronization array. These corrective actions include dynamic workload redistribution across heterogeneous processors, adaptive inference quantization, feature truncation, and temporal resynchronization of packet transmission intervals.The feedback control loop operates with millisecond granularity, thus enabling almost instantaneous adaptation to changing network or computing conditions.

[0068] The hardware implementation of the invention, the so-called latency-controlled communication device, integrates the latency control controller, the performance optimization unit, the AI ​​inference processor, and the transmission synchronization array into a single package. The device features a deterministic bus connection, a precise time protocol clock system, and a low-latency memory controller to ensure deterministic communication between the subsystems. It also includes an environmental monitoring circuit that continuously measures the thermal load and voltage levels of the processing units. If thermal or performance anomalies are detected, the latency control controller dynamically adjusts the processor frequency and workload distribution to prevent latency spikes caused by thermal throttling.

[0069] Technically, the orchestration controller employs a multi-layered control strategy. At the lower control layer, it performs event-driven latency corrections through deterministic scheduling. At the higher control layer, it performs predictive orchestration using reinforcement learning. The reinforcement learning controller uses a continuously updated policy function that maps observed system states—defined by latency vectors, inference queue size, and network delay—to orchestration actions such as rescheduling, quantization adjustment, or feature compression. The policy is optimized by a reward structure that penalizes latency violations while rewarding consistently low-latency operation and maintaining audio quality. Over time, this learning-based mechanism enables the orchestration controller to anticipate latency trends rather than merely reacting to them.

[0070] The cross-layer coordination enabled by this system ensures that latency optimization does not occur in isolation at a single stage. For example, if the orchestration controller predicts a delay at the inference stage, it simultaneously sends commands to the feature extraction unit to reduce the frame size and to the transmission synchronization array to slightly pre-buffer the output packets. This multi-stage correction adjustment ensures that latency variations are minimized simultaneously in all layers, thus preventing oscillatory correction processes and maintaining overall timing stability.

[0071] In operation, the overall system ensures an average call latency of less than one hundred milliseconds, imperceptible to human users and crucial for a natural dialogue flow. Closed-loop control and optimization occur continuously and dynamically adapt to fluctuations in computing load, network congestion, and environmental conditions. The architecture supports distributed deployment in hybrid networks, allowing inference components to run on cloud or edge nodes, while the hardware for latency control and synchronization is installed locally. Despite the physical distribution, the system ensures temporal coherence and consistency of orchestration through timestamp synchronization and predictive scheduling.

[0072] Through the described combination of predictive latency modeling, reinforcement learning-based orchestration, precision-adaptive inference control, and deterministic synchronization, the invention establishes a new class of AI-powered telephone systems that enable both intelligence and real-time capability. The system not only minimizes latency through reactive control but also anticipates it through predictive orchestration, thus enabling seamless, highly precise, and uninterrupted AI-powered telephone communication even in dynamically changing network and computing environments.

[0073] The proposed latency-aware orchestration and performance optimization system consists of a multi-layered device architecture designed for AI-powered, real-time telephone communication. It includes a voice acquisition interface (VAI), a feature extraction circuit (FEC), an AI inference coprocessor (AICP), a latency orchestration controller (LOC), a performance optimization feedback unit (PTFU), and a transmission synchronization array (TSA), all interconnected via a deterministic communication bus (DCB) to ensure sub-millisecond signal synchronization.

[0074] When an audio signal is received via the VAI, it is digitized and transmitted to the FEC. There, spectral-temporal features such as mel-frequency-cepstral coefficients (MFCCs), prosodic markers, and noise-corrected phonetic vectors are extracted. The extracted features are then forwarded to the AICP, which performs multi-stage inference processes, including automatic speech recognition, natural language processing, and emotional context modeling. Each inference stage is scheduled by the LOC based on predicted latency budgets derived from current telemetry data and system load analyses.

[0075] The Performance Tuning Feedback Unit (PTFU) acts as an adaptive optimizer, receiving telemetry data from all subsystems, including processor utilization, memory utilization, and real-time call quality indicators. Using a feedback loop based on reinforcement learning, the PTFU learns optimal orchestration configurations that minimize latency while maintaining accuracy. For example, if the latency deviation exceeds the threshold ΔL, the PTFU initiates a fine-grained redistribution of AI tasks between edge and local nodes or dynamically adjusts codec bitrates to reduce the overhead of data serialization.

[0076] The Transmission Synchronization Array (TSA) acts as a low-latency communication bus, synchronizing the transmission time of outgoing packets with the completion of inference events. The TSA includes phase-synchronized clocks and timestamp units that ensure synchronization between local and distributed nodes, effectively eliminating jitter accumulation in multi-hop VoIP environments.

[0077] The device integrates LOC, AICP, and TSA into a single enclosure, forming a latency-controlled communication hub (LOCH). The LOCH includes a real-time clock synchronization circuit, a dedicated AI inference processor, and a deterministic bus interface supporting the IEEE 1588 Precision Time Protocol (PTP). The modular design features removable AI processing modules for scalable deployment in enterprise telephony or call center servers. The enclosure also incorporates a vibration-damped mount for acoustic sensors, an embedded FPGA for real-time orchestration, and active thermal management to ensure high inference throughput even under low-latency conditions.

[0078] During operation, the LOCH performs a latency calibration upon connection establishment by measuring the end-to-end latency. During an ongoing conversation, any deviation from the target latency triggers a correction sequence in which the LOC dynamically redistributes inference tasks, compresses feature vectors, or throttles packet rates to restore real-time alignment.

[0079] The invention thus provides a hardware-based, AI-optimized communication control system capable of self-adaptive orchestration and latency adjustment, ensuring seamless voice interaction even under dynamic network and computing conditions.

[0080] The present invention relates generally to the field of telecommunications systems and, in particular, to systems and devices that enable AI-supported real-time telephone communication. It lies at the interface of digital signal processing, distributed AI computing, and communication systems engineering. Specifically, the invention relates to a latency-aware orchestration and performance optimization system that integrates predictive latency modeling, reinforcement learning-based scheduling, precision adaptive inference control, and deterministic synchronization to ensure real-time call continuity. The invention addresses the technical challenges of latency propagation in AI-supported telephony, including delays caused by inference computing, feature extraction, buffering, and network transmission.The described system achieves dynamic orchestration at the hardware and software levels to optimize inference execution, network packet synchronization, and feature-level data processing under varying bandwidth and computational load conditions. The technical field thus encompasses latency-optimized communication systems, hardware development for AI telephony, adaptive orchestration techniques, and performance-optimized inference architectures, which together ensure seamless, low-latency, and high-quality conversational interaction in modern intelligent communication environments.

[0081] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0082] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A system for latency-aware orchestration and performance optimization in AI-driven telephone communication. 102 Speech Recognition Unit 104 Feature extraction unit 106 processor for artificial intelligence inference 108 Latency Orchestration Controllers 110 Performance Optimization Unit 112 Transmission synchronization array

Claims

[1] A system for latency-aware orchestration and performance optimization in AI-driven telephone communication, consisting of: a speech capture unit configured to capture an analog audio signal from a telephone interface and convert the analog audio signal into a digital audio signal stream; a feature extraction unit that is operationally coupled with the speech acquisition unit and is configured to generate a feature representation of the digital audio signal stream through spectral decomposition, noise reduction, and temporal segmentation; an AI inference processor communicatively connected to the feature extraction unit, configured to run one or more AI models for automatic speech recognition, natural language understanding, and emotion recognition on the feature representation to generate intermediate results for inference; a latency orchestration controller coupled to the AI ​​inference processor, wherein the latency orchestration controller is configured to monitor latency across multiple processing stages, predict cumulative delay propagation using a hybrid latency estimation model, and orchestrate the execution scheduling of the AI ​​inference processor based on the predicted latency deviation; a performance optimization unit coupled with the latency orchestration controller and configured to dynamically adjust computational accuracy, inference batch size, and feature processing resolution based on latency thresholds and quality constraints set by the latency orchestration controller; and a transmission synchronization array configured to time-align the processed output generated by the AI ​​inference processor and transmit it to a remote communication node, with the transmission synchronization array maintaining deterministic time coordination between successive packets and the orchestrated inference results. [2] System according to claim 1, wherein the latency orchestration controller comprises a latency prediction processor configured to compute a latency distribution profile by analyzing the timestamp differences between successive processing events within the feature extraction unit, the AI ​​inference processor and the transfer synchronization array, and wherein the latency orchestration controller continuously updates a latency mapping table that defines intermediate delay values ​​for predictive orchestration. [3] System according to claim 1, wherein the latency orchestration controller is further configured to perform a recursive optimization operation to adjust the task scheduling order within the AI ​​inference processor based on the latency mapping table, thereby selectively prioritizing inference tasks of lower computational complexity during periods of detected latency exceedances and subsequently restoring full model operation once the latency deviation has fallen below a predefined threshold. [4] System according to claim 1, wherein the performance optimization unit comprises a circuit for adjusting the computational accuracy, configured to dynamically switch between floating-point and integer arithmetic processing states of the AI ​​inference processor depending on the current latency deviation, such that a reduction in accuracy occurs when the latency exceeds a predetermined upper limit, and a high-accuracy computation is restored when the latency is again within the target tolerance limits. [5] System according to claim 1, wherein the transmission synchronization array comprises a deterministic communication bus and a time synchronization circuit configured to align the output transmission time of each processed packet with a reference clock signal distributed to the speech capture unit, feature extraction unit and artificial intelligence inference processor, thereby ensuring synchronous operation and avoiding cumulative time drift during multi-stage processing. [6] System according to claim 1, wherein the latency orchestration controller is further configured to generate a predictive latency profile by combining the measured inference delay, the network packet transfer delay and the buffer queue delay, and computes a latency correction instruction which is transmitted to the performance optimization unit for the dynamic redistribution of computational tasks to the available inference processors in real time. [7] System according to claim 1, wherein the performance optimization unit is configured to perform an adaptive model reconfiguration process that truncates non-critical subgraphs of the artificial intelligence model when the latency exceeds the predefined tolerance limit, so that non-essential processing layers related to secondary attributes such as emotional tone or context weighting are temporarily bypassed without affecting the primary recognition accuracy. [8] System according to claim 1, wherein the transmission synchronization array further comprises a timestamp processor configured to assign a global time identifier to each packet leaving the AI ​​inference processor, and wherein the timestamp processor ensures a monotonic progression of the time identifiers so that the temporal order of the output packets is maintained across distributed network nodes. [9] System according to claim 1, wherein the latency orchestration controller comprises a delay compensation processor configured to analyze historical latency deviation data and generate a predictive delay compensation factor that is applied as a time offset adjustment in the transmission synchronization array to ensure a uniform round-trip latency across bidirectional communication links. [10] System according to claim 1, wherein the feature extraction unit comprises a dynamic segmentation circuit configured to change the size of the time window of the digital audio signal stream based on instantaneous latency feedback from the latency orchestration controller such that, as network latency increases, shorter segments are processed, thereby reducing inference delay without affecting temporal continuity.

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