Memory management system for appointment scheduling

1 . A computer-implemented method for managing memory allocation for appointment scheduling, the method comprising:

receiving, by a processor, an audio stream corresponding to a live conversation;

segmenting, by the processor, the audio stream into a sequence of time-based audio frames;

for each audio frame of the audio frames, generating, by the processor, a transcription and computing at least one of a speech-recognition confidence score associated with the transcription, and a domain-similarity score that quantifies semantic similarity between the transcription and a stored vocabulary using a cosine-distance function;

dynamically adjusting, by the processor, a predefined threshold for the speech recognition confidence score based on instantaneous processor-core utilization;

storing, in a working memory, the transcriptions for which at least one of the speech-recognition confidence score or the domain-similarity score exceeds the predefined threshold, the stored transcriptions being represented as fixed-length character encodings;

upon accumulation of a predetermined quantity of the stored transcriptions in memory, invoking, by the processor, a natural-language-understanding (NLU) model and supplying the stored transcriptions as input;

executing, by the processor, the NLU model to generate a structured intent representation that includes an appointment-booking intent and at least one time-related constraint;

querying, by the processor, an external calendar service for availability data based on the time-related constraint;

selecting, by the processor, a candidate time slot that satisfies the time-related constraint and is indicated as available in the availability data;

presenting, by the processor, the candidate time slot to a remote user within the live conversation; and

upon receiving the remote user confirmation:

storing the candidate time slot in a memory location reserved for a provisional booking,

monitoring calendar availability during a predetermined verification interval,

converting the provisional booking to a confirmed appointment if no conflict is detected during the interval, and otherwise presenting an alternative time slot to the remote user within a same session;

wherein discarding low-relevance transcriptions prior to memory allocation and delaying invocation of the NLU model until the predetermined quantity of the stored transcriptions is accumulated to reduce transient memory usage and processor cycles during live interaction.

2 . The method of claim 1 , further comprising concurrently handling a plurality of live conversations on separate execution threads and, to prevent overlapping bookings, maintaining in cache-aligned shared memory a lock-free ring buffer whose entries each store a time-slot identifier and a status flag, the method further comprising:

writing, by each thread, the provisional booking record into the ring buffer using an atomic fetch-and-add operation; and

promoting the status flag of the record to confirmed state via an atomic compare-and-swap operation only when no other confirmed record in the ring buffer has the same time-slot identifier.

3 . The method of claim 2 , wherein the ring buffer is dimensioned to occupy a single processor cache line, thereby allowing status-flag updates to complete without a cache-miss penalty.

4 . The method of claim 1 , wherein the stored vocabulary of appointment-related terms is compressed using a locality-sensitive-hashing structure to accelerate cosine-distance computation.

5 . The method of claim 1 , wherein the NLU model comprises a transformer-based sequence-to-sequence neural network distilled to a parameter footprint of less than 100 MB for edge deployment.

6 . The method of claim 1 , further comprising pre-fetching calendar availability data in the background while the NLU model executes.

7 . The method of claim 1 , wherein the time-related constraint includes an upper limit on start time and a minimum service duration.

8 . The method of claim 1 , further comprising generating a natural-language explanation of the confirmed appointment and transmitting the explanation as synthesized speech to the remote user.

9 . A system for managing memory allocation for appointment scheduling, the system comprising:

at least one multi-core processor;

a memory coupled to the processor;

an audio interface configured to capture a live audio stream;

a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the system to:

receive an audio stream corresponding to a live conversation;

segment the audio stream into a sequence of time-based audio frames;

for each audio frame of the audio frames, generate a transcription and computing at least one of a speech-recognition confidence score associated with the transcription, and a domain-similarity score that quantifies semantic similarity between the transcription and a stored vocabulary using a cosine-distance function;

dynamically adjust a predefined threshold for the speech recognition confidence score based on instantaneous processor-core utilization;

store, in a working memory, the transcriptions for which at least one of the speech-recognition confidence score or the domain-similarity score exceeds the predefined threshold, the stored transcriptions being represented as fixed-length character encodings;

upon accumulation of a predetermined quantity of the stored transcriptions in memory, invoke a natural-language-understanding (NLU) model and supplying the stored transcriptions as input;

execute the NLU model to generate a structured intent representation that includes an appointment-booking intent and at least one time-related constraint;

query an external calendar service for availability data based on the time-related constraint;

select a candidate time slot that satisfies the time-related constraint and is indicated as available in the availability data;

present the candidate time slot to a remote user within the live conversation; and

upon receiving the remote user confirmation; store the candidate time slot in a memory location reserved for a provisional booking, monitoring calendar availability during a predetermined verification interval, converting the provisional booking to a confirmed appointment if no conflict is detected during the interval, and otherwise presenting an alternative time slot to the remote user within a same session;

wherein discarding low-relevance transcriptions prior to memory allocation and delaying invocation of the NLU model until the predetermined quantity of the stored transcriptions is accumulated to reduce transient memory usage and processor cycles during live interaction.

10 . The system of claim 9 , wherein the memory includes a cache-aligned lock-free ring buffer whose entries each store a time-slot identifier and a status flag.

11 . The system of claim 9 , wherein the computer-readable medium further stores a transformer-based NLU model having fewer than 50 million trainable parameters.

12 . The system of claim 9 , wherein the audio interface comprises a digital signal processor that performs the segmentation of the audio stream into time-based frames in hardware.

13 . The system of claim 9 , further comprising a network interface that maintains persistent secure sockets to at least one external calendar service.

14 . The system of claim 9 , wherein the processor supports hardware-accelerated atomic fetch-and-add and compare-and-swap instructions used to implement the lock-free ring buffer.