System

A system comprising:

• circuitry configured to:
• receive, via a communication interface coupled to a packet-switched network, a compressed video bitstream captured by an imaging device having a wide-angle lens with a field of view of at least 120 degrees;
• decode the compressed video bitstream and generate pre-processed frame tensors;
• execute object recognition on the pre-processed frame tensors using a convolutional neural network including a backbone feature extractor with residual connections, a feature pyramid network, and a detection head, and apply non-maximum suppression to produce a set of object instances each having a bounding box, an object class, and a confidence score;
• execute face recognition by detecting facial regions using a face detection model and mapping each facial region to an embedding vector using a metric-learning model, and determining identity by cosine similarity comparison against stored reference embedding vectors;
• execute character recognition by detecting text regions, rectifying detected text regions, and executing an optical character recognition model to output recognized character strings;
• integrate the object instances, face recognition results, and recognized character strings over time using a multi-object tracking algorithm with Kalman filtering to generate structured scene information;
• assign importance scores to entities in the structured scene information on the basis of entity type, distance, relative motion, and recognition confidence, and select prioritized entities to generate scene interpretation information;
• construct a prompt sentence for a transformer-based generative AI model, the prompt sentence including an environment description derived from the scene interpretation information, user attribute constraints specifying language and maximum output length, and safety formatting instructions;
• input the prompt sentence to the transformer-based generative AI model and obtain a natural-language guidance text;
• verify the natural-language guidance text by checking for prohibited terms, ambiguous references, conflicting instructions, and length compliance, and apply corrective rewriting or regeneration when verification fails;
• convert the verified guidance text into audio data using a neural text-to-speech model;
• and
• transmit the audio data via the packet-switched network for output by a bone-conduction acoustic output device.

A method performed by circuitry of a system, the method comprising:

• receiving, via a communication interface coupled to a packet-switched network, image data captured by an imaging device associated with a user;
• executing learned discrimination processing on the image data using one or more discrimination-use learned models to perform object recognition, face recognition, and character recognition;
• integrating recognition results to generate structured scene information including types, positions, and distances of recognized entities;
• selecting and prioritizing elements of the structured scene information on the basis of user attribute information to generate scene interpretation information;
• generating a prompt sentence for a generative AI model on the basis of the scene interpretation information and dialogue history information;
• inputting the prompt sentence to the generative AI model and obtaining a natural-language guidance text;
• verifying the natural-language guidance text against predetermined safety criteria and expression-length constraints;
• converting the verified guidance text into audio data;
• and
• transmitting the audio data via the packet-switched network for output by an acoustic output device.