System

A system comprising:

• circuitry configured to:
• receive, via a communication interface coupled to a packet-switched network, structured input data and user profile data from a client terminal, the user profile data comprising preference history data, restriction data, and condition data retrieved from a relational database;
• construct a first prompt sequence by concatenating a role descriptor, a summary of the user profile data, and the structured input data, and input the first prompt sequence to a transformer-based neural network model comprising an embedding layer, a plurality of self-attention layers, feed-forward layers, and a softmax output layer to generate a plurality of inquiry data items;
• transmit the plurality of inquiry data items to the client terminal via the communication interface, receive response data from the client terminal, and extract a feature vector by tokenizing the response data, generating contextual embedding vectors via a bidirectional encoder model, and applying classification layers to detect entity references and constraint indicators;
• input the feature vector to a trained recommendation model to obtain compatibility scores for a plurality of candidate specifications, select at least one candidate specification satisfying constraint conditions, and instantiate the selected candidate specification into a specification data structure comprising material identifiers with quantity values, process method sequences, duration values, and geometry descriptors;
• construct a second prompt sequence based on the specification data structure and the feature vector, and input the second prompt sequence to a diffusion-based image synthesis model that encodes the second prompt sequence into a conditioning vector and iteratively applies denoising operations to a latent representation to generate synthesized image data;
• transmit the synthesized image data to the client terminal, receive feedback data, and convert the feedback data into updated specification parameters by mapping qualitative expressions in the feedback data to quantitative parameter adjustments via a trained regression model;
• generate, based on the updated specification parameters, structured command data by applying a slicing algorithm to geometry descriptors to produce layer definitions, computing per-layer toolpaths with coordinate positions and extrusion rates, and encoding movement instructions, extrusion instructions, and temperature instructions;
• and
• transmit the structured command data via the communication interface to a fabrication controller to execute a material processing operation by positioning a discharge mechanism along the per-layer toolpaths, dispensing materials at the extrusion rates, and controlling at least one heating element in accordance with the temperature instructions.

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

• receiving, via a communication interface coupled to a packet-switched network, structured input data from a client terminal;
• generating, based on the structured input data and context data retrieved from a database, a first prompt sequence for input to a transformer-based neural network model, and obtaining, from the transformer-based neural network model, a plurality of inquiry data items;
• transmitting the plurality of inquiry data items to the client terminal via the communication interface and receiving response data from the client terminal;
• extracting, by applying a bidirectional encoder model to the response data, a feature vector representing a set of constraint parameters and preference parameters;
• generating, based on the feature vector, a second prompt sequence for input to a diffusion-based image synthesis model, and obtaining synthesized image data from the diffusion-based image synthesis model;
• transmitting the synthesized image data to the client terminal via the communication interface and receiving feedback data from the client terminal;
• converting the feedback data into updated specification parameters by applying a trained regression model;
• generating, based on the updated specification parameters, structured command data comprising at least material identifiers, quantity values, process method indicators, and duration values;
• and
• transmitting the structured command data via the communication interface to a fabrication controller configured to execute a material processing operation in accordance with the structured command data.