Symbolic multidimensional matrices with semad identifiers for recognition-to-rendering workflows

Abstract

A computer-implemented system and method are disclosed for generating outputs including patent drawings, static images, vector line art, and animated sequences from inputs such as photographs, draft drawings, CAD data, and textual descriptions. The system constructs and maintains a symbolic scene state in a symbolic multidimensional matrix whose cells store semantic addresses (SEMADs) and structured records for primitives, pose, scale, attributes, relationships, and constraints. A reasoning process traverses and updates the symbolic scene state, including token-efficient serialization for language-model assistance. A symbolic validator / discriminator evaluates candidate symbolic interpretations as symbolic structures, enforces structural and drafting consistency across views, and produces bounded symbolic edit actions. The system applies the bounded edits to SEMAD records and selectively regenerates affected regions, views, labels, reference numerals, or leader-line placements, reducing computation and improving consistency. In some examples, a wireframe is generated and used to constrain generative linework to required geometry.

Description

[0001] Docket Number: SEMADS0001PCT

[0002] SYMBOLIC MULTIDIMENSIONAL MATRICES WITH SEMAD IDENTIFIERS FOR RECOGNITION-TO-RENDERING WORKFLOWS

[0003] CROSS-REFERENCE TO RELATED APPLICATIONS

[0004] This application claims the benefit of, and priority to, U.S. Provisional Patent Application No. 63 / 756,709, filed February 10, 2025, and entitled “METHODS AND APPARATUS FOR SUPPORT OF FIGURE DRAWING GENERATION WITH ARTIFICIAL INTELLIGENCE MODELS”, the entire contents of which are hereby incorporated by reference herein for all purposes.

[0005] FIELD OF THE INVENTION

[0006] The present methods and apparatus relate to computer-implemented generation and improvement of drawings using artificial intelligence models. In some examples, multimodal and language models convert image, CAD, and text inputs into a symbolic scene representation and generate drawings from that representation. A symbolic validator applies structural and drafting constraints and drives selective updates to affected drawing regions or views. In some examples, the drawings include figures for intellectual property submissions that satisfy jurisdictionspecific drafting requirements, including consistent views, leader lines, and reference numerals.

[0007] BACKGROUND OF THE INVENTION

[0008] Many drawing and modeling workflows create or revise depictions of objects, assemblies, and scenes using graphical tools, structured data, and descriptive inputs. Depending on the application, drawings may be produced from photographs, draft sketches, CAD files, or textual descriptions and updated iteratively as content or presentation requirements change. In some cases, it is desirable to represent a scene in a form that supports repeatable interpretation, validation, reuse, and controlled regeneration across different outputs or views.

[0009] In some non-limiting examples, drawings illustrate features of a design, method, apparatus, protocol, or system. For intellectual property submissions, drawings may be prepared to satisfy jurisdiction-specific formalities and to present consistent views and callouts. It may be beneficial to reduce inconsistency, improve repeatability, and support efficient updates when underlying content changes.Docket Number: SEMADS0001PCT

[0010] Accordingly, systems that assist in generating and improving drawings from flexible inputs — including text, images, and structured design data — can provide practical benefits, particularly when using machine-readable representations and constraint-aware processing. SUMMARY OF THE INVENTION

[0011] Methods, systems, apparatus, and computer-readable media are disclosed for generating, transforming, and improving drawings and related visual outputs for a variety of purposes, including, in some examples, figures for intellectual property submissions that satisfy jurisdiction-specific drafting requirements. In some examples, the disclosed techniques operate on inputs including images, draft drawings, CAD data, and textual or verbal descriptions, and produce outputs including patent drawings, static images, vector line art, diagrams, and animated sequences, including view-consistent figure sets and selectively regenerated revisions thereof.

[0012] In some examples, the system receives one or more inputs including images, textual descriptions, verbal descriptions, draft drawings, and structured design data such as CAD models or assembly files. The system constructs and maintains a symbolic scene state represented by a symbolic multidimensional matrix whose cells store semantic addresses (SEMADs) and structured records describing primitives, pose, scale, attributes, relationships, and constraints. A reasoning process traverses and updates the symbolic scene state and, in some examples, uses token-efficient serialization to interact with one or more language models. A symbolic validator or discriminator evaluates candidate symbolic interpretations as symbolic structures, enforces structural and drafting constraints, and produces bounded symbolic edit actions. In response, the system updates the symbolic scene state and selectively regenerates only affected regions, views, or drawing elements, which can reduce computational processing and improve consistency relative to full redraw.

[0013] In some examples, the system generates a wireframe or other intermediate structural representation from the symbolic scene state and uses the intermediate representation to constrain generative rendering. In examples directed to intellectual property figures, the system can generate and refine drafting elements such as reference numerals, callouts, and leader lines across one or more views while enforcing cross-view consistency.

[0014] In some examples, one or more models used by the system are trained or adapted using curated datasets, including datasets annotated for drawing type, structure, and complianceDocket Number: SEMADS0001PCT

[0015] criteria, and a quality assessment process may be used to select or weight training samples and to evaluate generated outputs.

[0016] BRIEF DESCRIPTION OF DRAWINGS FIGS. 1A-1B illustrate the symbolic multidimensional matrix (SMM) and example SEMAD cell / record structures, including sparse population and empty cells.

[0017] FIGS. 2A-2D illustrate example SMM / SEMAD encodings for mechanical assemblies, anatomical structures, and drafting semantics such as label and leader-line routing.

[0018] FIG. 3 illustrates an end-to-end workflow for generating and refining drawings (including patent drawings) using SMM / SEMAD scene state construction, reasoning, validation, rendering, and selective regeneration.

[0019] FIGS. 4A-4C illustrate SMM alignment to an input depiction and example linear simplification of SMM elements.

[0020] FIGS. 4D-4E illustrate connected-component consolidation and simplification of SMM regions in example scenes.

[0021] FIGS. 4F-4G illustrate additional region treatment operations and foveated refinement in which a selected region is expanded or resampled for higher-detail processing.

[0022] FIG. 5 illustrates an LLM / LMM traversal and symbolic discriminator / validator loop over the SMM, including constraint propagation, bounded updates, and selective regeneration.

[0023] FIGS. 6A-6B illustrate multimodal recognition, symbolic grounding, and reconciliation to produce a consistent symbolic representation from heterogeneous inputs.

[0024] FIG. 7 illustrates management of unknown or novel symbolic structures and updating a reusable library / registry of symbolic definitions, constraints, and validated structures.

[0025] DETAILED DESCRIPTION OF THE INVENTION

[0026] While the invention has been described in conjunction with specific embodiments, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the foregoing description. Accordingly, this description is intended to embrace all such alternatives, modifications, and variations as fall within its spirit and scope.

[0027] Methods and apparatus for creating drawings for a variety of purposes including but not limited to the creation of figures for intellectual property filings which are consistent with regional procedures and requirements are outlined. In some examples, a drawing is a deliberately constructed, information-bearing visual representation comprising one or more graphical marksDocket Number: SEMADS0001PCT

[0028] arranged within a coordinate frame, whether on a physical medium or in a digital format, wherein the graphical marks encode one or more properties of a subject including geometry, topology, spatial or logical relationships, or semantic meaning, in a form suitable for perception, interpretation, storage, transmission, and reproduction. In some examples, patent drawings are provided as black line drawings having clear, uniformly thick lines and sufficient contrast, and are prepared to remain legible upon reproduction. Applicable formal requirements may vary depending on the patent office or receiving authority to which the application is submitted.

[0029] For the purposes of creating figures for intellectual property filings, the training of an artificial intelligence model to generate them may require extensive exposure to patent specifications. The process of model formation may also include utilizing textual descriptions from the specification, extraction of structured figures, and identification and isolation of reference numerals and their leader lines as appropriate. The textual descriptions may serve as the primary guidance for associating text with generating visual representations, detailing components, relationships, and operational principles of an invention, hr some examples, as learning of textual associations occurs, searches may be performed in stored and processed specifications and drawings for words that may be considered synonyms to textual descriptions. Training various models with a corpus of patent illustrations along with specification reference Understanding the concept of reference numerals and leader lines may be crucial, as these elements establish clear associations between textual descriptions and the illustrated components. Furthermore, these lines may cross over drawing content and confuse feature identification. Leader lines may be intuited based on OCR detected reference numerals and relatively close location of leader line features. Leader lines may actually have numerous shapes that may need to be taught to a model for efficient identification, marking and in some examples removal once the appropriate information is obtained about what types of features in the drawing the leader line is associated with. Textual identifications are associated in the text of specifications in standard manners relative to the numerals that occur in the specifications. As non-limiting examples, leader lines may be straight linear features, may be linear features with different directional aspects. They may also be curvilinear features. In some cases, non-standard leader line features may be intuited based on the fact that in general the leader line style within a drawing may be consistent for all the leader lines. Accordingly, if simple leader line features are not found a search for common features to all the reference numeral locations may be identifiedDocket Number: SEMADS0001PCT

[0030] as a potential style. Tn some examples, novel designs to the model may be identified but segregated along with the drawing for a user review.

[0031] A well-structured patent specification includes a table of figures which describes the nature of the elements in the figure. Although the descriptions in the list of figures may be less specific than descriptions in the core of the specification, they may associate aspects of the figures that can be used as descriptors. For example, method figures may indicate a flow diagram class for a figure and the like. In some examples, the figures may have significant description referenced throughout the document. The systems of the present disclosure may be trained to recognize these references and correlate them with figure labels to enhance drawing comprehension. In some examples, models developed to associate textual identifiers with types of drawings and types of drawing elements may be useful in creating a discriminator for GAN type modelling processes.

[0032] Scraping data from the USPTO and other patent databases may allow the various models of the present disclosure to learn from a vast corpus of historical patent drawings, incorporating classification-based distinctions. For example, chemical structure illustrations may require different conventions than mechanical devices or method / process drawings, which often include flowcharts or step-by-step sequences. And these classifications may allow for separate GAN and discriminator model formation to allow for more optimal creating of different classes of drawings. Once appropriately trained, a query dialog of a user requesting the creation of a drawing from textual input or the improvement of draft drawings based on textual input may be analyzed by text interpretation models to assess which of various classes of drawing types would relate to the request and then associate a specific model type with that request. The process of training the model may allow for the identification of the potential of new classes of drawings which may be presented to users.

[0033] In some examples, a method to improve accuracy may involve training the model with source images that have been previously assessed by users on a grading system that distinguishes high-quality drawings from poor-quality ones. In some other examples, learning may be fostered from rejection data — such as office actions issued by the USPTO. Training the model with identified error types may refine the model’s ability to generate compliant drawings. Examiner-required amendments may provide valuable insights into the shortcomings of initial filings, enabling the model to adjust based on real-world feedback.Docket Number: SEMADS0001PCT

[0034] Patent documents may contain highly structured text that may benefit from deep linguistic processing to be effectively interpreted. Language models may be trained to extract meaning from specifications, particularly in understanding references in drawings. Reference numerals, figure labels, and claim dependencies may be correctly identified and mapped to corresponding descriptions.

[0035] One challenge in patent drafting may be the presence of complex tables, individual references, and cross-references within patent specifications. European patents, for instance, may often include reference numbers directly in the claims, which may represent different types of text the model to associate these numbers than may occur within the specification itself.

[0036] Nevertheless, such training may associate numbers with corresponding drawing elements. By training the language model on claim structures and keyword associations, it may enhance generated output in creating more precise drawings aligned with the claimed inventions.

[0037] Patent applicants may provide rough sketches, which need to be refined into formal patent drawings. A trained model for generative purposes may be able to recognize and enhance roughly drawn images by reconstructing them with clearer lines, proportional scaling, and adherence to standard conventions. Using a deep-learning approach, the model can convert rough inputs into structured illustrations, ensuring compliance with patent office standards. In some examples, the model may be able to recognize select features within an improved rough drawing and create reference numerals with appropriate leader lines to refer to the elements.

[0038] By incorporating multiple examples of poorly drawn figures and their corrected counterparts, the model may develop an understanding of what constitutes an acceptable patent drawing. This capability may reduce the burden on illustrators, enabling a more efficient drafting process. In some examples, there may be an additional language component that may accompany the rough drawing to help convey meaning of the rough sketch and to allow for more detailed information to be generated. Such additional added detail information may be suggested as additions to the improved rough sketch and dialogs may allow for user approval of the additions, rejections, and amendments as appropriate. In some examples, a textual description of the improved figure along with suggested improvements and suggested reference numerals along with reference numeral identification may be modeled by one or more of the artificial intelligence models of the present disclosure.Docket Number: SEMADS0001PCT

[0039] Many patents may reference products that may exist in developed or prototyped form. Tn some examples, there may be advantage to build model capabilities to process photographic, three-dimensional scanning or other physical based imagery and create line drawings. The capability may require training the model to bridge the gap between product photographs and corresponding patent illustrations historically to allow for a “reverse” generative process to take an inputted image from a user and to convert key features into an annotated line drawing that can be used or modified to create patent application drawings. Training the system with data from business websites and documents containing patent references, where such imagery is available in the public domain, may allow one input into the set of training data aimed at capabilities to recognize how real-world objects are represented in patent drawings.

[0040] By analyzing the structural features of products and their illustrated counterparts, the model may better translate three-dimensional product images into compliant line drawings. This ability is particularly useful for industrial designs, mechanical inventions, and consumer product patents where the visual representation may accurately convey form and function.

[0041] There may be numerous sources of input that can be important to training the different models of the present disclosure. For example, patent drawings can originate from various input sources, including photographs, engineering schematics, hand sketches, and verbal descriptions. The model training may be directed towards capability of managing these diverse input types, converting each into standardized patent drawings. In some examples, there may be separate models that are trained for different types of input. In some examples, model output may intentionally flow back and forth among the various different models to incorporate new types of information. In a non-limiting example, rough sketches and verbal descriptions may be utilized to create a first drawing output with various levels of sophistication including appropriate reference numerals, page headers and the like. Additional inputs such as photographic and scanning data on prototype devices that are embodiments at some level of the desired disclosure subjects may be used to supplement the patent drawings created from other starting purposes. Accordingly, there may be different model aspects that allow for different types of information to be combined into further refined drawing output. To form these models, training on general objects, prototypes, and engineering blueprints may provide utility in enhancing generalized modelling of what components may be incorporated into various functional devices.Docket Number: SEMADS0001PCT

[0042] Additionally, the systems may support textual inputs derived from patent specifications and descriptions as the starting point for modelling patent images. In some other examples, text may be derived from verbal descriptions provided by a user where a voice-to-text processing may initialize model inputs describe a drawing verbally or to describe a functionality verbally, which the model can then interpret and illustrate accordingly.

[0043] One of the critical tasks in automated patent drawing generation may, accordingly, be linking textual descriptions to corresponding graphical representations. The model may establish a lexicon that maps frequently used terms to specific visual structures. This capability may allow it to generate figures that accurately reflect the described invention, improving the consistency between written specifications and drawings. Here again, graded specification illustrations may be used in model sets to create linkages of effective and less effective implementation of drawings to textual input cues. Lexicon training from a corpus of already granted patent specifications along with their specification language and claim language may be a major starting point in model learning. In some examples, models may have regionally classified datasets since for example, European patent office requirements have some differences from USPTO differences. Nevertheless, there may be supplementary model refinement that may be derived by training on the different classified sources. Another supplementation may be used where discrete training from sources other than patent specification may occur for elements commonly found in patent specifications. For example, in a non-limiting sense, a corpus of the text of related and classified patent specifications (such as patents on physically defined apparatus) may be used to classify terms used to describe complete apparatus and alternatively words that are used to describe components of typical apparatus. Continuing the example, it may be found that for a corpus of patent specifications relating to physical descriptions of motors that the five most common elements found (hypothetically) may be 1) a nut, 2) a washer, 3) a bolt, 4) a locking plate, and 5) a stator. Accordingly, the model may be trained in a supplementary manner on three dimensional and two-dimensional representations of these five elements. With sufficient training in a variety of common place items for different types of classes of items, models may be useful in classifying elements in submitted drawings and learning how a breakdown of combinations of common and standard items may allow to the inference of the type of apparatus that a given drawing or image may belong to.Docket Number: SEMADS0001PCT

[0044] Although the modelling has been described with the function of generating high quality drawings from various input sources, a sophisticatedly trained model may be applied to additional value activities. For example, there may be value to a litigation activity to be able to take claim language from a patent under analysis and use the modeled awareness to search for elements in the historical record where patent drawings may indicate similarities in form or function to a claim structure. Such searching may eliminate vagaries of claim wording, where the same elements may be verbally described in different ways. The search for common drawing aspects may also cut across different language translation aspects where standard spoken translation models from say English to Russian may not work well for “Canadian English” patent claim language conventions to “Belarussian dialect” patent claim language conventions.

[0045] Accordingly, this type of logic may also be applied to non-patent sources of imagery such as journals, advertisements, and the like.

[0046] A well-designed user interface may be essential for refining and validating patent drawings. The interface may allow users to amend existing drawing input or model output to add reference numerals based on automated analysis or manual input. It may also support automated detection of figure numbering, docket references, and pagination, ensuring compliance with formal requirements.

[0047] In some examples, an optimization feature for reference numeral placement may minimize confusion by adjusting line positioning and avoiding cluttered areas. An optimization model may offer a user many options that may not be possible to envision directly without examples. Furthermore, such examples of automation may enhance readability and, therefore, reduce errors in application and patent submissions.

[0048] In some examples, one or more of the models may be capable of generating line diagrams from specific data structures, these may include photographic images as previously mentioned, software-generated graphics, Vector-Based Drawing Software, online drawing tools, CAD and BIM software files and the like. Training on raw design data from tools like Autodesk, Revit, and SolidWorks may ensure compatibility with industry-standard formats. It may also allow access to a large corpus of supplementary image resources for the common components of drawings as has been described previously. And, as will be described in following sections, in some examples, training data may be supplied by outside parties in incented models and the capability of incorporating and interpreting these various inputs before introducing the associatedDocket Number: SEMADS0001PCT

[0049] drawings with conceptual aspects of the present disclosure for model training. By incorporating user-generated line drawings and textual descriptions, the system may offer a dynamic approach to illustration generation, making it adaptable to different input types.

[0050] In some examples, a feature of the systems described in the present disclosure may be an ability to refine previously generated drawings. Users may be able to modify elements using graphical user interface controls or textual commands (e.g„ "increase size by 20%, " "add hatching"). By integrating automated change suggestions, the system may highlight areas for improvement, streamlining the editing process. And may be able to realize the suggested changes in some examples.

[0051] Many patents involve process-based inventions, requiring method illustrations such as flowcharts. In some examples, the system may generate these classes of drawings based on textual inputs and claims analysis, incorporating historical data to suggest appropriate layouts. Furthermore, the modeling capability may allow for an existing flow diagram to be analyzed for comparison of basis within a generating specification or for supplementation with additional steps or directions. In still further examples, a knowledge of existing process flows from prior art input may allow for the addition of new conceptual aspects to previously described content. Or, in similar but different examples, the ability to analyze process flows from drawing content may supplement the search for prior art similarities to existing drawings and claims. Automating step numbering and multi-page transitions may ensure logical and clear presentation of process diagrams.

[0052] For patents involving chemical inventions, the model must be trained on conventions for illustrating molecular structures, reaction pathways, and compound representations. By learning from chemical databases and patent examples, it may be possible for models of the present disclosure to accurately generate chemical diagrams that comply with IUPAC standards and to illustrate claims along with textual information as well. In addition, searches for prior art based on chemical illustrations may also be supplemented with these model capabilities.

[0053] Design patents emphasize ornamental features, necessitating distinct drawing conventions such as dashed lines for unclaimed portions and the like. Training on MPEP guidelines helps the model differentiate between design and utility patents, ensuring proper visual representation. Such understanding may help in the classification of raw drawings.

[0054] Furthermore, the generation of design patent drawings may be aided by the training to turnDocket Number: SEMADS0001PCT

[0055] imagery into drawings. It may be useful in some examples, to train specialized models that incorporate much of the training and capabilities of general models as described herein but to specifically relate to how rough drafts, textual information or other such inputs may be used to create design drawing consistent renderings. The tools may also be useful in a reverse sense to scrub web-based sources for items that may infringe the text and illustrations of design patents. Furthermore, searching may also be used for prior art purposes. By incorporating design elements from industrial specifications and design patents, the systems may generate more precise illustrations that meet patent office requirements.

[0056] In some advanced implementations, models may be able to leverage existing patent descriptions to abstract device elements and recognize classification-based distinctions. In some examples, learning acquired from such training may improve accuracy in generative processes. Accessing 3D model-based scanning technologies may further refine the assessment of figure quality.

[0057] By analyzing drawing rejections and patent office rules, the system may learn refined aspects of drawing quality, ensuring that generated illustrations comply with regulatory standards. Rejection learning and rule-based grading schemes may provide a framework for evaluating drawing effectiveness, with back-training on professional assessments to continuously improve accuracy.

[0058] In some examples, various inputs of the training datasets for the instant application may come from disparate sources. In some examples, the designers of algorithms may solicit input in initial phases of model training. In other examples, a continuous input of training data may be solicited. There may be models of compensation for imagery that may result when a third party provides diagrams, photographs, scan data, model data, or the like. In some examples, such input may be classified based on a quality measure for the input. There may be various methods of attributing value to an input that may be used in model creation. Block chain tracking of submitted imagery for model training from public and corporate sources. Block chain tracking may allow for the recording of information related to how important an image is in creating a model or in the utilization of a model. In a revenue sharing model a system may be configured that allocates a certain percentage of profits from creating drawings to content providers based on the relative importance of the content to the generation of generative or discriminator models.Docket Number: SEMADS0001PCT

[0059] The present disclosure may describe various systems and methods for generating and improving patent drawings. Key aspects may include teaching and training models. The system involves training models using patent specifications, textual descriptions, and reference numerals to understand and generate patent drawings. The systems may manage various input types, including pictures, sketches, engineering drawings, and verbal statements, to create patent drawings in some examples and more generally drawings for various purposes. It also may support input from business websites and documents with patent references. The system may automate the identification and optimization of figure numbering, reference numerals, and drawing elements. It may also suggest changes and improvements to drawings. Users may edit output drawings using graphical cursor control and textual descriptions, with automated change suggestions. In some examples, the system may support method illustrations, chemical structures, and design patent illustrations, with training based on MPEP guidelines and patent type distinctions. In some examples, the system may leam from existing patent descriptions, patent office actions, and other sources to define and improve drawing quality. There may be grading schemes for assessing drawing quality and supports customized training sessions. In some examples, input may include 3D model-based scanning and analysis, language model abstraction, and the ability to create constraint concepts based on patent office rules. In some examples, the invention may aim to enhance the generation, editing, and quality assessment of patent drawings through advanced automation and learning techniques.

[0060] In some examples, the systems may revolve around the automation and optimization of patent drawing generation using artificial intelligence, machine learning, and advanced image processing techniques. Methods and systems may leverage trained models to interpret textual descriptions, reference numerals, images, and engineering sketches to generate patent drawings with improved clarity and compliance. Additionally, some methods may focus on enhancing drawing quality by analyzing patent office actions, refining models through training, and automating figure numbering. Other examples may introduce user interfaces for editing, graphical cursor controls, and reviewing Al-assisted illustration suggestions to streamline the drafting process. Some examples may explore the generation of chemical structure diagrams. Some examples may consider using the models to search prior art for similar occurrences of an illustrated item both for prosecution purposes and litigation purposes. In some examples, input from 3D scanning for quality assessment may be utilized. Models may have the capability toDocket Number: SEMADS0001PCT

[0061] distinguish between design and utility patent illustrations based on MPEP guidelines. Moreover, novel techniques may be used for automating reference numeral placement, generating drawings from verbal inputs, and supporting method illustrations in patents. In general models may present a comprehensive framework for improving efficiency, accuracy, and compliance in patent drawing generation and editing.

[0062] Some examples relate to an automated system for generating patent drawings based on textual descriptions and reference numerals. These methods may involve training a model to interpret input data and produce drawings that align with patent office standards. The methods may leverage artificial intelligence (Al) to reduce manual drafting efforts, ensure consistency and accuracy. By automating the process, the method may significantly improve efficiency in preparing patent applications, including compliance with formatting and clarity requirements.

[0063] In some examples the methods may also relate to allowing additional input formats such as images, sketches, and engineering drawings. These sources may provide the model with more visual references, enabling it to generate highly accurate and detailed patent drawings. By incorporating various types of input, the system may cater to different technological fields where complex structures and assemblies need precise visual representation.

[0064] In some examples, the related systems may automate the identification of figure numbering and reference numerals. This may ensure that reference numerals are correctly placed and labeled and are included in related patent specifications. The systems may also include provisions for setup of a user interface (UI) for manual refinement, allowing users to review and adjust drawings.

[0065] In some examples, the related systems may introduce a UI feature that may allow users to modify drawings using graphical cursor controls and text inputs. This may make it easier for patent drafters to interact with the system, ensuring that automated suggestions can be reviewed and refined quickly.

[0066] In some examples a method for improving the quality of patent drawings through model training may be utilized. The system may access historical patent descriptions and analyze past patent office office actions which relate to drawing objections. By learning from these objections, the model may refine its output to enhance the production of drawings that meet patent office standards, reducing workload due to improper formatting, unclear illustrations, or missing details.Docket Number: SEMADS0001PCT

[0067] In some examples, the systems may introduce a quality assessment mechanism for evaluating patent drawings. By grading existing patent illustrations based on a refined model, the systems may provide feedback and ensure that newly generated drawings meet high standards. Automated grading system may serve as a pre-submission validation tool, helping applicants reduce costs.

[0068] This system may integrate or create a language model that reads patent specifications and suggests illustrations based on historical patent data. Since many patents require flowcharts, procedural diagrams, and sequence-based illustrations to explain inventions involving processes or methods, these too may be supported. By leveraging past examples, this system may assist in generating accurate method-based illustrations that align with industry norms.

[0069] Expanding on these capabilities, in some examples, automation for transitions between multiple page flow diagrams and step numbering in illustrations may be developed. Method illustrations may involve multiple steps that need to be clearly numbered and laid out logically. Model based automation may ensure consistency and readability, which may be crucial for understanding and interpreting process-based inventions.

[0070] In some examples, systems may focus on the generation of chemical structure diagrams based on textual input describing chemical processes. In patent applications related to pharmaceuticals, materials science, and chemistry, detailed molecular structures must be depicted accurately.

[0071] In some examples, systems may be introduced that differentiates between design and utility patent drawings. The system may utilize the training of models that have training based on MPEP and other such guidelines to ensure compliance with the specific requirements of each type of patent drawing. The system may distinguish between dashed lines and different view types in patent drawings.

[0072] Systems for image, drawing, and model generation

[0073] Symbolic Multidimensional Matrix as a Primary Scene Representation

[0074] In some examples, methods of scene representation and cognitive context may be directed to a Symbolic Multidimensional Matrix (SMM) as a primary scene representation. In some examples, the SMM may include semantic addresses (SEMADs) as described in following sections and a multidimensional matrix reasoning pipeline. A single semantic address may be referred to as a SEMAD in some examples. In some examples, a semantic address SEMAD is aDocket Number: SEMADS0001PCT

[0075] structured symbolic identifier for an entity, and may include a primitive or type code and one or more associated fields such as pose, scale, attributes, characterization data, or relationships. In some examples, SEMADs are stored and manipulated within a symbolic multidimensional matrix SMM representing a symbolic scene state; however, the same functions may be implemented using other structured symbolic representations, including tables, graphs, grids, lists, or combinations thereof. In an exemplary sense, patent drawing generation may be described as a concrete and readily verifiable example; however, the disclosed concepts may be applied more broadly across scene understanding, structure recognition, reasoning, simulation, and multimodal rendering and cognition.

[0076] Referring now to FIG. 1A, an illustration of a symbolic multidimensional matrix (SMM) is represented as a three-dimensional lattice 100 having a first axis 101. a second axis 102, and a third axis 103. The lattice 100 is an example slice of an N-dimensional symbolic address space in which each matrix element corresponds to a location in the address space and is configured to store symbolic identifiers and structured records rather than raw pixel values. In some examples, the figure further indicates that the matrix may extend beyond three dimensions, for example via a higher-dimensional extension indicator 104, such that additional axes can represent other indexing dimensions (e.g., view, hierarchy, semantic class, time, hypothesis layer, or other indexing schemes) while remaining addressable using the same symbolic formalism. In some examples, a SEMAD (semantic address) is a composite identifier formed from multiple components that, together, describe a referenced “thing” within a structured scene, model, or symbolic state. A SEMAD can function as an addressable handle for an object, region, part, concept, or other referent, and can be stored in one or more cells of an SMM so that symbolic reasoning and constraint-driven operations can be performed over the scene without requiring direct manipulation of pixel data.

[0077] In some examples, a SEMAD includes a primitive code, also referred to as a GCODE or SEMAD-PRIMITIVE, implemented as an alphanumeric token that denotes a unique part definition or concept definition. In some examples, primitive codes are organized in a taxonomic scheme, such that related items share a common family naming structure (e.g., fasteners may be represented as bolts labeled Bl, B2, ..., BX and washers labeled Wl, W2, ...), thereby supporting normalization, lookup, reuse, and hierarchical classification. In still further examples, a primitive code may take other forms other than standard alphanumeric characters such as iconsDocket Number: SEMADS0001PCT

[0078] or graphical codes such as QRCODES and the like. The encoded information may just provide a unique primitive code aspect. In still further examples, the coded information may also encode other information relating to the SEMAD as well.

[0079] In some examples, a SEMAD further includes pose and scale components. For instance, the SEMAD may specify an orientation using a rotational frame of reference (e.g., roll / pitch / yaw or an equivalent representation) and may include one or more scale factors along one or more dimensions. In some examples, a two-dimensional representation can be expressed using a scale vector where a third dimension is set to a null or zero value, while other examples use full three-dimensional scaling or other dimensionality-specific scale encodings.

[0080] In some examples, a SEMAD optionally includes one or more characterization datasets that describe properties of the referenced item in a structured form suitable for classification, constraint evaluation, or downstream reasoning. Such datasets can include discrete vectors or tags representing, for example, color, density, material descriptors, mass properties (including moments of inertia), surface or finish information, functional labels, or other attributes that help distinguish the referenced item and support consistent symbolic treatment across views and renderings.

[0081] In some examples, SEMAD structures are heterogeneous, such that not every SEMAD carries every possible field. For example, a liquid or gas may be assigned a primitive code while having a spatial reference expressed using a different mechanism, such as an index into a point cloud, a voxel field, a volumetric grid, or another spatial data structure. This is a non-limiting illustration that SEMAD composition can vary across object classes, modalities, or representational needs while still providing a stable symbolic reference used by the system.

[0082] In some examples, a SEMAD is considered valid even when certain optional characterization fields are absent, provided that minimum identifying structure is present (e.g., a primitive code and at least one form of addressability or association). In some examples, SEMADs and / or SEMAD-PRIMITIVEs are issued, reviewed, curated, or approved through a centralized registry that governs uniqueness and semantic meaning across projects, while in other examples SEMADs are locally scoped such that meaning is defined by a local schema, namespace, definition set, or project-specific ontology.

[0083] Continuing with FIG. 1A an illustration of a concrete embodiment of the SEMAD composition described above by illustrating how a SEMAD can be represented as both anDocket Number: SEMADS0001PCT

[0084] address string and a structured cell record within the SMM. In some examples, the SEMAD identifier 120 can include a primitive token that denotes a unique part or concept definition, together with embedded numeric indices and one or more scalar values that encode position, scale, weight, confidence, or similar numeric factors. In some examples, the stacked record lines 130, 140, 150, and the like correspond to SEMAD fields and associated datasets, including descriptive attributes (e.g., characterization vectors or tags), relationship and constraint assertions (e.g., attachment, adjacency, containment, alignment, permitted degrees of freedom), and metadata (e.g., provenance, confidence, approval state, or namespace / registry identifiers). In some examples, representing SEMADs in this structured manner allows a symbolic discriminator, traversal routine, or rendering pipeline to evaluate and enforce consistency using the SEMAD record contents — rather than relying on raw pixel appearance — thereby enabling robust reuse of part definitions, constraint-driven corrections, and cross-view structural validation.

[0085] Referring now to FIG. IB, a two-dimensional SMM 160 is illustrated for reference. Some scenes may directly correspond to such a matrix. For example, a two-dimensional input such as an image scan, or picture input may have at least one manifestation represented as a two-dimensional SMM. In other examples, such a two-dimensional scene may be derived as a slice of a higher order SMM matrix. In the illustration of FIG. IB, the representation may be of the front edge plane of the SMM. A 2D SMM can have positions with SEMAD instances 170 as well as empty 180 vertices. There may be many genesis activities of 2D SMM matrices in various contexts as are described herein.

[0086] The techniques described herein may be configured to improve structural consistency, enablement, and reusability across recognition, reasoning, and rendering workflows. In some examples or implementations, the SMM may operate as a canonical scene substrate that may persist across processing stages and modalities, such that outputs from recognition systems (e.g.. detectors, segmenters, CAD parsers, depth / point-cloud processors), reasoning systems (e.g., constraint solvers, graph optimizers), and rendering systems (e.g., illustrators, simulators) may reference a common symbolic state rather than separately interpreted pixel grids.

[0087] In some implementations or examples, the SMM may be a symbolic and primarily textual representation implemented as an N-dimensional address space whose axes may include spatial position and may further include one or more axes encoding semantic class, conceptual role, partDocket Number: SEMADS0001PCT

[0088] hierarchy, assembly membership, material or functional attributes, view index, time index, and / or hypothesis layer. In such examples, individual cells may store textual tokens and structured symbolic records rather than numeric pixel values, including one or more of: SEMAD identifiers, canonical names, attribute-value pairs, relationship descriptors, constraint expressions, provenance tags, and confidence measures. This textual formalism may decouple scene meaning from appearance and may support inspection, versioning, and auditability of scene state.

[0089] In some examples, the SMM may be populated from heterogeneous input paradigms including one or more of: photographs, video, depth maps, LiDAR or point clouds, multi-view image sets, CAD or BIM models, technical drawings, scanned documents, textual specifications, and sensor telemetry. In such implementations, observations may be mapped into textual and symbolic assertions (e.g., entity declarations, pose statements, relationship statements, and constraint statements), thereby enabling unified reasoning over heterogeneous evidence.

[0090] Referring now to FIG. 2A, an illustration is provided of a two-dimensional slice of a symbolic multidimensional matrix (SMM) 200 representing a SEMAD-based depiction of a mechanical assembly. The SMM slice 200 is shown as a grid of matrix elements, where one or more elements are populated with SEMADs 210 and other elements remain vacant 211. The depicted SMM slice 200 may reside within a larger SMM definition, for example having additional rows and columns indicated schematically by ellipses 213 and may be one of multiple slices or regions 214 used to represent the assembly in different neighborhoods, views, or hypothesis layers. Each populated matrix element corresponds to a location in the two-dimensional addressable space and is configured to store symbolic identifiers (e.g., SEMAD tokens) and associated structured records, rather than pixel values. In some examples, one or more matrix elements are populated with symbolic tokens representing detected or asserted objects of interest such as fasteners, plates, brackets, or other components, while many other matrix elements remain unpopulated to reflect a sparse symbolic representation of the scene. In some examples, an example populated matrix element includes a SEMAD-style identifier having a primitive portion and embedded numeric indices and / or scalar values (e.g., pose, scale, or confidence), thereby enabling downstream traversal, constraint enforcement, and rendering operations to proceed using symbolic structure rather than appearance.Docket Number: SEMADS0001PCT

[0091] In some implementations, the symbolic slice 200 is used to generate a deterministic structural depiction of the assembly. For example, transition 220 schematically represents a rendering process in which the S EMADs 210 are expanded into nominal geometry and projected into a selected view to produce a line-based wireframe 230 or skeleton depiction. The wireframe 230 may include centerlines, edges, and joint locations derived directly from the SEMAD records and constraints stored in the SMM, and therefore encodes assembly topology, part identity, and attachment relationships in a geometry-consistent manner.

[0092] In some examples, the deterministically generated wireframe 230 is then used as conditioning input for a generative image process, indicated schematically by transition 240. For instance, a generative image model may be provided with the wireframe 230 and one or more style, shading, or appearance prompts, and may synthesize a more realistic or stylistically refined depiction 250 that conforms to the structural constraints specified by the SMM slice 200.

[0093] Because the wireframe 230 is derived deterministically from the symbolic matrix, the generative depiction 250 remains constrained to preserve part counts, relative positions, attachment structure, and other geometric relationships encoded in the SEMAD records, while allowing flexible variation in rendering style, surface appearance, or background detail.

[0094] Referring now to FIG. 2B, an example application of the same SMM / SEMAD formalism to an anatomical domain is shown. In particular, FIG. 2B depicts a skeletal hand with an overlaid two-dimensional SMM slice 260 aligned to a plurality of anatomical elements. In some examples, selected matrix cells are populated with symbolic tokens corresponding to bones or bone groups, such that each populated cell provides a stable symbolic reference point aligned to a defined anatomical locus (e.g., a bone centerline, a joint region, or another predefined landmark). In some examples, one or more populated matrix elements include corresponding SEMAD tokens that, when expanded, encode a composite SEMAD identifier and an associated structured record describing pose, scale, characterization datasets, and / or relational constraints, such as adjacency, articulation linkages, or permitted degrees of freedom. In some examples, representing anatomical structure in the SMM supports consistent traversal, indexing, and constraint evaluation across multiple views while maintaining decoupling between pixel appearance and the underlying symbolic scene representation. For clarity, an isolated SMM 261 (shown schematically, with entries too small to read at the illustrated scale) is presented in frontDocket Number: SEMADS0001PCT

[0095] of the composite slice 260, and an isolated hand image 262 is shown behind the composite slice 260.

[0096] In some examples, processing using the SMM / SEMAD formalisms may proceed in either direction. In a first direction, an input depiction such as the hand image 262 (or other sensor-derived representation) may be processed according to the procedures described herein to detect anatomical elements, assign SEMAD identifiers, and populate the SMM 261, thereby producing a symbolic scene state that may be used for downstream reasoning, validation, constraint enforcement, simulation, measurement, classification, or cross-view correspondence. In a second direction, the flow may proceed oppositely, such that one or more processes populate or modify the SMM 261 (e.g., from a template anatomy model, clinician annotations, a learned anatomical prior, simulation output, or symbolic edits), and the populated SMM 261 is then rendered to produce an illustration of a hand image 262. In some examples, this bidirectional capability enables the same symbolic scene representation to serve both as an ingest target for perceptual understanding and as a generative source for producing consistent depictions.

[0097] Referring now to FIG. 2C, a close-up view 270 of the composite slice 260 is shown, illustrating a set of SEMADs corresponding to different hand bones. By way of example, SEMAD 271 corresponds to a first distal interphalangeal element 272. In some examples, the SMM is sparsely populated such that only cells associated with detected or relevant anatomical structures contain SEMAD entries, while a remainder of the matrix comprises blank cells 273.

[0098] The examples illustrated in FIGS. 2A-2C demonstrate that SEMADs provide a uniform symbolic addressing mechanism applicable across heterogeneous domains, including mechanical assemblies and anatomical structures, by storing SEMAD identifiers and associated structured records within an SMM slice aligned to an input depiction. In some examples, this decoupling enables a shared pipeline to normalize inputs from different modalities into a common symbolic scene state, where populated cells represent only objects or regions of interest, and where downstream reasoning, validation, and rendering decisions operate primarily on SEMAD records (e.g., primitives, pose / scale parameters, attributes, and constraints) rather than directly on raw pixel data. In some implementations, each populated cell may store a cell record comprising at least: (i) a SEMAD identifier; (ii) pose metadata including a coordinate frame and one or more of position, orientation, scale, tolerance, or kinematic state; and (iii) one or more relationship descriptors. In such examples, relationship descriptors may encode structural relations usable forDocket Number: SEMADS0001PCT

[0099] reconstruction and simulation, including at least one of attachment type, adjacency, containment, alignment, fastener engagement, feature correspondence joint constraints, clearance constraints, symmetry constraints, contact constraints, or temporal precedence.

[0100] Referring now to FIG. 2D, in some embodiments a symbolic scene matrix SMM 280 is used to encode not only semantic objects and attachment relationships, but also layout and routing rules for patent-style leader lines and labels. The SMM 280 may be represented as a two-dimensional grid of cells, in which one or more cells store semantic address tokens that identify parts or features and further specify leader attachment semantics. For example, a first SEMAD token 281 may encode a feature identifier together with a leader endpoint designation, such as a, LI, thereby indicating that a leader associated with index LI is to originate from a corresponding hole, fastener, or other feature encoded in the SMM 280. A leader endpoint cell 282 may designate a matrix-space endpoint for another leader index, such as h, L2, and may correspond to a label anchor location and associated label metadata for a feature referenced by the token.

[0101] The SMM 280 may additionally carry leader-routing constraints that define where a leader may traverse when routed from a feature anchor toward a label region. In the illustrated embodiment, an allowed leader routing cell 283 indicates a cell through which leader traversal is permitted, and a disallowed leader routing cell 284 indicates a cell through which leader traversal is prohibited. In some examples, the allowed and disallowed markings are overlaid on otherwise empty cells of the SMM 280 and are generated or refined by a symbolic discriminator, rule engine, or constraint solver that applies drafting rules including minimum clearance from depicted geometry, avoidance of dense feature regions, and prevention of crossings or ambiguous overlaps among multiple leaders.

[0102] To generate a patent-style illustration from the symbolic representation, a transformation from SMM to illustration space 285 maps the part placements, feature locations, and view parameters represented in the SMM 280 into an illustration layout. In addition, leader-routing rules driving image creation 286 are derived from the allowed and disallowed routing cells 283 and 284 and applied during leader path computation, such that candidate leader paths are constrained to traverse permitted regions and avoid prohibited regions after mapping into illustration space. In some examples, the routing computation operates on a grid induced from the SMM 280, on a vector graph derived from the SMM 280, or on a hybrid representation.Docket Number: SEMADS0001PCT

[0103] while still enforcing the same allowed and disallowed routing semantics encoded in the SMM 280.

[0104] As a result of applying these symbolic constraints, a first leader line in the illustration 287 is generated as a patent-style leader that originates at a feature encoded by the SMM 280 and is routed along an admissible path determined from the routing constraints. A second leader line in the illustration 288 may be generated for another feature, including a leader originating from a hole feature and terminating at a label region associated with the leader index L2. A label referencing SEMAD data 289 is placed at a terminal location of the second leader line and may include callout text, dimensions, reference indicia, or other annotation content derived from, or linked to, the SEMAD token corresponding to the referenced feature. In some implementations, the label content is generated by reading SEMAD-associated metadata such as feature type, part identifier, tolerance information, or dimensional parameters and formatting that metadata in a patent-drawing compliant style.

[0105] The illustration may further include an exclusion zone 290 in the illustration corresponding to disallowed leader routing regions encoded in the SMM 280, such that leaders are not drawn through the exclusion zone. In some examples, the exclusion zone 290 is rendered as a bounded region, stippled band, cross-hatched area, or other visual indicator and represents one or more areas mapped from disallowed leader routing cells 284, thereby ensuring that leader geometry does not intrude into reserved regions, functional envelopes, or annotation keep-out areas. In some implementations, edits to leader positions or label locations in the illustration are propagated back to the SMM 280 by updating associated SEMAD tokens and routing constraint markers, so that regenerated views or additional illustrations reuse consistent leader routing, label anchoring, and exclusion behavior across a set of patent drawings.

[0106] In addition, in some embodiments, learning and heuristics derived from large language model processing of the SMM 280, as described in later sections of the specification, are used to automate and optimize leader line placement, label routing, and reference numeral placement within the illustration. For example, an LLM may traverse serialized forms of the SMM 280 to identify congested regions, infer likely callout groupings, detect potential ambiguities in leader association, and propose updated allowed and disallowed routing cells 283, 284 or updated endpoint designations within SEMAD tokens to improve clarity and compliance with drafting conventions. In some examples, the LLM derives such adjustments by comparing the currentDocket Number: SEMADS0001PCT

[0107] SMM 280 and a candidate illustration layout against pattern libraries, prior accepted drawing structures, or rule sets learned from previously validated patent illustrations. Non-limiting input sources for such optimization include prior drawing drafts, user-supplied markup indicating preferred callout locations, and textual descriptions of parts and features that indicate which elements should be called out or emphasized. In these examples, the system uses the inferred priorities and constraints to automatically select leader attachment points, route leaders through permitted regions, assign or reassign reference numerals, and reserve exclusion zones 290, thereby reducing manual iteration while preserving traceability between each leader, each label, and the underlying SEMAD-linked feature representation encoded in the SMM 280.

[0108] In some examples, the pipeline may include a canonicalization operator configured to normalize the SMM into a consistent internal frame, ontology, and part taxonomy. In such implementations, canonicalization may perform one or more of: resolving duplicate identifiers, normalizing coordinate frames, mapping detected classes to SEMAD identifiers, standardizing part hierarchies, normalizing relationship schemas, and rewriting synonymous textual assertions into canonical forms. In some examples, the pipeline may include a constraint propagation operator configured to apply assembly, geometric, physical, or drafting constraints across one or more SMM dimensions such that updates in one region of the SMM may constrain permissible hypotheses, poses, and relationships in other regions.

[0109] LLM- and LMM-guided interaction with the textual SMM

[0110] In some implementations or examples, one or more large language models (LLMs) may interact with the SMM as a textual substrate for planning, explanation, querying, verification, and rule-based inference. For example, an LLM may be configured to generate or refine constraint expressions, produce candidate relationship assertions from textual specifications, reconcile inconsistent naming or taxonomy across sources, generate structured queries that may traverse the SMM across multiple axes, and / or produce human-readable rationales describing why particular hypotheses may be selected.

[0111] In some examples, a trained large multimodal model (LMM) may interact with the SMM to perform joint reasoning over one or more non-textual inputs (e.g„ images, video, depth) and the textual symbolic substrate. In such implementations, the LMM may encode selected portions of the SMM into token sequences (or other embeddings) and may align those tokens with input-derived tokens to ground symbolic assertions in observed evidence.Docket Number: SEMADS0001PCT

[0112] In some implementations or examples, the LMM may traverse the SMM along multiple axes, such as spatial neighborhoods, semantic layers, hierarchy levels, view-indexed layers, time-indexed layers, and hypothesis layers, to retrieve relevant cell records and assemble an LMM-consumable context. In such examples, uncertainties may be represented explicitly as competing hypotheses with confidence and provenance metadata, and the LMM may apply attention over both (i) SMM-derived textual tokens and (ii) input-derived tokens to resolve uncertainties by selecting mutually consistent entities and relationships, proposing missing constraints, and generating one or more SMM update actions.

[0113] In some examples, LLM outputs and LMM outputs may be combined in a hybrid loop in which the LMM may ground candidate symbolic assertions in perceptual evidence while the LLM may perform higher-level consistency checking, constraint authoring, and narrative explanation. In such implementations, the SMM may function as shared working memory, enabling iterative refinement via alternating steps of perception-conditioned updates and language-driven normalization and verification.

[0114] Referring now to FIG. 3, a flow diagram 300 illustrates an end-to-end runtime process for generating and refining patent drawings using a symbolic scene representation. A user interface and project input stage 310 may receive one or more inputs including, for example, images, drawings, CAD data, textual descriptions, constraints, and user directives. A recognition and normalization stage 320 may convert one or more of the inputs into symbolic assertions and may initialize or update a symbolic scene state 330, which may be implemented as a symbolic multidimensional matrix (SMM) storing SEMAD identifiers and structured cell records. In some examples, an LLM / LMM reasoning and traversal stage 340 may operate over the symbolic scene state 330 to propose hypotheses, enforce constraints, normalize structure, and / or select view and content directives for subsequent generation.

[0115] In some examples, a rendering and visibility determination stage 350 may generate one or more rendered views or figure directives from the symbolic scene state 330, and a candidate figure generation stage 360 may output one or more draft figures or candidate patent drawing views. A symbolic discriminator and structural validator 370 may evaluate the candidate figures and corresponding symbolic structures using one or more symbolic criteria, such as topology, part adjacency, attachment consistency, reference numeral consistency, leader-line consistency, view-to-view consistency, and constraint satisfaction. A final user interface 380 may present oneDocket Number: SEMADS0001PCT

[0116] or more candidate figures for review and selection, and approved figures may be promoted to final patent drawings 390, which may include consistent reference numerals, leader lines, view sets, and structural representations derived from the shared symbolic scene state.

[0117] Flow from the candidate figure generation stage 360 to the symbolic discriminator and structural validator 370 may indicate that the discriminator / validator receives both candidate outputs and associated symbolic hypotheses for evaluation. Symbolic corrections and scores 363 may be fed back from the candidate figures and draft outputs generation stage 360 to the symbolic scene state 330 to update that state. For example, the discriminator / validator may output a ranked set of candidate structural edits, constraint repairs, adjacency or attachment adjustments, normalized identifiers, or confidence-weighted updates to SEMAD records, and the symbolic scene state 330 may be updated accordingly so that subsequent rendering and candidate generation may operate on an improved symbolic substrate.

[0118] In some embodiments, flow from the symbolic discriminator and structural validator 370 to the rendering and visibility determination stage 350 may represent targeted regeneration 375 driven by validation results. Rather than regenerating an entire figure set, the discriminator / validator may direct the system to re-render a subset of views, regenerate only specific regions or objects, adjust camera or view parameters, and / or apply corrected visibility and occlusion logic for a subset of SEMAD-referenced items implicated by a detected inconsistency. Flow from the rendering and visibility determination stage 350 back to the symbolic scene state 330 may indicate additional symbolic corrections and / or scores 373 produced during rendering, such as inconsistencies in view alignment, occlusion ordering, missing parts, or constraint conflicts that may be more readily detected during visibility determination and then expressed as symbolic deltas applied to SEMAD records, relationships, and / or constraints within the SMM.

[0119] In some examples, flow from the final user interface 380 back to the user interface and project input stage 310, labeled user feedback 381, may indicate that user-supplied corrections and preferences may be incorporated as additional symbolic assertions or constraints. For example, user feedback may specify corrected part identities, revised constraints, view- selection preferences, target figure styles, inclusion or exclusion directives, and / or corrections to reference numeral placement, and such feedback may influence subsequent recognition, normalization, traversal, rendering, and validation steps. Representing the loop in FIG. 3 as symbolic updates toDocket Number: SEMADS0001PCT

[0120] the scene state 330 may enable iterative improvement without requiring direct pixel-level editing, and may support selective re-rendering, cross-view consistency, and discriminator-driven rejection and repair of structurally invalid candidates even when pixel appearance is superficially plausible.

[0121] For example, the system may generate and iteratively validate illustrations, animation frames or video sequences, simulation visualizations, and other rendered depictions in which a symbolic scene state is rendered into candidate output and then structurally re-parsed, checked against symbolic constraints, and selectively regenerated until the output satisfies the applicable rales and consistency requirements.

[0122] Rendering, illustration, simulation, and generative constraining

[0123] In some implementations or examples, the LMM and / or LLM may use the SMM to generate outputs including drawings, technical illustrations, instructional diagrams, exploded views, schematics, animations, and / or temporal simulations. In such examples, the view index dimension may store view-specific rendering directives (e.g., projection, styling, occlusion policy, callout / label policy) while referencing shared entities and relationships in the canonical SMM state, thereby enabling consistent multi-view generation across output formats.

[0124] In some examples, the system may render a line skeleton or other structural depiction from the SMM, including one or more of: wireframes, centerline graphs, edge maps, contour traces, or graph-based scene abstractions. In such implementations, rendered line skeletons derived from the SMM may be processed into constraints for downstream generative processes. For example, a line skeleton, constraint graph, or structured set of textual assertions may be provided as conditioning input to an image or video generation model, a vector-illustration generator, or a simulation Tenderer, thereby constraining generation to preserve topology, part relationships, alignment, and feature correspondence specified by the SMM.

[0125] In some implementations or examples, the system may include a symbolic discriminator configured to evaluate candidate generative outputs using the SMM and SEMADs-based scene representation rather than (or in addition to) pixel-level realism metrics. For example, a candidate generated image, frame, or illustration may be parsed (by an LMM and / or auxiliary recognition model) into proposed SEMADs assignments and relationships, and the symbolic discriminator may compare the parsed symbolic structure against constraints stored in the matrix to determine structural plausibility.Docket Number: SEMADS0001PCT

[0126] In some examples, the symbolic discriminator may evaluate one or more of: (i) object identity persistence; (ii) part counts; (iii) attachment and adjacency relations; (iv) feature correspondences; (v) alignment and symmetry relations; (vi) kinematic feasibility; and (vii) drafting conventions (e.g., occlusion policy, line style policy, callout policy). In such implementations, the discriminator may be style-invariant, such that it may accept multiple artistic render styles while rejecting outputs that violate topology, assembly structure, or semantic function.

[0127] In some implementations, discriminator feedback may be used to guide generation via one or more mechanisms including: rejection sampling, iterative regeneration, constrained decoding, or gradient-free search over generation seeds and conditioning parameters. In some examples, discriminator scores may be computed per-object, per-relationship, and per-region, and the system may apply targeted edits or localized regeneration to correct only the structurally invalid portions while preserving valid regions.

[0128] In some implementations or examples, a user may submit one or more inputs (e.g., a product photograph, a multi- view set, a CAD model, a scan, or a textual specification) and the system may populate a sparse SMM with candidate components and relationships. In such examples, the LMM may traverse the SMM across semantic and hypothesis dimensions and may apply attention to resolve ambiguities, the LLM may refine and canonicalize the resulting textual scene assertions, and the constraint propagation operator may refine the symbolic state without requiring full pixel-level reconstruction of the scene.

[0129] In some examples or implementations, the foregoing techniques may be applied to patent drawing generation as one application, among others. In that application, the SMM may be reused to render a perspective view and one or more orthographic views, and the pipeline may include a consistency validator configured to compare view-indexed render outputs against the shared SMM state and / or against one another to detect and reduce inconsistencies. In such implementations, the consistency validator may be configured to enforce one or more invariants including consistent part counts, stable part identifiers across views, consistent attachment relationships, alignment of corresponding features, and consistent fastener placement. In such examples, responsive to detecting an inconsistency, the system may update one or more SMM cell records and may re-render from the updated shared state.Docket Number: SEMADS0001PCT

[0130] Sparse, Hierarchical, and Adaptive Matrices

[0131] In some examples, techniques for scalability and computational efficiency may be directed to a symbolic multidimensional matrix, including controlling memory footprint, update cost, and reasoning latency as scene complexity increases.

[0132] Referring now to FIG. 4 A, an input depiction may be shown with an overlaid two-dimensional SMM grid 400, where the grid may correspond to a two-dimensional slice of a symbolic multidimensional matrix and where a plurality of matrix elements may correspond to addressable locations over the depiction. In some examples, only a subset of matrix elements may be populated with symbolic tokens representing objects or regions of interest, while many other matrix elements may remain unpopulated or may hold a default value (e.g., an “empty space” SEMAD), thereby forming a sparse symbolic representation suitable for downstream traversal, constraint evaluation, and rendering. For example, a hand depiction may be used to illustrate SEMADs 410 corresponding to hand bones 411, where each populated cell represents a stable symbolic address anchored to an anatomical locus and associated SEMAD record.

[0133] Referring now to FIG. 4B, an illustration of the raw SMM grid 400 may be shown in isolation. The SMM may include the located SEMADs 410 as well as default or empty-space SEMADs 415 for regions that are not populated with a specific object token. In some examples, this sparse population may be advantageous because it permits the system to store and update only those SEMAD records that are relevant to the current reasoning task, view selection, validation operation, or rendering output, while leaving background regions represented compactly.

[0134] Referring now to FIG. 4C, an example classification and simplification of matrix elements of the SMM grid 400 may be illustrated using linear simplification. In some examples, the SMM may be traversed or reduced along one or more axes (e.g., horizontal rows, vertical columns, diagonal scanlines, semantic groupings, hierarchical indices, or view / time axes), such that contiguous runs of identical SEMAD values may be consolidated. For example, when processing horizontally, entire rows of empty space 420 may be represented as a single compressed run that references a shared empty-space SEMAD. In other regions of the SMM, simplification may yield shorter compressed runs depending on local structure. In some examples, where populated SEMADs 430 occur near one another, little or no simplification may be available, and isolated empty cells 431 may remain between populated entries. In someDocket Number: SEMADS0001PCT

[0135] examples, linear simplification may provide computational benefits by reducing the number of cells that an LLM, LMM, or symbolic validator traverses when the objective is to reason about structure rather than to enumerate every background element.

[0136] Referring now to FIG. 4D, another type of simplification may be illustrated for the SMM grid 400 in which connected-component consolidation is performed. In some examples, all connected cells (e.g., 4-connected or 8-connected adjacency) sharing the same SEMAD may be consolidated into a single consolidated area 440 represented by a region-level record, boundary descriptor, or run-length / region encoding. After such consolidation, remaining distinct SEMADs 430 may be processed using the methods described herein, including constraint evaluation, crossview correspondence, hypothesis management, or downstream rendering. In some examples, connected-component consolidation and related region encodings may yield significant efficiencies for LLM traversal, LMM proposal generation, discriminator evaluation, and selective regeneration, because large uniform regions may be represented as single symbolic units while still preserving boundaries and adjacency relationships that are relevant to reasoning.

[0137] Referring now to FIG. 4E, an additional example scene may be shown to illustrate simplification in a more complex setting. In the illustrated example, a scene may include a sky region 450, a forest region 451, a grassland region 452, and an animal 460. In some examples, one or more recognition or segmentation processes may populate the SMM such that cells corresponding to sky are assigned a sky-feature SEMAD, cells corresponding to forest are assigned a forest-feature SEMAD, cells corresponding to grassland are assigned a grasslandfeature SEMAD, and cells corresponding to the animal are assigned one or more animal-feature SEMADs. In some examples, the SEMAD records associated with these tokens may encode additional metadata such as estimated depth ordering, occlusion relationships, texture or material descriptors, and / or constraints describing how the regions may be expected to interact (e.g., animal feet contacting grassland, forest behind animal, sky above horizon).

[0138] Proceeding to FIG. 4F, a result of simplification may be shown in which regional SEMAD identification is preserved while multiple low-information or low-interest cells are compressed into fewer symbolic regions. In some examples, the SMM may be segmented into region types based on an attention or interest measures. For example, a first subset of matrix elements may be designated as a high-interest region (e.g., the animal object), a second subset may be designated as a low-interest region (e.g., background sky, forest, and grassland), and aDocket Number: SEMADS0001PCT

[0139] third subset may be designated as uncertain or ambiguous. Tn some examples, such region typing may be derived from saliency estimation, segmentation confidence, constraint violations, user cues, task directives (e.g.. “focus on the animal”), or modality-specific heuristics. In some examples, like-region compression may consolidate substantially uniform or low-interest areas into region-level representations while preserving boundaries and adjacency constraints. Each consolidated zone may be represented by a shared symbolic descriptor, a region-level SEMAD record, and / or a region reference that stands in for a plurality of underlying matrix elements. In some examples, this compression reduces symbolic complexity and compute cost while preserving structural distinctions that are relevant to subsequent reasoning, validation, and rendering operations.

[0140] Referring now to FIG. 4G, an illustration of foveated refinement 480 may be provided in which a selected high-interest portion of the SMM grid 400 is expanded, resampled, or represented at a higher effective resolution relative to other portions of the grid. For example, FIG. 4G may depict an inset region in which matrix elements are more densely represented and / or more frequently populated with symbolic tokens, indicating that the system allocates additional representational capacity to a focal area predicted to influence structural validity, drawing quality, or user intent. In some examples, refinement may be implemented by instantiating a nested submatrix, increasing sampling density, increasing the number of stored symbolic hypotheses for the region, or enabling a higher-frequency update loop for the focal area during targeted regeneration. In the illustrated example, the foveated region may support creation or refinement of additional S EMADs for substructures of the animal, such as antlers 481, face 482, neck 483, legs 484, and body 485. In some examples, the supplemented SMM with foveated regions may improve efficiency by concentrating computing and symbolic hypothesis management on high-impact structures while maintaining compressed representations for background regions, thereby supporting faster convergence during validation, improved crossview consistency, and more reliable downstream rendering outputs.

[0141] In some examples, the multidimensional matrix may be configured to support sparse population, such that symbolic entries may be stored primarily (or exclusively) in regions of interest while other regions may remain uninstantiated, null, or represented by default tokens. Sparse population may reduce computational cost while preserving structural information relevant to the current task objective.Docket Number: SEMADS0001PCT

[0142] In some examples, a region of interest may be identified from one or more sources including: detection outputs, user selection, saliency maps, segmentation masks, motion cues, prior constraints, or model-driven queries. In some examples, a region of interest may be expressed as a slice across one or more axes of the matrix, including spatial neighborhoods, semantic layers, part hierarchy levels, view-indexed layers, time-indexed layers, and / or hypothesis layers.

[0143] In some examples, the matrix may be represented using one or more sparse data structures, including coordinate (COO) representations, compressed sparse row / column representations, hashed indices, dictionary-based stores, or chunked tiling. In some examples, sparse storage may be combined with a cell-record format in which each populated cell stores a structured record comprising a symbolic identifier, pose metadata, relationship descriptors, confidence values, and provenance metadata.

[0144] In some examples, the matrix may support hierarchical refinement in which a coarse representation may be progressively refined into finer-grained submatrices. In such examples, a coarse level may encode high-level entities (e.g., assemblies, subassemblies, major components, bounding volumes, or coarse poses), and a finer level may encode more detailed entities (e.g., features, interfaces, fasteners, threads, fillets, holes, and other substructures). Hierarchical refinement may be driven by task requirements, confidence thresholds, attention mechanisms, or constraints indicating that additional detail may be required.

[0145] In some examples, hierarchical refinement may be implemented via one or more of: (i) allocating nested submatrices linked to parent cells, (ii) increasing spatial resolution in a local neighborhood, (iii) splitting a cell into a plurality of child cells according to a refinement rule, and / or (iv) introducing additional semantic or hypothesis layers at a refined level. In some examples, parent-to-child links may preserve referential integrity such that a refined cell record may remain associated with a higher-level SEMAD identifier while adding lower-level identifiers for constituent parts or features.

[0146] In some examples, coarse matrices may be used to identify major components prior to allocating refined structure for fasteners, interfaces, or constraints. For example, a coarse layer may represent a housing and a cover as primary bodies with a coarse alignment relationship, and a subsequent refinement may allocate cells corresponding to interface features (e.g., holes,Docket Number: SEMADS0001PCT

[0147] bosses, flanges) and candidate fasteners that satisfy the inferred alignment and attachment constraints.

[0148] In some examples, refinement may be adaptive and may be triggered conditionally. For example, refinement may be performed when one or more ambiguity signals are detected, including: competing hypotheses exceeding a threshold count, confidence falling below a threshold, inconsistent relationship descriptors, constraint violations, or view-to-view inconsistencies during rendering. In some examples, refinement may also be triggered when a downstream consumer requests higher detail, such as a Tenderer requesting additional featurelevel structure for a close-up view, an instruction generator requesting explicit fastener representation, or a simulator requesting contact or joint constraints.

[0149] In some examples, the system may incorporate an attention-driven control policy in which refinement decisions may be guided by attention weights produced by an LMM and / or LLM reasoning module. In such examples, attention weights may be mapped to one or more matrix axes (e.g., spatial locus, semantic layer, hierarchy level, view index, time index, hypothesis layer) and may be used to allocate or deallocate matrix regions, increase or decrease local resolution, and / or select which hypotheses are retained for subsequent reasoning.

[0150] In some examples, matrix adaptation may include resolution reduction or pruning when detail is not required. For example, when a task objective changes from feature-level drafting to high-level assembly identification, refined submatrices may be compacted into coarse summaries, including aggregated identifiers, bounding volumes, canonical poses, and summarized constraints. In some examples, pruning may remove low-confidence hypotheses, collapse redundant entities, or replace detailed cell records with canonical references to higher-level entities, thereby controlling computational growth.

[0151] In some examples, the sparse and hierarchical configuration of the matrix may be coordinated with caching and incremental update techniques. For example, when a subset of the matrix is updated (e.g., due to hypothesis selection or constraint propagation), the system may recompute derived relationships and render directives only for affected regions rather than recomputing the entire matrix state. In some examples, this incremental update behavior may support interactive workflows in which a user iteratively supplies new evidence, requests additional views, or modifies constraints while the system maintains a consistent symbolic substrate.Docket Number: SEMADS0001PCT

[0152] In some examples, the foregoing sparse, hierarchical, and adaptive matrix techniques may be used in connection with a canonical scene substrate to support multiple applications, including but not limited to patent drawing generation, technical illustration, multimodal scene understanding, structure recognition from heterogeneous inputs, and simulation.

[0153] In some examples, the sparse, hierarchical, and adaptive representations described herein may be consolidated into a unified data structure that maintains a consistent SMM state while permitting multiple coexisting levels of detail. In some examples, the unified data structure may include a base symbolic matrix namespace for canonical identifiers and relationships, together with one or more linked layers (or views) that encode sparse occupancy maps, hierarchical submatrix pointers, and refinement metadata, such that refined cells remain referentially tied to parent cells and shared SEMAD identifiers.

[0154] In some examples, the unified data structure may be implemented as a chunked or tiled store in which each tile may contain a coarse summary record and optional child partitions that are instantiated only when refinement is triggered, thereby allowing the system to preserve coarse representations while selectively materializing finer structure.

[0155] In some examples, versioning or provenance fields may be maintained per cell or per tile to record when a representation was refined, compacted, or pruned, enabling rollback, auditability, and incremental recomputation.

[0156] In some examples, the unified data structure may expose a common query interface such that reasoning, LLM / LMM traversal, and rendering modules may retrieve either a coarse or refined form of a region depending on task relevance, attention weights, confidence thresholds, or output requirements, while still operating over a single canonical symbolic substrate.

[0157] In some implementations or examples, computational efficiency may be improved by reuse and instancing of SEMADs-bound geometry across the matrix. For example, a SEMAD identifier may reference a nominal parametric geometry template (e.g., a CAD-derived primitive, a feature library entry, or a parametric assembly subgraph) that is cached and reused across multiple instances of the same semantic object type. In such examples, individual matrix cell records may store instance- specific transforms (e.g., pose, orientation, scale, tolerances) and instance- specific attributes while referencing a shared nominal geometry binding.

[0158] In some examples, instancing may be applied to composite or hierarchical SEMADs such that an assembly-level SEMAD may expand into a reusable substructure that is instantiatedDocket Number: SEMADS0001PCT

[0159] multiple times with different transforms and / or role bindings. For example, repeated fasteners, repeated brackets, repeated ribs, repeated gears, or repeated circuit elements may be represented as multiple instances that share a single geometry definition and a single constraint bundle, thereby reducing memory footprint and amortizing computation across repeated structures.

[0160] In some implementations, the system may maintain a cache keyed by SEMAD identifiers and / or geometry-binding parameters, and the cache may store one or more of: tessellations, wireframe edge lists, line skeletons, normals / depth proxies, bounding volumes, and / or draftingready stroke primitives. In such examples, Tenderers and validators may retrieve cached structural artifacts for each instance and apply instance transforms at render time, thereby reducing repeated geometry processing while preserving structural correctness and multi-view consistency.

[0161] Matrix as Persistent Scene Memory

[0162] In some examples, techniques for scene representation and cognitive context may be directed to a symbolic multidimensional matrix configured as a persistent scene memory. In some examples, the symbolic multidimensional matrix may be maintained as a durable, queryable state that may persist across one or more stages of recognition, reasoning, and rendering, rather than being instantiated only transiently for a single inference pass. In some examples, persistence may be supported by maintaining, for one or more populated cells, one or more of: stable identifiers (e.g.. SEMAD identifiers), version identifiers, provenance records, confidence measures, and / or hypothesis sets representing alternative interpretations.

[0163] In some examples, the multidimensional matrix may serve as a single source of truth for a given scene state, such that multiple system components may read from and / or write to the same canonical symbolic substrate. For example, a recognition subsystem may populate candidate entities and poses as cell records, a reasoning subsystem may add or revise relationship descriptors and constraint expressions within the same cell records, and a rendering subsystem may consume the resulting symbolic state to generate one or more outputs while preserving referential integrity of identifiers across the pipeline. In some examples, the matrix may expose a uniform query interface that returns, for a requested region or slice, both asserted facts and uncertainty metadata (e.g., competing hypotheses and confidence), thereby enabling downstream components to operate directly on the persistent substrate.Docket Number: SEMADS0001PCT

[0164] In some examples, the multidimensional matrix may function as persistent memory retained across multiple views, frames, time steps, or output modalities. For example, a scene state populated from a photograph may be retained and subsequently reused to generate a perspective illustration, one or more orthographic views, an exploded diagram, an assembly instruction sequence, and / or a temporal simulation. In some examples, the matrix may include one or more view-indexed and / or time-indexed layers that may store view- specific directives (e.g., projection parameters, occlusion policies, label policies) and / or time-specific state (e.g., motion state, kinematic constraints), while referencing shared entities and relationships stored in the canonical substrate. In some examples, view- and time-specific layers may store deltas relative to the canonical substrate, thereby limiting duplication while enabling cross-view and cross-time consistency.

[0165] In some examples, the multidimensional matrix may be treated as a canonical representation rather than a derived artifact, such that downstream processes may operate directly on the matrix rather than on transient pixel data. In such examples, pixel- space observations may be treated as input evidence that generates or updates symbolic assertions in the matrix, and subsequent reasoning and rendering may proceed from those symbolic assertions. In some examples, the matrix may store explicit links between observations and assertions (e.g., provenance links to source frames, detector outputs, or user edits), thereby enabling auditability, rollback, and incremental refinement while decoupling persistent state from incidental details of a particular image capture.

[0166] In some examples, the matrix may be reused to generate multiple outputs that may be required to remain mutually consistent. For example, a single matrix state may be reused to generate multiple patent figures, multiple technical illustrations, and / or multiple frames of an animation, thereby reducing view-to-view or frame-to-frame drift that may occur when each output is inferred independently. In some examples, stable SEMAD identifiers may be retained across outputs such that the same entity may be referenced consistently across views and across time. In some examples, one or more invariants may be associated with the matrix state, including one or more of: part-count invariance, identifier stability, topology preservation for articulated structures, joint-limit satisfaction, and / or constraint satisfaction for attachment and alignment relationships.Docket Number: SEMADS0001PCT

[0167] In some examples, updates to the matrix may propagate to dependent outputs through one or more incremental recomputation mechanisms. For example, when a cell record is updated (e.g., a hypothesis selection is changed, a pose is refined, a relationship is added, or a constraint is revised), the system may identify affected downstream artifacts (e.g., a subset of views, annotations, callouts, simulation constraints, structural skeletons, or generative conditioning signals) and may regenerate only those affected portions. In some examples, such propagation may be implemented using one or more of: dependency graphs linking cell -record fields to derived artifacts, change logs, versioned cell records, caches keyed by identifiers and view / time indices, and / or event-driven triggers, such that outputs derived from the matrix may remain synchronized with the current canonical scene state.

[0168] In some examples, the persistent scene memory may support iterative interaction and refinement. For example, a user may provide additional inputs, corrections, or constraints over time, and the system may update the matrix while preserving prior state, provenance, and confidence metadata. In some examples, one or more LLM and / or LMM reasoning modules may traverse the persistent matrix to answer queries, resolve ambiguities, or propose updates, thereby using the matrix as a shared working memory across multiple reasoning cycles. In some examples, the LLM and / or LMM may generate structured update actions that may be applied to the matrix, including selecting among competing hypotheses, adding or removing relationship descriptors, rewriting constraints into canonical forms, and / or refining pose metadata.

[0169] In some examples, a complex articulated structure may be represented in the multidimensional matrix using a hierarchical SEMAD decomposition in which a higher-level SEMAD identifier may reference, invoke, or expand into a set of lower-level SEMAD identifiers corresponding to constituent parts. By way of illustration, a human hand may be modeled as an articulated assembly comprising a plurality of bones, and each bone may be assigned a distinct SEMAD identifier. In some examples, the plurality of bones may include approximately twentyseven bones, and the corresponding SEMAD identifiers may each be associated with a nominal geometric representation, such as a CAD primitive, a parameterized surface, a mesh template, or another canonical shape description. In some examples, invoking or dereferencing a SEMAD identifier may include instantiating corresponding cell records in one or more matrix regions and binding those records to one or more geometry sources and constraint bundles.Docket Number: SEMADS0001PCT

[0170] In some examples, a trained multimodal reasoning system, such as an LMM, may learn relationships among the constituent SEMAD identifiers, including kinematic connectivity, joint constraints, range-of-motion priors, and correlated motion patterns across common hand movements. For example, the LMM may learn that certain bones are connected by joints that permit rotation within bounded limits, that certain segments exhibit coupled motion during flexion or extension, and that certain poses are more anatomically plausible than others. In some examples, such learned context may be represented in the matrix as relationship descriptors and constraint expressions associated with the relevant SEMAD identifiers and / or stored as view-indexed or time-indexed state, optionally with confidence and provenance fields indicating whether a constraint is learned, asserted, or user- specified.

[0171] In some examples, a simplified SMM representation may include a higher-level SEMAD identifier representing the hand as a unitary structure. In such examples, invoking, dereferencing, or expanding the hand-level SEMAD identifier may cause the system to instantiate a subset or all of the constituent bone-level SEMAD identifiers, together with associated pose metadata and relationship descriptors. For example, the hand-level SEMAD identifier may expand into a set of bone-level identifiers corresponding to carpals, metacarpals, and phalanges, and may further instantiate joint constraints defining allowable relative motion among the expanded identifiers. In some examples, expansion may be selective, such that only a subset of bones and joints may be instantiated based on task relevance, attention weights, or ambiguity signals.

[0172] In some examples, the foregoing hierarchical invocation may support efficient scene memory and rendering by enabling the matrix to store a compact representation when fine detail is not required, while permitting selective refinement when anatomical detail is required for a target output. For example, a coarse matrix state may represent a hand as a single symbolic entity for high-level reasoning, and a refined matrix state may expand the entity into bone-level structure for precise illustration, simulation, or constraint evaluation. In some examples, the level of expansion may be selected based on task requirements, confidence thresholds, and / or attention weights produced by an LMM and / or LLM, and the selected level may be recorded in refinement metadata stored in one or more cell records.

[0173] In some examples, the expanded hand representation may be used to render an output directly, including generating one or more drawings, illustrations, animations, or temporal simulations in which the hand pose may be anatomically plausible under the stored and / orDocket Number: SEMADS0001PCT

[0174] learned constraints. In some examples, the expanded representation may additionally or alternatively be used to constrain a downstream generative process. For example, a line skeleton, joint graph, pose parameter set. constraint graph, or other structural depiction derived deterministically from the matrix state may be provided as conditioning input to an image or video generation model, thereby constraining generated imagery to satisfy anatomical connectivity and range-of-motion constraints and to preserve identifier-consistent structure across frames and views.

[0175] In some examples, this approach may enable a relatively small SMM dataset to enforce realism across a large output space. For example, by storing a compact set of SEMAD identifiers, nominal geometry bindings, and kinematic relationship constraints, the system may constrain generative outputs toward physically consistent and anatomically consistent hand configurations, even when the generative model is asked to produce novel viewpoints, lighting conditions, styles, or contexts. In some examples, the persistent matrix state may be reused across multiple frames of an animation or across multiple output modalities, and updates to hand pose, hypothesis selection, or constraints in the matrix may propagate through dependency-linked recomputation to maintain consistency as the hand moves or as the rendering target changes.

[0176] Language Models as Symbolic Reasoning Engines

[0177] In some examples, techniques described herein may employ one or more large language models (LLMs) as symbolic reasoning engines configured to operate on structured scene descriptions rather than as pixel processors. In some examples, the LLM may be configured to consume serialized symbolic structures derived from a symbolic multidimensional matrix (e.g„ an SMM), and to generate reasoning outputs that may be applied back to the matrix as updates, constraints, and / or validation results.

[0178] In some examples, the LLM may operate on a serialized representation of one or more matrix regions, layers, or slices rather than on raw pixel data. In such examples, the serialized representation may include one or more of: SEMAD identifiers, entity declarations, attributevalue pairs, pose metadata, relationship descriptors, hypothesis candidates, confidence measures, provenance tags, and / or constraint expressions. In some examples, serialization may preserve matrix addressing information (e.g., axes, indices, view / time labels) such that the LLM output may be mapped to specific cell records or matrix regions.Docket Number: SEMADS0001PCT

[0179] In some examples, symbolic tokens representing SEMADs and relationships may be encoded into a token stream for LLM consumption using a controlled schema. For example, entities may be expressed as typed records (e.g., ENTITY (id, class, attributes)), poses may be expressed as structured fields (e.g., POSE(id, frame, position, orientation, tolerance)), and relationships may be expressed as typed edges or constraint predicates (e.g., REL(type, a, b, parameters) and / or CONSTRAINT (name, scope, expression)). In some examples, the schema may include explicit markers for uncertainty such as competing hypotheses, confidence values, and provenance sources, enabling the LLM to reason over ambiguity explicitly rather than implicitly.

[0180] In some examples, the LLM may be configured to evaluate whether a proposed assembly configuration is structurally plausible. For example, given a set of SEMAD identifiers representing parts and a set of relationship descriptors representing attachments, alignments, and fastener engagements, the LLM may identify conflicts such as incompatible part roles, missing mates, cycles inconsistent with a hierarchy, violated constraints, or contradictory attachments (e.g., a fastener engaging two non-corresponding holes). In some examples, the LLM may output a plausibility score, a list of violated constraints, one or more candidate repairs, and / or one or more follow-up queries requesting additional detail or refinement of a matrix region.

[0181] In some examples, the LLM may assist in resolving ambiguous object relationships in technical drawings, scanned documents, or image-derived recognition outputs. For example, when multiple candidate relationships are stored in a hypothesis layer of the matrix (e.g., alternative adjacency or engagement relations), the LLM may select a mutually consistent subset based on semantic compatibility, learned priors, and stated constraints. In some examples, the LLM may propose additional relationship descriptors (e.g., inferred containment or alignment) that reconcile ambiguous evidence with canonical assembly patterns.

[0182] In some examples, the LLM may generate structured update actions that may be applied to the matrix, including one or more of: selecting among competing hypotheses; adding, removing, or rewriting relationship descriptors; generating or refining constraint expressions; normalizing identifiers into canonical forms; and / or proposing pose refinements within tolerance bounds. In some examples, the LLM output may include pointers to the corresponding matrix addresses (e.g., axis indices, entity identifiers, relationship IDs) such that the updates may be applied deterministically.Docket Number: SEMADS0001PCT

[0183] In some examples, the LLM may be used in combination with a multimodal model (e.g., an LMM) in a hybrid loop, in which the LMM may ground symbolic candidates in perceptual evidence and the LLM may perform higher-level symbolic validation, consistency checking, explanation, and constraint authoring. In some examples, the symbolic multidimensional matrix may function as a shared working memory between the LMM and the LLM, enabling iterative refinement of a canonical scene state across multiple reasoning cycles.

[0184] In some examples, an articulated structure such as a human hand may be represented in the matrix using a hierarchical SEMADs decomposition, and an LLM may operate on a serialized form of that decomposition to evaluate plausibility and resolve ambiguity without direct pixel processing. For example, a hand-level SEMAD identifier may be serialized together with an expansion record referencing a plurality of bone-level SEMAD identifiers, nominal geometry bindings, and joint constraints, and the LLM may evaluate whether a proposed pose or inferred connectivity satisfies one or more stored invariants (e.g., joint-limit satisfaction, topology preservation, or coupled motion patterns) while selecting among competing hypotheses in a hypothesis layer. In some examples, the LLM may propose repairs (e.g., revise a joint relationship, adjust a pose within tolerance, request a refinement of a submatrix region), and may emit structured update actions that are applied back to the matrix, after which constraint propagation and / or rendering may proceed from the updated canonical symbolic state. In some examples, a skeletal graph or constraint graph derived deterministically from the updated matrix state may be provided as a conditioning signal to a downstream generative model to constrain generated imagery toward anatomically consistent hand configurations across views or frames.

[0185] In some examples, the foregoing LLM-guided symbolic reasoning may be applied to patent drawing generation as a concrete drafting workflow. For example, a matrix state representing an assembly may be serialized into SEMADs- and relationship-based tokens and provided to an LLM to evaluate whether the assembly configuration implied by the matrix is structurally coherent and drafting-consistent (e.g., consistent part identity, attachment relationships, and feature correspondence) prior to rendering multiple figures. In some examples, the LLM may propose corrections to resolve ambiguities common in technical drawings — such as unclear engagement relationships, inconsistent part hierarchy assignments, or conflicting interface constraints — and may emit structured update actions that modify the matrix (e.g., selecting a hypothesis, refining a pose, or rewriting a constraint) such that subsequentDocket Number: SEMADS0001PCT

[0186] perspective and orthographic figures may be rendered from the updated canonical substrate. In some examples, because figure views may be generated from the same persistent matrix state, updates guided by the LLM may propagate to dependent figures, callouts, and annotations, thereby reducing view-to-view drift and improving consistency across a patent drawing set.

[0187] In some examples, outputs generated using LLM-guided symbolic reasoning may be used to drive one or more downstream processes including rendering, illustration, simulation, and generative conditioning. For example, a set of validated relationships and constraints may be consumed to generate drawings and multi- view illustrations with consistent identifiers or may be converted into a line skeleton or constraint graph that conditions an image or video generation process.

[0188] In some implementations or examples, the system may generate and place reference numerals (and associated callouts) using the SEMADs-based symbolic scene representation. For example, each SEMAD identifier (or each selected subset of SEMAD identifiers corresponding to claim-relevant parts) may be assigned a stable reference numeral that is retained across multiple figures and views. In such examples, the matrix may store a label policy and a mapping from SEMAD identifiers to reference numerals, thereby enabling consistent labeling across view-indexed layers.

[0189] In some examples, callout placement may be computed using geometry and relationship descriptors stored in the matrix. For example, a callout generator may select anchor points on rendered edges or feature landmarks derived from SEMADs-bound geometry, choose leader-line paths that avoid occluding critical edges, and place numerals in regions that preserve readability and comply with drafting conventions. In such implementations, placement may be optimized using constraints including non-overlap constraints, margin constraints, leader-line crossing constraints, and view-specific styling constraints.

[0190] In some implementations, reference numeral generation may be integrated with the consistency validator such that the process is responsive to a detected inconsistency (e.g., missing part depiction, duplicate part depiction, mismatched identity across views). The system may update the symbolic mapping and re-render affected figures while preserving already-stable numeral assignments where possible. In some examples, the matrix may store per-view deltas for callout placement (e.g., adjusted label positions) while retaining a canonical SEMAD-to-numeral mapping in the shared substrate.Docket Number: SEMADS0001PCT

[0191] Multidirectional Matrix Traversal

[0192] In some examples, techniques for symbolic reasoning and scene understanding may be directed to traversal of a symbolic multidimensional matrix to extract spatial meaning, relational structure, and task-relevant context. In some examples, traversal may be performed along multiple directional and relational paths within an N-dimensional address space, thereby enabling the system to retrieve and aggregate symbolic evidence that may not be obtainable from a single axis-aligned query.

[0193] In some examples, symbolic matrix traversal may be performed in one or more directions including horizontal, vertical, diagonal, planar, volumetric, and / or adjacency-based paths. For example, a spatial axis traversal may step across neighboring cells along one or more spatial dimensions, a planar traversal may sweep a two-dimensional slice at a fixed depth or layer index, and a volumetric traversal may iterate across a three-dimensional neighborhood. In some examples, traversal may further include non-spatial traversal across semantic, hierarchy, viewindex, time-index, and / or hypothesis axes, such that a traversal may follow a composite path that combines spatial and non-spatial movement.

[0194] In some examples, traversal may be implemented as a rule-governed walk, flood fill, neighborhood expansion, ray-cast, graph walk, or constraint-guided search. In some examples, traversal steps may be conditioned on one or more cell-record fields, including SEMAD identifiers, pose metadata, relationship descriptors, confidence values, provenance, and / or constraint satisfaction status. In some examples, a traversal may terminate based on reaching a boundary condition, satisfying a constraint predicate, exceeding a cost budget, or achieving a target confidence threshold.

[0195] In some examples, multidirectional traversals may serve as reasoning or learning signals by producing feature sets, subgraphs, chains of relationships, or aggregated descriptors that may be consumed by downstream modules. For example, traversal outputs may be serialized into token sequences for LLM reasoning, embedded as context vectors for LMM attention, or used as inputs to a scoring function that ranks competing hypotheses within a hypothesis layer. In some examples, traversal statistics (e.g„ path lengths, adjacency counts, constraint violations encountered) may be used as supervision signals during training or as heuristics during inference.

[0196] In some examples, diagonal or off-axis traversal may be used to detect stacked, layered, or occluded components. For example, in a representation in which components occupy adjacentDocket Number: SEMADS0001PCT

[0197] layers across a depth-like axis or a “layer index” axis, a diagonal traversal that couples movement across spatial position and layer index may indicate that one component lies above or below another and may provide evidence of stacking order, nesting, or occlusion. In some examples, such diagonal traversal may be used to infer hidden relationships (e.g., a cover over a housing) and may trigger refinement of a region of interest when ambiguity remains.

[0198] In some examples, adjacency-based traversal may be used to identify fasteners or connectors linking neighboring parts. For example, when relationship descriptors indicate attachment candidates or when a fastener SEMAD identifier is present in a region between two-part boundaries, adjacency traversal may identify that the fastener bridges two neighboring components, may infer an engagement relation (e.g., screw engages threaded boss), and may update corresponding cell records to reflect a consistent attachment configuration. In some examples, adjacency traversal may also detect missing fasteners (e.g., an expected attachment relation exists without a corresponding connector entity) and may insert a candidate connector hypothesis into the hypothesis layer.

[0199] In some examples, traversal may be constrained by pose metadata and relationship descriptors to follow physically meaningful paths. For example, a traversal may follow an axis of a fastener, may trace a chain of aligned holes, may walk along a mating surface boundary, or may follow a kinematic chain within an articulated assembly. In some examples, such constraint-guided traversal may support generation of line skeletons, joint graphs, or constraint graphs that may be used for rendering, simulation, or conditioning of downstream generative models.

[0200] In some examples, the system may be configured to compress traversal outputs and / or compress serialized matrix representations provided to an LLM or LMM to reduce token cost and improve latency. For example, rather than emitting a full dense listing of visited cells, a traversal operator may output a compact representation including one or more of: run-length encoded spans of empty / default cells, grouped summaries of repeated SEMAD identifiers, adjacency lists over detected entities, and / or canonicalized relationship triples.

[0201] In some implementations, traversal serialization may be performed using an ordering that preserves locality and reduces redundancy, such as a space-filling curve ordering (e.g., Morton / Z- order, Hilbert ordering) or a graph-walk ordering that follows adjacency relationships stored in cell records. In such examples, the system may preferentially serialize only non-null symbolic records and may represent intervening uninstantiated regions by compact placeholders,Docket Number: SEMADS0001PCT

[0202] thereby enabling the language model to consume a high-signal representation of structure without requiring a pixel-like token budget.

[0203] In some examples, the system may generate a multi-resolution serialization in which coarse summaries are provided first and finer detail is conditionally appended based on attention or uncertainty. For example, an initial serialization may include only entity-level SEMAD identifiers, coarse poses, and high-level relationships, while a refinement serialization may include feature-level SEMADs, tolerances, and constraint predicates for a selected region of interest. In such examples, token-efficient traversal compression may be used in iterative loops, where each loop step requests only the additional detail needed to resolve ambiguity, validate coherence, or generate an output.

[0204] In some examples, multidirectional traversal may be applied to an articulated structure represented by a hierarchical SEMAD decomposition, such as a human hand expanded into bone-level SEMAD identifiers and joint constraints. For example, traversal may proceed along a kinematic chain by following adjacency relations and joint descriptors from a wrist region through metacarpals to phalanges, while simultaneously traversing a hierarchy axis to retrieve parent-level grouping semantics (e.g., “index finger”) and a hypothesis axis to evaluate competing joint alignments or pose candidates. In some examples, traversal outputs may form a serialized chain of bone-to-bone relations and constraint predicates that may be consumed by an LMM / LLM attention mechanism to resolve ambiguity (e.g., selecting a plausible finger curl configuration) and to write back refined pose metadata and constraint satisfaction indicators into the matrix. In some examples, traversal may also produce a deterministic skeletal graph that may be reused as a stable conditioning signal for downstream generation, thereby preserving anatomical connectivity and range-of-motion constraints across views and frames even when the underlying generative model produces stylistic variation.

[0205] In some examples, traversal may be performed iteratively and adaptively, with traversal regions and directions selected based on task objectives, attention weights produced by an LLM and / or LMM, or ambiguity indicators stored in a hypothesis layer. In some examples, traversal results may be written back to the matrix as derived relationships, inferred constraints, updated confidence measures, and / or updated traversal metadata, thereby enabling subsequent passes to operate on an enriched and more consistent symbolic substrate.Docket Number: SEMADS0001PCT

[0206] In some examples, multidirectional traversal may be used in connection with patent drawing generation by extracting view-consistent structural information from the matrix prior to rendering. For example, traversal may identify adjacency-based fastener engagements, layered component ordering, and feature correspondences that should remain consistent between a perspective figure and one or more orthographic figures. In some examples, traversal results may be converted into drafting directives (e.g.. which edges to emphasize, which components are occluding, which features correspond across views, and where callouts should attach) and stored as view-indexed metadata, thereby reducing view-to-view drift and improving structural consistency across a patent figure set.

[0207] Aggregation of Multiple Traversals

[0208] In some examples, techniques described herein may be directed to aggregation of outputs from multiple traversal passes over a symbolic multidimensional matrix to form a consolidated interpretation of scene structure. In some examples, aggregation may improve robustness by combining complementary evidence obtained from different traversal directions, different matrix slices, different hierarchy levels, and / or different uncertainty hypotheses, thereby reducing ambiguity that may arise from any single traversal.

[0209] In some examples, results from multiple traversal passes may be aggregated to resolve ambiguity or reinforce consistent interpretations. For example, a first traversal may follow adjacency relationships to identify candidate attachments, a second traversal may follow an axis-aligned neighborhood to identify co-located features, and a third traversal may follow a hierarchy axis to retrieve assembly context; the resulting candidate relationships and constraints may then be combined into a single consolidated set of assertions associated with one or more matrix addresses.

[0210] In some examples, aggregation may be performed using one or more reconciliation mechanisms including probabilistic weighting, coherence evaluation, constraint satisfaction scoring, and / or consensus selection. For example, each traversal pass may produce one or more candidate interpretations with associated scores (e.g., confidence, cost, constraint violation counts, provenance reliability), and aggregation may compute a combined score that favors interpretations that are mutually consistent across passes. In some examples, coherence evaluation may include verifying that aggregated relationships do not violate part-hierarchy rules, kinematic constraints, attachment constraints, or view / time invariants stored in the matrix.Docket Number: SEMADS0001PCT

[0211] In some examples, consistent relationships detected across multiple passes may be reinforced. For example, if multiple traversals independently infer that a fastener engages a particular hole feature and attaches two neighboring parts, the relationship descriptor may be promoted from a hypothesis to an asserted relation, and a confidence value associated with the relation may be increased. In some examples, reinforcement may also include allocating refinement resources (e.g„ instantiating a refined submatrix) for regions implicated by consistently detected relationships.

[0212] In some examples, outlier interpretations may be suppressed. For example, if a particular traversal produces a relationship that is inconsistent with constraints inferred by other traversals, violates joint limits, produces an implausible topology, or relies on low-reliability provenance, the system may reduce its weight, demote it to a lower-ranked hypothesis, or remove it from the active hypothesis set. In some examples, outlier suppression may be implemented via thresholding, robust estimation, graph pruning, or conflict-resolution rules that prioritize constraint satisfaction and cross-pass agreement.

[0213] In some examples, aggregation may generate structured update actions that are written back into the matrix. For example, aggregation may (i) select among competing hypotheses in a hypothesis layer, (ii) revise pose metadata to a consensus pose estimate, (iii) add or rewrite relationship descriptors and constraints, and / or (iv) update confidence and provenance fields to reflect cross-pass reinforcement. In some examples, the matrix may store an aggregation record indicating which traversal passes contributed to a consolidated assertion, thereby supporting auditability and later rollback.

[0214] In some examples, aggregation of multiple traversals may be applied to an articulated structure represented by hierarchical SEMAD identifiers, such as a human hand decomposed into bone-level SEMADs and joint constraints. For example, a first traversal may follow a kinematic chain to propose a finger flexion configuration, a second traversal may sweep a spatial neighborhood to propose alternative bone alignments, and a third traversal may traverse a hypothesis axis to enumerate competing joint orientations; aggregation may reconcile these candidate pose interpretations by selecting a consensus configuration that satisfies joint limits and coupled-motion priors. In some examples, cross-pass reinforcement may increase confidence in stable bone-to-bone relationships, while suppressing outlier joint rotations that violate anatomical constraints, and the resulting consensus pose and constraints may be written back asDocket Number: SEMADS0001PCT

[0215] updated cell records that can deterministically generate a skeletal graph for downstream rendering or generative conditioning.

[0216] In some examples, aggregation may be performed iteratively and adaptively, with additional traversal passes triggered when residual ambiguity remains above a threshold, when conflicting hypotheses persist, or when a downstream consumer requests higher detail. In some examples, aggregation may be guided by attention weights produced by an LMM and / or LLM, where attention may prioritize which traversal results are emphasized and which matrix regions are revisited.

[0217] In some examples, aggregation of multiple traversals may be used to improve consistency and reduce ambiguity in patent drawing generation. For example, one traversal pass may infer attachment relationships and fastener engagements, another pass may infer stacking order and occlusion relationships, and another pass may infer feature correspondences needed for aligned orthographic projection; aggregation may consolidate these inferences into drafting-consistent assertions that are reused across multiple figures. In some examples, reinforced relationships may be promoted to asserted connections that drive callout placement and part labeling, while outlier interpretations may be suppressed to prevent view-to-view drift, inconsistent part counts, or mismatched fastener placement across a patent figure set.

[0218] LLM as Scene Coherence Evaluator

[0219] In some examples, techniques described herein may employ a large language model (LLM) as a scene coherence evaluator configured to perform global consistency checking over a symbolic scene representation stored in a symbolic multidimensional matrix (e.g., an SMM). In some examples, the LLM may operate on a serialized form of cell records, relationship descriptors, pose metadata, constraint expressions, and hypothesis sets, and may evaluate whether the overall scene state is coherent under one or more structural, physical, kinematic, or drafting constraints.

[0220] Referring now to FIG. 5, a process flow diagram 500 is illustrated showing traversal, reasoning, rendering, and validation performed over a symbolic scene representation. A symbolic scene state 501 may be implemented as a symbolic multidimensional matrix (SMM) that stores SEMAD identifiers and structured records describing objects, regions, attributes, and relationships. In some examples, a multimodal model (LMM) processes one or more perceptual inputs (e.g., images, drawings, CAD-derived views) to generate candidate symbolic assertions,Docket Number: SEMADS0001PCT

[0221] such as candidate SEMAD identifiers, candidate attributes, candidate relationships, and / or candidate constraints, and provides such candidates to the symbolic scene state 501 as proposed updates.

[0222] In some examples, a language model (LLM), which may use attention and sampling control 520, traverses the symbolic scene state 501 by reading, serializing, or otherwise extracting selected portions of SMM cell records according to one or more traversal policies. For example, traversal can follow spatial adjacency, semantic grouping, hierarchical decomposition, part-subpart relationships, hypothesis layers, view indices, constraint graphs, or other indexing axes represented by the SMM. In some examples, the LLM may propose symbolic edits including normalization, constraint repairs, relationship corrections, ordering or scheduling updates, hypothesis management, or view-selection directives, and writes such edits back to the symbolic scene state 501. In some embodiments, the LLM may be authorized to directly apply a bounded set of edits (e.g., label normalization, attribute completion, or reference disambiguation) while higher-impact structural modifications (e.g., topology changes, attachment changes, DOF changes) may be subject to a validation gate. Structural validation and / or a symbolic discriminator may block commitment of disallowed or constraint- violating edits and trigger corrective iteration in some examples.

[0223] In some examples, a candidate figure creation and rendering process 510 generates a candidate figure from the symbolic scene state 501. For instance, process 510 may include selecting one or more views, projecting or transforming geometry, generating linework and / or vector strokes, placing reference numerals and leader lines, and outputting a draft patent drawing figure in a form suitable for evaluation. In some examples, the output of the candidate figure creation and rendering process 510 is provided as a Draft Figure 515, which may represent a raster output, a vector drawing output, a stroke list, and / or another drawing representation used for downstream analysis. In some examples, process 510 may be conditioned by the attention and sampling control 520 and / or by constraints stored in the SMM (e.g., view consistency constraints, line style constraints, leader-line non-crossing constraints), such that the Draft Figure 515 is produced with structural regularities suitable for validation and correction.

[0224] In some examples, a segmentation and feature extraction process 530 receives the Draft Figure 515 and extracts one or more candidate regions, contours, keypoints, stroke primitives, leader- line candidates, reference numeral candidates, and / or feature descriptors. In someDocket Number: SEMADS0001PCT

[0225] examples, extraction process 530 may operate on raster pixels, vector paths, stroke sequences, or combinations thereof, and may prioritize localized regions of the Draft Figure 515 based on attention directives, uncertainty indicators, or previously detected errors.

[0226] In some examples, the system performs symbolic re-parsing 540 to convert the extracted features into symbolic structures suitable for insertion into, or comparison against, the symbolic multidimensional matrix (SMM). For example, symbolic re-parsing 540 may map detected shapes, strokes, or regions to SEMAD primitives, infer part boundaries, infer adjacency / attachment hypotheses, infer leader-line attachments and associated reference numeral assignments, and / or produce normalized symbolic assertions in a format consistent with the SMM’s schema. In some examples, symbolic re-parsing 540 may resolve ambiguous detections by emitting multiple competing hypotheses (e.g., alternate part identities, alternate attachment graphs, or alternate leader-line attachments), each with an associated confidence or score value.

[0227] In some examples, outputs of symbolic re-parsing 540 are provided to structural validation 550. which evaluates the candidate symbolic structures using symbolic criteria rather than pixel appearance. For instance, structural validation 550 may verify constraint satisfaction (e.g., permitted degrees of freedom, attachment compatibility, adjacency plausibility), topological correctness (e.g., part containment, graph consistency, non-intersection rules), identifier coherence (e.g., SEMAD normalization and registry / namespace consistency), and drawing- structure rules (e.g., valid use of reference numerals, leader-line attachment rules, nonoverlap or non-crossing leader line constraints, and consistency of referenced elements across multiple views). In some examples, structural validation 550 produces acceptance / rejection decisions, ranked alternatives, and / or symbolic deltas that specify how the candidate symbolic structures should be corrected.

[0228] In some examples, a Symbolic Corrections I Scores 554 flow from structural validation 550 to symbolic re-parsing 540 represents a feedback mechanism by which the validator supplies repair signals that guide subsequent parsing. For example, the validator may indicate that a candidate SEMAD primitive assignment is inconsistent with neighboring constraints, that a hypothesized attachment violates an allowed relationship type, or that a reference numeral conflicts with a previously established identifier mapping. In some examples, the symbolic corrections / scores 554 may include (i) explicit edit instructions (e.g., “replace relationship type,” “split a region,” “merge two segments,” “swap primitive assignment,” “re-attach leaderDocket Number: SEMADS0001PCT

[0229] line”), and / or (ii) score adjustments that re-rank competing hypotheses emitted by symbolic reparsing 540.

[0230] In some examples, a targeted regeneration 551 process represents targeted reprocessing triggered by validation outcomes, such that the system selectively re-executes upstream steps for only the portions of the Draft Figure 515 implicated by a detected inconsistency. For example, targeted regeneration 551 may instruct the system to adjust attention and sampling control 520 to increase sampling density in a suspected error region, to re-run segmentation / feature extraction process 530 on a localized patch of the Draft Figure 515, to request alternate hypotheses for a particular boundary or attachment, to re-place a subset of leader lines or reference numerals, and / or to re-render only those drawing elements that map to a specific subset of SEMAD addresses. In some examples, targeted regeneration 551 avoids recomputing the entire candidate figure by narrowing compute to high-impact regions and / or to the smallest symbolic subgraph needing repair, while preserving unaffected drawing portions.

[0231] In some examples, one or more bidirectional flows between the symbolic scene state 501, the candidate figure creation and rendering process 510, the segmentation and feature extraction process 530, and the structural validation 550 indicate that validation results can guide where candidate figure generation and / or perceptual extraction is focused next. For example, validator outputs may identify uncertain regions, ambiguous structures, low-confidence SEMAD assignments, or drawing-rule conflicts (e.g., leader line crossing), and the system may direct reextraction and / or localized re-rendering of those regions so that subsequent candidate updates improve the symbolic scene state. In some examples, the loop shown in FIG. 5 enables a closed-cycle symbolic refinement process in which perception-driven proposals (e.g., from an LMM), language-driven traversal, candidate rendering process 510 producing a Draft Figure 515, and validation-driven correction cooperate to produce structurally consistent symbolic states that support downstream rendering and patent drawing generation.

[0232] In some examples, outcomes 560 represent one or more terminal or intermediate actions selected based on the validation results. For example, an Accept outcome may indicate that the candidate figure (including the Draft Figure 515 and its corresponding symbolic structures) satisfies the structural validation criteria and can be promoted to subsequent presentation or finalization stages. In some examples, a Targeted Regeneration outcome may indicate that one or more localized inconsistencies were detected and that the system should iterate via feedbackDocket Number: SEMADS0001PCT

[0233] paths (e.g., targeted regeneration 551 and / or symbolic corrections and scores 554) to refine extraction, re-parsing, or constraint satisfaction for a limited subset of regions or objects. In some examples, a Symbolic Update (to SMM) outcome may indicate that validated symbolic structures (e.g., confirmed SEMAD assignments, corrected constraints, repaired attachment graphs, normalized identifier mappings, or vetted reference numeral associations) are committed back into the SMM so that subsequent figures and views are generated from an improved and more consistent symbolic scene state.

[0234] In some examples, committing the Symbolic Update (to SMM) outcome provides a technical advantage by ensuring that improvements discovered during validation (e.g., corrected identities, constraint repairs, or consistent reference numeral mappings) persist across subsequent iterations and across multiple candidate figures. In this manner, FIG. 5 depicts a closed-loop symbolic refinement process in which candidate figure creation and rendering 510 generates a draft drawing that is output as a Draft Figure 515, segmentation and feature extraction process 530 extracts structure from the Draft Figure 515, symbolic re-parsing 540 converts extracted evidence into structured symbolic hypotheses, and structural validation 550 enforces symbolic correctness and drives targeted regeneration 551 and symbolic corrections I scores 554 until an acceptable outcome is reached and the scene state is updated.

[0235] In some examples, the system generates a parsed symbolic structure of the Draft Figure 515 by transforming the drawing representation into one or more intermediate structural encodings prior to validation. For example, where the Draft Figure 515 is vector-based, the system may directly extract a stroke graph from vector paths, including vertices, segments, polylines, arcs, junctions, stroke order, and stroke grouping into candidate parts. Where the Draft Figure 515 is raster-based, the system may perform skeletonization or edge extraction to form an edge graph and / or medial-axis line skeleton suitable for identifying contours, junctions, and adjacency. In some examples, the system forms a region adjacency graph (RAG) by segmenting filled regions or bounded contours and encoding region-to-region boundaries and containment. In some examples, the system extracts a leader-line and callout graph by detecting leader-line primitives, arrowheads, endpoints, and text blocks, and by associating each leader line with a candidate attachment location on a drawing feature.

[0236] In some examples, the parsed symbolic structure includes reference numeral and label associations derived from the Draft Figure 515. For instance, the system may apply opticalDocket Number: SEMADS0001PCT

[0237] character recognition (OCR) to detect candidate reference numerals and text labels, normalize such labels (e.g., font variations, rotation, scale), and associate each numeral with a leader line and with a target feature in the extracted stroke / edge graph. In some examples, association is performed by geometric proximity, ray casting along a leader-line direction, intersection testing with candidate feature contours, or by using a learned association model that predicts a best attachment point given the leader-line geometry and nearby drawing features. In some examples, the resulting parsed symbolic structure includes a mapping between reference numerals and SEMAD identifiers, such that a numeral is treated as an external depiction of a stable SEMAD address across multiple views.

[0238] In some examples, when structural validation 550 detects a violation, the system identifies an affected portion of the Draft Figure 515 using a fault-to-structure localization procedure. For instance, the validator may output a set of implicated SEMAD identifiers, edges, constraints, or rule violations (e.g., “leader line crosses,” “reference numeral mismatch,” “attachment incompatible,” “missing part depiction,” “topology inconsistency”), and the system maps those implicated items to drawing primitives in the parsed symbolic structure (e.g., specific strokes, contours, leader lines, numeral glyphs, or regions). In some examples, the affected portion is defined as a minimal subgraph of the parsed symbolic structure that includes (i) the implicated nodes / edges and (ii) a bounded neighborhood of adjacent structures required to maintain drawing continuity (e.g., immediate neighbors in an adjacency graph, a local contour segment neighborhood, or a local assembly subgraph). In some examples, the system computes a geometric region (e.g., one or more bounding boxes, convex hulls, or masks) enclosing the implicated drawing primitives and uses that region as the target scope for regeneration.

[0239] In some examples, targeted regeneration 551 preserves unaffected portions of the candidate figure by “locking” drawing primitives that correspond to SEMADs not implicated by the validation failure. For example, the system may freeze unaffected strokes, contours, numeral placements, and leader lines, and restrict regeneration to only those primitives within the affected region mask and / or to those associated with the implicated SEMAD subset. In some examples, preservation includes maintaining stable reference numeral assignments for unaffected SEMADs, maintaining leader-line placements that already satisfy drafting rules, and maintaining view-level parameters (e.g.. viewpoint, scale, cropping) for portions outside the affected region so that changes remain localized and do not introduce secondary inconsistencies.Docket Number: SEMADS0001PCT

[0240] In some examples, targeted regeneration 551 operates by applying symbolic deltas to the SMM rather than rebuilding the entire symbolic scene state. For instance, the system may generate a localized set of symbolic edits (e.g„ reassign a SEMAD primitive, adjust an attachment relationship, correct a reference numeral association, modify a constraint parameter, or split / merge a segmented region) and commit only those deltas to the relevant SMM cells. In some examples, the system then re-executes candidate figure creation / rendering 510 only for the affected SEMAD subset and / or affected view index, thereby producing a revised Draft Figure 515 that differs only within the bounded region. In some examples, the system iterates until structural validation 550 indicates the localized inconsistency is resolved, while keeping previously validated unaffected drawing portions unchanged.

[0241] In some examples, the system generates a deterministic depiction from the symbolic scene state 501 (or from validated portions of the parsed symbolic structure) and uses that depiction as a conditioning input that constrains a downstream generative rendering process. For example, the deterministic depiction may include a line skeleton, edge map, contour set, joint graph, constraint graph, pose graph, or a composite control image that encodes required geometry and / or topology. In some examples, the deterministic depiction is provided to a generative model as a conditioning channel, such as a control image (e.g., edge map or skeleton image), a feature map, a graph embedding, a structured prompt that encodes adjacency and attachment, and / or a set of hard constraints applied during decoding or post-processing. In some examples, the generative process is configured so that generated strokes, contours, and placements are required to align with the deterministic depiction within a tolerance, thereby enforcing structural compliance while allowing stylistic variation in line thickness, shading, or rendering aesthetics.

[0242] In some examples, a failure of deterministic-depiction compliance is detected when the generated drawing deviates from the wireframe or constraint graph, such as by missing a required edge, introducing an extra contour, breaking a required junction, violating an attachment constraint, or displacing a reference numeral from an allowed region. In some examples, structural validation 550 measures deviation using one or more metrics including graph edit distance, junction correspondence, edge overlap score, skeleton alignment error, contour containment tests, and / or constraint satisfaction scores. In response to a detected deviation, targeted regeneration 551 may re-run generation only for the affected subset whileDocket Number: SEMADS0001PCT

[0243] increasing the strength of conditioning, tightening tolerances, modifying sampling control 520, or applying a hard constraint post-process that snaps generated primitives to the deterministic depiction. In this manner, the wireframe / constraint depiction operates as a structural guardrail that forces regeneration until the candidate figure satisfies both the depiction constraints and the symbolic drafting rules.

[0244] In some examples, the system implements a drafting rules engine that evaluates and optimizes patent-drawing compliance for candidate figures. For instance, the rules engine may compute an objective function over candidate numeral placements and leader-line geometries that penalizes (i) leader-line crossings, (ii) leader lines that intersect or occlude critical contours, (iii) numeral overlap with other numerals or linework, (iv) ambiguous attachment of a leader line to multiple nearby features, (v) numerals placed outside permitted margins or readability regions, and / or (vi) inconsistent numeral usage across views for the same SEMAD. In some examples, the system searches for a placement configuration that minimizes the objective subject to constraints (e.g.. fixed numeral-to-SEMAD mapping, fixed attachment feature, non-crossing constraints, minimum spacing, and / or view layout constraints). In some examples, the optimization is performed by heuristic search, integer programming, greedy improvement, simulated annealing, or a learned placement policy that outputs candidate layouts which are then symbolically validated.

[0245] In some examples following, example drawing aspects call out reference numerals as examples, these numerals that are out of context now, are not meant to relate to figures in this specification but rather as discussion examples. Accordingly, in some examples, outputs of the drafting rules engine are expressed as symbolic deltas and / or localized drawing edits, such as “move reference numeral 104 by Ax, Ay,” “reroute leader line for numeral 112 to attachment point P,” “swap callout order to reduce crossings,” or “reserve whitespace region R for numerals.” In some examples, the rules engine supplies these edits to symbolic re-parsing 540 and / or targeted regeneration 551, such that only the affected leader lines and numerals are regenerated while the remainder of the drawing is preserved. In some examples, the rules engine further enforces cross-view coherence by maintaining a persistent numeral registry keyed by SEMAD identifiers, so that once a numeral is assigned to a SEMAD, subsequent views and figures re-use that numeral unless an explicit re-indexing event is validated and committed to the SMM.Docket Number: SEMADS0001PCT

[0246] In some examples, a language model may evaluate physical plausibility or relational consistency of a symbolic scene representation by checking whether proposed entities and relationships can coexist without contradiction. For example, the LLM may evaluate whether attachment relationships are reciprocal where required, whether a part hierarchy is acyclic and consistent with assembly membership, whether declared interfaces are compatible (e.g., a fastener engagement implies a corresponding hole feature), and whether pose metadata implies overlapping occupancy that is inconsistent with allowed contact constraints. In some examples, the LLM may compute one or more coherence outputs including a plausibility score, a list of violated constraints, and one or more recommended repairs, optionally including a rationale and pointers to implicated matrix addresses.

[0247] In some examples, the LLM may adjust SEMAD hypotheses based on detected inconsistencies. For example, when multiple candidate SEMAD identifiers are stored in a hypothesis layer for a given region (e.g., alternative fastener types or alternative part classes), the LLM may down-rank hypotheses that produce constraint violations and up-rank hypotheses that improve global coherence. In some examples, the LLM may emit structured update actions (e.g., SELECT_HYPOTHESIS, REWRITE_REL, ADD_CONSTR AIN'T, REFINE_POSE) that may be applied deterministically to specific cell records using preserved addressing information in the serialized input.

[0248] In some examples, the model may flag impossible part intersections. For example, if pose metadata and nominal geometry bindings imply that two rigid components occupy the same space beyond a permitted tolerance, and no corresponding “cutaway,” “section,” or “interpenetration allowed” directive is present, the LLM may identify an intersection conflict and mark the implicated entities, relationships, or poses as inconsistent. In some examples, the LLM may propose corrective actions such as modifying a pose within tolerance, selecting an alternative hypothesis for one of the entities, inserting an intermediate component (e.g., a spacer), or triggering refinement of a local matrix region to obtain higher-resolution geometry or interface features.

[0249] In some examples, invalid fastener placements may be corrected by the system responsive to LLM-detected inconsistencies. For example, if a fastener axis is misaligned with a target hole feature, if a fastener length is incompatible with a stack-up thickness, or if an engagement relationship references incompatible mating features, the LLM may propose anDocket Number: SEMADS0001PCT

[0250] alternative fastener hypothesis, adjust the fastener pose, or revise the engagement relationship to match a compatible interface. In some examples, corrective actions may be validated by constraint propagation and / or by additional traversal passes, and the resulting corrected placement may be written back into the matrix as an asserted relationship with updated confidence and provenance.

[0251] In some examples, the LLM coherence evaluator may be invoked after aggregation of multiple traversal passes to validate that the consolidated interpretation satisfies global constraints and may further trigger additional traversal or refinement when residual ambiguity remains. In some examples, the LLM coherence evaluator may be used in a hybrid loop with an LMM, where the LMM grounds candidates in perceptual evidence and the LLM performs global symbolic consistency checking, including detecting contradictions that are not locally visible within a single region of the matrix.

[0252] In some examples, the coherence evaluator may be applied to an articulated structure represented by hierarchical SEMAD identifiers, such as a human hand expanded into bone-level SEMADs and joint constraints. For example, the LLM may evaluate whether an inferred hand pose violates joint limits, produces implausible bone intersections, breaks kinematic connectivity, or conflicts with coupled-motion priors stored as constraints. In some examples, when conflicting pose hypotheses exist for one or more joints (e.g., in a hypothesis layer), the LLM may adjust or select hypotheses that restore coherence and may emit update actions that refine joint orientations or restrict allowable motion ranges. In some examples, the corrected hand state may be used to deterministically derive a skeleton or joint graph that may condition downstream rendering or generative outputs, thereby preserving anatomical plausibility across frames and viewpoints even under stylistic variation.

[0253] In some examples, the LLM coherence evaluator may be used in connection with patent drawing generation to reduce drafting inconsistencies across a figure set. For example, the LLM may evaluate whether fastener placements, part interfaces, and assembly relationships remain mutually consistent between a perspective figure and one or more orthographic figures generated from a shared matrix state. In some examples, the LLM may flag conflicts such as a fastener engaging a hole in one figure but not in another, a part intersection inconsistent with the declared assembly constraints, or a mismatch in part count across views, and may propose structured corrections that update the canonical matrix prior to re-rendering. In some examples, thisDocket Number: SEMADS0001PCT

[0254] coherence evaluation may improve figure-to-figure consistency for callouts, hidden-line policies, and feature correspondences by ensuring that the underlying symbolic substrate satisfies global constraints before or during rendering.

[0255] Textual Grounding of Spatial and Structural Representations

[0256] In some examples, techniques described herein may be directed to bridging natural language with symbolic spatial and structural representations stored in a symbolic multidimensional matrix (e.g., an SMM). In some examples, text may be treated as a first-class input to scene reasoning, such that natural language may contribute constraints, intent, and semantic structure that may not be reliably inferred from perceptual evidence alone.

[0257] In some examples, natural language input may be mapped to symbolic matrix structures by converting text into one or more structured assertions, queries, or update actions that reference SEMADs identifiers, matrix addresses, and / or relationship descriptors. For example, a textual command such as “attach the cover to the housing using four screws” may be mapped into (i) entity references (e.g., COVER, HOUSING, SCREW), (ii) a multiplicity constraint (e.g., count = 4), (iii) one or more relationship descriptors (e.g., ATTACH(COVER, HOUSING) and FASTEN(SCREW_i, COVER, HOUSING)), and (iv) one or more placement or alignment constraints (e.g., hole correspondence and axis alignment). In some examples, the mapping may be performed by an LLM configured to parse the text into a controlled symbolic schema that is compatible with the cell-record and relationship formats stored in the matrix.

[0258] In some examples, textual descriptions may influence interpretation or refinement of SEMADs by biasing hypothesis selection, refinement triggers, and traversal priorities. For example, when an image-derived recognition subsystem proposes multiple SEMAD candidates for a component (e.g., a screw vs. a rivet, or a bolt vs. a pin), a textual description may provide disambiguating context (e.g., “bolted joint,” “threaded engagement.” “remove with a screwdriver”) that increases the weight of hypotheses consistent with the text and decreases the weight of incompatible hypotheses. In some examples, such influence may be recorded as provenance metadata indicating that a hypothesis adjustment was text-derived.

[0259] In some examples, a textual description specifying a bolted joint may constrain matrix interpretation. For example, the phrase “bolted joint” may be mapped to a constraint bundle requiring (i) a bolt or screw SEMAD hypothesis, (ii) an engagement relation with a threadedDocket Number: SEMADS0001PCT

[0260] feature or nut, (iii) axial alignment between the fastener and one or more corresponding holes, and (iv) a stackup relationship consistent with clamping of the joined parts. In some examples, when the matrix contains an attachment relationship without a compatible fastener engagement, the system may insert a fastener candidate into a hypothesis layer and allocate refinement resources to search for hole features or mating faces.

[0261] In some examples, text may resolve ambiguity between similar components by introducing functional or role-based constraints. For example, a description may identify a “hinge pin” versus a “fastener,” or a “spacer” versus a “washer,” and the system may update one or more cell records to reflect a role assignment that constrains allowable relationships and poses. In some examples, text may also specify intent (e.g., “open the lid,” “remove the cover,” “tighten the screw”), and the intent may be mapped to kinematic constraints, temporal state updates, or traversal objectives that guide subsequent reasoning and rendering.

[0262] In some examples, textual grounding may be applied iteratively, such that the system may alternate between (i) generating text-conditioned updates to the matrix and (ii) re-evaluating scene coherence using traversal, constraint propagation, and / or LLM coherence evaluation. In some examples, where text provides incomplete information, the system may generate follow-up questions or requests for clarification, and any user responses may be incorporated as additional symbolic assertions with associated provenance.

[0263] In some examples, natural language may be used to ground and constrain an articulated structure represented by hierarchical SEMAD identifiers, such as a human hand expanded into bone-level SEMADs and joint constraints. For example, a textual description such as “a clenched fist,” “index finger extended,” or “thumbs-up” may be mapped to pose intent constraints that restrict joint angles and coupled motion patterns across the bone-level kinematic chain. In some examples, when multiple pose hypotheses are present in a hypothesis layer (e.g., competing finger curl configurations), the text-derived intent constraints may bias selection toward anatomically plausible configurations consistent with the description, and the resulting pose may be written back into the matrix as updated joint parameters and constraint satisfaction indicators. In some examples, a skeletal graph derived deterministically from the updated matrix state may then condition a downstream generative model, enabling a small symbolic dataset to enforce consistent hand realism across viewpoints, frames, and styles.Docket Number: SEMADS0001PCT

[0264] In some examples, textual grounding may be used in connection with patent drawing generation by converting drafting instructions and specification language into symbolic constraints that guide figure generation. For example, text may specify that “the cover is bolted to the housing,” that “fasteners are omitted for clarity,” or that “a cross-sectional view is taken along line A-A,” and the system may map such text into view-indexed directives and relationship constraints stored in the matrix. In some examples, these text-derived directives may guide which components are included in a figure, how callouts are assigned, which edges are emphasized, and which occlusion or hidden-line policies apply, while preserving identifierconsistent structure across a multi-figure patent drawing set.

[0265] Image Recognition via Symbolic Scene Construction

[0266] In some examples, techniques described herein may be directed to scene-first image recognition in which recognition may be treated as construction and refinement of a symbolic scene representation rather than as production of isolated pixel-space labels. In some examples, image understanding may be performed as hypothesis testing within a symbolic multidimensional matrix (e.g., an SMM), where candidate entities, relationships, and constraints may be proposed, evaluated, and revised until a coherent symbolic scene state is obtained.

[0267] In some examples, one or more vision models may propose candidate SEMAD identifiers for assignment to one or more matrix cells based on image evidence. For example, a detector, segmenter, keypoint estimator, or feature extractor may propose a set of candidate classes for a region, and the system may map those candidates to SEMAD identifiers associated with nominal geometry, roles, and constraint bundles. In some examples, proposed candidates may be stored in a hypothesis layer together with confidence measures and provenance (e.g., which model produced the candidate and which features supported it).

[0268] In some examples, feature data may be mapped to semantic addresses probabilistically. For example, an image region may be associated with a probability distribution over candidate SEMAD identifiers, and the system may represent such distribution explicitly in the matrix as ranked hypotheses with confidence values. In some examples, the probability distribution may be updated iteratively based on additional evidence, including geometric consistency checks, adjacency relationships, and / or outputs of coherence evaluation.

[0269] In some examples, image-derived features may be interpreted in the context of neighboring symbolic objects rather than as isolated detections. For example, a candidateDocket Number: SEMADS0001PCT

[0270] fastener hypothesis may be evaluated in view of nearby hole features, mating faces, part boundaries, or expected attachment patterns, and the system may prefer candidate assignments that satisfy predicted engagement relations and alignments. In some examples, context may be retrieved by multidirectional traversal of the matrix, enabling the system to evaluate a local region against non-local constraints such as part hierarchy, assembly membership, and kinematic connectivity.

[0271] In some examples, ambiguous visual evidence may be resolved by enforcing structural consistency across the symbolic matrix. For example, when two candidate assignments compete (e.g„ screw vs. rivet, bolt vs. pin, housing feature vs. separate part), the system may evaluate which assignment yields a globally coherent set of relationships under stored constraints and may select the hypothesis that minimizes constraint violations or maximizes coherence across multiple traversal passes. In some examples, structural consistency enforcement may include one or more of: part-count invariance across views, reciprocal attachment relations, joint-limit satisfaction, avoidance of impossible interpenetrations, and / or consistency with text-derived intent constraints.

[0272] In some examples, the system may update the matrix using structured update actions responsive to recognition outcomes. For example, selecting a SEMAD hypothesis may instantiate a corresponding cell record including nominal geometry bindings and expected relationship descriptors, and constraint propagation may then infer additional implied relationships (e.g., likely mates, alignments, or connectors). In some examples, when consistency checks fail, the system may backtrack by demoting a selected hypothesis, promoting an alternative hypothesis, requesting refinement of a submatrix region, or triggering additional targeted feature extraction in an ambiguous region.

[0273] In some implementations or examples, the system may ingest hand sketches, line drawings, scanned drawings, and / or photos of drawings, and may construct a SEMADs-based symbolic scene representation in the matrix. For example, stroke geometry, edge maps, junctions, and contour groupings may be extracted from a sketch and mapped into candidate structural primitives and relationships, including candidate part boundaries, interface features, symmetry cues, and attachment cues.

[0274] In some examples, a multimodal model may propose candidate SEMAD identifiers conditioned on sketch-derived features, and the system may populate a sparse matrix withDocket Number: SEMADS0001PCT

[0275] probabilistic hypotheses that are refined by constraint propagation and coherence evaluation. In such implementations, the system may normalize noisy hand-drawn input by canonicalizing line quality, merging fragmented strokes, resolving occlusion ambiguities, enforcing orthogonality or symmetry constraints where appropriate, and mapping informal depiction conventions into a consistent symbolic representation.

[0276] In some examples, sketch ingestion may be used to produce drafting-ready outputs by converting sketch evidence into a canonical SEMAD scene that can be rendered as clean line art, orthographic views, exploded diagrams, and / or multi-figure patent drawings. In such examples, the system may preserve semantic identity across views even when the sketch contains inconsistent stylization or incomplete depiction, by prioritizing relational structure and constraint satisfaction in the symbolic substrate.

[0277] In some examples, scene-first recognition may be applied to an articulated structure such as a human hand by constructing a hierarchical SEMAD representation and resolving ambiguity through kinematic coherence. For example, a vision model may propose candidate SEMAD assignments for a hand region and for one or more bone-level substructures (e.g., metacarpals or phalanges) with uncertainty represented in a hypothesis layer; the system may then evaluate candidate joint orientations and bone-to-bone relationships by enforcing joint limits, connectivity constraints, and coupled-motion priors. In some examples, ambiguous evidence for a finger posture (e.g., partial occlusion) may be resolved by selecting the pose hypothesis that yields a coherent kinematic chain across the matrix and by writing back refined joint parameters and confidence values into the persistent scene substrate. In some examples, a skeleton or joint graph derived deterministically from the resulting symbolic state may condition downstream rendering or generative outputs to preserve anatomical realism.

[0278] In some examples, scene-first recognition may be used in connection with patent drawing generation by constructing a canonical symbolic assembly from one or more photographs, scans, or drawings and then rendering multiple figures from the resulting matrix state. For example, the system may propose SEMAD hypotheses for parts, interfaces, and fasteners, resolve ambiguities by enforcing attachment and alignment consistency across the matrix, and store view-indexed directives needed for orthographic and perspective rendering. In some examples, because recognition produces a persistent symbolic substrate rather than independent per- view detections,Docket Number: SEMADS0001PCT

[0279] the resulting figures may exhibit reduced view-to-view drift and improved consistency of part counts, fastener placements, and callout targets across a multi-figure patent drawing set.

[0280] Rendering Pipelines Driven by Symbolic Structure

[0281] In some examples, techniques described herein may be directed to structure-driven rendering in which one or more rendering pipelines may be driven directly by symbolic scene state stored in a symbolic multidimensional matrix (e.g., an SMM), rather than by pixel-based inputs. In some examples, appearance may be derived from symbolic geometry, pose metadata, and relationship descriptors associated with SEMAD identifiers, thereby enabling rendering outputs to be generated from a canonical structural substrate even when pixel observations are incomplete, noisy, or stylistically variable.

[0282] In some examples, rendering processes may be driven directly by symbolic matrix representations by traversing matrix regions and instantiating geometry associated with SEMAD identifiers. For example, each SEMAD identifier may be bound to a nominal geometric representation (e.g., a CAD primitive, parameterized surface, mesh template, or procedural model), and pose metadata stored in cell records may place the geometry into a shared coordinate frame. In some examples, relationship descriptors and constraint expressions may further specify assembly configuration, such as attachment points, alignment constraints, and feature correspondences, thereby enabling a renderer to reconstruct a consistent assembly without reliance on image textures.

[0283] In some examples, structural fidelity may be preserved independently of appearance by decoupling geometry and topology from rendering style. For example, a single canonical matrix state may be used to render a photorealistic image, a technical illustration, a wireframe, an edge-only depiction, a line skeleton, or an exploded diagram, while preserving the same underlying identifiers, poses, and part-to-part relationships. In some examples, view-indexed directives stored in the matrix may control projection type, line-weight policies, hidden-line, or occlusion policies, shading style, and callout / label conventions, enabling consistent structure across diverse output modalities.

[0284] In some examples, the renderer may generate line-based output by converting instantiated geometry into one or more structural representations, such as edge graphs, silhouette curves, contour traces, centerlines, or wireframes. In some examples, such line-based outputs may beDocket Number: SEMADS0001PCT

[0285] generated deterministically from the matrix state, enabling consistent topology across views and across time. In some examples, line skeletons or edge graphs derived from the matrix may additionally be used as constraints or conditioning inputs for downstream generative processes, thereby constraining generated imagery or animation to preserve the structural relationships encoded in the matrix.

[0286] In some examples, wireframe patent drawings may be generated directly from SEMADs by instantiating nominal geometry for one or more SEMAD identifiers, projecting the geometry into one or more selected views, and applying drafting policies stored as view-indexed directives. In some examples, drafting policies may include edge emphasis rules, hidden-line rules, line simplification rules, sectioning rules, and callout placement constraints, and may be applied consistently because the underlying structural state is shared.

[0287] In some examples, multiple consistent views may be rendered from the same matrix by rendering a plurality of view-indexed layers that reference shared entities and relationships in a canonical substrate. For example, a perspective view and one or more orthographic views may be produced by applying different projection parameters and view directives while reusing the same SEMAD identifiers, poses, and assembly constraints, thereby reducing view-to-view drift that may occur when each view is inferred independently. In some examples, if the matrix state is updated (e.g., a pose is refined or a relationship is corrected), the Tenderer may regenerate affected views based on the updated canonical state.

[0288] In some examples, structure-driven rendering may be applied to an articulated structure represented by hierarchical SEMAD identifiers, such as a human hand in which a hand-level SEMAD may expand into bone-level SEMAD identifiers with nominal geometry bindings and joint constraints. For example, a Tenderer may instantiate nominal bone geometries and apply joint poses stored in the matrix to produce a line skeleton, wireframe, or shaded render of the hand in a selected pose, while preserving anatomical connectivity independently of surface appearance. In some examples, the system may render multiple frames over time by reading time-indexed joint parameters from the matrix, thereby producing temporally coherent animations. In some examples, a skeletal graph derived deterministically from the matrix may be used as a stable conditioning signal for a generative Tenderer, enabling stylistic variation while maintaining anatomically plausible structure.Docket Number: SEMADS0001PCT

[0289] In some examples, structure-driven rendering may be used in connection with patent drawing generation by rendering a set of figures from a shared symbolic matrix state that serves as the canonical assembly description. For example, the system may generate a perspective figure and multiple orthographic figures from the same SEMADs-based structure, apply view-indexed drafting directives for hidden lines and callouts, and maintain consistent part identifiers across the figure set. In some examples, because the figures may be generated from a single persistent substrate, updates to the matrix (e.g., correcting a fastener engagement or refining a part pose) may propagate to regenerate affected figures, thereby improving consistency of part counts, feature correspondences, and fastener placements across the patent drawing set.

[0290] Structural Constraints for Generative Models

[0291] In some examples, techniques described herein may be directed to constraining one or more generative models using symbolic structural information derived from SEMAD identifiers and associated symbolic multidimensional matrices (e.g., an SMM). In some examples, generation may be guided or enforced by a canonical symbolic substrate such that synthesized outputs preserve structural relationships, topology, and pose consistency specified in the matrix, independently of photometric appearance or stylistic rendering choices.

[0292] In some examples, generative image synthesis processes may be constrained by symbolic structural information by providing the generative model with one or more structural conditioning signals derived from the matrix state. For example, a Tenderer may deterministically derive one or more of: a line skeleton, edge graph, contour map, segmentation layout, depth proxy, pose parameter set, joint graph, or constraint graph from SEMAD-linked geometry and pose metadata, and such derived representations may be provided as conditioning inputs to an image or video generation process. In some examples, the conditioning inputs may be view-indexed or time-indexed to enforce cross-view or temporal consistency.

[0293] In some examples, constraints may operate as hard constraints or soft constraints. For example, a hard constraint may require satisfaction of explicit structural predicates (e.g., permitted attachments, joint limits, part-count invariants, non-interpenetration beyond tolerance), and a soft constraint may bias generation toward a preferred structural configuration using prompts, weights, penalties, or guidance schedules. In some examples, a hard constraint may be enforced by rejecting samples that violate one or more predicates, and a soft constraint may beDocket Number: SEMADS0001PCT

[0294] enforced by modifying sampling trajectories or scores to favor samples that better match the symbolic structure.

[0295] In some examples, generated images violating structure may be rejected based on one or more validators that evaluate consistency between a generated output and the matrix-derived structural constraints. For example, a validator may compare a generated image against a derived skeleton or layout, evaluate whether predicted keypoints or edges correspond to the expected topology, or evaluate whether inferred attachments and interfaces match matrix-stored relationships. In some examples, validation may be performed using one or more of: a recognition model that re-parses the generated image into a symbolic form, a geometric evaluator that measures deviation from expected line structure, and / or an LLM coherence evaluator that checks relational consistency of a serialized interpretation.

[0296] In some examples, diffusion-based generation may be biased toward valid assemblies by injecting structural guidance during sampling. For example, the diffusion process may be conditioned on matrix-derived structural representations, and guidance terms may penalize deviations from expected pose parameters, part adjacency patterns, or interface alignments. In some examples, the guidance may be applied progressively, such that coarse structural layout is enforced early in sampling and finer constraints (e.g., fastener placement, feature correspondence) are enforced at later steps. In some examples, structural guidance may be adapted based on confidence values or uncertainty stored in a hypothesis layer, such that constraints are relaxed for uncertain regions and strengthened for asserted relationships.

[0297] In some examples, the system may maintain traceability between the canonical matrix state and the generative output by storing provenance records indicating which SEMAD identifiers, constraints, and derived structural signals were used to condition a given generation. In some examples, this traceability may support iterative refinement in which a user adjusts constraints (e.g.. select a different fastener type, modify a pose, request a different view) and the system regenerates output while preserving identifier-consistent structure.

[0298] In some examples, structural constraints for generative models may be applied to an articulated structure such as a human hand represented by hierarchical SEMAD identifiers and joint constraints. For example, a hand-level SEMAD may expand into bone-level SEMADs with nominal geometry bindings and range-of-motion constraints, and the system may derive a skeletal graph and pose parameters from the matrix to condition an image or video generator. InDocket Number: SEMADS0001PCT

[0299] some examples, hard constraints may enforce joint-limit satisfaction and connectivity of the kinematic chain, while soft constraints may bias generation toward a text-specified pose intent (e.g., “thumbs-up”) or toward a motion prior learned by an LMM. In some examples, generated outputs that depict anatomically implausible finger intersections or broken connectivity may be rejected or corrected by revising sampling guidance and / or updating the matrix state, thereby enabling a relatively small symbolic dataset to preserve hand realism across viewpoints and styles.

[0300] In some examples, structural constraints may be used in connection with patent drawing generation by conditioning a generative Tenderer or illustration model on matrix-derived wireframes, edge maps, or layout constraints corresponding to a canonical assembly state. For example, a diffusion-based illustration process may be guided to produce clean line art that preserves part counts, fastener placements, and feature correspondences defined in the matrix, while still allowing stylistic choices such as line weight or shading. In some examples, outputs that violate drafting-consistency constraints (e.g.. a missing fastener, a duplicated part, or a mismatched interface) may be rejected and regenerated, thereby improving consistency across a multi-figure patent drawing set derived from the same persistent symbolic substrate.

[0301] Temporal and Evolutionary’ Scene Modeling

[0302] In some examples, techniques described herein may be directed to time-indexed symbolic scene modeling in which temporal evolution of a scene may be represented symbolically within a multidimensional matrix rather than by interpolating pixel values between frames. In some examples, temporal state may be encoded as time-indexed layers or time-indexed slices of a symbolic multidimensional matrix (e.g., an SMM), thereby enabling temporal reasoning, simulation, and rendering to be performed over persistent identifiers, constraints, and relationships.

[0303] In some examples, scenes may be represented as sequences of time-indexed multidimensional matrices. For example, a scene may be represented as a series of matrix states Mtfor time indices t, where each state may store one or more of: SEMAD identifiers, pose metadata, relationship descriptors, constraints, and uncertainty hypotheses. In some examples, time-indexed states may be stored as full snapshots, as incremental deltas from a canonical baseline state, or as event-driven updates (e.g., “part A rotates by 9,” “fastener engaged.” “constraint activated”), thereby reducing storage overhead while preserving temporal semantics.Docket Number: SEMADS0001PCT

[0304] In some examples, SEMAD identifiers may persist across time indices such that the same entity may be referenced consistently over time. For example, a component may retain a stable SEMAD identifier while its pose metadata changes across time, and relationship descriptors may be updated to reflect evolving interactions such as contact, engagement, separation, or kinematic coupling. In some examples, identifier persistence may support tracking, auditability, and consistent association of annotations, callouts, and constraints across frames.

[0305] In some examples, motion of components may be simulated symbolically by updating pose metadata in accordance with one or more stored constraints. For example, the matrix may store kinematic joints, attachment relationships, allowable ranges of motion, and / or temporal rules (e.g., sequence constraints), and a symbolic simulator may compute time-indexed pose updates that satisfy these constraints. In some examples, simulation may be driven by an input intent (e.g., “open lid,” “tighten screw,” “rotate lever”), by a learned motion prior produced by an LMM, and / or by a control policy that selects which constraints are active at a given time.

[0306] In some examples, temporal consistency may be enforced across animation frames by applying invariants and coherence checks to the time-indexed sequence. For example, the system may enforce one or more of: identifier stability across time, part-count invariance, joint-limit satisfaction, avoidance of impossible interpenetrations beyond tolerance, continuity bounds on pose changes, and preservation of declared attachments unless an explicit disengagement event occurs. In some examples, temporal coherence evaluation may be performed by an LLM coherence evaluator operating on serialized time-indexed slices, and / or by aggregation of traversal outputs across time that detect discontinuities or inconsistent relationships.

[0307] In some examples, temporal modeling may be coordinated with rendering and generative processes. For example, a Tenderer may consume time-indexed matrix states to generate animation frames with consistent structure, and a generative model may be conditioned on time-indexed skeleton graphs or constraint graphs derived from the matrix. In some examples, when a generated frame deviates from the symbolic temporal constraints, the system may reject the frame, adjust guidance, or update the symbolic state and regenerate, thereby maintaining temporal structure while permitting stylistic variation.

[0308] In some examples, temporal and evolutionary modeling may be applied to an articulated structure such as a human hand represented by a hierarchical SEMAD decomposition into bonelevel identifiers with joint constraints. For example, a hand-level SEMAD may persist acrossDocket Number: SEMADS0001PCT

[0309] time while bone-level poses evolve according to joint limits and coupled-motion priors, and the matrix may store time-indexed joint parameters that represent a grasp, pinch, or gesture sequence. In some examples, temporal consistency may be enforced by ensuring continuity of finger curl trajectories, preventing anatomically implausible bone intersections, and preserving kinematic connectivity across frames; the resulting time-indexed skeletal graph may be used to drive a Tenderer or to condition a generative video model so that generated hand motion remains anatomically plausible across styles and viewpoints.

[0310] In some implementations or examples, the symbolic multidimensional matrix may support predictive or generative evolution of a scene state across time-indexed layers. For example, responsive to a current matrix state and one or more constraints (e.g., kinematic constraints, assembly constraints, physical plausibility constraints, drafting constraints, or user intent), an LLM and / or LMM may be configured to generate one or more candidate next-state updates to the matrix, including changes to SEMAD assignments, pose metadata, relationship descriptors, and / or constraint expressions.

[0311] In some examples, the system may generate intermediate matrix states between an initial time index and a target time index to support animation, simulation, and temporally coherent rendering. For example, a sequence of time-indexed deltas may be produced such that each delta represents a bounded update (e.g., limited joint rotation, incremental translation, incremental assembly step, or incremental deformation) that respects one or more constraint bundles stored in the matrix. In some implementations, intermediate states may be selected by optimizing a cost function that penalizes constraint violations, discontinuities in identity persistence, and / or implausible transitions.

[0312] In some examples, the system may maintain multiple competing predicted transition hypotheses, each with confidence and provenance metadata. For example, the matrix may store alternative future actions (e.g., “fastener engages.” “panel slides,” “hinge rotates,” “component detaches”) as hypothesis-layer entries at future time indices, and a coherence evaluator may prune predicted trajectories that yield collisions, broken attachments, invalid kinematics, or drafting-inconsistent outcomes. In some examples, predicted transitions may be conditioned on textual instructions (e.g., assembly instructions, repair steps, or user intent) such that language provides a high-level transition plan while the matrix enforces structural feasibility at each intermediate step.Docket Number: SEMADS0001PCT

[0313] In some examples, temporal scene modeling may be used in connection with patent drawing generation by producing sequences of figures that depict motion, assembly progression, or operational states from a shared symbolic substrate. For example, the system may generate a sequence of illustrations showing a part moving between positions, a fastener being engaged, or a cover transitioning from closed to open, with SEMAD identifiers and callouts remaining consistent across the sequence. In some examples, time-indexed constraints may ensure that each depicted state is structurally valid and that relationships and part identities remain consistent, thereby improving clarity and consistency of multi-figure patent disclosures that illustrate operation over time.

[0314] Neuro-lnspired Visual Processing and Efficiency

[0315] In some examples, techniques described herein may be directed to improving efficiency of scene understanding by drawing on biological principles of visual perception, including selective attention, foveation, and structure-first interpretation. In some examples, computational efficiency may be achieved by prioritizing construction and refinement of a symbolic scene substrate (e.g., an SMM) and by allocating compute preferentially to regions and hypotheses that are most relevant to a task objective or most uncertain.

[0316] In some examples, symbolic scene construction may be informed by biological models of visual perception in which early processing emphasizes structure (e.g., boundaries, edges, and relational cues) and later processing refines semantic assignments and fine detail. For example, the system may first generate a sparse structural scaffold (e.g., edges, contours, keypoints, adjacency candidates) and map the scaffold into preliminary SEMAD hypotheses within the matrix, and may subsequently refine poses, interfaces, and relationship descriptors as additional evidence is gathered. In some examples, this staged construction may reduce sensitivity to texture noise and lighting variation by relying initially on shape and relational cues.

[0317] In some examples, attention and foveation may guide symbolic resolution by controlling where and at what level of detail the matrix is populated or refined. For example, an LMM and / or LLM may generate attention weights over image regions and / or matrix slices, and the system may allocate higher-resolution matrix refinement to high-attention regions while maintaining coarse representations elsewhere. In some examples, foveation may be implemented by dynamically increasing spatial resolution, instantiating nested submatrices, and / or expandingDocket Number: SEMADS0001PCT

[0318] hierarchical SEMAD into constituent SEMADs only for regions that exceed one or more attention thresholds or ambiguity thresholds.

[0319] In some examples, edge-first processing may reduce noise by emphasizing stable geometric cues prior to semantic commitment. For example, the system may extract edges, silhouettes, and centerlines and use them to propose candidate part boundaries, interfaces, and skeleton graphs that seed SEMAD hypotheses, rather than relying solely on dense pixel classification. In some examples, the extracted structural cues may be used to constrain subsequent recognition (e.g., requiring that a fastener axis align with a detected line feature or that a mating face correspond to a contour boundary), thereby reducing false positives and increasing coherence.

[0320] In some examples, attention may focus computation on regions of interest by triggering additional recognition passes, targeted feature extraction, additional matrix traversal, or higher-detail constraint checking only when needed. For example, if a region contains competing SEMAD hypotheses above a threshold count, confidence below a threshold, or inconsistent relationship descriptors, the system may allocate additional compute to that region, while suppressing computation for regions that are stable and constraint-consistent. In some examples, the system may maintain an attention map over the matrix itself (e.g., over spatial tiles, hierarchy levels, and hypothesis layers) and may use the attention map to schedule incremental updates and to limit recomputation to affected dependencies.

[0321] In some examples, the foregoing neuro-inspired prioritization may be combined with learning-based policies that improve efficiency over time. For example, the system may learn that certain structural configurations are high-yield indicators for downstream tasks (e.g., fastener regions for assembly inference, joint regions for kinematic motion) and may bias attention and refinement toward those regions in future processing. In some examples, such learned policies may be represented as parameters of an LMM, or as rule sets derived from an LLM, while the SMM remains the canonical memory substrate that records the resulting symbolic state.

[0322] In some examples, attention-guided symbolic resolution may be applied to an articulated structure such as a human hand by allocating refinement preferentially to joints and fingertips, which may carry high informational content for pose and intent. For example, the system may construct an initial coarse hand scaffold from edges and keypoints, assign a hand-level SEMADDocket Number: SEMADS0001PCT

[0323] hypothesis, and then selectively expand into bone-level SEMADs and joint constraints in high-attention regions where ambiguity exists (e.g., occluded fingers or overlapping digits). In some examples, foveation may increase matrix resolution around a fingertip region to refine phalanx pose estimates while keeping the palm region at a coarse level, and the resulting refined skeletal graph may constrain downstream rendering or generative output to preserve anatomically plausible connectivity with reduced compute.

[0324] In some examples, neuro-inspired efficiency techniques may be used in connection with patent drawing generation by prioritizing symbolic refinement of edges, interfaces, and calloutrelevant structures that contribute most to drafting clarity. For example, the system may perform edge-first processing to build a clean structural scaffold for line art and may apply attention-guided refinement to regions containing fasteners, interface features, or functional joints that must be accurately depicted across multiple figures. In some examples, by limiting high-resolution processing to such high-value regions while maintaining a canonical symbolic substrate, the system may reduce computational cost while still producing structurally consistent multi- view patent drawings.

[0325] In some implementations or examples, the system may represent unknown, partially observed, or abstract objects using placeholder SEMAD identifiers that may be progressively refined. For example, a matrix cell record may store a placeholder SEMAD indicating an “unknown component,” “unknown fastener,” “unknown interface.” or “unknown subassembly.” along with one or more candidate roles, attribute constraints, and relationship constraints inferred from context. In such examples, the placeholder SEMAD may include provenance indicating the source of uncertainty (e.g., occlusion, low resolution, conflicting evidence, incomplete text specification).

[0326] In some examples, progressive refinement may be performed by iteratively updating placeholder SEMADs based on additional evidence, traversal-derived context, and coherence evaluation. For example, as additional views, frames, or user-provided specifications become available, the system may refine an unknown placeholder into a more specific SEMAD identifier, instantiate a parametric geometry binding, and update relationships and constraints accordingly. In some implementations, refinement may preserve identity persistence by maintaining a stable internal identifier while updating the semantic class binding and geometry binding.Docket Number: SEMADS0001PCT

[0327] Referring now to FIG. 6A, an example recognition and symbolic encoding process 600 according to the present disclosure is illustrated. An image input 610, which may include a photograph, scanned drawing, rendered view, or other depiction of a scene, may be supplied to a multimodal recognition module 620. The multimodal recognition module 620 may comprise one or more multimodal or vision-language models configured to perform visual interpretation of the image input 610. including segmentation, feature extraction, object hypothesis generation, spatial abstraction, and / or candidate relationship inference over the depicted scene.

[0328] Within the multimodal recognition module 620, a symbolic discriminator 630 may evaluate one or more candidate symbolic interpretations generated from the image evidence. In some examples, the symbolic discriminator 630 may receive proposed symbolic assignments for objects, parts, regions, attributes, and / or relationships and may apply one or more symbolic criteria including taxonomy compatibility for SEMAD primitives, structural constraints, adjacency / attachment plausibility, cross-view or prior-state consistency, and identifier normalization rules maintained by the system. In some examples, the symbolic discriminator 630 may output acceptance / rej ection decisions, confidence scores, ranked alternatives, and / or symbolic correction signals (e.g., constraint repairs, relationship adjustments, or disambiguation directives). In response, the multimodal recognition module 620 may selectively re-run one or more internal steps (e.g., localized re- segmentation, feature re-extraction, or alternate hypothesis generation) for only the affected regions or structures until a structurally consistent interpretation is obtained.

[0329] Based on evaluation performed by the symbolic discriminator 630, an accepted symbolic representation may be produced as a semantic address (SEMAD) vector and / or a slice of a symbolic scene matrix (SMM), shown at the representation 640. In some examples, the representation 640 may include SEMAD identifiers and associated structured records, such as pose and scale parameters, characterization datasets, attributes, and relational constraints (e.g., adjacency, attachment, permitted degrees of freedom, containment, or ordering constraints). In some examples, the representation 640 may further include confidence values, provenance indicators, and / or multiple competing hypotheses stored as alternative rows, layers, or candidate records within a persistent symbolic scene state. The bracketed row in the block of representation 640 may indicate an example selected or active SEMAD vector within a larger symbolic scene state that may store multiple alternatives, hypotheses, or related scene records. The resultingDocket Number: SEMADS0001PCT

[0330] SEMAD and / or SMM representation 640 may be stored as part of the persistent symbolic scene state and may be further processed, reconciled with symbolic representations derived from other modalities, or used as input to downstream traversal, validation, rendering, and / or generative workflows described elsewhere in the specification.

[0331] Referring now to FIG. 6B, a depiction of a multimodal grounding and reconciliation workflow 650 for constructing a common symbolic scene representation from heterogeneous input modalities is illustrated. As shown, a visual input, such as an image, drawing, or rendered scene 655, is provided to a multimodal machine learning model 670, which performs imagebased interpretation including segmentation, feature extraction, and generation of candidate symbolic representations corresponding to detected objects, regions, or relationships. In parallel, a textual input, such as a written description, specification text, or claim language 660, is provided to a language model 680, which derives one or more symbolic hypotheses corresponding to semantic entities, attributes, or relationships expressed in the text. The symbolic candidates produced by the multimodal model 670 and the language model 680 may be incomplete, ambiguous, or partially inconsistent when considered independently.

[0332] The symbolic candidates from both modalities are provided to a multimodal reconciliation module 690, which iteratively aligns, constrains, and reconciles the symbolic hypotheses. In some examples, the reconciliation module 690 matches symbols across modalities, resolves conflicts, enforces known constraints, propagates relationships, and prunes inconsistent interpretations, exchanging symbolic information between modalities until one or more convergence criteria are met. The output of the reconciliation process is a unified symbolic representation 695, such as a semantic address (SEMAD) vector or a slice of a symbolic scene matrix (SMM), representing a common symbolic scene state agreed upon by both visual and textual interpretations. The unified symbolic representation 695 may include selected symbols, relationships, and confidence-weighted alternatives, and may be stored, traversed, or further processed by downstream components, including validation, rendering, regeneration, or training workflows as described with respect to other figures.

[0333] Referring now to FIG. 7, an illustration is provided of how the system may manage unknown symbolic structures and may grow a library of symbolic definitions over time. In some examples, a candidate semantic address SEMAD 710 may be produced by one or more upstream components, such as recognition, traversal, or reasoning stages, when a structure may beDocket Number: SEMADS0001PCT

[0334] detected that does not confidently match existing symbolic definitions. In some examples, the candidate SEMAD 710 may additionally or alternatively be emitted during symbolic re-parsing and / or structural validation (e.g., when parsing a draft figure or when comparing parsed symbolic structure to a symbolic scene state) when no canonical SEMAD mapping may satisfy one or more constraints for the detected structure. The candidate SEMAD 710 may include one or more placeholder or unknown fields (e.g., an unknown primitive code, an unknown constraint template, an unresolved relationship type, an unresolved degree-of-freedom parameter, and / or an unresolved attribute or characterization value) and may be treated as an “unknown” or partially specified symbolic entity within the symbolic multidimensional matrix (SMM).

[0335] The candidate SEMAD 710 may first be provided to a primary membership or classification test 720. The membership test 720 may compare the candidate against a library or registry of existing SEMAD classes, for example by evaluating similarity of primitive codes, attribute signatures, relationship patterns, and / or taxonomic proximity relative to known entries (schematically indicated by the symbols within the representation test 720). In some examples, the membership test 720 may compute one or more scores including (i) constraint satisfaction of the candidate against class templates, (ii) graph similarity between a candidate adjacency / attachment subgraph and stored class relationship patterns, (iii) similarity of attribute signatures or characterization vectors, and / or (iv) primitive-code and synonym-table matching. In some examples, the membership test 720 may select a best matching class when one or more scores exceed a threshold and when constraint compatibility is satisfied. When the candidate may be mapped to an existing class — such as via synonym resolution, shape-family matching, relationship-pattern matching, and / or constraint compatibility — the system may normalize the candidate to a canonical SEMAD definition 730.

[0336] In some examples, canonicalization 730 may include rewriting the candidate identifier to reference a registry-approved primitive, filling in missing characterization fields, selecting, or binding a class- specific constraint template, and updating SMM cell records that referenced the candidate to instead reference the canonical SEMAD entry, thereby maintaining a consistent part taxonomy. In some examples, canonicalization 730 may preserve provenance metadata indicating that the mapping was inferred automatically, inferred with high confidence, or confirmed by an extended process, and such provenance may be stored with the SEMAD record and / or the SMM cell record for later audit, validation, or debugging.Docket Number: SEMADS0001PCT

[0337] When the membership test 720 may fail to identify a suitable match (e.g., because similarity scores may fall below a threshold, constraints may be incompatible, or multiple competing classes may remain ambiguous), the candidate SEMAD 710 may be routed to an extended evaluation or review stage 740. The extended evaluation stage 740 may aggregate heterogeneous evidence, including one or more exemplary images 742, previously stored symbolic hypotheses 744, and optionally explicit user or curator inputs 746, which may be provided through an interactive interface. In some examples, the extended evaluation stage 740 may be performed fully automatically, and in other examples the extended evaluation stage 740 may incorporate user or curator inputs as a validation gate for high-impact updates such as creation of new SEMAD classes. In some examples, the extended stage 740 may perform additional checks such as cross-modal consistency tests, registry lookups maintained by the system, taxonomy or ontology queries maintained by the system, and / or rule-based checks to determine whether the candidate may be reconciled with an existing SEMAD class despite the initial failure at the representation test 720.

[0338] If, based on the extended evaluation stage 740, the candidate SEMAD may be determined to correspond to an existing class, the system may output a resolved canonical representation 750. In some examples, the resolved representation 750 may associate the candidate with a canonical SEMAD identifier while preserving provenance information indicating that the mapping was approved via extended evaluation. The SMM and associated data structures may then be updated to reference the canonical identifier, thereby ensuring that future traversals, validations, and renderings may treat the entity as an instance of the resolved class, and that subsequent candidate figures may reuse the same canonical constraints, relationship templates, and identifier normalizations.

[0339] When extended evaluation stage 740 may still fail to map the candidate SEMAD to any existing class, the system may initiate a new class creation operation 760. The new class creation operation 760 may propose a new SEMAD primitive or schema entry and may generate one or more associated definition artifacts, such as default geometry bindings, attribute schemas, characterization-field definitions, and constraint templates for the new class, optionally subject to registry or curator approval. In some examples, new class creation operation 760 may also define one or more normalization rules and synonym mappings so that future observations may be mapped to the newly created class with reduced ambiguity. Upon acceptance, the new SEMADDocket Number: SEMADS0001PCT

[0340] class may be inserted into a SEMAD library or registry 770, represented schematically as an expanded set of symbolic entries including a newly added entry corresponding to the candidate. In this way, the SEMAD library or registry 770 may grow over time, and subsequent recognition, traversal, parsing, or reasoning passes may normalize similar future observations directly to the newly created class without repeating the extended evaluation process.

[0341] In some examples, the updated library or registry 770 may be used across recognition, reasoning, validation, and rendering workflows as a shared source of canonical definitions, such that the same SEMAD entries and associated constraints may be reused for part identification, constraint evaluation, targeted regeneration, and generative conditioning in downstream drawing, animation, simulation, or analysis pipelines. In some examples, normalization to canonical SEMAD definitions and growth of the registry 770 may improve downstream drawing generation by stabilizing cross-view identity persistence, preserving consistent reference numeral assignments, enabling reuse of class- specific drafting-rule and constraint templates during validation, and reducing repeated ambiguity resolution for recurring structures.

[0342] In some examples, the refined symbolic scene matrix and associated SEMAD structures produced as described herewith provide a canonical, machine-searchable representation of a visual scene or a generated illustration. Rather than searching using pixel-level similarity or free-text keywords alone, the system may derive one or more normalized descriptors from the SMM and SEMADs, such as an ordered list of SEMAD tokens, a graph of feature-to-feature relationships, a constraint field summary, or a compact hash of a serialized symbolic scene state. Because these descriptors are derived from stable structural properties of the depicted assembly or layout, they may remain consistent across stylistic variations, viewing angle changes, lineweight differences, or drafting conventions, thereby improving retrieval of identical or semantically equivalent outputs.

[0343] In some examples, the system uses the normalized descriptors as query keys to search characterized databases, including distributed collections such as the world wide web, as well as manufacturer-maintained repositories such as parts supplier catalogs, CAD libraries, or product documentation systems. For example, a SEMAD graph that encodes a fastener pattern, a hole spacing arrangement, and attachment relationships can be used to identify matching subassemblies across different suppliers even when part numbers, product names, or image appearances differ. In such cases, the system may perform exact-match retrieval using aDocket Number: SEMADS0001PCT

[0344] canonical hash, near-match retrieval using edit distance over SEMAD sequences, or subgraph matching to find partial correspondences, and may return candidate matches together with a confidence score and an explanation trace indicating which SEMAD primitives, dimensions, and relationships caused the match.

[0345] In further examples, technical drawings, including patent drawings, may be treated as especially suitable search targets and search keys because they intentionally depict functional structure in a normalized manner and often omit non-essential surface texture or photographic noise. In some examples, the system generates, from a patent-style illustration, an SMM and SEMAD representation that captures feature topology, attachment semantics, and callout structure, and then uses that representation to search for common illustrations in an art field, such as prior patent publications, technical manuals, standards documents, product datasheets, and archived engineering drawings. By using structure-derived descriptors, the system may locate drawings that depict the same mechanism or a closely related arrangement even when the drawings differ in viewpoint, reference numeral scheme, line styling, or drafting conventions, thereby supporting identification of related art, common design motifs, or re-used subassemblies across a body of technical documentation.

[0346] In some examples, these search capabilities further enable reuse and acceleration of illustration generation workflows. When a match or near-match is found, the system may import associated metadata such as manufacturer part identifiers, tolerances, material properties, compatible fastener specifications, or previously validated drawing callouts, and may use that metadata to populate labels, reference numerals, and drafting notes in a regenerated illustration. Additionally, the system may store newly generated SMM and SEMAD structures back into a local corpus as characterized entries, enabling subsequent projects to retrieve prior outputs by structure rather than by file name or manual tagging, and thereby reducing redundant drafting and improving consistency across a family of patent drawings, CAD exports, and technical documentation outputs.

[0347] In some embodiments, SEMAD and SMM based patterns learned during multimodal model processing may further support predictive generation of simulation outputs without performing a full physical model calculation for each query. For example, by training on corpora of prior simulations indexed by boundary condition descriptors encoded as SEMAD tokens and constraint fields within an SMM, the system may learn mappings from classes of boundaryDocket Number: SEMADS0001PCT

[0348] conditions, geometry configurations, and interaction rules to expected qualitative or quantitative outcomes, such as deformation modes, flow directionality, contact regions, or motion trajectories. In these examples, the system may generate an approximate simulation output, such as a predicted state sequence, deformation field, or response curve, by retrieving and adapting previously characterized patterns that are structurally similar in SEMAD and SMM space, optionally with lightweight correction steps or constraint enforcement to maintain consistency with the specified boundary conditions. Accordingly, for certain classes of problems, the SMM and SEMAD representation functions as a surrogate state description that enables synthesis of plausible simulation behavior from learned symbolic correspondences, reducing computation cost while preserving traceable linkage between the specified boundary conditions and the generated simulation output.

[0349] Extensible and Future-Proof Variations

[0350] In some examples, techniques described herein may be implemented in non-limiting embodiments that support extension to additional domains while preserving a core architecture based on a symbolic multidimensional matrix (e.g., an SMM), semantic addressing (SEMAD), and matrix-driven reasoning, traversal, and rendering. In some examples, extensibility may be achieved by defining new SEMAD identifiers, new nominal geometry bindings, new relationship descriptor schemas, and / or new constraint bundles, while maintaining a stable matrix substrate, traversal operators, coherence evaluation, and update mechanisms.

[0351] In some examples, the systems and methods may be extended to support additional object types or domains by adding new SEMAD libraries corresponding to domain- specific ontologies. For example, a mechanical-domain SEMAD library may be extended with electrical components, fluid components, biomedical structures, architectural elements, or manufacturing fixtures, each with associated roles, attribute schemas, and expected relationships. In some examples, new SEMAD entries may include bindings to one or more geometry sources (e.g., parameterized primitives, CAD templates, symbol glyphs) and to one or more constraint bundles (e.g., connectivity rules, clearance rules, joint rules), thereby enabling new classes of scenes to be represented and reasoned over without altering the core matrix formalism.

[0352] In some examples, abstract or conceptual scenes may be represented symbolically by mapping non-physical entities to SEMAD identifiers and representing relationships as typed constraints and directed links rather than as literal geometry. For example, a conceptualDocket Number: SEMADS0001PCT

[0353] workflow, a network topology, a causal diagram, or a software architecture may be represented in the matrix using symbolic entities and relationship descriptors (e.g., dependency, containment, influence, data flow), and the system may generate outputs such as diagrams, maps, or explanatory illustrations from the same matrix-driven pipeline. In some examples, “spatial” axes of the matrix may encode conceptual proximity, layout constraints, or graph embedding coordinates rather than physical coordinates.

[0354] In some examples, the framework may be applied to architecture, construction, or scientific visualization by representing domain-relevant structures as matrix-addressable symbolic entities with persistent identifiers and constraints. For example, architectural scenes may include floors, walls, doors, beams, HVAC elements, and fixtures, while scientific scenes may include molecules, organ systems, experimental apparatus, or simulated fields. In some examples, time-indexed layers may represent construction phases, experimental steps, or simulation evolution, and view-indexed layers may represent plan views, sections, elevations, or visualization perspectives, all referencing shared canonical entities and relationships.

[0355] In some examples, the core architecture may remain unchanged while supporting new domains by maintaining a stable set of operations that act over matrix contents rather than domain-specific code paths. For example, multidirectional traversal, aggregation of traversal results, LLM / LMM coherence evaluation, hypothesis selection, constraint propagation, and structure-driven rendering may operate over cell records and relationship descriptors using schemas that are extensible through typed fields. In some examples, domain-specific behavior may be introduced through configuration (e.g., new constraint bundles, new traversal rules, new validation predicates) associated with SEMAD libraries rather than through modifications to the underlying substrate.

[0356] In some examples, new SEMAD libraries may be added without altering the core system by registering new identifiers, attribute schemas, relationship types, and validators. For example, a library registration process may provide (i) a set of SEMAD identifiers, (ii) nominal geometry bindings or symbol glyph bindings, (iii) default relationship templates and constraints, (iv) optional serialization rules for LLM consumption, and (v) optional rendering directives. In some examples, once registered, the new library may be immediately usable for recognition, reasoning, and rendering, including hypothesis assignment, traversal-based context retrieval, coherence evaluation, and generation of outputs constrained by the newly introduced domain semantics.Docket Number: SEMADS0001PCT

[0357] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include,” “including,” and “includes” mean including but not limited to.

[0358] The phrases “at least one.” “one or more,” and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A. B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.

[0359] The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted the terms “comprising,” “including,” and “having” can be used interchangeably.

[0360] Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in combination in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

[0361] As has been mentioned, the illustrations depict aspects of exemplary embodiments, and the relative scale of illustrated features may be exaggerated for depiction of various aspects. Accordingly, the scale of features illustrated is not intended to limit the scope of the elements of the disclosure.

Claims

Docket Number: SEMADS0001PCTCLAIMSWhat is claimed is:

1. A computer- implemented method of generating a drawing, comprising:receiving, by one or more processors, an input comprising at least one of (i) a multimodal scene input or (ii) a user-supplied scene description;generating or updating a symbolic matrix model (SMM) representing at least a portion of the scene, the SMM comprising a plurality of cells each storing one or more semantic addresses (SEMADs) defining objects, parts, or regions of the scene; generating, from the SMM, a drawing representation;applying, to the SMM, a symbolic structural validator configured to evaluate one or more candidate symbolic interpretations of the scene as symbolic structures derived from SEMADs stored in the SMM according to one or more structural constraints represented in the SMM;updating the SMM based on an output of the symbolic structural validator; and generating an updated drawing representation based on the updated SMM; wherein generating the drawing representation comprises rendering at least a portion of the scene based on the SMM, including generating one or more view directives or drafting directives from the SMM and parameterizing the rendering using the view directives or drafting directives;wherein updating the SMM comprises applying one or more bounded symbolic edit actions produced by the symbolic structural validator to identified SEMAD records or relationships; andwherein generating the updated drawing representation comprises selectively regenerating only a subset of the drawing representation corresponding to SEMADs implicated by the bounded symbolic edit actions, thereby reducing computational processing and memory bandwidth relative to regenerating an entire drawing representation from scratch while improving structural consistency of the drawing representation under constraints encoded in the SMM.Docket Number: SEMADS0001PCT2. The method of claim 1 , wherein generating the drawing representation comprises generating a wireframe representation based on the SMM and generating a rendered drawing based on the wireframe representation, wherein the rendered drawing is constrained to conform to the wireframe representation.

3. The method of claim 1, further comprising generating a plurality of patent- application figures from the SEMADs, including linework, reference numerals, and leader lines, enforcing one or more drawing compliance rules including leader-routing or exclusion-zone constraints during rendering, and maintaining consistent reference numerals for corresponding elements across the plurality of figures.

4. The method of claim 1, wherein the SMM stores relationships between SEMADs including one or more of attachment, adjacency, containment, alignment, constraint, or correspondence.

5. The method of claim 1, further comprising canonicalizing the SMM by resolving duplicate SEMAD assignments, normalizing reference frames, or rewriting synonymous symbolic assignments.

6. The method of claim 1, wherein the SMM is sparse such that unpopulated cells represent absence of an object, part, region, or relation.

7. The method of claim 1, wherein the symbolic structural validator produces a ranked set of symbolic edits, and updating the SMM comprises applying at least one symbolic edit selected from the ranked set.

8. The method of claim 1, wherein selectively regenerating only the subset of the drawing representation comprises restricting regeneration to a view region, a layer, or a set of drawing primitives that correspond to the SEMADs implicated by the bounded symbolic edit actions.

9. A computer-implemented method of operating a language model with a symbolic scene representation, the method comprising:storing a symbolic scene state in a symbolic multidimensional matrix (SMM) comprising a plurality of cells that store SEMAD identifiers and structured records describing at least objects, relationships, constraints, and confidence measures;Docket Number: SEMADS0001PCTperforming multidirectional traversal of the SMM according to one or more traversal policies that select cells based on at least one of spatial adjacency, hierarchy, semantic grouping, view index, time index, or a hypothesis layer;producing, from the traversal, a token-efficient serialization of selected cell records for input to a language model;using the language model to determine one or more symbolic update actions based on the token-efficient serialization; andwriting the one or more symbolic update actions back into the SMM to update the symbolic scene state for subsequent traversal and downstream rendering or analysis.

10. The method of claim 9, wherein producing the token-efficient serialization comprises compressing empty or default cells using run-length encoding and emitting an adjacency list over detected entities.

11. The method of claim 9, wherein producing the token-efficient serialization comprises generating a multi-resolution serialization that provides a coarse summary first and conditionally appends finer detail responsive to uncertainty or attention.

12. The method of claim 9, wherein the traversal policies include traversal along a kinematic chain using joint descriptors stored in the structured records to generate a deterministic skeletal or constraint graph.

13. The method of claim 9, wherein the SMM includes a hypothesis layer that stores multiple competing interpretations for at least one region, and the symbolic update actions select among the competing interpretations.

14. The method of claim 9, further comprising aggregating results from multiple traversal passes by computing a combined score that favors interpretations that are mutually consistent and satisfy stored constraints.

15. The method of claim 14, wherein aggregating further comprises outlier suppression that demotes or removes an interpretation that violates constraints or conflicts with other traversal passes, and storing an audit record identifying which traversal passes contributed to a consolidated assertion.Docket Number: SEMADS0001PCT16. A system for creating patent drawings for a patent application, the system comprising:one or more processors and memory storing instructions that, when executed, cause the system to:receive at least one of a draft drawing, an image, CAD data, or textual description of an invention;generate and maintain a symbolic scene state as a symbolic multidimensional matrix (SMM) storing SEMADs and structured records for parts and relationships; generate a set of patent drawing figures for inclusion in the patent application, the patent drawing figures including at least one of a perspective view, an orthographic view, an exploded view, a section view, or a flow diagram, and including reference numerals and leader lines;apply a symbolic discriminator to evaluate the set of patent drawing figures using at least one rule set for structural and drafting consistency derived from the SEMADs and structured records; andin response to the evaluation, cause a targeted update to the symbolic scene state and regenerate at least one of (i) a leader line placement, (ii) a reference numeral assignment, or (iii) a figure view, to improve compliance and consistency across the set of patent drawing figures.

17. The system of claim 16. wherein the symbolic discriminator evaluates cross-view consistency by verifying that identified parts and relationships remain consistent between a perspective figure and one or more orthographic figures.

18. The system of claim 16. wherein generating the set of patent drawing figures comprises generating view-indexed drafting directives that specify at least one of emphasized edges, occluding components, feature correspondences, or callout attachment locations.

19. The system of claim 16, wherein the symbolic discriminator evaluates compliance with leader-line and reference numeral rules by validating consistency between referenced parts in the SMM and callouts rendered in the patent drawing figures.

20. The system of claim 16, wherein the targeted update to the symbolic scene state is limited to a bounded set of edits comprising at least one of label normalization, attribute completion,Docket Number: SEMADS0001PCTreference disambiguation, or constraint repair, while blocking commitment of disallowed edits that violate stored constraints.

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