4 results about "Symbolic Systems" patented technology

A system and a method for verifying authenticity of a physical copy and a digital copy of a document are disclosed. The method comprises registering a document in a repository by storing details related to the document in a location of the repository. A symbology for the document is generated. The symbology is an identifier of the location of the repository comprising the document. The symbology is associated with either a physical or a digital copy of the document. The digital copy of the document is printed to generate a printed copy. The printed copy or the physical copy of the document is scanned to generate a scanned image. The document and the details related to the document present at the location of the repository are accessed. The scanned image is compared with the document stored in the repository to determine the authenticity of either the physical copy or the digital copy of the document.

The invention discloses an automatic labeling method and system based on a neural symbol combination framework, and belongs to the field of information processing. The method comprises the following steps: S1, inputting multi-modal data, and preprocessing the multi-modal data; s2, respectively extracting features of each mode, and generating a joint embedded vector by adopting a multi-head attention mechanism; s3, logical reasoning is executed based on the domain knowledge base, and interpretable labeling rules are generated; s4, mapping the joint embedded vector to a predicate space of symbol logic for rule matching; s5, detecting whether a matching conflict exists or not based on a logic constraint solving algorithm; if the conflicts exist, manual auditing is triggered, the rule weight is updated based on the Bayesian network, and the steps S3-S5 are executed again; and if no conflict exists, outputting a labeling result. According to the method, collaborative optimization of data driving and knowledge driving is realized by fusing the perception ability of the neural network and the logical reasoning ability of the symbol system.

The invention particularly relates to a multi-level self-cognition system based on a large model, and relates to the technical field of large models. A neural symbol world model module; a large language model cognition core module; and a hierarchical decision planning system module. According to the method, deep integration of perception, cognition and decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-modal perception encoder adopts layered encoding and a cross-modal attention mechanism, so that the semantic alignment problem of multi-source perception data is effectively solved, and the understanding ability of the system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.

A data processing system includes a processor; and a memory in communication with the processor. The memory contains executable instructions that, when executed by the processor alone or in combination with other processors, cause the data processing system to perform functions of: detecting column headers within an unstructured text using a trained classifier; prompting a Large Language Model (LLM) to produce a table sketch based on detected headers and the unstructured text; generating candidate rows for lines of the unstructured text not included in the table sketch using a symbolic system; ranking the candidate rows based on consistency with a consistency ranker; and assembling a final table based on the unstructured text by adding candidate rows based on rank to the table sketch.