Patents
Literature
Patsnap Eureka AI that helps you search prior art, draft patents, and assess FTO risks, powered by patent and scientific literature data.

3 results about "Symbolic Systems" patented technology

Symbolic Systems (Sym Sys) is an interdisciplinary academic program at Stanford University focusing on computers and minds, specifically the relationship between natural and artificial systems that represent, process, and act on information. The program aims to prepare majors with the vocabulary, theoretical background, and technical skills necessary to research questions about language, information, and intelligence, both human and machine. Core requirements of the program include courses in symbolic logic, artificial intelligence, mathematics of computation, probability and statistics, programming, cognitive psychology, philosophy of mind, and interdisciplinary approaches to cognitive science.

Method and system for verifying authenticity of a document

ActiveUS20180154676A1Digital data information retrievalPaper-money testing devicesSymbolic SystemsDigital copy
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.
Owner:VERIDOC SYSTEMS LLC +1

Large model-based multi-level ownership cognition 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.
Owner:杭州长望智创科技有限公司

Recognizing tables in unstructured text

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.
Owner:MICROSOFT TECHNOLOGY LICENSING LLC