3 results about "Vector space model" patented technology

The application discloses an AI Agent intelligent adaptation skill calculation method. The method comprises the following steps: constructing a three-dimensional unified vector space model of AI Agent capability image, skill demand label and task constraint condition; performing hard rule preliminary screening, filtering the skills that do not meet the strong constraint condition based on resource, time delay, permission dependence and necessity label hit constraint; calculating the discrete label accurate matching score, the Embedding semantic similarity, the capability adaptation weighted score and the quality stability correction score of the skills that pass the preliminary screening, wherein the upper limit of the single capability adaptation ratio is capped at 1; performing weighted summation on the scores of the above four dimensions to obtain a comprehensive matching total score, and scheduling based on the threshold and the total score ranking; and updating the AI Agent proficiency and the historical success rate in reverse after calling is completed. The application improves the accuracy, efficiency and scene adaptability of AI Agent skill adaptation.

The application provides an automatic reading method and system for antibody identification results based on a vector space model, which comprises the following steps: constructing an antibody specificity vector model, mapping the antigen spectrum corresponding to each antibody specificity in an antibody identification pedigree table into an antibody specificity vector with a fixed dimension; mapping the reaction results of antibody identification of a serum sample to be tested into a to-be-tested vector through the antibody specificity vector model; calculating the similarity between the to-be-tested vector and the antibody specificity vector; and performing reading based on reading logic according to the similarity calculation results to obtain antibody identification results. The antibody identification results can be automatically read by calculating the vector similarity, the antibody identification can be realized quickly, accurately and standardly, and the work efficiency is improved significantly, and the manpower is liberated.

A method for automated therapy discovery includes: accessing a corpus of scientific publications; compiling a population of semantic concepts from the corpus of scientific publications into a vector space model; deriving domains of concepts in the vector space model based on proximity to domain descriptors in the vector space model; deriving association scores and action characteristics between connected concepts, based on proximity and action descriptors in the vector space model; generating a semantic network; receiving a query for a target concept and a target domain at a research portal; isolating a set of edges between a target node and a subset of nodes; identifying subsets of concepts along the set of edges; generating hypotheses for directions and magnitudes of effects of subsets of concepts on the target concept based on association scores and action characteristics stored in connections along the set of edges; and returning hypotheses to the research portal.