Abstract: An automatic artwork review system validates an artwork or a product label based on a received label specification document. Text extracted from the product label is chunked into sentences and words. Character-wise comparison is executed to identify the best match text from the label specification document for the sentence chunks from the product label. If the corresponding best match texts bears a similarity higher than a predetermined threshold to selected text including one or more sentence chunks, no errors are raised. If the similarity of the best match text to the selected text is not higher than the predetermined threshold, the specific errors occurring in the selected text and the particular portions where such errors are present are identified. The information regarding the errors can be output via one or more of an output user interface or a label compliance report.

Abstract: An automatic artwork review system validates an artwork or a product label based on a received label specification document. Text extracted from the product label is chunked into sentences and words. Character-wise comparison is executed to identify the best match text from the label specification document for the sentence chunks from the product label. If the corresponding best match texts bears a similarity higher than a predetermined threshold to selected text including one or more sentence chunks, no errors are raised. If the similarity of the best match text to the selected text is not higher than the predetermined threshold, the specific errors occurring in the selected text and the particular portions where such errors are present are identified. The information regarding the errors can be output via one or more of an output user interface or a label compliance report.

Abstract: A claims preprocessor processes claim data to identify claims that are to be adjudicated. Each claim includes at least one claim exception. The claims preprocessor further prioritizes the claim exception of each identified claim based on the claim data. A robotic process automator then orchestrates adjudication of the identified claims based on claim data. Further, a rules engine adjudicates the identified claims based on pre-defined rules. Subsequently, a fall out handler determines if any of the identified claims are incorrectly adjudicated and identify an issue associated with incorrect claims adjudication on determining that any of the identified claims are incorrectly adjudicated. A self learner then provides feedback to rules engine based on a decision tree and information received from fall out handler, the feedback being usable to resolve the issue. The information received from fall out handler is indicative of issue associated with incorrect claims adjudication.