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: An automation optimization engine is disclosed that provides a closed loop nodal analysis on a process performance tree. By analyzing an observed process and identifying automatable tasks from non-automatable tasks from the observed process, the automation optimization engine then applies machine learning techniques to generate a recommendation report identifying steps for implementing in the observed process to convert the non-automatable tasks into automatable tasks. Optimization is thus achieved to close the loop to further automate the observed process.

Abstract: An automation optimization engine is disclosed that provides a closed loop nodal analysis on a process performance tree. By analyzing an observed process and identifying automatable tasks from non-automatable tasks from the observed process, the automation optimization engine then applies machine learning techniques to generate a recommendation report identifying steps for implementing in the observed process to convert the non-automatable tasks into automatable tasks. Optimization is thus achieved to close the loop to further automate the observed process.

Abstract: An automation identification diagnostic tool (AIDT) is disclosed that records and analyzes actions within a program that are taken by an agent to accomplish certain tasks. By recording the action steps to accomplish a task in a recording session data file, the AIDT is then able to analyze the recording session data file to identify steps for conversion to automated tasks.

Abstract: An automation optimization engine is disclosed that provides a closed loop nodal analysis on a process performance tree. By analyzing an observed process and identifying automatable tasks from non-automatable tasks from the observed process, the automation optimization engine then applies machine learning techniques to generate a recommendation report identifying steps for implementing in the observed process to convert the non-automatable tasks into automatable tasks. Optimization is thus achieved to close the loop to further automate the observed process.

Abstract: An automation identification diagnostic tool (AIDT) is disclosed that records and analyzes actions within a program that are taken by an agent to accomplish certain tasks. By recording the action steps to accomplish a task in a recording session data file, the AIDT is then able to analyze the recording session data file to identify steps for conversion to automated tasks.