Abstract: A strategic method for process control wherein the method includes, for a predetermined process juncture, the steps of: (A) defining an interconnection cell having associated therewith (i) at least one set of input data or at least one set of process control parameters, and (ii) at least one set of output data; (B) assigning at least one boundary value to at least one set of the sets associated with the defined interconnection cell; (C) using the assigned at least one boundary value, forming a plurality of discrete respective set combinations, and (D) for the interconnection cell, processing data from the plurality of respective formed set combinations into respective corresponding data record clusters. The strategic method for process control is a continuation-in-part of a Knowledge-Engineering Protocol-Suite, (U.S. patent application Ser. No. 09/588,681 filed on 7 Jun.

Abstract: A system for automatic control of a process, comprising a process model using data and further comprising a data model for generating data for said process model and an empirical data extractor for extracting data from said process for said model, and wherein said data used by said process model is interchangeable between data obtained by said data model and data obtained by said extractor. The data model may be a partly statistical partly empirical orthogonal process model. The system is useful in allowing control systems using empirical prediction methods to perform automatic control before having built up a full results database.

Abstract: A Knowledge-Engineering Protocol-Suite is presented that generally includes methods and systems, apparatus for search-space organizational validation, and appurtenances for use therewith. The protocol-suite includes a search-space organizational validation method for synergistically combining knowledge bases of disparate resolution data-sets, such as by actual or simulated integrating of lower resolution expert-experience based model-like templates to higher resolution empirical data-capture dense quantitative search-spaces. Furthermore, from alternative technological vantages, the suite relates to situations where this synergetic combining is beneficially accomplished, such as in control systems, command control systems, command control communications systems, computational apparatus associated with the aforesaid, and to quantitative modeling and measuring tools used therewith.

Abstract: In an automatic decision-making system, a method and a tool for the reduction of the dimension of data mining, which is automatically coupled to an empirical predictor of the system. The method includes a qualitative modeling of the interrelations between various objects whose attributes are relevant to a score made by the predictor according to which decisions are made, wherein this relevancy is determined by an input of a domain expert to the problem in hand. The model is called a Knowledge-Tree and its conclusions are represented by a graphical symbolization called the Knowledge-Tree map. Data mining, which follows the construction of the Knowledge-Tree map regards only datasets which are associated with logical and validated branches of the knowledge tree. Because the expert input which reduces the dimension of data mining was completed prior to data mining, interception by human reasoning is not needed after data mining and the decision making process can proceed automatically.

Abstract: A method of modeling a monitorable stage in a process is provided.

Abstract: A method and system to enhance yield in multi-process manufacturing. The method comprising the translation of a performance parameter of a product into input variables to operate tools carrying our cooperating processes which built a structural element which determines the performance parameter, wherein the individual tools are process controlled. The method further comprising the integration of process control of individual separate processes or of stages in a process, into a combined and comprehensive (modular) process control in which the process parameters of a process are enslaved to accomplish the target output of the final process. Hence target output values of intermediate processes are dynamically reassigned during the manufacturing with respect to their initially designed values, in accordance with the output of their cooperating processes.

Abstract: A system for automatic control of a process, comprising a process model using data and further comprising a data model for generating data for said process model and an empirical data extractor for extracting data from said process for said model, and wherein said data used by said process model is interchangeable between data obtained by said data model and data obtained by said extractor. The data model may be a partly statistical partly empirical orthogonal process model. The system is useful in allowing control systems using empirical prediction methods to perform automatic control before having built up a full results database.

Abstract: A method and system to enhance yield in multi-process manufacturing. The method comprising the translation of a performance parameter of a product into input variables to operate tools carrying our cooperating processes which built a structural element which determines the performance parameter, wherein the individual tools are process controlled. The method further comprising the integration of process control of individual separate processes or of stages in a process, into a combined and comprehensive (modular) process control in which the process parameters of a process are enslaved to accomplish the target output of the final process. Hence target output values of intermediate processes are dynamically reassigned during the manufacturing with respect to their initially designed values, in accordance with the output of their cooperating processes.

Abstract: Apparatus and associated method for constructing a quantifiable model, comprising: an object definer for converting user input into at least one cell having inputs and outputs, a relationship definer for converting user input into relationships associated with said cells such that each said relationships is associatable with said cells via one of said inputs and outputs, a quantifier for analyzing a data set to be modeled to assign quantitative values to said relationships and to associate said quantitative values with said associated inputs and outputs, thereby to generate a quantitative model. The model is useful in automatic decision-making and process control and for process simulation and study. The model building methodology provides for structured and quantity reduced investigation of process data since a qualitative model is used to guide the data analysis. The methodology also allows for obtaining new information regarding such a process through the resulting quantitative model.

Abstract: In an automatic decision-making system, a method and a tool for the reduction of the dimension of data mining, which is automatically coupled to an empirical predictor of the system. The method includes a qualitative modeling of the interrelations between various objects whose attributes are relevant to a score made by the predictor according to which decisions are made, wherein this relevancy is determined by an input of a domain expert to the problem in hand. The model is called a Knowledge-Tree and its conclusions are represented by a graphical symbolization called the Knowledge-Tree map. Data mining, which follows the construction of the Knowledge-Tree map regards only datasets which are associated with logical and validated branches of the knowledge tree. Because the expert input which reduces the dimension of data mining was completed prior to data mining, interception by human reasoning is not needed after data mining and the decision making process can proceed automatically.