DETAILED ACTION
This action is responsive to the preliminary amendment filed 05/15/2024.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-3 and 5 are objected to for minor informalities.
Claims 1-5 are rejected under 35 U.S.C. 112(b).
Claims 1-5 are rejected under 35 U.S.C. 101.
Claims 1-5 are rejected under 35 U.S.C. 102(a)(1).
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Recipe Maker and Optimizer using Updatable Models
Claim Objections
Claims 1-3 and 5 are objected to because of the following informalities:
In claim 1, “PLC” should appear is parenthesis after the full term is recited in its first occurrence in the claim. Acronyms should generally be defined in the claim before they are used elsewhere in the claim.
In claim 1, “Process Recipes” and “Recipe Maker” should not be capitalized.
In claim 1, “’variable to calculate’” should be rephrased to remove the apostrophes.
In claim 2, “The software tool from claim 1 also has” should read “The software tool of claim 1, further comprising”.
In claim 3, “A methodology within the software tool’s recipe maker from claim 1 that” should read “The software tool of claim 1, wherein the recipe maker module further comprises a feature that”. At the very least, “recipe maker” should recite “recipe maker module” for proper antecedent basis with claim 1.
In claim 5, “CV” and “PID” should appear is parenthesis after the full terms are recited in each of their first occurrences in the claim.
Appropriate correction equivalent to or in line with the examiner’s suggestions is required.
Drawings
The drawings are objected to because Figures 2 and 5-15 are unclear and difficult to read. Higher quality black and white versions of the drawings should be submitted.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-5 are rejected as failing to define the invention in the manner required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
All the claims are narrative in form and replete with indefinite language. The structure which goes to make up the device must be clearly and positively specified. The structure must be organized and correlated in such a manner as to present a complete operative device. The claims must be in one sentence form only. Note the format of the claims in the patent(s) cited.
Applicant should amend the claims to more clearly recite what applicant regards as the invention. Consider the following re-draft of claim 1, which the examiner is assuming is the intent of the applicant based on the specification and the current form of claim 1.
Note: This draft is presented for exemplary purposes of clear claim language in an effort for compact prosecution and is not meant to indicate what the examiner considers the broadest reasonable interpretation of the current claim language of claim 1. It also does not consider issues under 35 U.S.C. 101, 102, or 103. Furthermore, official amendments to the claims should not recite any new matter. See MPEP 608.04 and 2163.06.
1. A software tool for real-time industrial or laboratory process management comprising a recipe maker module which performs a method comprising:
interfacing with a programmable logic controller (PLC);
collecting data, the data including historical data, from the PLC;
determining trends in the historical data;
crafting process recipes from the determined trends in the historical data;
employing regression, machine learning, and/or vector analysis to develop one or more models, wherein the one or more models determine relationships between a plurality of variables in the process recipes;
receiving a user selection of a variable of the plurality of variables, the selected variable having a setpoint to be calculated to meet targeted outcomes of other variables of the plurality of variables; and
adjusting the setpoint for the selected variable based on the targeted outcomes of the other variables of the plurality of variables.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “advanced” in claim 1 is a relative or subjective term which renders the claim indefinite. The term “advanced” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear from the claim and from the specification what would be considered an “advanced” algorithm, and whether a person of ordinary skill in the art considers an algorithm to be advanced would be subjective and subject to change with continued advancements in technology. It is therefore unclear what algorithms are encompassed by the term “advanced algorithms”. See MPEP 2173.05(b).IV.
Claim 1 further recites “a user selectable ’variable to calculate’ for targeted outcomes”. There is insufficient antecedent basis for this limitation in the claim. It is unclear what the “targeted outcomes” are of. The examiner is assuming the term is referring to targeted outcomes of the user selectable variable; however, the term can also be construed to refer to targeted outcomes of the “industrial or laboratory process”. The wording of the claim limitation as a whole creates multiple disparate interpretations that render the claim ambiguous.
Claims 1-5 as a whole should be amended to clearly recite what the applicant regards as the invention, including actively reciting the relationships between the elements of the claims, which would avoid antecedent basis issues. See MPEP 2173.05(e).
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “accurately” in claim 2 is a relative term which renders the claim indefinite. The term “accurately” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear from the claim and from the specification what would be considered an “accurate” computation. See MPEP 2173.05(b).I. and 2173.05(b).IV.
Claim 2 further recites “intended outcomes”, “actual results”, and “related variables”. There is insufficient antecedent basis for this limitation in the claim. It is unclear what the “outcomes” and “results” are of. The examiner is assuming these elements are referring to the intended outcomes and the actual results of the “industrial or laboratory process”. It is unclear to what the “related variables” are related to. The examiner is assuming the “related variables” are related to the “designated dependent variable”. See MPEP 2173.05(e).
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “enhancing” in claim 3 is a relative term which renders the claim indefinite. The term “enhancing” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear from the claim and from the specification what quantity or metric of the predictions is being “enhanced” and it is further unclear what degree of change in the predictions would be considered an enhancement. See MPEP 2173.05(b).I. and 2173.05(b).IV.
from the claim terms what structure or steps are encompassed by the claim.
Claim 3 further recites “enhancing predictions based on recent trends and patterns”. There is insufficient antecedent basis for this limitation in the claim. It is unclear what element is being predicted in the claim. It is therefore unclear what predictions are being enhanced with the “recent trends and patterns”. It is further unclear what the “recent trends” and the “patterns” are referring to; however, the examiner is assuming they are trends and patterns of the “fresh datasets”. See MPEP 2173.05(e).
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites “A method for enhancing Proportional-Integral-Derivative (PID) loop performance through predictive modeling, which involves predicting a Control Variable (CV) output to achieve a new Process Variable (PV) setpoint upon adjustment.” The claim recites using “predictive modeling, which involves predicting a Control Variable (CV) output to achieve a new Process Variable (PV) setpoint upon adjustment” to enhance PID loop performance. The claim is therefore a “use” claim, which fails to set forth any steps involved in “enhancing Proportional-Integral-Derivative (PID) loop performance” as it merely recites doing so by using predictive modeling and then defines what predictive modeling is. The attempt to claim a process without setting forth any steps involved in the process therefore renders the claim indefinite. See MPEP 2173.05(q). The same analysis applies to “uses the prediction as an advanced baseline for immediate control response, thus improving the speed and accuracy of achieving desired PV setpoints”, which is also indefinite given the guidance in MPEP 2173.05(q).
The claim further recites “This method applies the predicted CV output to reach the PV setpoint more efficiently than conventional PID loop adjustments and uses the prediction as an advanced baseline for immediate control response, thus improving the speed and accuracy of achieving desired PV setpoints.” This is functional language that, in the context of the claim, renders the claim indefinite. See MPEP 2173.05(g), a relevant excerpt of which is reproduced below:
Examiners should consider the following factors when examining claims that contain functional language to determine whether the language is ambiguous: (1) whether there is a clear cut indication of the scope of the subject matter covered by the claim; (2) whether the language sets forth well-defined boundaries of the invention or only states a problem solved or a result obtained; and (3) whether one of ordinary skill in the art would know from the claim terms what structure or steps are encompassed by the claim. These factors are examples of points to be considered when determining whether language is ambiguous and are not intended to be all inclusive or limiting. Other factors may be more relevant for particular arts. The primary inquiry is whether the language leaves room for ambiguity or whether the boundaries are clear and precise.
The limitation from claim 4 above (underline added for emphasis) is ambiguous because 1) it is not clear what the scope of the claim is as it lacks any structure or method steps for accomplishing the stated goal, 2) it merely states the problem being solved, i.e. “more efficient” PID loop adjustments or “improving the speed and accuracy of achieving desired PV setpoints”, and 3) a person of ordinary skill in the art would not know from the claim terms what structure or steps are encompassed by the claim.
Claim 4 further recites “upon adjustment”. There is insufficient antecedent basis for this limitation in the claim. It is unclear what element is being adjusted in the claim. The examiner is assuming a CV output is adjusted to a predicted CV output in order to meet a new PV setpoint that enhances the PID performance (i.e. reduces error between the system output and the desired setpoint, as a person of ordinary skill in the art would construe). See MPEP 2173.05(e).
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites “This method identifies discrepancies, to flag potential abnormal equipment or process performance and employs the analysis for preventative maintenance and operational adjustments, preventing minor issues from escalating into significant disruptions.” This is functional language that, in the context of the claim, renders the claim indefinite. See MPEP 2173.05(g).
The limitation above (underline added for emphasis) is ambiguous because 1) it is not clear what the scope of the claim is as it lacks any structure or method steps for accomplishing the stated goal other than the potential abnormal equipment or process performance being flagged (i.e. it is unclear how or under what conditions the equipment or performance is flagged), 2) it merely states the problem being solved, i.e. “for preventative maintenance and operational adjustments”, “preventing minor issues from escalating into significant disruptions”, and 3) a person of ordinary skill in the art would not know from the claim terms what structure or steps are encompassed by the claim.
The limitation “employs the analysis for preventative maintenance and operational adjustments, preventing minor issues from escalating into significant disruptions” could also be considered a “use” limitation. The attempt to claim a process of preventive maintenance using “the analysis” without setting forth any steps involved in the process renders the claim indefinite. MPEP 2173.05(q)
Claim 5 further recites the limitations "each recipe’s dependent variable" and “each CV’s performance”. There is insufficient antecedent basis for this limitation in the claim. Since claim 5 is an independent claim, it is unclear to what “each recipe’s dependent variable” and “each CV’s performance” is referring. There is no previous recitation of multiple recipes, the recipes each having a dependent variable, and there is no previous recitation of multiple CVs (assumed to mean Control Variables), the CVs each having a performance. See MPEP 2173.05(e).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to software per se. See MPEP 2106.03.I. Claim 1 recites “a software tool for real-time industrial or laboratory process management” without reciting any structural limitations. Products that do not have a physical or tangible form, and computer programs per se in this case in particular, are not considered among the four statutory subject matter categories for patent eligibility. Claims 2-3 are rejected due to their dependencies on claim 1.
Claims 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Broadest Reasonable Interpretation of the Claims
The broadest reasonable interpretation of claim 1 is a software tool that interfaces with a programmable logic controller, collects and trends data, uses algorithms to craft process recipes from historical data, and features a module that employs regression, machine learning, and/or vector analysis to develop models, determine variable relationships, and adjust setpoints based on a user selectable targeted outcome for a variable. The tool being for an industrial or laboratory environment is an intended use that does not carry significant weight. See the 35 U.S.C. 112(b) rejections of claim 1 regarding the term “advanced”, which does not limit what algorithms are used.
The BRI of claim 2 is a modification of claim 1 to include an error-checking mechanism that compares the intended outcomes with actual results and uses an error adjustment method on a designated dependent variable by adjusting input variables. This method generally describes closed-loop feedback, a very well-known method in the area of control theory and control system design. See, for example, Chapter 2.1 Pages 5-6 of Astrom, published in 1995 (included in the references cited). See the 35 U.S.C. 112(b) rejections of claim 2 regarding the term “accurately”. The related variables being computed “accurately” is an intended outcome that does not carry significant weight.
The BRI of claim 3 is a modification of claim 1 where the models are updated with fresh datasets. The limitation “enhancing predictions based on recent trends and patterns” is an intended outcome that does not carry significant weight. See the 35 U.S.C. 112(b) rejections of claim 3.
See the 35 U.S.C. 112(b) rejections of claim 4. The only part of claim 4 that carries weight is “A method for enhancing Proportional-Integral-Derivate (PID) loop performance through predictive modeling, which involves predicting a Control Variable (CV) output to achieve a new Process Variable (PV) setpoint upon adjustment”. Given the indefiniteness of the claim, the examiner is interpreting this limitation to mean that a CV output is adjusted to a predicted CV output in order to meet a new PV setpoint that enhances the PID performance (i.e. reduces error between the system output and the desired setpoint, as a person of ordinary skill in the art would construe). This method generally describes closed-loop feedback using PID control, where a model of a system is used to calculate a control variable that meets a desired setpoint (i.e. “model following”), which can be re-calculated for any new setpoint, a very well-known method in the area of control theory and control system design. See, for example, Chapter 7.4 Pages 284-287 of Astrom, published in 1995 (included in the References Cited). The remaining portion of claim 4 (i.e. lines 4-6 starting with “applies…”) is an intended outcome that does not carry any weight as it does not include any method steps to arrive at the stated outcome.
See the 35 U.S.C. 112(b) rejections of claim 5. The only parts of claim 5 that carry weight are “A method for evaluating and enhancing process and equipment performance post-process run by analyzing the outcomes of each recipe’s dependent variable and each CV’s performance within PID loops against expected values from predictive models” and “identifies discrepancies to flag potential abnormal equipment or process performance”. This method generally describes flagging abnormal equipment or performance (i.e. quality control) by using analysis (i.e. statistical or mathematical analysis) of real outcomes of the process compared to the expected outcomes made by predictive models. Quality control, as generally described by this claim, is a very well-known method in the art.
The limitation “analyzing the outcomes of each recipe’s dependent variable and each CV’s performance within PID loops against expected values from predictive models” also generally describes PID control with on-line fault detection using a process model, which is also a well-known process in the art. See, for example, Chapters 6.7-6.8 Pages 268-270 of Astrom, published in 1995 (included in the References Cited).
Furthermore, the italicized portion of the preamble is an intended outcome that does not carry significant weight. The remaining portion of claim 5 (i.e. lines 5-6 starting with “employs…”) is an intended outcome that does not carry any weight as it does not include any method steps to arrive at the stated outcome.
Step 1: Is the claim directed to one of the four statutory categories?
Claims 1-3 are not directed to a statutory category, as they recite software per se; however, they can be amended to be directed to a machine or an article of manufacture that implement a process.
Claim 4 is directed to a process.
Claim 5 is directed to a process.
Step 2A Prong 1: Does the claim recite an abstract idea?
The BRI of claim 1 includes the following limitations:
A software tool for real-time industrial or laboratory process management interfaces with a PLC, collects and trends data, uses advanced algorithms to craft Process Recipes from historical data, and features a Recipe Maker module that employs regression, machine learning, and/or vector analysis to develop models, determine variable relationships, and adjust setpoints based on a user selectable 'variable to calculate' for targeted outcomes.
The underlined potion of the claim recites mathematical concepts and mathematical calculations, in particular. See MPEP 2106.04(a)(2).I.C.
Trending data, using algorithms to create processes from historical data, using regression, machine learning, and/or vector analysis to develop models, determining relationships between variables, and adjusting setpoints based on a targeted outcome are all mathematical calculations. Other than interfacing with a PLC, collecting data, and receiving a user selection of a target outcome, the claim recites an abstract idea.
The BRI of claim 2 includes the following limitations:
an error-checking mechanism comparing intended outcomes with actual results and uses an error adjustment method on a designated dependent variable, adjusting input values based on discrepancies to compute related variables accurately
As discussed in the claim interpretation section, this claim generally recites using closed-loop feedback, which is a mathematical calculation, as further supported by reference to Astrom as cited above. The claim could also be considered a mental process that is capable of being performed in the human mind with the use of a physical aid, such as pen and paper. See MPEP 2106.04(a)(2).III.B. The claim therefore is also directed to the abstract idea of claim 1 due to its dependency while also reciting another abstract idea.
The BRI of claim 3 includes the following limitations:
A methodology within the software tool's recipe maker from claim 1 that updates existing models with fresh datasets, enhancing predictions based on recent trends and patterns.
The claim is directed to the abstract idea of claim 1 due to its dependency. While “enhancing predictions” carries little weight due to it being unclear how or by what measure the predictions are being enhanced and what the predictions are even of, “predicting” using “recent trends” and “patterns”— recited generally as it is in the claim— is also a mathematical calculation.
The BRI of claim 4 includes the following limitations:
A method for enhancing Proportional-Integral-Derivative (PID) loop performance through predictive modeling, which involves predicting a Control Variable (CV) output to achieve a new (PV) setpoint upon adjustment.
As discussed in the claim interpretation section, this method generally describes closed-loop feedback using PID control, which is a mathematical concept. See MPEP 2106.04(a)(2).I.C.
Predicting a control variable output that would result in a new setpoint being achieved using a model is a mathematical calculation, as further supported by reference to Astrom as cited above. The claim could also be considered a mental process that is capable of being performed in the human mind with the use of a physical aid, such as pen and paper. See MPEP 2106.04(a)(2).III.B.
The BRI of claim 5 includes the following limitations:
A method for evaluating and enhancing process and equipment performance post-process run by analyzing the outcomes of each recipe's dependent variable and each CV's performance within PID loops against expected values from predictive models… identifies discrepancies to flag potential abnormal equipment or process performance
The underlined potion of the claim recites mathematical concepts and mathematical calculations. See MPEP 2106.04(a)(2).I.C. As discussed in the claim interpretation section, this method generally describes closed-loop feedback using PID control, which is a mathematical concept, as further supported by reference to Astrom as cited above. Identifying the discrepancy could be construed as an error calculation that the person performing the calculation considers unideal, thus identifying abnormal process performance.
The claim could also be considered a mental process that is capable of being performed in the human mind with the use of a physical aid, such as pen and paper. See MPEP 2106.04(a)(2).III.B. The limitation “analyzing the outcomes of each recipe's dependent variable and each CV's performance within PID loops against expected values from predictive models” could be performed in the human mind or with pen and paper by comparing, qualitatively or quantitatively, the values of the dependent variables and control variable performance to what is expected. The expected values could be manually calculated using the model of the system or be a list of values expected by an operator or user. Based on the outcome, the user can then manually make note of or tag a piece of equipment or a process performance that is underperforming, thus identifying “discrepancies to flag potential abnormal equipment or process performance”.
Step 2A Prong 2: Does the claim recite additional elements that integrate the abstract idea into a practical application?
Regarding claim 1, the limitations “software tool”, “interfaces with a PLC, collects… data” and the ‘variable to calculate’ being “user selectable” are additional limitations. Claim 3 comprises the additional limitations “updates existing models with fresh datasets”. Claims 2 and 4-5 do not recite any additional limitations other than the abstract idea.
The limitations “interfaces with a PLC, collects… data” and “user selectable ‘variable to calculate’” are insignificant extra-solution activity, namely pre-solution activity. See MPEP 2106.05(g), which states “An example of pre-solution activity is a step of gathering data for use in a claimed process.” Both the limitations “collects… data” and that the “variable to calculate” is “user selectable” are mere data gathering steps needed to perform the claimed process. The data being gathered from a PLC and from a user is not significant enough to integrate the claim into a practical application. With these limitations, the claims as a whole are not integrated into a practical application. The limitation “updates existing models with fresh datasets” in claim 3 is also insignificant extra-solution activity that amounts to mere data gathering. Claim 3 therefore does not integrate the abstract idea into a practical application.
The limitation of claim 1 that the abstract idea is being performed by “a software tool” in the preamble further does not integrate the abstract idea into a practical application. A software tool is a generic computer element that, even if amended to be recited with tangible structural elements so that the claim is considered more than software, would merely be an object on which the method operates. A general-purpose computer that applies a judicial exception, such as the abstract idea of claim 1, by use of conventional computer functions does not qualify as a particular machine that integrates the abstract idea into a practical application. See MPEP 2106.05(b).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
As discussed in the preceding section, claims 1 and 3 recite additional elements that amount to insignificant extra-solution activity, such as mere data gathering, or to generic computer components that implement the abstract idea. Claims 2 and 4-5 do not recite any additional elements outside of their recited abstract ideas. The claims therefore do not recite any additional elements that amount to significantly more than the mathematical concepts or mental processes discussed in Step 2A Prong 1. See MPEP 2106.05.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1-3 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WILLIS (US 2006/0184264 A1).
Regarding Claim 1, WILLIS discloses a software tool for real-time industrial or laboratory process management (¶ 32-33, 78, Figs. 6-8: A software tool is provided for managing a process, which can be in an industrial or a laboratory setting and in this case is a semiconductor manufacturing process, in real-time.)
interfaces with a PLC, (¶ 21, 25, 36, 44: The software tool (see Fig. 1 element 120, Fig. 3 element 330 and ¶ 21) is associated with a controller that interfaces with the controller of a “manufacturing equipment system (MES)” 130. The controller of a MES is equivalent to a PLC.)
collects… data (¶ 25, 37, 40, 44, 79, 95, 100, 103: Data related to the manufacturing process is collected and provided to the controller associated with the software tool.)
and trends data, (¶ 112, 118-119, 143: Trends in the obtained data are determined in order to optimize a process model and the recipe parameters.)
uses advanced algorithms to craft Process Recipes from historical data, (¶ 47-51, 67-68, 89, 114, 118, 121-122, 151-152: Recipe parameters are computed based on the trends in the data, which is historical data obtained from the manufacturing equipment (see for example, ¶ 40, 95). Recipes can be recalculated or updated and new recipes can be generated. Recipes are established using models and algorithms, such as multivariate analysis models and models developed using PLS, selecting the best-fit model for solving the recipe parameters)
and features a Recipe Maker module that employs regression, machine learning, and/or vector analysis to develop models, determine variable relationships, (¶ 112, 118, 150-151, 164-165: A process model is developed that represents a verified relationship between process variables needed to achieve a desired result. The model is continuously updated based on the trends in the captured data. The model is developed using a training dataset and a partial least squares analysis, which is a type of regression.)
and adjust setpoints based on a user selectable 'variable to calculate' for targeted outcomes. (¶ 104, 109-110, 235, Fig. 3 target CD 315 and target calculation 320: A “target calculation” is of a “target CD [critical dimension]”, which is a “variable to calculate”. The variable is calculated by an equation “that correlates one set of data with another set of data”. The variable is user selectable (see ¶ 47), as shown in Fig. 8 interface element 812 and 814 and discussed in ¶ 231-236. Based on the user selectable variable to calculate, a desired result (i.e. setpoint), is adjusted and fed into the control process. ¶ 131: A predicted result, which is also used as a setpoint in an error calculation (see claim 2), is also calculated based on the desired outcome.)
Regarding Claim 2, WILLIS further discloses an error-checking mechanism comparing intended outcomes (Fig. 3 target calculation 320 and predicted result calculation 355) with actual results (Fig. 3 actual result calculation 350) and uses an error adjustment (Fig. 3 error calculation 360) method on a designated dependent variable (Fig. 3 target CD 315), adjusting input values based on discrepancies (Fig. 3 EWMA filter 370) to compute related variables accurately. (¶ 110, 124, 131-144: An error between a desired or predicted result and an actual result is calculated. The error signal is fed through a filter to remove discrepancies due to noise. The cleaned error signal is provided as feedback to update the models and calculate the recipe parameters more accurately in order to improve the performance of the process.)
Regarding Claim 3, WILLIS further discloses a methodology within the software tool's recipe maker from claim 1 that updates existing models with fresh datasets, enhancing predictions based on recent trends and patterns. (¶ 23, 111-113, 118-119, 221-222, 234: The existing process models are updated with data obtained from the process, including input trends and metrology data. The models are updated to better predict the parameters of the process to reduce error. Update buttons are also included in a GUI of the software tool to refresh the model and recipe calculation with the updated data.)
Claim 4-5 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by ASTROM (“PID Controllers, 2nd Edition”, Copyright 1995).
Regarding Claim 4, ASTROM discloses a method for enhancing Proportional-Integral-Derivative (PID) loop performance (Bottom of Page 284: More accurate control of the setpoint response (i.e. enhanced performance) of a PID control loop is obtained by using a reference model that gives the desired response to setpoint changes.)
through predictive modeling, (Pages 284-285, Fig. 7.9 “Model”: “The reference model is typically chosen as a dynamic system of first or second order… The model and the feedforward elements are then designed to obtain the desired setpoint response”. Predictive models are designed to obtain a desired setpoint response.)
which involves predicting a Control Variable (CV) output to achieve a new Process Variable (PV) setpoint upon adjustment. (Page 285: “The signal uff is such that it will produce the desired output if the models are correct. The error e will differ from zero when the output deviates from its desired behavior. The feedback path will then generate the appropriate actions. When implementing the system the boxes labeled model and feedforward are often combined into one unit which has the command signal yc as input and ysp and uff as outputs.” The signal uff is the CV output predicted using the model and feedforward elements to achieve a PV setpoint yc. The system adjusts based on the error determined by the feedback loop.)
This method applies the predicted CV output to reach the PV setpoint more efficiently than conventional PID loop adjustments and uses the prediction as an advanced baseline for immediate control response, thus improving the speed and accuracy of achieving desired PV setpoints. (As discussed in the 101 and 112 rejections of claim 4 above, the examiner is giving this limitation no patentable weight as it merely states a desired outcome of a method without reciting any method steps and is indefinite.
Regardless, ASTROM teaches that a predicted control variable is applied to reach a setpoint “more efficiently than conventional PID loop adjustments”, the prediction being used as a baseline for immediate control response:
Pages 284-285, Chapter 7.4 Model Following, Fig. 7.9: “In some cases it is desirable to have more accurate control of the setpoint response. This can be achieved by using a reference model that gives the desired response to setpoint changes… It is necessary that the feedback loop be very fast relative to the response of the reference model. The system can be improved considerably by introducing feedforward as shown in Figure 7.9. In this system we have also feedforward from the command signal. (Compare with Section 7.3.) The signal uff is such that it will produce the desired output if the models are correct. The error e will differ from zero when the output deviates from its desired behavior. The feedback path will then generate the appropriate actions… Model following is used when precise setpoint following is desired.” A feedforward loop uses a predicted response of a system using a model as a baseline to predict a CV output (i.e. the signal uff) that is applied to track a desired setpoint more accurately. The model and the feedforward loop augment the conventional PID feedback loop.)
Regarding Claim 5, ASTROM discloses a method for evaluating and enhancing process and equipment performance post-process run (Page 262-263, Chapter 6.7 Integrated Tuning and Diagnosis: A conventional process is described for evaluating and enhancing process and equipment performance post-process run to identify if the problem, such as oscillations in a system output, are due to the controller or equipment issues.)
by analyzing the outcomes of each recipe's dependent variable and each CV's performance within PID loops against expected values from predictive models. (Pages 268-269, Fig. 6.18: “A common approach to fault detection is shown in Figure 6.18. If a model of the process is available, the control signal can be fed to the input of the process model. By comparing the output of the model with the true process output, one can detect when the process dynamics change. If the model is good, the difference between the model output and the process output (e) is small. If the process dynamics change, e will no longer be small, since the two responses to the control signal are different. It is also possible to compare other signals in the process and the model rather than the output signals. These fault detection methods are called observer-based methods. Another fault detection approach is to use a recursive parameter estimator in the same way as the model-based continuous adaptive controller, and to base the detection on the changes in the parameter estimates. These methods are called identification-based methods.” The output of a process using a PID controller is compared to an expected output from a model to determine a level of error of the process variable in order to detect a fault in the process.)
This method identifies discrepancies, to flag potential abnormal equipment or process performance (Pages 262-263, 268-269: “Friction in the valve results in stick-slip motion. This phenomena is shown in Figure 6.11. Because of the static friction, the process output will oscillate around the setpoint. The valve will only move when the control signal has changed sufficiently since the previous valve movement. When the valve moves, it moves too much. This causes the stick-slip motion. The pattern in Figure 6.11, where the measurement signal is close to a square wave and the control signal is close to a triangular wave, is typical for stick-slip motion… If the disturbances are generated inside the loop, the cause can be either friction in the valve or a badly tuned controller. Whether friction is present or not can be determined by making small changes in the control signal and checking if the measurement signal follows, as shown in Figure 6.10. If friction is causing the oscillations, the solution to the problem is valve maintenance… Most controllers have a primitive form of diagnosis in the use of alarms on limits on the measured signals. The operator thus gets an alarm when signals exceed certain specified alarm limits. More sophisticated detection procedures, where alarms are given when problems like those mentioned above arise, will be available in industrial products within the next few years… it is desirable to supply the adaptive controllers with on-line detection methods, so that reasons for bad control-loop performance, other than poor controller tuning, are detected.” Multiple conventional methods are disclosed for identifying the source of poor performance, flagging (i.e. by an alarm) either abnormal equipment, such as valves, due to discrepancies caused by friction, noise, or nonlinearities or faulty controller tuning.)
and employs the analysis for preventative maintenance and operational adjustments, preventing minor issues from escalating into significant disruptions. (As discussed in the 101 and 112 rejections of claim 5 above, the examiner is giving this limitation no patentable weight as it merely states a desired outcome of a method without reciting any method steps and is indefinite.
Regardless, ASTROM teaches “and employs the analysis for preventative maintenance and operational adjustments, preventing minor issues from escalating into significant disruptions”, as discussed above with reference to the relevant portions of Pages 262-263 and 268-269. Valve maintenance and controller auto-tuning are preventative maintenance and operational adjustments that prevent issues from escalating.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The references are listed in the same order as they appear in the Notice of References Cited.
Broese teaches combining models using a neural network to calculate process parameters given at leas one precomputed parameter. (Abstract)
Klimasauskas teaches an error correction model for an adaptive controller. (Fig. 4A)
Blevins teaches a model predictive controller for simulating process control outputs. (Abstract)
Hales teaches controlling the setpoints for manipulated variables based on constraints of other variables to optimize a process. (¶ 25-29, Fig. 2)
Ferguson teaches training a support vector machine using historical data to optimize the output of a controller for a production process. (Fig. 4, ¶ 35-60). Hartman teaches another embodiment.
Guerlain teaches a software tool for evaluating the performance of a model predictive controller for a chemical process. (Fig. 3)
Huang teaches creation of model to fine tune process recipes. (Fig. 2, 5A)
Mehta teaches management of a plurality of models for a process control system, including tuning of PID parameters. (¶ 8, Figs. 4-6)
Dao teaches generating recipes for PID control process. (Fig. 5)
Wojsznis teaches a model predictive controller for a chemical process including wireless feedback. (¶ 20, Fig. 5)
SayyarRodsari teaches data gathering from an industrial automation process including modeling target variables using clustering techniques. (Fig. 13, ¶ 41-44)
Salat teaches implementing a PID controller in a PLC using support vector regression for a black box process. (Abstract)
Vats teaches a GUI visualizing the effects changes to one process variable has on other process variables. (¶ 30-35, Fig. 2A)
Aaron teaches automatic control loop parameter variation based on analysis of collected data. (Abstract, Fig. 3)
Zhao teaches automatic model development to enhance a process control system using historical data. (Abstract, Fig. 2E)
Havlena teaches using machine learning to automatically tune a PID controller. (¶ 80-85, Fig. 3)
Zhuo teaches generating a control model that outputs a manipulated variable corresponding to an indicated variable and a process variable of a predetermined system using machine learning. (¶ 6, Fig. 3)
El-Ferik teaches using an AI algorithm with a PID controller to minimize error, as well as a advisory software tool, to reduce striction in a industrial process. (¶ 34-42, Figs. 1, 4B, 5)
Miller teaches a process for a biopharmaceutical product, including creating recipes, calculating dependent variables, and providing a GUI for managing the process and recipes. (Abstract, Figs. 2, 5)
Ridely teaches a tool for real-time industrial process guidance, including a user specifying a process variable to be controlled by a model predictive controller. (¶ 6-10, Figs. 3)
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/RAMI R OKASHA/Primary Examiner, Art Unit 2118