DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 07/07/2022
Claims 1-12 are presented for examination
Information Disclosure Statement
The IDS dated 07/07/2022 and 03/24/2023 has been reviewed. See attached.
Drawings
The drawings dated 07/07/2022 have been reviewed. They are accepted.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Abstract
The abstract dated 07/07/2022 has been reviewed. It has 142 words, and contains no legal phraseology. It is accepted.
Claim Interpretation
It is not immediately clear what the term “smoothness” is meant to refer to in the context of the disclosure. For the purposes of this examination, based on the plain meaning of the term and the use of the term in the disclosure and without unnecessarily reading in limitations from the specification, determining that the “smoothness” is conformed to is interpreted as determining whether data is regular, even, or consistent, either with itself or with other data sets, by some measure without significant deviations.
Further, see MPEP 2111.01(II) "Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment." Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004)”
Claim Objections
Claims 8-9 objected to because of the following informalities:
Claims 8 and 9 recite “the parameter extraction module 210;” the reference character 210 was not previously used when referring to the parameter extraction module. Further, it is best practice to enclose reference characters in parentheses, i.e. “the parameter extraction module (210).” (see MPEP 608.01(m)) It is recommended further to make use of this reference character consistent, i.e. it should be used with every recitation of the parameter extraction module or alternatively removed altogether.
Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-5, and 11 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.
Claim 2 recites the limitation " (D) determining whether the plurality of parameter sets meet a termination criterion, to generate a third determination result; and (E) generating the plurality of parameter sets according to the third determination result.” There is insufficient antecedent basis for this limitation in the claim. Particularly, it is unclear if the plurality of parameter sets “generated” in step (E) are meant to refer the sets selected in step (C) and whether the sets for which the termination criterion is determined in step (D) refer to the same sets selected in step (C); if they all refer to the same sets, i.e. sets are selected in step (C) then those same sets are checked for the termination criterion in step (D), how are the sets being “generated” based on a determination performed on themselves? If they are not the same sets, which sets get each processing step, and how do these relate to the singular “parameter set” introduced in claim 1? With this in mind, this claims is rendered indefinite.
Claim 3 recites the limitation "wherein the parameter extraction module generates the plurality of parameter sets according to the plurality of parameter sets" There is insufficient antecedent basis for this limitation in the claim. Particularly, it is unclear what is being referred to which each recitation of “the plurality of parameter sets” Is each set referring to the sets generated in claim 2 step C, claim 2 step D, claim 2 step E, or claim 1? Further are these different groups of sets, or are they referring to the same sets? If they are the same sets, how are the sets being generated “according to” themselves before ever existing? With this in mind, this claim is rendered indefinite.
Claim 4 recites “wherein the parameter extraction module performs a genetic algorithm on the plurality of parameter sets to generate the plurality of parameter sets” There is insufficient antecedent basis for this limitation in the claim. Similarly to claims 2 and 3, it is unclear what is being referred to which each recitation of “the plurality of parameter sets” Is each set referring to the sets generated in claim 2 step C, claim 2 step D, claim 2 step E, claim 3, or claim 1? Further are these different groups of sets, or are they referring to the same sets? If they are the same sets, how are the sets being generated by “perform{ing} a genetic algorithm” on themselves before ever existing? With this in mind, this claim is rendered indefinite.
Claims 2-4 describe the parameter extraction module as generating a “plurality of parameter sets” when attempting to further specify the operation of the module while claim 1 describes the parameter extraction module as generating a singular “parameter set.” These limitations are contradictory, with the language of claim 1 explicitly limiting the number of parameter sets to a single set (i.e. the claim recites generating “a parameter set”). With this in mind, these claims are rendered indefinite.
Claim 11 recites the limitation “the first slope;” There is insufficient antecedent basis for this limitation in the claim. The “first slope” was introduced in claim 10, from which claim 11 does not depend.
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 1-12 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
an analysis module, coupled to the parameter extraction module, for determining whether the plurality of devices conform to a trend according to the parameter set, to generate a first determination result, and for determining whether the plurality of devices conform to a smoothness according to the first determination result and the parameter set, to generate a second determination result;
Determining whether the data corresponding to the devices conforms to a trend (i.e. determining that the measured data is positively correlated with the model/parameter-derived data) is a mental process equivalent to observing both sets of data, i.e. by plotting both sets with a paper and pencil, and judging, based on observing the data, if the lines representative of each set have the same direction (upwards or downwards.)
Determining whether the data corresponding to the devices conforms to a “smoothness” is a mental process equivalent to again plotting both sets of data with a paper and pencil, and judging whether the lines representative of the model/parameter-derived data is consistent with that of the measured data. In other words, judging whether the lines appear to be similar and do not significantly diverge.
Should it be found that this is not a mental process, it is also an example of a mathematic concept.
a device model parameter generation module, coupled to the analysis module, for generating a plurality of device model parameters according to the second determination result and the parameter set.
Given the mental determination that the parameter data conforms to the trend and the smoothness, i.e. the determination that no further modification to the parameters is needed, generating the final device model parameters is a mental process equivalent to creating a representation of them, for example by writing out the parameters of the parameter set with a pencil and paper.
Should it be found that this is not a mental process, it is also an example of insignificant post-solution activity.
The claims also recite a mathematic concept:
an analysis module, coupled to the parameter extraction module, for determining whether the plurality of devices conform to a trend according to the parameter set, to generate a first determination result, and for determining whether the plurality of devices conform to a smoothness according to the first determination result and the parameter set, to generate a second determination result;
As per the specification, the operations of determining whether the devices conform to the trend and conform to the smoothness both consist of determining whether slopes representative of data sets have the same sign ([Par 24] “For example, the analysis module220 determines that the plurality of devices conform to the trend and the first determination result indicates that the plurality of devices conform to the trend, when the first comparison result indicates that the signs of the second slope and the first slope are the same (e.g., both are positive or both are negative).” [Par 25] “The analysis module220 compares the plurality of third slopes with the first slope (e.g., whether the signs are the same) to generate a plurality of second comparison results. The analysis module220 determines whether the plurality of devices conform to the smoothness according to the plurality of second comparison results, to generate the second determination result.”)
With this in mind, determining if two slopes have the same sign is a mathematic comparison between the two slopes. Further, calculations such as the Pearson correlation coefficient (PCC) allow for such comparisons to be performed formulaically.
Step 2A – Prong 2: Integrated into a Practical Solution?
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
a user module, for obtaining a plurality of parameter set configurations and a plurality of measurement data of a plurality of devices;
Generally “obtaining” data without any specifics to how this data obtaining is performed amounts to no more than mere data gathering.
a parameter extraction module, coupled to the user module, for performing a plurality of parameter extractions on the plurality of parameter set configurations and the plurality of measurement data, to generate a parameter set;
Extracting parameters from a set of data amounts to no more than gathering data about that dataset in a conventional way, and therefore amounts to no more than mere data gathering. Further, see WURC evidence for this element under step 2B.
a simulation module, coupled to the parameter extraction module, for performing a plurality of simulations according to the plurality of parameter set configurations and the plurality of measurement data, to generate a plurality of simulation results;
Simulating data to generate a result without specifics recited as to what the simulation consists of or how it works is merely the act of gathering data representative of that simulation result for use by the abstract idea and therefore amounts to no more than mere data gathering. Further, see WURC evidence for this element under step 2B.
Should it be found that this is not mere data gathering, this step is also an example of mere instructions to apply.
Post-Solution Activity:
a device model parameter generation module, coupled to the analysis module, for generating a plurality of device model parameters according to the second determination result and the parameter set.
This step merely is merely the act of presenting the results of the abstract idea, i.e. presenting the parameter set in a final form after analyzing them mentally through the analysis process; therefore, this step amounts to no more than insignificant post-solution activity. Further, see WURC evidence for this element under step 2B.
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
a simulation module, coupled to the parameter extraction module, for performing a plurality of simulations according to the plurality of parameter set configurations and the plurality of measurement data, to generate a plurality of simulation results;
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that simulation results are generated without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations;
Moreover, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “A device model parameter generation system, comprising: a user module; a parameter extraction module; a simulation module; an analysis module; a device model parameter generation module;” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are mere data gathering, insignificant post-solution activity, or mere instructions to apply and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
a user module, for obtaining a plurality of parameter set configurations and a plurality of measurement data of a plurality of devices;
Generally “obtaining” data without any specifics to how this data obtaining is performed amounts to no more than mere data gathering.
A claim element that amounts to merely gathering data is not indicative of integration into a
practical solution nor evidence that the claim provides an inventive concept or significantly more, as exemplified by ((MPEP 2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
a parameter extraction module, coupled to the user module, for performing a plurality of parameter extractions on the plurality of parameter set configurations and the plurality of measurement data, to generate a parameter set;
Extracting parameters from a set of data amounts to no more than gathering data about that dataset in a conventional way, and therefore amounts to no more than mere data gathering. Further, see WURC evidence for this element below.
A claim element that amounts to merely gathering data is not indicative of integration into a
practical solution nor evidence that the claim provides an inventive concept or significantly more, as exemplified by ((MPEP 2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93; iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
a simulation module, coupled to the parameter extraction module, for performing a plurality of simulations according to the plurality of parameter set configurations and the plurality of measurement data, to generate a plurality of simulation results;
Simulating data to generate a result without specifics recited as to what the simulation consists of or how it works is merely the act of gathering data representative of that simulation result for use by the abstract idea and therefore amounts to no more than mere data gathering. Further, see WURC evidence for this element below.
A claim element that amounts to merely gathering data is not indicative of integration into a
practical solution nor evidence that the claim provides an inventive concept or significantly more, as exemplified by ((MPEP 2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93; iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
Should it be found that this is not mere data gathering, this step is also an example of mere instructions to apply.
Post-Solution Activity:
a device model parameter generation module, coupled to the analysis module, for generating a plurality of device model parameters according to the second determination result and the parameter set.
This step merely is merely the act of presenting the results of the abstract idea, i.e. presenting the parameter set in a final form after analyzing them mentally through the analysis process; therefore, this step amounts to no more than insignificant post-solution activity. Further, see WURC evidence for this element below.
This element merely acts on the results of the previous abstract steps. A claim element that merely acts on a series of previous abstract steps is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.)
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
a simulation module, coupled to the parameter extraction module, for performing a plurality of simulations according to the plurality of parameter set configurations and the plurality of measurement data, to generate a plurality of simulation results;
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that simulation results are generated without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; Additionally, see WURC evidence for this element below.
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Moreover, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “A device model parameter generation system, comprising: a user module; a parameter extraction module; a simulation module; an analysis module; a device model parameter generation module;” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
In addition, the following are also considered as well-understood, routine, and conventional activities, as discussed in MPEP § 2106.05(d):
a parameter extraction module, coupled to the user module, for performing a plurality of parameter extractions on the plurality of parameter set configurations and the plurality of measurement data, to generate a parameter set … a device model parameter generation module, coupled to the analysis module, for generating a plurality of device model parameters according to the second determination result and the parameter set.
is a well-understood, routine, and conventional activity, as evidenced by:
A Hybrid Genetic Algorithm for MOSFET Parameter Extraction ([Page 1111 Col 1 Par 1, Col 2 Par 2])
Reid (US 20160378717 A1) ([Par 49, Fig. 1]
Trombley ([Par 18-20])
Optimized Extraction of MOS Model Parameters ([Page 163 Col 1 Par 1-2])
Effective parameter extraction using multiple-objective function for VLSI circuits ([Abstract, Page 121 Col 2 Par 2])
a simulation module, coupled to the parameter extraction module, for performing a plurality of simulations according to the plurality of parameter set configurations and the plurality of measurement data, to generate a plurality of simulation results;
is a well-understood, routine, and conventional activity, as evidenced by:
What is a SPICE Simulation in Electronics Design? ([Page 1 Par 1-3])
Tips for Using LTspice for Power Circuit Design ([Par 1-3])
VLSI design techniques for analog and digital circuits ([Chapter 4 Page 237 Par 1- Page 238 Par 1])
How Does Circuit Simulation Work? ([Page 5 Par 5])
Optimized Extraction of MOS Model Parameters ([Abstract, Page 163 Col 1 Par 1-2])
Effective parameter extraction using multiple-objective function for VLSI circuits ([Abstract, Page 121 Col 2 Par 2])
As per MPEP § 2106.05(d), an additional element that is “no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality,” does not integrate a judicial exception into a practical application, nor provide significantly more.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 12 The elements of claim 12 are substantially the same as those of claim 1. Therefore, the elements of claim 12 are rejected due to the same reasons as outlined above for claim 1.
Claim 2 recites “wherein the operation of performing the plurality of parameter extractions on the plurality of parameter set configurations and the plurality of measurement data comprises:
(A) generating a plurality of candidate parameter sets according to a parameter set configuration in the plurality of parameter set configurations and a measurement data in the plurality of measurement data;
Generating a plurality of candidate parameter sets is a mental process equivalent to observing the rules set by the parameter set configuration and the measurement data and generating data that relates to both, as by writing that data down with a pencil and paper. For example, if the configuration limits all generated parameters to multiples of 5, and a measurement data element is {13}, a person could generate data elements {10, 15, etc.}
(B) providing the plurality of candidate parameter sets to the simulation module, and obtaining the plurality of simulation results;
Simulating data to generate a result without specifics recited as to what the simulation consists of or how it works is merely the act of gathering data representative of that simulation result for use by the abstract idea and therefore amounts to no more than mere data gathering.
Should it be found that this is not mere data gathering, this step is also an example of mere instructions to apply.
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that simulation results are generated without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; Additionally, see WURC evidence for this element below.
What is a SPICE Simulation in Electronics Design? ([Page 1 Par 1-3])
Tips for Using LTspice for Power Circuit Design ([Par 1-3])
VLSI design techniques for analog and digital circuits ([Chapter 4 Page 237 Par 1- Page 238 Par 1])
How Does Circuit Simulation Work? ([Page 5 Par 5])
Optimized Extraction of MOS Model Parameters ([Abstract, Page 163 Col 1 Par 1-2])
Effective parameter extraction using multiple-objective function for VLSI circuits ([Abstract, Page 121 Col 2 Par 2])
(C) selecting a plurality of parameter sets from the plurality of candidate parameter sets according to the plurality of simulation results;
Selecting this data is a mental process equivalent to arbitrarily judging which parameter sets should be further processed based on an observation of the simulation results. For example, if the simulation results indicate that parameter sets that include a {4} perform well, a person could reasonably judge that all the sets containing a {4} should be selected from the candidates and further processed.
(D) determining whether the plurality of parameter sets meet a termination criterion, to generate a third determination result; and
Determining whether this data meets a “termination criterion” is a mental process equivalent to judging whether or not the data meets an arbitrarily chosen condition, such as when the average of all the values in the set is less than a certain number, a {16} appears in the set, etc.
(E) generating the plurality of parameter sets according to the third determination result.”
Generating a plurality of sets “according to the third determination result” is a mental process that is merely equivalent to generating arbitrary data, as by writing it down, when it is determined that the other data has met the arbitrarily chosen condition.
Claim 3 recites “wherein the parameter extraction module generates the plurality of parameter sets according to the plurality of parameter sets, when the third determination result indicates that the plurality of parameter sets meet the termination condition.”
Generating a plurality of sets “according to the third determination result” is a mental process that is merely equivalent to generating arbitrary data, as by writing it down, when it is determined that the other data has met the arbitrarily chosen condition.
Claim 4 recites “wherein the parameter extraction module performs a genetic algorithm on the plurality of parameter sets to generate the plurality of parameter sets and returns to the operation (B), when the third determination result indicates that the plurality of parameter sets do not meet the termination condition.”
Using a generic “genetic algorithm” with no particularities recited as to what the algorithm consists of or how it works is merely the act of gathering data representative of the result of performing the algorithm for use by the abstract idea and therefore amounts to no more than mere data gathering
Should it be found that this is not mere data gathering, this step is also an example of mere instructions to apply.
Applying a computer to perform the operations of a generic “genetic algorithm” at a high level of generality is simply the act of instructing a computer to perform generic functions to perform the steps of that algorithm, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that parameter sets are generated using the algorithm without reciting how this is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations;
Additionally, specifying the return to operation (B) when the termination condition is not met is equivalent repeating the steps of operations (B)-(D) until it is mentally determined that the termination condition is met. Therefore, this is merely an extension of the mental process, mere instructions to apply, and data gathering steps.
Claim 5 recites “wherein the genetic algorithm comprises a crossover operation or a mutation operation.”
This merely clarifies some of the operations performed by the genetic algorithm, and is therefore merely an extension of the mere data gathering steps and the mere instructions to apply the exception.
Claim 6 recites “wherein the analysis module determines whether the plurality of devices conform to the smoothness to generate the second determination result, when the first determination result indicates that the plurality of devices conform to the trend.”
Specifying that the smoothness determination is performed when the devices are determined to conform to the trend merely specifies the order and conditions under which the steps are performed, and is therefore merely an extension of the mental process, mathematic concept, mere data gathering steps, and mere instructions to apply.
Claim 7 recites “wherein the device model parameter generation module generates the plurality of device model parameters according to the parameter set, when the second determination result indicates that the plurality of devices conform to the smoothness.”
Specifying that the device model parameter generation is performed when the devices are determined to conform to the smoothness merely specifies the order and conditions under which the steps are performed, and is therefore merely an extension of the mental process, mathematic concept, mere data gathering steps, and mere instructions to apply.
Claim 8 recites “wherein the analysis module returns to the parameter extraction module 210 and the parameter extraction module 210 performs the plurality of parameter extractions on the plurality of parameter set configurations and the plurality of measurement data, when the second determination result indicates that the plurality of devices do not conform to the smoothness.”
Specifying that the process returns to the parameter extraction step if it is mentally/mathematically determined that the smoothness is not conformed to merely specifies conditions under which steps are repeated, and is therefore merely an extension of the mental process, mathematic concept, mere data gathering steps, and mere instructions to apply.
Claim 9 recites “wherein the analysis module returns to the parameter extraction module 210 and the parameter extraction module 210 performs the plurality of parameter extractions on the plurality of parameter set configurations and the plurality of measurement data, when the first determination result indicates that the plurality of devices do not conform to the trend.”
Specifying that the process returns to the parameter extraction step if it is mentally/mathematically determined that the trend is not conformed to merely specifies conditions under which steps are repeated, and is therefore merely an extension of the mental process, mathematic concept, mere data gathering steps, and mere instructions to apply
Claim 10 recites “wherein the operation of determining whether the plurality of devices conform to the trend according to the parameter set to generate the first determination result comprises:
generating a first slope according to the plurality of measurement data, and generating the trend according to the first slope;
generating a second slope according to the plurality of devices corresponding to the plurality of simulation results, and
comparing the second slope with the first slope to generate a first comparison result; and
generating the first determination result according to the first comparison result.”
Generating a slopes according to the data is a mental process equivalent to determining linear slopes representative of the data sets; for example, plotting the data points for each set with a pencil and paper and drawing visually estimated lines of best fit through each plot for each data set.
As per the specification, determining the trend of each slope merely consists of determining the sign of the slope, i.e. upwards or downwards, ([Par 24] “For example, the trend is up when the first slope is positive. The trend is down when the first slope is negative.”) which can be judged by visually observing the plots, i.e. whether the line is going upwards or downwards.
Finally, comparing the slopes to generate a comparison result is a mental process equivalent to observing both slopes and making judgements about certain characteristics between them, such as that they are both positive, negative, etc.
Claim 11 recites “wherein the operation of determining whether the plurality of devices conform to the smoothness to generate the second determination result comprises:
performing an interpolation operation on the plurality of devices to generate a plurality of virtual devices, and generating a plurality of third slopes according to the plurality of virtual devices;
comparing the plurality of third slopes with the first slope to generate a plurality of second comparison results; and
determining whether the plurality of devices conform to the smoothness according to the plurality of second comparison results, to generate the second determination result.”
Performing an interpolation operation to generate “virtual” devices is a mental process that is equivalent to coming up with devices with data somewhere between that of other devices. For example, if a first device is defined by the parameter set {1, 1, 1} and a second device is defined by the parameter set {3, 3, 3}, a person could reasonably interpolated between the two to create a new “virtual” device defined by the parameter set {2,2,2}
Generating a slopes according to the data is a mental process equivalent to determining linear slopes representative of the data sets; for example, plotting the data points for each set with a pencil and paper and drawing visually estimated lines of best fit through each plot for each data set.
Finally, comparing the slopes to generate a comparison result is a mental process equivalent to observing the slopes and making judgements about certain characteristics between them, such as that they are all positive, negative, etc.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
(1) Claims 1-3, and 6-12 are rejected under 35 U.S.C. 103 as being unpatentable over Reid (US 20160378717 A1) in view of Trombley (US 20100169849 A1)
Claim 1. Reid teaches A device model parameter generation system, comprising: ([Par 47] “The present invention is directed to a method of generating semiconductor device model parameters.”) a user module, for obtaining a plurality of parameter set configurations and a plurality of measurement data of a plurality of devices; ([Par 7-8] “According to a first aspect of the present invention, there is provided a method for execution in at least one processor of at least one computer, the method for generating semiconductor device model parameters, the method comprising the steps of: (a) receiving semiconductor device performance data of a plurality of statistical instances of semiconductor devices, for a plurality of coordinates in process space, wherein at least some of the semiconductor device performance data is obtained, at least in part, from a data set acquired by measurement; [Par 61-62]” At step 302, TCAD simulations of a plurality (n.sub.s=1000) of statistical instances of semiconductor devices are performed for a plurality of coordinates in process space, to produce simulation results (406 in FIG. 4). The TCAD simulation model is calibrated by acquiring a data set by measurements on physical semiconductor devices and fitting the TCAD parameters to the measured data set. … The differences between the statistical instances for a given coordinate in process space relate to different modelled configurations of random variability sources, which affect semiconductor device performance. The random variability sources may comprise at least one of RDD, LER and gate granularity.” [Par 63] “The TCAD simulation results or measurement results thus obtained are received by the computer on which extraction and parameter generation are performed. In both cases, at least some of the semiconductor device performance data is obtained, at least in part, from a data set acquired by measurement.” [Examiner’s note: each random variability source is considered a “parameter set configuration” as each is a configuration that determines how parameters are generated]) a parameter extraction module, coupled to the user module, for performing a plurality of parameter extractions on the plurality of parameter set configurations and the plurality of measurement data, to generate a parameter set; ([Par 7] “According to a first aspect of the present invention, there is provided a method for execution in at least one processor of at least one computer” [Par 9] "(b) extracting model parameters from the semiconductor device performance data to produce a plurality of individual model instances each corresponding to the respective statistical instances for the plurality of coordinates in process space;” “[Par 62] “The differences between the statistical instances for a given coordinate in process space relate to different modelled configurations of random variability sources, which affect semiconductor device performance. The random variability sources may comprise at least one of RDD, LER and gate granularity.” [Par 63] “The TCAD simulation results or measurement results thus obtained are received by the computer on which extraction and parameter generation are performed. In both cases, at least some of the semiconductor device performance data is obtained, at least in part, from a data set acquired by measurement. The data set may comprise current-voltage (IV) characteristics obtained by measurements of test structures or process control monitors (PCM). Such test structures may also be subjected to electrical and thermal stress in order to determine the performance characteristics under the influence of process-induced device variations.” [Par 64] “At step 304, compact model parameters are extracted from the semiconductor device performance data (TCAD simulation results or physical device measurement results) to produce a plurality (n.sub.s*n.sup.m) of individual model instances (410 in FIG. 4) each corresponding to the respective statistical instances (406 in FIG. 4) for the plurality of coordinates in process space.”) a simulation module, coupled to the parameter extraction module, for performing a plurality of simulations according to the plurality of parameter set configurations and the plurality of measurement data, to generate a plurality of simulation results; ([Par 7] “According to a first aspect of the present invention, there is provided a method for execution in at least one processor of at least one computer…” [Par 10-12] “c) modeling statistics of the extracted model parameters by processing the individual model instances to determine, for each coordinate in process space: moments describing non-normal marginal distributions of the extracted model parameters; and correlations between the extracted model parameters” [Par 62] “The differences between the statistical instances for a given coordinate in process space relate to different modelled configurations of random variability sources, which affect semiconductor device performance. The random variability sources may comprise at least one of RDD, LER and gate granularity.” [Par 63] “The TCAD simulation results or measurement results thus obtained are received by the computer on which extraction and parameter generation are performed. In both cases, at least some of the semiconductor device performance data is obtained, at least in part, from a data set acquired by measurement.” [Par 64] “At step 304, compact model parameters are extracted from the semiconductor device performance data (TCAD simulation results or physical device measurement results) to produce a plurality (n.sub.s*n.sup.m) of individual model instances (410 in FIG. 4) each corresponding to the respective statistical instances (406 in FIG. 4) for the plurality of coordinates in process space.” [Par 65] “At step 304, a set of uniform model parameters relating to the uniform semiconductor device are extracted from the semiconductor device performance data (simulation or measurement results) with no variations. The uniform model parameters are used to re-extract from the semiconductor device performance data a subset (n.sub.p) of the uniform model parameters for each of the statistical instances. The subset of model parameters are selected to capture intrinsic statistical variability of semiconductor device performance. The variability arises from intrinsic parameter fluctuations due to configurations of random variability sources that affect semiconductor device performance. The random variability sources may comprise at least one of RDD, LER and poly or metal gate granularity. Statistical variability of semiconductor device performance also arises from process-induced device variations.” [Par 66] “At step 306, statistics of the extracted compact model parameters are modeled by processing the individual model instances to determine, for each coordinate in process space: moments describing non-normal marginal distributions of the extracted compact model parameters; and correlations between the extracted compact model parameters; Response surface modeling may be used to calculate moments and correlations at selected combinations of intermediate process-dependent device parameters. Generalized Lambda Distribution (GLD) parameters may be calculated to fit the interpolated moments by fitting each determined marginal distribution to a Generalized Lambda Distribution (GLD) using the method of moments.”[Par 68] “At step 310, SPICE simulation is performed” [Par 69] “Extraction software 408 performs the step 304 of FIG. 3 to extract parameters for the model cards 410 from the TCAD simulation results 406. These model cards 410 are input to a compact model generator 412, which performs the steps 306 and 308 of FIG. 3” [Par 99] “Computer 714 uses the generated semiconductor device model parameters in a SPICE simulation as part of the IC design flow.”) an analysis module, coupled to the parameter extraction module, for ([Par 7] “According to a first aspect of the present invention, there is provided a method for execution in at least one processor of at least one computer…” [Par 10-12] “c) modeling statistics of the extracted model parameters by processing the individual model instances to determine, for each coordinate in process space: moments describing non-normal marginal distributions of the extracted model parameters; and correlations between the extracted model parameters” [Examiner’s note: modeling the statistics to determine the correlations performs the dual duty of both the simulation and analysis]) ([Par 64] “At step 304, compact model parameters are extracted from the semiconductor device performance data (TCAD simulation results or physical device measurement results) to produce a plurality (n.sub.s*n.sup.m) of individual model instances (410 in FIG. 4) each corresponding to the respective statistical instances (406 in FIG. 4) for the plurality of coordinates in process space.”) [Par 64] “At step 304, compact model parameters are extracted from the semiconductor device performance data (TCAD simulation results or physical device measurement results) to produce a plurality (n.sub.s*n.sup.m) of individual model instances (410 in FIG. 4) each corresponding to the respective statistical instances (406 in FIG. 4) for the plurality of coordinates in process space.”) ([Par 13] “(d) generating semiconductor device model parameters using the determined moments and the determined correlations, for a selected coordinate in process space” [Par 65-67] “At step 304, a set of uniform model parameters relating to the uniform semiconductor device are extracted from the semiconductor device performance data (simulation or measurement results) with no variations. The uniform model parameters are used to re-extract from the semiconductor device performance data a subset (n.sub.p) of the uniform model parameters for each of the statistical instances. The subset of model parameters are selected to capture intrinsic statistical variability of semiconductor device performance…. At step 306, statistics of the extracted compact model parameters are modeled by processing the individual model instances to determine, for each coordinate in process space: moments describing non-normal marginal distributions of the extracted compact model parameters; and correlations between the extracted compact model parameters… At step 308, compact model parameters for SPICE model cards are obtained by generating multivariate Gaussian variates and using the determined moments and correlations, for a selected coordinate in process space. This step may also include applying a Probability Integral Transform to obtain a random sample of variates. The correct moments are attained via the Probability Integral Transform; at the multivariate Gaussian generation stage, the numbers are standard normal.” [Par 95] “To generate parameter instances that follow the specified probabil