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 12/22/2022 Claims 1-13 are presented for examination Information Disclosure Statement The IDS dated 12/22/2022 has been reviewed. See attached. Drawings The drawings dated 12/22/2022 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 12/22/2022 has been reviewed. It has 140 words, and contains no legal phraseology. It is accepted. Claim Interpretation Based on a review of the specification and in view of the plain meaning of the terms, the features are understood to be high-level labels or categories for measurements , metrics , or other attributes related to the building (e.g. actual direct measurements of conditions, aggregate metrics like a high-level ‘ventilation performance’ metric, design elements such as wall thickness, etc , ) the measurements / metrics /attributes themselves being the feature values, with the evaluation values being credits or ratings related to these metrics. For example, an evaluation value might be a ventilation or air comfort score of 8 credits or 80% based on the determination that the feature value for fresh air rate within the building is 0.3 L/sm^2 ; these might both be related to a “ventilation efficiency” feature. See ([Par 74] “ As illustrated in Fig. 4, as the ventilation performance, which is the feature value of Air03, increases, an evaluation value of Air03 increases. That is, it can be seen that Air03 is evaluated more highly by improving the ventilation performance. On the other hand, as the ventilation performance, which is the feature value of Air03, increases, the evaluation value of Comfort82 decreases. That is, it can be seen that Comfort82 is evaluated more poorly by improving the ventilation performance. In other words, the features Air03 and Comfort82 show dichotomy in relation to the feature value of the ventilation performance. ” [Par 76] “I n the area A1, for example, a name of a feature focused upon and a condition of achievement are displayed. In the example illustrated in Fig. 5, the feature focused upon is air category No. 01 "air quality standards" (Air01), and the condition of achievement is "particulate matter less than XX [ppm]" ) The claims make numerous references to a “first feature” and “second features.” It is unclear what the relationship between these features are other than that the “second features” are a plurality of features besides the first feature. With this in mind, anything involving 3 or more different features or metrics is interpreted as containing both a first and second features. Claim 10 recites the term “sub-dependence” in reference to a dependance between an evaluation value and second features. Based on the language of the claim and [Par 89] of the specification describing it as being related to ‘other features’ ( “S ince a target value of a feature (here, Air01) corresponding to the currently displayed feature value and target values (i.e., sub-target values) of other features (here, Air03 and Comfort82) at a time when the feature value is changed are visually indicated on a single axis of change of the feature value, a relationship between these values can be intuitively evaluated. The target values of the other features are set, on the basis of dependence (i.e., sub-dependence) of evaluation values of the other features upon the feature value set for the feature, ” ) the term is interpreted as referring to a dependence between an evaluation value and any feature or features beyond a first feature , i.e. output variable y depends on input x and sub-depends of variables m and b in the equation y=mx+b. Any dependency/correlation determination that allows the determination of more than a single dependence is interpreted as satisfying this. Claim Objections Claim FILLIN "Enter claim indentification information" \* MERGEFORMAT 3 objected to because of the following informalities: Claim 3 recites “based on magnitude…” This is simple grammatical error and should instead read “based on a magnitude” 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 appl icant regards as his invention. Claim s 5, and 10- FILLIN "Enter claim indentification information" \* MERGEFORMAT 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 FILLIN "Enter claim identification information" \* MERGEFORMAT 5 recites the limitation " FILLIN "Enter appropriate information" \* MERGEFORMAT the second feature .” There is insufficient antecedent basis for this limitation in the claim. No individually identified “second feature” was ever previously introduced, merely the set of a plurality of “second feature s” was introduced. As it has only been described in the context of a plurality otherwise, it is unclear which of this set of second features is being referred to in claim 5. Similarly to claim 5, claim 10 recites the limitation " the second feature .” There is insufficient antecedent basis for this limitation in the claim. No individually identified “second feature” was ever previously introduced, merely the set of a plurality of “second feature s” was introduced. As it has only been described in the context of a plurality otherwise, it is unclear which of this set of second features is being referred to in claim 10. Claim FILLIN "Enter claim identification information" \* MERGEFORMAT 11 contains the trademark/trade name FILLIN "Enter appropriate name" \* MERGEFORMAT WELL building standard . Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph. See Ex parte Simpson , 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe FILLIN "Enter appropriate information" \* MERGEFORMAT a particular building standard and, accordingly, the identification/description is indefinite. 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 1-13 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more. Claim 1 (Statutory Category – Process) 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.” A building evaluation method for evaluating a space inside a building on a basis of features used for certain building certification, the building evaluation method comprising: Evaluating a building using a certification standard is a mental process equivalent to observing features of a building and checking them against the requirements of the certification to judge whether those requirements have been met. For example, if a certification requirement is that a building have a ceiling height of at least 10 feet, evaluating the building for that feature would be the process of observing the height of the ceilings of the building to judge if they are at least 10 feet and, based on this judgement, noting if the requirement is met. finding, among the second features, correlation features, whose calculated correlation coefficients satisfy a certain condition and outputting the correlation features. Finding these features is a mental process equivalent to judging the set of features and their associated correlation coefficients to determine which satisfy a certain arbitrarily chosen condition, and indicating these determined features, for example by writing these features out with a pencil and paper. For example, if the chosen condition was that the correlation coefficient be greater than 0, this would consist of writing out all the features that have a correlation coefficient greater than 0. Should it be found that the outputting is not a mental process it is also an example of insignificant post-solution activity. The claims also recite a mathematic concept , particularly: conducting a thermo-fluid analysis on the space using a feature value of the space that is set for a first feature and whose change causes a change in an evaluation value of the first feature, the thermo-fluid analysis being conducted under each of two or more conditions between which the feature value is different; The specification defines such a thermo-fluid analysis as being the solving of an algorithm such as the navier-stokes equations ([Par 54] “ In the thermo-fluid analysis, an algorithm based on a finite element method or a finite volume method, such as Navier-Stokes equations, may be used …”) With this in mind, it is clear that this limitation is merely a textual placeholder for a mathematic calculation, and therefore is merely a mathematic concept. Should it be found that this is not a mathematic concept, it is also an example of mere data gathering and mere instructions to apply. calculating, on a basis of a result of the thermo-fluid analysis, a correlation coefficient of an evaluation value of each of second features in relation to the feature value, the features being the first feature and the second features; and Calculating a numeric value such as a coefficient is merely the process of mathematic calculation, and therefore amounts to no more than a mathematic concept. See (MPEP 2106.04(a)(2)(C) “ A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. ” 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. Data gathering: conducting a thermo-fluid analysis on the space using a feature value of the space that is set for a first feature and whose change causes a change in an evaluation value of the first feature, the thermo-fluid analysis being conducted under each of two or more conditions between which the feature value is different; When recited at such a high level of generality, without any details as to how the analysis is performed, conducting a generic thermo-fluid analysis (i.e. performing a generic CFD analysis) amounts to no more than merely gathering data representative of the output of such an analysis. Should it be found that this is not an example of mere data gathering, it is also an example of mere instructions to apply. Post-solution activity: … outputting the correlation features. Generically “outputting” these features when recited at such a high level of generality, without explaining how this output is produced, is merely the act of presenting the results of the abstract idea. 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: conducting a thermo-fluid analysis on the space using a feature value of the space that is set for a first feature and whose change causes a change in an evaluation value of the first feature, the thermo- fluid analysis being conducted under each of two or more conditions between which the feature value is different; A pplying a computer to perform a generic “thermo- fluid ” 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 the thermo-fluid analysis is “ conducted ” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations . 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”) Step 2B: Claim provides an Inventive Concept? No, as discussed with respect to Step 2A, the additional limitations are insignificant extra-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. Data gathering: conducting a thermo-fluid analysis on the space using a feature value of the space that is set for a first feature and whose change causes a change in an evaluation value of the first feature, the thermo-fluid analysis being conducted under each of two or more conditions between which the feature value is different; When recited at such a high level of generality , without any details as to how the analysis is performed, conducting a generic thermo-fluid analysis (i.e. performing a generic CFD analysis) amounts to no more than gathering data representative of the output of the analysis. 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 Should it be found that this is not an example of mere data gathering, it is also an example of mere instructions to apply. Post-solution activity: … outputting the correlation features. Generically “outputting” these features when recited at such a high level of generality, without explaining how this output is produced, is merely the act of presenting the results of the abstract idea. 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: conducting a thermo-fluid analysis on the space using a feature value of the space that is set for a first feature and whose change causes a change in an evaluation value of the first feature, the thermo-fluid analysis being conducted under each of two or more conditions between which the feature value is different; A pplying a computer to perform a generic “thermo- fluid ” 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 the thermo-fluid analysis is “ conducted ” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations . 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”) 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 13 The elements of claim 13 are substantially the same as those of claim 1 . Therefore, the elements of claim 13 are rejected due to the same reasons as outlined above for claim 1. 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 building evaluation apparatus … an analyzer … a calculator … an outputter” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept. Claim 2 recites “ wherein, in the certain building certification, each of the features is classified as a precondition, which needs to be achieved for the building to obtain the certain building certification, or a non-precondition, which need not be achieved for the building to obtain the certain building certification, and ” This merely clarifies features of the building certification, and therefore is merely an extension of the mental process of evaluating whether building features meet the requirements of the certification. wherein, in the finding, an output priority level of, among the found correlation features, a correlation feature corresponding to one of the preconditions is set higher than an output priority level of a correlation feature corresponding to one of the non-preconditions and the correlation features are output in accordance with set output priority levels. This merely clarifies how the found features are ordered when output, and is therefore merely an extension of the mental process and insignificant post-solution activity. Claim 3 recites “ displaying the output correlation features in modes based on magnitude of the calculated correlation coefficients. ” This merely further clarifies how the found features are presented when output, and is therefore merely an extension of the mental process and insignificant post-solution activity. Claim 4 recites “wherein the certain condition is that an absolute value of the calculated correlation coefficient be larger than a threshold. ” This merely clarifies the form of the certain condition, and is therefore merely an extension of the mental process of judging whether features meet that condition. Further, determining whether the absolute value of a first number is larger than a threshold number is a mental process equivalent to considering the value of each, ignoring the positive/negative sign of the first number, and judging which is larger. This kind of simple mathematic comparison and determination of the absolute value of a number is also mathematic concept. Claim 5 recites “ wherein the certain condition is that, in descending order of an absolute value of the calculated correlation coefficient, the second feature be one of a certain number of features with the largest absolute values. ” This merely clarifies how the certain condition is evaluated, and is therefore merely an extension of the mental process of judging the features to see if they meet the condition. This process could be completed, for example, by listing all the of the features and their associated correlation coefficients, listing them in descending order, and choosing an arbitrary number of features/coefficients from the top of the list. Claim 6 recites “ wherein, in the finding, information regarding the feature value is output along with the correlation features. ” This merely clarifies how the information is output, and is therefore merely an extension of the mental process and insignificant post-solution activity. Claim 7 recites “ wherein, in the finding, a selected feature value and a feature and a correlation feature corresponding to the selected feature value are output ” This merely clarifies how the information is output, and is therefore merely an extension of the mental process and insignificant post-solution activity. Claim 8 recites “ wherein, in the calculating, dependence of the evaluation value of the first feature upon the feature value is also determined on a basis of the result of the thermo-fluid analysis, and ” As an extension of the calculating step, this is also merely part of the calculation and therefore a mathematical process. This type of determination as to the influence each independent variable has on an output variable can be performed mathematically through a variety of techniques such as sensitivity analysis, uncertainty analysis and quantification, aspects of principal component analysis, etc. Further, t his is also equivalent to performing a mental process in conjunction with the simulation for each of the feature values, by varying each of the values, running the simulation, and determining which has the most influence on the evaluation result obtained from the simulation output. For example, if increasing a feature value related to ventilation performance in the simulated space before running the simulation results in an increase in an evaluation related to airflow comfort, but increasing a value related to the illumination level in the building does not have much of an effect on that airflow comfort evaluation, it can be judged that the airflow comfort evaluation value depends more on the ventilation performance than the intensity of the lighting in the building. . “ wherein, in the finding, a relative relationship between a target value that is a level of the feature value necessary for the evaluation value of the first feature to satisfy a reference value, which is provided for the first feature and is a reference for achieving the first feature, and that is calculated from the reference value on a basis of the determined dependence and a current value, which is a level of the feature value under a current condition, is output as the information regarding the feature value. ” This a mental process equivalent to determining how far a particular feature value is from the level necessary to satisfy an associated requirement/evaluation. For example, if an evaluation for air quality is 100% met when detected airborne particulate matter is less than 100 ppm, based on a determination that the evaluation depends on particulate matter concentration, and the current value of airborne particulate matter is 200 ppm resulting in an air quality evaluation of 50%, it can be determined that the current feature value is about twice its target value. This can also be performed through pure mathematic calculation. Claim 9 recites “ wherein each of the features includes two or more sub-features, each of which is provided with a sub-reference value, wherein a reference value is satisfied when an evaluation value of the feature satisfies all the sub-reference values of the two or more sub-features, and wherein a target value is calculated on a basis of calculated dependence using a sub-reference value whose difference from a current value is the largest among the two or more sub-reference values. ” This merely establishes a hierarchy of features and clarifies how the higher-level features are evaluated in view of their child sub-features, which is merely an extension of the mental process and mathematic concept. Further, numerically calculating a target value in such a manner is merely a mathematic calculation. Claim 10 recites “ wherein, in the calculating, sub-dependence of the evaluation value of each of the second features upon the feature value is also determined on a basis of the result of the thermo-fluid analysis, and ” As an extension of the calculating step, this is also merely part of the calculation and therefore a mathematical process. This type of determination as to the influence each independent variable has on an output variable can be performed mathematically through a variety of techniques such as sensitivity analysis, uncertainty analysis and quantification, aspects of principal component analysis, etc. Further, this is also equivalent to performing a mental process in conjunction with the simulation for each of the feature values, by varying each of the values, running the simulation, and determining which has the most influence on the evaluation result obtained from the simulation output. For example, if increasing a feature value related to ventilation performance in the simulated space before running the simulation results in an increase in an evaluation related to airflow comfort, but increasing a value related to the illumination level in the building does not have much of an effect on that airflow comfort evaluation, it can be judged that the airflow comfort evaluation value depends more on the ventilation performance than the intensity of the lighting in the building. Performing this for sub-dependence merely consists of performing this process for a different set of features. “ wherein, in the finding, a relative relationship between a sub-target value that is a level of the feature value necessary for the evaluation value of the second feature to satisfy a sub-reference value, which is provided for the second feature and is a reference for achieving the second feature, and that is calculated from the sub-reference value on a basis of the determined sub-dependence, a target value, and a current value is output as the information regarding the feature value. ” This a mental process equivalent to determining how far a particular feature value is from the level necessary to satisfy an associated requirement/evaluation. For example, if an evaluation for air quality is 100% met when detected airborne particulate matter is less than 100 ppm, based on a determination that the evaluation depends on particulate matter concentration, and the current value of airborne particulate matter is 200 ppm resulting in an air quality evaluation of 50%, it can be determined that the current feature value is about twice its target value. This can also be performed through pure mathematic calculation. Performing this for sub-targets, sub-references, and sub-dependences merely consists of performing this process for a different set of features. Claim 11 recites “ wherein the certain building certification is WELL Building Standard (registered trademark). ” This merely clarifies the standards by which the features of the building are judged, and is therefore merely an extension of the mental process, mathematic concept, mere data gathering, insignificant post-solution activity, and mere instructions to apply. Claim 12 recites “ A non-transitory computer-readable storage medium storing a program for causing a computer to perform the building evaluation method according to claim 1. “ This limitation merely introduces a generic computer structure to perform the method of claim 1. As such, 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 non-transitory computer-readable storage medium storing a program for causing a computer to perform the building evaluation ” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept. 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- 7, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over McLean ( US 20100106674 A1 ) in view of A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost (Hereinafter Wang ) Claim 1. McLean teaches A building evaluation method for evaluating a space inside a building on a basis of features used for certain building certification, the building evaluation method comprising: ([Par 13-16] “ The integrated analysis and design environment 100 is a flexible, integrated system for building performance system assessment. It can enable comparison and evaluation of alternative design strategies, from concept to completion and beyond. The integrated analysis and design environment can evaluate and help maximize the sustainable potential of a building throughout its lifecycle. The integrated analysis and design environment 100 can also interact intelligently with external software products (e.g., computer aided design (CAD), building information modeling (BIM)-importing geometry). … The index comparison module 165 can be configured to generate, different indices (e.g., a climate energy index and a building energy index) and/or to compare such indices. The additional indices module 170 can be configured to generate indices, for example, based on other aspects of climate not already present as a module, such as, but not limited to, wind and water. The integrated analysis and design environment 100 can include: a shared model 120, a solar module 125, a daylight module 130, an energy module 135, a cost module 140, an egress module 145, a mechanical and electrical design (M&E) module 150, a computational fluid dynamics (CFD) module 155, a value module 160, a climate module 127, a materials module 126, and a builder module 121. The integrated analysis and design environment 100 can follow a shared process using the shared model 120: geometry can be created, data can be assigned (e.g., BIM), and analysis can be carried out. For example, within the integrated analysis and design environment 100, a user may use the shared model 120 to create geometry. Apache can then be used to add data (e.g., BIM), such as constructions and activities, and Suncast can then be used to analyze solar information. The Suncast results can also be used through feed back as a precursor to Apache for thermal simulations. Data from Apache simulations can then provide the starting data for a CFD simulation (and so on). This can result in better productivity and holistic design. ” [Par 22] “ The daylight module 130 can be configured to test the look and performance of different lighting designs, including prediction of light levels, maximization of daylight, minimization of glare, visualize ambiance for different configurations, and test Leadership in Energy and Environmental Design (LEED) daylight rating. ” [Fig. 3A] Shows that LEED, BREEM, and Greenstar credits are evaluated, note that these are all building certification standards.) conducting a thermo-fluid analysis on the space using a feature value of the space that is set for a first feature and whose change causes a change in an evaluation value of the first feature, the thermo-fluid analysis being conducted under each of two or more conditions between which the feature value is different; ([Par 21] “ The CFD module 155 can be configured to simulate airflow, ensure optimum ventilation in a design, produce detailed comfort predictions for different areas of a room, assess strategies such as ventilated facades, for example, and/or visualize results and communicate such results with graphics. ” [Par 32] “ At 225, an analysis of the simulation results can be performed to produce metrics associated with the building (e.g., building energy use intensity kwh/m.sup.2 yr, glazing area % of wall area, heating load w/m.sup.2, fresh air rate L/sm.sup.2, surface area to volume ration, etc.). The simulation results can also produce patterns (e.g., peaks, averages, trends, range testing, coincidence of variables, selected metric(s) value testing, etc.) that, when analyzed, also contain metric information. The metrics can be associated with (and grouped into) climate information, natural resources information, urban design information, building form information, building thermal information, building light information, materials information, water information, and/or sustainability information. ” [Fig. 3A] Shows that LEED, BREEM, and Greenstar credits are evaluated (equivalent to evaluation values ) [Par 54] “ FIGS. 10A-10L illustrate another example of a working navigator, according to one embodiment. FIG. 10A illustrates a user interface, according to one embodiment. FIG. 10B illustrates the user interface with the drop down menu of navigator workflow options 1005, according to one embodiment. FIG. 10C illustrates a climate navigator 1015 that has been chosen, according to one embodiment. FIG. 10D illustrates the climate navigator 1015 with user notes 1020. FIG. 10E illustrates the climate navigator with the climate metrics 1025 chosen and a climate report generated, according to one embodiment. FIG. 10F illustrates the climate navigator with the time/date/user stamps 1030 utilized, according to one embodiment. FIG. 10G illustrates a model data navigator 1035, according to one embodiment. FIG. 10H illustrates the model geometry navigator 1035 with the location and weather item 1040 (completed in the previous climate navigator 1015) already completed. This illustrates how navigator components can be kept up to date in all workflows as workflows are changed. FIG. 10I illustrates the model geometry navigator 1035 after progressing through the workflow sections checked off (1045) (e.g., can be in a color such as green) so the user can minimise the groups to see progress and required actions. FIG. 10J illustrates a sustainability navigator 1050, according to one embodiment, where a generated report can be produced including a dynamic "video" of the daylight results and a dynamic table of the results. FIG. 10K illustrates a water navigator 1055, where a generated report of water simulation is produced including charts and result details, according to one embodiment. FIG. 10L illustrates a LEED navigator 1060, where a generated report can be produced including a dynamic "video" of the comfort results, a dynamic table of the results, and the credits likely to be achieved with the design, according to one embodiment. ” [Fig 10L] Notice how several analysis runs with different conditions are present in the Analysis Overview list. [Par 35] “ The metrics shown in FIGS. 3A-3H are examples of determined output from 225 in FIG. 2. Note these are just examples of groups, and that many other groups can be utilized. ” [Examiner’s note: the system of performs several analyses of the building under different conditions using CFD simulation (see the multiple analysis runs listed in Fig. 10L,) generates metrics based on those simulations (equivalent to feature values) and based on those generated metrics create s results and reports including credits/ratings using certification standards (equivalent to evaluation values)] calculating, on a basis of a result of the thermo-fluid analysis, ([Par 21] “ The CFD module 155 can be configured to simulate airflow, ensure optimum ventilation in a design, produce detailed comfort predictions for different areas of a room, assess strategies such as ventilated facades, for example, and/or visualize results and communicate such results with graphics .”) a correlation coefficient of an evaluation value of each of second features in relation to the feature value, the features being the first feature and the second features; and ([Par 32] “ At 225, an analysis of the simulation results can be performed to produce metrics associated with the building (e.g., building energy use intensity kwh/m.sup.2 yr, glazing area % of wall area, heating load w/m.sup.2, fresh air rate L/sm.sup.2, surface area to volume ration, etc.). The simulation results can also produce patterns (e.g., peaks, averages, trends, range testing, coincidence of variables, selected metric(s) value testing, etc.) that, when analyzed, also contain metric information. The metrics can be associated with (and grouped into) climate information, natural resources information, urban design information, building form information, building thermal information, building light information, materials information, water information, and/or sustainability information. ” [Par 35] “ FIGS. 3A-3H show examples of metrics. FIG. 3A illustrates many types of metrics: climate, urban, building form, building thermal, building light, building water, and sustainability . … FIG. 3H illustrates sustainability metrics. ” [Examiner’s note: the described ‘sustainability metrics’ include the credits for various certification standards, i.e. evaluation metrics. Testing for coincidence of variables including these sustainability metrics and other metrics, as described, is equivalent to determining the correlation between the various feature values and evaluation values ]) finding, among the second features, ([Par 35] “ FIGS. 3A-3H show examples of metrics. FIG. 3A illustrates many types of metrics: climate, urban, building form, building thermal, building light, building water, and sustainability. ) correlation features, whose calculated correlation coefficients satisfy a certain condition and outputting the correlation features. McLean does not explicitly teach calculating a correlation coefficient; finding correlation features, whose calculated correlation coefficients satisfy a certain condition and outputting the correlation features. Wang makes obvious calculating a correlation coefficient; finding correlation features, whose calculated correlation coefficients satisfy a certain condition and outputting the correlation features. ([Page 9 Col 1 Par 1 – Col 2 Par 3] “ PRCC, the sensitivity index, derived from MLR is applied to analyze the main factors affecting building performance. The larger the sample size, the more stable the indicators will be. … The input variables involved include not only thermo-physical parameters but also parameters that have potential effects on EUI and thermal comfort such as WWR. SA has the complementary role of ordering by importance, the strength, and relevance of the inputs in determining the variation in the output. The SA results are determined and motivated by the thermo-physical characteristic, and also affected by the uncertainty of input variables [ HYPERLINK "https://www.sciencedirect.com/science/article/pii/S0360544219324181" \l "bib41" 41 ]. For variables that are highly correlated in thermo-physical properties, it is also possible to have a relatively low order of influence when the parameter uncertainty (constrained by PH standards) is small. For the EUI, PRCC and R2 of the regression model for different sample sizes are exhibited in HYPERLINK "https://www.sciencedirect.com/science/article/pii/S0360544219324181" \l "fig6" Fig. 6 and HYPERLINK "https://www.sciencedirect.com/science/article/pii/S0360544219324181" \l "tbl4" Table 4 . For different sample sizes, the PRCCs are slightly different but the overall trend can be used as the basis for measuring important variables, and the fitting performance of the regression model is perfect with R2 higher than 0.90. This indicates that the regression analysis model is robust. ACH (x9) is recognized as the most important contributor. It accounts for over 95% of the explainable output variation. The U-value of the external wall (x1) and roof (x4), as well as the WWR of the south (x14) are ranked after and contribute about 40%. The U-value of the external window (x7), the SHGC (x8) and WWR of the north (x16) have some influence with PRCC about 30%. The solar absorptance of coating (x10 and x11) also has some influence on PRCC, about 10%. The remaining factors are considered relatively less important with individual contributions less than 10%. In addition, the impact of variables on the annual cooling load and the annual heating load also are shown in Fig. 6 . The results of SA of EUI are different from both HEUI and CEUI, which indicates that the EUI is not only determined by the cooling or the heating energy demand. Finally, obtaining the PRCC indices of CTR. The model achieved acceptable fitting performance with R 2 of 0.83. For CTR, the density (x 5 ) and heat capacity (x 6 ) of roof and SHGC (x 8 ) are the most important factors with PRCC around 0.6. Followed by WWR (x 13 -x 16 ) and the length of overhang (x 19 ). The U-value of window (x 7 ), ACH (x 9 ), building orientation (x 12 ) and fin depth (x 20 ) have almost no impact on CTR. ” [Fig. 6] shows model output, showing the correlation coefficient (PRCC) of each feature/variable [Examiner’s note: determining the most important variables/features that have the highest influence/correlation, as described above, is equivalent to finding features whose correlation coefficients satisfy a certain condition, that condition being that the correlation coefficients for those variables/features be higher than those of other variables/features] ) Wang is analogous art because it is within the field of building optimization. It would have been obvious to one of ordinary skill in the art to combine it with Mc L ean before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to better optimize the building for energy efficiency and eco-friendliness. As noted by Wang, the demand for buildings that follow the highly efficient “passive house” model has grown significantly in recent years. However, previous optimization methods based on this model have had significant drawbacks, such as limited optimization scope, resulting in designs that are not fully optimal ([Page 1 Col 1 Par 1] “Population growth, increased demand for indoor environment and global warming have led to a sharp increase in energy consumption for buildings heating and cooling, which accounts for 20% of global energy consumption [ HYPERLINK "https://www.sciencedirect.com/science/article/pii/S0360544219324181" \l "bib1" 1 ]. Especially, the energy consumption by the residential building is increasing at approximately 30% annually worldwide [ HYPERLINK "https://www.sciencedirect.com/science/article/pii/S0360544219324181" \l "bib2" 2 ]. Therefore, developing sustainable buildings has increasingly become a very important task, and Passive House (PH) has emerged as the preferred concept for architects and subject for researchers in most countries. PH are buildings that need 80%–90% less heating energy than conventional buildings to provide comfortable indoor conditions, while the incremental cost of their construction is only 5%–10% [ HYPERLINK "https://www.sciencedirect.com/science/article/pii/S0360544219324181" \l "bib3" 3 ]. Many countries have introduced PH standards. ” [Page 2 Col 2 Par 3 – Page 3 Col 1 Par 1] “ The existing problems on PH buildings: Most research related to PH buildings focuses on performance assessments, some of which only raised the phenomenon of the indoor overheating risk in PH buildings, but did not propose solutions to solve the problem. In addition, PH standards have only constrained the range of energy consumption and certain design parameters, but it is difficult to guarantee an optimal solution. In summary, there is still a lack of systematic optimization methods to guide the passive design of PH buildings in engineering applications. ”) To this end, Wang presents an improved optimization model that better considers windowing and ventilation to more accurately model the building, as well as financial analysis, ultimately resulting in better optimization. ([Page 3 Col 1 Par 2] “ The corresponding innovation: This paper establishes an optimization model for PH buildings. Different from previous research, the optimization model considers the effect of windowing for natural ventilation on the indoor thermal environment. The relationship between 20 passive design parameters and two building properties including energy demand and thermal comfort was constructed and the optimization scheme was explored under the constraints of PH standards. Finally, an economic analysis of the Pareto frontier solution was carried out. The optimization framework produces more practical and detailed design guidance. ”) Overall, one of ordinary skill in the art would have recogni