DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Benjamin et al. (US 2021/0150083) (hereinafter Benjamin).
Regarding claims 1, 11, and 20, Benjamin teaches a computer-implemented method, computer-readable media (ph. [0027],[0029], memory 116 and 136), and system comprising memory and processor (ph. [0026], client 110 processor 112 and memory 116; ph. [0028], server 130 processor 132 and memory 136) to perform the method, the method for generating a modular design for a construction project, the method comprising:
executing one or more geometry optimization systems to generate a first plurality of design options for the construction project, wherein each design option included in the first plurality of design options comprises a site layout and a set of building designs for a set of buildings included in the site layout (ph. [0037], “generate successive generations of design options 222. Each design option 222 describes a multi-family housing project corresponding to the parcel of land described in site information 214. Design generator 220 performs various geometric construction techniques to generate, for each design option 222, geometry that describes one or more buildings, parking lots and spaces, walkways, landscaping features, and other design elements typically found in multi-family housing projects.”);
for each design option included in the first plurality of design options:
computing a first set of performance metrics for the site layout associated with the design option (fig. 6, step 604; ph. [0061], “At step 604, design evaluator 330 generates a first metric that quantifies compliance of the first design option 222 with a first construction regulation. In particular, geometric evaluator 234 within design evaluator performs a geometric analysis on the first design option 222 using parcel data 312, structural data 314, and material data 316 to generate structural metrics 330. Each structural metric 332 represents the degree to which the first design option 222 meets specific geometric criteria, including those associated with construction regulations. For example, a given structural metric 330 could indicate a degree to which the first design option 222 complies with a maximum FAR value associated with the parcel of land.”) and a second set of performance metrics for the set of building designs associated with the design option (fig. 6, step 606; ph. [0062], “At step 606, design evaluator 330 generates a second metric that quantifies performance of the first design option 222 relative to a first financial target. Specifically, financial evaluator 236 within design evaluator 330 executes a performance analysis on the first design option 222 using parcel data 312, structural data 314, and material data 316 to generate financial metrics 332 included in design metrics 232. Each financial metric 332 represents the degree to which the first design option 222 performs according to a specific financial target. For example, a given financial metric 332 could indicate a total construction cost associated with the first design option 222.”); and
aggregating the first set of performance metrics and the second set of performance metrics into a set of overall performance metrics for the design option (fig. 6, step 608; ph. [0063], “At step 608, design generator 220 computes a sensitivity analysis on a design space that includes the first design option 222 to determine a plurality of design features that are correlated with the first metric and the second metric. In so doing, design feature recombinator 310 within design generator 220 identifies specific design features of the first design option 222 that contribute to the Pareto efficiency of that design option 222 relative to design metrics 232.”); and
generating a first set of candidate designs for the construction project based on the first plurality of design options and the sets of overall performance metrics associated with the first plurality of design options (fig. 6, step 610; ph. [0063]-[0064], “determine a plurality of design features that are correlated with the first metric and the second metric. In so doing, design feature recombinator 310 within design generator 220 identifies specific design features of the first design option 222 that contribute to the Pareto efficiency of that design option 222 relative to design metrics 232. These design features correspond to the position of the first design option 222 within the design space. At step 610, design generator 220 generates a second design option that includes the plurality of design features. In particular, design feature recombinator 310 within design generator implements a recombination process with the design features derived from the first design option 222 to produce a subsequent generation of design options 222 that includes the second design option 222. Design feature recombinator 310 may implement any technically feasible approach when doing so, including techniques derived from genetic programming, multi-objective optimization, and so forth. In one embodiment, only a subset of the design options 222 included in the first generation of design options 222 includes the plurality of design features, while each design option 222 included in the subsequent generation of design options 222 includes the plurality of design features.”).
Regarding claims 2 and 12, Benjamin teaches the computer-implemented method of claim 1 and media of claim 11, further comprising:
generating a second plurality of design options based on one or more modifications to the first set of candidate designs; and generating a second set of candidate designs for the construction project based on the second plurality of design options and at least one additional set of performance metrics associated with the second plurality of design options. (ph. [0039], “Design evaluator 230 generates design metrics 232 via geometry evaluator 234 and financial evaluator 236 and then provides those design metrics to design generator 220 in order to inform the generation of additional design options 222. In this manner, design generator 220 and design evaluator 230 operate iteratively in order to generate successive generations of design options 222 having design metrics 232 that improve over time while continuing to meet design criteria 212 and construction regulations 216…In various embodiments, multi-objective solver 224 implements one or more evolutionary and/or genetic algorithms in order to modify design options 222, based on corresponding design metrics 232, to generate successive generations of design options 222. Via the above techniques, design generator 220 is configured to explore a vast multi-dimensional design space that includes a multitude of design options 222 having widely varying characteristics.”).
Regarding claims 3 and 13, Benjamin teaches the computer-implemented method of claim 1 and media of claim 11, further comprising
generating a second plurality of design options by performing at least one of: mutating one or more attributes of a candidate design included in the first set of candidate designs; or replacing a first attribute of a first candidate design included in the first set of candidate designs with a second attribute. (ph. [0039], “In various embodiments, multi-objective solver 224 implements one or more evolutionary and/or genetic algorithms in order to modify design options 222, based on corresponding design metrics 232, to generate successive generations of design options 222. Via the above techniques, design generator 220 is configured to explore a vast multi-dimensional design space that includes a multitude of design options 222 having widely varying characteristics.”).
Regarding claims 4 and 14, Benjamin teaches the computer-implemented method of claim 1 and media of claim 11, further comprising: determining a set of attributes associated with the first set of candidate designs and one or more modifications to the first set of candidate designs; and executing the one or more geometry optimization systems to generate a second plurality of design options based on the set of attributes (ph. [0037], “design generator 220 may include a multi-objective solver 224 that is configured to implement a set of mathematical optimization algorithms to generate the various design elements found in each design option 222.”; ph. [0038], “design evaluator 230 includes a geometry evaluator 234 that evaluates geometric attributes of design options 222 as well as a financial evaluator 236 that evaluates financial attributes of design options 222. Geometric evaluator 234 can generate any technically feasible type of metric associated with the geometry of a given design option 222, including the total square footage of the design option, the overall habitable volume of the design option, the floor area ratio (FAR) of the design option, the building coverage ratio (BCR) of the design option, and so forth. Financial evaluator 236 can generate any technically feasible type of metric that describes financial projections for a given design option 222, including the construction cost of the design option, the projected rental revenue of the design option, the return-on-investment (ROI) associated with the design option, and so forth.”; ph. [0039], ““In various embodiments, multi-objective solver 224 implements one or more evolutionary and/or genetic algorithms in order to modify design options 222, based on corresponding design metrics 232, to generate successive generations of design options 222. Via the above techniques, design generator 220 is configured to explore a vast multi-dimensional design space that includes a multitude of design options 222 having widely varying characteristics.”).
Regarding claims 5 and 15, Benjamin teaches the computer-implemented method of claim 1 and media of claim 11, wherein executing the one or more geometry optimization systems comprises:
determining a set of input parameters associated with the construction project; and generating, via the one or more geometry optimization systems, the first plurality of design options based on the set of input parameters (ph. [0023], “the design engine and design evaluator interoperate iteratively in the above manner to generate successively improved generations of design options. During or after any given iteration, one or more of the design options can be modified based on user input.”; ph. [0040], “design generator 220 is configured to obtain configuration data 242 from a user 240 and to then generate and/or modify design options 222 based on that data. Configuration data 242 may include any technically feasible type of data that can be used to guide the generation of design options 222, including financial targets for design options 222, design preferences related to the layout and/or orientation of buildings within the multi-family housing project, target occupancy metrics for the multi-family housing project, design rules and/or constraints that reflect various aesthetic preferences of user 240 and/or the landowner, landowner feedback in general, and so forth.”; ph. [0048], “Pareto analyzer 300 analyzes design options 222 based on some or all design metrics 232 to determine a subset of design options 222 that reside along a Pareto frontier relative to those design metrics. Each design option 222 included in the subset may be considered Pareto efficient relative to other design options 222 because the design metrics corresponding to those design options 222 strike an optimal balance relative to one another. Pareto analyzer 300 can perform the above analysis based on any combination of design metrics 232 to determine any subset of design options 222 considered Pareto efficient. Persons skilled in the art will understand that both “Pareto frontier” and “Pareto efficient” are well known terms in the field of multi-objective optimization.”).
Regarding claim 6, Benjamin teaches the computer-implemented method of claim 5, wherein the set of input parameters includes at least one of a site boundary, a set of building modules, one or more example building layouts, a number of buildings, a setback requirement, a separation between buildings, or a building size (ph. [0035], “Site information 214 includes data and metadata associated with a given parcel of land, including a geographic location, a set of property boundaries, a property area value, geometric attributes of the parcel such as topology and/or topography, and so forth.”; ph. [0036], “Site analyzer 210 processes site information 214 and construction regulations 216 to synthesize a set of design criteria 212 that feasible designs should meet. A given design criterion 212 may include a design objective or a design constraint. A design objective could be, for example, that designs should maximize the amount of area that is devoted to buildings versus landscaping or other design features. A given design constraint could be, for example, that designs should not include buildings that are placed outside of property boundaries or exceed a given height.”).
Regarding claims 7 and 16, Benjamin teaches the computer-implemented method of claim 1 and media of claim 11, wherein generating the first set of candidate designs comprises: generating a ranking of the first plurality of design options based on the sets of overall performance metrics associated with the first plurality of design options; and selecting the first set of candidate designs based on the ranking (ph. [0054], “Pareto analyzer 300 within design generator 220 computes a Pareto frontier 420 that includes design options 222 which optimally balance BCR and ROI values relative to other design options 222. Pareto analyzer 300 also filters design options 222 that reside along Pareto frontier 420 to eliminate design options 222 having disallowed BCR values that fall beneath minimum BCR value 430 or above maximum BCR value 432, In this manner, Pareto analyzer 300 computes subset 422 of design options 222. Design feature recombinator 310 subsequently extracts a set of design features from subset 422 that may promote both desirability and feasibility, and then generates a subsequent generation of design options 222 that incorporates these design features.”).
Regarding claim 8, Benjamin teaches the computer-implemented method of claim 1, wherein the first set of performance metrics and the second set of performance metrics are computed based on one or more simulations (ph. [0041], “In various embodiments, design engine 120 may perform computer simulations of various design elements included in each design option 222 to generate one or more designs.”; ph. [0021], “The design evaluator includes a geometric evaluator that performs a geometric analysis on the design options using the aforesaid datasets to generate a set of structural metrics. A given structural metric represents the degree to which a corresponding design option meets specific geometric criteria, including those associated with construction regulations. The design evaluator also includes a financial evaluator that performs a performance analysis on the design options using the aforesaid datasets to generate a set of financial metrics. A given financial metric represents the degree to which a corresponding design option performs according to a specific financial target.”).
Regarding claim 9, Benjamin teaches the computer-implemented method of claim 1, wherein the first set of performance metrics and the second set of performance metrics include at least one of a construction cost, a material cost, an operating income, a carbon accounting metric, or a habitability metric (ph. [0038], “Financial evaluator 236 can generate any technically feasible type of metric that describes financial projections for a given design option 222, including the construction cost of the design option, the projected rental revenue of the design option, the return-on-investment (ROI) associated with the design option, and so forth.”).
Regarding claims 10 and 19, Benjamin teaches the computer-implemented method of claim 1 and media of claim 11, wherein each design option included in the first plurality of design options further includes at least one of a façade, a landscape, a unit layout, a tree layout, a window design, a wall design, or a door design (ph. [0037], “Each design option 222 describes a multi-family housing project corresponding to the parcel of land described in site information 214. Design generator 220 performs various geometric construction techniques to generate, for each design option 222, geometry that describes one or more buildings, parking lots and spaces, walkways, landscaping features, and other design elements typically found in multi-family housing projects.”).
Regarding claim 17, Benjamin teaches the one or more non-transitory computer-readable media of claim 11, wherein executing the one or more geometry optimization systems comprises: executing a first geometry optimization system to generate a plurality of site layouts for the construction project; and for each site layout included in the plurality of site layouts, executing a second geometry optimization system to generate a set of building layouts for the set of buildings included in the site layout (ph. [0040], “Configuration data 242 may include any technically feasible type of data that can be used to guide the generation of design options 222, including financial targets for design options 222, design preferences related to the layout and/or orientation of buildings within the multi-family housing project, target occupancy metrics for the multi-family housing project”; ph. [0044], “During an initial iteration, design generator 220 generates design options 222 based on design trends 202, design criteria 212, and configuration data 242 according to the techniques described in conjunction with FIG. 2.”; ph. [0045], “Geometric evaluator 234 performs a geometric analysis on each design option 222 using parcel data 322, structural data 324, and material data 326 to generate structural metrics 330 included in design metrics 232. Each structural metric 330 represents the degree to which a corresponding design option 222 meets specific geometric criteria, including those associated with construction regulations. For example, a given structural metric 330 could indicate a degree to which a design option 222 complies with a maximum FAR value associated with the parcel of land. In another example, a given structural metric 330 could indicate a degree to which a building design included in a design option 222 intersects a slant plane associated with the parcel of land.”; ph. [0047], “Design evaluator 230 provides design metrics 232 to design generator 220, thereby initiating a subsequent iteration. During the subsequent iteration, design generator 220 implements Pareto analyzer 300 and design feature recombinator 310 to analyze design options 222 using design metrics 232 in order to generate a subsequent generation of design options 222.”; ph. [0048], “Pareto analyzer 300 analyzes design options 222 based on some or all design metrics 232 to determine a subset of design options 222 that reside along a Pareto frontier relative to those design metrics. Each design option 222 included in the subset may be considered Pareto efficient relative to other design options 222 because the design metrics corresponding to those design options 222 strike an optimal balance relative to one another. Pareto analyzer 300 can perform the above analysis based on any combination of design metrics 232 to determine any subset of design options 222 considered Pareto efficient. Persons skilled in the art will understand that both “Pareto frontier” and “Pareto efficient” are well known terms in the field of multi-objective optimization.”).
Regarding claim 18, Benjamin teaches the one or more non-transitory computer-readable media of claim 17, wherein executing the one or more geometry optimization systems further comprises executing a third geometry optimization system to generate a set of design details for at least one of the site layout or a building layout included in the set of building layouts (ph. [0050], “Design generator 220 provides the subsequent generation of design options 222 to design evaluator 230, and the process described above then repeats across multiple iterations and multiple successive generations of design options. Each successive generation of design options 222 may have improved design metrics 232 relative to a previous generation of design options 222.”; ph. [0040], “Configuration data 242 may include any technically feasible type of data that can be used to guide the generation of design options 222, including financial targets for design options 222, design preferences related to the layout and/or orientation of buildings within the multi-family housing project”; ph. [0044], “In so doing, design evaluator 230 generates parcel data 322 that describes geometric aspects of the parcel of land, structural data 324 that describes the structural configuration of design options 222”; ph. [0037], “Design generator 220 performs various geometric construction techniques to generate, for each design option 222, geometry that describes one or more buildings, parking lots and spaces, walkways, landscaping features, and other design elements typically found in multi-family housing projects. In one embodiment, design generator 220 may include a multi-objective solver 224 that is configured to implement a set of mathematical optimization algorithms to generate the various design elements found in each design option 222.”).
Response to Arguments
§102 Rejection Argument
Applicants argue:
“That is, Benjamin uses the first metric and the second metric separately to identify correlations between particular features of the design option and performance according to the respective metrics. In that regard, Benjamin does not disclose combining or otherwise aggregating the first metric and the second metric to generate a set of overall performance metrics for the design option, as recited in claim 1.” (Remarks, pg. 9)
However, the examiner respectfully disagrees because finding the Pareto optimal solution between the first and second metrics is well within the broadest reasonable interpretation of the term aggregation. To aggregate means to “bring different things together”1. As explained in the rejection Benjamin teaches a set of design metrics 232 which includes structural metrics 330 (ph. [0045], “generate structural metrics 330 included in design metrics 232.”) and the financial metrics 332 (ph. [0046], “generate financial metrics 332 included in design metrics 232.”). The fact that the design metrics 330 and financial metrics 332 are brought together as the set of design metrics 232 should read on the aggregation. However, Benjamin goes beyond that and after generating structural metrics 330 in step 604 (ph. [0061]), and generating a second set of financial metrics 332 in step 606 (ph. [0062]), it performs the further aggregating step of finding the Pareto efficient combination/solution of the various metrics in the set of design metrics 232 that includes structural metrics 330 and financial metrics 332 (see step 608; ph. [0063] as mapped to the aggregation step above). So, even if one doesn’t consider the combining of the metrics 330 and metrics 332 into metrics 232 to be aggregation, this finding of the Pareto optimal solution/combination of the first metric and the second metric (as well as other metrics in the multi-objective optimization) most certainly reads on the broadest reasonable interpretation of aggregation (ph. [0048], “Pareto analyzer 300 analyzes design options 222 based on some or all design metrics 232 to determine a subset of design options 222 that reside along a Pareto frontier relative to those design metrics. Each design option 222 included in the subset may be considered Pareto efficient relative to other design options 222 because the design metrics corresponding to those design options 222 strike an optimal balance relative to one another. Pareto analyzer 300 can perform the above analysis based on any combination of design metrics 232 to determine any subset of design options 222 considered Pareto efficient. Persons skilled in the art will understand that both “Pareto frontier” and “Pareto efficient” are well known terms in the field of multi-objective optimization.”).
Accordingly, Applicants’ argument is found to be unpersuasive, and the rejection has been maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Standford, “Pareto Optimality” (Jan 12, 2021) teaches Pareto multi-objective optimization.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN W WATHEN whose telephone number is (571)270-5570. The examiner can normally be reached M-F 9-5:30pm.
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BRIAN W. WATHEN
Primary Examiner
Art Unit 2151
/BRIAN W WATHEN/ Primary Examiner, Art Unit 2151
1 https://dictionary.cambridge.org/us/dictionary/english/aggregate
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