DETAILED ACTION
Claims 1 – 22 have been presented for examination.
This office action is in response to submission of the amendments on 06/16/2025.
The instant office action relies on Choi et at. “AN INTELLIGNENT, INTEGRATED BUILDING DESIGN SYSTEM” and Sarao et al. (WO 2016/138531) and Segev et al. (US 2021/0073449) which are cited on the IDS.
Response to Provisional Application Support for the Present Claims
Applicant’s arguments have been fully considered and they are persuasive. Therefore, the effective priority date of the instant application is to the provisional application dated 09/14/2020.
Response to Objections to the Specification
Applicant’s amendments to the specification overcome the objection. Therefore, it is withdrawn.
Response to Double Patenting
Applicant requests that the double patenting rejections be held in abeyance. In order for a response to be fully responsive, it must respond to every ground of rejection in the prior Office action, and can hold objections in abeyance (see MPEP 714.02). Examiner notes that the double patenting is a rejection since it is to an issued US Patent (i.e., it is not a provisional rejection). Notwithstanding Applicant's request to hold the double patenting rejection in abeyance, Applicant's reply appears throughout to be a bona fide attempt to advance the application. Therefore, Applicant's reply is given further examination.
Response to Claim Rejections under 35 U.S.C. § 101
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “In addition, the amended claims are not directed towards mental processes because the claimed steps are not practically performed in the human mind or using pen/paper. See MPEP § 2106.04(a)(2)(111). Specifically, the amended claims recite the specific step of selecting, via a machine learning model executed by a processor, one or more column locations from a set of candidate column locations based on a structural stability associated with the one or more column locations. This step quite clearly requires the use of a computing device is not a step that can be performed in someone's mind or using pen/paper. In particular, a machine learning model cannot be executed by a processor to select column locations from candidate column locations based on structural stability without use of a computing device” (emphasis added)
Applicant argues that the claimed invention cannot be executed wit the use of a computing device. Examiner notes that implementing an abstract idea using a generic computer cannot provide an inventive concept. Further, it is clearly disclosed that the claimed invention was previously performed by a structural engineer (see the instant application Paragraph 3 “structural design, structural engineers typically place columns in floorplans of architectural building designs provided by their clients”).
Applicant argues: “In that regard, the claimed approach is directed towards the practical application of enabling utilizing a machine learning model to select column locations in a structural design based on structural stability of the column locations. See Application, paragraphs [0063] - [0064]. Through this practical application, the claimed approach imparts the technological improvement of automatically performing column placement in a structural design via machine learning techniques to expedite the column placement task, while consuming fewer computing resources. See id., paragraph [0064].” (emphasis added)
Applicant argues that the recited “selecting” amounts to a practical application. Examiner notes that an abstract idea cannot be a practical application of itself, and the recited “selecting” is part of the abstract idea but for the recitation of generic computer components.
Applicant further argues that the claimed invention is “enabling utilizing a machine learning model” resulting in a technological improvement to “expedite the column placement task” for performance of the “selecting”. Examiner notes that claim 1 does not recite the manner in which the machine learning model is trained. Further, claim 8 recites using a reward function that includes a first reward for placing a column at a preferred column location, without reciting any details of the implementation reward function itself. Therefore, the recitation of “via a machine learning model executed by a processor” amounts to generically implementing the “selecting” on a general-purpose computer such that it amounts to mere automation of a manual process which does not amount to an improvement in computer functionality (see MPEP 2106.05(a)(I) “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase”). Therefore, the “expedite the column placement task” results merely from automating the abstract idea.
Applicant further argues that the claimed invention results in “consuming fewer computing resources”. Looking to the disclosure, the consumption of fewer computing resources is linked to an RL agent in combination with various user actions with regard to placement rules (see the instant application Paragraph 8). Looking to the claimed invention, this specific combination of user actions and placement rules does not appear to be explicitly recited, and the disclosure is that the column placement task “may be performed more efficiently and/or consume fewer resources” (see the instant application Paragraph 8). Therefore, Applicant’s argument that the invention as claimed results in “consuming fewer computing resources” is not persuasive.
Applicant argues: “In addition, these limitations place substantive and meaningful limits on the scope of the claims and similarly place substantive and meaningful limits on any purported abstract idea, thereby integrating any purported abstract idea into the practical application of generated computer-aided designs.
In that regard, the present Application makes clear that a technical problem that existed in the prior art prior to the development of the claimed approach was that in the conventional structural design technique, engineers manually place columns in floorplans of architectural building designs using structural design software. This conventional design technique is tedious and time-consuming, especially for buildings that do not have the same architectural layout on every floor(~, residential buildings). In residential projects with medium complexity, structural engineers may spend days or a week identifying and reviewing appropriate column locations for a building design. See Application, paragraphs [0003] - [0004] and [0008]. The present Application also makes clear that one of technical advantages of the claimed approach is that the claimed approach utilizes a machine learning model to automatically select column locations in the structural design based on structural stability of the column locations. See id., paragraphs [0063] - [0064]. This new functionality of the claimed approach addresses the deficiencies in conventional structural design, which resulted in inefficient and time-consuming manual placement of column locations in a building design. See id., paragraphs [0003] - [0004] and [0008]. Thus, among other things, the claimed approach solves the above technical problem that existed in the prior art.”
Applicant’s arguments about substantive and meaningful limits on the abstract idea, and addressing the deficiencies with manual column placement are not persuasive based on the preceding remarks.
Response to Claim Rejections under 35 U.S.C. § 103
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “In the rejections, the Examiner maps the machine learning model, recited in prior claim 1, to the genetic algorithm, disclosed in Grierson; the one or more column locations, recited in prior claim 1, to one or more column-member locations within the design topology, disclosed in Grierson; and the structural stability associated with the one or more column locations, recited in prior claim 1, to the performance constraints associated with the one or more column-member locations, disclosed in Grierson. See Office Action, pages 21-22. However, Grierson does not teach or suggest any element corresponding to the set of candidate column-member locations, as recited in amended claim 1. Rather, Grierson discloses only that the topological variables can refer to different patterns of column members and a particular design topology can include or not include a left column member for a frame. See Grierson, page 151, left column; page 152, left column; Figs. 1-2. However, Grierson does not teach or suggest selecting one or more column-member locations from a set of candidate column-member locations. Grierson is silent in this regard.” (emphasis added)
Applicant argues that the teaching of Grierson of “a particular design topology can include or not include a left column member for a frame” does not teach the amended claim 1 “selecting, via a machine learning model executed by a processor, one or more column locations from a set of candidate column locations based on a structural stability associated with the one or more column locations”. Examiner notes that the amended portion of claim 1 does not meaningfully change the “selecting” as it does not directly relate to the column locations. Specifically, the amended portion only changes how the machine learning performs the “selecting”, without changing the results of the “selecting”. Examiner further notes that the recited “selecting” covers the selecting of columns for any purpose (i.e., for inclusion in a design topology), and that the set of location at which the columns can be included is known (i.e., set of candidate column-member locations). Therefore, Applicant’s arguments are not persuasive.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 and 11 and 22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and 13 and 20 of U.S. Patent No. 11941327 (henceforth ‘327), respectively. The claims of the instant application and the claims of ‘327 are compared in the table below.
Instant application
‘327
1. A computer-implemented method for generating floor plans, the method comprising:
1. A method comprising:
applying one or more placement rules to a floorplan of a building to generate a set of candidate column locations in the floorplan;
selecting, via a machine learning model executed by a processor, one or more column locations from a set of candidate column locations based on a structural stability associated with the one or more column locations; and
selecting, via a first reinforcement learning (RL) agent that comprises a first machine learning model executed by a processor, one or more column locations from the set of candidate column locations based on a structural stability of the one or more column locations; and
generating a floorplan for at least a portion of a building that includes the one or more column locations.
outputting the floorplan that includes the one or more column locations as a structural design for the building
Instant application
‘327
11. One or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:
13. A non-transitory computer readable medium storing instructions that, when executed by a processor, cause the processor to perform the steps of:
applying one or more placement rules to a floorplan of a building to generate a set of candidate column locations in the floorplan;
selecting, via a machine learning model executed by the one or more processors, one or more column locations from a set of candidate column locations based on a structural stability associated with the one or more column locations; and
selecting, via a first reinforcement learning (RL) agent that comprises a first machine learning model executed by a processor, one or more column locations from the set of candidate column locations based on a structural stability of the one or more column locations; and
generating a floorplan for at least a portion of a building that includes the one or more column locations.
outputting the floorplan that includes the one or more column locations as a structural design for the building
Instant application
‘327
22. A system, comprising: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of:
20. A system, comprising: a memory that stores instructions, and a processor that is coupled to the memory and, when executing the instructions, is configured to:
execute a first reinforcement learning (RL) agent that generates a set of gridlines in a floorplan of a building based on a set of structural importances of a set of walls in the floorplan;
apply one or more placement rules to one or more intersections of the set of gridlines to generate a set of candidate column locations and one or more pre-placed columns in the floorplan;
selecting, via a machine learning model executed by the one or more processors, one or more column locations from a set of candidate column locations based on a structural stability associated with the one or more column locations; and
select, via a second RL agent that comprises a machine learning model executed by the processor, one or more column locations from the set of candidate column locations based on a structural stability of the one or more column locations; and
generating a floorplan for at least a portion of a building that includes the one or more column locations.
output the floorplan that includes the one or more column locations as a structural design for the building.
Claim 1 of ‘327 recites all of the limitations of claim 1 of the instant application, except that they are slightly narrower. For example, the “selecting” of ‘327 is with respect to a RL agent machine learning model, and claim 1 of the instant application is with regard to any type of machine learning model. The, “outputting the floorplan” of ‘327 implicitly requires that it be generated prior to being output, and claim 1 of the instant application recites “generating a floorplan”. Therefore, claim 1 of ‘327 anticipates claim 1 of the instant application.
Claim 13 of ‘327 recites all of the limitations of claim 11 of the instant application, except that they are slightly narrower. For example, the “selecting” of ‘327 is with respect to a RL agent machine learning model, and claim 13 of the instant application is with regard to any type of machine learning model. The, “outputting the floorplan” of ‘327 implicitly requires that it be generated prior to being output, and claim 1 of the instant application recites “generating a floorplan”. Therefore, claim 13 of ‘327 anticipates claim 11 of the instant application.
Claim 20 of ‘327 recites all of the limitations of claim 22 of the instant application, except that they are slightly narrower. For example, the “select” of ‘327 is with respect to a RL agent machine learning model, and claim 22 of the instant application is with regard to any type of machine learning model. The, “output the floorplan” of ‘327 implicitly requires that it be generated prior to being output, and claim 22 of the instant application recites “generating a floorplan”. Therefore, claim 20 of ‘327 anticipates claim 22 of the instant application.
Further, the following maps claims from the instant application to the issued patent as being anticipated by the claim in the issued patent, where I denotes instant application and P defined the issued patent:
Claim 2 (I): Claim 1 (P)
Claim 3 (I): Claim 2 (P)
Claim 4 (I): Claim 3 (P)
Claim 5 (I): Claim 4 (P)
Claim 6 (I): Claim 6 (P)
Claim 7 (I): Claim 5 (P)
Claim 8 (I): Claim 9 (P)
Claim 9 (I): Claim 10 (P)
Claim 10 (I): Claim 1 (P)
Claim 12 (I): Claim 13 (P)
Claim 15 (I): Claim 14 (P)
Claim 16 (I): Claim 15 (P)
Claim 17 (I): Claim 14 (P)
Claim 18 (I): Claim 18 (P)
Claim 19 (I): Claim 18 (P)
Claim 20 (I): Claim 18 (P)
Claim 21 (I): Claim 19 (P)
Claim 17 of ‘327 depends from and requires all of the limitations of claim 13. Claims 13 and 17 of ‘327 recites all of the limitations of claim 13 of the instant application except that the updating is “via the machine learning model”. However, Mathews, J. “Optimisation and Decision Support during the Conceptual Stage of Building Design” (henceforth “Mathews (Thesis)”) teaches updating, via a machine learning model, one or more column locations selected from a set of candidate column locations based on one or more additional placement rules (see Page 109 configuration information can be read again during a genetic algorithm execution to reflect changes “The configuration file was read during the execution of a genetic experiment to initialize a design problem and was accessible at other times to update the design specification.”, and Page 106 design concepts are desirably revised, where additional column-free zones could be added or existing ones changed having predictable results).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the claimed invention to combine the non-transitory computer readable medium to perform the steps disclosed by claim 13 and 17 of ‘327 with the column-free zones disclosed by Mathews (Thesis). One of ordinary skill in the art would have been motivated to make this modification in order to desirably exclude columns from being placed in specific areas (Mathews (Thesis) Page 64).
Claim 13 of ‘327 recites all of the limitations of claim 14 of the instant application except “detecting a conflict between the one or more placement rules and the one or more additional placement rules” and “generating an alert in response to detecting the conflict”. However, Segev et al. (US 2021/0073449) (henceforth “Segev (449)”) teaches detecting a conflict between one or more placement rules and one or more additional placement rules; and (see Paragraph 136 the building elements comprise columns “A BIM analysis may include but is not limited to an analysis of a BIM model using a combination of geometric analysis, semantic analysis, and machine learning methods aimed at extracting specific information regarding the architectural features, geometry and BIM objects in a floor plan … For example, walls and columns”, and Paragraph 150 conflicts between requirements is detected "In some instances, two or more functional requirements may conflict with each other. ... Thus, functional requirements may include rules for resolving conflicting functional requirements”). Segev (449) further teaches generating an alert in response to detecting the conflict (see Paragraph 192 an alert can be generated for non-conformance of placement of a building element “The system may generate an alert to the user or otherwise display the updated conformance information based on the modifications.”).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the claimed invention to combine the non-transitory computer readable medium to perform the steps disclosed by claim 13 of ‘327 with the functional requirement conflict detection in a building design disclosed by Segev (449). One of ordinary skill in the art would have been motivated to make this modification in order to desirably resolve the conflict (Segev (499) Paragraph 239 “The system might evaluate both options ( e.g., a generative analysis where more than one option is considered) and select a solution that gets closest to achieving the conflicting specifications”).
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 – 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent claim 1 recites at Step 1 a statutory category (i.e. a process) method for generating floor plans, the method comprising: selecting one or more column locations from a set of candidate column locations based on a structural stability associated with the one or more column locations; and generating a floorplan for at least a portion of a building that includes the one or more column locations. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “selecting” and “generating” amount to modeling actions recited at a high-level of generality, and requiring nothing more than observations, judgements, or evaluations which are reasonably performed in the mind. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: that the method is computer-implemented; and that the selecting is via a machine learning model executed by a processor. The method being “computer-implemented” is recited at a high-level of generality such that it amounts to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(d)(I)). The “via a machine learning model executed by a processor” generically implements the “selecting” on a general-purpose computer such that it amounts to mere automation of a manual process which does not amount to an improvement in computer functionality (see MPEP 2106.05(a)(I) “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase”). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “computed-implemented” amount(s) to insignificant data gathering. Further the “via a machine learning model executed by a processor” amounts to mere automation of a manual process which does not amount to an improvement in computer functionality. For at least these reasons, the claim is not patent eligible.
Dependent claim 2 - 10 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): In claim 2 applying one or more placement rules to an initial floorplan to generate the set of candidate column locations; In claim 3 updating the one or more column locations selected from the set of candidate column locations based on one or more additional placement rules; In claim 4 detecting a conflict between the one or more placement rules and the one or more additional placement rules; In claim 5 generate a set of gridlines in the floorplan based on a at least one structural importance associated with at least one wall included in the floorplan; In claim 6 matching a first parameter included in a placement rule to an intersection of two gridlines included in the set of gridlines, and applying a category to the intersection based on a second parameter included in the placement rule; In claim 7 wherein the set of gridlines in the floorplan is further generated based on a minimum beam span associated with the building and a maximum beam span associated with the building. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “applying” and “updating” and “detecting” and “generate a set of gridlines” and “matching” and “further generated” amount to modeling actions recited at a high-level of generality, and requiring nothing more than observations, judgements, or evaluations which are reasonably performed in the mind. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: In claim 3 that the updating is via the machine learning model; In claim 4 generating an alert in response to detecting the conflict; In claim 5 executing a second machine learning model to generate the set of gridlines; In claim 8 wherein the machine learning model is trained using a reward function that includes a first reward for placing a column at a preferred column location; In claim 9 wherein the reward function further includes a second reward that is associated with a randomly generated floorplan that includes one or more placed columns; In claim 10 wherein the machine learning model comprises a first reinforcement learning (RL) agent executed by a processor. For example, the “via a machine learning model” and “executing a second machine learning model” and “machine learning model comprises” generically implements the “updating” and “generate a set of gridlines” and “selecting” on a general-purpose computer such that it amounts to mere automation of a manual process which does not amount to an improvement in computer functionality (see MPEP 2106.05(a)(I) “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase”). The “generating an alert” amounts to insignificant data outputting since it is recited at a high-level of generality with regard to how the data is outputted. The “trained” and “reward function further includes” amounts to reciting the words “apply it” since it recites the idea of an outcome that is merely based on specific reward function, but does not explicitly disclose the manner in which the training is implemented in combination with the reward function. The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the “via a machine learning model” and “executing a second machine learning model” and “machine learning model comprises” amounts to mere automation of a manual process which does not amount to an improvement in computer functionality. The “generating an alert” comprises well-understood, routine, and conventional activity since they are generic with regard to how the data is outputted which reasonably includes any electronic means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). The “trained” and “reward function further includes” amounts to reciting the words “apply it” since it recites the idea of an outcome. Considering the additional elements in combination does not add anything more than when considering them individually since the data-outputting step and apply it steps require no more than generic computer functions. For at least these reasons, the claim is not patent eligible.
Independent claim 11 recites at Step 1 a statutory category (i.e. a manufacture) one or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: selecting one or more column locations from a set of candidate column locations based on a structural stability associated with the one or more column locations; and generating a floorplan for at least a portion of a building that includes the one or more column locations. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “selecting” and “generating” amount to modeling actions recited at a high-level of generality, and requiring nothing more than observations, judgements, or evaluations which are reasonably performed in the mind. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: that the selecting is via a machine learning model executed by the one or more processors. The “via a machine learning model executed by the one or more processors” generically implements the “selecting” on a general-purpose computer such that it amounts to mere automation of a manual process which does not amount to an improvement in computer functionality (see MPEP 2106.05(a)(I) “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase”). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “via a machine learning model executed by the one or more processors” amounts to mere automation of a manual process which does not amount to an improvement in computer functionality. For at least these reasons, the claim is not patent eligible.
Dependent claim 12 - 21 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): In claim 12 applying one or more placement rules to an initial floorplan to generate the set of candidate column locations; In claim 13 updating the one or more column locations selected from the set of candidate column locations based on one or more additional placement rules; In claim 14 detecting a conflict between the one or more placement rules and the one or more additional placement rules; and generating an alert in response to detecting the conflict; In claim 15 generate a set of gridlines in the floorplan based on a at least one structural importance associated with at least one wall included in the floorplan; In claim 16 matching a first parameter included in a placement rule to an intersection of two gridlines included in the set of gridlines, and applying a category to the intersection based on a second parameter included in the placement rule; In claim 17 wherein the set of gridlines in the floorplan is further generated based on a minimum beam span associated with the building and a maximum beam span associated with the building; and In claim 21 wherein the steps further comprise adding one or more pre-placed columns to the floorplan based on the one or more placement rules. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “applying” and “updating” and “detecting” and “generate a set of gridlines” and “matching” and “further generated” and “adding” amount to modeling actions recited at a high-level of generality, and requiring nothing more than observations, judgements, or evaluations which are reasonably performed in the mind. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: In claim 13 that the updating is via the machine learning model; In claim 15 executing a second machine learning model to generate the set of gridlines; In claim 18 wherein the machine learning model is trained using a reward function that includes a first reward for placing a column at a preferred column location; In claim 19 wherein the reward function further includes a second reward that is associated with a randomly generated floorplan that includes one or more placed columns; and In claim 20 wherein the machine learning model is trained based on a set of randomly generated floorplans and a reward function that comprises a first reward for placing a column at a preferred column location and a second reward that is based on an area of a randomly generated floorplan that is covered by one or more placed columns. For example, the “via a machine learning model” and “executing a second machine learning model” generically implements the “updating” and “generate a set of gridlines” on a general-purpose computer such that it amounts to mere automation of a manual process which does not amount to an improvement in computer functionality (see MPEP 2106.05(a)(I) “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase”). The “trained” and “reward function further includes” amounts to reciting the words “apply it” since it recites the idea of an outcome that is merely based on specific reward function, but does not explicitly disclose the manner in which the training is implemented in combination with the reward function. The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the “via a machine learning model” and “executing a second machine learning model” amounts to mere automation of a manual process which does not amount to an improvement in computer functionality. The “trained” and “reward function further includes” amounts to reciting the words “apply it” since it recites the idea of an outcome. Considering the additional elements in combination does not add anything more than when considering them individually since the data-outputting step and apply it steps require no more than generic computer functions. For at least these reasons, the claim(s) are not patent eligible.
Independent claim 22 recites at Step 1 a statutory category (i.e. a machine) system, comprising: selecting one or more column locations from a set of candidate column locations based on a structural stability associated with the one or more column locations; and generating a floorplan for at least a portion of a building that includes the one or more column locations. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “selecting” and “generating” amount to modeling actions recited at a high-level of generality, and requiring nothing more than observations, judgements, or evaluations which are reasonably performed in the mind. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of; that the selecting is via a machine learning model executed by the one or more processors. The “memories” and “processors” are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(d)(I)). The “via a machine learning model executed by the one or more processors” generically implements the “selecting” on a general-purpose computer such that it amounts to mere automation of a manual process which does not amount to an improvement in computer functionality (see MPEP 2106.05(a)(I) “iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase”). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “memories” and “processors” amount to no more than mere instructions to apply the judicial exception using generic computer components. The additional elements do not amount to a particular machine (see MPEP 2106.05(b)(I)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further the “via a machine learning model” amounts to mere automation of a manual process which does not amount to an improvement in computer functionality. For at least these reasons, the claim is not patent eligible.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter, subject to overcoming the 101 and double patenting rejections:
None of the prior art of record taken individually or in combination discloses the claim 10 method comprising: “wherein the machine learning model comprises a first reinforcement learning (RL) agent executed by a processor”, in combination with the remaining elements and features of the claim. It is for these reasons that the applicant’s invention defines over the prior art of record.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 8, 11, 18, 20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Grierson et al. “Optimal sizing, geometrical and topological design using a genetic algorithm” (henceforth “Grierson”) in view of Choi et at. “AN INTELLIGNENT, INTEGRATED BUILDING DESIGN SYSTEM” (henceforth “Choi”). Grierson and Choi are analogous art because they solve the same problem of column placement, and because they are from the same field of endeavor of building design.
With regard to claim 1, Grierson teaches a computer-implemented method for generating floor plans, the method comprising:
selecting, via a machine learning model executed by a processor, one or more column locations from a set of candidate column locations (Grierson Page 152, Left and Figure 1 and 2 a genetic algorithm selects whether or not to include column members from a set of predefined locations “Finally, in addition to variable member properties and support positions, it is assumed that the left column member for the frame in Fig. 1 may or may not exist, such that the topology of the frame may be either that in Fig. 1 or that in Fig. 2”, and Page 155, Right “On average, a typical run of the GA evolved through 12 generations, took 15 seconds CPU time on a SUN-Sparc/1 system”)
based on a structural stability associated with the one or more column locations; and (Grierson Page 151, Left the selection takes into account design constraints “(1b) defines the m performance constraints (i.e. Pr = specified maximum allowable values of displacements/stresses/etc.).”)
Grierson does not appear to explicitly disclose: generating a floorplan for at least a portion of a building that includes the one or more column locations.
However, Choi teaches:
generating a floorplan for at least a portion of a building that includes the one or more column locations. (Choi Figure 3 and Figure 8 columns are defined in a floorplan
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It would have been obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the column member topology optimization algorithm disclosed by Grierson with the software for a floorplan comprising support columns disclosed by Choi. One of ordinary skill in the art would have been motivated to make this modification in order to create a floorplan for export showing columns (Choi Figure 8).
With regard to claim 11, it recites the same steps as claim 1, which is taught by Grierson in view of Choi. Claim 11 further recites: one or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps.
Choi teaches:
one or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps. (Choi Page 6, Bottom code is stored in a database for use by the program that performs desired activities “Data generated by a subsystem is immediately stored in the central database and retrieved by any subsystems that need it.”)
It would have been obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the column member topology optimization algorithm disclosed by Grierson with the software for a floorplan comprising support columns disclosed by Choi. One of ordinary skill in the art would have been motivated to make this modification in order to create a floorplan for export showing columns (Choi Figure 8).
With regard to claim 22, it recites the same steps as claim 1, which is taught by Grierson in view of Choi. Claim 22 further recites: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps.
Grierson in view of Choi further teaches:
one or more processors, and when executing instructions, are configured to perform the steps (Grierson Page 155, Right “On average, a typical run of the GA evolved through 12 generations, took 15 seconds CPU time on a SUN-Spare/1 system”)
one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps (Choi Page 6, Bottom code is stored in a database for use by the program that performs desired activities “Data generated by a subsystem is immediately stored in the central database and retrieved by any subsystems that need it.”)
It would have been obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the column member topology optimization algorithm disclosed by Grierson with the software for a floorplan comprising support columns disclosed by Choi. One of ordinary skill in the art would have been motivated to make this modification in order to create a floorplan for export showing columns (Choi Figure 8).
With regard to claim 8 and 18, Greirson in view of Choi teaches all the elements of the parent claim 1 and 11, and further teaches:
wherein the machine learning model is trained using a reward function that includes a first reward for placing a column at a preferred column location. (Grierson Figure 5 design space exploration results in columns being in preferred locations, based on optimizing structural properties (first reward for placing column)
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With regard to claim 20, Grierson in view of Choi teaches all the elements of the parent claim 11, and further teaches:
wherein the machine learning model is trained based on a set of randomly generated floorplans and a reward function that comprises a first reward for placing a column at a preferred column location and (Grierson Figure 5 design space exploration results in columns being in preferred locations, based on optimizing structural properties (first reward for placing column) using a genetic algorithm (randomly generated floorplans)
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a second reward that is based on an area of a randomly generated floorplan that is covered by one or more placed columns. (Grierson Page 155, Left the cross-sectional area of each column is also optimized, where each column arrangement is random as translated to a floorplan by Choi “where the cross-sectional area A i and length L i of each member i are identified from Tables 1 and 2 upon decoding the binary string for the design to its base-10 equivalent.”)
Claims 2 – 3 and 12 - 13 are rejected under 35 U.S.C. 103 as being unpatentable over Grierson in view of Choi, and further in view of Mathews, J. “Optimisation and Decision Support during the Conceptual Stage of Building Design” (henceforth “Mathews (Thesis)”). Grierson and Choi and Mathews (Thesis) are analogous art because they solve the same problem of column placement, and because they are from the same field of endeavor of building design.
With regard to claim 2 and 12, Grierson in view of Choi teaches all the elements of the parent claim 1 and 11, and does not appear to explicitly disclose: ap