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 .
Status of the Claims
This Office Action is in response to the Application filed on December 21, 2022. Claims 1-22 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 8, 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1-22 are objected to because of the following informalities:
In regards to claims 1, 8, and 9 the claims recite “e.g.” which is an abbreviation that typically means for example. It is suggested to replace this abbreviation for the full meaning of the word.
In regards to claims 1, 8, and 9 the claims recite “aka” which is an abbreviation that typically means also known as. It is suggested to replace this abbreviation for the full meaning of the word.
In regards to claims 1 and 9, the claims recite “the system comprising”, however a system has not been introduced within the claim. It appears as though this was intended to recite --the method comprising--.
In regards to claims 1, 8, and 9, the claim recites “the best net”, however this was previously introduced as “at least one best net”. For examination purposes, the claim has been interpreted to read -- at least one best net--.
In regards to claims 2-7 and 10-22, the claims start with either “a method” or “a system”, however the method or system was previously introduced in the independent claims, therefore this should be amended to --The method-- or --the system-- accordingly.
In regards to claim 3, the claim recites “two nets”, however this was previously introduced as “at least two nets”. For examination purposes, the claim has been interpreted to read -- at least two nets --.
In regards to claim 9, the claim recites “a method for method for”, which appears to be a typo and should read -- a method for --.
In regards to claim 19, the claim recites “said machine”, however a machine has not been introduced within the claim. It appears as though this was intended to recite -- a machine--.
In regards to claims 19 and 20, the claim recites “the user”, however a user has not been introduced within the claim. It appears as though this was intended to recite -- a user--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-22 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.
Regarding claims 1, 8, and 9, the phrase "e.g." renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Regarding claims 1 and 9, the phrase "may include" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
The term “most of” in claim 11 is a relative term which renders the claim indefinite. The term “most of” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. One of ordinary skill in the art would not be reasonably apprised of the scope of the invention because it is unclear if there is a threshold for “most of” the nets having a weight, or if this is a majority of the nets, or some other measure. For examination purposes the claim has been interpreted as though a majority (in other words, over 50%) of the nets have a weight.
In regards to claims 2-7, 10, and 12-22, the claims are dependent upon a rejected claim and are therefore rejected.
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.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim recites a computer program product, which is not within one of the four statutory categories. The claim can be reasonably interpreted as including transitory forms of signal transmission and as a computer program per se, and therefore the claim fails to recite statutory subject matter (S4ee MPEP 2106.03).
Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis - Step 1
Claims 1-7, 13-16, and 22 recite a method/process, therefore claims 1-7, 13-16, and 22 are within at least one of the four statutory categories.
Claims 8, 10-12 and 17-21 recite a system/apparatus, therefore claims 8, 10-12 and 17-21 are within at least one of the four statutory categories.
101 Analysis - Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recites mathematical concepts and/or mental processes (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for deploying at least one physical connector within faces, including at least one of walls, ceilings and floors, of an architectural structure e.g. a building, wherein each physical connector interconnects physical locations along the faces of the architectural structure, the system comprising using a hardware processor for:
a. Displaying a representation of a 3d architectural structure including a polyhedron which has faces corresponding to at least one of a wall, a ceiling and a floor of the structure, wherein the polyhedron defines plural 2 dimensional nets aka polyhedral nets or net representations or net arrangements,
b. Accepting a human designer's selection of locations, on the faces of the polyhedron, at which to position physical resource nodes such as water outlets, electrical outlets, taps, sewer junctions, or intersections between physical connectors, wherein each node belongs to a physical resource network such as a water pipe network, electrical network or sewage network; and
c. for each physical resource network within a set of physical resource networks which may include at least one of: a water pipe network, a sewage pipe network, an electrical cable network, to be deployed in the architectural structure:
for each of the plural nets, generating a logical graph including
logical nodes which represent at least some of said physical resource nodes respectively; and
edges, which represent required physical connectors between said physical resource nodes, respectively and weights defined for each of the edges;
scoring each net's logical graph and, accordingly, selecting at least one best net from among the plural nets; and
deploying at least one physical resource node, and at least one of water pipes, sewage pipes, and electrical cables in the architectural structure according to a routing plan which interconnects plural nodes belonging to said physical resource network and which is derived from the logical graph generated for the best net from among the plural nets.
These limitations, as drafted, is a system that, under its broadest reasonable interpretation, covers performance of the limitation as a mental process and organizing human activity. That is, nothing in the claim elements preclude the steps from practically being performed as mental process and organizing human activity. For example, " for each physical resource network… generating a logical graph including logical nodes… and edges…" and " scoring each net’s logical graph...", and encompass mental processes as a human can perform these limitations using observations, evaluations, judgments, and/or opinions. " for each physical resource network… generating a logical graph including logical nodes… and edges…" involves a human evaluating and/or making a judgement where to place logical nodes and edges on a logical graph, which can be performed mentally or using paper and pencil and “" scoring each net’s logical graph..." involves a human making a judgment or using paper and pencil to determine a score of a net’s logical graph and therefore determine the best net. The limitation of “deploying at least one physical resource node…”, under its broadest reasonable interpretation, is involving a user to deploy the physical node and at least one of water pipes, sewage pipes, and electrical cables according to the routing plan, where there is no structure within the claims that recite of the device being capable of deploying this items, therefore it would require a human to physically perform this process. Thus, the claim recites at least a mental process and organizing human activity.
101 Analysis - Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the "additional limitations" while the bolded portions continue to represent the "abstract idea"):
A method for deploying at least one physical connector within faces, including at least one of walls, ceilings and floors, of an architectural structure e.g. a building, wherein each physical connector interconnects physical locations along the faces of the architectural structure, the system comprising
using a hardware processor for:
a. Displaying a representation of a 3d architectural structure including a polyhedron which has faces corresponding to at least one of a wall, a ceiling and a floor of the structure, wherein the polyhedron defines plural 2 dimensional nets aka polyhedral nets or net representations or net arrangements,
b. Accepting a human designer's selection of locations, on the faces of the polyhedron, at which to position physical resource nodes such as water outlets, electrical outlets, taps, sewer junctions, or intersections between physical connectors, wherein each node belongs to a physical resource network such as a water pipe network, electrical network or sewage network; and
c. for each physical resource network within a set of physical resource networks which may include at least one of: a water pipe network, a sewage pipe network, an electrical cable network, to be deployed in the architectural structure:
for each of the plural nets, generating a logical graph including
logical nodes which represent at least some of said physical resource nodes respectively; and
edges, which represent required physical connectors between said physical resource nodes, respectively and weights defined for each of the edges;
scoring each net's logical graph and, accordingly, selecting at least one best net from among the plural nets; and
deploying at least one physical resource node, and at least one of water pipes, sewage pipes, and electrical cables in the architectural structure according to a routing plan which interconnects plural nodes belonging to said physical resource network and which is derived from the logical graph generated for the best net from among the plural nets.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of " A method for deploying at least one physical connector within faces, including at least one of walls, ceilings and floors, of an architectural structure e.g. a building” the examiner submits that this limitation characterizes the method as being associated with a physical connector of an architectural structure, which merely amounts to indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself (see MPEP 2106.05(h)). Additionally, “a hardware processor” is merely a generic computing component used to perform the abstract idea. Additionally, the claim limitation “Displaying a representation of a 3d architectural structure…” and “Accepting a human designer's selection of locations “ does not amount to an inventive concept since it is insignificant extra-solution activity as it is merely a form of data collection and outputting to a display (MPEP § 2106.05(g)). The examiner submits that these limitations are mere data collection and outputting components to apply the above-noted abstract idea within an indicated field of use (MPEP §2106.05).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular process for safety performance evaluation, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B
Regarding Step 2B in the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “Displaying a representation of a 3d architectural structure…” and “Accepting a human designer's selection of locations “ amounts to extra-solution data gathering and outputting. Additionally, the specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). With respect to the displaying function, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), indicated that the mere displaying of data is a well understood, routine, and conventional function. With respect to “Displaying a representation of a 3d architectural structure…” and “Accepting a human designer's selection of locations “ it was ruled within Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015), which are recited within MPEP 2106.05(d)(II) that mere data collection or receiving/obtaining and transmitting of data over a network is well-understood, routine, and conventional function when it is claimed in a merely generic matter, as it is here. Additionally, “hardware processor” are each generic computing components that merely apply the judicial exception (See 2106.05(f)). Additionally, " A method for deploying at least one physical connector within faces, including at least one of walls, ceilings and floors, of an architectural structure e.g. a building” is merely a technological environment or field of use as the limitations merely link the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claims 8 and 9 recites analogous limitations to that of claim 1, and are therefore rejected by the same premise.
Dependent claims 2-7 and 10-22 specify limitations that elaborate on the abstract idea of claims 1, 8, and 9, and thus are directed to an abstract idea nor do the claims recite additional limitations that integrate the claims into a practical application or amount to "significantly more" for similar reasons.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 8-12, and 17-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khouri et al. (AN EFFICIENT ALGORITHM FOR SHORTEST PATH IN THREE DIMENSIONS WITH POLYHEDRAL OBSTACLES; hereinafter Khouri; see attached NPL document for citations) in view of Levy et al. (US 20210073446; hereinafter Levy).
In regards to claim 1, Khouri discloses of a method for deploying at least one physical connector within faces, including at least one of walls, ceilings and floors, of an architectural structure e.g. a building, wherein each physical connector interconnects physical locations along the faces of the architectural structure (“The purpose of the example is not to show a realistic application of the algorithm, but rather to illustrate some of the properties of the algorithm. The example involves finding the minimal distance path on a closed surface. Therefore, the method of unfolding (6) can be used to find the exact solution, which is then compared to the iterated result. The problem (10) is: Consider a room dimensioned 12 meters wide, 12 meters high and 30 meters long. The starting point is on one end wall 1 meter off the floor and 6 meters from the side walls. The destination point is at the antipodal position (i.e., 1 meter from the ceiling and 6 meters from the sides). Find the minimum distance path that lies on the surface of the room (Page 163 right column), “The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Page 161 left column))” , the system comprising
using a hardware processor (“The system of equations obtained is tridiagonal since the local minima of the path at every edge is directly affected only by the elements of the previous and those of the next edge in the sequence. We could store the elements of the matrix along the principal diagonal, the upper and lower diagonal. Unlike other general matrix methods, the computer storage required is proportional to n. A tridiagonal matrix solver is also much more efficient computationally than a general matrix solver” (Page 163 right column)) for:
a. Displaying a representation of a 3d architectural structure including a polyhedron which has faces corresponding to at least one of a wall, a ceiling and a floor of the structure, wherein the polyhedron defines plural 2 dimensional nets aka polyhedral nets or net representations or net arrangements (“The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Page 161 left column), “In the CAD system for pipe routing developed by the authors, the operator is alerted to the problem and given the option of choosing an alternative path. This could be done by replacing edge Ei in the sequence with another edge of those which intersect at the endpoint. For non-convex polyhedral obstacles, the possibility exists, that the minimum distance path is constrained to intersect the endpoint of one of the edges” (Page 163 left column), “The purpose of the example is not to show a realistic application of the algorithm, but rather to illustrate some of the properties of the algorithm. The example involves finding the minimal distance path on a closed surface. Therefore, the method of unfolding (6) can be used to find the exact solution, which is then compared to the iterated result.” (Page 163 right column), “By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” Page 164 left column), see also Figs 1-4),
…
c. for each physical resource network within a set of physical resource networks which may include at least one of: a water pipe network, a sewage pipe network, an electrical cable network, to be deployed in the architectural structure (“The purpose of the example is not to show a realistic application of the algorithm, but rather to illustrate some of the properties of the algorithm. The example involves finding the minimal distance path on a closed surface. Therefore, the method of unfolding (6) can be used to find the exact solution, which is then compared to the iterated result. The problem (10) is: Consider a room dimensioned 12 meters wide, 12 meters high and 30 meters long. The starting point is on one end wall 1 meter off the floor and 6 meters from the side walls. The destination point is at the antipodal position (i.e., 1 meter from the ceiling and 6 meters from the sides). Find the minimum distance path that lies on the surface of the room (Page 163 right column), “The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Page 161 left column))”:
for each of the plural nets, generating a logical graph (“The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Page 161 left column), “The purpose of the example is not to show a realistic application of the algorithm, but rather to illustrate some of the properties of the algorithm. The example involves finding the minimal distance path on a closed surface. Therefore, the method of unfolding (6) can be used to find the exact solution, which is then compared to the iterated result.” (Page 163 right column), “By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” Page 164 left column), see also Figs 1-4), including
logical nodes which represent at least some of said physical resource nodes respectively (“Many industrial applications require routing a minimum length path in the presence of polyhedral obstacles from a starting point to a destination point as in Figure 1. Typical applications include of path planning for a robot manipulator and the routing piping systems. length minimization of an arbitrary route is often Path helpful in improving the productivity of a process and reducing costs. The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Page 161 left column), see also Figs 1-4, wherein the start and destination locations are nodes); and
edges, which represent required physical connectors between said physical resource nodes, respectively and weights defined for each of the edges (“Many industrial applications require routing a minimum length path in the presence of polyhedral obstacles from a starting point to a destination point as in Figure 1. Typical applications include of path planning for a robot manipulator and the routing piping systems. length minimization of an arbitrary route is often Path helpful in improving the productivity of a process and reducing costs. The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Page 161 left column), “By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” Page 164 left column) see also Figs 1-4);
scoring each net's logical graph and, accordingly, selecting at least one best net from among the plural nets (“Many industrial applications require routing a minimum length path in the presence of polyhedral obstacles from a starting point to a destination point as in Figure 1. Typical applications include of path planning for a robot manipulator and the routing piping systems. length minimization of an arbitrary route is often Path helpful in improving the productivity of a process and reducing costs. The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Page 161 left column), “By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” Page 164 left column); and
deploying at least one physical resource node, and at least one of water pipes, sewage pipes, and electrical cables in the architectural structure according to a routing plan which interconnects plural nodes belonging to said physical resource network and which is derived from the logical graph generated for the best net from among the plural nets (“Many industrial applications require routing a minimum length path in the presence of polyhedral obstacles from a starting point to a destination point as in Figure 1. Typical applications include of path planning for a robot manipulator and the routing piping systems. Path length minimization of an arbitrary route is often helpful in improving the productivity of a process and reducing costs. The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Page 161 left column), see also Page 164 left column and Figs 1-4).
However, Khouri does not specifically disclose of b. Accepting a human designer's selection of locations, on the faces of the polyhedron, at which to position physical resource nodes such as water outlets, electrical outlets, taps, sewer junctions, or intersections between physical connectors, wherein each node belongs to a physical resource network such as a water pipe network, electrical network or sewage network.
Levy, in the same field of endeavor, teaches of b. Accepting a human designer's selection of locations, on the faces of the polyhedron, at which to position physical resource nodes such as water outlets, electrical outlets, taps, sewer junctions, or intersections between physical connectors, wherein each node belongs to a physical resource network such as a water pipe network, electrical network or sewage network (“In some embodiments of the present disclosure, the generative analysis may account for other variables, such as user inputs, when determining the equipment placement locations and technical specifications. The user inputs may include any constraints or other parameters in addition to the functional requirements that may inform the generative analysis results. In some embodiments, these inputs may be enforced strictly, in which the generative analysis will not consider solutions that do not satisfy the user inputs. In other embodiments, the inputs may be preferences, and the generative analysis may weigh solutions that satisfy the inputs more favorably than those that do not. In some embodiments, the user may define a preferred area for equipment placement, and identifying the first technical specification and first equipment placement location may be based on the preferred area.” (Para 0178), “The selected auxiliary equipment may include one or more of wiring, conduits, ducts, pipes, and/or cable trays.” (Para 0543), “In this context, an interface may allow a user to select one or more elements on the screen. Further, an interface may allow a user to apply certain operations to those elements, to add, edit, and/or delete elements, and/or to interact with the elements in any way. Selecting and assigning functional requirements may include allowing a user to identify and attach one or more appropriate functional requirements to a room, and embodiments may include repeating this process for a plurality of rooms, one room at a time.” (Para 0535), see also Para 0515 and 0231).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the faces of the polyhedron with physical resource nodes, as taught by Khouri, to include being selected by a human designer, as taught by Levy, with a reasonable expectation of success in order to allow the user to apply certain operations to the elements and determine the functional requirements of a room (Levy Para 0535).
In regards to claim 2, Khouri in view of Levy teaches of a method according to claim 1 wherein nodes can only be interconnected by connectors or edges lying within edge-joined polyhedron faces “By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” Page 164 left column).
In regards to claim 3, Khouri in view of Levy teaches of a method according to claim 2 wherein at least two nets from among the plural nets differently define how pairs of first and second nodes, in first and second polyhedron faces, can be interconnected, because in one of the two nets, the first and second polyhedron faces are edge-joined and in another of the two nets, the first and second polyhedron faces are not edge-joined (“By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” (Khouri Page 164 left column), “It is applicable when the edges are parallel or intersecting. The local problem is solved by unfolding all the planes of the surface in one common plane. The shortest of all paths joining two points on the surface is a geodesic, in this case a straight line bisecting the edges at equal angles. Bajaj (7) improved the efficiency of planar numerical unfolding by applying parallel calculations performed with shared memory. Using n processors for n edges, the solution was obtained in O(log n) time.” (Khouri Page 162 left column), Khouri see also Figs 1-4).
In regards to claim 4, Khouri in view of Levy teaches of a method according to claim 1 wherein the architectural structure comprises a room (“The purpose of the example is not to show a realistic application of the algorithm, but rather to illustrate some of the properties of the algorithm. The example involves finding the minimal distance path on a closed surface. Therefore, the method of unfolding (6) can be used to find the exact solution, which is then compared to the iterated result. The problem (10) is: Consider a room dimensioned 12 meters wide, 12 meters high and 30 meters long. The starting point is on one end wall 1 meter off the floor and 6 meters from the side walls. The destination point is at the antipodal position (i.e., 1 meter from the ceiling and 6 meters from the sides). Find the minimum distance path that lies on the surface of the room (Khouri Page 163 right column).
In regards to claim 5, Khouri in view of Levy teaches of a method according to claim 1 wherein the architectural structure comprises a common area coupling two rooms and wherein the method also comprises identifying the common area and pre-processing by identifying a mediation point in the common area and its location and connectivity requirements (“In BIM, objects may describe actual architectural elements such as walls or doors and can hold a large amount of information regarding them. For example, a wall element may contain a wall geometry, a physical makeup of a wall (bricks, concrete or gypsum), a finishing type of a wall, a fire resistance rating of a wall, locations of openings in a wall and parametric relationships between the wall and other architectural features. Additionally, abstractions such as floors and rooms can be supported by BIM software, which may automatically identify enclosed spaces and provide the user with a simple way to store information regarding the spaces.” (Levy Para 0683), “In a BIM model, a large amount of information may be contained within semantic, textual fields as object properties and attributes. In this type of file, a semantic analysis may yield information regarding the type and the physical makeup of architectural elements and equipment. For example, a semantic analysis may determine a type of wall, its structure, its fire rating, or its dimensions. For example, a semantic analysis may determine the model number, family, dimensions, 2D image, 3D image and other metadata associated with a BIM object of a desk. A semantic analysis of a BIM file may also yield information regarding the type, model or manufacturer, technical characteristics, 2D image, 3D image and other metadata associated with of equipment. In these files a semantic analysis may also yield relationships or associations between different elements in the floor plan, for example an association of a wall with a door within it, or a wall with the room it borders.” (Levy Para 0331), see also Levy Para 0543 and 0671 and Khouri Page 163 right column).
The motivation of combining Khouri and Levy is the same as that recited for claim 1 above.
In regards to claim 6, Khouri in view of Levy teaches of a method according to claim 1 wherein said generating and scoring is performed for each physical resource network within the set of networks, for a first ordering of the physical resource networks within the set of networks and is then performed again at least once, for each physical resource network within the set of networks, for at least one second ordering of the physical resource networks within the set of networks which differs from said first ordering, thereby to yield at least two best nets (“By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” (Khouri Page 164 left column), “It is applicable when the edges are parallel or intersecting. The local problem is solved by unfolding all the planes of the surface in one common plane. The shortest of all paths joining two points on the surface is a geodesic, in this case a straight line bisecting the edges at equal angles. Bajaj (7) improved the efficiency of planar numerical unfolding by applying parallel calculations performed with shared memory. Using n processors for n edges, the solution was obtained in O(log n) time.” (Khouri Page 162 left column), “It is interesting to note that the ranking of the three candidate paths reverses order depending on whether the sum of the squares of the lengths or the sun of the lengths is minimized. From Table 1, candidate path 1 appears to be the shortest path and candidate path 3 appears to be the longest path when the sum of the squares of the length is minimized. When the true minimum length path is found, it becomes clear that path 3 is the shortest path and path 1 is the longest path among the three candidates.” (Khouri Page 164 left column), Khouri see also Figs 1-4).
and wherein a selection is made between said at least two best nets thereby to define a most preferred net and then, at least one physical resource node, and at least one of water pipes, sewage pipes, and electrical cables in the architectural structure, are deployed according to the logical graph generated for the most preferred net (“By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” (Khouri Page 164 left column), “It is applicable when the edges are parallel or intersecting. The local problem is solved by unfolding all the planes of the surface in one common plane. The shortest of all paths joining two points on the surface is a geodesic, in this case a straight line bisecting the edges at equal angles. Bajaj (7) improved the efficiency of planar numerical unfolding by applying parallel calculations performed with shared memory. Using n processors for n edges, the solution was obtained in O(log n) time.” (Khouri Page 162 left column), “It is interesting to note that the ranking of the three candidate paths reverses order depending on whether the sum of the squares of the lengths or the sun of the lengths is minimized. From Table 1, candidate path 1 appears to be the shortest path and candidate path 3 appears to be the longest path when the sum of the squares of the length is minimized. When the true minimum length path is found, it becomes clear that path 3 is the shortest path and path 1 is the longest path among the three candidates.” (Khouri Page 164 left column), “The algorithm presented in this paper is currently being used to aid the operator in a computer aided design system for pipe routing. Based on a visual observation of the obstacle space, a path is assumed. The path is corrected to yield the shortest length touching on the assumed sequence of edges. Through the use of interactive graphics, different path candidates are observed and tested for minimum length by the operator.” (Khouri Page 161 left column)” Khouri see also Figs 1-4).
In regards to claims 8-9, the claims recite analogous limitations to claim 1 and are therefore rejected on the same premise.
In regards to claims 10, the claim recites analogous limitations to claim 1 and are therefore rejected on the same premise.
In regards to claim 11, Khouri in view of Levy teaches of a system according to claim 8 and wherein a single logical graph, but for weights, is used for most of said plural nets (“By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” (Khouri Page 164 left column), “The weights introduced are updated after each iteration to reflect recent changes in the lengths of each segment. The resulting tridiagonal matrix n linear equations is solved for every parameter, t.. The location of the points of contact of die path on every edge is found using Equation 1 permitting iterative improvement of the path. The iterated path converges to the path which is locally shortest, using the change in path length between iterations as the stopping
criteria.” (Khouri Page 163 left column)).
In regards to claim 12, Khouri in view of Levy teaches of a system according to claim 11 and wherein a single logical graph, but for weights, is used for all of said plural nets (“By unfolding the sides about the ceiling of the room, the two specified points can be connected by a straight line perpendicular to the edges. The weights, initially set to unity give the exact solution of 42.0 meters. The second path crosses three edges as shown in Figure 3. The iterated path length is 40.7 meters. Finally, the shortest path of 40.0 meters crosses four edges along the surface of the room and is shown in Figure 4.” (Khouri Page 164 left column), “The weights introduced are updated after each iteration to reflect recent changes in the lengths of each segment. The resulting tridiagonal matrix n linear equations is solved for every parameter, t.. The location of the points of contact of die path on every edge is found using Equation 1 permitting iterative improvement of the path. The iterated path converges to the path which is locally shortest, using the change in path length between iterations as the stopping
criteria.” (Khouri Page 163 left column)).
In regards to claim 17, Khouri in view of Levy teaches of a system according to claim 8 wherein the system has a first mode, Mode [i], in which users are prompted to select better plans and/or rank or mark plans thereby to define user preferences, and machine learning of said user preferences occurs without machine- modification of presented plans (“Moreover, the inventive approach may reduce to minutes or a few hours projects that might take many weeks or months. In some embodiments, the results of a process of automatically identifying and prompting users to select areas of interest or disinterest may be used to train machine learning models.” (Levy Para 0231), “In some embodiments, the method may include providing a prompt querying acceptance or denial of an application of the function requirement to individual spaces of the plurality of spaces by a user. For example, a functional requirement (Function “X”) may be a applied to set of rooms with a certain function. But a room within the set may have originally had a designation of Function “Y” before the semantic enrichment process. A prompt can alert the user to accept this change or override the change. The prompt can be in the form of a pop-up, a modal, or the suitable interface item, and may be textual. For example, a prompt may ask: “Room A originally had Function Y, are you sure you want to apply the functional requirement associated with Function X?” A prompt may also be graphical, for example a space may be shown as a certain color, the color indicating acceptance is required. Other types of graphical prompts are also possible.” (Levy Para 0377)).
The motivation of combining Khouri and Levy is the same as that recited for claim 1 above.
In regards to claim 18, Khouri in view of Levy teaches of a system according to claim 9 wherein the system has an additional mode, Mode [ii], in which the system guesses user preferences and accordingly, assesses its own accuracy and wherein said machine learning continues without machine-modification of presented plans “where each xi is a score associated with objective i for n objectives and the coefficients ai are weights. Scores may include a cost score, a coverage score, or other score associated a design goal. Scores and weights may be by any positive or negative real numbers. In some examples, weights may be predetermined based on stored data. Alternatively, or additionally, weights may be determined based on an optimization process that includes feedback received after generating solutions that consider multiple types of scores, such as a machine learning process. In one non-limiting example of an optimization process to improve coverage while minimizing cost by weighting a cost score more heavily than a coverage score, P may be calculated as:” (Levy Para 0180), “Some embodiments may further include displaying a score evaluating the equipment placement location. In this context a score may indicate a numeric appraisal of the performance of one or more pieces of equipment at a certain location. The score may indicate a degree of conformance to one or more functional requirements, such as an amount of coverage of the equipment, a number of functional requirements that are conformed to, or other means of evaluating conformance to the functional requirements. The score may also take into account other factors, such as whether user-defined constraints were met (e.g., preferred placement areas, etc.), equipment cost, equipment availability, equipment size, equipment noise, energy consumption, or other parameters that may indicate the desirability of the equipment placement location. The score may be represented as a number on a scale (e.g., a score from 1-100, a score from 1-10, etc.), a text-based grading scale (e.g., B+, Excellent, etc.), a percentage (e.g., percentage of conformance, percentage of coverage, etc.), or any other representation of how well the equipment is placed.” (Levy Para 0185)).
The motivation of combining Khouri and Levy is the same as that recited for claim 1 above.
In regards to claim 19, Khouri in view of Levy teaches of a system according to claim 18 wherein the system has an additional mode, Mode [iii], in which the system takes into account historical preferences by presenting plans to the user which comprise an optimized derivation and/or which are presented in an order biased by a likelihood that each plan will be chosen by the user and wherein the system transitions from mode ii to mode iii when said accuracy achieves a given accuracy threshold (“As one of skill in the art will appreciate, machine learning may include training a model to perform a task, the training including providing example training data to the model and iteratively optimizing model parameters until training criteria are satisfied. For example, a model may be trained to classify data using labelled datasets. In some embodiments, a model may be trained to use training input data to produce an output that closely matches training output data. Model training may include hyperparameter tuning, sizing of mini-batches, regularization and changes in network architectures. It should be understood that systems and methods contemplated herein include using available machine learning platforms and/or libraries to train and/or manage models (e.g., TENSORFLOW, PYTHON, MATLAB, KERAS, MICROSOFT COGNITIVE TOOLKIT, and/or any other machine learning platform). In some embodiments, training of machine learning models may be supervised and/or unsupervised. Training data may take many forms including, for example, the annotation of elements including but not limited to various architectural features (e.g. doors, door sills, windows, walls, rooms, etc.) and equipment (e.g. sensors, furniture, cabinetry, lighting fixtures, HVAC ducting, etc.).” (Levy Para 0130), “Generative analysis may include implementing a trained model to provide a solution. As an example, machine learning model 380 may accept floor plan 370 as input and provide solution 390 identifying sensor placement location to maximize sensor coverage. Machine learning model 380 may have been previously trained according to training process 360. As depicted in solution 390, a solution may include placement of two sensors to maximize sensor coverage. More generally, the generative analysis process illustrated in FIG. 3B may be applied to train models to provide solutions that at least partly conform to any functional requirement for any floor plan, and the solutions may include one or more equipment placement locations.” (Levy Para 0205), see also Levy Para 0212).
The motivation of combining Khouri and Levy is the same as that recited for claim 1 above.
In regards to claim 20, Khouri in view of Levy teaches of a system according to claim 9 wherein the system has an additional mode, Mode [iii], in which the system takes into account historical preferences by presenting plans to the user which comprise an optimized derivation and/or which are presented in an order biased by a likelihood that each plan will be chosen by the user (“As one of skill in the art will appreciate, machine learning may include training a model to perform a task, the training including providing example training data to the model and iteratively optimizing model parameters until training criteria are satisfied. For example, a model may be trained to classify data using labelled datasets. In some embodiments, a model may be trained to use training input data to produce an output that closely matches training output data. Model training may include hyperparameter tuning, sizing of mini-batches, regularization and changes in network architectures. It should be understood that systems and methods contemplated herein include using available machine learning platforms and/or libraries to train and/or manage models (e.g., TENSORFLOW, PYTHON, MATLAB, KERAS, MICROSOFT COGNITIVE TOOLKIT, and/or any other machine learning platform). In some embodiments, training of machine learning models may be supervised and/or unsupervised. Training data may take many forms including, for example, the annotation of elements including but not limited to various architectural features (e.g. doors, door sills, windows, walls, rooms, etc.) and equipment (e.g. sensors, furniture, cabinetry, lighting fixtures, HVAC ducting, etc.).” (Levy Para 0130), “Generative analysis may include implementing a trained model to provide a solution. As an example, machine learning model 380 may accept floor plan 370 as input and provide solution 390 identifying sensor placement location to maximize sensor coverage. Machine learning model 380 may have been previously trained according to training process 360. As depicted in solution 390, a solution may include placement of two sensors to maximize sensor coverage. More generally, the generative analysis process illustrated in FIG. 3B may be applied to train models to provide solutions that at least partly conform to any functional requirement for any floor plan, and the solutions may include one or more equipment placement locations.” (Levy Para 0205), see also Levy Para 0212).
The motivation of combining Khouri and Levy is the same as that recited for claim 1 above.
In regards to claim 21, Khouri in view of Levy teaches of a system according to claim 20 wherein in mode iii, the user continues to rank and/or mark and/or select and the machine continues to learn (“Moreover, the inventive approach may reduce to minutes or a few hours projects that might take many weeks or months. In some embodiments, the results of a process of automatically identifying and prompting users to select areas of interest or disinterest may be used to train machine learning models.” (Levy Para 0231), “In some embodiments, the method may include providing a prompt querying acceptance or denial of an application of the function requirement to individual spaces of the plurality of spaces by a user. For example, a functional requirement (Function “X”) may be a applied to set of rooms with a certain function. But a room within the set may have originally had a designation of Function “Y” before the semantic enrichment process. A prompt can alert the user to accept this change or override the change. The prompt can be in the form of a pop-up, a modal, or the suitable interface item, and may be textual. For example, a prompt may ask: “Room A originally had Function Y, are you sure you want to apply the functional requirement associated with Function X?” A prompt may also be graphical, for example a space may be shown as a certain color, the color indicating acceptance is required. Other types of graphical prompts are also possible.” (Levy Para 0377), “Generative analysis may include implementing a trained model to provide a solution. As an example, machine learning model 380 may accept floor plan 370 as input and provide solution 390 identifying sensor placement location to maximize sensor coverage. Machine learning model 380 may have been previously trained according to training process 360. As depicted in solution 390, a solution may include placement of two sensors to maximize sensor coverage. More generally, the generative analysis process illustrated in FIG. 3B may be applied to train models to provide solutions that at least partly conform to any functional requirement for any floor plan, and the solutions may include one or more equipment placement locations.” (Levy Para 0205)).
The motivation of combining Khouri and Levy is the same as that recited for claim 1 above.
Allowable Subject Matter
Claims 7, 13-16, and 22 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101 and 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
In regards to claim 7, the closest prior art of record is Khouri et al. (AN EFFICIENT ALGORITHM FOR SHORTEST PATH IN THREE DIMENSIONS WITH POLYHEDRAL OBSTACLES; hereinafter Khouri; see attached NPL document for citations) in view of Levy et al. (US 20210073446; hereinafter Levy) further in view of Liu et al. (CN 209695601; hereinafter Liu; see attached English translation). Khouri in view of Levy in view of Liu teaches of a method according to claim 1.
However, Khouri in view of Levy in view of Liu does not fully teach of plural routing plans are generated by at least one routing algorithm and wherein, in at least one initial mode of operation, the method prompts a user to rate at least some of the plural routing plans thereby to generate user ratings. It is noted that the prior art teaches of training an artificial intelligence system using user inputs and scoring values for iterations of the desired output. However the prior art does not fully teach of at least one routing algorithm and wherein, in at least one initial mode of operation, the method prompts a user to rate at least some of the plural routing plans thereby to generate user ratings for the deployment of physical connectors in an architectural structure, in combination with the remaining claim limitations. Therefore the claim contains allowable subject matter.
In regards to claims 13-16 and 22, the claims are dependent upon a claim containing allowable subject matter and are therefore allowable subject matter as well.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Parekh et al. (US 20230146207) discloses of a computing system that displays a constructure infrastructure and allows a user to generate a design pathing of the piping and cables of the project.
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/KYLE J KINGSLAND/ Primary Examiner, Art Unit 3663