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 .
Response to Amendments
The amendment filed February 2, 2026 has been entered. Claims 1 and 17 were amended to include partial limitation of previous claim 11. No claim was canceled. No claim was added. Claims 1-20 remain pending in the instant application.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are not found persuasive. The amendments to claims 1 and 17 merely adds the partial limitation already addressed in claim 11 in the previous action.
Applicant argues that execution of the machine learning models are not mental processes. However, the presented claims do not positively recite executing machine learning models. Therefore, the argument is moot in view of how it is claimed. The Examiner suggests positively reciting these executing steps to overcome the current 35 USC 101 rejections under abstract ideas.
In terms of prior art rejections under 35 USC 103, applicant argues that (1) Gallo does not teach determining locations and labels for the individual elements within the plan; and (2) Gallo does not teach determining a type for the plan.
In response to (1), Examiner submits that Gallo’s orientation does provide locations and labels in order for the orientation to be useful. The specific features argued are not claimed. It is suggested to amend the claims to add specific details distinguishing the claimed features from Gallo or any other prior art of record.
In response to (2), Examiner points to the claim language presented. Specifically, applicant argues that Gallo teaches an orientation plan with is different from “determining a type for the plan”, which involves determining whether an electrical, plumbing, general floor plan, etc. drawing has been provided to the system. Claim language does not provide this distinction. Adding such details may overcome Gallo as the prior art teaching those specific limitations. Gallo still teaches the feature as claimed under the broadest reasonable interpretation.
As stated before, the applicant’s amendment has already been addressed as part of claim 11 previously. No new amendment is made. Therefore, the claims remain rejected as shown below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea of a mental process without significantly more.
Claim 1 - Step 1 (Statutory Category - Process)
Step 2A - Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(III) "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions."
Further, the MPEP recites "The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation."
receiving a document depicting a sheet of a blueprint of a structure;
MPEP 2106.05 (g) Insignificant Extra-Solution Activity has found mere data gathering and post solution activity to be insignificant extra-solution activity. The above step is merely gathering the data on elements to be used in the calculation:
identifying, with a first machine learning model, a portion of the document that contains a plan of the structure;
Identifying a portion of the document is an observation of the document and a judgement to determine which portion to select. The judgement is based on a plan of the structure and further evaluation may be done to determine which portion. The machine learning model is recited at a high level of generality and includes a mental process.
determining, with a second machine learning model, a type for the plan depicted within the portion of the document;
Determining the type for the plan is an observation of the portion of the document and an evaluation/judgement to determine the type used for this portion. The machine learning model is recited at a high level of generality and includes a mental process.
selecting, based on the type of the plan, a third machine learning model, wherein the third machine learning model is a segmentation model;
Selection of the model is a mental process of observation and evaluation.
determining, with a third machine learning model and based on the contents of the portion of the document, (i) locations of individual elements within the plan and (ii) labels for the individual elements within the plan;
A portion of the document is observed and the locations of individual elements and the labels for the individual elements within the plan are determined based on an evaluation of the appropriate location and label. The machine learning model is recited at a high level of generality and includes a mental process. Therefore, the claim recites a mental process.
generating a vector version of the floor plan based on the locations and labels for individual elements within the floor plan.
Post solution Activity which can also be mentally performed using pen and paper.
Step 2A- Prong 2: Integrated into a Practical Solution?
No, none of the limitations alone or in combination is integrated into a practical application.
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed above in step 2A-prong 2, the additional elements of mere data gathering and post solution activity does not provide an inventive concept. Further, the claims related to the following example in 2106.04(a)(2)(A) Abstract Idea Groupings "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group V. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
Further, the claims related to the following example in 2106.05(g) "obtaining information about transactions using the Internet to verify credit card transactions, CyberSource V. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011). The claim is ineligible.
Claim 2:
Claim 2 recites the method of claim 1, wherein the first machine learning model
identifies a bounding box that surrounds the portion of the document that contains the plan of the building. Identifying a bounding box is an observation of the portion of the document that contains the plan and then an evaluation to identify where the bounding box goes.
Claim 3:
Claim 3 recites the method of claim 2, wherein the portion of the document is extracted from the document and provided to the second machine learning model. Extracting a portion from the document is an observation and evaluation of collecting the data for the machine learning model which is recited at a high level of generality.
Claim 4:
Claim 4 recites the method of claim 1, wherein each of the predefined plurality of types of plans corresponds to at least one machine learning model from among a plurality of machine learning models, and wherein selecting the third machine learning model comprises selecting the third machine learning model from among the plurality of machine learning models as corresponding to the type for the plan Selecting from among a predefined plurality of plans including plans that corresponds to at least one machine learning model among a plurality of machine learning models, is an observation and where the selection step is recited at a high level of generality.
Claim 5:
Claim 5 recites the method of claim 4, wherein the plurality of types of floor plans
includes at least one of a structural plan, an electrical plan, a plumbing plan, an HVAC plan, a life and safety plan, and/or a fire suppression plan. Selecting from among a plurality of types of floor plans is an observation and where the selection step is recited at a high level of generality.
Claim 6:
Claim 6 recites the method of claim 1, wherein locations and labels for individual
elements are determined responsive to determining that the type for the floor plan is a structural plan. Determining locations and labels responsive to determining that the type for the floor plan is a structural plan is an observation and where the determination step is recited at a high level of generality.
Claim 7:
Claim 7 recites the method of claim 1, wherein generating the vector version of the floor plan includes, for each element of at least a subset of the individual elements: generating a vector version of the element based on a label corresponding to the element and contents of the floor plan at a location associated with the element; scaling the vector version of the element based on the location associated with the element; and placing the vector version of the element within the vector version of the floor plan based on the location associated with the element. MPEP 2106.04(a)(2)(111) "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Further, the MPEP recites "The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation."
generating a vector version of the element based on a label corresponding to the element and contents of the floor plan at a location associated with the element
Generating a vector version of the element based on a label corresponding to the element and contents of the floor plan at a location associated with the element is an observation of the document and a judgement to determine which label and corresponding label to select. The judgement is based on labels that correspond to the element and contents of the floor plan at a location associated with the element and further evaluation may be done to determine which label. The vector version generation is recited at a high level of generality and includes a mental process.
scaling the vector version of the element based on the location associated with the element;
Scaling the vector version of the element based on the location associated with the element is an observation based on the observed location and an evaluation/judgement to determine how to scale the vector based on the observed location. Scaling the vector version is recited at a high level of generality and includes a mental process.
placing the vector version of the element within the vector version of the floor plan based on the location associated with the element.
Placing a vector version of the element within the vector version of the floor plan based on the location associated with the element is an observation based on an observed location and an evaluation/judgment to determine how to place the vector based on the observed location.
Placing a vector version is recited at a high level of generality and includes a mental process.
Therefore, the claim recites a mental process.
Step 2A- Prong 2: Integrated into a Practical Solution?
Post solution Activity:
generating a vector version of the floor plan based on the locations and labels for
individual elements within the floor plan; scaling the vector version of the element based on the location associated with the element; and placing the vector version of the element within the vector version of the floor plan based on the location associated with the element. The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed above in step 2A-prong 2, the additional elements of mere data gathering and post solution activity does not provide an inventive concept. Further, the claims related to the following example in 2106.04(a)(2)(A) Abstract Idea Groupings "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group V. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). The claim is ineligible.
Claim 8:
Claim 8 recites the method of claim 1, wherein receiving the document includes
receiving multiple documents depicting multiple sheets of the blueprint of the structure, and
wherein the method is repeated at least in part for multiple floor plans depicted in each of at least a subset of the multiple documents.
Receiving the documents including receiving multiple documents depicting multiple sheets of the blueprint of the structure where the method is repeated is an observation and evaluation of receiving multiple documents is recited at a high level of generality.
Claim 9:
Claim 9 recites the method of claim 8, wherein the method further comprises combining multiple vector versions of the multiple floor plans to generate a three-dimensional representation of the structure.
Combining multiple vector versions after receiving multiple documents depicting multiple sheets of the blueprint of the structure where the method is repeated to generate a three-dimensional representation of the structure is an observation and evaluation of combining multiple vector versions of the multiple floor plans is recited at a high level of generality.
Claim 10:
Claim 10 recites the method of claim 9, wherein the method further comprises, prior to determining the locations and labels for individual elements: identifying a common match line within two or more of the multiple documents; and combining the two or more of the multiple documents to generate a single floor plan, wherein the single floor plan is provided to the third machine learning model for use in determining the locations and labels for individual elements.
Identifying a common match line within two or more of the multiple documents; and combining the two or more of the multiple documents to generate a single floor plan is an observation and the third machine learning model is recited at a high level of generality and includes a mental process.
Claim 11:
Claim 11 recites the method of claim 1, wherein at least one of (i) the first model is an object recognition model, (ii) the second model is a classifier model, and/or (iii) the third model is a segmentation model.
Recognition machine learning models that go to a second classifier and third
segmentation machine learning model is an observation and the machine learning models are recited at a high level of generality and includes a mental process.
Claim 12:
Claim 12 recites the method of claim 1, wherein the structure includes at least one of, a building, a vehicle, an infrastructure component, a ship, a spacecraft, an aircraft, a tank, and/or an appliance.
Receiving a document depicting a sheet of a blueprint of a structure where the structure which may include a building, a vehicle, an infrastructure component, a ship, a spacecraft, an aircraft, a tank, and/or an appliance is an observation and evaluation of collecting the data for the machine learning models are recited at a high level of generality and includes a mental process.
Claim 13:
Claim 13 recites the method of claim 1, wherein the structure includes components for one or more of a vehicle, a ship, a spacecraft, an aircraft, a tank, an artillery, and/or a weapon.
Receiving a document depicting a sheet of a blueprint of a structure where the structure which may include components for one or more of a vehicle, a ship, a spacecraft, an aircraft, a tank, an artillery, and/or a weapon is an observation and evaluation of collecting the data for the machine learning models are recited at a high level of generality and includes a mental process.
Claim 14:
Claim 14 recites the method of claim 1, wherein the floor plan includes an exterior portion surrounding the structure and the vector version of the floor plan includes a representation of the exterior portion.
Receiving a document depicting a sheet of a blueprint of a structure where the structure is a floor plan and where an exterior portion surrounding the structure and the vector version of the floor plan includes a representation of the exterior portion is an observation and evaluation of collecting the data for the machine learning models are recited at a high level of generality and includes a mental process.
Claim 15:
Claim 15 recites the method of claim 1, wherein the vector version of the floor plan is at least one of (i) a two-dimensional vector representation of the floor plan and (ii) a three-dimensional vector representation of the floor plan."
Receiving a document depicting a sheet of a blueprint of a structure where the structure is a floor plan and where the vector version of the floor plan is at least one of (i) a two-dimensional vector representation of the floor plan and (ii) a three-dimensional vector representation of the floor plan is an observation and evaluation of collecting the data for the machine learning models are recited at a high level of generality and includes a mental process.
Claim 16:
Claim 16 recites the method of claim 1, wherein the vector version of the floor plan allows a user to navigate a three-dimensional representation of the floor plan.
Receiving a document depicting a sheet of a blueprint of a structure where the structure is a floor plan and where the vector version of the floor plan allows a user to navigate a three-dimensional representation of the floor plan is an observation and evaluation of collecting the data for the machine learning models are recited at a high level of generality and includes a mental process.
Claims 17-20:
Claims 17-20 are system claims corresponding to method claims 1, 7, 11, and 2,
respectively. Claims 1, 7, 11, and 2 are rejected under 35 U.S.C. 101. Claims 17-20 are rejected for the same reasons for being substantiality similar. The additional limitations of "A system comprising: a processor; and a memory storing instructions which, when executed by the processor, cause the processor to:" are rejected as a general-purpose computer under MPEP 2106.05(f) Mere Instructions To Apply An Exception. MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
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.
Claims 1-7 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over GALLO (US 20210150088 A1), hereinafter GALLO, and in further view of LIU (A local order metric for condensed phase environments), hereinafter LIU, and in further view of EDER (US 20210279957 A1), hereinafter EDER.
Claim 1:
Claim 1 is rejected because GALLO teaches receiving a document depicting a sheet of a blueprint of a structure and identifying, with a first machine learning model, a portion of the document that contains a plan of the structure; GALLO ([0056] "the algorithm/data processing module 402 receives the room layout description 302 and configuration file 404 (receiving a document depicting a sheet of a blueprint of a structure) as input and outputs a generated synthetic floorplan" where the PDF drawing is prepared/pre-processed. Because later steps in the workflow are based on object (a type for the plan) detection (identifying) in images (depicted within the portion of the document), the input PDF files (document) may be preprocessed where a first machine learning model (first machine learning model) can receive documents and where blueprint structures (a portion of the document that contains a plan of the structure) are identified (identified).") See also GALLO ([Figure 8].)
GALLO also teaches determining, with a second machine learning model, a type for the plan depicted within the portion of the document GALLO ([0089-0090] "The object detection model (first machine learning model) (resulting from object detection 820) only gives the size and the type of the bounding boxes, but the orientation of the symbol is still missing, which makes the automatic symbol placement difficult. Step 822 (second machine learning model) provides for determining (based on the synthetic floor plan (a type for the plan) design drawing dataset) this orientation (locations depicted within the portion of the document). From the object detection 816, there are already a lot of symbol instances inside the floor plan images generated through the synthetic dataset (in inference graph 806) and the orientation of the synthetic symbols are already known, such orientation information (locations depicted within the portion of the document) can be used for orientation information (locations depicted within the portion of the document) learning.
GALLO also teaches selecting a third machine learning model based on the type of plan, wherein the third machine learning model is a segmentation model ([0077] "To summarize step 814 of determining/extracting the drawing area, an ML model (third segmentation model) may be used to segment the raster image into multiple sections, and the ML model (third segmentation model) identifies (recognizes) fixed patterns of a layout of the floor plan drawing. Thereafter, the one or more multiple sections that include/ define the design drawing area are selected.
GALLO also teaches determining, with the third machine learning model and based on the contents of the portion of the document, (i) locations of individual elements within the plan and (ii) labels for the individual elements within the plan GALLO ([0090] "Thus, at step 830, the known symbols orientation (labels and locations depicted within the portion of the document) (in the synthetic floor plan (a type for the plan) design drawing dataset), another machine learning model 832 (third machine learning model) (i.e., the symbol classification (label contents within the plan) and orientation ML model) is trained (determining) to predict the orientation (location content of individual elements) of the detected symbols (labels for the individual elements within the plan). The training at step 830 utilizes the symbols (labels for the individual elements within the plan) from a symbol legend 828 to generate (selecting) the symbol classification (labels based on the type for the plan) and orientation ML model that is then stored in and accessed via database/inference graph 832.") See also GALLO ([Figure 8]).
Although Applicant argues that the cited prior art references do not discuss multiple model structures similar to those recited in the claims as amended, the Examiner disagrees because in Examiner's Non-Final Office Action, Examiner cites GALLO specification paragraph [0056], [0064], and [Figure 8] because those GALLO references teach claim 1 and similarly recited claim 17 limitations. Examiner cites GALLO [0089-0090 and figure 8] here to further demonstrate why GALLO teaches claim 1 and 17 claim limitations.
GALLO does not explicitly teach generating a vector version of the floor plan based on the locations and labels for individual elements within the floor plan.
However, LIU teaches generating a vector version of the floor plan based on the
locations and labels for individual elements within the floor plan LIU ([Section 5.3 Qualitative Evaluations I pdf page 6 of 9] "Figure 5 shows an input floorplan image, the reconstructed vector representation visualized by our own renderer, and a popup 3D model for each example. In our rendering, a wall junction is highlighted as a disk with a red border, an opening primitive is shown with a black dashed line, an object primitive is shown as an icon with different styles, depending on the inferred semantics. We also change the background color of each room based on its type.
The popup 3D model is generated by extruding wall primitive to a certain height, adding window or door textures at the location of opening primitives (an opening becomes a window, if it faces outside), and placing 3D objects models in the bounding box of icon primitives.") See also LIU ([Figure 5].)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of LIU with GALLO as the references deal with methods and systems for improved generation of vector versions of plans for structures. LIU would modify GALLO by generating a vector version of the floor plan based on the locations and labels for individual elements within the floor plan. The benefits of doing so significantly outperforms existing methods and achieves 90% precision and gets to the range of production-ready performance. (LIU [Abstract]).
The combination of GALLO and LIU does not explicitly teach selecting, based on the type for the plan, a third machine learning model.
However, EDER teaches selecting, based on the type for the plan, a third machine learning model EDER ([0053] "The results of the present disclosure may be achieved by one or more machine learning models that cooperatively work with each other to generate a virtual representation. For example, in an embodiment, a first machine learning model may be configured to generate a 3D model, a second machine learning model may be trained to generate semantic segmentation or instance segmentation information or object detections from a given input image, a third machine learning model may be configured to estimate pose information (select) associated with a given input image (based on the type for a plan), and a fourth machine learning model may be configured to spatially localize metadata to an input image or an input 3D model ( e.g., generated by the first machine learning model). In another embodiment, a first machine learning model may be configured to generate a 3D model, a second machine learning model may be trained to generate semantic segmentation or instance segmentation information or object detections from a given input 3D model or images, a third machine learning model may be configured to spatially localize (select) metadata (based on the type) to an input 3D model or images (for a plan).")
See also EDER ([0127] "FIGS. 10, 11A-1C, and 14A-14B illustrate an example graphical user interface for selecting regions (selecting based on the type for the plan) of a 3D model (third machine learning model), deleting a region of a 3D model, or selecting semantic regions within a 3D model of a location (e.g., a portion of a room).") See also EDER ([Figure 10], [Figures 13B-13E]).")
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of EDER with GALLO and LIU as the references deal with methods and systems for improved generation of vector versions of plans for structures. EDER would modify GALLO and LIU by selecting, based on the type for the plan, a third machine learning model. The benefits of doing so provides a system that resolves several impediments in existing 3-dimensional (3D) visualization systems by creating a virtual representation of a location, and enabling this representation to be a platform for collaborative interaction for services and/or tasks to be performed by a user. (EDER [0005]). Accordingly, claim 1 is rejected based on the combination of these references.
Claim 2:
Claim 2 is rejected because the combination of GALLO, LIU, and EDER teaches claim 1.
GALLO also teaches wherein the first machine learning model identifies a bounding box that surrounds the portion of the document that contains the plan of the building GALLO ([0079] "After the drawing area 904 is identified, the vectorized elements inside this area are extracted. The vectorized elements are grouped (surrounds) to form a candidate's area (portion that contains the plan of the building) (i.e., a candidate area of candidate elements) for symbol recognition (identifies a bounding box). Candidates/candidate elements can be filtered out by analyzing (i.e., based on) the size of their bounding box. FIGS. 10A-10C illustrate an exemplary group extraction from graphic drawing PDF files in accordance with one or more embodiments of the invention. Each group may be in different color. In this regard, FIG. 10A illustrates a graphic drawing PDF file. In FIG. 10B, vectorized elements 1002, 1004, 1006, 1008 etc. are grouped. FIG. 10C illustrates the resulting extracted vectorized elements that have bounding boxes 1110 (only some bounding boxes are shown for illustration purposes).") See also GALLO ([Figure 10A-10C].) Accordingly, claim 2 is rejected based on the combination of these references.
Claim 3:
Claim 3 is rejected because the combination of GALLO, LIU, and EDER teaches claim 2.
GALLO also teaches wherein the portion of the document is extracted from the document and provided to the second machine learning model GALLO ([0091] "Once the model has been generated/trained, step 822 (second machine learning model) may be performed to actually use the model to determine the orientation of the symbols in a drawing. In step 834, since the orientation of the symbols are usually aligned with wall directions, there are limited directions for the symbols---e.g., four (4) directions (left, right, up, down) or more detailed 360-degree directions. Thus, it is enough to use a classification model (at step 834) as well as the object orientation model (at step 836) for symbol orientation prediction. For example, the nearest wall of the detected symbols can also be queried in the floor plan drawing and the direction of the wall can be used to further validate the predicted orientation. Accordingly, at step 822, the orientation of the object symbol instances is determined based on the ML model trained in step 830.")
See also GALLO ([0096] "After the orientation is determined, BIM elements (e.g., electrical BIM elements) (portion of the document) are automatically/autonomously extracted/fetched according to symbol object label (extracted from the document) at step 844.
FIG. 12 illustrates a prediction of exemplary electrical symbols (also referred to as the symbol orientation classification) with four (4) labels in accordance with one or more embodiments of the invention. As illustrated the four (4) labels are label=0, label=1, label=2, and label=3. The fetching step 844 (extracted from the document) of BIM elements essentially provides for fetching a BIM 3D element that corresponds to the 2D symbols detected, oriented, filtered, and adjusted (i.e., in steps 816, 822, 838, and 842) (provided to the second machine learning model). In this regard, based on the classification of the symbol (determined at step 834), there is a one-to-one (l-to-1) mapping to a BIM element for that class. Accordingly, based on the symbol label/classification at step 844, the appropriate BIM element can be fetched.") See also
GALLO ([0097] "At step 846, the (electrical) symbols are automatically/autonomously placed in the floor plan drawing (e.g., in accordance with the extracted size and orientation information).") Accordingly, claim 3 is rejected based on the combination of these references.
Claim 4:
Claim 4 is rejected because the combination of GALLO, LIU, and EDER teach claim 1.
GALLO teaches wherein the type is selected from among a predefined plurality of types of plans GALLO ([0075] "At steps 812-814, the (electrical) drawing area (type) to be examined is extracted/determined. In this regard, a drawing area (type) may be automatically/autonomously identified/determined/extracted by parsing the PDF content. As set forth herein, while the figures and text may refer to electrical drawing processing, embodiments (type) of the invention are not limited to electrical drawings and may include processing (selection) of any type (among a predefined plurality of types of plans) of design drawing with symbols.")
However, the combination of GALLO and LIU does not teach corresponds to at least one machine learning model from among a plurality of machine learning models, and wherein selecting the third machine learning model comprises selecting the third machine learning model from among the plurality of machine learning models as corresponding to the type for the plan.
However, EDER teaches corresponds to at least one machine learning model from among a plurality of machine learning models, and wherein selecting the third machine learning model comprises selecting the third machine learning model from among the plurality of machine learning models as corresponding to the type for the plan EDER ([0053] "The results of the present disclosure may be achieved by one or more machine learning models (at least one machine learning model from among a plurality of machine learning models) that cooperatively work with each other (selecting the third machine learning model from among a plurality of machine learning models) to generate a virtual representation (corresponding to the type for the plan). For example, in an embodiment, a first machine learning model may be configured to generate a 3D model, a second machine learning model may be trained to generate semantic segmentation or instance segmentation information or object detections from a given input image, a third machine learning model may be configured to estimate pose information associated with a given input image (corresponding to the type for the plan), and a fourth machine learning model may be configured to spatially localize metadata to an input image or an input 3D model (e.g., generated by the first machine learning model). In another embodiment, a first machine learning model may be configured to generate a 3D model, a second machine learning model may be trained to generate semantic segmentation or instance segmentation information or object detections from a given input 3D model or images, a third machine learning model may be configured to spatially localize metadata to an input 3D model or images (selecting third machine learning model corresponding to the type for the plan). In an embodiment, two or more of the machine learning models may be combined into a single machine learning model by training the single machine learning model accordingly. In the present disclosure, a machine learning model may not be identified by specific reference numbers like "first," "second," "third," and so on, but the purpose of each machine learning model will be clear from the description and the context discussed herein (selecting third machine learning model corresponding to the type for the plan). Accordingly, a person of ordinary skill in the art may modify or combine one or more machine learning models to achieve the effects discussed herein. Also, although some features may be achieved by a machine learning model, alternatively, an empirical model, an optimization routine, a mathematical equation (e.g., geometry based), etc. may be used.")
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of EDER with GALLO and LIU as the references deal with methods and systems for improved generation of vector versions of plans for structures. EDER would modify GALLO and LIU by corresponding to at least one machine learning model from among a plurality of machine learning models, and wherein selecting the third machine learning model comprises selecting the third machine learning model from among the plurality of machine learning models as corresponding to the type for the plan. The benefits of doing so provides a system that resolves several impediments in existing 3-dimensional (3D) visualization systems by creating a virtual representation of a location, and enabling this representation to be a platform for collaborative interaction for services and/or tasks to be performed by a user. (EDER [0005]). Accordingly, claim 4 is rejected based on the combination of these references.
Claim 5:
Claim 5 is rejected because the combination of GALLO, LIU, and EDER teach claim 4.
GALLO also teaches wherein the plurality of types of floor plans includes at least one of a structural plan, an electrical plan, a plumbing plan, an HVAC plan, a life and safety plan, and/or a fire suppression plan GALLO ([0037] "To solve the problems of the prior art, embodiments (plurality of types of floor plans) of the invention programmatically generate a (synthetic) dataset (structural plan, electrical plan, plumbing plan, HVAC plan, life and safety plan, and/or fire suppression plan). Such synthetic data sets may consist of any architectural floor plan element such as electrical symbols, HVAC, furniture, lighting, etc. The output of the synthetic dataset generation framework can be a layout for a floor plan in vector format (e.g., 2D vector space) or an image (e.g., with the elements). FIG. 1 illustrates an example of a generated synthetic floor plan electrical design drawing 102 in accordance with one or more embodiments of the invention.")
See also GALLO ([0076-0077] "in design drawings, there may be some fixed patterns on the layout of the drawing, such as the border, caption(s), notes, title, and the real drawing area. Embodiments of the invention are only interested in the drawing area. Accordingly, to focus on the drawing area and to reduce computational overhead, the machine learning model may be trained to segment the rasterized PDF image into multiple sections and only the segments with a particular pattern reflecting the desired drawing area/drawing type (e.g., electrical drawings) will be passed through the pipeline for processing/recognition. FIG. 9 illustrates the segmentation of a whole graphical drawing page into different sections in accordance with one or more embodiments of the invention.") See also GALLO ([0075].) Accordingly, claim 5 is rejected based on the combination of these references.
Claim 6:
Claim 6 is rejected because the combination of GALLO, LIU, and EDER teach claim 1.
GALLO does not explicitly teach wherein locations and labels for individual elements are determined responsive to determining that the type for the floor plan is a structural plan.
However, LIU teaches wherein locations and labels for individual elements are determined responsive to determining that the type for the floor plan is a structural plan LIU ([Section 3 Stratified floorplan representation I Column 2 I Paragraph 1] "The junction layer also has two types of per-pixel probability distribution maps over different semantic types. The first map distinguishes if a pixel belongs to a wall (label) or a certain room type (location). The map (location) is a probability distribution function (PDF) over 12 different classes (determining responsiveness), as there are 11 room types (type for the floor plan is a structural plan): living-room, kitchen, bedroom, bathroom, restroom, washing-room, balcony, closet, corridor, pipe space (structural plan), or outside. The second map (label) distinguishes (determining) if a pixel belongs to an opening, a certain icon type or empty (individual elements). The map is a PDF over 10 different classes, as there are 8 icon types: cooking counter, bathtub, toilet, washing basin, refrigerator, entrance mat, column, or stairs (labels for individual elements). Note that a pixel (labels and locations) can be both a bathroom (location) (first map) and a bathtub icon (label) (second map).")
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of LIU with GALLO as the references deal with methods and systems for improved generation of vector versions of plans for structures. LIU would modify GALLO by generating a vector version of the floor plan based on the locations and labels for individual elements within the floor plan. The benefits of doing so significantly outperforms existing methods and achieves 90% precision and gets to the range of production-ready performance. (LIU [Abstract]). Accordingly, claim 6 is rejected based on the combination of these references.
Claim 7:
Claim 7 is rejected because the combination of GALLO, LIU, and EDER teach claim 1.
Claim 7 is rejected because it is the method embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1. Accordingly, claim 7 is rejected based on the combination of these references.
Claim 11:
Claim 11 is rejected because the combination of GALLO, LIU, and EDER, teach claim 1.
GALLO also teaches wherein at least one of (i) the first model is an object recognition model, (ii) the second model is a classifier model, and/or (iii) the third model is a segmentation model GALLO ([0076] "the machine learning model (first model) may be trained to segment the rasterized pdf image (object recognition model) into multiple sections and only the segments.") See also GALLO ([0082] "in order to recognize the small symbols inside the big drawing, embodiments (second classifier model) of the invention may tile (classify) the synthetic electrical drawing images into small tiles (i.e. such as those illustrated in FIGS 7A and 7B) with fixed dimension so that symbols can be recognized inside that tile when the image is scaled to 224*224 or other low-resolution size.") See also GALLO ([0077] "To summarize step 814 of determining/extracting the drawing area, an ML model (third segmentation model) may be used to segment the raster image into multiple sections, and the ML model (third segmentation model) identifies (recognizes) fixed patterns of a layout of the floor plan drawing. Thereafter, the one or more multiple sections that include/ define the design drawing area are selected.") Accordingly, claim 11 is rejected based on the combination of these references.
Claim 12:
Claim 12 is rejected because the combination of GALLO, LIU, and EDER, teach claim 1.
GALLO also teaches wherein the structure includes at least one of, a building, a vehicle, an infrastructure component, a ship, a spacecraft, an aircraft, a tank, and/or an appliance. GALLO ([0152] "obtaining a synthetic floor plan design drawing dataset (structure), wherein synthetic symbol labels (an infrastructure component), synthetic symbol locations, and synthetic symbol orientations of synthetic data in the synthetic floor plan design drawing dataset are known.") Accordingly, claim 12 is rejected based on the combination of these references.
Claim 13:
Claim 13 is rejected because the combination of GALLO, LIU, and EDER, teach claim 1.
GALLO also teaches wherein the structure includes components for one or more of a vehicle, a ship, a spacecraft, an aircraft, a tank, an artillery, and/or a weapon. GALLO ([0039] "The room layout (structure) can be deduced by/from existing CAD drawings, exhaustively generated (of all possible results) (including components for one or more of a vehicle, a ship, a spacecraft, an aircraft, a tank, an artillery, and/or a weapon) [Per Galle], generated by generative design or created by machine learning algorithms such as GAN (generative adversarial network) [Zheng] or with shape grammars and reinforcement learning [RuizMontiel]. In other words, at step 202, a room layout/ floorplan for one or more rooms of a floorplan drawing is obtained (e.g., via one or more different methodologies). Further, in the room layout, a room description defines a semantic for one or more of the rooms.")
Accordingly, claim 13 is rejected based on the combination of these references.
Claim 14:
Claim 14 is rejected because the combination of GALLO, LIU, and EDER, teach claim 1.
GALLO does not explicitly teach wherein the floor plan includes an exterior portion surrounding the structure and the vector version of the floor plan includes a representation of the exterior portion.
However, LIU teaches wherein the floor plan includes an exterior portion surrounding the structure and the vector version of the floor plan includes a representation of the exterior portion LIU ([Section 4.2.2 Integer Programming I pdf page 5 of 9] "Loop constraints: Bedroom, bathroom, restroom, balcony, closet, pipe-space, and the exterior boundary must form a closed loop (allowing some walls that stick out). It turns out that this high-level rule can be enforced by local constraints at every wall junction. We use a T-shaped wall junction to explain our idea in Fig. 4. Room types must be the same for a pair of walls with arrows of the same color in the figure.") See also LIU ([Figure 4]). Figure 4: Loop constraints can be enforced locally at each junction. The room types must be the same for each pair of walls marked with the same color.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of LIU with GALLO as the references deal with methods and systems for improved generation of vector versions of plans for structures. LIU would modify GALLO wherein the floor plan includes an exterior portion surrounding the structure and the vector version of the floor plan includes a representation of the exterior portion. The benefits of doing so significantly outperforms existing methods and achieves 90% precision and gets to the range of production-ready performance. (LIU [Abstract]). Accordingly, claim 14 is rejected based on the combination of these references.
Claim 15:
Claim 15 is rejected because the combination of GALLO, LIU, and EDER, teach claim 1.
GALLO teaches wherein the vector version of the floor plan is at least one of (i) a two-dimensional vector representation of the floor plan and (ii) a three- dimensional vector representation of the floor plan GALLO ([0039] "At step 202, the algorithm starts to generate the room layout/floorplan (vector version of the floor plan) in a 2D vector space (2D vector representation of the floor plan). The room layout consists of a set of 2D positions that correspond to the beginning and end of all walls and a set of room descriptions that defines the semantic of the room. See also GALLO ([0036] "Synthetic data generation for other machine learning applications may exist in the prior art. However, the prior art has failed to address the problems associated with floor plan drawings. For example, [Cinnamon] US Patent Application Publication 20190057520 generates 2D images by re-projecting 3D objects (3D vector representation of the floor plan).") Accordingly, claim 15 is rejected based on the combination of these references.
Claim 16:
Claim 16 is rejected because the combination of GALLO, LIU, and EDER, teach claim 1.
GALLO does not explicitly teach wherein the vector version of the floor plan allows a user to navigate a three-dimensional representation of the floor plan.
However, LIU teaches wherein the vector version of the floor plan allows a user to navigate a three-dimensional representation of the floor plan LIU ([Section 2 Related Work I pdf page 2 of 9] "Besides the raster-to-vector transformation problem, the Computer Vision community has tackled various problems involving floorplan images. Photograph to floorplan alignment has been an active research topic in recent years, with applications ranging from image localization [19, 10], navigation (allows a user to navigate) [22, 10], to real-estate content creation [17]. Pointcloud to floorplan alignment (vector version of the floor plan) has also been studied for building-scale 3D scanning (a three-dimensional representation of the floor plan) [23]. Indoor 3D reconstruction from images [13], depth-images [9], or laser-scanned pointclouds [20, 8, 16, 24] are also closely related to floorplan modeling. However, the critical difference is that these 3D reconstruction methods recover a surface representation (i.e., a wall as two surfaces instead of one thickened plane), and do not explicitly model the wall structure as a skeleton for instance. The surface-based representation suffices for rendering, but cannot allow standard floorplan editing operation, such as moving walls to change room sizes, or thinning walls for a new style. The goal of this paper is to recover (allowing a user to navigate) a vector representation of a floorplan (vector version of the floor plan) as a CAD model (three-dimensional representation of the floor plan), which enables human post-processing or further computing applications.")
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of LIU with GALLO as the references deal with methods and systems for improved generation of vector versions of plans for structures. LIU would modify GALLO wherein the vector version of the floor plan allows a user to navigate a three-dimensional representation of the floor plan. The benefits of doing so significantly outperforms existing methods and achieves 90% precision and gets to the range of production-ready performance. (LIU [Abstract]). Accordingly, claim 16 is rejected based on the combination of these references.
Claim 17:
Claim 17 is rejected because the combination of GALLO, LIU, and EDER teach claim 1.
Claim 17 is rejected because it is the system embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1. See GALLO ([0065] "In the workflow, the system (system embodiment) first automatically identifies a drawing area to exclude certain information (e.g., captions, notes, and other areas) so the pipeline can focus on the recognition and modeling of the drawing area.") Accordingly, claim 17 is rejected based on the combination of these references.
Claim 18:
Claim 18 is rejected because the combination of GALLO, LIU, and EDER teach claim 17.
Claim 18 is rejected because it is the system embodiment of claim 7 with similar limitations to claim 7, and is such rejected using the same reasoning found in claim 7. See GALLO ([0065] "In the workflow, the system (system embodiment) first automatically identifies a drawing area to exclude certain information (e.g., captions, notes, and other areas) so the pipeline can focus on the recognition and modeling of the drawing area.") Accordingly, claim 18 is rejected based on the combination of these references.
Claim 19:
Claim 19 is rejected because the combination of GALLO, LIU, and EDER teach claim 17.
Claim 19 is rejected because it is the system embodiment of claim 11 with similar limitations to claim 11, and is such rejected using the same reasoning found in claim 1. See GALLO ([0065] "In the workflow, the system (system embodiment) first automatically identifies a drawing area to exclude certain information (e.g., captions, notes, and other areas) so the pipeline can focus on the recognition and modeling of the drawing area.") Accordingly, claim 19 is rejected based on the combination of these references.
Claim 20:
Claim 20 is rejected because the combination of GALLO, LIU, and EDER teach claim 17.
Claim 20 is rejected because it is the system embodiment of claim 2 with similar limitations to claim 2, and is such rejected using the same reasoning found in claim 2. See GALLO ([0065] "In the workflow, the system (system embodiment) first automatically identifies a drawing area to exclude certain information (e.g., captions, notes, and other areas) so the pipeline can focus on the recognition and modeling of the drawing area.") Accordingly, claim 20 is rejected based on the combination of these references.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over GALLO, in view of LIU, in view of EDER, and in further view of TYULYAEV (US 20200387553 A1), hereinafter TYULYAEV.
Claim 8:
Claim 8 is rejected because the combination of GALLO, LIU, and EDER teach claim 1.
Claim 8 is rejected because GALLO teaches wherein receiving the document includes receiving multiple documents depicting multiple sheets of the blueprint of the structure GALLO ([0056] "FIG. 4 illustrates an overview of the data flow through the algorithm and processing of FIG. 2 in accordance with one or more (multiple documents) embodiments of the invention. In particular, the algorithm/data processing module 402 receives (wherein receiving) the room layout description 302 (one document) and configuration file 404 (multiple documents depicting multiple sheets) as input, performs the steps of FIG. 2, and outputs a generated synthetic floor plan 102 (blueprint of the structure). The output 102 can be a floor plan in vector format or an image. Further, the parameters/symbols can be found manually or randomly and may be determined using a machine learning algorithm such as GAN that decides if the output design matches the customer data.")
The combination of GALLO, LIU, and EDER does not explicitly teach wherein the method is repeated at least in part for multiple floor plans depicted in each of at least a subset of the multiple documents.
However, TYULYAEV teaches and wherein the method is repeated at least in part for multiple floor plans depicted in each of at least a subset of the multiple documents TYULYAEV ([Abstract] ""a device receives a technical document that includes content comprising: shapes that depict assets, lines that connect to the shapes and that depict connections to the assets, and text describing one or more of the assets and/or connections.") See also TYULYAEV ([0021] "the document management platform may repeat the process for each binary mask.") See also TYULYAEV ([0032] "the document management platform may repeat the process for each binary mask.")
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of TYULYAEV with GALLO, LIU, and EDER as the references deal with methods and systems for improved generation of vector versions of plans for structures. TYULYAEV would modify GALLO, LIU, and EDER wherein the method is repeated at least in part for multiple floor plans depicted in each of at least a subset of the multiple documents. The benefits of doing so allows the document management platform efficiently and effectively identifies and establishes associations between different content within the technical document, between content across multiple technical documents, and / or the like. (TYULYAEV [0006]). Accordingly, claim 8 is rejected based on the combination of these references.
Claim 9:
Claim 9 is rejected because the combination of GALLO, LIU, EDER, and TYULYAEV teach claim 8.
GALLO teaches generate a three-dimensional representation of the structure GALLO ([0096] "The fetching step 844 of BIM elements essentially provides for fetching a BIM 3D (generating a three-dimensional representation) element that corresponds (representation) to the 2D symbols (structure) detected, oriented, filtered, and adjusted (i.e., in steps 816, 822, 838, and 842).")
The combination of GALLO, LIU, and EDER do not explicitly teach wherein the method further comprises combining multiple vector versions of the multiple floor plans.
However, TYULYAEV teaches TYULYAEV ([0016] "In this way, the document management platform efficiently and effectively identifies and establishes associations (combining) between different content (multiple vector versions) within the technical document, between content across multiple technical documents (multiple floor plans), and/or the like.") See also TYULYAEV ([0022] "The set of blueprint documents may include (combine) a set of design documents, a set of architectural drawings, a set of floor plans (multiple floor plans) The set of blueprint documents may include (combine) content, such as shapes that represent one or more assets (multiple vector versions) depicted within particular blueprint documents (of the multiple floor plans), lines that connect to the shapes and that depict connections to particular assets (e.g., physical connections, electrical connections, virtual connections, and/or the like), text describing particular assets and/or connections, and/or the like.") See also TYULYAEV ([Figure 4 I Labels [430][440][450][460].)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of TYULYAEV with GALLO, LIU, and EDER as the references deal with methods and systems for improved generation of vector versions of plans for structures. TYULYAEV would modify GALLO, LIU, and EDER wherein the method further comprises combining multiple vector versions of the multiple floor plans. The benefits of doing so allows the document management platform efficiently and effectively identifies and establishes associations between different content within the technical document, between content across multiple technical documents, and / or the like. (TYULYAEV [0006]). Accordingly, claim 9 is rejected based on the combination of these references.
Claim 10:
Claim 10 is rejected because the combination of GALLO, LIU, EDER, and TYULYAEV teach claim 9.
GALLO teaches wherein the single floor plan is provided to the third machine learning model for use in determining the locations and labels for individual elements GALLO ([0090] "Thus, at step 830, the known symbols orientation (labels and locations) (in the synthetic floor plan (a single floor plan) design drawing dataset), another machine learning model 832 (third machine learning model) (i.e., the symbol classification (label contents within the plan) and orientation ML model) is trained (determining) to predict the orientation (location content of individual elements) of the detected symbols (labels for the individual elements). The training at step 830 utilizes the symbols (labels for the individual elements) from a symbol legend 828 to generate (selecting) the symbol classification (labels based on the single floor plan) and orientation ML model that is then stored in and accessed via database/inference graph 832.") See also GALLO ([Figure 8]).
The combination of GALLO, LIU, and EDER do not explicitly teach identifying a common match line within two or more of the multiple documents; and combining the two or more of the multiple documents to generate a single floor plan.
However, TYULYAEV teaches identifying a common match line within two or more of the multiple documents TYULYAEV ([0073] "For example, if a dotted line has ten line segments, the document management platform may identify ten cluster groups and may determine a distance between each neighboring cluster group. If the distance (common match line) between each neighboring cluster group (two or more of the multiple documents) matches, the document management platform may identify the cluster groups (e.g., which are part of particular line segments in the dotted line) as being part of a dotted line and may filter the dotted line as described above.") YULYAEV also teaches combining the two or more of the multiple documents to generate a single floor plan TYULYAEV ([0065] "In some implementations (not shown), the document management platform may identify spatial coordinates for combinations of shapes. For example, the blueprint document (single floor plan) might include shapes that depict multiple types of valves. In this case, the document management platform may identify that multiple types of valves are used within the blueprint document (single floor plan) and may identify a particular group of spatial coordinates that identifies all values and/or groups of spatial coordinates (two or more of the multiple documents) that identify specific types of valves. In this way, a user may be able to interact (combine) with a user interface to search (generate) a digitized copy of the blueprint document (single floor plan) for locations (two or more of the multiple documents) within the blueprint document (single floor plan) of a specific type of valve, for locations within the blueprint document of all valves, and/or the like.") See also TYULYAEV ([0022] "The set of blueprint documents (single floor plan) may include a set of design documents, a set of architectural drawings, a set of floor plans, a set of construction drawings, a set of whiteprint plan documents, and/or the like (two or more of the multiple documents).") See also TYULYAV ([0031, 0080, 0092].)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of TYULYAEV with GALLO, LIU, and EDER as the references deal with methods and systems for improved generation of vector versions of plans for structures. TYULYAEV would modify GALLO, LIU, and EDER combining the two or more of the multiple documents to generate a single floor plan. The benefits of doing so allows the document management platform efficiently and effectively identifies and establishes associations between different content within the technical document, between content across multiple technical documents, and / or the like. (TYULYAEV [0006]). Accordingly, claim 10 is rejected based on the combination of these references. Accordingly, claim 10 is rejected based on the combination of these references.
Conclusion
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/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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