DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 8/30/2023
Claims 1 – 20 are presented for examination.
Priority
ADS dated 8/24/2023 claims domestic benefit of 63328428 dated 4/7/2022.
Information Disclosure Statement
No IDS provided.
Drawings
The drawings dated 8/30/2023 have been reviewed. They are accepted.
Specification
The abstract dated 4/7/2023 has 100 words, 8 lines, and no legal phraseology. It is accepted.
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.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites the limitation “the object”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 11, 9, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 (US 2019/0026956 A1) in view of Hubner_2021 (Pose Normalization of Indoor Mapping Datasets Partially Compliant to the Manhattan World Assumption, July 16, 2021)
Claim 1. Gausebeck_2019 makes obvious “A method for automatic floorplan generation (par 31: “the disclosed subject matter is directed to employing one or more machine learning models… various elements described in connection with the disclosed techniques can be embodied in computer implemented system of device and/or a different form such as a computer-implemented method…”;
Par 56: “… in accordance with various embodiments, a 3D model can be viewed and rendered from various perspectives… different views or perspectives of the model can be… a floor plan mode…”; Page 161: “… machine learning object recognition technique to automatically identify defined objects and features… e.g., walls, floors, ceiling, windows, doors, furniture, people, buildings, etc.)… used as input to one or more augmented 3D-from 3D models…” par 179: “… facilitates capturing 2D images and deriving 3D data from the 2D images in accordance with various aspects and embodiments described herein. In this regard, the user device… can also include 3D model generation component 118 to generate reconstructed 3D models based on the 3D data… and a display/rendering to facilitate presenting the 3D reconstructed model at the user device 1402 (e.g., via a device display)… the display/rendering component 1408 can… facilitates accessing or otherwise receiving 3D models and/or representation of the 3D models (e.g., including 3D floorplan models, 2D floorplan models… and displaying them via a display of the user device…”), comprising: Gathering mesh triangles for a space comprising one or more walls and a floor (Par 70: “… a mesh comprising a series of triangles… a 3D model of an interior environment of building can comprise mesh data (e.g., a triangle mesh, a quad mesh, a parametric mesh, etc.)… in one example, the captured 3D data can be configured in a triangle mesh format, a quad mesh format…”; par 78: “… a floorplan model can be a simplified representation of surfaces (e.g., walls, floors, ceilings, etc.)…”); Receiving a request for a floorplan for the space (Par 262: “a user enters commands or information into the computer 3521 through input device(s) 3536… mouse, trackball, stylus, tough pad, keyboard, microphone, joystick, game pad…”; Par 80: “… classes of items can be toggled in a floorplan via a viewer on a remote device (e.g., via a user interface on a remote client device…”; Par 81: “… a floorplan model in 2D or 3D… and/or associated aligned 2D and 3D data can be rendered at a user device 130 via a display 132… the user device 130 can generate a graphical user interface (GUI)…”; par 178: “… computer-executable instructions…”; par 244: “… computer-executable instructions… the 2D-from -3D processing modules… when executed by the at least one processor 3320 facilitates performance of operations defined by the computer-executable instructions…”)
And Generating a floorplan based on the floor and walls” (par 80: “… a 2D floorplan model can include surfaces (e.g., walls, floors, ceiling, etc.), portals (e.g., door openings) and/or window openings associated with derived 3D data 116 used to generate a 3D model and projected to a flat 2D surface… a floorplan can be viewed at a plurality of different heights with respect to vertical surfaces (e.g., walls) via a viewer on a remote device…”).
Hubner_2021 makes obvious “Determining a facing direction of the floor from the mesh triangles;
Rotating the mesh triangles until the floor is horizontal; (page 5/39: “… we further presuppose the individual indoor mapping geometries to have normal vectors… triangle meshes do already have normal vectors inherent in the geometries of the individual triangles… the present method aims at rotating the given indoor mapping geometries to a pose with respect to the surrounding coordinate system for which the largest possible fraction of normal vectors is aligned with the three Cartesian coordinate axes. This comprises an optional leveling step to orient horizontal surfaces like floors and ceilings to be orthogonal to a chosen vertical axis if this not already achieved by the data acquisition process…” Figure 2. The normal vectors ~ni of the triangle mesh shown in Figure 1…”) Determining a primary wall facing direction from the mesh triangles; Rotating the mesh triangles so the primary wall facing direction is parallel to a major axis or other desired direction” (Figure 1: “… the green bounding box on the top-down-view on the right-hand side illustrates the alignment along the dominant Manhattan World structure considered as ground truth pose while the red bounding box illustrates the pose by 30 degrees around the vertical axis as exemplarily used…”
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Page 11/39: “… data can be rotated by the thus refined angle around the vertical axis to achieve the alignment of the building geometry with the horizontal coordinate axis. In the case of triangle mesh, it is sufficient to rotate the vertices of the triangles as the respective normal vectors….”; page 32/39: “… method for the automated pose normalization of indoor mapping data like point clouds and triangle meshes. The aim of the proposed method is to align an indoor mapping point cloud or triangle mesh along the coordinate axes in a way that a chosen vertical axis points upwards with respect to the represented building structure, i.e., the chosen vertical axis is expected to be orthogonal to horizontal floor and ceiling surfaces…”).
Gausebeck_2019 and Hubner_2021 are analogous art because they are from the same field of endeavor called digital models including models of buildings. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Hubner_2021.
The rationale for doing so would have been that Gausebeck_2019 teaches to model buildings using points and/or meshes. Hubner_2021 teaches that when modeling buildings using point and/or meshes to identify the primary face of the building and then align the building with a coordinate system known as the Manhattan World assumption and is a principle in computer vision and robotics that states that man-made environments are build around a single, dominant Cartesian coordinate system where edges align with three mutually orthogonal directions. Therefore, it would have been obvious to combine Gausebeck_2019 and Hubner_2021 for the benefit of having model data that is adjusted to be compliant with the principle of Manhattan World assumption to obtain the invention as specified in the claims.
Claim 11. The limitations of claim 11 are substantially the same as those of claim 1 and are therefore rejected due to the same reasons as outlined above for claim 1. Additionally, Gausebeck_2019 makes obvious the further limitations of “a system for automatic floorplan generation, comprising: a database comprising mesh triangles for a space comprising one or more walls and a floor; and a server comprising a central processing unit, memory, an input port to receive the mesh triangles from the database, and an output port, wherein the central processing unit is configured to:” (Par 70: “… a mesh comprising a series of triangles… a 3D model of an interior environment of building can comprise mesh data (e.g., a triangle mesh, a quad mesh, a parametric mesh, etc.)… in one example, the captured 3D data can be configured in a triangle mesh format, a quad mesh format…”; FIG. 1: Memory, processor, bus, database; FIG. 5).
Claim 9. Gausebeck_2019 makes obvious “further comprising: Adding one or more objects to the floorplan to represent objects within the space” ((par 77: “… in some embodiments, the 3D models can include photorealistic 3D representations of an object. The 3D model generation component 118 can further remove objects photographed (e.g., walls, furniture, fixtures, etc.) from the 3D model, integrate new 2D and 3D graphical objects on or within the 3D model in spatially aligned positions relative to the 3D model…”; Par 89: “… a user can navigate through a 3D model of an interior living space. The living space can include walls, furniture, and other objects…”).
Claim 10. Gausebeck_2019 makes obvious “wherein the objects each comprise one or more of a thermostat, sensor, and furniture” (par 77: “… in some embodiments, the 3D models can include photorealistic 3D representations of an object. The 3D model generation component 118 can further remove objects photographed (e.g., walls, furniture, fixtures, etc.) from the 3D model, integrate new 2D and 3D graphical objects on or within the 3D model in spatially aligned positions relative to the 3D model…”; Par 89: “… a user can navigate through a 3D model of an interior living space. The living space can include walls, furniture, and other objects…”).
Claims 4, 5 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 in view of Hubner_2021 in view of Oesau_2014
Claim 4. Oesau_2014 makes obvious “wherein the floor plan is a drafting-style floor plan” (Fig. 4,:
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page 70 section 2.1 Horizontal slicing: “walls are assumed to be vertical and perpendicular to floor and ceiling… in this step the point cloud is vertically partitioned into horizontal slices… samples sharing similar heights… horizontal structures show up as peaks in the point distribution along the vertical axis… the peaks from the point distribution we create a histogram. The bin size has to manually specified, a default value of 5 -10 cm is suggested… to ensure a minimum distance between two peaks, close peaks within a small distance h are clustered. We choose a default value of twice the bin size for h. The z coordinate of each maximum is denotes by Mi € { 1, …, Nm}. The point cloud is now split at points around the peak into horizontal structure-slices. Containing the peaks and representing floor and ceiling, and into wall-slices, covering the remaining parts representing the walls… the split points are located by walking through the bins in the distribution…”
EXAMINER NOTE: Instant specification paragraph 65 states that Figure 13 of the instant specification is a drafting-style floor plan 125. Paragraph 13 if the instant specification states: “Figure 13 is a block diagram showing, by way of example, a drafting-style floor plan that results from slicing the mesh of flat walls.).
Gausebeck_2019 and Oesau_2014 are analogous art because they are from the same field of endeavor called indoor scene reconstruction. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Oesau_2014. The rationale for doing so would have been that Gausebeck_2019 teaches to uses extract wall, ceiling, floor features from data and to build/recreate a model. Oesau_2014 teaches a method of slicing data along the z-axis in order to extract and recreate building features from data. Therefore, it would have been obvious to combine Gausebeck_2019 and Oesau_2014 for the benefit of recreating indoor features from data to obtain the invention as specified in the claims.
Claim 5. Oesau_2014 makes obvious “further comprising: Replacing the mesh triangles of the walls with rectangular blocks; and Generating the drafting-style floorplan based on the rectangular blocks” (Fig. 13 illustrates walls and ceiling represented as rectangular blocks, Fig. 14 illustrates a triangle mesh, Fig. 15 illustrates rectangular blocks overlayed on the triangle mesh.
EXAMINER NOTE: These figures illustrate that walls, ceilings can be represented by triangle meshes and/or replaced with rectangular blocks. Par 18 if the instant specification states: “Figure 12 is a block diagram showing, by way of example, flat walls after rectangle construction and mesh replacement has been performed” Accordingly, rectangular blocks are simply a shades area the shape of the building feature as the illustrated walls are rectangular shaped.
Fig. 20 illustrates a segment of drafting style floorplan (e.g., lines similar instant specification Figure 13) of a wall slice and the wall is also illustrated as shaded surfaces the shape of the wall and the walls are generally rectangular shaped.).
Claims 2, 12, 3, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 in view of Hubner_2021 in view of Rosinol_2020 (3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans, June 16, 2020).
Claim 2, 12. Gausebeck_2019 makes obvious further comprising: Annotating the mesh triangles by positioning each object relative to the mesh triangles that represent the space” (par 80: “… floors, walls and ceilings can be dimensioned (e.g., annotated) with an associated size… floorplan model (e.g., rooms) can be associated with a textual data (e.g., a name). Measurement data (e.g., square footage, etc.) associated with surfaces can also be determined based on the derived 3D data corresponding to the respective surfaces and associated with the respective surfaces. These measurements can be displayed in association with viewing and/or navigation of the 3d floorplan model…”)
Rosinol_2020 makes obvious “Annotating the mesh triangles with locations of one or more objects” (page 4: “… metric-semantic mesh… a semantically annotated 3D mesh… edges connecting triplets of points… our metric-semantic mesh includes everything in the environment that is static… places and structures… place attributes only include a 3D position, but can also include a semantic class (e.g., back or front of the room) and an obstacle-free bounding box around the place position… structures include nodes describing structural elements in the environment, e.g., walls, floor, ceiling, pillars. The notion of structure captures elements often called “stuff”…” EXAMINER NOTE: Each face of the generated 3D triangular mesh is automatically annotated with semantic labels (e.g., walls, floors, or specific furniture categories). Further, the engine extracts nearby objects from the mesh, estimates their bounding boxes or fits CAD models to them, and directly annotates the spatial model with the precise, locations, and structural relationships (such as adjacency or inclusion within a room) of those indoor objects.)
Gausebeck_2019 and Rosinol_2020 are analogous art because they are from the same field of endeavor called modeling spatial relationships including indoor environments. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Rosinol_2020. The rationale for doing so would have been that Gausebeck_2019 teaches to have a triangle mesh to model spatial environments such is inside a building. Rosinol_2020 teaches to include actionable information that supports planning and decision making in triangle meshes by annotating those meshes with information such as location/position of objects in the indoor environment because such information provides information about, for example, travers ability. Therefore, it would have been obvious to combine Gausebeck_2019 and Rosinol_2020 for the benefit of perception of the travers ability of an indoor floorplan to obtain the invention as specified in the claims.
Claim 3, 13. Gausebeck_2019 makes obvious “further comprising: Rotating the objects that were positioned relative to the mesh triangles with each object maintaining its relative positions with respect to the mesh triangles” ( par 91: “… a mode wherein a user perceives the model such that the user is outside or above the model and can freely rotate a model about a central point as well as move the central point around to model… pitched up or down, rotated left or right around a vertical axis… those motions may maintain a constant distance to the central point. Thus, the pitch and rotation around-a vertical-asis motions of this viewpoint may be though of as vertical and horixontal travel, respectively, on the surface of a sphere centered on the central point…”)
Claims 6, 7 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 in view of Hubner_2021 in view of Winkenback_1994 (Computer-Generated Pen-and-Ink Illustration, Computer Graphics Proceedings, Annual Conference Series, 1994).
Claim 6. Winkenback_1994 makes obvious “wherein the floorplan is pen-and-ink floorplan” (Title: “computer-generated pen-and-ink illustration”; abstract: “… pen-and-ink illustrations, and shows how a great number of them can be implemented as part of an automated rendering system… we demonstrate these techniques using complex architectural models…”; page 92 section 1.2: “… principles of traditional pen-and-ink illustration… these principles can be used to guide the design of an automated system…”)
Gausebeck_2019 and Winkenback_1994 are analogous art because they are from the same field of endeavor called architectural illustrations. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Winkenback_1994.
The rationale for doing so would have been that Gausebeck_2019 teaches to have an automated system that generates architectural illustrations and Winkenback_1994 teaches principles of pen-and-ink illustrations and teaches to implement automated rendering systems with these principles for the purpose of communicating the complex information in a comprehensible and effective manner (introduction par 2). Therefore, it would have been obvious to combine Gausebeck_2019 and Winkenback_1994 for the benefit of communicating complex information effectively to obtain the invention as specified in the claims.
Claim 7. Gausebeck_2019 makes obvious “further comprising: Generating a pen-and-ink floorplan comprising slicing the mesh triangles using planes parallel to the floor at different altitudes” (par 80: “… in yet another aspect, a floorplan can be viewed at a plurality of different heights with respect to vertical surfaces (e.g., walls) via a viewer…”)
Claims 8 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 in view of Hubner_2021 in view of Winkenback_1994 in view of Oesau_2014
Claim 8. Oesau_2014 makes obvious “further comprising: arranging slices from the mesh triangle slicing as a stack of the slices” (Fig. 3, page 70 section 2.1 Horizontal slicing: “walls are assumed to be vertical and perpendicular to floor and ceiling… in this step the point cloud is vertically partitioned into horizontal slices… samples sharing similar heights… horizontal structures show up as peaks in the point distribution along the vertical axis… the peaks from the point distribution we create a histogram. The bin size has to manually specified, a default value of 5 -10 cm is suggested… to ensure a minimum distance between two peaks, close peaks within a small distance h are clustered. We choose a default value of twice the bin size for h. The z coordinate of each maximum is denotes by Mi € { 1, …, Nm}. The point cloud is now split at points around the peak into horizontal structure-slices. Containing the peaks and representing floor and ceiling, and into wall-slices, covering the remaining parts representing the walls… the split points are located by walking through the bins in the distribution…”).
Gausebeck_2019 and Oesau_2014 are analogous art because they are from the same field of endeavor called indoor scene reconstruction. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Oesau_2014. The rationale for doing so would have been that Gausebeck_2019 teaches to uses extract wall, ceiling, floor features from data and to build/recreate a model. Oesau_2014 teaches a method of slicing data along the z-axis in order to extract and recreate building features from data. Therefore, it would have been obvious to combine Gausebeck_2019 and Oesau_2014 for the benefit of recreating indoor features from data to obtain the invention as specified in the claims.
Claims 14, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 in view of Hubner_2021 in view of Xu_2019 (CN 106133756 B).
Claim 14. Gausebeck_2019 makes obvious “A method for automatic floorplan generating (par 31: “the disclosed subject matter is directed to employing one or more machine learning models… various elements described in connection with the disclosed techniques can be embodied in computer implemented system of device and/or a different form such as a computer-implemented method…”;
Par 56: “… in accordance with various embodiments, a 3D model can be viewed and rendered from various perspectives… different views or perspectives of the model can be… a floor plan mode…”; Page 161: “… machine learning object recognition technique to automatically identify defined objects and features… e.g., walls, floors, ceiling, windows, doors, furniture, people, buildings, etc.)… used as input to one or more augmented 3D-from 3D models…” par 179: “… facilitates capturing 2D images and deriving 3D data from the 2D images in accordance with various aspects and embodiments described herein. In this regard, the user device… can also include 3D model generation component 118 to generate reconstructed 3D models based on the 3D data… and a display/rendering to facilitate presenting the 3D reconstructed model at the user device 1402 (e.g., via a device display)… the display/rendering component 1408 can… facilitates accessing or otherwise receiving 3D models and/or representation of the 3D models (e.g., including 3D floorplan models, 2D floorplan models… and displaying them via a display of the user device…”), comprising: obtaining a collection of mesh data for a space comprising one or more walls, a floor, and ceiling (Par 70: “in various embodiements, the 3D model generation component 118 can employ the derived 3D data… to generate reconstructed 3D models… a 3D model can include a collection of points… the collection of points can be associated with each other (e.g., connected) by geometric entities. For example, a mesh comprising a series of triangles, lines… for example, a 3D model of an interior environment of building can comprise mesh data…”; par 78: “… a floorplan model can be a simplified representation of surface (e.g., walls, floors, ceilings, etc.), portals (e.g., door openings) and/or windows openings…”; par 80: “… a 3D floorplan model can comprise edges, of each floor, wall, and ceiling…”; Page 161: “… machine learning object recognition technique to automatically identify defined objects and features… e.g., walls, floors, ceiling, windows, doors, furniture, people, buildings, etc.)… used as input to one or more augmented 3D-from 3D models…”); Reorienting the mesh data to generate a representation of the floor for a floorplan (par 91: “… orbit mode represents a mode wherein a user perceives the model such that the user is outside or above the model and can freely rotate a model about a central point as well a move the central point around the model… for example, a viewpoint may be pitched up or down, rotated left or right around a vertical axis, zoomed in or out, or moved horizontally. The pitch, rotation-around-a-vertical-axis, and zoom motions may be relative to a central point, such as defined by an (X,Y,Z) coordinate… horizontal plan may be visible, and its height may be defined by a global height of the floor of the 3D model…”; par 92: “the floor plan mode presents views of a 3D model that is orthogonal or substantially orthogonal to a floor of the 3D model (e.g., looking down at the model from directly above, such with respect to 3D floorplan model 300… the controls for floor plan mode may be identical to those described in the context of orbital mode…”) Identifying one or more annotations within the mesh data (par 252: “… neural network model to predict semantic labels (e.g., walls, ceilings, doors, etc.) without requiring human annotation of the dataset…”); Reorienting a representation of the annotations based on the floor and primary walls; [view] the mesh data at one or more altitudes; Projecting from the mesh data onto a plane; and projecting the annotation on the plane ( par 78: “… a floorplan model can include one or more dimensions associated with surfaces (e.g., walls, floors, ceilings, etc.)…”: par 80: “… a 3D floorplan model can comprise… lines for floors, walls, ceilings can be dimensioned (e.g., annotated) with an associated size… displays of individual items (e.g., dimensions)… can be toggled in a floorplan… a 2D floorplan model can include surfaces (e.g., walls, floors, ceilings, etc.), portals (e.g., door openings) and/or window openings associated with derived 3D data 116 used to generated a 3D model and projected to a flat 2D surface. In yet another aspect, a floorplan can be viewed at a plurality of different heights with respect to vertical surfaces…”; par 91: “… horizontal plane may be visible, and its height may be defined by a global height of the floor of the 3D model…” par 129: “… auxiliary data can be used by the 3D model generation component 118 to facilitate aligning images (and with their associated derived 3D data 116), captured at different capture positions and/or orientations relative to one another in a three-dimensional coordinate system…”
EXAMINER NOTE: the above teaches that floors, walls, ceilings have annotations and that the annotations can be shown in the projected flat 2D surface along with the walls, floors, ceilings, doors, windows. This further teaches that floorplans may be viewed at different vertical heights. The above also teaches to align data, including derived data relative to one another by re-orientation.)
While viewing a floorplan a different vertical heights may properly imply to those of ordinary skill in the art the claimed “slice” because the 2D projection at that height provides a sliced view across the building at that height, Gausebeck_2019 does not explicitly recites “slicing” nor “slices” nor “sliced”.
While Gausebeck_2019 teaches to reorient mesh data and to rotate mesh data around an axis and teaches a coordinate system, Gausebeck_2019 does not explicitly recite “Reorienting the mesh data to identify at least one of the walls as primary; Aligning a representation of the primary walls with coordinate axis;”
Hubner_2021 makes obvious “Reorienting the mesh data to identify at least one of the walls as primary; Aligning a representation of the primary walls with coordinate axis;” (Figure 1: “… the green bounding box on the top-down-view on the right-hand side illustrates the alignment along the dominant Manhattan World structure considered as ground truth pose while the red bounding box illustrates the pose by 30 degrees around the vertical axis as exemplarily used…”
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Page 11/39: “… data can be rotated by the thus refined angle around the vertical axis to achieve the alignment of the building geometry with the horizontal coordinate axis. In the case of triangle mesh, it is sufficient to rotate the vertices of the triangles as the respective normal vectors….”; page 32/39: “… method for the automated pose normalization of indoor mapping data like point clouds and triangle meshes. The aim of the proposed method is to align an indoor mapping point cloud or triangle mesh along the coordinate axes in a way that a chosen vertical axis points upwards with respect to the represented building structure, i.e., the chosen vertical axis is expected to be orthogonal to horizontal floor and ceiling surfaces…”).
Gausebeck_2019 and Hubner_2021 are analogous art because they are from the same field of endeavor called digital models including models of buildings. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Hubner_2021.
The rationale for doing so would have been that Gausebeck_2019 teaches to model buildings using points and/or meshes. Hubner_2021 teaches that when modeling buildings using point and/or meshes to identify the primary face of the building and then align the building with a coordinate system known as the Manhattan World assumption and is a principle in computer vision and robotics that states that man-made environments are build around a single, dominant Cartesian coordinate system where edges align with three mutually orthogonal directions. Therefore, it would have been obvious to combine Gausebeck_2019 and Hubner_2021 for the benefit of having model data that is adjusted to be compliant with the principle of Manhattan World assumption to obtain the invention as specified in the claims.
Xu_2019 makes obvious “slicing” and “slices” and “sliced” (page 6/21: “… object projection at different heights of a plurality of two-dimensional (2D) image slices…”; page 12/21: “… characterize the feature description device capturing 3D shape information of vertical slice of the 3D binary large object…”; page 15/21: “… the system can be self-adapting 3D at different height slices to 2D projection technique for extracting morphological features…” EXAMINER NOTE: the above citations teach the extraction of morpholocial features of a 3D object at a specific height which means the taking of a 2D horizontal cross section (i.e., slice) of the object and to measure its shape, structure, and geometry at that height level.
Gausebeck_2019 and Xu_2019 are analogous art because they are from the same field of endeavor called extracting/viewing features of an height of an object. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Xu_2019.
The rationale for doing so would have been that Gausebeck_2019 teaches to have a floorplan of a 3D object that displays physical features including, for example, dimensional annotations of these features and that this can be viewed at a plurality of different vertical heights. See paragraph 80. Xu_2019 teaches that morphological features can be extracted from a 3D object by taking a height slice to 2D projection. Therefore, it would have been obvious to combine Gausebeck_2019 and Xu_2019 for the benefit of extracting morphological features such a dimension features from height slices in order to annotate the morphological features (i.e., physical features) of the floorplan at the various vertical heights to obtain the invention as specified in the claims.
Claim 16. Gausebeck_2019 makes obvious “further comprising: Computing flat walls from the mesh data” (par 89: “in various embodiments, in association with generating a 3D model of an environment, the 3D model generation component 118 can determine positions of objects, barriers, flat planes, and the liked… can identify barriers, walls, objects (e.g., countertops…”).
Claims 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 in view of Hubner_2021 in view of Xu_2019 in view of Lee_2021 (Clustering-Based Plane Segmentation Neural Network for Urban Scene Modeling, Sensors, December 15, 2021
Claim 15. Lee_2021 makes obvious “wherein the reorienting of the floor and the primary walls uses modified spherical coordinates K-means clustering” (page 4/14 section 3.1 Hybrid K-Means Clustering… Spherical K-means clustering [30] is suitable for clustering high-dimensional data using the cosine distance between vectors. Inspired by K-means ++ and spherical K-means clustering methods, we propose a hybrid K-means clustering method that clusters data into planes using both Euclidean distance and cosine distance…”
Gausebeck_2019 and Lee_2021 are analogous art because they are from the same field of endeavor called clustering data. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Lee_2021. The rationale for doing so would have been that Gausebeck_2019 teaches to cluster data into planes for features such as walls, floors, and ceiling and Lee_2021 teaches a hybrid k-means clustering method that uses both Euclidean distance and cosine distance to cluster nearby points in the same direction for better plane segmentation results.
Therefore, it would have been obvious to combine Gausebeck_2019 and Lee_2021 for the benefit of better segmentation results of data into planes to obtain the invention as specified in the claims.
Claims 17 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 in view of Hubner_2021 in view of Xu_2019 in view of Chen_2021 (Block-DBSCAN: Fast clustering for large scale data, Pattern Recognition 109 2021 107624).
Claim 17. Chen_2021 makes obvious “wherein the flat walls are computed using a modified DBSACN algorithm in three dimensions, wherein the modified DBSCAN algorithm uses a block-shaped neighborhood when counting neighboring points” (Fig. 2 (c) “… core block…”; Fig. 4 “… blocks…”)
Gausebeck_2019 and Chen_2021 are analogous art because they are from the same field of endeavor called clustering data. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Chen_2021. The rationale for doing so would have been that Gausebeck_2019 teaches to cluster data and Chen_2021 teaches to use a modified DBSAN called BLOCK-DBSCAN for large scale data which improves the performance of DBSCAN algorithms. Therefore, it would have been obvious to combine Gausebeck_2019 and Chen_2021 for the benefit of clustering large datasets to obtain the invention as specified in the claims.
Claims 18, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gausebeck_2019 in view of Hubner_2021 in view of Xu_2019 in view of Oesau_2014 (Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut, ISPRS Journal of Photogrammetry and Remote Sensing 90 2014 68-82).
Claim 18. Oesau_2014 makes obvious “wherein the mesh data is sliced at a set of altitudes evenly spaced between the ceiling and floor” (Fig. 3, page 70 section 2.1 Horizontal slicing: “walls are assumed to be vertical and perpendicular to floor and ceiling… in this step the point cloud is vertically partitioned into horizontal slices… samples sharing similar heights… horizontal structures show up as peaks in the point distribution along the vertical axis… the peaks from the point distribution we create a histogram. The bin size has to manually specified, a default value of 5 -10 cm is suggested… to ensure a minimum distance between two peaks, close peaks within a small distance h are clustered. We choose a default value of twice the bin size for h. The z coordinate of each maximum is denotes by Mi € { 1, …, Nm}. The point cloud is now split at points around the peak into horizontal structure-slices. Containing the peaks and representing floor and ceiling, and into wall-slices, covering the remaining parts representing the walls… the split points are located by walking through the bins in the distribution…”).
Gausebeck_2019 and Oesau_2014 are analogous art because they are from the same field of endeavor called indoor scene reconstruction. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Gausebeck_2019 and Oesau_2014. The rationale for doing so would have been that Gausebeck_2019 teaches to uses extract wall, ceiling, floor features from data and to build/recreate a model. Oesau_2014 teaches a method of slicing data along the z-axis in order to extract and recreate building features from data. Therefore, it would have been obvious to combine Gausebeck_2019 and Oesau_2014 for the benefit of recreating indoor features from data to obtain the invention as specified in the claims.
Claim 19. Gausebeck_2019 makes obvious “wherein the sliced mesh data is projected onto the same place to produce a composite line drawing of the three-dimensional field” (par 79: “… in another example, a floorplan model can comprise a series of lines…”; par 80: “… a 3D floorplan model can comprise… lines for floors, walls, ceilings… associated with derived 3D data 116 used to generated a 3D model and projected to a flat 2D surface…”).
Claim 20. Hubner_2021 makes obvious “wherein the sliced mesh data is drawn in a semi-transparent color to produce a composite line drawing that displays tall flat features in a dark color and short rounded features in a light color” (Figure 5 (a) (b) which illustrates flat wall surfaces as grey and smaller rounded point type features as red. EXAMINER NOTE: Fig. 11 shows “light colors” that include red on short rounded features. Fig. 12 shows “dark color” includes grey on flat surfaces.).
Conclusion
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/BRIAN S COOK/Primary Examiner, Art Unit 2187
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