DETAILED ACTION
This Office Action is a first Office Action on the merits of the application. Claims 1 - 20 are presented for examination. Claims 1 - 7, 12 - 15, and 17 - 19 are rejected.
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 .
Specification Objection
The disclosure is objected to because of the following informalities: Equation 2 in paragraph [0051] is blurry and a bit difficult to read. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 7, and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6 and 7 recite “train an image generation network of the image synthesizer adversarially against a discriminator network of the discriminator”, but it is unclear how much the image generation network needs to be trained to be considered “adversarially” trained. The phrase is unclear and renders the claim vague and indefinite.
Suggested language: Amend the phrase to recite “train an image generation network of the image synthesizer against a discriminator network of the discriminator”.
Claim 15 recites “training, by the at least one hardware processor, an image generation network adversarially against a discriminator network that classifies the generated floor plan as real or not-real”, but it is unclear how much the image generation network needs to be trained to be considered “adversarially” trained. The phrase is unclear and renders the claim vague and indefinite.
Suggested language: Amend the claim to recite “training, by the at least one hardware processor, an image generation network against a discriminator network that classifies the generated floor plan as real or not-real”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4, 5, 12 - 14, and 17 - 19 are rejected under 35 U.S.C. 103 as being unpatentable over Koning et al. (“Queries on Semantic Building Digital Twins for Robot Navigation”), hereinafter “Koning”, and further in view of Nevdahs et al. (U.S. Patent 11,493,939 B1), hereinafter “Nevdahs”.
As per claim 1, Koning discloses:
a digital twin-based floor layout generation apparatus comprising at least one hardware processor (Koning, page 3, lines 29 - 34 discloses the use of a digital twin, a graph database and REVIT program, indicating a computer is used to perform the steps, in which a computer typically includes at least one processor and at least one form of memory.)
a convolutional message passing network analyzer, executed by the at least one hardware processor, to receive, for a floor plan that is to be generated, an activity map that includes movement of at least one user within a digital twin of a specified area (Koning, page 7, lines 1 - 8 discloses a metric map derived from an algorithm and a digital twin of a building, to obtain a path of movement between areas, and page 8, lines 9 - 13 discloses a path of movement includes areas that can be traversed as well as areas that cannot be traversed.)
Koning does not expressly disclose:
generate, based on the activity map, embedding vectors for each room type of a plurality of room types in the specified area; and
an image synthesizer, executed by the at least one hardware processor, to:
receive an input boundary feature map; and
generate, based on an analysis of the embedding vectors for each room type of the plurality of room types and based on an analysis of the input boundary feature map, the floor plan.
Nevdahs however discloses:
generate, based on the activity map, embedding vectors for each room type of a plurality of room types in the specified area (Nevdahs, col 4, ln 3 - 9 discloses trajectory generated in the pre-mapping portion using a floorplan, interpreted as an initial floorplan due to the pre-mapping process being performed, with the trajectory obtained based on a user walking through the area holding a camera drone (SCD).)
an image synthesizer, executed by the at least one hardware processor, to receive an input boundary feature map (Nevdahs, col 1, ln 49 - 54 discloses a detailed mapping procedure performed to obtain features of a space within a building, after the generation of an initial floorplan, and col 4, ln 49 - 67 through col 5, ln 1 - 2 adds a user with the camera drone (SCD) walking around objects in rooms to determine locations during the pre-mapping phase, to sense the environment.)
generate, based on an analysis of the embedding vectors for each room type of the plurality of room types and based on an analysis of the input boundary feature map, the floor plan (Nevdahs, col 1, ln 49 - 51 discloses an initial floorplan generated, and col 1, ln 58 - 61 adds the detailed mapping, interpreted to use the initial floorplan, used to generated a more detailed floorplan with interest areas and restricted areas, and col 5, ln 22 - 31 adds the trajectories obtained during the pre-mapping phase used to obtain the detailed mapping, recited above as used to obtain the more detailed floorplan.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
As per claim 12, Koning discloses:
a method for digital twin-based floor layout generation, the method comprising receiving, by at least one hardware processor, for a floor plan that is to be generated, an activity map that includes movement of at least one user within a digital twin of a specified area (Koning, page 7, lines 1 - 8 discloses a metric map derived from an algorithm and a digital twin of a building, to obtain a path of movement between areas, with page 8, lines 9 - 13 discloses a path of movement includes areas that can be traversed as well as areas that cannot be traversed, and page 3, lines 29 - 34 discloses the use of a digital twin, a graph database and REVIT program, indicating a computer is used to perform the steps, in which a computer typically includes at least one processor.)
Koning does not expressly disclose:
receiving, by the at least one hardware processor, an input boundary feature map; and
generating, by the at least one hardware processor, based on an analysis of the activity map and based on an analysis of the input boundary feature map, the floor plan.
Nevdahs however discloses:
receiving, by the at least one hardware processor, an input boundary feature map (Nevdahs, col 1, ln 49 - 54 discloses a detailed mapping procedure performed to obtain features of a space within a building, after the generation of an initial floorplan, and col 4, ln 49 - 67 through col 5, ln 1 - 2 adds a user with the camera drone (SCD) walking around objects in rooms to determine locations during the pre-mapping phase, to sense the environment.)
generating, by the at least one hardware processor, based on an analysis of the activity map and based on an analysis of the input boundary feature map, the floor plan (Nevdahs, col 1, ln 49 - 51 discloses an initial floorplan generated, and col 1, ln 58 - 61 adds the detailed mapping, interpreted to use the initial floorplan, used to generated a more detailed floorplan with interest areas and restricted areas.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
As per claim 17, Koning discloses:
a non-transitory computer readable medium having stored thereon machine-readable instructions, the machine-readable instructions, when executed by at least one hardware processor, cause the at least one hardware processor to (Koning, page 3, lines 29 - 34 discloses the use of a digital twin, a graph database and REVIT program, indicating a computer is used to perform the steps, in which a computer typically includes at least one processor and at least one form of memory.)
receive, for a floor plan that is to be generated, an activity map that includes movement of at least one user within a digital twin of a specified area (Koning, page 7, lines 1 - 8 discloses a metric map derived from an algorithm and a digital twin of a building, to obtain a path of movement between areas, and page 8, lines 9 - 13 discloses a path of movement includes areas that can be traversed as well as areas that cannot be traversed.)
Koning does not expressly disclose:
generate, based on an analysis of the activity map, the floor plan.
Nevdahs however discloses:
generate, based on an analysis of the activity map, the floor plan (Nevdahs, col 4, ln 36 - 67 through col 5, ln 1 - 11 discloses a user with a camera drone moving from one room to another to map locations, producing a pre-mapping phase with trajectory paths, and produce a two or three-dimensional mapping, and col 6, ln 32 - 42 discloses the mapped rooms and trajectory processed to obtain a two-dimensional or three-dimensional floorplan.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
For claim 2: The combination of Koning and Nevdahs discloses claim 2. The digital twin-based floor layout generation apparatus according to claim 1, wherein
the specified area includes a residence or a factory (Nevdahs, col. 13, ln 43 - 47 discloses the camera capturing information of a building includes a residential facility building.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs, and the additional teaching of the time of building a floorplan is created, also found in Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
For claim 4: The combination of Koning and Nevdahs discloses claim 4: The digital twin-based floor layout generation apparatus according to claim 1, wherein the convolutional message passing network analyzer is executed by the at least one hardware processor to generate, based on the activity map, the embedding vectors for each room type of the plurality of room types in the specified area by: utilizing embedding layers to embed the activity map to generate the embedding vectors of a specified dimension (Nevdahs, col 4, ln 3 - 4 discloses the trajectories obtained in the pre-mapping, shown in FIG. 1 as two dimensional (x and y movements).)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs, and the additional teaching of the trajectory dimensions for building a floorplan, also found in Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
For claim 5: The combination of Koning and Nevdahs discloses claim 5: The digital twin-based floor layout generation apparatus according to claim 1, further comprising:
a discriminator, executed by the at least one hardware processor, to: classify the generated floor plan as real or not-real (Nevdahs, col 6, ln 43 - 56 discloses using machine learning to classify objects regarding objects for the floorplan, and validating the floorplan for verification the accuracy of the graphical presentation and the mapped building space.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs, and the additional teaching of the validation of the floorplan with the building space mapped, also found in Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
For claim 13: The combination of Koning and Nevdahs discloses claim 13: The method of claim 12, wherein:
generating, by the at least one hardware processor, based on the analysis of the activity map and based on the analysis of the input boundary feature map, the floor plan further comprises generating, by the at least one hardware processor, based on the activity map, embedding vectors for each room type of a plurality of room types in the specified area (Nevdahs, col 4, ln 3 - 9 discloses trajectory generated in the pre-mapping portion using a floorplan, interpreted as an initial floorplan due to the pre-mapping process being performed, with the trajectory obtained based on a user walking through the area holding a camera drone (SCD).)
generating, by the at least one hardware processor, based on an analysis of the embedding vectors for each room type of the plurality of room types and based on the analysis of the input boundary feature map, the floor plan (Nevdahs, col 1, ln 49 - 51 discloses an initial floorplan generated, and col 1, ln 58 - 61 adds the detailed mapping, interpreted to use the initial floorplan, used to generated a more detailed floorplan with interest areas and restricted areas, and col 5, ln 22 - 31 adds the trajectories obtained during the pre-mapping phase used to obtain the detailed mapping, recited above as used to obtain the more detailed floorplan.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
For claim 14: The combination of Koning and Nevdahs discloses claim 5: The method of claim 12, further comprising:
classifying, by the at least one hardware processor, the generated floor plan as real or not-real (Nevdahs, col 6, ln 43 - 56 discloses using machine learning to classify objects regarding objects for the floorplan, and validating the floorplan for verification the accuracy of the graphical presentation and the mapped building space.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs, and the additional teaching of the validation of the floorplan with the building space mapped, also found in Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
For claim 18: The combination of Koning and Nevdahs discloses claim 18. The non-transitory computer readable medium according to claim 17, wherein the machine readable instructions to generate, based on the analysis of the activity map, the floor plan, when executed by the at least one hardware processor, further cause the at least one hardware processor to receive an input boundary feature map (Nevdahs, col 1, ln 49 - 54 discloses a detailed mapping procedure performed to obtain features of a space within a building, after the generation of an initial floorplan, and col 4, ln 49 - 67 through col 5, ln 1 - 2 adds a user with the camera drone (SCD) walking around objects in rooms to determine locations during the pre-mapping phase, to sense the environment.)
generate, based on the analysis of the activity map and based on an analysis of the input boundary feature map, the floor plan (Nevdahs, col 1, ln 49 - 51 discloses an initial floorplan generated, and col 1, ln 58 - 61 adds the detailed mapping, interpreted to use the initial floorplan, used to generated a more detailed floorplan with interest areas and restricted areas.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs, and the additional teaching of the features of a space to map and the areas for more details in the mapping and floorplan generation, also in Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
For claim 19: The combination of Koning and Nevdahs discloses claim 19. The non-transitory computer readable medium according to claim 18, wherein the machine readable instructions to generate, based on the analysis of the activity map and based on the analysis of the input boundary feature map, the floor plan, when executed by the at least one hardware processor, further cause the at least one hardware processor to generate, based on the activity map, embedding vectors for each room type of a plurality of room types in the specified area (Nevdahs, col 4, ln 3 - 9 discloses trajectory generated in the pre-mapping portion using a floorplan, interpreted as an initial floorplan due to the pre-mapping process being performed, with the trajectory obtained based on a user walking through the area holding a camera drone (SCD).)
generate, based on an analysis of the embedding vectors for each room type of the plurality of room types and based on the analysis of the input boundary feature map, the floor plan (Nevdahs, col 1, ln 49 - 51 discloses an initial floorplan generated, and col 1, ln 58 - 61 adds the detailed mapping, interpreted to use the initial floorplan, used to generated a more detailed floorplan with interest areas and restricted areas, and col 5, ln 22 - 31 adds the trajectories obtained during the pre-mapping phase used to obtain the detailed mapping, recited above as used to obtain the more detailed floorplan.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning with the floorplan generated from movement with trajectory paths teaching of Nevdahs, and the additional teaching of the generated trajectories of travel by a user to produce a floorplan, also in Nevdahs. The motivation to do so would have been because Nevdahs discloses the benefit of generating a detailed floorplan from detailed mapping, which can be used to obtain additional information of a building space using a traveling or trajectory path obtained to update a floorplan or provide information for an alarm event (Nevdahs, col 1, ln 58 - 67 through col 2, ln 1 - 3).
Claims 3, 6, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Koning et al. (“Queries on Semantic Building Digital Twins for Robot Navigation”), hereinafter “Koning”, and further in view of Nevdahs et al. (U.S. Patent 11,493,939 B1), and further in view of Mura et al (“Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories”), hereinafter “Mura”.
For claim 3, the combination of Koning and Nevdahs discloses the apparatus of claim 1.
The combination of Koning and Nevdahs does not expressly disclose:
wherein the convolutional message passing network analyzer is executed by the at least one hardware processor to generate, based on the activity map, the embedding vectors for each room type of the plurality of room types in the specified area by: passing the activity map through a series of graph convolution layers.
Mura however discloses:
wherein the convolutional message passing network analyzer is executed by the at least one hardware processor to generate, based on the activity map, the embedding vectors for each room type of the plurality of room types in the specified area by: passing the activity map through a series of graph convolution layers (Mura, page 6, right col, ln 11 - 23 discloses walk trajectory for input maps, used in a neural network for objects and footprints, and page 7, left col, ln 2 - 6 adds the use of convolution layers.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning and the floorplan generated from movement with trajectory paths teaching of Nevdahs with the convolutional layers regarding neural networks and input maps using walk trajectory teaching of Mura. The motivation to do so would have been because Mura discloses the benefit of evaluations performed using real-world trajectories as well as simulated trajectories to compared against an image-to-image translation baseline method, to provide viability in the technique and provides recovering ability for floor plans with minimal walk trajectory data (Mura, page 1, Abstract, 16 - 18).
For claim 6: The combination of Koning, Nevdahs, and Mura discloses claim 6: the apparatus of claim 5, further comprising: an image synthesizer trainer, executed by the at least one hardware processor, to: train an image generation network of the image synthesizer adversarially against a discriminator network of the discriminator (Mura, page 11,left col, ln 3 - 7 discloses baseline approach using an image of a walking trajectory for a floor plans translated, and page 11, right col, ln 9 - 15 discloses using simulated and real-world test data regarding training of trajectory for floor plans, and a comparison of each.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning and the floorplan generated from movement with trajectory paths teaching of Nevdahs with the convolutional layers regarding neural networks and input maps using walk trajectory teaching of Mura, and the additional teaching of walking trajectory, simulated and real-world, compared to a baseline image of trajectory regarding floor plans, also found in Mura. The motivation to do so would have been because Mura discloses the benefit of evaluations performed using real-world trajectories as well as simulated trajectories to compared against an image-to-image translation baseline method, to provide viability in the technique and provides recovering ability for floor plans with minimal walk trajectory data (Mura, page 1, Abstract, 16 - 18).
For claim 15: The combination of Koning, Nevdahs, and Mura discloses claim 15: The method of claim 14, further comprising:
training, by the at least one hardware processor, an image generation network adversarially against a discriminator network that classifies the generated floor plan as real or not-real discriminator (Mura, page 11,left col, ln 3 - 7 discloses baseline approach using an image of a walking trajectory for a floor plans translated, and page 11, right col, ln 9 - 15 discloses using simulated and real-world test data regarding training of trajectory for floor plans, and a comparison of each, with page 5, left col, ln 21 - 28 adds validation performed for training of simulated data regarding the floor plan.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the path of movement in an area, a metric map produced and a digital twin of a building teaching of Koning and the floorplan generated from movement with trajectory paths teaching of Nevdahs with the convolutional layers regarding neural networks and input maps using walk trajectory teaching of Mura, and the additional teaching of walking trajectory, simulated and real-world, compared to a baseline image of trajectory regarding floor plans and validation performed, also found in Mura. The motivation to do so would have been because Mura discloses the benefit of evaluations performed using real-world trajectories as well as simulated trajectories to compared against an image-to-image translation baseline method, to provide viability in the technique and provides recovering ability for floor plans with minimal walk trajectory data (Mura, page 1, Abstract, 16 - 18).
Allowable Subject Matter
Claims 7 - 11, 16, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art of Koning et al. (“Queries on Semantic Building Digital Twins for Robot Navigation”) discloses a path of movement in an area, a metric map produced and a digital twin of a building Nevdahs et al. (U.S. Patent 11,493,939 B1) discloses floorplan generated from movement with trajectory paths, and Mura et al (“Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories”) discloses convolutional layers regarding neural networks and input maps using walk trajectory.
However, none of the references cited, including the prior art of Koning, Nevdahs, and Mura, taken either alone or in combination with the prior art of record discloses:
Claim 7, obtaining a minimized objective by a trainer and a maximized objective by a network, with a training of an image synthesizer against a different network, in combination with the remaining elements and features of the claimed invention. It is for these reasons that the applicants’ invention defines over the prior art of record.
Claims 8, 16, and 20, wherein a weight sum of losses is minimized regarding the generated floorplan, in combination with the remaining elements and features of the claimed invention. It is for these reasons that the applicants’ invention defines over the prior art of record.
Dependent claims 9 - 11 are allowable under 35 U.S.C. 103 for depending from claim 8, an allowable base claim under 35 U.S.C. 103.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CEDRIC D JOHNSON whose telephone number is (571)270-7089. The examiner can normally be reached M-Th 4:30am - 2:00pm, F 4:30am - 11:30am.
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/Cedric Johnson/ Primary Examiner, Art Unit 2186
June 13, 2026
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