DETAILED ACTION
This action is in reply to the submission filed on 2/26/2025.
Claims 1-20 are currently pending and have been examined.
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 without significantly more.
Step 1: the claims fall under statutory categories of processes and/or machines.
Step 2A Prong 1: the claims recite: accessing a construction document associated with a construction project, generating classified pages identifying a page type and region, generating segmented pages comprising object(s) corresponding to a building element, analyzing the objects to determine coordinates and properties, and determining a bill of materials from the coordinates and properties. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes, including concepts performed in the mind, including an observation, judgement, evaluation and/or opinion.
Step 2A Prong 2: Said judicial exception is not integrated into a practical application because the claims as a whole, looking at the additional elements: providing the pages to a first machine learning (ML) model, and providing the classified pages to a second ML model, memory, processor, NTCRM and instructions, individually and in combination, merely use a computer (see MPEP 2106.05f.) The claims use these machines in their ordinary capacity for the purpose of applying the abstract idea(s). Therefore, these limitations are invoking computers or other machinery merely as a tool to perform an existing process, such that it amounts to no more than mere instructions to apply the exception. Then, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, and the claim is directed to an abstract idea.
Step 2B: Said claims recite additional elements as listed above, which are not sufficient to amount to significantly more than the judicial exception because, as mentioned in Step 2A Prong 2, they use computers or other machinery to perform an abstract idea in such a way that amounts to no more than mere instructions to apply the exception using computers or other machinery. Mere instructions to apply an exception using computers or other machinery cannot provide an inventive concept. Therefore, the claim is not patent eligible.
Claims 2, 6, 13 and 17 recite displaying classified pages/visualizing objects on a user device/display device, receiving user input on device associated with a region of a classified page, and adjusting the region based on inputs received. Claim 3 recites substantially similar language. These claims are directed to performing the judicial exception using computing technology in its ordinary capacity.
Claims 4, 14 and 18 recite generating a segmentation mask identifying a roof. The claims further recite calculation roof area, creation of a masked image from the segmentation mask, analyzing the image to identify distinct shapes and edges, identifying structural lines from the edges, determining slopes of roof and associating slopes with building elements. These limitations are part of the judicial exception, and using the second ML model to generate the mask is seen as using computing technology in its ordinary capacity to perform the exception.
Claims 5, 15 and 19 recite receiving a first mask of interior wall locations and a second of exterior wall locations, calculating dimensions of walls and associating the dimensions with one or more of the building elements. This is part of the judicial exception. The claims also recite training the second ML model to identify wall segments, said model capable of sending masks. Claims 10 and 11 also detail the training of the second ML model to include wall identification and object associated roof slope and area. Claims 7 and 20 specify the first ML model is at least one of a CNN, R-CNN, or transformer-based vision model. These limitations are seen as using the claimed computing technology in its ordinary capacity to perform the exception.
Claims 8 and 9 detail elements of the data types and content/analysis.
The claims are not subject matter eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that forms the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) or 102(a)(2) as being anticipated by Austern (US 2023/0153485).
Claims 1, 12 and 16. Austern teaches a method comprising:
accessing a construction document associated with a construction project, the construction document comprising pages; (paragraph 5 accessing a floor plan)
providing the pages to a first machine learning model (para. 7 showing machine learning method analysis to floor plan) to generate classified pages, wherein each classified page identifies a page type and a region; (para. 7 area of interests and room openings)
providing the classified pages to a second machine learning model to generate segmented pages comprising one or more objects, each object corresponding to a building element; (para. 135 determining walls, doors, windows and technical aspects) (paragraphs 131 and 132 various models used and maintained for analysis)
analyzing the one or more objects to determine coordinates and properties; and (para. 132 showing determination of coordinates of objects in plan, as well as dimensions and contours)
determining, from the coordinates and the properties, a bill of materials associated with the construction project. (para. 14 bill of material (BOM) output)
Claim 12 additionally: a memory and a processor coupled to the memory and configured to perform the operations. (para. 109 computer embodiment)
Claim 16 additionally: a non-transitory computer readable medium comprising instruction, that when executed by a processor, cause the processor to perform operations. (para. 109 computer embodiment)
Claims 2, 13 and 17. Austern teaches the method of claim 1, further comprising:
presenting one or more of the classified pages on a display of a user device; (para. 13 displaying output)
receiving input from the user device, wherein the input is associated with a region of a first classified page of the classified pages; and (para. 13 input for adjusting results) (para. 125 manually adjusting room contours)
adjusting the region of the first classified page based on inputs received from a user device. (para. 13 input for adjusting results) (para. 125 manually adjusting room contours)
Claim 3. Austern teaches the method of claim 1, further comprising:
presenting one of the segmented pages on a display of a user device; and (para. 13 displaying output)
adjusting one or more objects of the one of the segmented pages based on inputs received from the user device. (para. 13 input for adjusting results) (para. 155 showing manual configuration of object data)
Claims 4, 14 and 18. Austern teaches the method of claim 1, wherein the second machine learning model generates a segmentation mask (para. 131 showing segmentation masking on floor plan) that identifies a presence of a roof on a first classified page of the classified pages, (para. 321 roof as one of spaces being analyzed) the method further comprising:
calculating, from the segmentation mask, an area of the roof; (para. 578 dimensions of roof as determination by system) (para. 123 extraction of room area)
applying the segmentation mask to the first classified page to create a masked image; (para. 335 showing image generation with masks)
analyzing the masked image to identify distinct shapes and edges of the distinct shapes; (para. 335 showing edge and shape recognitions for identifying objects in plan)
identifying, from the distinct edges, structural lines; (para. 936 showing room segmentation using edge analysis)
determining, from the structural lines, one or more slopes associated with the roof; and (para. 132 showing determination of line length and direction and location of geometric objects on floor plan, including certain angles of walls and room boundaries)
associating the one or more slopes with the building elements. (para. 132 showing determination of line length and direction and location of geometric objects on floor plan, including certain angles of walls and room boundaries)
Claims 5, 15 and 19. Austern teaches the method of claim 1, wherein the second machine learning model is trained to identify one or more wall segments, the method further comprising:
receiving from the second machine learning model, a first mask representing first locations of interior walls (para. 335 showing interior wall determination) and a second mask representing second locations of exterior walls; (para. 173 showing location of exterior walls determined) (para. 128 masks for segmentation)
calculating first dimensions of the interior walls from the first mask and second dimensions of the exterior walls from the second mask; and (para. 121 calculate wall geometry)
associating first dimensions of the interior walls and second dimensions of the exterior walls with one or more of the building elements. (para. 121 calculate wall geometry)
Claim 6. Austern teaches the method of claim 1, further comprising: visualizing the one or more objects of the segmented pages on a display device. (para. 121 visualization of analysis in representation of space from floor plan)
Claims 7 and 20. Austern teaches the method of claim 1, wherein the first machine learning model comprises one or more of a convolutional neural network (CNNs), a transformer-based vision model, and a region-based CNN (R-CNN). (para. 128 Mask RCNN for segmentation, CNN for classification)
Claim 8. Austern teaches the method of claim 1, wherein the page type is one or more of electrical, structural, elevation, flat roof, sloped roof, mechanical, and plumbing. (para. 123 showing room tagging type including electrical, mechanical and boiler room)
Claim 9. Austern teaches the method of claim l, further comprising adjusting one or more of a size and a shape of one or more of the pages prior to providing the pages to the first machine learning model. (para. 131 showing manipulation of floor plan/cropping/reduced size prior to model input)
Claim 10. Austern teaches the method of claim 1, wherein the second machine learning model is trained to identify one or more of an interior wall and an exterior wall. (para. 335 showing interior wall determination) (para. 173 showing location of exterior walls determined)
Claim 11. Austern teaches the method of claim 1, wherein the second machine learning model is trained to identify, for each of the one or more objects, an associated roof slope and area. (para. 578 dimensions of roof as one of the spaces’ determination by system) (para. 123 extraction of room area including contours, para. 124 vector generation for room contours))
Conclusion
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/AARON TUTOR/Primary Examiner, Art Unit 3627
