DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This is in response to applicant’s amendment/response filed on 08/27/2026, which has been entered and made of record. Claims 1-2 and 11-12 have been amended. Claims 1-20 are pending in the application.
Response to Arguments
Applicant's arguments filed on 08/27/2026 have been fully considered but they are rendered moot in view of the new grounds of rejection presented below (as necessitated by the amendment to claims 1 and 11).
Applicants state that “In response to the rejections of claims 1, 2, 3, 11, 12, and 13, Applicant respectfully traverses. It appears that the Office misunderstands the claimed limitations in view of the instant specification. For example, claims 1 and 12 recite the limitations "measuring one of more lengths and one or more areas of the image file." This limitation defines automatically measuring the length or area on the image file in order to find different attributes like length or area, as disclosed in paragraph [0023] of the specification, reproduced below. It is not related to the digital size of the file "for example, 10KB, 10 MB, GB or TB, etc." as understood by the Examiner.1 Furthermore, the instant invention is related to automatically identifying different attributes on an image file in order to estimate a cost. ….Applicant respectfully requests withdrawal of the 112(b) rejection. If, however, the Examiner disagrees, the Examiner is invited to telephone the undersigned who will be happy to work with the Examiner in a joint effort to derive mutually acceptable language”. The examiner disagrees. 1. Based on definition, an image file is a digital graphic saved in a specific format to store visual data on a computer or online and an image has measuring one of more lengths and one or more areas. 2. identifying walls, doors, and room layouts from image (not image file). So, I suggest change image file to image to overcome 112(b).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 11 recite limitation “automatically labeling one or more objects including adding new objects based on the one or more measured lengths and one or more measured areas on the image file to generate a labeled image”.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention because in specification include par 0031, it only describe automatically labeling one or more objects including adding new objects on the image file to generate a labeled image or automatically labeling the one or more measured lengths and one or more measured areas on the image file to generate a labeled image, but does not disclose automatically labeling one or more objects including adding new objects based on the one or more measured lengths and one or more measured areas on the image file to generate a labeled image. This is a new matter rejection. Support was not found for these new limitations in the original specification. MPEP 2163 II A (b) states “To comply with the written description requirement of 35 U.S.C. 112, para. 1, or to be entitled to an earlier priority date or filing date under 35 U.S.C. 119, 120, or 365(c), each claim limitation must be expressly, implicitly, or inherently supported in the originally filed disclosure. When an explicit limitation in a claim “is not present in the written description whose benefit is sought it must be shown that a person of ordinary skill would have understood, at the time the patent application was filed, that the description requires that limitation.”
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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 1 and 11 recite the limitations “measuring one or more lengths and one or more areas of the image file”, claims 2 and 12 recite the limitations “detecting one or more drawing elements of the image file”, claims 3 and 13 recite the limitations “adjustments for the one or more lengths and/or areas of the image file”, and claims 4 and 15 recite the limitations “detecting one or more line segments of the image file” that are indefinite. They are vague and ambiguous because an image or data file is normally measuring in term of size (for example, 10KB, 10 MB, GB or TB, etc.). It is unclear what is meant by the length or area or drawing elements or line segments of image file because the normal meaning of data or image file are usually expressed in bytes. A clearly and precisely defined metes and bounds of the claimed invention are required.
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.
Claim(s) 1-5, 7-9, and 11-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. PGPubs 2022/0327264 to Murphy et al. in view of U.S. PGPubs 2023/0316702 to Kar et al..
Regarding claim 1, Murphy et al. teach a method for planning and cost engineering using an artificial intelligence (AI) model comprising (abstract):
receiving one or more input documents, the input documents including at least architectural design information (par 0011, “A two-dimensional reference, such as a design floorplan is input into an AI engine and the AI engine converts aspects of the floorplan into components that may be processed by the AI engine, such as, for example, a pasteurized version of the floorplan”, par 0016, “provides for apparatus and methods related to receiving as input design plans (either physical or electronic) and generating one or more pixel patterns based upon automated processing of the design plans”, Fig 1A, par 0056-0058, “At step 100, a design plan (which may be design plan or dynamic architectural design file (e.g., a Revit® compatible file) indicating aspects of a Powles et al.; is input into a controller or other data processing system using a computing device …. the design plan may be files extensions that include but are not limited to: DWG, DXF, PDF, TIFF, PNG, JPEG, GIF, or other type of file based upon a set of engineering drawings”);
converting the input documents into an image file (par 0059, “The input process may occur with a user creating, scanning into, or accessing such a file containing a raster graphics image or a vector graphics image. The user may access the file on a desktop or standalone computing device or, in some embodiments, via an application running on a smart device. In some embodiments, a user may operate a scanner or a smart device with a charged couple device to create the file containing the image on the smart device”, par 0063, “If a file type of the received design plan, such as an input architectural floor plan technical drawing, includes at least a portion that is not already in raster graphics image format (for example that it is in vector format), then the input architectural floor plan technical drawing may be transformed to a pixel or raster graphics image format in step 102”);
automatically setting a scale unit based on at least one known measurement of the input documents (par 0072-0073, “a scale (e.g.,; FIG. 2B item 217) is associated with the two-dimensional reference. In preferred embodiments, the scale is based upon a portion of the two-dimensional reference dedicated for indicating a scale, such as a ruler of a specific length relative to features included in a technical drawing included in the two-dimensional reference. The software then performs a pixel count on the image and applies this scale to the bitmapped image. Alternatively, a user may input a drawing scale for a particular image, drawing or other two-dimensional reference. The drawing scale, may for example, be in inches: feet, centimeters: meters, or any other appropriate scale”, par 0116-0117, “A scale 217 may be used to indicate a size of features included in a technical drawing included in the two-dimensional reference. As indicated above, executable software may be operative with a controller to count pixels on an image and apply a scale to a bitmapped image. Alternatively, a user may input a drawing scale for a particular image, drawing or other two-dimensional reference. Typical units referenced in a scale include inches: feet, centimeters: meters, or any other appropriate unit”);
verifying the scale unit (par 0072-0073, par 0116-0117, “a scale 217 may be determined by manually measuring a room, a component, or other empirical basis for assessing a relative size. Examples therefore include a scale included as a printed parameter on two-dimensional reference or obtained from dimensioned features in the drawing. For example, if it is known that a particular wall is thirty feet in length, a scale may be based upon a length of the wall in a particular rendition of the two-dimensional reference and proportioned according to that length”);
automatically measuring one or more lengths and one or more areas of the image file based on the architectural design information (par 0009, “he present invention uses AI to auto-detect, measure, and classify components of Powles et al. plans, and ascertain whether requirements relating, to Powles et al. design are in compliance with a relevant code according to the AHJ”, par 0128, “in addition to method steps operative to calculate a value for a variable representative of an area, a controller may be operative to generate a value for element lengths, which values may also be calculated. For example, if ceiling heights are measured, presented on drawings, or otherwise determined then volume for the room and surface area calculations for the walls may be made.”, par 0275, “Various symbols and icons may be overlayed on the design to indicate various artifacts, corners, stairs, measurements such as door width, access to measurements or design data of various kinds and the like. The user may be able to zoom to portions of the drawing for more detailed review and/or the display of more detailed measurements and design data”); and
automatically labeling one or more objects including adding new objects based on the one or more measured lengths and one or more measured areas on the image file to generate a labeled image (par 0079, “The interaction of two elements at a vertex may define one or more new elements. For example, an intersection of two lines at a vertex may be assessed by the AI as an angle that is formed by this combination “, par 0084, “a boundary may be selected and “dragged” to a new location. The user interface may enable a user to select a line end, a polygon portion, an apex, or other convenient portion and move the selected portion to a new position and thereby redefine the line and/or polygon. An area that includes a boundary as a border will be redefined based upon the modification to the boundary. As such, an area of a room or unit may be redefined by a user via the user interlace. Changing an area of a room and/or unit in turn be used as a basis for modifying an occupant load, defining an egress path, classifying a space, or other purpose “, par 0097, “The user interface 125 includes a reproduction of the pictorial representation 122 and an overlay 124 with one or more user manipulatable components, such as, by way of non-limiting, example: boundaries, line segments, polygons, images, icons, points, and the like. The line segments may have calculated lengths that may be mathematically manipulated and/or summarized. Aspects such as polygons, line segments, shapes, icons, and points may be counted, added, subtracted, extrapolated, and other functions performs on them”, par 0130, “referring now to FIG. 3A, a user interface 300 with a replication view 301A may include replication of an original floor plan represented by a two-dimensional reference, without any controller added features, vectors, lines, or polygons integrated or overlaid into the floorplan”, par 0157-0159,” Referring now to FIG. 5C, a line segment view 501C may include identified boundary line segments 510 and vertices 511 may also be presented as an overlay of the regions 502-509 illustrated as delineated symbolic hatches or colors etc., as illustrated in FIG. SC … integrated and/or overlaid aggregations of some or all of: regions; spaces; patterned portions; line segments; polygons; symbols; icons or other portions of the user interfaces may be assembled and presented in a user output and our user interface, or as input into another automated process”, par 0273, “Examples of improvements which the system may recommend to a user may include adding a new door or other feature to create a new path of egress. In other examples, design elements such as doors may be relocated to address non-compliance. Other examples may include adding structure to block aspects such as dead ends. Still further examples may address non-compliance aspects by adding features such as by suggesting the adding of a doorway to a dead end corridor as a non-limiting example”).
But Murphy et al. keep silent for teaching wherein the labeled image is used to train the Al model, and calculating a cost estimate, based on the labeled image.
In related endeavor, Kar et al. teach automatically labeling one or more objects based on the one or more measured lengths and one or more measured areas on the image file to generate labeled image, wherein the labeled image is used to train the Al model (par 0024, “an ML parts identification model that identifies and labels the parts and the states of the various parts of the damaged object from the images. The output from the parts identification model can include one or more images of the damaged object with parts identified and annotated with corresponding states. In an example, the parts identification model can include a Faster Region-based convolution neural network (Faster R-CNN). The Faster R-CNN model is also trained on labeled training data that includes images of other objects belonging to the object category of the damaged object. The images of the other objects are labeled with the various parts in different states. Different objects of the same object category labeled with respective parts and annotated with their corresponding states such as damaged, non-damaged, repair annotations and replace annotations are included in the training data for the parts identification model. When the parts identification model is able to identify the parts the extent of damage is estimated as a percentage of damaged parts versus the non-damaged parts “, par 0030, “The images 110 are accessed by the cause predictor 104 which analyzes the images 110 to determine or predict a cause for the damaged object. The cause for the damage can be identified from a plurality of causes that are predetermined for an object category associated with the damaged object. Different objects of the object category may have different causes for damages. Also, different types of damage to an object may result due to different causes “, par 0035, “the models trainer 112 can include or may access different types of training data required to train the various ML models within the damage detection and estimation system 100 including the sub-models 1422 in the cause prediction model 142 and the parts identification model 162. Training data sets 122, 124 with labeled images can be used for supervised training of the ML models. The training data 122 can store sub-sets of images of objects belonging to the same object category such as the damaged object captured in the images 110 that are damaged due to various causes. Each of the sub-sets can be used to train a corresponding one of the sub-models 1422 in identifying a cause of damage for one particular portion of the damaged object. The images for the training data sets 122, 124 can be provided to a software, such as Labelimg which is a graphical image annotation tool and which can label object bounding boxes in images, where the images are manually annotated so that the images map to a specific label name and coordinates into an XML file (e.g., <labelname>fender</labelname>, <xmin>433</xmin>, etc.)”), and calculating a cost estimate, based on the labeled image (par 0032-0033, “The images 110 are also provided to the parts identifier 106 for image segmentation and for identification of the various parts of the damaged object from the images 110. The parts identifier 106 also includes an ML parts identification model 162 for identifying and labelling the various damaged, non-damaged and repairable parts of the damaged object from the images 110. In an example, the parts identification model 162 can include a faster RCNN that is trained via labeled data for the identification of the various parts. The damaged parts are those parts which are identified as both requiring replacement as well as repairable parts which can be fixed without the need for replacements. The parts identifier 106 can also be configured to produce a damage assessment of the damaged object in terms of an overall percentage and in terms of damage percentage for individual parts of the damaged object. The identification of the damaged, non-damaged, repairable and replacement parts 164 enables the damage detection and estimation system 100 to automatically assess the cost of fixing the damaged object”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Murphy et al. to include wherein the labeled image is used to train the Al model, and calculating a cost estimate, based on the labeled image as taught by Kar et al. to use an ensemble machine learning (ML) model to identify different objects through analyze obtained images to determine corresponded cost for each object.
Regarding claim 2, Murphy et al. as modified by Kar et al. teach all the limitation of claim 1, and Murphy et al. further teach further comprising; automatically detecting one or more drawing elements of the image file (par 0009, “he present invention uses AI to auto-detect, measure, and classify components of Powles et al. plans, and ascertain whether requirements relating, to Powles et al. design are in compliance with a relevant code according to the AHJ”); automatically classifying each of the one or more drawing elements into one or more classifications (par 0108, par 0163-0166, “a controller executing an AI engine or other method steps, may be operative, in some embodiments, to classify individual line segments 602-611 of a boundary 601 and present a user interface 600C indicating the classified individual line segments 602-611. The AI engine may be trained, and subsequently operative, to classify individual line segments 602-611 included in a boundary $01 in different classes. As a non-limiting example, an AI engine may classify walls as interior walls, exterior walls and/or demising walls that separate internal spaces”); linking each of the one or more classifications to at least a material (par 0165, “functionality may be allocated to classified individual line segments 602-611, such as, by way of non-limiting example, a process that generates an estimated materials list for a region or an area defined by a boundary, based on the regions or area's characteristics and its classification”); calculating, based on the linked material, the cost estimate for each of the one or more drawing elements (par 0054, par 0099, par 0173-0174, “a derived area or region comprising a room and/or a boundary, perimeter or other beginning and end indicator may allow for a Powles et al. estimate that may integrate choices of materials with associated raw materials costs and with labor estimates all scaled with the derived parameters. The boundary determination function may be integrated with other standard construction estimation software and feed its calculated parameters through APIs”).
Regarding claim 3, Murphy et al. as modified by Kar et al. teach all the limitation of claim 2, and Murphy et al. further teach further comprising; receiving one or more user provided adjustments for the one or more lengths and/or areas of the image file (par 0074, par 0120, “a controller is operative to generate a user interface with dynamic components that may be manipulated by one or both of user interaction and automated processes. Any or all of the components in a user interface may be converted to a version that allows a user to modify an attribute of the component, such as the length, size, beginning point, end point, thickness, or other attribute. In some embodiments, a boundary may be treated as a component and manipulated in like manner”, par 0217-0219, “a dynamic component may include a line segment and/or arcuate segment, and methods of practice may include one or more of: receiving an instruction via a user interactive interface to modify a parameter of the line segment, and the method further includes the step of modifying the parameter of the line segment based upon the instruction received via the interactive user interface. The parameter of the line segment may include a length of the line segment and the method may additionally include modifying a length of a wall based upon the modifying the length of the line segment”); automatically recalculating, based on the user provided adjustments, the cost estimates for each of the one or more drawing elements (par 0099, “ a first user interface 125 rendition, may be modified by a user to create a second user interface 125, and submitted to AI analysis to ascertain compliance with a selected code. Some embodiments may also calculate costs, expenses, man hours or other variable associated with changes to a design plan in, order to bring the design plan into compliance”, par 0216, “The steps may be performed multiple times and may include two or more two dimensional references with results of the process be compared one against the other to ascertain when a change has been made to a two dimensional reference that places a Powles et al. in compliance with a selected code. In various embodiments, a change in subsequent two dimensional references may be used to generate a change in one or more of a take off, labor costs, project management input or other aspects that may impact construction of a Powles et al. and/or associated costs”).
Regarding claim 4, Murphy et al. as modified by Kar et al. teach all the limitation of claim 2, and Murphy et al. further teach further comprising; automatically detecting one or more line segments of the image file (par 0071, “The features and boundaries may be determined, for example, via algorithmically processing an input design plan image with a trained AI model. As a non-limiting example, the AI engine may process a raster file that is converted for output as an image file of a floorplan (as illustrated in FIG. 2B, a boundary is represented as line, a boundary may also be represented as a polygon, which may be a patterned polygon or other user discernable representation, such as a colored line etc)”, par 0078, “some embodiments that utilize the recognition step transforms features such as windows, doorways, vias and the like to other features and may be remove them and/or replace them as elements—such as line segments, vectors, or polygons referenceable to other neighboring features “, par 0115, par 0157, “determination of a length of a wall section, or size of a feature may be supported via a boundary that includes a line segment”).
Regarding claim 5, Murphy et al. as modified by Kar et al. teach all the limitation of claim 3, and Murphy et al. further teach further comprising; modifying the one or more line segments based off a user-provided input (par 0074, par 0120, “a controller is operative to generate a user interface with dynamic components that may be manipulated by one or both of user interaction and automated processes. Any or all of the components in a user interface may be converted to a version that allows a user to modify an attribute of the component, such as the length, size, beginning point, end point, thickness, or other attribute. In some embodiments, a boundary may be treated as a component and manipulated in like manner”, par 0217-0219, “a dynamic component may include a line segment and/or arcuate segment, and methods of practice may include one or more of: receiving an instruction via a user interactive interface to modify a parameter of the line segment, and the method further includes the step of modifying the parameter of the line segment based upon the instruction received via the interactive user interface. The parameter of the line segment may include a length of the line segment and the method may additionally include modifying a length of a wall based upon the modifying the length of the line segment”); and automatically adjusting, by the AI and in substantially real-time, the cost estimates for each of the one or more drawing elements based on the modified line segments (par 0099, “ a first user interface 125 rendition, may be modified by a user to create a second user interface 125, and submitted to AI analysis to ascertain compliance with a selected code. Some embodiments may also calculate costs, expenses, man hours or other variable associated with changes to a design plan in, order to bring the design plan into compliance”, par 0216, “The steps may be performed multiple times and may include two or more two dimensional references with results of the process be compared one against the other to ascertain when a change has been made to a two dimensional reference that places a Powles et al. in compliance with a selected code. In various embodiments, a change in subsequent two dimensional references may be used to generate a change in one or more of a take off, labor costs, project management input or other aspects that may impact construction of a Powles et al. and/or associated costs”).
Regarding claim 7, Murphy et al. as modified by Kar et al. teach all the limitation of claim 3, and Murphy et al. further teach wherein the AI incorporates at least one user provided annotation of a new element to improve future analysis (par 0081, “At step 106A, In some embodiments, components presented in an interactive user interface may be analyzed by a user and refinements may be made to one or more components (e.g., size, shape and/or position of the component). In some embodiments, user modifications may also be input back to the AI engine train the AI engine. User modifications provided back to the AI Engine may be referenced to make subsequent AI processes more accurate and/or enable additional types of AI processes”, par 0281-0282, “The tiles may also include view-specific elements 2212. View specific elements may be details and annotation elements that appear only when specific views 2220 are activated. Annotation elements 2232 may include keynotes, comments, tags, dimensions, and other such annotations. Detail elements 2233 may include detail lines, filling of various aspects and other such components. ….Still further examples may derive from capturing design elements in displays of various kinds. Finally, such access to external file types may be used to verify models generated in the standard manners as have been described or add information such as annotations, descriptions, and other such aspects”).
Regarding claim 8, Murphy et al. as modified by Kar et al. teach all the limitation of claim 2, and Murphy et al. further teach further comprising: utilizing Object Content Recognition (OCR) to extract text from the input documents (par 0191, “ Logic 926 may also perform optical character recognition (OCR.) or other post-scan processing on the bit-mapped image to convert it to text.”); performing a compliance check on the input document against at least one code (par 0013-0014, “Variables are specified that will be used. to assess compliance and a compliance determination is made based upon values for the specified variables. In some embodiments, the variables may include some or all of; occupancy load; travel distance; travel distance from a furthest point from a pint of egress; egress capacity; common path; dead end; a function of space; or other factor on which a determination of compliance and/or lack of compliance may be based. The AI engine will generate values for some, or all of the variables referenced to determine compliance”, par 0099, par 0126, “the AI may generate a likelihood that a region or area represented by one or both of a polygon or pattern of dots, includes a common path or dead end or an area definable for determining an occupancy load, egress capacity, travel distance and/or other factor that may influence a decision on compliance with a local code”); automatically flagging at least one conflict against the compliance code (par 0014, “Some embodiments may also include, in the case where the conditions/components were not met by a floorplan, the portions referenced in determining non-compliance. Still further, some embodiments may include suggested changes and/or options for sets of changes to the floor plan that may be implemented in order to achieve compliance”, par 0089-0091, “the user interface may employ a simple yes/no indicator for compliance with a requirement of a selected code. In other embodiments, the user interface may visually indicate portion of the design plan that was referenced in determining a state of compliance or on-compliance, e.g.; a doorway that is thirty six inches or more and therefore in compliance or a hallway that is only twenty four inches wide and therefore not in compliance”).
Regarding claim 9, Murphy et al. as modified by Kar et al. teach all the limitation of claim 8, and Murphy et al. further teach further comprising; automatically suggesting at least one design modification to resolve the at least one conflict against the compliance code (par 0014, “Some embodiments may also include, in the case where the conditions/components were not met by a floorplan, the portions referenced in determining non-compliance. Still further, some embodiments may include suggested changes and/or options for sets of changes to the floor plan that may be implemented in order to achieve compliance “, par 0091, “he present invention may use the AI to generate suggested modifications to a design plan in order to transition the design plan from a state of non-compliance to a state of compliance. In addition, the user interface may indicate other actions, in addition to a modification to the design plan, (e.g., reduce an occupant load) in order to conform to code”, par 0237, “a process executed by an AI engine may ascertain Powles et al. attributes and analyze the Powles et al. attributes may be modified in order to bring the Powles et al. into compliance. A user interface may present suggested modifications to a user. Some embodiments may also include designation and/or ranking of variables that may be modified in order to bring a Powles et al. into compliance”, par 0237, “ where there are non-compliant aspects, a trained artificial intelligence analysis of numerous examples of non-compliant designs and their subsequent design changes that reinediated the issues may be used to suggest modifications based on an analysis of the design under review”).
Regarding claim 11, Murphy et al. teach a system for planning and cost engineering using an artificial intelligence comprising (abstract); an artificial intelligence (AI) model (par 0062); a non-transitory computer-readable storage medium storing computer executable instructions (par 0189, par 0227); and a processor configured to execute the computer executable instructions to perform the steps of (par 0183). The remaining limitations of the claim are similar in scope to claim 1 and rejected under the same rationale.
Regarding claims 12-13, Murphy et al. as modified by Kar et al. teach all the limitation of claim 11, the claims 12-13 are similar in scope to claims 2-3 and are rejected under the same rational.
Regarding claim 14, Murphy et al. as modified by Kar et al. teach all the limitation of claim 13, and Murphy et al. further teach wherein the user input method is one of a mouse, a keyboard, or a touchscreen device (par 0185, “Input facilities may exist in the form of buttons, scroll wheels, or other tactile Sensors such as touchpads. In some embodiments, input facilities may include a touchscreen display”).
Regarding claim 15, Murphy et al. as modified by Kar et al. teach all the limitation of claim 13, and Murphy et al. further teach wherein the user input method is configured to allow a user to manually resize or adjust one or more boundaries used to calculate the one or more lengths and/or areas of the image file (par 0069, “a training database may utilize a collection of design data that may include one or more of: a combination of a vector graphic two-dimensional references such as floor plans and associated raster graphic version of the two-dimensional references, raster graphic patterns associated with features; and a determination of boundaries may be automatically or manually derived.”, par 0074, par 0120, “a controller is operative to generate a user interface with dynamic components that may be manipulated by one or both of user interaction and automated processes. Any or all of the components in a user interface may be converted to a version that allows a user to modify an attribute of the component, such as the length, size, beginning point, end point, thickness, or other attribute. In some embodiments, a boundary may be treated as a component and manipulated in like manner”).
Claim(s) 6, 10, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPubs 2022/0327264 to Murphy et al. in view of U.S. PGPubs 2023/0316702 to Kar et al., further in view of U.S. PGPubs 2022/0391627 to Powles et al..
Regarding claim 6, Murphy et al. as modified by Kar et al. teach all the limitation of claim 2, but keep silent for teaching wherein the AI includes at least a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Long Short-Term Memory network (LSTM).
In related endeavor, Powles et al. teach wherein the AI includes at least a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Long Short-Term Memory network (LSTM) (par 0064, “The semantic segmentation algorithms may include one or more of YOLO algorithms, YOLACT (‘You Only Look at Coefficients’) algorithms, SSD algorithms, CNN (‘Convolutional Neural Networks’) configured to perform pixel-by-pixel classification of each pixel in the image, FCN (‘Fully Convolutional Networks’) trained for image segmentation, and Mask R-CNN (‘Region based Convolutional Neural Networks’). The text recognition algorithms may include one or more of RNN (‘Recurrent Neural Networks’) configured to process variable length sequences of text, LSTM (‘Long Short-Term Memory’) to process sequences of images or text, and NLP (‘Natural Language Processing’) algorithms such as Word2Vec.”, also par 0293).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Murphy et al. as modified by Kar et al. to include wherein the AI includes at least a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Long Short-Term Memory network (LSTM) as taught by Powles et al. to combine the architectural design document analysis system of Murphy et al. with the support for convolutional neural networks of Powles et al., because such systems and methods allow for the use of different neural network models in performing architectural design document analysis (Powles et al.: para [0064]). Furthermore, both Murphy et al. and Powles et al. are directed to systems and methods for architectural design document analysis.
Regarding claim 10, Murphy et al. as modified by Kar et al. teach all the limitation of claim 1, but keep silent for teaching further comprising; automatically exporting the labeled image as a CSV file to a project management software.
In related endeavor, Powles et al. teach further comprising; automatically exporting the labeled image as a CSV file to a project management software (par 432, “This information can be present in any machine or human readable format, including but not limited to binary files, text files, JavaScript Object Notation (JSON), or Comma-separated variable (CSV) files”, also par 0463-0465).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Murphy et al. as modified by Kar et al. to include further comprising; automatically exporting the labeled image as a CSV file to a project management software as taught by Powles et al. to combine the architectural design document analysis system of Murphy et al. with the support for the export of CSV files of Powles et al., because such systems and methods allow for the use of different file formats in performing and outputting architectural design document analysis (Powles et al.: para [0432]). Furthermore, both Murphy et al. and Powles et al. are directed to systems and methods for architectural design document analysis.
Regarding claim 16, Murphy et al. as modified by Kar et al. teach all the limitation of claim 11, the claim 16 is similar in scope to claim 6 and is rejected under the same rational.
Regarding claim 17, Murphy et al. as modified by Kar et al. and Powles et al. teach all the limitation of claim 16, and Powles et al. teach wherein the AI model further includes Natural Language Processing (NLP) techniques (par 0064, “The semantic segmentation algorithms may include one or more of YOLO algorithms, YOLACT (‘You Only Look at Coefficients’) algorithms, SSD algorithms, CNN (‘Convolutional Neural Networks’) configured to perform pixel-by-pixel classification of each pixel in the image, FCN (‘Fully Convolutional Networks’) trained for image segmentation, and Mask R-CNN (‘Region based Convolutional Neural Networks’). The text recognition algorithms may include one or more of RNN (‘Recurrent Neural Networks’) configured to process variable length sequences of text, LSTM (‘Long Short-Term Memory’) to process sequences of images or text, and NLP (‘Natural Language Processing’) algorithms such as Word2Vec.”, also par 0293-0296) and Murphy et al. further teach wherein the instructions further comprise; utilizing Object Content Recognition (OCR) to extract text from the input documents; performing a compliance check on the input document against at least one code; and automatically flagging at least one conflict against the compliance code (similar term as claim 8 so see rejection of claim 8).
Regarding claim 18, Murphy et al. as modified by Kar et al. and Powles et al. teach all the limitation of claim 17, the claims 18 is similar in scope to claim 9 and is rejected under the same rational.
Regarding claim 19, Murphy et al. as modified by Kar et al. teach all the limitation of claim 12, but keep silent for teaching wherein the cost estimations are further based on at least a cost data library, and further include at least labor, taxes, and shipping.
In related endeavor, Powles et al. teach wherein the cost estimations are further based on at least a cost data library, and further include at least labor, taxes, and shipping (par 0034, par 0209, “The knowledge representation and linkage module also provides output data 314 which summarises the quantifiable characteristics of the objects identified on the architectural plan. In one example of the technology, this is a full breakdown of the items and materials used including lengths, counts, locations and volumes of each item. The quantifiable characteristics can then be compared against a supplier database of products in order to determine the products required to construct the Powles et al., and a material cost database to determine the costs of the products”)
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Murphy et al. as modified by Kar et al. to include wherein the cost estimations are further based on at least a cost data library, and further include at least labor, taxes, and shipping as taught by Powles et al. to combine the architectural design document analysis system of Murphy et al. with the support for a cost data library of Powles et al., because such systems and methods allow for a database for querying actual costs related to construction (Powles et al.: para [0209]). Furthermore, both Murphy et al. and Powles et al. are directed to systems and methods for architectural design document analysis.
Regarding claim 20, Murphy et al. as modified by Kar et al. and Powles et al. teach all the limitation of claim 19, and Powles et al. further teach wherein the cost estimates are further based off at least one user provided cost (par 0415, “Another use of the human in the loop module is for completing the required process of producing an estimate, providing architectural plan comparisons, or generating CAD or BIM files. To achieve this process the present technology can present a complete, or near complete output file to a user/human, and automatically flag or otherwise highlight features which fall below a predetermined confidence level, or where an information inconsistency exists”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jin Ge whose telephone number is (571)272-5556. The examiner can normally be reached 8:00 to 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Chan can be reached at (571)272-3022. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JIN . GE
Examiner
Art Unit 2619
/JIN GE/Primary Examiner, Art Unit 2619
