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 .
Responsive to communications on 03/20/2026
Claims 1-20 pending in the application
Claims 1-3, 6-7, 13-15, and 18-20 amended
Claims 4-5, 8-12, and 16-17 original
Final Action
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.
Interview
Interview was conducted on 03/12/2026. Please see Examiner Interview Summary Record for relevant information and notes regarding the interview.
Beginning Response to Arguments
Response to Arguments Regarding Objections to the Specifications
Issue: Specifications were objected to for minor informalities. Par 28 was objected to for a typographic error. Par 36 and 81 were objected to for usage of terms used in commerce. Response: Applicant has amended paragraph 28, as well as paragraph 36 for informalities. Applicant argues terms in paragraphs 81 are generic terms. Rule: The MPEP 608.01(v) states that a trade name may be used in patent applications if “its meaning is well-known to one skilled in the relevant art and is satisfactorily defined in the literature” Analysis: Examiner confirms amended specifications on 3/20/2026 corrects previous objections posed for par 28 and 36. Examiner agrees that the objected terms in par 81 are generic and well known to one skilled in the art. Conclusion: Examiner withdraws objection to the specification in light of the amendments to the specification and applicant arguments.
Response to Arguments Regarding Objections to the Claims
Issue: Claims 1, 13, and 18 were objected to for clarity. Claims 2, 14, and 19 were also objected for clarity. Claims 3, 15, and 20 were also objected to for clarity. Response: Applicant has amended the claims. Analysis: Examiner confirms amended claims overcome and clarify the objections. Amendments in claim 1, 13, and 18 clarify where the first symbol is received from, and also remove the objected to “combination thereof.” Claims 2, 14, and 19 similarly remove the objected to “combination thereof” limitation. Claims 3, 15, and 20 add the term “at least one of” and remove “or a combination thereof” which clarifies the scope of the claim. Conclusion: Examiner withdraws claim objections in light of the amendments to the claims.
Response to Arguments regarding 112 Rejections
Issue: Claims 1, 3, 6, 7, 13, 15, 18, and 20 were rejected under 112(b) for being indefinite. Response: Applicant has amended claims 1, 3, 6, 7, 13, 15, 18, and 20. Applicant also notes that the term “a detailed view” in claim 6 is a understood term of the art, and clarifies its meaning to be further specifying a portion of a document or the like. Analysis:1. Claims 1 , 13, and 18 “to detect the correlation between the one or more symbols and the metadata of the plurality of 2D building drawings” was found to lack antecedent basis. The examiner does not find that the amendments correct this specific 112(b) issue. The claim introduces “a correlation between an elevation, a floorplan, and a section of the plurality of 2D building drawings” and then says to “detect the correlation between the one or more symbols and the metadata of the plurality of 2D building drawings;” These seem to be different correlations, and therefore the correlation referenced lacks antecedent basis and the rejection is maintained by the examiner.
2. Claims 1, 13, and 18 the correlation between the at least one first symbol and at least one second symbol of the second 2D building drawing; was found to lack antecedent basis. The examiner does not find that the amendments correct this specific 112(b) issue. The claim says to “based, at least in part, on the correlation between the at least one first symbol selected via the user interface from the first 2D building drawing and at least one second symbol of the second 2D building drawing;” This correlation lacks antecedent basis since it was not introduced previously into the claim, and therefore the rejection is maintained by the examiner.
3. Claim 3 wherein correlating the at least one first symbol and the at least one second symbol further comprises: was found to lack antecedent basis. The claim has been amended to recite “detecting the correlation of the at least one first symbol and the at least one second symbol further comprises:” The examiner does not locate the referenced step of detecting a correlation of at least on first symbol and at least one second symbol introduced previously into the claim. Therefore this limitation lacks antecedent basis and the rejection is maintained by the examiner.
4. Claim 6. The elevation of the 3D design model. Was found to lack antecedent basis. The claim has been amended to recite “an elevation.” This overcomes the previous 112(b) rejection. Therefore the rejection is withdrawn by the examiner.
5. Claim 6. A detailed view. Was found to lack antecedent basis. As stated in the argument, the applicant argues that this is a term in the art. The examiner accepts the applicants arguments. Therefore the rejection is withdrawn by the examiner.
6. Claim 7. “the categorized plurality of 2D building drawings was found to lack antecedent basis. The claim introduces “a categorized plurality of 2D building drawings” Therefore this amendment overcomes the previous 112(b) rejection. Therefore the rejection is withdrawn by the examiner.
7. Claims 15 and 20 “wherein associating the at least one first symbol and the at least one second symbol further comprises” was found to lack antecedent basis. These claims have been amended to state “detecting the correlation.” Please see claim 3 as to why the examiner views this limitation as still lacking antecedent basis. Therefore the rejection is maintained by the examiner.
Conclusion: Certain rejections have been removed due to amendments and arguments while others have been maintained, please see section 112.
Response to Argument Regarding 101 Rejection
Issue: Claims 1, 4-13, and 15-18 were previously rejected under 101 Analysis.
Response: Applicant amended independent claims 1, 13, and 18 to overcome the 101 rejection. Applicant amendments pertain to “training a machine learning model … receiving training data … splitting the training data … train the machine learning model …. Validate and iteratively adjust the machine learning model to reduce error … process … via the trained machine learning model … enable navigation between aligned … building drawing(s).” Applicant cites August 4, 2025 USPTO memorandum stating that claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within the mental process grouping. Applicant argues that the amended claim limitations regarding AI do not recite a mental process because the steps cannot be practically performed in the human mind. Applicant also cites 2019 PEG stating arguing that if a method cannot be practically performed in the mind, that the claim is no longer a mental process. Applicant argues that it is mentally impossible to perform amended steps above, including “training a machine learning …” Applicant also argues that even if the examiner believes the claim recites an abstract idea, that the claim also integrates the exception the exception into a practical idea. The applicant argues that the improvement pertains to an improvement in a technology field, where there is an improvement of “enabling navigation between the aligned … drawing(s).” Applicant quotes 12/5/2025 memorandum, citing Enfish precedent, which states that software can make improvements to computer technology. Applicant then argues that the claim as proposed provides a concrete improvement to the technology of data monitoring and analysis. Applicant also draws parallels between their claimed invention and claim 3 PEG example 47, where improvements in the specifications constituted an improvement to a technical field. Lastly, applicant reminds examiner of the USPTO memorandum requirements that a rejection should only be made whether it is more likely than not that it should be rejected under 101 analysis. Applicant submits that claim 1 is more likely eligible than not. Applicant cites same arguments for similar independent claims 13, 18 and the dependent claims
Rule: “2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence” states “Many claims to AI inventions are eligible as improvements to the functioning of a computer or improvements to another technology or technical field. While the courts have not provided an explicit test for how to evaluate the improvements consideration, they have instead illustrated how it is evaluated in numerous decisions. These decisions and a detailed explanation of how USPTO personnel should evaluate this consideration are provided in MPEP sections 2106.04(d)(1) and 2106.05(a).
A key point of distinction to be made for AI inventions is between a claim that reflects an improvement to a computer or other technology described in the specification (which is eligible) and a claim in which the additional elements amount to no more than (1) a recitation of the words “apply it” (or an equivalent) or are no more than instructions to implement a judicial exception on a computer, or (2) a general linking of the use of a judicial exception to a particular technological environment or field of use (which is ineligible).[66] “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome.” [67] AI inventions may provide a particular way to achieve a desired outcome when they claim, for example, a specific application of AI to a particular technological field ( i.e., a particular solution to a problem).[68] In these situations, the claim is not merely to the idea of a solution or outcome and amounts to more than merely “applying” the judicial exception or generally linking the judicial exception to a field of use or technological environment. In other words, the claim reflects an improvement in a computer or other technology.[69] … An improvement in the judicial exception itself is not an improvement in the technology … In contrast, an improvement can be provided by one or more additional elements or by the additional element(s) in combination with the recited judicial exception … below the USPTO identifies other examples of claims that improve technology and are not directed to a judicial exception from Federal Circuit decisions: … Claims to a packet monitor to identify disjointed connection flows as belonging to the same conversational flow were directed to an improvement in computer technology and not an abstract idea,Packet Intel. LLC v. NetScout Sys., Inc., 965 F.3d 1299, 1308-10 (Fed. Cir. 2020).”
Analysis: Regarding applicant arguments towards the recited AI training steps, While training an AI model may encompass mathematic or abstract steps, examiner agrees that the recited steps of “training a model…2D building drawings…validate … Iteratively adjust” cannot reasonably performed in the human mind. However, examiner notes that the original abstract idea pertained to the mental process of aligning 2D images, which the examiner still believes to be a mental process. The process of aligning 2D drawings together to form a 3D design model has been performed by architects mentally for many years prior to the invention of digital technology, and the examiner maintains that it is a mental process. Thus, the examiner agrees with the applicants interpretation that the claim must be evaluated to see if the abstract idea is applied towards improvements to computer technology. The example given in the rules, “a packet monitor to identify disjointed connection flows as belonging to the same conversational flow” was found by the examiner to be similar to the outlined improvement specified in the claimed invention and its specifications, where the examiner interprets the above example as relating to the claimed invention. “a users may experience difficulty while navigating through these 2D building drawings, especially because 2D building drawings do not efficiently present the overall shape and layout of the structure. Alignment of the 2D building drawings within a 3D space is important to improve organization, navigation, accessibility, and efficiency while evaluating the 2D building drawings. “Based on this fact in light of the amendments, the claim applies the judicial exception of finding connections between multiple 2D drawings into an improvement of a technological environment, in that it helps find these connections quickly to help users align and navigate drawings.
Conclusion: Examiner withdraws the 101 rejection in light of the amendments when the claim is viewed as a whole and in light of the specifications.
Response to Argument regarding 103 Rejection
Issue: Applicant argues against previous 103 rejections in light of the claims. Applicant argues that references fail to teach the amended portions of “training a machine learning library … receiving training data … splitting the training data” Response: As was brought up in the interview, the prior art of Murphy does disclose the training of a machine learning model. Applicant agrees that the prior art of Murphy does disclose the training of a machine learning model, however, applicant argues that the training of Murphy is done using known information and lacks the need to make corrections to the training model. This is due to the recitation of “verified outputs” in Murphy. Applicant argues that Murphy relies on known outcomes and expert modifications. Applicant argues that Murphy teaches away from using training data in a two part format which validates and then iteratively adjusts the model as recited in claim 1. Applicant argues that murphy “teaches away” the limitations of claim 1. Rule: The MPEP 2143.01(I) states “The court stated that "the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed” Analysis: Examiner notes that Murphy_2021 does not actually teach away the limitations as suggested by the applicant. This is because while Murphy_2021 does disclose a different way of generating the “validation data” (through an expert modification), using a different methodology does not “teach away” a combination reference using Murphy_2021. The prior art of Murphy_2021 does not criticize or discourage the splitting of training data therefore, it does not teach away the claimed process. Conclusion: As the scope of the claim has been amended, new prior art and mapping may be introduced to map to the new claim limitations. Please see section 103 where the rejection was maintained in light of new mappings and prior art.
Issue: Applicant argues against previous 103 rejections in light of the claims. Applicant argues that references fail to teach the amended portions of “processing the plurality of 2D building drawings .. receiving a selection .. retrieving a second 2D building drawing” Response: Applicant reiterates that these claims were mapped to the secondary reference of Dosch. Applicant argues that Dosch does not cure the deficiencies present in Murphy. The examiner stated that Dosch makes obvious a selection via a user interface. The applicant disagrees. Applicant argues that Dosch only teaches an interactive system, but does not specify what is interactive in the system. Therefore applicant argues that Dosch does not teach “receiving a selection..” and “retrieving a second 2D building drawing.” Analysis and Conclusion: Previously the claim was interpreted as only requiring the selection of a drawings, which the examiner interpreted as mapping to the prior art of Dosch, since the claim has been narrowed in scope to require a specific selection of a symbol when it was initially allowing a combination, new prior art or mappings may be introduced into the claim. Please see section 103, where the rejection has been maintained in light of the amendments and new mapping to existing prior art.
Issue: Applicant argues dependent claims inheriting from the independent claims be made allowable due to their reliance on the dependent claims in light of the amendments. Analysis and Conclusion: Since the rejection of the independent claims was maintained, so will the rejection of the dependent claims. Therefore the rejections are maintained.
End Response to Arguments
Claim Objections
Claim 3 is objected to for the following informalities:
Claim 3:
Claim 3 is inconsistent in its claim scope.
extracting data associated with at least one of the at least one first symbol, the at least one second symbol, and the metadata associated with the first 2D building drawing and the second 2D building drawing, (This limitation recites “at least one of” which implies that only one is needed)
and linking the at least one first symbol of the first 2D building drawing and the at least one second symbol of the second 2D building drawing based, at least in part, on the extracted data and metadata. (“and” implies that both are needed)
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 are 1-20 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, 13, and 18 recite the limitation "to detect the correlation between the one or more symbols and the metadata of the plurality of 2D building drawings;" There is insufficient antecedent basis for this limitation in the claim, because the claim does not introduce a correlation between the one or more symbols and the metadata of the plurality of 2D building drawings;
Claims 1, 13, and 18 recite the limitation “based, at least in part, on the correlation between the at least one first symbol selected via the user interface from the first 2D building drawing and at least one second symbol of the second 2D building drawing; “ There is insufficient antecedent basis for this limitation in the claim, because the claim does not introduce a correlation between a first and second symbol in the drawings.
Claims 3, 15, and 20 recite the limitation: “detecting the correlation of the at least one first symbol and the at least one second symbol further comprises:” The examiner does not locate the referenced step of detecting a correlation of at least on first symbol and at least one second symbol introduced previously into the claim. Therefore there is insufficient antecedent basis for this limitation in the claim.
Claims 2, 4-12, 14, 6-17 and 19 are rejected based on their dependence to the rejected claims.
Examiner note: The examiner would like to place a note to expedite prosecution in relation to the 112(b) rejection. From the examiners understanding, “a correlation” as referenced is a correlation between two different drawings (i.e: elevation, floorplan etc.) which is represented by symbols on the drawings (ie: elevation symbol, section symbol)
See claim 1 “wherein the one or more symbols indicates a correlation between an elevation, a floorplan, and a section of the plurality of 2D building drawings.” This limitation defines “a correlation” as between two different drawings of the plurality of 2D building drawings. This states that the symbol contains the correlation.
However, later on in the claim it is stated “detect the correlation between the one or more symbols and the metadata of the plurality of 2D building drawings;” and “ the correlation between the at least one first symbol selected via the user interface form the first 2D drawing and at least one second symbol of the second 2D building drawing” in this context, the correlation is occurring between two symbols (which are present in the drawings), but not two drawings themselves. Rather than the symbol containing the correlation, this is a correlation between different symbols.
The examiner proposes multiple solutions to overcome the 112(b) rejections. The examiner recommends using the word “a” when the context relates to a different correlations. Alternatively, the examiner recommends introducing “a correlation” as correlating between different symbols rather than drawings. Another alternative could be to define “an elevation, a floorplan, and a section of the plurality of 2D building drawings” as symbols.
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 1-2, 6-7, 12-14, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220292230 A1 “METHODS AND APPARATUS FOR ARTIFICIAL INTELLIGENCE CONVERISON OF A TWO-DIMENSIONAL REFERENCE INTO AN ACTIONABLE INTERFACE” (Muprhy_2021) , US 20200387788 A1 “METHODS AND SYSTEMS FOR AUTOMATICALLY DETECTING DESIGN ELEMENTS IN A TWO-DIMENSIONAL DESIGN DOCUMENT” (Alves_2020) and US 20060100722 A1 “Method And Apparatus For Spatially Coordinating, Storing And Manipulating Computer Aided Design Drawings” (Bell_2006)
Claim 1:Murphy_2021 makes obvious A computer-implemented method (par 6: “Accordingly, the present disclosure provides methods and apparatus for analyzing two-dimensional (2D) documents”) for displaying two- dimensional (2D) design images in a three-dimensional (3D) space, comprising: (“par 67: “A three dimensional model may be effectively built based upon a sequenced stacking of the disparate drawings (Examiner note: 2D design images) representing different levels of elevations.”)
receiving a plurality of 2D building drawings from a device (par 27: “ In some embodiments, the 2D representation may include technical drawings such as blueprints, floorplans, design plans and the like. The AI analysis may include determination of boundaries and/or features indicated in the 2D representation. “,) wherein the plurality of 2D building drawings include one or more symbols and metadata, ((par 43: “ As may be observed in reference to FIG. 2A, a given two-dimensional reference may have a number of elements that an observer and/or an AI engine may classify as features 201-209 such as, for example, one or more of: exterior walls 201; interior walls 202; doorways 204; windows 203; plumbing components, such as sinks 205, toilets 206, showers 207, water closets or other water or gas related items; kitchen counters 209 and the like.“ … par 103: “ In some embodiments, a drawing may be geolocated by user entry of data associated with the location of a project associated with the input architectural plans.” (Examiner note: where location is an example of metadata). and wherein the one or more symbols indicate a correlation between an elevation, a floorplan, and a section of the plurality of 2D building drawings; (par 67: “In some embodiments, a controller may be provided with two dimensional references that include a series of architectural drawings with disparate drawings representing different elevations within a structure. A three dimensional model may be effectively built based upon a sequenced stacking of the disparate drawings representing different levels of elevations (Examiner note: Where these disparate drawings stacked on top of each other references floor plans. See par 92: which describes ‘multiple disparate regions 502-509.’” In figure 5, where the regions are part of a floor plan) . In other examples, the series of drawings may include cross sectional representation as well as elevation representation. A cross section drawing, for example, may be used to infer a common three-dimensional nature that can be attributed to the features, boundaries and areas that are extracted by the processes discussed herein. Elevation drawings may also present a structure in a three-dimensional perspective. Feature recognition processes may also be used to create three-dimensional model aspects.”)
training a machine learning model using a library of sample 2D building drawings, sample symbols, and sample metadata, wherein training the machine learning model includes :receiving training data including the sample 2D building drawings, the sample symbols, and the sample metadata; (par 42 – 44: “In some embodiments, 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. An exemplary AI-processed two-dimensional reference that includes a floorplan 210, with boundaries 211 predicted, is shown in FIG. 2B, based on the floorplan of FIG. 2A. As may be observed in reference to FIG. 2A, a given two-dimensional reference may have a number of elements that an observer and/or an AI engine may classify as features 201-209 such as, for example, one or more of: exterior walls 201; interior walls 202; doorways 204; windows 203; plumbing components, such as sinks 205, toilets 206, showers 207, water closets or other water or gas related items; kitchen counters 209 and the like. The two-dimensional references may also include narrative or text 208 of various kinds throughout the two-dimensional references. Identification and characterization of various features 201-209 and/or text may be included in the input two-dimensional references. Generation of values for variables included in generating a bid may be facilitated by splitting features into groups called ‘disparate features’ 201-209 and boundary definitions and generation of a numerical value associated with the features, wherein numerical values may include one or more of: a quantity of a particular type of feature; size parameters associated with features, such as the square area of a wall or floor; complexity of features (e.g. a number of angles or curves included in a perimeter of an area; a type of hardware that may be used to construct a portion of a building, a quantity of a type of hardware that may be used to construct a portion of the building; or other variable value (Examiner note: metadata.”)
par 41: “In some embodiments, a structure of the artificial neural network may include multiple layers including input layers and hidden layers with designed interconnections with weighting factors. For learning optimization, the input architectural floor plan technical drawings may be used for artificial intelligence (AI) training to enhance the AI's ability to detect what is inside a boundary. “ .. par 106:” Teams of experts may review the results of the AI processing and make corrections as required. Corrected drawings may be provided to the AI engine for renewed training.”.) Examiner note: Where one ordinarily skilled in the art understands training a neural network to be an iterative adjustment of reduce errors.
processing the plurality of 2D building drawings, via the trained machine learning model,to detect the correlation between the one or more symbols and the metadata of the plurality of 2D building drawings; (par 44: “Identification and characterization of various features 201-209 and/or text may be included in the input two-dimensional references. Generation of values for variables included in generating a bid may be facilitated by splitting features into groups called ‘disparate features’ 201-209 and boundary definitions and generation of a numerical value associated with the features, wherein numerical values may include one or more of: a quantity of a particular type of feature; size parameters associated with features, such as the square area of a wall or floor; complexity of features (e.g. a number of angles or curves included in a perimeter of an area; a type of hardware that may be used to construct a portion of a building, a quantity of a type of hardware that may be used to construct a portion of the building; or other variable value.” … par 111: “In some embodiments of the present invention, a recognized feature may be accompanied on a drawing with textual description which may also be recognized by the AI image recognition capabilities. The textual description may be assessed in the context of the recognized physical features in its proximity and used to supplement the feature identification. Identified feature elements may be compared to a database of feature elements, and matched elements may be married to the location on the architectural plan. In some embodiments, text associated with dimensioning features may be used to refine the identity of a feature. For example, a feature may be identified as an exterior window, but an association of a dimension feature may allow for a specific window type to be recognized. As well, a text input or other narrative may be recognized to provide more specific identification of a window type.”
and generating a 3D design model by aligning the first 2D building drawing and the second 2D building drawing in the 3D space ( par 67: “ A three dimensional model may be effectively built based upon a sequenced stacking of the disparate drawings representing different levels of elevations.”),
Murphy_2021 does not expressly recite And splitting the training data into a first training data set and a second training data set, wherein the first training data set is used to train the machine learning model and the second training data set is used to validate
receiving a selection, via a user interface of the device, at least one first symbol in the first 2D building drawing,
retrieving a second 2D building drawing from the plurality of 2D building drawings based, at least in part, on the correlation between the at least one first symbol selected via the user interface from the first 2D building drawing and at least one second symbol of the second 2D building drawing;
wherein the 3D design model is displayed on the user interface to enable navigation between the aligned first 2D building drawing and the second 2D building drawing.
Alves_2020 however makes obvious And splitting the training data into a first training data set and a second training data set, wherein the first training data set is used to train the machine learning model and the second training data set is used to validate and (par 54: “At step 715, which may be optional depending on the state of the received training data 135, the model training system 120 may prepare the received training data 135 for model training. Data preparation may involve randomizing the ordering of the training data 135, visualizing the training data 135 to identify relevant relationships between different variables, identifying any data imbalances, splitting the training data 135 into two parts where one part is for training a model and the other part is for validating the trained model, de-duplicating, normalizing, correcting errors in the training data 135, and so on. Data preparation may also involve pre-processing image data, before any image data is fed into a machine learning algorithm. However, such pre-processing may not be needed especially with deep learning (e.g., neural networks), as filters in the neural networks may learn on the provided data set.” (Examiner note: Where the usage of deep learning/neural networks inherently implies a process which iteratively adjusts a machine learning model, which is what standard machine learning entails) … par 55: “At step 720, the model building system 120 may train a machine learning model using the training data. The trained machine learning model could analyze a design document to detect one or more design elements and their respective types/designations. At step 725, the model building system 120 may validate the trained machine learning model. For example, the machine learning model may be validated by analyzing a set of training data that are known to represent certain object classes (e.g., certain design element types). Accordingly, the accuracy of the machine learning model may be determined. Once the validation step is complete, at step 730, the model building system 120 may store the trained (and validated) machine learning model in a system memory or storage. (Examiner note: As one ordinarily skilled in the art would recognize, that if the validation fails that the model would have to “relearn” which is a process of iterative adjustment with training data similarly to as outlined in Murphy_2021)
Murphy_2021 and Alves_2020 are analogous art to the claimed invention because they are from the same field of endeavor called using machine learning models to classify architectural floor plans.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Murphy_2021 and Alves_2020. The rationale for doing so would have been a simple substitution of known elements to obtain predictable results. The prior art of Murhy_2021 makes obvious a machine learning model training methodology which involves the usage of a “retraining” as well as the usage of a neural network to optimizes the ability of an AI to detect floor plan symbols. Par 39: “ At step 103, training (and/or retraining) of the AI engine is performed.” … par 41: “ In some embodiments, a structure of the artificial neural network may include multiple layers including input layers and hidden layers with designed interconnections with weighting factors. For learning optimization, the input architectural floor plan technical drawings may be used for artificial intelligence (AI) training to enhance the AI's ability to detect what is inside a boundary.”) This “retraining” is done with validated data, see par 106:” Teams of experts may review the results of the AI processing and make corrections as required. Corrected drawings may be provided to the AI engine for renewed training.” The prior art of Alves_2020 also includes training and validating a neural network model, this is done as outlined above by splitting the data into training data and validating data. Par 54: “splitting the training data 135 into two parts where one part is for training a model and the other part is for validating the trained model, de-duplicating, normalizing, correcting errors in the training data 135, and so on. Data preparation may also involve pre-processing image data, before any image data is fed into a machine learning algorithm. However, such pre-processing may not be needed especially with deep learning (e.g., neural networks), as filters in the neural networks may learn on the provided data set.” Splitting data into training data and validating data is a known methodology in the art of machine learning for training a model. One ordinarily skilled in the art would recognize that the training and relearning process of Murphy_2021, which uses user generated “corrected drawings” could be swapped with the “validation data” of Alves_2020, as they are both the use of validation data to further optimize and adjust a model.
Therefore, it would have been obvious to combine the model training of Murphy_2021 with the splitting of training and testing data of Alves_2020 to obtain the predictable result of validating and iteratively improving a machine learning model obtain the invention as specified in the claims.
Muprhy_2021 and Alves_2020 do not expressly recite receiving a selection, via a user interface of the device, at least one first symbol in the first 2D building drawing,
retrieving a second 2D building drawing from the plurality of 2D building drawings based, at least in part, on the correlation between the at least one first symbol selected via the user interface from the first 2D building drawing and at least one second symbol of the second 2D building drawing;
wherein the 3D design model is displayed on the user interface to enable navigation between the aligned first 2D building drawing and the second 2D building drawing.
Bell_2006 however, makes obvious receiving a selection, via a user interface of the device, at least one first symbol in the first 2D building drawing, (par 89: “As shown in FIG. 26, when the user is in the appropriate plan file, there can also be a series of "switch discipline icons" which allow one to view different floor plans related to various disciplines such as structural, mechanical, electrical, or interior elevations. As an example, when the plan of a room is being viewed, the user could click to the room symbol and turn on the interior wall level or reference file, depending on the filing organization, to view the interior elevations.”)
retrieving a second 2D building drawing from the plurality of 2D building drawings based, at least in part, on the correlation between the at least one first symbol selected via the user interface from the first 2D building drawing and at least one second symbol of the second 2D building drawing; (par 88: “s also shown in FIG. 25, next to the level designation icon is a discipline icon (electrical, mechanical, plumbing, etc.) that allows the user to open any given floor plan file in the desired discipline. Note that for this illustration only one floor level has been shown for clarity. In reality, there would be a floor level switch shown for every level. For example, one can open the 1.sup.st floor architecture plan or the 1.sup.st floor electric plan, etc. Once one has switched to the desired floor plan, on that plan there can be a further series of "switch file icons" that take the user to a more detailed plan file or to other aspects of the building such as section, detail etc. Preferably, the switch file icons are based on information related to standard architectural graphic symbols such as section cuts, elevation symbol etc.”) Examiner note: Where this makes obvious a user selecting a symbol, and then a second 2D building drawing retrieved based on the correlation between the symbols.
wherein the 3D design model is displayed on the user interface to enable navigation between the aligned first 2D building drawing and the second 2D building drawing. (par 53-54: “ In accordance with the invention, however, these separate plans are laid over the MDP file whereby all of these files exist in the same three-dimensional coordinates in space. As illustrated in FIG. 1, this feature of the invention may be illustrated as a series of transparent sheets, or separate CAD files, which are overlaid on top of each other but where each layer of information is independent but congruent. Unlike existing CAD systems, the plan file, structural file, electrical file, and the like are coordinated in space to the MDP file. The significance of this will be explained in detail below. The orthographic projection of elevations from a plan is the standard method of drawing exterior elevations, sections, and the like (i.e., looking at the object from outside of the object). The reason for orthographic projection for elevations is to keep the horizontal elements of the drawing to scale and avoid distortion caused by perspective. The standard method for projecting elevations is to project the elevation below the plan, that is, with the top closest to the plan and the bottom furthest from the plan as shown in FIG. 2.” Examiner note: Where these drawings (like elevations) are aligned, and are used to enable navigation, see par 88: “Once one has switched to the desired floor plan, on that plan there can be a further series of "switch file icons" that take the user to a more detailed plan file or to other aspects of the building such as section, detail etc.”
Muprhy_2021, Alves_2020 and Bell_2006 are analogous art to the claimed invention because they are from the same field of endeavor called Architectural floor planning. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Muprhy_2021, Alves_2020 and Bell_2006. The rationale for doing so would have been to follow a teaching and motivation proposed in the prior art. Murphy_2021 par 147 teaches “147: “Still further method steps within the scope of the present invention include, one or more of: associating a cost with the items to be included in the construction of the building and the aggregate quantity of the type of labor, wherein the items to be included in the construction of the building may include at least one of: a plumbing fixture, wall board, flooring, and electrical wiring. The method may additionally include the step of training the AI engine based upon a human identifying portions of a two-dimensional representation to indicate that it includes a particular type of item; or to identify portions of the two dimensional representation that include a boundary. The AI engine via may also be trained by reference to a boundary allocation hierarchy.” Bell_2006 states par 89-90: “Further, the reference file on/off display would be switches that turn on or off the destination file as a reference file as opposed to switching active files. This replaces the Reference file display sequence required when using a reference toolbox as in prior art CAD systems. This will allow quick reference to different layers of information to be displayed such as electrical, mechanical, structural, details, etc. with the plan to display the inter-relationships therewith. As will be apparent to those skilled in the art, the method of the invention allows one to work and move through massive amounts of information held in innumerable design files and have a seamless way to relate them together with graphic icons for easy recall.“ Therefore, it would have been obvious to combine Murphy_2020 and Bell_2002 inventions of detection of different features which also includes wiring and plumbing, with switches of Bell_2006 to view that information for the benefit of having quick reference to many different types of information to obtain the invention as specified in the claims.
Claim 2:
The computer-implemented method of claim 1, further comprising:
Murphy_2021 makes obvious detecting, via the trained machine learning model, at least one of the one or more symbols and the metadata associated with the plurality of 2D building drawings, (par 43: “As may be observed in reference to FIG. 2A, a given two-dimensional reference may have a number of elements that an observer and/or an AI engine may classify as features (Examiner note: Symbols) 201-209 such as, for example, one or more of: exterior walls 201; interior walls 202; doorways 204; windows 203; plumbing components, such as sinks 205, toilets 206, showers 207, water closets or other water or gas related items; kitchen counters 209 and the like.… par 149: ““Artificial Intelligence” as used herein means machine-based decision making and machine learning”)
Claim 6:
The computer-implemented method of claim 1, further comprising:
Muprhy_2021 makes obvious generating an exploded view of the 3D design model, wherein the exploded view changes a position of an elevation of the 3D design model to provide a detailed view of an interior of the 3D design model. (par 67: “ A three dimensional model may be effectively built based upon a sequenced stacking of the disparate drawings representing different levels of elevations. In other examples, the series of drawings may include cross sectional representation as well as elevation representation.” Examiner note: Where the examiner understands a detailed view to be a more specific view. Where changing an elevation to see the specific elevation is a detailed view.
Claim 7:
The computer-implemented method of claim 1, further comprising:
Muprhy_2021 does not expressly recite generating a categorized plurality of 2D building drawings by subject based, at least in part, on the metadata, wherein the subject includes civil engineering, structural engineering, architectural engineering, mechanical engineering, electrical engineering, plumbing, or a combination thereof; and generating a user interface element to switch between the categorized plurality of 2D building drawings within the 3D space.
Bell_2006 however makes obvious generating a categorized plurality of 2D building drawings by subject based, at least in part, on the metadata, wherein the subject includes See figure 26 which depicts multiple reference icons which categorize the plurality of the 2D building drawings, where the icons show architecture, mechanical, structural, electrical, and plumbing. Par 99: “ FIG. 26 illustrates the switch discipline file icon and the additional option of adding a reference display switch below for each discipline. Thus, if one wishes to have a reference file turned on for a specific discipline, one can turn on the circle button below each discipline to switch on or off given disciplines. Again, these buttons will be hover buttons which when activated will turn on or off the display of a reference file. This macro will be established in the same way as the switch file icons except it will be using the reference file tool box arrangement to establish the reference file which is turned on and off with the icon button. In this fashion, the CAD designer can move quickly back and forth to different disciplines of the design to coordinate and adjust various disciplines in various files, i.e., electrical files with architectural files with structural files, etc.”) Examiner note: Where an icon for mechanical for instance covers the mechanical engineering subject.
Bell_2006 does not explicitly teach “Civil engineering”
However, Bell_2006 makes obvious “Civil engineering” by teaching Par 13: “The system and method of organizing CAD drawings in accordance with the invention is ideally used by engineers, architects, planners, and all businesses required to keep track of physical property and the physical design and description of three-dimensional objects. As used herein, physical property can include buildings, roads, utilities, bridges, transportation, landscape, hardscape, molecules, and the like. “ … Par 86: “Thus, cities and governments can request that all information be coordinated with this method of spatially organizing the location of CAD drawings. Then a city can have the same information for each building, road, utility, and the like systematically and spatially coordinated.”
While Bell_2006 does not explicitly tech the use of a civil engineering icon to categorize the plurality of drawings, Bell_2006 does show that an intended use of the program is to do work involving roads and bridges (aka civil engineering). Therefore, it would have been obvious one of ordinary skill in the art to modify the invention of Bell_2006 UI to switch between disciples, to include civil engineering as a discipline, as it is an intended use case of the invention.
As stated previously, Murphy_2021, Alves_2020, and Bell_2006 are all analogous arts, as they are all software that deals with analyzing 2D floorplans. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Murphy_2021, Alves_2020 and Bell_2006. The rational for doing so would have been for a motivation provided by Bell_2006. Murphy_2021 par 147 teaches “147: “Still further method steps within the scope of the present invention include, one or more of: associating a cost with the items to be included in the construction of the building and the aggregate quantity of the type of labor, wherein the items to be included in the construction of the building may include at least one of: a plumbing fixture, wall board, flooring, and electrical wiring. The method may additionally include the step of training the AI engine based upon a human identifying portions of a two-dimensional representation to indicate that it includes a particular type of item; or to identify portions of the two dimensional representation that include a boundary. The AI engine via may also be trained by reference to a boundary allocation hierarchy.” Bell_2006 states par 89-90: “Further, the reference file on/off display would be switches that turn on or off the destination file as a reference file as opposed to switching active files. This replaces the Reference file display sequence required when using a reference toolbox as in prior art CAD systems. This will allow quick reference to different layers of information to be displayed such as electrical, mechanical, structural, details, etc. with the plan to display the inter-relationships therewith. As will be apparent to those skilled in the art, the method of the invention allows one to work and move through massive amounts of information held in innumerable design files and have a seamless way to relate them together with graphic icons for easy recall.“ Therefore, it would have been obvious to combine Murphy_2020 and Bell_2002 inventions of detection of different features which also includes wiring and plumbing, with switches of Bell_2006 to view that information for the benefit of having quick reference to many different types of information to obtain the invention as specified in the claims.
Claim 12:
Murphy_2021 makes obvious The computer-implemented method of claim 1, wherein the plurality of 2D building drawings are in portable document format (PDF), and wherein the plurality of 2D building drawings include one or more of architectural drawings, engineering drawings, or construction drawings. (par 30: “ At step 100, a 2D representation, such as, by way of nonlimiting example: drawing files, architectural floor plans; technical drawings; or other two-dimensional document indicating aspects of a building; is input into a controller or other data processing system using a computing device. “ … par 32: “ In some embodiments, the two-dimensional reference input 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.”)
Claim 13:Claim 13 is an effective duplicate of claim 1, and is therefore rejected under the same rational. Additionally, Muprhy_2021 makes obvious the additional limitations of A system (par 13: “Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.”) for displaying two-dimensional (2D) design images in a three-dimensional (3D) space,
(par 67: “In some embodiments, a controller may be provided with two dimensional references that include a series of architectural drawings with disparate drawings representing different elevations within a structure. A three dimensional model may be effectively built based upon a sequenced stacking of the disparate drawings representing different levels of elevations. In other examples, the series of drawings may include cross sectional representation as well as elevation representation. A cross section drawing, for example, may be used to infer a common three-dimensional nature that can be attributed to the features, boundaries and areas that are extracted by the processes discussed herein. Elevation drawings may also present a structure in a three-dimensional perspective. Feature recognition processes may also be used to create three-dimensional model aspects.”) comprising: one or more processors; (par 6 “with the aid of computer processors”) and at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: (par 13: “The present invention provides for systems of one or more computers that can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform artificial intelligence operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. “)
Claim 14:Claim 14 is an effective duplicate of claim 2 except that It depends on claim 13 and is therefore rejected under the same rational as claims 2 and 13.
Claim 18:
Claim 18 is an effective duplicate of claim 1, and is therefore rejected under the same rational. Additionally, Muprhy_2021 makes obvious the additional limitations of A non-transitory computer readable medium for displaying two-dimensional (2D) design images in a three-dimensional (3D) space, thenon-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising: ((par 13: “The present invention provides for systems of one or more computers that can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform artificial intelligence operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. “ … par 67: “In some embodiments, a controller may be provided with two dimensional references that include a series of architectural drawings with disparate drawings representing different elevations within a structure. A three dimensional model may be effectively built based upon a sequenced stacking of the disparate drawings representing different levels of elevations. In other examples, the series of drawings may include cross sectional representation as well as elevation representation. A cross section drawing, for example, may be used to infer a common three-dimensional nature that can be attributed to the features, boundaries and areas that are extracted by the processes discussed herein. Elevation drawings may also present a structure in a three-dimensional perspective. Feature recognition processes may also be used to create three-dimensional model aspects.”)
Claim 19:
Claim 19 is an effective duplicate of claim 2 except that it depends on claim 18 and is therefore rejected under the same rational as claim 2 and 18.
Claims 3, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy_2021, Alves_2020, Bell_2006, and further in view of “Reconstruction of the 3D Structure of a Building from the 2D Drawings of its Floors” (Dosch_2002)
Claim 3:The computer-implemented method of claim 2, wherein detecting the correlation of the at least one first symbol and the at least one second symbol further comprises:
Murphy_2021 does not expressly recite extracting data associated with at least one of the at least one first symbol, the at least one second symbol, and the metadata associated with the first 2D building drawing and the second 2D building drawing,
and linking the at least one first symbol of the first 2D building drawing and the at least one second symbol of the second 2D building drawing based, at least in part, on the extracted data and metadata.
Dosch_2002 however makes obvious extracting data associated with at least one of the at least one first symbol, the at least one second symbol, and the metadata associated with the first 2D building drawing and the second 2D building drawing, (page 1 abstract: “A set of features is extracted from the set of architectural symbols available for each floor. A graph of matching hypothesis between the most pertinent features of two consecutive floors is then constructed.”)
and linking the at least one first symbol of the first 2D building drawing and the at least one second symbol of the second 2D building drawing based, at least in part, on the extracted data and metadata. (Page 1 abstract “A maximal clique detection algorithm supplies the best set of matches. The geometrical transformation allowing each floor to be aligned with respect to its lower floor is computed from the pairs of matching features. The final 3D model of the building is obtained by heaping the 3D models of the consecutive floors”).
Murphy_2021 and Dosch_2002 are analogous art to the claimed invention because they are from the same field of endeavor of digital floor plan analysis for architectural buildings and design. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Murphy_2021 and Dosch_2002. The rational for doing so would have been to apply a known technique to a known method to produce a predictable result. Murphy_2021 teaches that par 67: “A three dimensional model may be effectively built based upon a sequenced stacking of the disparate drawings representing different levels of elevations.” Dosch_2022 provides a method for doing so, being matching features/ symbols page 1 abstract: “A set of features is extracted from the set of architectural symbols available for each floor. A graph of matching hypothesis between the most pertinent features of two consecutive floors is then constructed. A maximal clique detection algorithm supplies the best set of matches. The geometrical transformation allowing each floor to be aligned with respect to its lower floor is computed from the pairs of matching features. The final 3D model of the building is obtained by heaping the 3D models of the consecutive floors.” Therefore, it would have been obvious to combine the symbol detection algorithms, correlations, and generation of 3D models of Murphy_2021 with the method of doing so by aligning symbol information of Dosch_2002 to yield the predictable result of generating a 3D model by stacking disparate drawings of different elevations to obtain the invention as specified in the claims.
Claim 15:
Claim 15 is an effective duplicate of claim 3 except that is depends on claim 13, and is therefore rejected under the same rational as claims 13 and 3.
Claim 20:
Claim 20 is an effective duplicate of claim 3 except that is depends on claim 18, and is therefore rejected under the same rational as claims 18 and 3.
Claims 4-5, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy_2021, Alves_2020, Bell_2006 and further in view of “Making a Split View in Model Space (autodesk_2016)
Claim 4:
Murphy_2020 makes obvious The computer-implemented method of claim 1, further comprising:
displaying the first 2D building drawing in a first viewing mode in the user interface of the device;
(par 69:“Referring now to FIGS. 3A-3C a user interface 300 may generate multiple different user views, each view has different aspects related to the two-dimensional reference drawing inputted.”)
Par 67: “ a controller may be provided with two dimensional references that include a series of architectural drawings with disparate drawings representing different elevations within a structure.” … par 69:“ a user interface 300 may generate multiple different user views, each view has different aspects related to the two-dimensional reference drawing inputted. “)
Murphy_2021, Alves_2020, and Bell_2006 do not explicitly recite and transitioning from the first viewing mode to a split-screen viewing mode based, at least in part, on the selection of the at least one first symbol from the first 2D building drawing, wherein the first 2D building drawing is shown within a first display area and the second 2D building drawing is shown within a second display area adjacent to the first display area.
Autodesk_2016 however, makes obvious and transitioning from the first viewing mode to a split-screen viewing mode based, at least in part, on the selection of the at least one first symbol from the first 2D building drawing, wherein the first 2D building drawing is shown within a first display area and the second 2D building drawing is shown within a second display area adjacent to the first display area.
Par 1: “Whether you are working in large drawings or simply have a need to be zoomed in to more than one location at a time, one approach is to split the model space views.“ Examiner note: Where the first viewing mode is the view before opening the viewport configuration, and the split screen viewing mode is done after setting up the viewport configuration. Where the transition is done when the user clicks on the viewport configuration button. For example, see figure 3 annotated below. Please note that while the figure also depicts a 3D view, the claim language does not specify that there cannot also be a 3D view. Also further respect and understand the “viewport configuration” button as shown allows a variety of different views, where the “Two: Vertical” view also represents two adjacent displays.
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Murphy_2021, Alves_2020, Bell_2006 and Autodesk_2016 are all analogous arts, as they deal with working with architectural floor plans and drawings in computer systems. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Murphy_2021, Murphy_2021, Alves_2020, Bell_2006, Autodesk_2016. The rational for doing so would have been due to a motivation as proposed by Autodesk_2016.
Autodesk_2016 par 1 states: “Whether you are working in large drawings or simply have a need to be zoomed in to more than one location at a time, one approach is to split the model space views.” Murphy_2020 states the use of CAD files (par 32: “The engineering drawings may be hand drawings, or they may be computer-generated drawings, such as may be created as the output of CAD files associated with software programs such as AutoDesk™, Microstation™ etc.”). Therefore, Since Murphy_2020 already takes in CAD drawings as input, it would have been obvious to combine the method of claim 1 of Murphy_2021, Alves_2020, and Bell_2006 when working with multiple architectural drawings, alongside the different views as outlined by Autodesk_2016 for the benefit of being able to look at the multiple floors for analysis and aligning of Murphy_2020, especially with large drawings and at the same time to obtain the invention as specified in the claims.
Claim 5:
Murphy_2020 makes obvious The computer-implemented method of claim 1, further comprising:
displaying the plurality of 2D building drawings in a first viewing mode in the user interface of the device; and (par 69:“Referring now to FIGS. 3A-3C a user interface 300 may generate multiple different user views, each view has different aspects related to the two-dimensional reference drawing inputted.”)
(par 67: “ a controller may be provided with two dimensional references that include a series of architectural drawings with disparate drawings representing different elevations within a structure. A three dimensional model may be effectively built based upon a sequenced stacking of the disparate drawings representing different levels of elevations.”)
Murphy_2021, Alves_2020, and Bell_2006 do not explicitly recite transitioning from the first viewing mode to a second viewing mode based, at least in part, on the selection of one or more 2D building drawings, wherein the plurality of 2D building drawings are shown within a first display area and the 3D design model is shown within a second display area adjacent to the first display area.
Autodesk_2016 makes obvious recite transitioning from the first viewing mode to a second viewing mode based, at least in part, on the selection of one or more 2D building drawings, wherein the plurality of 2D building drawings are shown within a first display area and the 3D design model is shown within a second display area adjacent to the first display area.
Par 1: “Whether you are working in large drawings or simply have a need to be zoomed in to more than one location at a time, one approach is to split the model space views. For those of us who use Civil 3D or work in AutoCAD 3D we sometimes split the view to take a look at the 3D model while we work on the 2D plan and profile.” (Examiner note: Please again see figure 3 as shown depicts a first viewing mode before clicking the viewport configuration button, and a transition to a second viewing mode upon clicking the button. Where the viewports are determined and chosen by the user ie: a selection of one or more drawings. Where the 3D design mode, as shown by the “Four left” or “Twin vertical” views, can be shown adjacent to the first display area. Whereas depicted in the image, the 3D model is adjacent (ie: adjoined) to the plurality of 2D drawings chosen. )
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Murphy_2021, Alves_2020, Bell_2006 and Autodesk_2016 are all analogous arts, as they deal with working with architectural floor plans and drawings in computer systems. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Murphy_2021, Murphy_2021, Alves_2020, Bell_2006, and Autodesk_2016. The rational for doing so would have been due to a motivation as proposed by Autodesk_2016.
Autodesk_2016 par 3 states : “In the example below I used a simple 3D model of a gear showing you how we can work on the geometry while viewing the 3D model in an alternate view. You can pan and zoom independently in each viewport as well as change your visual style as shown below. This is extremely helpful when editing a model and having the ability to view the updated part while you make changes.” Murphy_2021 states the use of CAD files (par 32: “The engineering drawings may be hand drawings, or they may be computer-generated drawings, such as may be created as the output of CAD files associated with software programs such as AutoDesk™, Microstation™ etc.”). Therefore, Since Murphy_2021 already takes in CAD drawings as input, it would have been obvious to combine the method of claim 1 of Murphy_2021 when working with multiple architectural drawings, alongside the different views as outlined by Autodesk_2016 for the benefit of being able to look at the multiple floors of the 3D model for analysis and aligning of Murphy_2021 at the same time to obtain the invention as specified in the claims.
Claim 16:Claim 16 is an effective duplicate of claim 4 except that it depends on claim 13 and is therefore rejected under the same rational as claims 4 and 13.
Claim 17:Claim 17 is an effective duplicate of claim 5 except that it depends on claim 13 and is therefore rejected under the same rational as claims 5 and 13.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy_2021, Alves_2020, Bell_2006 and further in view of US 20080062195 A1 (Brown_2008)
Claim 8:The computer-implemented method of claim 1,
Murphy_2021, Alves_2020, and Bell_2006 do not explicitly recite further comprising: generating a user interface element to indicate a hierarchical relationship between the plurality of 2D building drawings, wherein the user interface element is a tree structure.
Brown_2008 makes obvious further comprising: generating a user interface element to indicate a hierarchical relationship between the plurality of 2D building drawings, wherein the user interface element is a tree structure. (Par 35: “Panel 240 shows a hierarchy of drawing files included in the project folder specified in text box” … par 36: “ panel 240 displays a "Select Project Drawing" tree 234 that shows the matching drawings in the project drawing folder.”)
Murphy_2021, Alves_2020, Bell_2006, and Brown_2008 are all analogous arts as they pertain to methods to process and display 2D architectural floor plan drawings. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Murphy_2021, Alves_2020, Bell_2006 and Brown_2008. The rational for doing so would have been the use of a known technique to improve the similar product in the same way. Murphy_2021 teaches receiving files of drawings, par 30: “ Referring now to FIG. 1, a general flow diagram showing some preferred embodiments of the present invention is illustrated. At step 100, a 2D representation, such as, by way of nonlimiting example: drawing files, architectural floor plans; technical drawings; or other two-dimensional document indicating aspects of a building; is input into a controller or other data processing system using a computing device.“ However, Murphy_2021 does not explicitly state how those files were organized. Brown_2008 organizes the files in a hierarchy tree structure. One ordinarily skilled in the art would have been able to apply this known improvement of storing files in a tree hierarchy from Brown_2008 to the invention of Murphy_2021 to achieve the claimed invention.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy_2021, Alves_2020, Bell_2006 and further in view of “United States National CAD Standard – V6 Module 6 Symbols” (CAD_2014)
Claim 9:
The computer-implemented method of claim 1, wherein the one or more symbols include
Murphy_2021, Alves_2020, Bell_2006 don’t explicitly recite a callout symbol, and wherein the callout symbol includes a callout number, a start point, and an end point to indicate an arrangement of the plurality of 2D building drawings.
Cad_2014 makes obvious a callout symbol, and wherein the callout symbol includes a callout number, a start point, and an end point to indicate an arrangement of the plurality of 2D building drawings. (Cad_2014 symbol 73, Where the examiner interprets symbol 73 as a callout symbol with all the limitations of the claim.)
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Murphy_2021, Alves_2020, Bell_2006, and Cad_2014 are analogous arts as they all pertain to 2D floor plan and architectural drawings. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine references Murphy_2021, Alves_2020, Bell_2006 and Cad_2014. The rational for doing so would have been simple substitution of known elements to achieve a predictable result. Murphy_2021, Alves_2020, Bell_2006 includes symbols/features for detection, but do not specify those symbols. Cad_2014 is a document with national standards for symbols, so any symbol listed is well known in the art. Therefore, it would have been obvious to combine the symbol detection methods for floorplans in Murphy_2021, Alves_2020, Bell_2006 and to substitute one of those symbols with a callout symbol to obtain the invention as specified in the claims.
Claim 10:
computer-implemented method of claim 1,
Murphy_2021, Alves_2020, Bell_2006 don’t explicitly recite wherein the one or more symbols include a section symbol, and wherein the section symbol includes a drawing number, a sheet number, a view direction sign, and a guideline icon to indicate a correlation between the floorplan and the section of the plurality of 2D building drawings.
Cad_2014 makes obvious recite wherein the one or more symbols include a section symbol, and wherein the section symbol includes a drawing number, a sheet number, a view direction sign, and a guideline icon to indicate a correlation between the floorplan and the section of the plurality of 2D building drawings. (Cad_2014 Symbol 78)
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Murphy_2021, Alves_2020, Bell_2006, and Cad_2014 are analogous arts as they all pertain to 2D floor plan and architectural drawings. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine references Murphy_2021, Alves_2020, Bell_2006 and Cad_2014. The rational for doing so would have been simple substitution of known elements to achieve a predictable result. Murphy_2021, Alves_2020, Bell_2006 include symbols/features for detection, but do not specify those symbols. Cad_2014 is a document with national standards for symbols, so any symbol listed is well known in the art. Therefore, it would have been obvious to combine the symbol detection methods for floorplans in Murphy_2021, Alves_2020, Bell_2006 and to substitute one of those symbols with a section symbol to obtain the invention as specified in the claims.
Claim 11:
The computer-implemented method of claim 1,
Murphy_2021, Alves_2020, Bell_2006 don’t explicitly recite wherein the one or more symbols include an elevation symbol, and wherein the elevation symbol includes a drawing number, a sheet number, and a view direction sign to indicate a correlation between the floorplan and the elevation of the plurality of 2D building drawings.
Cad_2014 makes obvious wherein the one or more symbols include an elevation symbol, and wherein the elevation symbol includes a drawing number, a sheet number, and a view direction sign to indicate a correlation between the floorplan and the elevation of the plurality of 2D building drawings. (Cad_2014 Symbol 27)
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Murphy_2021, Alves_2020, Bell_2006, and Cad_2014 are analogous arts as they all pertain to 2D floor plan and architectural drawings. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine references Murphy_2021, Alves_2020, Bell_2006, and Cad_2014. The rational for doing so would have been simple substitution of known elements to achieve a predictable result. Murphy_2021, Alves_2020, Bell_2006 include symbols/features for detection, but do not specify those symbols. Cad_2014 is a document with national standards for symbols, so any symbol listed is well known in the art. Therefore, it would have been obvious to combine the symbol detection methods for floorplans in Murphy_2021, Alves_2020, Bell_2006 and to substitute one of those symbols with a elevation symbol to obtain the invention as specified in the claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMAD HUSSAM SHALABY whose telephone number is (571)272-7414. The examiner can normally be reached Mon-Fri 7:30am - 5pm.
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, Emerson Puente can be reached at 5712723652. 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.
/A.H.S./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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