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 .
Detailed Action
Claims 1-40 are pending.
Response to Amendment
This action is in response to the Amendment filled on 07/11/2026. The amendment has been entered. Claims 1,5,7,15,21,22,26,28-30,32,38 and 39 have been amended. Claims 1-40 are pending, with claims 1,21 and 32 being independent in the instant application.
Response to Arguments
Applicant's Arguments/Remarks filed on 07/11/2026 on page 12-22 regarding 35 U.S.C. 101 rejections have been fully considered and found persuasive in view of the amended claims and presented Arguments/Remarks by the Applicant. Therefore, the previous rejection regarding 35 U.S.C. 101 being withdrawn in this current office action.
Applicant's Arguments/Remarks on page 22-34 regarding 35 U.S.C. 103 rejections have been fully considered and found persuasive in view of the amended claims and presented Arguments/Remarks by the Applicant. Specifically, Examiner agrees with the Applicant's Arguments/Remarks on page 23-34 stated: “As amended, claim 1 recites a specific ordered change-order processing workflow that is not taught or suggested by Austern, Segev, Das, or any combination thereof. In particular, the cited references fail to teach or suggest the amended limitations”. However, a new ground of rejections is necessitated by Applicant's claim amendments. Therefore, the previous rejections regarding 35 U.S.C.103 are being amended in this current office action. (See analysis below Claim Rejections-35 U.S.C. §103).
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham, v. John Deere Co., 383 U.S.1.148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
7. Claim 1-6,9,12,14,16,20-26,28, and 32-35 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy et al. (Pub. No. US2022/0292240A1), in view of John Bews (US20240169193A1) (hereinafter Bews) and further in view of a review paper “Development and validation of the Housing Environmental Quality Assessment Tool (HEQAT) for children with ADHD using the Delphi process” by Sima Alizadeh et al. (hereinafter Alizadeh, paper published on 2025-01-28).
Regarding claim 1, Murphy teaches a computer-implemented system for processing building design plans and authorizing change orders, (Murphy disclosed in page 1 para [0008]: “the present disclosure provides methods and apparatus for analyzing two-dimensional (sometimes referred to as “2D") documents such as design plans with the aid of artificial intelligence (sometimes referred to herein as “AI”) to make sure that the design plans are in compliance with requirements set forth by a authority having jurisdiction (“AHJ”) for various aspects of a resulting a building”).
Murphy teaches the system comprising: a display screen configured to present an interactive user interface; a digital storage medium comprising an executable software code; (Murphy disclosed in page 9 para [0108]: “in some embodiments of the present invention, an interactive user interface may be generated that presents a user with a display of one or more boundaries and pattern or color filled areas arranged as a reproduction of an two - dimensional reference input into the AI engine.” In page 13-14 para [0166-0167]: “The processor 802 is also in communication with a storage device 803. … The storage device 803 may comprise any appropriate information storage device, including … semi-conductor memory devices such as Random Access Memory (RAM) devices and Read Only Memory (ROM) devices. The storage device 803 can store a software program 804 with executable logic for controlling the processor 802. The processor 802 performs instructions of the software program 804, and thereby operates in accordance with the present disclosure.”).
and Murphy teaches a controller operating one or both of: an Artificial Intelligence (AI) engine and a Generative Adversarial Network (GAN) engine, wherein the controller comprises a processor, and wherein the executable software code, when executed by the processor, (Murphy disclosed in page 5 para [0064]: “In some non-limiting examples of the present invention, a generative adversarial network may include a controller with an AI engine operative to generate a user interface that includes dynamic components.” In page 7 para [0085]: “The controller is operative to generate a user interface 125 on a user computing device 126. The user computing device may include a smart device, workstation, tablet, laptop or other user equipment with a processor, storage and display.” Further, in page 14 para [0167]: “The storage device 803 can store a software program 804 with executable logic for controlling the processor 802. The processor 802 performs instructions of the software program 804, and thereby operates in accordance with the present disclosure.”).
Murphy teaches causes the processor to: a. receive, by the controller, an initial design plan of at least a portion of a building, the initial design plan including a plurality of design elements and a plurality of associated sub-plans including at least one of: an electrical sub-plan, a plumbing sub-plan, an HVAC sub-plan, and a structural sub- plan; (Murphy disclosed in page 3 para [0039]: “According to the present invention , a controller is operative to execute artificial intelligence (AI) processes and analyze one or more two dimensional representations of at least a portion of a building (or other structure) for which a compliance determination will be generated …”. In para [0041]: “The present invention provides for an interface illustrating a path integrated into the design plan. … Similarly, the AI engine may apply machine learning to a two dimensional reference to determine values for variables that relate to other conditions for compliance, …”. Further, in para [0043]: “in some embodiments, AI analysis may include determination of architectural aspects, such as doorways, windows, angles in walls, curves in walls, plumbing fixtures, piping, wiring, electrical equipment or boxes; duct work; HVAC fixtures and/or equipment;”).
Murphy teaches b. represent, by the controller, at least a portion of the initial design plan as multiple dynamic components in the interactive user interface, the multiple dynamic components corresponding to the plurality of design elements; (Murphy disclosed in page 7 para [0093]: “Referring now to FIG. 1D, tables 140 list exemplary parameters that may be values for variables used in AI engine processes to determine whether a design plan is in compliance with a set of required code parameters and/or best practices. As described herein, a design plan may be defined as one or more areas or regions. An area may be associated with variables. Values for the variables may be generated by the AI engine and/or provided via user input …”. In page 9 para [0112]: “Referring to FIG. 2D, an exemplary user interface 230 illustrates a user interface floorplan model 231 with boundaries 236-237 between adjacent regions 233-235 with interior boundaries 236-237 that may be included in an appropriate region of a dynamic component 231.” Further in page 16 para [0202]: “In another aspect a dynamic component may a include a line 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.”).
Murphy teaches c. generate, with the controller, the interactive user interface based upon artificial intelligence analysis of the initial design plan, wherein the interactive user interface allows a user to view, select, and interact with the multiple dynamic components of the initial design plan; (Murphy disclosed in page 12 para [0153]: “a user interface may include one or more vertex 701-704 (e.g., points where two or more line segments meet) that may be user interactive such that a user may position the one or more vertex 701-704 at a user selected position. User positioning may include, for example, user drag and drop of the one or more vertex 701-704 at a desired location or entering a desired position, such as via coordinates. … User interactive portions of a user interface 700 are not limited to vertex 701-704 and can be any other item 701-709 in the user interface 700 that may facilitate achievement of a purpose by allowing one or both of: the user, and the controller, to control dynamic sizing and/or placement of an item 701-709.” In page 16 para [0201]: “In some embodiments dynamic components may include a polygon and a method further of practice may include the steps of: receiving an instruction via the user interactive interface to modify a parameter of the polygon, and modifying the parameter of the polygon based upon the instruction received via the interactive user interface.”).
Murphy teaches d. allow, via the interactive user interface, user selection of a specific location on the initial design plan or a selected dynamic component of the multiple dynamic components to initiate a change order request; (Murphy disclosed in page 7 para [0088]: “In some embodiments, a first user interface 125 rendition, may be modified by a user to create a second user interface 125, and submitted to Al 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. Change order renditions provided as options to bring a design plan into compliance with a selected code may also be provided with a unique identifier, time and/or date stamped to create a continuum of work, as related to original projects and compliance initiated changes.”).
Murphy teaches e. receive, via the interactive user interface, detailed user input for the change order request from the user, the detailed user input specifying modification to one or more design elements of the initial design plan; (Murphy disclosed in page 9 para [0116]: “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. There may be numerous dimensional calculations that may be made based on the different types of model output and the user inputted calibration factors and other parameters entered by the user.” In page 15 para [0181]: “In some embodiments, the user may be given access to movement of boundary elements and vertices of boundary elements. In examples where lines or vectors are used to represent boundaries and surrounding area, a user may move vertices between lines or center points of lines … In some examples, a user may elect to move multiple or all layers in an equivalent manner. … A user may be presented with a user interface that includes dynamic representations of a takeoff representation and associated values and changes may be input by a user. … Accordingly, in various embodiments, a controller and a user may manipulate aspects of a user interface and AI engine.”).
Murphy teaches i. present, through the interactive user interface, the detected potential conflicts or challenges (Murphy disclosed in page 13 para [0163]: “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. … In some embodiments, a list of variances or discovered potential issues may be presented to a user on a display or in a report form.”).
Murphy teaches j. allow, through the interactive user interface, the user to update the change order request; (Murphy disclosed in page 11 para [01396-0138]: “In a non-limiting example, accurate automatic classification of room spaces may allow for a combination of all interior spaces to be made and presented to a user. Overlays and boundary displays can accordingly be displayed for such aggregations. There may be numerous functionality and purpose to automatic classification of regions from an input drawing. … An Al engine may utilize a combination of factors to classify a region, but it may be clear that the context of recognized text may provide direct evidence upon which to infer a decision. For example, a recognized textual comment in a region may directly identify the space as a bedroom, which may allow the AI engine to make a set of hierarchical assignments to space and neighboring spaces, such as adjoining bathrooms, closets, and the like.” Further in page 13 para [0162]: “In some embodiments, various feature types and text may be associated into separate layers of a processed architectural design. Thus, a user interface or other output display or on reports, different layers may be illustrated at different times along with associated display of estimation results.”).
Murphy teaches k. generate, by the controller, one or more updated design plans corresponding to one or more of the plurality of associated sub-plans affected by the change order request, wherein the one or more updated design plans include at least one of: an updated electrical design plan, an updated plumbing design plan, an updated HVAC design plan, and an updated structural design plan; (Murphy disclosed in page 7 para [0088]: “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. Change order renditions provided as options to bring a design plan into compliance with a selected code may also be provided with a unique identifier, time and/or date stamped to create a continuum of work, as related to original projects and compliance initiated changes.” In page 8 para [0099]: “In some embodiments, a recognition step may function to replace or ignore a feature. For example, for a task goal of the result shown in FIG. 2B, features such as windows 203, and doorways, 204, may be recognized and replaced with other features consistent with exterior walls 201 or interior walls 202 (as shown in FIG. 2A). Other features may be removed, such as the text 208, the plumbing features and other internal appliances and furniture which may be shown on drawings used as input to the processing.”
The disclosure above “result shown in FIG. 2B, features such as windows 203, and doorways, 204, may be recognized and replaced with other features consistent with exterior walls 201 or interior walls 202 (as shown in FIG. 2A)” corresponds to claim limitation “the one or more updated design plans include an updated structural design plan”. Further disclosure “Change order renditions provided as options to bring a design plan into compliance with a selected code may also be provided with a unique identifier” corresponds to claim limitation “one or more updated design plans corresponding to associated sub-plans affected by the change order request”).
Murphy teaches generate, by the controller, one or more updated construction constraints corresponding to each of the one or more updated design plans, the one or more updated construction constraints comprising recalculated values for one or more of: time, cost, labor, and materials required to implement the one or more updated design plans; (Murphy disclosed in page 13 para [0157-0158]: “Aspects that are determined by a controller running an Al engine to be represented in a two-dimensional reference may be used to generate an estimate of what will be required to complete a project. … For example, a derived area or region comprising a room and/or a boundary, perimeter or other beginning and end indicator may allow for a building estimate that may integrate choices of materials with associated raw materials costs and with labor estimates all scaled with the derived parameters. ... In other examples, the boundary determination function may be supplemented with the equivalent functions of construction estimation to directly provide parametric input to an estimation function. For example, the parameters derived by the boundary determinations may result in estimation of needed quantities like cement, lumber, steel, wall board, floor treatments, carpeting and the like. Associated labor estimates may also be calculated.”).
The disclosure above teaches the limitation “the one or more updated construction constraints comprising recalculated values for one or more of: time, cost, labor, and materials required to implement the one or more updated design plans”).
Murphy teaches transmit, by the controller, each corresponding updated design plan of the one or more updated design plans along with the corresponding one or more updated construction constraints to the stakeholders (Under BRI, examiner would construe the claim element “stakeholder” as participants or team member, who has active involvement. Murphy disclosed in page 5 para [0061]: “an AI engine may ascertain features included in the two dimensional representation, the AI engine may additionally ascertain that a feature is located within a particular set of boundaries or external to the set of boundaries … The features and boundaries may be determined, for example, via algorithmically processing an input design plan image with a trained AI model. … 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,” In page 6 para [0070]: “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 Al processes more accurate and/or enable additional types of AI processes.”
The disclosures above “features and boundaries may be determined, for example, via algorithmically processing an input design plan image with a trained AI model; user modifications may also be input back to the AI engine train the AI engine” teaches the limitation “transmit, by the controller corresponding updated design plan along with one or more updated construction constraints to the stakeholders”).
However, Murphy doesn’t explicitly teach the limitations “f. analyze, by the controller, the detailed user input for the change order request to simulate the change order request within the initial design plan to detect potential conflicts or challenges resulting from impacts on one or more of: surrounding spaces, overlapping building systems, interdependencies between the plurality of associated sub-plans, design considerations, one or more interrelated design elements, and one or more initial constraints of the initial design plan, g. categorize, by the controller, the detected potential conflicts or challenges based on their severity and type; h. generate, by the controller, one or more recommended modifications configured to reduce the detected potential conflicts or challenges; i. present, through the interactive user interface, the one or more recommended modifications to the user;
Bews teaches f. analyze, by the controller, the detailed user input for the change order request to simulate the change order request within the initial design plan to detect potential conflicts or challenges resulting from impacts on one or more of: surrounding spaces, overlapping building systems, interdependencies between the plurality of associated sub-plans, design considerations, one or more interrelated design elements, and one or more initial constraints of the initial design plan, (Bews disclosed in para [0038]: “In some embodiments, apparatus 100 may determine a divergence element 124 as a function of a compliance threshold 140. In one or more embodiments, the compliance threshold 140 may be determined as a function of the construction constraints 128 and/or the geographical constraints 132. For instance, and without limitation, apparatus 100 may receive user input 108 which may include a design plan 116. In some embodiments, apparatus 100 may determine a divergence element 124 of design plan 116 by comparing it to compliance threshold 124. … In other embodiments, compliance threshold 124 may be received from an external computing device, user input, machine-learning module, and/or other forms of communication. ... A machine learning model may be trained with training data correlating user data to compliance threshold 124. Training data may be received from user input, external computing devices, and/or previous iterations of processing. A machine learning model may be configured to input user data 108 and output one or more divergence elements of user data 108.”
The disclosures above “apparatus 100 may receive user input 108 which may include a design plan” and further, “determine a divergence element 124 as a function of a compliance threshold 140 and the compliance threshold 140 may be determined as a function of the construction constraints” corresponds to claim limitations “the detailed user input within the initial design plan” and “detect potential conflicts or challenges resulting from impacts on one or more initial constraints of the initial design plan.” respectively. The disclosure “apparatus 100 may determine a divergence element 124 of design plan 116 by comparing it to compliance threshold; compliance threshold received from an external computing device, user input, machine-learning module” corresponds to claim limitation “analyze, by the controller the change order request to simulate the change order request within the initial design plan to detect potential conflicts or challenges”.).
Bews teaches g. categorize, by the controller, the detected potential conflicts or challenges based on their severity and type; (Bews disclosed in para [0036]: “In some embodiments, determination of compliance with the threshold may include a buffer or degree to which the user design is outside the threshold, a buffer or degree to which there is room to make modification while under the threshold, use of an optimization program to find the best or closest match under or over the threshold and the like. ... Also, “threshold” could include multiple component thresholds for different categories, for example, there may be a structural requirement threshold, a plumbing threshold, an electrical threshold and the like. … In another embodiment, there may be an identification of reasons why and/or categories stating that it is not within the threshold, for example, the electrical outlets of the user design may not be within the specified distance, or the plumbing may be constructed of the incorrect piping material.” In para [0038]: “In some embodiments, apparatus 100 may determine a divergence element 124 of design plan 116 by comparing it to compliance threshold 124. … A machine learning model may be configured to input user data 108 and output one or more divergence elements of user data 108.” In para [0078]: “Still referring to FIG. 7, at step 720, method 700 includes determining a divergence element related to compliance of user design as a function of the compliance threshold and design plan.”
The disclosure above “determination of compliance with the threshold may include a buffer or degree to which the user design is outside the threshold, a buffer or degree” corresponds to claim limitation “the detected potential conflicts or challenges”. Further, the disclosure “threshold” could include multiple component thresholds for different categories, e.g., a structural requirement threshold, a plumbing threshold, an electrical threshold; identification of reasons why and/or categories stating that it is not within the threshold, for example, the electrical outlets of the user design may not be within the specified distance, or the plumbing may be constructed of the incorrect piping material” correspond to claim limitation “categorize, by the controller (e.g., machine learning model configured to output one or more divergence elements), the detected potential conflicts or challenges (divergence element) based on their severity and type”).
Bews teaches h. generate, by the controller, one or more recommended modifications configured to reduce the detected potential conflicts or challenges; (Bews disclosed in para [0036]: “In another embodiment, there may be an identification of reasons why and/or categories stating that it is not within the threshold, for example, the electrical outlets of the user design may not be within the specified distance, or the plumbing may be constructed of the incorrect piping material. In other embodiments, if the user design does not fall within a buffer or degree of the threshold there may be an identification of one or more actions or degrees of change necessary to come within the threshold, … for example there may be a recommendation of adding an electrical outlet. In another embodiment, there may be an identification of a forecast of probability of success for a given attempt to come within threshold. In an embodiment, ways to offset a change in one category for a change in another to optimize cost and/or time may be identified, for example, spending less on emissions due to a determination of being under a threshold and using that to offset the additional costs of complying to a code such as a fire code or a plumbing code.”).
and Bews teaches present, through the interactive user interface, the one or more recommended modifications to the user; (Bews disclosed in para [0036]: “In an embodiment, ways to offset a change in one category for a change in another to optimize cost and/or time may be identified, for example, spending less on emissions due to a determination of being under a threshold and using that to offset the additional costs of complying to a code such as a fire code or a plumbing code. A display of any of those potential outputs and/or combinations of them, may be displayed in a GUI. For example, the user may input one or more modifications to the GUI, redo any or all steps described above and display the results. The recommendation may be determined by processor 104 in a database or processor 104 may initiate a classifier which matches sets of inputs to improvement actions by for example, utilizing a first machine learning process to determine how close the input is to the threshold and then utilizing a second machine learning process which uses the same input and provides recommendations to achieve an improved result.”).
Murphy and Bews are analogous art because they are related to have generative artificial intelligence (AI) models in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy and Bews before him or her, to modify the quantifying/analyzing initial building design plan of Murphy, to include detecting potential conflicts or challenges in initial/original design plan change in Bews’s teaching. The suggestion or motivation for doing so would have been obvious by Bews because “In some embodiments, determination of compliance with the threshold may include a buffer or degree to which the user design is outside the threshold, a buffer or degree to which there is room to make modification while under the threshold, use of an optimization program to find the best or closest match under or over the threshold and the like. In another embodiment, there may an output of a probability that the user design will be found to be under a threshold. Also, “threshold” could include multiple component thresholds for different categories, for example, there may be a structural requirement threshold, a plumbing threshold, an electrical threshold and the like.” (Bews disclosed in para [0036]).
Neither Murphy nor Bews explicitly teaches the limitations “the one or more initial constraints comprise contractually agreed constraints established among stakeholders of the initial design plan; m. identify, by the controller, one or more affected stakeholders corresponding to each of the one or more updated design plans; and n. updated design plan of the one or more updated design plans along with the set of updated construction constraints to the one or more affected stakeholders corresponding to that updated design plan for review and approval.
wherein Alizadeh teaches the one or more initial constraints comprise contractually agreed constraints established among stakeholders of the initial design plan; (Alizadeh disclosed in page 68 heading ‘MATERIAL AND METHODS’: “we developed a new focused assessment tool. … Adjustments were also made to allow for a more comprehensive evaluation of the building (rather than just IEQ) in combination with identified ADHD aspects and the built environment features … Lastly, the opinions of experts and stakeholders were used for the final amendments and revision of the survey. As a result, a focused survey was developed to be inclusive and specifically target housing for individuals with ADHD. … In the design of our tool, technical factors, which are the indicators of a building’s physical systems dealing with the survival attributes of its users, comprised safety and security, interior condition and maintenance, structure and layout, and cleanliness. We used eight variables for the measurement of technical-related indicators, including the ratings of their suitability and checking for any relevant issues.”
In page 71 heading ‘Analysis of the panel’s review and measuring consensus’ (left col.): “For data analysis, descriptive statistics were used to provide the frequencies of responses for each question. A content validity index (CVI) proposed … was also used to establish the percentage of agreement. This method seeks to ascertain how much consensus among the panel specifies item acceptance or deletion by calculating the ratio of items decided to be content valid by experts through ratings. … Level 1 aggregates the panel’s feedback for items with 80% and above agreement. There was majority agreement (80% and above) among panel members for the validity of 51 items out of the original draft survey that had 69 items in total. Therefore, it was assumed that these items were fundamentally valid and remained unchanged.”).
Alizadeh teaches m. identify, by the controller, one or more affected stakeholders corresponding to each of the one or more updated design plans; (Alizadeh disclosed in page 68 heading ‘Indicators and assessment criteria of HEQAT’: “The focus of a post-occupancy evaluation tool can be organised into five general groups of building elements, including technical, functional, behavioural, symbolic, and economic factors. These consist of indicators representing signs, elements, and items that assess certain qualities of a building feature …”. In page 70 heading ‘Panel recruitment’: “In this study, we aimed for a purposive sample of experts and stakeholders with balance across expert types. The expert team was purposefully selected to include those with relevant experience and expertise in the field of either ADHD or the built environment. … For the selection of stakeholders, parents of children with ADHD were included.” Further, in page 71 heading ‘Analysis of the panel’s review and measuring consensus’: “Level 2 aggregates the panel’s feedback for items with 80% to 70% agreement. There was 80% to 70% congruence among panel members for the validity of 14 items out of the original draft survey with 69 items. Therefore, these items were changed and modified based on the feedback received and included in round 2 for the panel’s review and degree of congruence; … The data and feedback from the first round formed a report to run the second round of Delphi. All comments were listed in the report, and the modified or changed items (according to the panel’s feedback in round 1) … The panel reviewed and judged the second draft of the Housing Environmental Quality Assessment Tool with 76 items. Analysis of the data from round 2 showed that the feedback and ratings were aggregated into three levels: Level 1 aggregates the panel’s feedback for items with 80% and above agreement. There was a majority agreement (80% and above) among panel members for the validity of 12 items out of 14 items that were changed or modified after round 1.”
The disclosure above “the validity of 14 items out of the original draft survey with 69 items, therefore, these items were changed and modified based on the feedback received and included in round 2 for the panel’s review and degree of congruence” corresponds to claim limitation “identify one or more updated design plans”. Further, the disclosure “expert team was purposefully selected to include those with relevant experience and expertise in the field of either ADHD or the built environment. For the selection of stakeholders, parents of children with ADHD were included” corresponds to claim limitation “affected stakeholders”).
and Alizadeh teaches n. updated design plan of the one or more updated design plans along with the set of updated construction constraints to the one or more affected stakeholders corresponding to that updated design plan for review and approval. (Alizadeh disclosed in page 71 heading ‘Analysis of the panel’s review and measuring consensus’: “Level 2 aggregates the panel’s feedback for items with 80% to 70% agreement. There was 80% to 70% congruence among panel members for the validity of 14 items out of the original draft survey with 69 items. Therefore, these items were changed and modified based on the feedback received and included in round 2 for the panel’s review and degree of congruence; … The data and feedback from the first round formed a report to run the second round of Delphi. All comments were listed in the report, and the modified or changed items (according to the panel’s feedback in round 1) … The panel reviewed and judged the second draft of the Housing Environmental Quality Assessment Tool with 76 items. Analysis of the data from round 2 showed that the feedback and ratings were aggregated into three levels: Level 1 aggregates the panel’s feedback for items with 80% and above agreement. There was a majority agreement (80% and above) among panel members for the validity of 12 items out of 14 items that were changed or modified after round 1.” In page 72 heading ‘Determining the tool’s overall content validity and reliability’: “After the second round of Delphi, final refinements and modifications were made based on the panel’s feedback. All the items with validation of more than 70% were included in the final version of the tool, and the final validated tool with 74 items was developed. ... Of the 74 items reviewed and rated by the panel over a 2-round Delphi process, a total of 70 items were judged to be valid with a CVI of more than 0.78. ... No items attained less than 78% agreement on validity. As a result, the tool’s overall content validity was calculated as 95% …”. In page 72-73 heading ‘Discussion and conclusion’: “We believe that this assessment tool will be useful in identifying housing factors that may influence the symptoms and behaviours of children with ADHD, thus assisting in the development of solutions for improving their housing environmental quality. Improving the overall housing suitability and environmental quality could help improve physical comfort and quality of life for children with ADHD and their parents. This study represents a first step in building awareness among clinical experts and consumers.”).
Murphy, Bews and Alizadeh are analogous art because they are related to have generative and data analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews and Alizadeh, before him or her, to modify analyzing change order request to determine an impact on one or more initial design constraints of Murphy’s teaching, to include Alizadeh’s teaching to authorize or approve change order request and further identifying affected stakeholders by the change order request. The suggestion/motivation for doing so would have been obvious by Alizadeh because “The tool was validated using the Delphi process and the panel’s reviews. The high level of consensus among the panel supported the HEQAT with a high content validity of 95 per cent. A set of assessment criteria for housing quality was generated and validated by employing logical and scientifically recognized processes in developing content validity. These assessment criteria can be beneficial in supporting a standard for other assessment tools and reproductions. Moreover, this procedure may serve as an application or a model for developing and validating future criterion-referenced assessment tools in building quality and mental health.” (Alizadeh disclosed in page 73 heading ‘Discussion and conclusion’ (left col.)).
Regarding Claim 2, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Murphy teaches the Al engine is further configured to simulate the impact of the change order request on the initial design plan and provide visual feedback through the interactive user interface. (Murphy disclosed in page 7 para [0088]: “In some embodiments, a first user interface 125 rendition, may be modified by a user to create a second user interface 125, and submitted to Al 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. Change order renditions provided as options to bring a design plan into compliance with a selected code may also be provided with a unique identifier, time and/or date stamped to create a continuum of work, as related to original projects and compliance initiated changes.” The disclosure “first user interface 125 rendition, may be modified by a user to create a second user interface 125, and submitted to Al analysis to ascertain compliance with a selected code” corresponds to claim limitation “simulate the impact of the change order request on the initial design plan”.
In page 16 para [0199-0200]: “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 building 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 building … The method additionally determining a scale of the components included in the two-dimensional representation and/or generating a user interface including user interactive areas to change at least one of: a size and shape of at least one of the dynamic components.” The disclosure “determining a scale of the components included in the two-dimensional representation and/or generating a user interface including user interactive areas to change at least one of: a size and shape of at least one of the dynamic components” corresponds to claim limitation “provide visual feedback through the interactive user interface” (related to design change)).
Regarding Claim 3, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Murphy teaches the controller is configured to integrate external data sources, such as building codes and material availability, into an evaluation of the change order request. (Murphy disclosed in page 7 para [0088]: “In some embodiments, a first user interface 125 rendition, may be modified by a user to create a second user interface 125, and submitted to Al 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. Change order renditions provided as options to bring a design plan into compliance with a selected code may also be provided with a unique identifier, time and/or date stamped to create a continuum of work, as related to original projects and compliance initiated changes.” Further, in page 16 para [0199]: “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 building 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 building …”).
Regarding Claim 4, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Murphy teaches the interactive user interface includes tools for annotating the one or more design elements with comments, measurements, and other relevant data. (Murphy disclosed in page 11 para [0138]: “In some embodiments, a type may be inferred from text located on an input drawing or other two-dimensional reference. An Al engine may utilize a combination of factors to classify a region, but it may be clear that the context of recognized text may provide direct evidence upon which to infer a decision. For example, a recognized textual comment in a region may directly identify the space as a bedroom, which may allow the AI engine to make a set of hierarchical assignments to space and neighboring spaces, such as adjoining bathrooms, closets, and the like.”).
Regarding Claim 5, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Murphy teaches the interactive user interface allows the user to view a comparison between the one or more initial constraints and the one or more updated construction constraints. (Murphy disclosed in page 13 para [0161-0163]: “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. … 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. … a text input or other narrative may be recognized to provide more specific identification of a window type. Identified features may be associated with a specific item within a features database. The item within the features database may have associated records that precisely define a vector graphics representation of the element. … Thus, a user interface or other output display or on reports, different layers may be illustrated at different times along with associated display of estimation results. … 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. The calculations of raw material, labor and the like may then be adjusted for prevailing conditions in the selected geographic location. ... The databases associated with the systems may associate a geolocation with a set of codes, standards and the like and review the discovered design elements for compliance. In some embodiments, a list of variances or discovered potential issues may be presented to a user on a display or in a report form.”
The disclosure teaches the limitation “the interactive user interface allows the user to view a comparison (e.g., a list of variances or discovered potential issues may be presented to a user on a display or in a report form”) between the one or more initial constraints (e.g., “user entry of data associated with the location of a project associated with the input architectural plans”) and the one or more updated construction constraints” (e.g., “calculations of raw material, labor and the like may then be adjusted for prevailing conditions in the selected geographic location. The databases associated with the systems may associate a geolocation with a set of codes, standards and the like and review the discovered design elements for compliance”), as disclosed above).
Regarding Claim 6, Murphy, Bews and Alizadeh teach the system of claim 1, further Murphy teaches a database storing historical change orders and their outcomes, which the Al engine uses to predict potential challenges and solutions for new change orders (Murphy disclosed in page 13 para [0163-0164]: “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. The calculations of raw material, labor and the like may then be adjusted for prevailing conditions in the selected geographic location. Similarly, the geolocation of the drawing may drive additional functionality. The databases associated with the systems may associate a geolocation with a set of codes, standards and the like and review the discovered design elements for compliance. In some embodiments, a list of variances or discovered potential issues may be presented to a user on a display or in a report form. In some embodiments, a function may be offered to remove user entered data and other personally identifiable information associated in the database with a processing of a graphic image. In some embodiments, a feature determination that is presented to a user in a user interface may be assessed as erroneous in some way by the user. The user interface may include functionality to allow the user to correct the error. The resulting error determination may be included in a training database for the AI engine to help improve its accuracy and functionality.”
The disclosures “databases associated with the systems may associate a geolocation with a set of codes, standards and the like and review the discovered design elements for compliance” and “a list of variances or discovered potential issues may be presented to a user on a display or in a report form” correspond to claim limitation “a database storing historical change orders and their outcomes and potential challenges”. Further, the disclosure “the user interface may include functionality to allow the user to correct the error and resulting error determination may be included in a training database for the AI engine to help improve its accuracy and functionality” correspond to claim limitation “the Al engine uses to predict potential challenges and solutions for new change orders”).
Regarding Claim 9, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Murphy teaches the Al engine is configured to provide automated suggestions for optimizing the initial design plan based on user-defined priorities, such as cost efficiency or sustainability. (Murphy disclosed in page 16 para [0199]: “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 building 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 building and/or associated costs.” It has been discussed in page 15 para [0181] that users may be given multiple menu options to select disparate elements for processing and adjustment, accordingly a controller and a user can manipulate aspects of a user interface and AI engine.
The disclosure above teaches the limitation “the Al engine is configured to provide automated suggestions for optimizing the initial design plan based on user-defined priorities, such as cost efficiency”).
Regarding Claim 12, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Murphy teaches the Al engine is configured to identify potential conflicts between the change order request and existing design elements, and to propose resolutions. (Murphy disclosed in page 16 para [0199]: “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 building 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 building and / or associated costs.”).
Regarding Claim 14, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Murphy teaches the Al engine is configured to learn from user interactions to improve accuracy and relevance of its analyses and suggestions. (Murphy disclosed in page 13 para [0163-0164]: “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. The calculations of raw material, labor and the like may then be adjusted for prevailing conditions in the selected geographic location. … In some embodiments, a list of variances or discovered potential issues may be presented to a user on a display or in a report form. In some embodiments, a function may be offered to remove user entered data and other personally identifiable information associated in the database with a processing of a graphic image. In some embodiments, a feature determination that is presented to a user in a user interface may be assessed as erroneous in some way by the user. The user interface may include functionality to allow the user to correct the error. The resulting error determination may be included in a training database for the AI engine to help improve its accuracy and functionality.”).
Regarding Claim 16, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Murphy teaches the interactive user interface includes a feature for visualizing the changes in the initial design plan in three dimensions, enhancing user understanding of the impact. (Murphy disclosed in page 9-10 para [0117]: “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. … 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.”
In page 10 para [0119]: “The replication view 301A, may also include one or more fixtures 302. A rasterized version (or pixel version) of the fixtures 302 may be identified via an AI engine. If a pattern is present that is not identified as a fixture 302, a user may train the AI engine to recognize the pattern as a fixture of a particular type. The controller may generate a tally of multiple fixtures 302 identified in the two dimensional reference. The tally of multiple fixtures 302 may include some or all of the fixtures identified in the two dimensional reference and be used to generate an estimate for completion of a project illustrated by …”. This disclosure teaches the limitation “a feature for visualizing the changes, enhancing user understanding of the impact” (of change)).
Regarding Claim 20, Murphy, Bews and Alizadeh teach the system of claim 1, however Murphy and Alizadeh do not explicitly teach the limitation “the controller is configured to identify conflicts between the change order request and one or more design considerations stored in a design consideration database”.
wherein Bews teaches the controller is configured to identify conflicts between the change order request and one or more design considerations stored in a design consideration database. (Bews disclosed in para [0038]: “In some embodiments, apparatus 100 may determine a divergence element 124 as a function of a compliance threshold 140. In one or more embodiments, the compliance threshold 140 may be determined as a function of the construction constraints 128 and/or the geographical constraints 132. For instance, and without limitation, apparatus 100 may receive user input 108 which may include a design plan 116. In some embodiments, apparatus 100 may determine a divergence element 124 of design plan 116 by comparing it to compliance threshold 124. … In other embodiments, compliance threshold 124 may be received from an external computing device, user input, machine-learning module, and/or other forms of communication. ... A machine learning model may be trained with training data correlating user data to compliance threshold 124. Training data may be received from user input, external computing devices, and/or previous iterations of processing. A machine learning model may be configured to input user data 108 and output one or more divergence elements of user data 108.” Further, in para [0039] it has been discussed that as referred to FIG. 1, apparatus 100 determine compliance threshold 140 through generating a web index query. A “query” as used in this disclosure is a search function that returns data. Apparatus 100 may generate a query to search through databases for similar attributes.
To identify conflicts between the change order request and one or more design considerations, e.g., determine a “divergence element” corresponds to conflict, which is a function of a compliance threshold (in present disclosure), further determining compliance threshold is performed by using a ‘query’ (a search function that returns data). Therefore, it is understood that one or more design considerations stored in a design consideration database to identify the conflicts).
Murphy and Bews are analogous art because they are related to have generative artificial intelligence (AI) models in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy and Bews before him or her, to modify the quantifying/analyzing initial building design plan of Murphy, to include detecting potential conflicts or challenges in initial/original design plan change in Bews’s teaching. The suggestion or motivation for doing so would have been obvious by Bews because “In some embodiments, determination of compliance with the threshold may include a buffer or degree to which the user design is outside the threshold, a buffer or degree to which there is room to make modification while under the threshold, use of an optimization program to find the best or closest match under or over the threshold and the like. In another embodiment, there may an output of a probability that the user design will be found to be under a threshold. Also, “threshold” could include multiple component thresholds for different categories, for example, there may be a structural requirement threshold, a plumbing threshold, an electrical threshold and the like.” (Bews disclosed in para [0036]).
Regarding Claim 21, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh as discussed above for substantially similar rationale. In addition, claim 21 recites following limitation:
Murphy teaches a method for managing change order in a design plan of a building, (Murphy disclosed in page 1 para [0008]: “the present disclosure provides methods and apparatus for analyzing two-dimensional (sometimes referred to as “2D") documents such as design plans with the aid of artificial intelligence (sometimes referred to herein as “AI”) to make sure that the design plans are in compliance with requirements set forth by a authority having jurisdiction (“AHJ”) for various aspects of a resulting a building”. In page 6 para [0075]: “At step 109, one or both of the user and an automated process on a controller may specify a code for which a compliance determination based upon the Al generated boundaries. In some embodiments, a selection of a set of codes to apply to the floor plan may be automated, for example, based upon a geographic or geopolitical area in which the building resides or will be constructed. … a user may select that a set of floorplans be analyzed with the Al engine to assess compliance with Americans with Disabilities Act (ADA) compliance and National Fire protection Association code, or other code adopted by an authority having jurisdiction.”).
Regarding claim 22, Murphy, Bews and Alizadeh teach the method of claim 21, is incorporating the rejections of claim 5, because claim 22 has substantially similar claim language as claim 5, therefore claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh as discussed above for substantially similar rationale.
Regarding claim 23, Murphy, Bews and Alizadeh teach the method of claim 21, wherein Murphy teaches the change order input is added by selecting a specific spot on the design plan displayed on the interactive user interface and providing a description for the change order. (Murphy disclosed in page 13 para [0161-0162]: “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. … 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. Identified features may be associated with a specific item within a features database. … Therefore, an input graphic design may be reconstituted within the system to locate wall and other boundary elements and then to superimpose a database element graphic associated with the recognized feature. In some embodiments, various feature types and text may be associated into separate layers of a processed architectural design. Thus, a user interface or other output display or on reports, different layers may be illustrated at different times along with associated display of estimation results.”).
Regarding claim 24, Murphy, Bews and Alizadeh teach the method of claim 21, further Murphy teaches receiving, by the controller, design considerations including at least one of regulatory requirements, structural integrity parameters, and user-defined preferences. (Murphy disclosed in page 1 para [0008-0009]: “the present disclosure provides methods and apparatus for analyzing two-dimensional (sometimes referred to as “2D") documents such as design plans with the aid of artificial intelligence (sometimes referred to herein as “AI”) to make sure that the design plans are in compliance with requirements set forth by a authority having jurisdiction (“AHJ”) for various aspects of a resulting a building, … Specifically , the present invention uses AI to auto detect, measure, and classify components of building plans, and ascertain whether requirements relating to building design are in compliance with a relevant code according to the AHJ, such as, but not limited to: occupancy load, travel distance to exit a building from within disparate areas, common paths, egress capacity, treatment of dead ends, percentage of accessible public entrance (versus services entrances); … accessible routes within a building and on a building site, including egress; and elevator dimension requirements.” This disclosure teaches the limitation “receiving, by the controller, design considerations including at least one of regulatory requirements”).
Regarding claim 25, Murphy, Bews and Alizadeh teach the method of claim 21, further Murphy teaches determining compliance of the change order input with the design considerations. (Murphy disclosed in page 6-7 para [0080]: “the 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.”).
Regarding claim 26, Murphy, Bews and Alizadeh teach the method of claim 21, wherein Murphy teaches the interactive user interface allows the user to input additional constraints, including at least one of a fixed budget, timeline, the labor, and material choice. (Murphy disclosed in page 11 para [0134]: “an AI engine or other process run by a controller based upon the two dimensional reference, such as a floor plan, design plan or architectural blueprint. The variables include aspects that may affect an amount of time, worker hours, and materials involved in a project based upon the two dimensional reference.” In page 13 para [0158]: “a derived area or region comprising a room and/or a boundary, perimeter or other beginning and end indicator may allow for a building estimate that may integrate choices of materials with associated raw materials costs and with labor estimates all scaled with the derived parameters. … the parameters derived by the boundary determinations may result in estimation of needed quantities like cement, lumber, steel, wall board, floor treatments, carpeting and the like. Associated labor estimates may also be calculated.”).
Regarding claim 28, Murphy, Bews and Alizadeh teach the method of claim 21, however, Murphy and Bews do not explicitly teach the limitation “receiving, by the controller, a response from the one or more affected stakeholders by the change order input, wherein the response includes one of an approval, a rejection, and a modification condition”.
further Alizadeh teaches receiving, by the controller, a response from the one or more affected stakeholders by the change order input, wherein the response includes one of an approval, a rejection, and a modification condition. (Alizadeh disclosed in page 71 heading ‘Analysis of the panel’s review and measuring consensus’: “Level 2 aggregates the panel’s feedback for items with 80% to 70% agreement. There was 80% to 70% congruence among panel members for the validity of 14 items out of the original draft survey with 69 items. Therefore, these items were changed and modified based on the feedback received and included in round 2 for the panel’s review and degree of congruence; … The data and feedback from the first round formed a report to run the second round of Delphi. All comments were listed in the report, and the modified or changed items (according to the panel’s feedback in round 1) … The panel reviewed and judged the second draft of the Housing Environmental Quality Assessment Tool with 76 items. Analysis of the data from round 2 showed that the feedback and ratings were aggregated into three levels: Level 1 aggregates the panel’s feedback for items with 80% and above agreement. There was a majority agreement (80% and above) among panel members for the validity of 12 items out of 14 items that were changed or modified after round 1.” In page 72 heading ‘Determining the tool’s overall content validity and reliability’: “After the second round of Delphi, final refinements and modifications were made based on the panel’s feedback. All the items with validation of more than 70% were included in the final version of the tool, and the final validated tool with 74 items was developed. ... Of the 74 items reviewed and rated by the panel over a 2-round Delphi process, a total of 70 items were judged to be valid with a CVI of more than 0.78. ... No items attained less than 78% agreement on validity. As a result, the tool’s overall content validity was calculated as 95% …”. In page 72-73 heading ‘Discussion and conclusion’: “We believe that this assessment tool will be useful in identifying housing factors that may influence the symptoms and behaviours of children with ADHD, thus assisting in the development of solutions for improving their housing environmental quality. Improving the overall housing suitability and environmental quality could help improve physical comfort and quality of life for children with ADHD and their parents. This study represents a first step in building awareness among clinical experts and consumers.”
The disclosure above teaches the limitation “a response from the one or more affected stakeholders by the change order input, wherein the response includes one of an approval”).
Murphy, Bews and Alizadeh are analogous art because they are related to have generative and data analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews and Alizadeh, before him or her, to modify analyzing change order request to determine an impact on one or more initial design constraints of Murphy’s teaching, to include Alizadeh’s teaching to authorize or approve change order request and further identifying affected stakeholders by the change order request. The suggestion/motivation for doing so would have been obvious by Alizadeh because “The tool was validated using the Delphi process and the panel’s reviews. The high level of consensus among the panel supported the HEQAT with a high content validity of 95 per cent. A set of assessment criteria for housing quality was generated and validated by employing logical and scientifically recognized processes in developing content validity. These assessment criteria can be beneficial in supporting a standard for other assessment tools and reproductions. Moreover, this procedure may serve as an application or a model for developing and validating future criterion-referenced assessment tools in building quality and mental health.” (Alizadeh disclosed in page 73 heading ‘Discussion and conclusion’ (left col.)).
Regarding Claim 32, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 32 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh as discussed above for substantially similar rationale. In addition, claim 32 recites following limitation:
Murphy teaches an apparatus for managing change order in a design plan of a building, (Murphy disclosed in page 1 para [0008]: “the present disclosure provides methods and apparatus for analyzing two-dimensional (sometimes referred to as “2D") documents such as design plans with the aid of artificial intelligence (sometimes referred to herein as “AI”) to make sure that the design plans are in compliance with requirements set forth by a authority having jurisdiction (“AHJ”) for various aspects of a resulting a building”. In page 6 para [0075]: “At step 109, one or both of the user and an automated process on a controller may specify a code for which a compliance determination based upon the Al generated boundaries. In some embodiments, a selection of a set of codes to apply to the floor plan may be automated, for example, based upon a geographic or geopolitical area in which the building resides or will be constructed. … a user may select that a set of floorplans be analyzed with the Al engine to assess compliance with Americans with Disabilities Act (ADA) compliance and National Fire protection Association code, or other code adopted by an authority having jurisdiction.” The disclosure “artificial intelligence” (AI) corresponds to “apparatus”).
Regarding claim 33, Murphy, Bews and Alizadeh the apparatus of claim 32, is incorporating the rejections of claim 20, because claim 33 has substantially similar claim language as claim 20, therefore claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh as discussed above for substantially similar rationale.
Regarding claim 34, Murphy, Bews and Alizadeh the apparatus of claim 32, wherein Murphy teaches the interactive user interface provides a drag-and-drop functionality for moving the one or more design elements within the design plan. (Murphy disclosed in page 6 para [0073]: “Some embodiments include an interface that enables user modifications of boundaries and areas defined by the modified boundaries. For example, 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. … As such, an area of a room or unit may be redefined by a user via the user interface. Changing an area of a room and/or unit may in turn be used as a basis for modifying an occupant load, defining an egress path, classifying a space, or other purpose.”).
Regarding claim 35, Murphy, Bews and Alizadeh the apparatus of claim 32, wherein Murphy teaches the controller determines whether the change order request affects structural integrity by analyzing interdependencies between structural components. (Murphy disclosed in page 13 para [0160-0161]: “In some two dimensional references, furniture, desks, beds, and the like may be depicted in designated spaces. Al pattern recognition capabilities can also be trained to recognize each of these features and many other such features commonly included in design drawings. … 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.”
The disclosure “a recognized feature may be accompanied on a drawing with textual description which may also be recognized by the AI image recognition capabilities” corresponds to claim limitation “the controller determines the change order request” (e.g., adding textual description on a drawing). Further, the disclosure “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” corresponds to claim limitation “determines whether the change order request affects structural integrity by analyzing interdependencies between structural components”).
Claims 7,11,15,29,36 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh and further in view of Segev et al. (Pub. No. US2021/0073449A1).
Regarding Claim 7, Murphy, Bews and Alizadeh teach the system of claim 1, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the executable software code, when executed by the processor, causes the processor to send alerts to the stakeholders via one or more of: email, SMS, and a project management platform”.
wherein Segev teaches the executable software code, when executed by the processor, causes the processor to send alerts to the stakeholders via one or more of: email, SMS, and a project management platform. (Segev disclosed in page 22 para [0188]: “In some embodiments, the materials list may include in image of the equipment. The materials list may be displayed on the screen or exported as text, a table, an image ... In some embodiments, the materials list may be electronically transmitted by email or via the internet, for example, to a third-party or third-party software application, an enterprise resource planning (ERP) system, or any other entity that may use the information.” Further, in para [0192]: “In some embodiments, the system may reanalyze the equipment placement location and specification to determine whether it still conforms to the functional requirement (or a degree of conformance to the functional requirement). The system may generate an alert to the user or otherwise display the updated conformance information based on the modifications.”).
Murphy, Bews, Alizadeh and Segev are analogous art because they are related to have generative artificial intelligence (AI) models in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Segev, before him or her, to modify the quantifying/analyzing initial building design plan of Murphy, to include analyzing change order request to determine an impact on one or more initial constraints of Segev. The suggestion or motivation for doing so would have been obvious by Segev because “the generative analysis may result in multiple options for equipment placement locations and / or technical specifications. For example, the generative analysis may result in multiple solutions that conform to the received functional requirements, or that have similar degrees of conformation to the received functional requirements. In some embodiments, the disclosed methods may include presenting a predetermined number or percentage of solutions that best conform to the functional requirement (e.g., the top 5 results, the top 10% of results, or various other thresholds).” (Segev disclosed in page 22 para [0190]).
Regarding Claim 11, Murphy, Bews and Alizadeh teach the system of claim 1, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the executable software code, when executed by the processor, causes the processor to allow the users to order materials directly through the interactive user interface”.
wherein Segev teaches the executable software code, when executed by the processor, causes the processor to allow the users to order materials directly through the interactive user interface. (Segev disclosed in page 21-22 para [0188]: “In some embodiments, the materials list may also include secondary and/or auxiliary equipment required to physically construct a building system … A material list may include equipment models (e.g., a model number or model name), quantities, pricing information, specific properties (e.g., materials of construction, sizing, equipment types, or other properties), a physical description, and any other information associated with the equipment that may be useful for purchasing, tracking, or installation of the equipment. In some embodiments, the materials list may include in image of the equipment. The materials list may be displayed on the screen or exported as text, a table, an image (e.g., JPG, PDF, etc.), or any other suitable format.”).
Murphy, Bews, Alizadeh and Segev are analogous art because they are related to have generative artificial intelligence (AI) models in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Segev, before him or her, to modify the quantifying/analyzing initial building design plan of Murphy, to include analyzing change order request to determine an impact on one or more initial constraints of Segev. The suggestion or motivation for doing so would have been obvious by Segev because “the generative analysis may result in multiple options for equipment placement locations and / or technical specifications. For example, the generative analysis may result in multiple solutions that conform to the received functional requirements, or that have similar degrees of conformation to the received functional requirements. In some embodiments, the disclosed methods may include presenting a predetermined number or percentage of solutions that best conform to the functional requirement (e.g., the top 5 results, the top 10% of results, or various other thresholds).” (Segev disclosed in page 22 para [0190]).
Regarding Claim 15, Murphy, Bews and Alizadeh teach the system of claim 1, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the system is configured to track a status of the change orders and provide updates to the one or more affected stakeholders throughout an authorization process”.
wherein Segev teaches the system is configured to track a status of the change orders and provide updates to the one or more affected stakeholders throughout an authorization process. (Segev disclosed in page 22 para [0191-0192]: “. The user interface may also allow the user to rotate the equipment, change an equipment placement height, modify a coverage area of the equipment, modify a size or shape of the equipment, or any other modifications that may be made to the placement locations. Similarly, the user may modify one or more technical specifications identified during the generative analysis. For example, the user may select a different model of equipment, a different equipment size, a different equipment rating or capacity, or various other parameters. Once such changes are made, the system might run a simulation, compare the simulation result with one or more functional requirements, and provide an output as to the extent of conformance of the changes with the functional requirement (s). … In some embodiments, the system may reanalyze the equipment placement location and specification to determine whether it still conforms to the functional requirement (or a degree of conformance to the functional requirement). The system may generate an alert to the user or otherwise display the updated conformance information based on the modifications.”).
Murphy, Bews, Alizadeh and Segev are analogous art because they are related to have generative artificial intelligence (AI) models in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Segev, before him or her, to modify the quantifying/analyzing initial building design plan of Murphy, to include analyzing change order request to determine an impact on one or more initial constraints of Segev. The suggestion or motivation for doing so would have been obvious by Segev because “the generative analysis may result in multiple options for equipment placement locations and / or technical specifications. For example, the generative analysis may result in multiple solutions that conform to the received functional requirements, or that have similar degrees of conformation to the received functional requirements. In some embodiments, the disclosed methods may include presenting a predetermined number or percentage of solutions that best conform to the functional requirement (e.g., the top 5 results, the top 10% of results, or various other thresholds).” (Segev disclosed in page 22 para [0190]).
Regarding claim 29, Murphy, Bews and Alizadeh teach the method of claim 21, however, Murphy, Bews and Alizadeh do not explicitly teach the limitation “the one or more affected stakeholders include one or more of a building owner, a contractor, a sub-contractor, a secondary owner, a regulatory authority, and a design consultant.
wherein Segev teaches the one or more affected stakeholders include one or more of a building owner, a contractor, a sub-contractor, a secondary owner, a regulatory authority, and a design consultant. (Segev disclosed in page 22 para [0188]: “In some embodiments, the materials list may include in image of the equipment. The materials list may be displayed on the screen or exported as text, a table, an image ... In some embodiments, the materials list may be electronically transmitted by email or via the internet, for example, to a third-party or third-party software application, an enterprise resource planning (ERP) system, or any other entity that may use the information.” The disclosure “third-party or third-party software application, an enterprise resource planning (ERP) system” corresponds to “affected stakeholders include a contractor or a sub-contractor”).
Murphy, Bews, Alizadeh and Segev are analogous art because they are related to have generative artificial intelligence (AI) models in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Segev, before him or her, to modify the quantifying/analyzing initial building design plan of Murphy, to include analyzing change order request to determine an impact on one or more initial constraints of Segev. The suggestion or motivation for doing so would have been obvious by Segev because “the generative analysis may result in multiple options for equipment placement locations and / or technical specifications. For example, the generative analysis may result in multiple solutions that conform to the received functional requirements, or that have similar degrees of conformation to the received functional requirements. In some embodiments, the disclosed methods may include presenting a predetermined number or percentage of solutions that best conform to the functional requirement (e.g., the top 5 results, the top 10% of results, or various other thresholds).” (Segev disclosed in page 22 para [0190]).
Regarding claim 36, Murphy, Bews and Alizadeh teach the apparatus of claim 32, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the controller notifies one or more affected parties about the change order request and receives their approvals or conditions via the interactive user interface”.
wherein Segev teaches the controller notifies one or more affected parties about the change order request and receives their approvals or conditions via the interactive user interface. (Segev disclosed in page 22-23 para [0192-0193]: “In some embodiments, the system may constrain the modifications by the user such that they still conform to the functional requirement. For example, the system may allow the user to move a piece of equipment only within a specified region that conforms to the functional requirement. In some embodiments, the system may reanalyze the equipment placement location and specification to determine whether it still conforms to the functional requirement (or a degree of conformance to the functional requirement). The system may generate an alert to the user or otherwise display the updated conformance information based on the modifications. … In some embodiments, the modification may be based on a user input, which may override the functional requirements that are received by the system. In some embodiments, the modification may be automatic, for example, to adjust the functional requirement according to an applicable code or standard, a preferred value (e.g., a minimum value set by a user or organization), a value determined by a calculation (e.g., to adjust the functional requirement for a different application), or any other predefined value.”).
Murphy, Bews, Alizadeh and Segev are analogous art because they are related to have generative artificial intelligence (AI) models in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Segev, before him or her, to modify the quantifying/analyzing initial building design plan of Murphy, to include analyzing change order request to determine an impact on one or more initial constraints of Segev. The suggestion or motivation for doing so would have been obvious by Segev because “the generative analysis may result in multiple options for equipment placement locations and / or technical specifications. For example, the generative analysis may result in multiple solutions that conform to the received functional requirements, or that have similar degrees of conformation to the received functional requirements. In some embodiments, the disclosed methods may include presenting a predetermined number or percentage of solutions that best conform to the functional requirement (e.g., the top 5 results, the top 10% of results, or various other thresholds).” (Segev disclosed in page 22 para [0190]).
Regarding claim 39, Murphy, Bews and Alizadeh teach the apparatus of claim 32, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the controller generates sub-plans, such as electrical or plumbing plans, based on the set of updated constraints resulting from the change order request”.
wherein Segev teaches the controller generates sub-plans, such as electrical or plumbing plans, based on the set of updated constraints resulting from the change order request. (Segev disclosed in page 14 para [0148]: “The functional requirement may be specific to a particular room within a floor plan, or may apply to multiple rooms. … In some embodiments, different types of functional requirements (for example defining noise level and camera coverage requirements) can be applied to the same room or area.” In page 17 para [0162]: “A generative analysis may use a variety of input data including: room geometric and architectural features such as area, volume, ceiling height, number of boundary segments, boundary complexity, number and locations of doors … For example, the data may refer to the distance between a piece of equipment in a room and the nearest exit point, the distance between a piece of equipment to a corridor, or to an equipment room, head-end or electrical panel. A generative analysis may also consider equipment technical specifications and functional requirements, both within rooms and outside of them. This analysis may seek to avoid duplicating equipment, avoid code and compliance issues, fix clashing locations, and harmonize technical specifications or manufacturers across all rooms in a given level or a project.”
The disclosure “the data may refer to the distance between a piece of equipment in a room and the nearest exit point, the distance between a piece of equipment to a corridor, or to an equipment room, head-end or electrical panel; generative analysis may also consider equipment technical specifications and functional requirements, both within rooms and outside of them. This analysis may seek to avoid duplicating equipment, avoid code and compliance issues, fix clashing locations, and harmonize technical specifications or manufacturers across all rooms in a given level or a project” corresponds to claim limitation “generates sub-plans, such as electrical plans, based on the set of updated constraints resulting from the change order request”).
Murphy, Bews, Alizadeh and Segev are analogous art because they are related to have generative artificial intelligence (AI) models in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Segev, before him or her, to modify the quantifying/analyzing initial building design plan of Murphy, to include analyzing change order request to determine an impact on one or more initial constraints of Segev. The suggestion or motivation for doing so would have been obvious by Segev because “the generative analysis may result in multiple options for equipment placement locations and / or technical specifications. For example, the generative analysis may result in multiple solutions that conform to the received functional requirements, or that have similar degrees of conformation to the received functional requirements. In some embodiments, the disclosed methods may include presenting a predetermined number or percentage of solutions that best conform to the functional requirement (e.g., the top 5 results, the top 10% of results, or various other thresholds).” (Segev disclosed in page 22 para [0190]).
Claims 8,10,18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh and further in view of a research paper “Integrating ChatGPT, Bard, and Leading-Edge Generative Artificial Intelligence in Architectural Design and Engineering: Applications, Framework, and Challenges” by Nitin Liladhar Rane et al. (hereinafter Rane, paper published on 2023).
Regarding Claim 8, Murphy, Bews and Alizadeh teach the system of claim 1, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the interactive user interface allows for real-time collaboration among multiple users, enabling simultaneous review and discussion of the change orders”.
wherein Rane teaches the interactive user interface allows for real-time collaboration among multiple users, enabling simultaneous review and discussion of the change orders. (Rane disclosed in page 98-99 section 3.1.11 (last para): “ChatGPT plays a crucial role in developing conversational interfaces for architectural visualization. Architects and designers can interact with the model to explore design alternatives, receive instant feedback, and discuss ideas in a natural language format. This conversational approach enhances the iterative process, allowing architects to refine their designs based on real-time feedback from the model. Another valuable application of ChatGPT is in the generation of design documentation. The model assists in automatically generating annotations, explanations, and labels for architectural drawings and visualizations. … ChatGPT contributes to the creation of Virtual Reality (VR) or Augmented Reality (AR) experiences for architectural visualization. Users can navigate and interact with virtual architectural spaces, receiving contextual information and insights generated by the model through natural language input.” In page 104 section 4.1: “In the sphere of building systems, ChatGPT or GAI can finds significant application in architectural design and planning … Architects can leverage the model to generate design concepts, fine-tune ideas, and receive real-time feedback on design choices, fostering a more intuitive and collaborative design experience. The model’s proficiency in understanding and generating human-like text facilitates seamless communication, enabling architects to receive suggestions and alternatives that enhance the creative process.”).
Murphy, Bews, Alizadeh and Rane are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Rane, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include real-time collaboration among multiple users, enabling simultaneous review and discussion of change orders of Rane’s teaching. The suggestion/motivation for doing so would have been obvious by Rane because “The paper also investigates the role of generative AI in promoting Sustainability and Environmental Design, showcasing its potential to optimize energy efficiency, reduce environmental impact, and enhance overall sustainability. From reshaping design theories to optimizing construction processes, these AI tools stand as invaluable collaborators in the quest for innovative, sustainable, and culturally responsive built environments. A thoughtful and ethical approach to AI integration will be paramount in ensuring that the future of architecture and engineering is not just efficient but also deeply human.” (Rane disclosed in page 92 and 114).
Regarding Claim 10, Murphy, Bews and Alizadeh teach the system of claim 1, wherein Alizadeh teaches to generate approval of the change order request (Alizadeh disclosed in page 71 heading ‘Analysis of the panel’s review and measuring consensus’: “Level 2 aggregates the panel’s feedback for items with 80% to 70% agreement. There was 80% to 70% congruence among panel members for the validity of 14 items out of the original draft survey with 69 items. Therefore, these items were changed and modified based on the feedback received and included in round 2 for the panel’s review and degree of congruence; … The data and feedback from the first round formed a report to run the second round of Delphi. All comments were listed in the report, and the modified or changed items (according to the panel’s feedback in round 1) … The panel reviewed and judged the second draft of the Housing Environmental Quality Assessment Tool with 76 items. Analysis of the data from round 2 showed that the feedback and ratings were aggregated into three levels: Level 1 aggregates the panel’s feedback for items with 80% and above agreement. There was a majority agreement (80% and above) among panel members for the validity of 12 items out of 14 items that were changed or modified after round 1.” In page 72 heading ‘Determining the tool’s overall content validity and reliability’: “After the second round of Delphi, final refinements and modifications were made based on the panel’s feedback. All the items with validation of more than 70% were included in the final version of the tool, and the final validated tool with 74 items was developed. ... Of the 74 items reviewed and rated by the panel over a 2-round Delphi process, a total of 70 items were judged to be valid with a CVI of more than 0.78. ... No items attained less than 78% agreement on validity. As a result, the tool’s overall content validity was calculated as 95% …”).
However, Murphy, Bews and Alizadeh do not explicitly teach the limitation “the executable software code, when executed by the processor, causes the processor to generate a detailed report outlining a rationale for the change order including any conditions or modifications required”.
Rane teaches the executable software code, when executed by the processor, causes the processor to generate a detailed report outlining a rationale for the change order including any conditions or modifications required. (Rane disclosed in page 105 section 5 (2nd and 4th para): “In the domain of project planning and scheduling, ChatGPT assists in creating and optimizing construction schedules. Through natural language input, project managers can collaborate with ChatGPT to develop preliminary schedules, identify potential bottlenecks, and explore alternative scenarios. The model’s language understanding capabilities allow it to interpret complex scheduling constraints and dependencies, providing valuable insights to optimize project timelines and resource utilization. … ChatGPT enhances the accessibility and usability of BIM by acting as an interface for querying BIM databases using natural language. This streamlined access improves collaboration among project stakeholders and supports better-informed decision-making throughout the construction lifecycle. … ChatGPT assists in generating standardized reports, flagging potential issues based on predefined criteria, and proposing solutions or preventive measures. This real-time collaboration accelerates issue resolution and enhances overall project quality.”).
Murphy, Bews, Alizadeh and Rane are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Rane, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include real-time collaboration among multiple users, enabling simultaneous review and discussion of change orders of Rane’s teaching. The suggestion/motivation for doing so would have been obvious by Rane because “The paper also investigates the role of generative AI in promoting Sustainability and Environmental Design, showcasing its potential to optimize energy efficiency, reduce environmental impact, and enhance overall sustainability. From reshaping design theories to optimizing construction processes, these AI tools stand as invaluable collaborators in the quest for innovative, sustainable, and culturally responsive built environments. A thoughtful and ethical approach to AI integration will be paramount in ensuring that the future of architecture and engineering is not just efficient but also deeply human.” (Rane disclosed in page 92 and 114).
Regarding Claim 18, Murphy, Bews and Alizadeh teach the system of claim 1, however, Murphy, Bews and Alizadeh do not explicitly teach the limitation “the Al engine is configured to assess an environmental impact of the change order request and suggest eco-friendly alternatives”.
wherein Rane teaches the Al engine is configured to assess an environmental impact of the change order request and suggest eco-friendly alternatives. (Rane disclosed in page 106 section 8.2-8.3: “The selection of construction materials significantly impacts a building’s environmental footprint. ChatGPT aids in choosing sustainable materials by considering factors such as embodied carbon, recyclability, and overall environmental impact. Additionally, the model conducts life cycle assessments, evaluating the environmental impact of materials from extraction and production to use and disposal. … ChatGPT can analyze geographical and environmental data to optimize the use of resources like water, timber, and land. For example, the model can offer insights into water conservation strategies, recommend sustainable forestry practices for obtaining wood, and propose designs that minimize a building’s ecological footprint on the surrounding landscape.” In section 8.6 disclosed: “ChatGPT contributes by generating recommendations for designs that promote indoor air quality, natural lighting, and overall occupant health. It suggests layouts that encourage physical activity, the integration of green spaces, and the use of non-toxic materials, embracing a holistic approach to sustainability that prioritizes both environmental and human factors.”).
Murphy, Bews, Alizadeh and Rane are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Rane, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include real-time collaboration among multiple users, enabling simultaneous review and discussion of change orders of Rane’s teaching. The suggestion/motivation for doing so would have been obvious by Rane because “The paper also investigates the role of generative AI in promoting Sustainability and Environmental Design, showcasing its potential to optimize energy efficiency, reduce environmental impact, and enhance overall sustainability. From reshaping design theories to optimizing construction processes, these AI tools stand as invaluable collaborators in the quest for innovative, sustainable, and culturally responsive built environments. A thoughtful and ethical approach to AI integration will be paramount in ensuring that the future of architecture and engineering is not just efficient but also deeply human.” (Rane disclosed in page 92 and 114).
Regarding Claim 19, Murphy, Bews and Alizadeh teach the system of claim 1, however, Murphy, Bews and Alizadeh do not explicitly teach the limitation “the controller is configured to generate a summary of a change order process, including key decisions, stakeholder feedback, and final outcomes, for archival and review purposes”.
wherein Rane teaches the controller is configured to generate a summary of a change order process, including key decisions, stakeholder feedback, and final outcomes, for archival and review purposes. (Rane disclosed in page 103 section 3.4.10.: “ChatGPT participates in design review processes, collaborating with peers to provide insights, explanations, and alternative perspectives. It contributes to the collective intelligence of the design team, fostering a more comprehensive review process.” In page 105 section 5 (3rd para): “ChatGPT or GAI can also plays a significant role in risk management, analyzing historical project data, current conditions, and external factors to identify potential risks … In decision support, ChatGPT acts as a knowledge repository and decision-making tool for construction managers, offering expert advice on various aspects of project execution based on relevant literature, best practices, and project data. This includes support in areas such as material selection, construction methodologies, and cost estimation, enabling more informed decision-making and improved project outcomes.” Further, in 107 section 7.2: “ChatGPT offers a streamlined approach by generating comprehensive project documentation based on inputs from project managers or engineers. This encompasses the creation of reports, summarization of project updates, and even the drafting of emails or communications to stakeholders, ensuring a thorough and easily retrievable record of project advancement.”).
Murphy, Bews, Alizadeh and Rane are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Rane, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include real-time collaboration among multiple users, enabling simultaneous review and discussion of change orders of Rane’s teaching. The suggestion/motivation for doing so would have been obvious by Rane because “The paper also investigates the role of generative AI in promoting Sustainability and Environmental Design, showcasing its potential to optimize energy efficiency, reduce environmental impact, and enhance overall sustainability. From reshaping design theories to optimizing construction processes, these AI tools stand as invaluable collaborators in the quest for innovative, sustainable, and culturally responsive built environments. A thoughtful and ethical approach to AI integration will be paramount in ensuring that the future of architecture and engineering is not just efficient but also deeply human.” (Rane disclosed in page 92 and 114).
Claims 13 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh, and further in view of a conference paper “An Integrated Bim-Power BI Approach for Data Extraction and Visualization” by Y. Kadcha et al. (hereinafter Kadcha, paper published in 2022).
Regarding Claim 13, Murphy, Bews and Alizadeh teach the system of claim 1, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the interactive user interface includes a dashboard displaying key metrics related to the change order request, such as cost impact, timeline adjustments, and resource allocation.”
wherein Kadcha teaches the interactive user interface includes a dashboard displaying key metrics related to the change order request, such as cost impact, timeline adjustments, and resource allocation. (Kadcha disclosed in page 69 section 3.1 (right col.): “Our solution tries to meet these requirements by designing an extraction method that is object-based and user-oriented. It proposes some relevant use cases for the AEC community: cost extract, clash coordination, plan extraction, and change detection.” In page 71 section 4.1 (left col.): “The result of the visualization of cost elements dashboard (Figure 7) that can serve quantity surveyors, construction cost consultants, or sales managers in their missions of controlling and supervising the financial situation of the construction project. As each element is well defined by its cost, it’s possible to control the expenses and ensure that the initial budget is not exceeded.” Further in page 72 section 4.4 (left col.): “In addition to the color-coded visual displayed on Revit, our solution also provides a simple dashboard (Figure 10) to choose which items to display based on the type of change that has occurred, while providing the user with all the related information. This comparison tool gives designers a better understanding of the changes made to the project, especially when multiple professionals are working on the model, and allows team leaders to accurately and easily visualize and identify the work progress.” The disclosure above teaches the limitation “the interactive user interface includes a dashboard displaying key metrics related to the change order request, such as cost impact”).
Murphy, Bews, Alizadeh and Kadcha are analogous art because they are related to have computer-assisted evaluation in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Kadcha, before him or her, to modify the change order related to building spatial/geometric configuration of Murphy, to include vision-based progress monitoring methods such as a dashboard displaying key metrics related to the change order, cost impact of Kadcha. The suggestion/motivation for doing so would have been obvious by Kadcha because “To address this main issue about the extraction of information from BIM models, our study aims to propose a solution that allows extracting information related to four different analyses whose importance is crucial in the AEC (Architecture, Engineering, and Construction) domain, which are clash detection, cost analysis, plans extraction, and change detection, and to visualize the results in user-friendly dashboards. In our solution used the Dynamo Revit’s plugin to perform targeted analysis on the BIM model and extract relevant data, as well as the power BI, BI software for their visualization on interactive and understandable dashboards allowing a correct sharing of information between the various stakeholders of the projects and a good decision-making process. (Kadcha disclosed in page 67-68 section 1 and page 72 section 4.4 (last para, left col.)).
Regarding claim 40, Murphy, Bews and Alizadeh teach the apparatus of claim 32, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the interactive user interface includes options for defining fixed or flexible construction constraints, such as cost ceilings or labor hours”.
wherein Kadcha teaches the interactive user interface includes options for defining fixed or flexible construction constraints, such as cost ceilings or labor hours. (Kadcha disclosed in page 69 section 3.2.1 (right col.): “This application is based on detecting all the elements of the BIM model and targeting the cost parameter for each of them then providing the user with the cost value in an easy-to-read format. … The cost extraction script processes each category separately; for each item, the values of the significant parameters are extracted. A significant parameter here means any parameter that can be useful for the cost evaluation.” Further, in page 71 section 4.1 (left col.): “The result of the visualization of cost elements dashboard (Figure 7) that can serve quantity surveyors, construction cost consultants, or sales managers in their missions of controlling and supervising the financial situation of the construction project. As each element is well defined by its cost, it’s possible to control the expenses and ensure that the initial budget is not exceeded.”
The disclosure above teaches the limitation “the interactive user interface includes options for defining fixed or flexible construction constraints, such as cost ceilings”).
Murphy, Bews, Alizadeh and Kadcha are analogous art because they are related to have computer-assisted evaluation in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Kadcha, before him or her, to modify the change order related to building spatial/geometric configuration of Murphy, to include vision-based progress monitoring methods such as a dashboard displaying key metrics related to the change order, cost impact of Kadcha. The suggestion/motivation for doing so would have been obvious by Kadcha because “To address this main issue about the extraction of information from BIM models, our study aims to propose a solution that allows extracting information related to four different analyses whose importance is crucial in the AEC (Architecture, Engineering, and Construction) domain, which are clash detection, cost analysis, plans extraction, and change detection, and to visualize the results in user-friendly dashboards. In our solution used the Dynamo Revit’s plugin to perform targeted analysis on the BIM model and extract relevant data, as well as the power BI, BI software for their visualization on interactive and understandable dashboards allowing a correct sharing of information between the various stakeholders of the projects and a good decision-making process. (Kadcha disclosed in page 67-68 section 1 and page 72 section 4.4 (last para, left col.)).
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh and further in view of an Article “Performance Analysis and Assessment of BIM-Based Construction Support with Priority Queuing Policy” by Nam-Hyuk Ham et al. (hereinafter Ham, article published on 2023).
Regarding Claim 17, Murphy, Bews and Alizadeh teach the system of claim 1, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the system is configured to prioritize the change orders based on urgency, impact, and stakeholder input.”
wherein Ham teaches the system is configured to prioritize the change orders based on urgency, impact, and stakeholder input. (Ham disclosed in page 12 (1st and 2nd para): “project participants raised BIM RFIs to the BIM staff, the frequency of BIM RFIs was high in the order of framing, finishing, and installation of facilities (mechanical, electrical, and plumbing) (Table 1) … The data in Table 2 reveals that many different sources of information are used to respond to BIM RFIs … In this study, considering these properties of BIM RFIs, we aim to apply a priority policy according to the purpose of information use. … In addition, as a result of the analysis based on the classified data of BIM RFIs, it can be determined that the continuous management of the shape of the BIM model is as important in the construction phase as in the design phase. Therefore, in this study, the priority of design review and changes was set to Level 1, and the priority of constructability review, process review, and quantity take-off and review, which show high frequency among the purposes of information utilization in the construction phase, was set to Level 2. Finally, the priority of interference review, shop drawing review, safety management, and others (e.g., visualization) was set to Level 3 (Table 4). This study aims to analyze how the performance of the BIM staff responding to BIM RFIs is improved when the above-described priority policy is applied.”
The disclosure above “In this study, considering these properties of BIM RFIs, we aim to apply a priority policy according to the purpose of information use; the priority of design review and changes was set to Level 1, and the priority of constructability review, process review, and quantity take-off and review, was set to Level 2; the priority of interference review, shop drawing review, safety management, and others (e.g., visualization) was set to Level 3” correspond to claim limitation “prioritize change orders based on urgency, impact,”. Further, the disclosure “how the performance of the BIM staff responding to BIM RFIs is improved when the above-described priority policy is applied” corresponds to claim limitation “prioritize change orders based on stakeholder input”).
Murphy, Bews, Alizadeh and Ham are analogous art because they are related to have computer-assisted evaluation in Building information modeling and Construction. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Ham, before him or her, to modify the change order related to building spatial/geometric configuration of Murphy, to include prioritize design change orders based on urgency/need, impact and user’s input/feedback of Ham. The suggestion/motivation for doing so would have been obvious by Ham because “interactions between the BIM staff providing BIM services and the project participants requesting BIM services from the viewpoint of micro-level management. In this study, with the aim of improving the performance of BIM-based construction support, we performed an analysis of the properties of the BIM request for information (RFI) in the construction phase, proposing a method for performance analysis and assessment which considers the competencies of the BIM staff that handle and process such requests. This study verified that, through the application of a priority policy according to the purpose of the information use in the construction phase, the performance of the BIM staff can be improved, and the waiting time of project participants to receive responses to the BIM RFIs can be reduced.” (Ham disclosed in page 1 under ‘Abstract’).
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh and further in view of an NPL paper “Minimising the impact of resource consumption in the design and construction of buildings” by Stephen Pullen et al. (hereinafter Pullen, paper published on 2012).
Regarding Claim 27, Murphy, Bews and Alizadeh teach the method of claim 21, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the controller calculates surplus materials resulting from the change order and provides automated suggestions for utilizing the surplus materials elsewhere in the building.”
wherein Pullen teaches the controller calculates surplus materials resulting from the change order and provides automated suggestions for utilizing the surplus materials elsewhere in the building. (Pullen disclosed in page 4 section 3.4 and 3.5: “The commercial head office of the Built Environs company at 100 Hutt Street, Adelaide was refurbished in 2007/2008. This received a Green Building Council of Australia five star rating and showcased the company’s sustainability credentials. The amount of re-used and recycled materials was exemplary at 95.1% and this far exceeded the Green Star rating requirements The re-used materials included recycled timber used for noggins in new partition walls, surplus concrete reinforcing mesh which was used in the reception area after a powder coating treatment, black ceasar stone (which had originally been reclaimed from a prominent South Australian public building), recycled mechanical spiral ductwork and re-used wire mesh from surplus stock on previous projects used in the stair balustrades. … For the demolition of the MEC building, the re-use and recycling of materials amounted to some 56 percent (by weight). For the Montreal building, a different measurement system was used during the demolition but 9000 cubic metres of materials were diverted from landfill. Both case studies provided exemplary recycling rates which are greater than that normally realised.”
The prior arts Murphy and Bews teaches the controllers or processors to perform any automated task or claimed invention. Therefore, the limitations of claim 27 are combinedly taught by the prior arts Murphy, Bews, Alizadeh and Pullen).
Murphy, Bews, Alizadeh and Pullen are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Pullen, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include calculating and utilizing surplus materials/resources change order in building design in Pullen’s teaching. The suggestion/motivation for doing so would have been obvious by Pullen because “a framework to guide the research project in its aim of developing a clear pathway to minimize resource usage and waste reduction. It is likely that the comprehensive adoption of procedures and strategies to minimize the impact of resource construction will necessitate a change in the attitudes and culture of all stakeholders involved in the construction of buildings. The aim of the project is to develop a clear route to take building procurement teams (i.e. the client, architects, designers, planners, engineers, building contractors and facility managers) from current levels of knowledge and practice in the minimization of resource usage and waste reduction towards international best practice and total waste elimination. (Pullen disclosed in page 1 under ‘Abstract’ and ‘Introduction’).
Claims 30, 31 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh and further in view of an NPL “Integrating Building Information modelling (BIM) and Artificial Intelligence (AI) for smart construction schedule, cost, quality, and safety management: challenges and opportunities” by Nitin Rane (hereinafter Rane1, NPL published on 2023).
Regarding Claim 30, Murphy, Bews and Alizadeh teach the method of claim 21, however Murphy, Bews and Alizadeh do not explicitly teach the limitation “the set of updated constraints includes a breakdown of labor requirements for implementing the change order.”
wherein Rane1 teaches the set of updated constraints includes a breakdown of labor requirements for implementing the change order. (Rane1 disclosed in page 7-8 heading ‘Labor Cost Management’: “Effectively managing labor costs in construction projects necessitates intricate analysis of factors like skill levels, wages, and project timelines. AI-powered analytics scrutinize labor data from past projects, considering variables such as skillsets, productivity, and overtime patterns. Utilizing machine learning algorithms, AI predicts labor requirements based on project specifications, ensuring the deployment of the right number of skilled workers at the right time. BIM complements this process by creating detailed 3D models, allowing simulations of construction processes. AI algorithms analyze these simulations, optimizing labor workflows, reducing idle time, and enhancing productivity. AI-driven tools monitor worker performance, identifying areas for improvement and training, ultimately augmenting labor efficiency and reducing costs.”).
Murphy, Bews, Alizadeh and Rane1 are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Rane1, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include Rane1’s teaching to have updated constraints includes recommendations for reallocation of budget savings and breakdown of labor requirements for implementing the change order. The suggestion/motivation for doing so would have been obvious by Rane1 because “The amalgamation of Building Information Modelling (BIM) and Artificial Intelligence (AI) within smart construction practices signifies a crucial advancement in the construction field. Our study delves into the challenges and potential directions of this integration, underscoring its accomplishments and the challenges that demand attention for successful implementation. Undeniably, the integration of BIM and AI has brought about a transformative shift in the planning, execution, and supervision of construction projects. Leveraging AI algorithms and machine learning, stakeholders now benefit from improved predictive analytics, intelligent decision-making, and streamlined workflows. By combining BIM's digital representations with AI's data processing abilities, project managers can foresee scheduling conflicts, optimize resource allocation, and enhance overall project efficiency.” (Rane1 disclosed in page 15 under ‘Conclusion’).
Regarding Claim 31, Murphy, Bews and Alizadeh teach the method of claim 21, however Murphy and Bews do not explicitly teach the limitation “approving the change order”.
Alizadeh teaches approving the change order. (Alizadeh disclosed in page 71 heading ‘Analysis of the panel’s review and measuring consensus’: “Level 2 aggregates the panel’s feedback for items with 80% to 70% agreement. There was 80% to 70% congruence among panel members for the validity of 14 items out of the original draft survey with 69 items. Therefore, these items were changed and modified based on the feedback received and included in round 2 for the panel’s review and degree of congruence; … The data and feedback from the first round formed a report to run the second round of Delphi. All comments were listed in the report, and the modified or changed items (according to the panel’s feedback in round 1) … The panel reviewed and judged the second draft of the Housing Environmental Quality Assessment Tool with 76 items. Analysis of the data from round 2 showed that the feedback and ratings were aggregated into three levels: Level 1 aggregates the panel’s feedback for items with 80% and above agreement. There was a majority agreement (80% and above) among panel members for the validity of 12 items out of 14 items that were changed or modified after round 1.” In page 72 heading ‘Determining the tool’s overall content validity and reliability’: “After the second round of Delphi, final refinements and modifications were made based on the panel’s feedback. All the items with validation of more than 70% were included in the final version of the tool, and the final validated tool with 74 items was developed. ... Of the 74 items reviewed and rated by the panel over a 2-round Delphi process, a total of 70 items were judged to be valid with a CVI of more than 0.78. ... No items attained less than 78% agreement on validity. As a result, the tool’s overall content validity was calculated as 95% …”).
However, Murphy, Bews and Alizadeh do not explicitly teach the limitation ““the interactive user interface allows real-time collaboration between multiple users for discussing”.
wherein Rane1 teaches the interactive user interface allows real-time collaboration between multiple users for discussing. (Rane1 disclosed in page 10 (in 2nd to 3rd para): “BIM and AI synergize to enable real-time monitoring and quality control through IoT (Internet of Things) devices and sensors. These devices, embedded within the construction site and integrated with BIM models, collect a plethora of data, ranging from temperature and humidity to structural stress and equipment performance. AI algorithms process this real-time data, identifying deviations from the expected norms and patterns that could indicate quality issues. … Real-time monitoring of structural components can identify deformations or stress concentrations, highlighting areas that require immediate attention. By enabling proactive quality control measures, real-time monitoring ensures that construction teams can address issues promptly, preventing them from escalating into major quality concerns. … Natural language processing (NLP) algorithms, a subset of AI, enable BIM systems to comprehend and respond to human language. This capability enhances communication between stakeholders, allowing project managers, architects, engineers, and contractors to interact with BIM systems conversationally. Queries related to project specifications, quality standards, or design intent can be answered in real-time, fostering a collaborative environment where decisions are made based on accurate and up to-date information.”).
Murphy, Bews, Alizadeh and Rane1 are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Rane1, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include Rane1’s teaching to have updated constraints includes recommendations for reallocation of budget savings and breakdown of labor requirements for implementing the change order. The suggestion/motivation for doing so would have been obvious by Rane1 because “The amalgamation of Building Information Modelling (BIM) and Artificial Intelligence (AI) within smart construction practices signifies a crucial advancement in the construction field. Our study delves into the challenges and potential directions of this integration, underscoring its accomplishments and the challenges that demand attention for successful implementation. Undeniably, the integration of BIM and AI has brought about a transformative shift in the planning, execution, and supervision of construction projects. Leveraging AI algorithms and machine learning, stakeholders now benefit from improved predictive analytics, intelligent decision-making, and streamlined workflows. By combining BIM's digital representations with AI's data processing abilities, project managers can foresee scheduling conflicts, optimize resource allocation, and enhance overall project efficiency.” (Rane1 disclosed in page 15 under ‘Conclusion’).
Regarding Claim 38, Murphy, Bews and Alizadeh teach the apparatus of claim 32, however, Murphy, Bews and Alizadeh teach do not explicitly teach the limitation “the set of updated constraints includes recommendations for reallocation of budget savings to other design elements or additional features”.
wherein Rane1 teaches the set of updated constraints includes recommendations for reallocation of budget savings to other design elements or additional features. (Rane1 disclosed in page 6 heading ‘Clash Detection and Conflict Resolution’: “BIM models play a crucial role in clash detection, identifying conflicts during the design phase. When integrated with AI, these clashes can be automatically analyzed and resolved. AI algorithms assess the impact of clashes on the construction schedule and propose alternative solutions.” Further, in page 7 Table 2 in 4th row, under column ‘Type of Cost’, where Design and Planning Costs stated AI automates design evaluations and generates cost-efficient alternatives, when Role of Artificial Intelligence (AI) is present. Also, AI analyzes BIM designs, automating evaluations for streamlined and cost effective planning, when Integration of BIM and AI is present).
Murphy, Bews, Alizadeh and Rane1 are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Rane1, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include Rane1’s teaching to have updated constraints includes recommendations for reallocation of budget savings and breakdown of labor requirements for implementing the change order. The suggestion/motivation for doing so would have been obvious by Rane1 because “The amalgamation of Building Information Modelling (BIM) and Artificial Intelligence (AI) within smart construction practices signifies a crucial advancement in the construction field. Our study delves into the challenges and potential directions of this integration, underscoring its accomplishments and the challenges that demand attention for successful implementation. Undeniably, the integration of BIM and AI has brought about a transformative shift in the planning, execution, and supervision of construction projects. Leveraging AI algorithms and machine learning, stakeholders now benefit from improved predictive analytics, intelligent decision-making, and streamlined workflows. By combining BIM's digital representations with AI's data processing abilities, project managers can foresee scheduling conflicts, optimize resource allocation, and enhance overall project efficiency.” (Rane1 disclosed in page 15 under ‘Conclusion’).
Claim 37 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy, Bews and Alizadeh and further in view of a blog “On-site Optimization: Real-time Material Tracking for Construction Sites” by Jonny Parker (hereinafter Parker, published on 2023).
Regarding Claim 37, Murphy, Bews and Alizadeh teach the apparatus of claim 32, however, Murphy, Bews and Alizadeh teach do not explicitly teach the limitation ““the controller integrates real-time data from external databases to verify material availability for the change order request.”
wherein Parker teaches the controller integrates real-time data from external databases to verify material availability for the change order request. (Parker disclosed in page 2 heading ‘What is construction inventory management software?’ (3rd para): “Centralizing material tracking to one construction inventory management software system allows teams to track project materials, collect data, and schedule reorders from any device with access to the system.” In page 4 heading ‘Accurate item counts’: “Software-based material tracking employs barcode systems to scan tools and materials in and out of the database. Therefore, every site with access to the system has access to the data on the locations of tools, the sites that need them, …”. Further, in same page 4 heading ‘Automated reordering’: “Real-time item counts enable you and your admins to accurately reorder tools and materials without wasting money on overorders or wasting time waiting for deliveries to arrive. The software can be set to auto-order when certain materials get below a designated level to ensure your workflow never halts because of a delivery schedule.”).
Murphy, Bews, Alizadeh and Parker are analogous art because they are related to have generative analysis in Architecture, Engineering, and Construction projects. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Murphy, Bews, Alizadeh and Parker, before him or her, to modify analyzing change order request to determine an impact on one or more initial constraints of Murphy, to include Parker’s teaching to integrate real-time (on-site) data from available databases in order to verify material availability for the change order request. The suggestion/motivation for doing so would have been obvious by Parker because “Construction jobs rely on end-to-end visibility of materials to avoid delays and meet expectations. Workers need to get the needed materials for each job and team leaders need assurances that the materials are to-spec with the job requirements. The key to on-site material tracking in 2023 and beyond is versatile construction inventory management software. These solutions enable real-time material visibility to help you meet expectations, as well as provide valuable data that allows companies in the competitive construction industry to compare the performance of different suppliers, teams, and strategies.” (Parker disclosed in page 1 in 1st and 2nd para).
Conclusion
8. The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. An NPL “Generative Design in Building Information Modelling (BIM): Approaches and Requirements” Wei Ma et al. conducted a critical review of current approaches for developing GD (generative design) in BIM (building information modelling), and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. The significance is to support designers in the building industry on the proper methods selection for developing GD-BIM. It is clear from the review and analysis that programming skills are necessary for designers to develop GD-BIM, and different types of programming languages have different suitability based on development objectives and GD components. Accordingly, three novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development, as well as a set of reference guides, are proposed regarding development method selection, skill learning, and improvement. The review in this paper aims to guide designers in the building industry to select proper methods or formulate skill-improving paths to develop GD-BIM and provides an inspired map for researchers to explore new knowledge.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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