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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 30 December 2025 has been entered.
This Continued Examination Office Action is in reply to the Request for Continued Examination filed on 30 December 2025.
Claims 1 has been amended. Claims 9 and 19 are listed as amended but there are no apparent indications of any limitations added or deleted within these claims. Claims 21, 22 are new and have been added.
Claims 1, 5-22 are currently pending and have been examined.
Response to Amendment
In the previous office action, Claims 1-20 were rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Applicants have not amended now Claims 1, 5-22 to provide statutory support and the rejection is maintained.
Claims 1, 5-18 were rejected under 35 USC 112(b) or 35 USC 112(pre-AIA ), second paragraph as being indefinite for lack of antecedence. Independent Claim 1 recites the limitation "storing said multiplied price in said database in said non transitory computer readable media" (emphasis added). There is insufficient antecedent basis for this limitation in the claim. The Examiner is not sure how to interpret this emphasized phrase limitation but will interpret it as reciting "storing a multiplied price in said database in said non transitory computer readable media". Applicants has not amended now Claims 1, 5-22 to correct for proper antecedence and this rejection is maintained.
Response to Arguments
Applicants’ arguments filed 30 December 2025 have been fully considered but they are not persuasive.
In the remarks regarding the 35 USC § 101 rejection for Claims 1, 5-22, Applicants argue that: the claims are not directed to an abstract idea, and even if they were, they would amount to significantly more than the abstract idea. Examiner respectfully disagrees. Commensurate with the 2019 revised patent subject matter eligibility guidance (2019 PEG), the October 2019 Update: Subject Matter Eligibility (“October 2019 Update”) and updated with the addition of new Examples 47-49 published July 2024, the claims are continued analyzed based on these new guidelines and is detailed below in the maintained rejection under 35 USC 101.
In the remarks regarding the 35 USC § 102(a)(1) rejection for Claims 1, 5-22, Applicants amendments necessitated a new ground of rejection and the arguments are moot. It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. The Examiner has a duty and responsibility to the public and to Applicant to interpret the claims as broadly as reasonably possible during prosecution. In re Prater, 415 F.2d 1 393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 5-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 1, 5-22 are each focused to a statutory category of invention, namely “process; system” sets.
Step 2A: Prong One: Claims 1, 5-22 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention is directed to an abstract idea without significantly more. The claims recite steps for, generally, “estimating a cabinet installation”. The claims encompass processing information for representative Claim 1 by:
“creating a cabinet brand and storing said brand in a database in a non-transitory computer readable media;
determining via a sensor a set of dimensions of a room;
communicating with said sensor to store said dimensions of said room in a memory;
creating a basic set of floor plans based upon said dimensions stored in said memory and storing said plans in said database in said non transitory computer readable media;
providing a set of stock cabinets for filling each floor plan;
providing costs for raw materials for creating the cabinets and storing said costs in said database in said non transitory computer readable media;
providing a brand cost multiplier for each brand of cabinet and storing said cost multiplier in said database in said non transitory computer readable media;
receiving a selection of a cabinet brand;
receiving a selection of a layout;
rendering a furnished layout with cabinets on a screen;
receiving a selection of at least one brand;
applying said brand cost multiplier which is stored in said non transitory computer readable media in said database via a microprocessor to the raw material costs of the furnished layout;
storing said multiplied price in said database in said non transitory computer readable media;
rendering on said screen at least one price for the furnished layout based upon the brand selected”
The claims as a whole recite certain groupings under the categories:
(a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations. The process involves calculating a new price based on a multiplier (original cost X multiplier = new price), which constitutes a mathematical formula/calculation.
(b) Certain methods of organizing human activity – commercial/business interaction; business relations; managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) The claim focuses on gathering data (dimensions), storing data (brand/costs), performing calculations (brand cost multiplier), and rendering a price. Applying a "brand cost multiplier" to raw material costs for a "layout selection" is fundamentally a method of commercial marketing and sales, specifically, "providing costs for raw materials" "providing a brand cost multiplier" and "receiving a selection of a cabinet brand/layout"
which is a method of organizing human activity.
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Storing room dimensions, selecting layouts, and calculating costs via a "microprocessor" are methods traditionally done by hand or with generic computer tools (like spreadsheets).
See MPEP § 2106.04(a) II C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components.
Prong Two: Claims 1, 5-22: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Therefore, the claims contain computer components (“sensor; database; memory; non transitory computer readable media; microprocessor”, etc.) (e.g., see Applicants’ published Specification ¶'s 4-11, 31-35) that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) (“The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point”). See also Genetic Technologies Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1547 (Fed. Cir. 2016) (steps of DNA amplification and analysis are not “sufficient” to render claim 1 patent eligible merely because they are physical steps). Conversely, the presence of a non-physical or intangible additional element does not doom the claims, because tangibility is not necessary for eligibility under the Alice/Mayo test. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 118 USPQ2d 1684 (Fed. Cir. 2016) (“that the improvement is not defined by reference to ‘physical’ components does not doom the claims”). See also McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102 (Fed. Cir. 2016), (holding that a process producing an intangible result (a sequence of synchronized, animated characters) was eligible because it improved an existing technological process). Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. See MPEP § 2106.05(f) (h).
Step 2B: As explained in MPEP § 2106.05, Claims 1, 5-22 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “sensor; database; memory; non transitory computer readable media; microprocessor”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ published Specification ¶'s 4-11, 31-35) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “computer network 10” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int' l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 1, 5-22 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 5-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent Claim 1 recites the limitation "storing said multiplied price in said database in said non transitory computer readable media" (emphasis added). There is insufficient antecedent basis for this limitation in the claim. The Examiner is not sure how to interpret this emphasized phrase limitation but will interpret it as reciting "storing a multiplied price in said database in said non transitory computer readable media". Clarification is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 5-22 are rejected under 35 U.S.C. 103 as being unpatentable over Bruce et al. (Bruce) (US 11,494,794) in view of Miller (US 2021/0183128).
invention.
With regard to Claims 1, 19, Bruce teaches a process/system for estimating a cabinet installation (A facility for estimating the cost of a remodeling project is described. The facility accesses a project cost model that predicts project costs determined from a photograph based upon project characteristics. The facility applies the access project cost model to characteristics of a distinguished project to obtain an estimated cost. The facility causes the obtained estimated cost to be displayed) comprising the steps of: at least one database server having at least one microprocessor, said at least one database server for storing a database; at least one application server having at least one microprocessor; at least one sensor having at least one microprocessor (FIG. 1 is a high-level block diagram showing a typical environment in which a software, hardware, and/or firmware facility implementing the functionality described herein; environment 100; The computer system 150 also includes one or more of the following: a network connection device 174 for connecting to a network (for example, the Internet 140) to exchange programs and/or data via its networking hardware, such as switches, routers, repeaters, electrical cables and optical fibers, light emitters and receivers, radio transmitters and receivers, and the like, an information input device 175, and an information output device 176. In some embodiments, the facility operates on the server computer system to perform some or all of the following activities: receive information about observations used to train the model; training the model using observations; receiving information about projects whose cost is to be estimated; and applying the model to estimate the cost of these projects) (see at least col. 3, line 11-col. 4, line 3, Abstract), said sensor configured to:
creating a cabinet brand (appliance presence and type; categories; the facility receives information about sample completed remodeling projects; this information includes photographs of the remodeled room after remodeling, as well as information about the actual cost of the project and an indication of the date on which and geographic location in which the project was performed. The facility subjects the photos from each project to a visual analysis process that discerns from the visual information in the photos characteristics of the project that have a relationship to the cost of the project, such as attribute values (e.g., project type =kitchen, number of cabinet doors=12), tags (e.g., #island), and/or an overall quality score for the project (e.g., quality=8).) and storing said brand in a database in a non transitory computer readable media (see at least col. 2, lines 7-61; col. 3, line 11-col. 4, line 57; col. 7, lines 35-67);
creating a basic set of floor plans (FIG. 2 is a data flow diagram showing how the facility estimates the costs of remodeling projects. The facility trains a model 240 on the basis of a number of observations 210, 220, and 230. This training is sometimes referred to as “fitting” the model. Each observation, such as observation 210, is comprised of a geographic location 212, a cost 213 manually estimated for performing the project at the location, a date 214 for which the cost of the project is estimated, and characteristics 211 of the project, determined by performing a visual analysis on one or more photos 201 depicting the end result of the project. As discussed in detail elsewhere herein, in some embodiments, an observation also includes information about how the cost 213 was determined, such as information identifying a contractor who either actually performed the project for the specified cost, or who estimated the specified cost for the project on the basis of the photos and/or characteristics; FIG. 4 is an image diagram showing a sample photo of a project to be used as an observation. This photo 400 shows the finished state of a remodel project for a kitchen. This photo is a basis for determining characteristics of the project including room dimensions, quality and/or expensiveness of the materials and labor, appliance presence and type, cabinet and finish count and type, etc.) based upon said dimensions stored in said memory (FIG. 4 is an image diagram showing a sample photo of a project to be used as an observation. This photo 400 shows the finished state of a remodel project for a kitchen. This photo is a basis for determining characteristics of the project including room dimensions, quality and/or expensiveness of the materials and labor, appliance presence and type, cabinet and finish count and type, etc.) and storing said plans in said database in said non transitory computer readable media (see at least col. 4, lines 11-57);
providing a set of stock cabinets for filling each floor plan (FIG. 6 is a display diagram showing a sample visual user interface presented by the facility in order to obtain a location and a set of costs for an observation project, such as from a contractor. The display 600 includes a photograph 610 depicting the finished state of the project whose cost is being estimated. It further identifies a contractor 601 who is providing the estimate. It includes a text entry field 602 for entering information about the location for which the user is estimating the project, such as zip code or other information identifying a geographic location, and a text entry field 603 for entering timing information for which the user is estimating the project, such as a date. The display further includes an area 620 showing characteristics that have been attributed to the project, such as on the basis of the visual information in the photograph. The contents of this area can be scrolled using scrolling controls 641-644. The display further includes an estimate area 660. The estimate area 660 includes a number of categories of the project for which the user is to estimate both a labor cost and a materials cost. For example, the user would enter a labor cost for the counters category into text entry field 672, and a materials cost for the counters category into text entry field 682. In some embodiments, for at least certain categories, the user is prompted to add a per-unit cost for the category rather than an overall cost for the category. For example, where, as here, the project involves installing nine upper cabinets and nine lower cabinets, the user interface may indicate for a cabinets category that labor and materials costs should be estimated for each of the 18 cabinets, rather than across all 18 cabinets) (see at least col. 7, lines 35-67);
providing costs for raw materials for creating the cabinets (FIG. 6 is a display diagram showing a sample visual user interface presented by the facility in order to obtain a location and a set of costs for an observation project, such as from a contractor. The display 600 includes a photograph 610 depicting the finished state of the project whose cost is being estimated. It further identifies a contractor 601 who is providing the estimate. It includes a text entry field 602 for entering information about the location for which the user is estimating the project, such as zip code or other information identifying a geographic location, and a text entry field 603 for entering timing information for which the user is estimating the project, such as a date. The display further includes an area 620 showing characteristics that have been attributed to the project, such as on the basis of the visual information in the photograph. The contents of this area can be scrolled using scrolling controls 641-644. The display further includes an estimate area 660. The estimate area 660 includes a number of categories of the project for which the user is to estimate both a labor cost and a materials cost. For example, the user would enter a labor cost for the counters category into text entry field 672, and a materials cost for the counters category into text entry field 682. In some embodiments, for at least certain categories, the user is prompted to add a per-unit cost for the category rather than an overall cost for the category. For example, where, as here, the project involves installing nine upper cabinets and nine lower cabinets, the user interface may indicate for a cabinets category that labor and materials costs should be estimated for each of the 18 cabinets, rather than across all 18 cabinets) and storing said costs in said database in said non transitory computer readable media (see at least col. 7, lines 35-67);
providing a brand cost multiplier for each brand of cabinet (the display further includes an estimate area 660. The estimate area 660 includes a number of categories of the project for which the user is to estimate both a labor cost and a materials cost. For example, the user would enter a labor cost for the counters category into text entry field 672, and a materials cost for the counters category into text entry field 682. In some embodiments, for at least certain categories, the user is prompted to add a per-unit cost for the category rather than an overall cost for the category. For example, where, as here, the project involves installing nine upper cabinets and nine lower cabinets, the user interface may indicate for a cabinets category that labor and materials costs should be estimated for each of the 18 cabinets, rather than across all 18 cabinets) and storing said cost multiplier in said database in said non transitory computer readable media (see at least col. 7, lines 35-67; col. 8, lines 7-63);
receiving a selection of a cabinet brand (The display 500 includes a copy 510 of the photograph received for the project shown in FIG. 4. It further includes a region 520 into which a human user inputs characteristics of the project portrayed in the photo. For example, it includes text entry fields 521-525 into which the user can enter values for five different project attributes. Region 400 further includes radio buttons 526-530, one of which the user can select in order to specify one of five shown enumerated values for a Cabinet Door project attribute. The user can operate scrolling controls 541-544 to expose and complete the portion of region 520 that is hidden in the view shown. After doing so, the user activates a submit button 550 to submit the project characteristics entered into this area on behalf of the project whose photo is shown) (see at least col. 4, line 58-col. 5, line 8);
receiving a selection of a layout (FIG. 2 is a data flow diagram showing how the facility estimates the costs of remodeling projects. The facility trains a model 240 on the basis of a number of observations 210, 220, and 230. This training is sometimes referred to as “fitting” the model. Each observation, such as observation 210, is comprised of a geographic location 212, a cost 213 manually estimated for performing the project at the location, a date 214 for which the cost of the project is estimated, and characteristics 211 of the project, determined by performing a visual analysis on one or more photos 201 depicting the end result of the project. As discussed in detail elsewhere herein, in some embodiments, an observation also includes information about how the cost 213 was determined, such as information identifying a contractor who either actually performed the project for the specified cost, or who estimated the specified cost for the project on the basis of the photos and/or characteristics) (see at least col. 4, lines 11-57);
rendering a furnished layout with cabinets on a screen (FIG. 6 is a display diagram showing a sample visual user interface presented by the facility in order to obtain a location and a set of costs for an observation project, such as from a contractor. The display 600 includes a photograph 610 depicting the finished state of the project whose cost is being estimated. It further identifies a contractor 601 who is providing the estimate. It includes a text entry field 602 for entering information about the location for which the user is estimating the project, such as zip code or other information identifying a geographic location, and a text entry field 603 for entering timing information for which the user is estimating the project, such as a date. The display further includes an area 620 showing characteristics that have been attributed to the project, such as on the basis of the visual information in the photograph. The contents of this area can be scrolled using scrolling controls 641-644. The display further includes an estimate area 660. The estimate area 660 includes a number of categories of the project for which the user is to estimate both a labor cost and a materials cost. For example, the user would enter a labor cost for the counters category into text entry field 672, and a materials cost for the counters category into text entry field 682. In some embodiments, for at least certain categories, the user is prompted to add a per-unit cost for the category rather than an overall cost for the category. For example, where, as here, the project involves installing nine upper cabinets and nine lower cabinets, the user interface may indicate for a cabinets category that labor and materials costs should be estimated for each of the 18 cabinets, rather than across all 18cabinets. In some cases, this enables the model to generalize costs in such categories from observations having certain counts of the relevant feature to projects to be estimated having different counts. The user may use scrolling controls 691-694 to scroll the estimate area and display all of the text entry fields in corresponding categories, and enter an estimated cost into each text entry field. After doing so, the user operates a submit button 699 in order to associate the entered geographic location and cost information with the observation project) (see at least col. 7, line 35-col. 8, line 6);
receiving a selection of at least one brand (The user may use scrolling controls 691-694 to scroll the estimate area and display all of the text entry fields in corresponding categories, and enter an estimated cost into each text entry field. After doing so, the user operates a submit button 699 in order to associate the entered geographic location and cost information with the observation project) (see at least col. 7, line 35-col. 8, line 6);
applying said brand cost multiplier which is stored in said non transitory computer readable media in said database via a microprocessor to the raw material costs of the furnished layout (The display further includes an area 620 showing characteristics that have been attributed to the project, such as on the basis of the visual information in the photograph. The contents of this area can be scrolled using scrolling controls 641-644. The display further includes an estimate area 660. The estimate area 660 includes a number of categories of the project for which the user is to estimate both a labor cost and a materials cost. For example, the user would enter a labor cost for the counters category into text entry field 672, and a materials cost for the counters category into text entry field 682. In some embodiments, for at least certain categories, the user is prompted to add a per-unit cost for the category rather than an overall cost for the category. For example, where, as here, the project involves installing nine upper cabinets and nine lower cabinets, the user interface may indicate for a cabinets category that labor and materials costs should be estimated for each of the 18 cabinets, rather than across all 18cabinets. In some cases, this enables the model to generalize costs in such categories from observations having certain counts of the relevant feature to projects to be estimated having different counts. The user may use scrolling controls 691-694 to scroll the estimate area and display all of the text entry fields in corresponding categories, and enter an estimated cost into each text entry field. After doing so, the user operates a submit button 699 in order to associate the entered geographic location and cost information with the observation project) (see at least col. 3, line 11-col. 4, line 57, col. 7, line 35-col. 8, line 6);
storing a multiplied price in said database in said non transitory computer readable media (A facility for estimating the cost of a remodeling project is described. The facility accesses a project cost model that predicts project costs determined from a photograph based upon project characteristics. The facility applies the access project cost model to characteristics of a distinguished project to obtain an estimated cost. The facility causes the obtained estimated cost to be displayed) comprising the steps of: at least one database server having at least one microprocessor, said at least one database server for storing a database; at least one application server having at least one microprocessor; at least one sensor having at least one microprocessor (FIG. 1 is a high-level block diagram showing a typical environment in which a software, hardware, and/or firmware facility implementing the functionality described herein; environment 100; The computer system 150 also includes one or more of the following: a network connection device 174 for connecting to a network (for example, the Internet 140) to exchange programs and/or data via its networking hardware, such as switches, routers, repeaters, electrical cables and optical fibers, light emitters and receivers, radio transmitters and receivers, and the like, an information input device 175, and an information output device 176. In some embodiments, the facility operates on the server computer system to perform some or all of the following activities: receive information about observations used to train the model; training the model using observations; receiving information about projects whose cost is to be estimated; and applying the model to estimate the cost of these projects) (see at least col. 3, line 11-col. 4, line 3, Abstract);
rendering on said screen at least one price for the furnished layout based upon the brand selected (FIG. 7 is a table diagram showing estimates performed by different contractors for different aspects of a project to be used as an observation to train the facility's model. The table 700 is made up of rows such as rows 701-706, each corresponding to a different category of a remodeling project. For example, row 701 corresponds to the demolition phase of the project, where existing contents of the room are removed, such as down to the studs and subfloor, while row 702 corresponds to purchasing materials for and constructing counters as part of the project. Each of the rows is divided into the following columns: a category column indicating the category whose cost is estimated by each contractor; and, for each of the four contractors providing estimates, a labor column (column 712, 714, 716, and 718) specifying the cost of labor predicted to be required for the category to which the row corresponds, as well as a materials column (column 713, 715, 717, and 719) indicating the cost of materials expected to be required to perform the category of the project to which the row corresponds. For example, the contents of row 702 indicate that, for the counters category of the project depicted in Table 700, contractor A estimates the labor cost to be $4,320 and the materials cost to be $2,880; contractor B estimates the labor cost to be $2,052 and the materials cost to be $1,368; contractor C estimates the labor cost to be $1,980 and the materials cost to be $1,320; and contractor D estimates the labor cost to be $918 and the material cost to be $612) (see at least col. 8, lines 9-36);
Bruce does not specifically teach determining via a sensor a set of dimensions of a room; communicating with said sensor to store said dimensions of said room in a memory. Miller teaches determining via a sensor a set of dimensions (Application 105 executes a machine learning (ML) algorithm configured to identify home improvement design solutions based on visual data selected by a user via Mobile Computing Device 100. The term “visual data” (also referred to as a “visual data portfolio”) may include an image, plurality of images, video, or plurality of videos, collected via a sensor (e.g., a camera). The ML algorithm may identify home improvement design solutions based on one or more attributes of visual data selected by the user—such as, e.g., calculated dimensions of an area to be renovated, identified design components, internet browser metadata, color schemes, styles, or user feedback. The ML algorithm may derive from a training data set that includes a plurality of tagged images stored in Image Recognition RDB 193. Each image of a training data set may include metadata tags assigned by a user, one or more properties identified by Image Recognition Software, and/or third-party data associated with a given image. In response to executing a ML algorithm on visual data selected by a user, Application 105 may display one or more design components in an augment reality (AR) or virtual reality (VR) environment via Mobile Computing Device 100—e.g., Application 105 may display one or more design components in Design View of step 259. In response to executing a ML algorithm on visual data selected by a user, Application 105 may generate a three-dimensional (3D) digital representation of a design component based on one or more properties of the design component and a pre-built 3D model scaffold) of a room (collecting visual data of a room via a mobile computing device, including calculating dimensions of at least one aspect of the room based on an interaction with a touch enabled display of the mobile computing device; displaying a plurality of project resource templates related to improvement projects; selecting a project resource template in response to a user input; displaying a plurality of design components based on a selected project resource template and calculated dimensions of the at least one aspect of the room; displaying an augmented reality environment of the room on the touch-enabled display, the augmented reality environment configured to allow a user to engage with a selected design component to manipulate a location and orientation of the selected design component within the augmented reality environment; area to be renovated); communicating with said sensor (The term “visual data” (also referred to as a “visual data portfolio”) may include an image, plurality of images, video, or plurality of videos, collected via a sensor (e.g., a camera). The ML algorithm may identify home improvement design solutions based on one or more attributes of visual data selected by the user—such as, e.g., calculated dimensions of an area to be renovated) to store said dimensions of said room in a memory (System processes and calculations (SaaS processing) occur in the “online” mode, when required data is collected on the Computing Device 100 and relayed to the System Processes Server 155 via the Main Web Server 110. Project Name 165, Photo Image 170 and Dimension measurements 180 are processed by the System Processes Server 155 and then stored along with Project Guide URLs 185 and Tools and Materials 187 in the Project Information RDB 160) in analogous art of construction project management for the purposes of: “an image, plurality of images, video, or plurality of videos, collected via a sensor (e.g., a camera). The ML algorithm may identify home improvement design solutions based on one or more attributes of visual data selected by the user—such as, e.g., calculated dimensions of an area to be renovated, identified design components, internet browser metadata, color schemes, styles, or user feedback” (see at least paragraphs 13, 35-38, 66; Abstract).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the method and system for construction project management using photo imaging measurements as taught by Miller in the system of Bruce, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With regard to Claim 5, Bruce teaches the step of selecting at least one additional brand and rendering at least a second price for the furnished layout based upon the second brand selected (see at least col. 7, line 35-col. 8, line 6; Claim 1).
With regard to Claim 6, Bruce teaches the step of selecting at least a third brand, and then rendering at least a third price for the third brand (see at least col. 7, line 35-col. 8, line 6; Claim 1).
With regard to Claim 7, Bruce teaches the step of providing a feed for real time updating of raw materials (see at least col. 4, lines 11-57).
With regard to Claim 8, Bruce teaches wherein said step of providing a brand cost multiplier comprises inputting data into a database for average costs for each model of each brand (see at least col. 4, lines 11-57).
With regard to Claim 9, Bruce teaches wherein said step of providing a brand cost multiplier comprises using a microprocessor for calculating an average difference between each brand by averaging the cost of each model for each brand, determining the amount of raw materials in each model of each brand and then determining a brand cost multiplier based upon the cost difference between the raw materials and the total cost of a finished product (see at least TABLE’s 2-4; col. 10, lines 47-67).
With regard to Claim 10, Bruce teaches wherein the step of providing a set of stock cabinets comprises providing a set of pre-set dimensions for different sized cabinets (see at least TABLE’s 2-4; col. 10, lines 47-67).
With regard to Claim 11, Bruce teaches wherein the step of providing a set of stock cabinets comprises providing a set of cabinets of different shape (see at least TABLE’s 2-4; col. 10, lines 47-67).
With regard to Claim 12, Bruce teaches wherein the step of providing a set of stock cabinets comprises providing a location for each cabinets including a location of each cabinets in either an upper cabinet position or under counter position (see at least TABLE’s 2-4; col. 10, lines 47-67).
With regard to Claim 13, Bruce teaches wherein the step of editing the rendering of the cabinet layout (see at least col. 11, lines 53-60).
With regard to Claim 14, Bruce teaches wherein the step of editing the rendering of the cabinet layout further comprises changing the cost estimate for at least one brand based upon the changed layout (see at least TABLE’s 2-4; col. 10, lines 47-67).
With regard to Claim 15, Bruce teaches inputting data including at least the floor plan, and the brand cost multiplier into an artificial intelligence (Al) server (see at least col. 2, lines 7-67).
With regard to Claim 16, Bruce teaches the step of inputting a set of stock cabinets into the Al server (see at least TABLE’s 2-4; col. 2, lines 7-67; col. 10, lines 47-67).
With regard to Claim 17, Bruce teaches the step of inputting the layout into the Al server (see at least TABLE’s 2-4; col. 2, lines 7-67; col. 10, lines 47-67).
With regard to Claim 18, Bruce teaches wherein said Al server is configured to provide questions to a consumer to gather information for providing a final cabinet layout (see at least col. 7, lines 35-67).
With regard to Claim 20, Bruce teaches determine a price of the layout based upon a brand multiplier stored in said application server and wherein the application server is configured to determine a price of an additional layout of an additional model for cabinets to provide a user with a price option for a selection of cabinets (see at least col. 4, line 58-col. 7, line 29; TABLEs 1-3).
With regard to Claims 21, 22, Bruce does not specifically teach wherein said sensor comprises an optical sensor for determining an axial distance in a room to determine the dimensions of a room. Miller teaches wherein said sensor comprises an optical sensor for determining an axial distance in a room to determine the dimensions of a room (the term “visual data” (also referred to as a “visual data portfolio”) may include an image, plurality of images, video, or plurality of videos, collected via a sensor (e.g., a camera). The ML algorithm may identify home improvement design solutions based on one or more attributes of visual data selected by the user—such as, e.g., calculated dimensions of an area to be renovated) n analogous art of construction project management for the purposes of: “an image, plurality of images, video, or plurality of videos, collected via a sensor (e.g., a camera). The ML algorithm may identify home improvement design solutions based on one or more attributes of visual data selected by the user—such as, e.g., calculated dimensions of an area to be renovated, identified design components, internet browser metadata, color schemes, styles, or user feedback” (see at least paragraphs 13, 35-38, 66; Abstract).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the method and system for construction project management using photo imaging measurements as taught by Miller in the system of Bruce, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Miller (US 12,190,429)
Lopez et al. (US 8,266,005)
Slaughenhoupt (WO 2012/075101 A2)
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624