DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/25/2026 has been entered.
Response to Amendment
The amendment filed 03/25/2026 has been entered. As directed, claims 21, 29, 36, and 39-40 have
been amended, no claim have been canceled and added. Thus claims 21-40 remain pending in the application.
Response to Arguments
With respect to the Applicant’s argued rejection under 35 § U.S.C. 101 in “Applicant
Arguments/Remarks Made in an Amendment,”:
Applicant argues:
…
Applicants' have amended independent claims 21, 29 and 36 to recite the particular manner of training the machine-learning function. Consequently, Applicants' believe that those claims, too, are "directed to an improvement in computer functionality versus being directed to an abstract idea" under the Enfish case (Enfish, LLC v. Microsoft Corp. 822 F.3d 1327 (Fed. Cir. 2016).
For example, Applicants' independent claim 21 recites, among other limitations:
a) training a function using a machine learning algorithm by,
i) receiving as input training data a plurality of 2D plant- layout schemas each including a 2D arrangement of a plurality of 2D plant objects;
ii) for each 2D plant-layout schema, receiving, as output training data, identifiers and location data associated with one or more of the plurality of 2D plant objects; and
iii) training, by the machine learning algorithm, the function based on the input training data and the output training data;
d) applying the function trained by the machine learning algorithm to the input data for detecting a set of 2D plant objects, and providing a set of identifiers and location data on the detected set of 2D plant objects as output data from the function, the location data including a set of coordinates representing locations of the set of detected 2D plant objects on the given 2D schema;
Applicants' independent claim 29 recites similar limitations, among others.
Applicants' independent claim 36 was amended to recite, among other limitations:
c) apply a function trained by a machine learning algorithm to the input data for detecting a set of 2D plant objects, and provide a set of identifiers and location data on the detected 2D plant object set as output data from the function, the location data including a set of coordinates representing locations of the set of detected 2D plant objects on the given 2D schema, the function being trained using the machine learning algorithm by:
i) receiving as input training data a plurality of 2D plant- layout schemas each including a 2D arrangement of a plurality of 2D plant objects;
ii) for each 2D plant-layout schema, receiving, as output training data, identifiers and location data associated with one or more of the plurality of 2D plant objects; and
iii) training, by the machine learning algorithm, the function based on the input training data and the output training data;
Thus, Applicants have clarified claims 21, 29 and 36 by explicitly reciting the steps by which the function is trained by the machine learning algorithm prior to its use in generating the 3D model of the plant-layout, as claimed. Consequently, all of Applicants' claims recite a particular manner of training the machine-learning function.
Applicants' invention of claims 21, 29 and 36, like that of claims 39 and 40 (for which the rejection under 35 U.S.C. § 101 has been withdrawn), improves the functioning of a computer and/or provides an improvement to another technology or technical field. In particular, Applicants' invention is directed to an improvement to how a machine learning model itself operates, which is, in fact, an improvement to computer functionality versus being directed to an abstract idea. Claims directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field are patent eligible under Step 2A because they are not "directed to" the recited judicial exception. See, for example, M.P.E.P. § 2106.04(d)(1) (i.e., "A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field. The application or use of the judicial exception in this manner meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment, and thus transforms a claim into patent-eligible subject matter. Such claims are eligible at Step 2A because they are not "directed to" the recited judicial exception.")
Further, as discussed in the previous Amendment, in the precedential Patent Office case of Ex parte Desjardins, Appeal 2024-00567 (Decided September 26, 2025), Director Squires found that an improvement to how a machine learning model itself operates, and not, for example, to an identified mathematical calculation, is, in fact, an improvement to computer functionality versus being directed to an abstract idea. In Ex parte Desjardins, Director Squires states, among other things:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that "[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes." 822 F.3d at 1339. Moreover, because "[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can," the Federal Circuit held that the eligibility determination should turn on whether "the claims are directed to an improvement to computer functionality versus being directed to an abstract idea." Id. at 1336.
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. 21. The Specification also recites that the claimed improvement allows artificial intelligence (Al) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. [emphasis added by Applicants]
Page 6 of the instant application, lines 9 - 23, make it clear that the claimed machine language training of a function in accordance with the present invention, improves the functioning of the computer/technology by, among other things:
Enabling automatic generation of a 3D CAD model of a plant layout departing from its 2D schema without required human intervention by the plant layout engineer;
Rendering the process of generating a 3D model of plant layout more efficient;
Enabling upgrading the capability of several existing manufacturing planning software applications;
Enabling time savings; and
Allowing layout planners to provide a Software as a Service ("SaaS") module whereby they can upload a 2D layout schema and get as result a populated 3D digital scene where plant equipment objects are automatically positioned.
Thus, the use of particularly obtained input training data and output training data to train a function via a machine learning algorithm and providing the machine learning trained function for generating the 3D model of the plant layout by applying the machine learning trained function to a given 2D schema of the plant-layout as input data, to achieve the foregoing advantages stated in Applicant's specification is believed to constitute an improvement to how the machine learning model itself operates, and thus, provides an improvement to the functioning of the computer and/or to another technology or technical field.
Additionally, as discussed above, Applicants' claims relate to training a function in a particular way with particular data via machine learning algorithms to, among other things, enable automatic generation of a 3D CAD model of a plant layout departing from its 2D schema without requiring human intervention by the plant layout engineer, thus increasing efficiency and time savings. Therefore, Applicants' claims are analogous to the claims found to be subject matter eligible in Example 39 of the Subject Matter Eligibility Examples: Abstract Ideas issued to be used in conjunction with the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG). In Example 39, it was found that a certain steps of a computer technology for identifying human faces in digital images that introduced an expanded training set would, unfortunately increase false positives when classifying non-facial images. Example 39 goes no to state:
Accordingly, the second feature of applicant's invention is the minimization of these false positives by performing an iterative training algorithm, in which the system is retrained with an updated training set containing the false positives produced after face detection has been performed on a set of non-facial images. This combination of features provides a robust face detection model that can detect faces in distorted images while limiting the number of false positives.
Claim 1 in Example 39 was found to not recite any of the judicial exceptions enumerated in the 2019 PEG, because:
... the claim does not recite any mathematical relationships formulas, or calculations. While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims. Further, the claim does not recite a mental process because the steps are not practically performed in the human mind. Finally, the claim does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people. Thus, the claim is eligible because it does not recite a judicial exception.
Like the claimed invention in Example, 39, Applicant's currently claimed invention recites a particularly claimed function trained by a machine language algorithm using particularly recited input and output training data to improve object detection using output training data generated for training the machine learning function. See, for example, page 10 of the instant application, lines 13 - 20. Like claim 1 of Example, 39, claims 32 and 33 do not recite a mathematical concept, are not practically performed in the human mind, and do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Thus, like claim 1 of Example 39, Applicants' claims are believed to be subject matter eligible, because they do not recite a judicial exception.
Consequently, Applicants' claims are believed to provide an improvement to how a machine learning model itself operates, and thus, is an improvement to computer functionality versus being directed to an abstract idea, per Ex parte Desjardins and Example 39 of the 2019 PEG. Consequently, Applicants' claims are believed to be subject matter eligible because they do not recite a judicial exception.
As such, Applicants' claims are believed to be directed to statutory subject matter under 35 U.S.C. § 101.
(see Response filed 03/25/2026 [pages 12-18]).
Applicant’s arguments have been considered but are not persuasive.
First, Applicant argues that amended claims 21, 29, and 36 recite a particular manner of training the machine learning function. However, the Examiner respectfully disagrees. The claims recite receiving input training data, receiving output training data, and training a function based on the input and output training data. These limitations merely identify the type of data used for training, but they do not recite a particular machine learning architecture, a particular training objective, a particular loss function, a particular parameter update technique, or any specific modification to how the machine learning model itself operates. Rather, the claims broadly recite training and applying a machine learning function to detect 2D plant objects and output identifiers and location data. The machine learning function is used in its ordinary capacity as a tool for learning from training data and making predictions on input data. Although the claims include additional training data limitations, the added limitations do not show improvement of the operation of the machine learning model itself, the computer functionality, or another technology or technical field. Instead, the claims use the trained machine learning function as part of an automated process for detecting objects and generating a 3D plant layout. Therefore, the added training limitations do not integrate the judicial exception into a practical application.
Second, Applicant argues that the claims are directed to an improvement in computer functionality under Enfish and Ex parte Desjardins. However, the pending claims are distinguishable. In Desjardins, the claim reflected a specific improvement to how the machine learning model itself operated. However, the claims do not recite any comparable improvement to the internal operation of a machine learning model. The claim do not recite how the machine learning model parameters are adjusted or how the machine learning model performance is improved, or how error are reduced. Instead, the claims use a trained function as a tool to process a 2D schema, detect 2D plant objects, and provide identifier and location information. The subsequent selecting and arranging of 3D plant objects is based on the detected information. Thus, the alleged improvement is directed to automating the generation of a 3D plant layout from a 2D schema, not to improving computer functionality or the operation of the machine learning model itself. See MPEP 2106.05(a), II.: "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” See also the court decision for RECENTIVE ANALYTICS, INC. v. FOX CORP. , No. 23-2437 (Fed. Cir. 2025): “We hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
Third, Applicant relies on portions of the specification describing benefits such as automatic generation of a 3D CAD model, reduced human intervention, increased efficiency, time savings, and automatic positioning of plant equipment objects. However, these asserted benefits are not sufficient because the claims must themselves recite the alleged technical improvement. The claims recite the desired result of detecting objects, outputting identifiers and location data, selecting corresponding 3D plant objects, and generating a 3D model. The claims do not recite a specific technical mechanism that improves computer operation, CAD technology, or machine learning technology. Therefore, the asserted benefits are related to automation of a design task rather than integration of the judicial exception into a practical application. As explained in MPEP 2106.05(a), I.: “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).”
Fourth, Applicant argues that the claims are analogous to Example 39 of the 2019 PEG, and do not recite a judicial exception. The Examiner respectfully disagrees. In Example 39, the claim only recites a specific training process, which does not recite any of the judicial exceptions. In contrast, the pending claims recite steps that, under their broadest reasonable interpretation in light of specification, covers performance of the limitation in the mind but for the recitation of generic computer components, include observing/evaluating 2D plant objects, determining and associating identifiers and location information with the objects, selecting corresponding 3D objects, and arranging the selected objects according to location data. These concepts can be performed mentally or with pen and paper.
For the reasons discussed above, applicant’s arguments have been considered but are not
persuasive. The claims are directed to abstract ideas (mental process), are not integrated judicial exception into a practical application, and do not recite additional elements amount to significantly more than the judicial exception. The rejection of claims 21 and similar claims 29, and 36 under 35 U.S.C. § 101 is maintained.
Applicant’s arguments, see “Applicant Arguments/Remarks Made in an Amendment,” pages 18-29,
filed 03/25/2026, with respect to the rejection(s) under 35 U.S.C 103 of claim(s) 21, 29, 36, and 39-40 under statutory basis for the previous rejection have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
The newly applied references:
Tremblay US20190251397A1 teaches training a neural network function using paired input image data and task specific training data, wherein the task specific training data includes annotations indicating locations of rendered objects of interest, such as bounding box coordinates, and object identifies associated with the rendered objects of interest (see e.g., [0020], [0032], and [0035]).
Wang AU2018100585A4 teaches apply a machine learning based object detection module to a 2D floor plan input to detect and recognize objects, record coordinates for the detected elements, and output a JSON file containing coordinates and object type information, and further teaches selecting appropriate 3D models according to the information in the JSON file and generating a 3D model based on the detected object information (see e.g., [0005], [0024], [0026], and [0027]).
Therefore, the combination of “MPDS plant design” by Cad-Schroer, hereinafter “Schroer”, published in Feb, 2019 in view of Tremblay and Wang together teach or suggest limitations of claims 21, 29, 36 and 39-40. Therefore, the rejection of claims 21, 29, 36 and 39-40 under 35 U.S.C. 103 is maintained.
Claim Objections
Claims 21, 24, 32, 37 and 40 are objected to because of the following informalities:
Claims 21, 24, 32, 37 and 40 are objected to for inconsistent terminology because the claims alternate between “plant-layout” and “plant layout.” For clarity and consistency, Applicant should amend the claim to use one term consistently, such as “plant-layout”.
Claims 21 and 40 are objected to for inconsistent terminology because the claims alternate between “2D-schema” and “2D schema.” For clarity and consistency, Applicant should amend the claim to use one term consistently, such as “2D schema”.
Claims 21 and 40 are objected to for inconsistent terminology because the claims alternate between “3D-model” and “3D model.” For clarity and consistency, Applicant should amend the claim to use one term consistently, such as “3D model”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 27, 30, 35 and 37 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.
Claim 27 recites “digital plant objects,” which renders the claim indefinite because it is unclear if the “digital plant objects” refer to the previously recited “the 2D plant object,” “the 3D plant object” or a combination of both. For the purpose of substantive examination, the examiner interprets “digital plant objects” as referring back to the 2D plant objects and/or 3D plant objects.
Claim 35 recites the same limitation, “digital plant objects”, and is rejected for the same reasons as set forth above with respect to Claim 27.
Claim 30 recites the limitation “the 2D schema of the plant layout includes …”. There is insufficient antecedent basis for this limitation in the claim.
Claim 37 recites the limitation “the 2D schema of plant layout includes …”. There is insufficient antecedent basis for this limitation in the claim.
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.
The claim(s) 21-38 are rejected under 35 USC § 101 because the claimed invention is directed to
judicial exception an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates, and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 21-28 are directed to method and fall within the statutory category of processes;
Yes, Claim 29-35 are directed to system and falls within the statutory category of machines;
Yes, Claims 36-38 are directed to non-transitory computer-readable medium and fall within the statutory category of articles of manufactures.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claim 21: The limitations of “d) … detecting a set of 2D plant objects, and providing a set of identifiers and location data on the detected set of 2D plant objects …, the location data including a set of coordinates representing locations of the set of detected 2D plant objects on the given 2D schema,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the human mind. For example, a person is capable of observing and evaluating a set of 2D plant objects in the 2D drawing, recognizing the 2D plant objects, determining corresponding identifiers and determining associated location information including coordinates. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
Claim 21: The limitations of “e) selecting a set of 3D plant objects from the plant catalogue having identifiers associated with the set of identifiers of the detected set of 2D plant object,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the human mind. For example, after recognizing the detected 2D plant objects and their corresponding identifiers, a person is capable of observing and evaluating 3D plant objects in a plant catalogue/list, determining corresponding identifiers associated with the detected 2D plant objects, and selecting 3D plant objects based on the associated identifiers. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
Claim 21: The limitations of “f) generating the 3D model of the plant-layout by arranging the selected set of 3D plant objects in accordance with a correspondence of the location data of the output data,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the human mind. For example, after identifying corresponding 3D plant objects and associated location information from the 2D schema, a person is capable of observing and evaluating the location information, mentally determining corresponding positions for the selected 3D plant objects, and mentally arranging the selected 3D plant objects according to the corresponding location information to form a corresponding plant-layout model. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
If a claim limitation, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea under step 2A, Prong One (See MPEP 2106.04(a)(2)(III)).
Claims 29 and 36 recite the similar elements as claim 21, and are rejected for the same reasons under 35 U.S.C. 101.
Therefore, claims 21, 29, and 36 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 21, 29, and 36: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements - "A data processing system, comprising: a processor; and an accessible memory; the data processing system configured to:” and “A non-transitory computer-readable medium encoded with executable instructions that, when executed, cause one or more data processing systems to:,” which is mere instruction to implement an abstract idea on a computer, or merely uses a computer as tool to perform an abstract idea with the broad reasonable interpretation, which does not integrate a judicial exception into practical application. See MPEP § 2106.05(f)).
Further, the following additional element – “a) … by, i) receiving as input training data a plurality of 2D plant-layout schemas each including a 2D arrangement of a plurality of 2D plant objects; ii) for each 2D plant-layout schema, receiving, as output training data, identifiers and location data associated with one or more of the plurality of 2D plant objects …; b) providing access to a plant catalogue of a plurality of identifiers of the plurality of 3D plant objects, at least one of the plurality of identifiers of the plurality of 3D plant objects being associated with an identifier of a corresponding 2D plant object; c) receiving data on a given 2D schema of the plant-layout as input data,” are merely a recitation of insignificant extra-solution activity such as data gathering (i.e., receiving/accessing data), which does not integrate a judicial exception into practical application. (see MPEP 2106.05(g)).
Further, the limitation “A method for generating, by a data processing system, a 3D-model of a plant layout departing from a 2D-schema of the plant-layout, … comprising: a) training a function using a machine learning algorithm … iii) training, by the machine learning algorithm, the function based on the input training data and the output training data; … d) applying a function trained by a machine learning algorithm to the input data for detecting …; e) selecting … e) generating …” which are merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and applying a computing component to perform generic steps (e.g., generating, training, applying, and selecting) at high level of generality is simply the act of instructing a computer to perform generic functions, which is merely an instruction to apply a computer to the judicial exception and does not integrate judicial exception into practical application. see MPEP 2106.05(f). Alternately, the limitations merely link the use of the judicial exception to a particular technological environment (i.e., machine learning techniques) or field of use. Therefore, limiting an abstract idea to implementation using a generic machine learning algorithm or trained function does not integrate the exception into a practical application. See MPEP § 2106.05(h).
Examiner note: the additional limitations, including “a data processing system,” “a machine learning algorithm,” “a trained function,” “a plant catalogue,” receiving data, accessing catalogue, and generating the 3D model of the plant-layout, merely recite generic computer implementation, insignificant extra-solution activity, and field of use, which are used to perform the recited mental process. For example , the limitations broadly recite receiving/accessing data, training and applying a broadly recited machine learning function to analyze information, selecting corresponding objects based on identifies, and outputting/generating a model based on the analyzed information, without reciting any particular technological manner for improving computer functionality, machine learning technology, image processing technology, CAD technology, or 3D modeling technology itself. In particular, the claim does not recite a specific machine learning architecture, a specific object detection technique, a specific data structure for the plant catalogue, a particular improvement in generating or rendering 3D models, or any technological mechanism that improves the functioning of a computer or another technology. Instead, the additional limitations merely use generic computing components and a broadly recited machine learning function as tools to automate the recited mental processes of recognizing objects, associating identifiers, selecting corresponding objects, and arranging objects according to location information. As explained in MPEP 2106.05(a), I.: “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).” See also the court decision for RECENTIVE ANALYTICS, INC. v. FOX CORP. , No. 23-2437 (Fed. Cir. 2025): “We hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Thus the claim merely implements the abstract idea on a generic computer in a particular technological environment relating to plant-layout modeling and machine learning, which does not integrate the judicial exception into a practical application. See MPEP §§2106.05(a), (b), (f), (g) and (h).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 21, 29, and 36 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 21, 29, and 36: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; … iv. Storing and retrieving information in memory, …
As explained by the Supreme Court: in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". (Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014). See also Yu v. Apple Inc.: 1 F.4th 1040 (Fed. Cir. 2021)).
Examiner note: as explained above, the additional limitations merely use generic computer components to carry out the recited abstract idea. The additional limitations do not provide any further technological feature improvement beyond the abstract recognition, association, selection, and arrangement process itself. The claim does not recite a specific technical improvement, specialized computer operation, or unconventional arrangement of components that would transform the judicial exception into patent eligible subject matter. Therefore, the independent claims do not provide an inventive concept sufficient to amount to significantly more than the abstract idea under Step 2B.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 21, 29, and 36 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 22-28, 30-35 and 37-38 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 22-28, 30-35 and 37-38 are also rejected for incorporating the deficiency of their independent claims 21, 29, and 36.
Claim 22 recites “The method according to claim 21, which further comprises providing the 2D schema of the plant-layout with a set of schema annotations providing schema information.”
This merely specifies the plant layout 2D schema with a set of schema annotations providing schema information refers to claim 1 of the received data; therefore, it merely a recitation of insignificant extra-solution data gathering (i.e., receiving data) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Therefore, the claim 22 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 23 recites “The method according to claim 21, which further comprises providing additional layout data.”
This merely specifies the plant layout 2D schema with additional layout data refers to claim 1 of the received data; therefore, it merely a recitation of insignificant extra-solution data gathering (i.e., receiving/providing data) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Therefore, the claim 23 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 24 recites “The method according to claim 21, which further comprises interpreting at least one of additional layout data or schema annotation information by a coded rule module to provide a selection of adjusting steps to the 3D model of the plant layout.”
This merely specifies a coded rule module to provide a selection of adjusting steps to the plant layout 3D model based on received additional data; therefore, it merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and applying a computer component (i.e., coded rule module) to perform a selecting of adjusting step at high level of generality is simply the act of instructing a computer to perform generic functions, which is merely an instruction to apply a computer to the judicial exception or significant more. Therefore, the claim 24 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 25 recites “The method according to claim 24, which further comprises at least one of providing the coded rule module as a knowledge graph or providing additional layout data with manufacturing process semantic information.”
This merely specifies additional layout data with manufacturing process semantic information is provided; therefore, it merely a recitation of insignificant extra-solution data gathering (i.e., receiving/providing data) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Therefore, the claim 25 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 26 recites “The method according to claim 21, which further comprises providing the plant catalogue as a standard catalogue, a specific catalogue or a combination of a standard catalogue and a specific catalogue. ”
This merely further defines different plant catalogue refers to claim 1 of access and retrieve data; it merely a recitation of insignificant extra-solution data gathering (i.e., receiving and retrieve data) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Therefore, the claim 26 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 27 recites “The method according to claim 21, which further comprises providing digital plant objects as CAD objects.”
This merely further defines plant objects are digital plant objects as CAD objects refers to claim 1 of receive data; therefore, it merely a recitation of insignificant extra-solution data gathering (i.e., receiving/providing data) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Therefore, the claim 27 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 28 recites “The method according to claim 21, which further comprises training a Machine Learning function with a You Only Look Once algorithm.”
This merely further defines the ML algorithm is a You Only Look Once algorithm; therefore, it merely linking the use of the judicial exception to a particular technological environment or field of use (e.g., You Only Look Once algorithm). Therefore, the claim 28 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claims 30-35 and 37-38 recite the similar elements as claims 22-27, and are rejected for the same reasons under 35 U.S.C. 101.
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.
Claim(s) 21-23, 26-27, 29-31 and 34-40 are rejected under 35 U.S.C. 103 as being unpatentable
over “MPDS plant design” by Cad-Schroer, hereinafter “Schroer”, published in Feb, 2019 in view of Tremblay US20190251397A1 and Wang AU2018100585A4.
Claim 21, Schroer teaches A method for generating, by a data processing system, a 3D-model of a plant layout departing from a 2D-schema of the plant-layout, the plant-layout including an arrangement of a plurality of plant objects, the plant-layout being representable by the 2D-schema of the plant-layout and by the 3D model of the plant-layout, the 2D schema of the plant-layout including a 2D arrangement of a plurality of 2D plant objects and the 3D model of the plant layout including a 3D arrangement of a plurality of 3D plant objects (Page.2, Meeting the Challenges of 3D Plant Design, “The MPDS4 plant design software is scalable, modular, multi-user capable, user friendly, integrates with existing systems, and easily handles huge amounts of data.” Page.3, 2D/3D Integration, “Imported 2D layouts can form the basis of 3D buildings. Use 2D P&IDs to drive 3D Piping.” Large-scale 3D Plant Design, “Use MPDS4 for large-scale, rules based, multi-user, cross-discipline plant assembly directly in 3D. The software offers a comprehensive range of add-on modules for all the main plant engineering disciplines, including Piping, Steel, Hangers & Supports, P&ID, HVAC Ducting, Mechanical Handling, and Electrical Design.” Examiner note: the reference teaches ““Imported 2D layouts can form the basis of 3D buildings. Use 2D P&IDs to drive 3D Piping,” thereby teaching generation of 3D plant layout information based on corresponding 2D plant layout schema information, and further teaches plant engineering disciplines including piping, steel, hangers and supports, P&ID, HVAC ducting, mechanical handing and electrical design, thereby supporting that the plant layout includes a plurality of plant objects represented in corresponding 2D and 3D arrangements. The recited “data processing system” is mapped to the computer based system used to execute the MPDS4 plant design software), the method comprising:
i)(Page.3, 2D/3D Integration, “Imported 2D layouts can form the basis of 3D buildings. Use 2D P&IDs to drive 3D Piping.” See also 2D figure in the middle of page 3 showing the 2D plant layout drawing/schemas including a 2D arrangement of a plurality of 2D plant objects/components);
ii) for each 2D plant-layout schema, (Page.3, the 2D figure showing a 2D plant layout drawing/schema with multiple 2D plant objects/components, object labels/identifies, and relative location/arrangement information within the 2D drawing/schema); and
iii)
b) providing access to a plant catalogue of a plurality of identifiers of the plurality of 3D plant objects, at least one of the plurality of identifiers of the plurality of 3D plant objects being associated with an identifier of a corresponding 2D plant object (page.3, Catalog-driven Design, “MPDS4’s customisable libraries of 3D parametric catalog components support rules-driven, standard-compliant, accurate design. Users specify, place and auto-route components, which are automatically modelled in 3D. An optional 3D Component Editor allows designers to create custom parametric catalog components.” Large-scale 3D Plant Design, “… The software offers a comprehensive range of add-on modules for all the main plant engineering disciplines, including Piping, Steel, Hangers & Supports, ....” 2D/3D Integration, “Use 2D P&IDs to drive 3D Piping.” Examiner note: The reference teaches a plant catalogue because MPDS4 includes “customisable libraries of 3D parametric catalog components,” and further teaches that “2D P&IDs” are used “to drive 3D Piping,” indicating correspondence between 2D P&IDs plant objects and 3D piping/catalog components. A POSITA would understand that catalog components and P&IDs objects within a rules-driven plant design system are associated through component designations, labels, symbols, or catalog entries used to relate the 2D schematic objects to corresponding 3D modeled components. Thus, Schroer teaches identifies of 3D plant objects associated with identifies of corresponding 2D plant objects);
c) receiving data on a given 2D schema of the plant-layout as input data (page.3, 2D/3D Integration, “Imported 2D layouts can form the basis of 3D buildings.”);
d) (Page.3, the 2D figure showing a 2D plant layout drawing/schema with multiple 2D plant objects/components, object labels/identifies, and relative location arrangement information within the 2D drawing/schema);
e) selecting a set of 3D plant objects from the plant catalogue having identifiers associated with the set of identifiers of the detected set of 2D plant objects (page.3, 2D/3D Integration, “Use 2D P&IDs to drive 3D Piping.” Catalog-driven Design, “MPDS4’s customisable libraries of 3D parametric catalog components support rules-driven, standard-compliant, accurate design. Users specify, place and auto-route components, which are automatically modelled in 3D.” Examiner note: the reference teaches that that “2D P&IDs” are used “to drive 3D Piping,” which supports selecting corresponding 3D piping/catalog components based on 2D P&IDs objects or identifies, and further teaches that users specify, place, and auto-route components, which are automatically modeled in 3D, supporting that the corresponding 3D plant objects are selected from the plant catalogue for the 3D model. Thus, Schroer teaches selecting 3D plant objects from the plant catalogue having identifies associated with corresponding 3D plant objects); and
f) generating the 3D model of the plant-layout by arranging the selected set of 3D plant objects (page.3, 2D/3D Integration, “Imported 2D layouts can form the basis of 3D buildings. Use 2D P&IDs to drive 3D Piping.” Catalog-driven Design, “… Users specify, place and auto-route components, which are automatically modelled in 3D.” Page.2, “MPDS4 offers extremely high productivity through its wide range of specialised toolsets for design tasks such as piping layout, steelwork design and HVAC duct routing, all driven by extensive, and extensible, component libraries supplied with the software.” Examiner note: the reference teaches generating a 3D plant/building model from 2D layout information because it states “Imported 2D layouts can form the basis of 3D buildings. Use 2D P&IDs to drive 3D Piping,” and further teaches arranging modeling selected 3D plant components because users “specify, place and auto-route components, which are automatically modelled in 3D,” and because piping layout, steelwork design, and HVAC duct routing are driven by component libraries).
However, Schroer fails to teach a) training a function using a machine learning algorithm by, i) receiving as input training data; ii) receiving, as output training data, identifiers and location data; and iii) training, by the machine learning algorithm, the function based on the input training data and the output training data.
Tremblay teaches a) training a function using a machine learning algorithm by, i) receiving as input training data; ii) receiving, as output training data, identifiers and location data; and iii) training, by the machine learning algorithm, the function based on the input training data and the output training data ([0020], “The generated training data may be used to train neural networks for object detection and segmentation tasks … More specifically, the neural network model is trained to detect objects of interest and ignore other objects in the images.” [0032], “The task-specific training data is not included as part of the input image data, but is instead paired with the input image data. During supervised training of a neural network model, the task-specific training data is ground truth labels corresponding to the rendered object(s) of interest that are compared with an output generated by the neural network model when the input image is processed by the neural network model.” [0035], “In an embodiment, the task-specific training data is annotations indicating locations of the rendered object of interest. For example, the locations may be (x, y, width, height) coordinates of 2D bounding boxes enclosing each rendered object of interest. In an embodiment, for segmentation, the task-specific training data is the rendered objects of interest having each pixel within a rendered object of interest replaced with an object identifier associated with the rendered object of interest.” Examiner note: the reference teaches training a neural-network function using paired input image data and task-specific training data. The claimed “input training data” is mapped to input image data, and the claimed “output training data” is mapped to task-specific training data/ground-truth labels paired with the input image data. The reference further teaches that the task-specific training data includes object identifiers and location annotations, including (x, y, width, height) coordinates” corresponding to detected objects. Thus, Tremblay teaches training a function using a machine-learning algorithm based on input training data and corresponding output training data).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schroer to incorporate the teachings of Tremblay and applying machine learning based neural network training using paired input image data and corresponding task-specific training data including object identifier and location annotations in order to improve automated object detection and recognition techniques. In this case, Schroer teaches 2D plant layout drawings/schemas including arrangements of plant objects used for generating corresponding 3D plant layout models. Tremblay teaches training a neural network using paired input image data and corresponding task-specific training data including object identifiers and location annotations for object detection and segmentation tasks. The combination of teaching would predictably provide benefit of improving automated detection, identification, and localization of plant objects from 2D plant layout drawings/schemas, thereby improving automation, processing efficiency, and reliability when generating 3D plant layout models.
However, Schroer and Tremblay fail to teach d) applying the function trained by the machine learning algorithm to the input data for detecting a set of 2D objects, and providing a set of identifiers and location data on the detected set of 2D objects as output data from the function, the location data including a set of coordinates representing locations of the set of detected 2D objects on the given 2D schema. f) arranging the selected set of 3D objects in accordance with a correspondence of the location data of the output data.
Wang teaches d) applying the function trained by the machine learning algorithm to the input data for detecting a set of 2D objects, and providing a set of identifiers and location data on the detected set of 2D objects as output data from the function, the location data including a set of coordinates representing locations of the set of detected 2D objects on the given 2D schema ([0005], “The embodiments describe a technology including back-end and front-end services that would allow transforming 2D floor plans to 3D models, this would be done by recognizing the features that are of interest and generating models from the corresponding features. The recognition process is divided into two parts, comprising the detection of the infrastructure (e.g. wails, doors, windows, rooms and preferably balconies) and the detection of the furniture (e.g. sofas, dining tables, beds, etc.), using the techniques and algorithms in computer vision and machine learning.” [0024], “… detect and recognize the furniture from the 2D floor plan; and record the coordinates for all elements; and create a JSON file which stores the data of the furniture …” [0027], “the furniture detection module 304 which would take the raw data 314 as the input and output a JSON file 310, which contains the coordinates and the type of the furniture it has recognized.” [0026], “… the GPU server 108 is used for detecting furniture via Faster R-CNN (Regional Convolutional Neural Network) …” Examiner note: the reference teaches applying a machine-learning based detection function (Faster R-CNN) to input floor plan image data for detecting 2D objects/furniture from the 2D floor plan. The furniture detection model takes the uploaded floor plan image as input and outputs a JSON file containing recognized furniture information including “coordinates and the type of the furniture it has recognized. Thus, the recognized furniture “type” corresponds to identifiers of detected 2D objects, and the recorded coordinate for all elements” correspond to location data including a set of coordinates representing locations of detected 2D objects on the 2D floor plan schema).
f) arranging the selected set of 3D objects in accordance with a correspondence of the location data of the output data ([0005], “The embodiments describe a technology including back-end and front-end services that would allow transforming 2D floor plans to 3D models, this would be done by recognizing the features that are of interest and generating models from the corresponding features.” [0027], “output a JSON file 310, which contains the coordinates and the type of the furniture it has recognized … when generating the models of the furniture … choose the appropriate 3D model of the furniture from its local storage, according to the information in the JSON file.” Examiner note: the reference teaches arranging/generating 3D furniture models according to detected object information and corresponding location data extracted from the 2D floor plan. In particular, the furniture detection module output a JSON file containing “coordinates and the type of the furniture it has recognized,” and choose the appropriate 3D model of the furniture … according to the information in the JSON file.” Thus, the detected object location data is used to place/arrange corresponding selected 3D objects in the generated 3D model).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schroer and Tremblay to incorporate the teachings of Wang and applying machine learning based object detection to detect objects from a 2D schema and output corresponding identifiers and coordinate based location data for use in generating and arranging corresponding 3D objects in a generated 3D model in order to improve conversion of 2D layouts into corresponding 3D models using detected object information and associated positional data. The combination of teaching would predictably provide benefit of improving object placement, model generation accuracy and efficiency when generating 3D model from 2D plant layout drawing/schemas.
Claim 22, Schroer teaches The method according to claim 21, which further comprises providing the 2D schema of the plant-layout with a set of schema annotations providing schema information (Page.3, the 2D figure showing a 2D plant layout drawing/schema with multiple 2D plant objects/components, object labels/identifies, connection lines, dimensions, and relative location/arrangement information within the 2D drawing/schema)).
Claim 23, Schroer further teaches The method according to claim 21, which further comprises providing additional layout data (Page.3, Downstream Data, “MPDS4 automatically generates BOMs, parts lists, reports and 2D drawings directly from the plant design. There are interfaces to ISOGEN™, pipe stress analysis software, ERP and EDM systems.” Examiner note: the automatically generated BOMs, parts lists, reports, and interface data for ISOGEN, pipe stress analysis software, ERP, and EDM systems corresponds to additional layout data because they provide further information associated with the plant layout beyond the 2D schema/drawing itself).
Claim 26, Schroer further teaches The method according to claim 21, which further comprises providing the plant catalogue as a standard catalogue, a specific catalogue or a combination of a standard catalogue and a specific catalogue (Page.3, Catalog-driven Design, “MPDS4’s customisable libraries of 3D parametric catalog components support rules-driven, standard-compliant, accurate design.” Catalog-driven Design, “Users specify, place and auto-route components, which are automatically modelled in 3D. An optional 3D Component Editor allows designers to create custom parametric catalog components.” Examiner note: the reference teaches a standard catalogue because the 3D parametric catalog components support “standard-compliant” design, and further teaches a specific/custom catalogue because the 3D Component Editor allows designers to create custom parametric catalog components).
Claim 27, Schroer further teaches The method according to claim 21, which further comprises providing digital plant objects as CAD objects (page.3, Catalog-driven Design, “MPDS4’s customisable libraries of 3D parametric catalog components support rules-driven, standard-compliant, accurate design.” Catalog-driven Design, “Users specify, place and auto-route components, which are automatically modelled in 3D. An optional 3D Component Editor allows designers to create custom parametric catalog components.” Examiner note: the reference teaches digital 3D plant objects because MPDS4 provides 3D parametric catalog components that are specified, placed, auto-routed, and automatically modeled in 3D. The 3D Component Editor further supports creating custom parametric catalog components. These digital 3D parametric components correspond to CAD objects because they are computer modeled components used in a plant design software environment).
The elements of claims 29-31 and 34-40 are substantially the same as those of claims 21-23 and 26-27. Therefore, the elements of claims 29-31 and 34-40 are rejected due to the same reasons as outlined above for claims 21-23 and 26-27. Particularly, Claims 39 and 40 are rejected for substantially overlap with the training related limitations previously addressed and discussed with respect to Claim 21, including the use of identifiers and location data comprises coordinates as output training data. Further, the additional limitation “A data processing system, comprising: a processor; and an accessible memory; the data processing system configured to:” and “A non-transitory computer-readable medium encoded with executable instructions that, when executed, cause one or more data processing systems to:” (see Schroer, the process is performed by a data processing system and A non-transitory computer-readable medium encoded with executable instructions (i.e., MPDS4 software on a computer platform)).
Claim(s) 24 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Schroer and
Tremblay and Wang as applied to claim 21 and 29 above, and further in view of Wu US20070266346A1.
Claim 24, Schroer further teaches The method according to claim 21, which further comprises (See Schroer, page.3, 2D/3D Integration, “Imported 2D layouts can form the basis of 3D buildings. Use 2D P&IDs to drive 3D Piping.”).
However, Schroer and Tremblay and Wang fail to teach interpreting at least one of additional layout data or schema annotation information by a coded rule module to provide a selection of adjusting steps.
Wu teaches interpreting at least one of additional layout data or schema annotation information by a coded rule module to provide a selection of adjusting steps ([0018], “DFM utilities 130 may provide corrective actions and solutions to the designer to guide for design improvement and tuning … DFM utilities 130 may also include a checker 134 that is integrated with DFM rules, is able to automatically check the layout for any DFM rule violation, and/or provides suggestions to eliminate the violation. DFM utilities 130 may include an enhancer 136 that is capable of automatically adjusting the layout to meet the DFM rules or eliminate identified hotspots.” Examiner note: the reference teaches a coded rule module because its DFM utilities/checker/enhancer are integrated with DFM rules and automatically check layout data for rule violations. The DFM utilities provide corrective actions, solutions, and suggestions to eliminate violations, and the enhancer automatically adjusts the layout to meet the DFM rules. Thus, Wu teaches interpreting layout related data using coded design/manufacturing rules to provide a selection of adjusting steps for adjusting the model/layout).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schroer and Tremblay and Wang to incorporate the teachings of Wu and applying rule based checking and corrective action utilities to interpret layout related data and provide suggested adjustment steps in order to improve automated validation and correction of layout and model design error. The combination of teaching would predictably provide benefit of improving design quality, reducing manual correction, and increasing efficiency in adjusting generated layout models to satisfy applicable design or manufacturing rules.
The elements of claim 32 is substantially the same as those of claims 24. Therefore, the elements of claim 32 is rejected due to the same reasons as outlined above for claim 24.
Claim(s) 25 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Schroer and
Tremblay and Wang and Wu as applied to claim 24 and 32 above, and further in view of Debord (“PROPOSITION OF A DESIGN RULES FRAMEWORK,” published in 2018).
Claim 25, Schroer and Tremblay and Wang and Wu fail to teach The method according to claim 24, which further comprises at least one of providing the coded rule module as a knowledge graph or providing additional layout data with manufacturing process semantic information.
Debord teaches at least one of providing the coded rule module as a knowledge graph or providing additional layout data with manufacturing process semantic information (Figure.7; Page.7, 3.3 Design rules framework, “Ontology-based knowledge representation allows to capture, model, share, reuse, and maintain design knowledge. To create our ontology we used Protégé [23] which relies on the language OWL.” Page.8, “coupling the reasoning capabilities of OWL with inference rules enables the attribution of the design rules of interest (4) to a design rules set (5) that matches the designer’s need.” Examiner note: the reference teaches an ontology based design rules framework implemented using OWL and Protégé, wherein the ontology framework represents design knowledge, design rules, concepts and relationships in a structured ontology representation. The framework further uses inference rules to attribute applicable design rules to a design rule set matching a designer’s need, thereby corresponding to a coded rule module that applies rule based logic. Further, the ontology framework represents interconnected concepts and relationships in a structured knowledge representation, the framework corresponds to providing the coded rule module as knowledge graph).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schroer and Tremblay and Wang and Wu to incorporate the teachings of Debord and applying ontology based knowledge representation and inference rule processing for managing and applying design rules and design context relationships in order to enhance rule based processing and automated decision making associated with plant layout generation and modification. The combination of teachings would predictably provide the benefit of improving automation, consistency, adaptability, and rule driven layout generation in manufacturing and plant design environments.
The elements of claim 33 is substantially the same as those of claims 25. Therefore, the elements of claim 33 is rejected due to the same reasons as outlined above for claim 25.
Claim(s) 28 is rejected under 35 U.S.C. 103 as being unpatentable over Schroer and Tremblay and
Wang (‘585) as applied to claim 21 above, and further in view of Wang US20200074215A1.
Claim 28, Schroer and Tremblay and Wang (‘585) fail to teach The method according to claim 21,
which further comprises training a Machine Learning function with a You Only Look Once algorithm.
Wang (‘215) teaches training a Machine Learning function with a You Only Look Once algorithm ([0038] Training data 126 can include images with pre-marked or pre-labeled areas indicating final locations for multiple parts of a vehicle. Training data 126 can also include a plurality of sample images of a predetermined class corresponding to a respective part of a vehicle. Server 108 can receive training data 126 (as training data 128), and use training data 128 to train a neural model based on an algorithm, such as the You Only Look Once v2 (“yolov2”) algorithm, which can result in a trained algorithm (function 129). Server 108 can subsequently use the trained algorithm to detect and identify vehicle parts on incoming captured images (e.g., captured image 122 of vehicle 120, as received from computing device 104 and as taken by user 102 using computing device 104).”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schroer and Tremblay and Wang (‘585)to incorporate the teachings of Wang (‘215) and applying You Only Look Once v2 (“yolov2”) algorithm in order to achieve high-speed, real-time object detection by framing the process as a single regression problem rather than a multi-stage pipeline.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
P. Henderson et al., “Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision,” published in Nov 2018, discloses a unified framework tackling two problems … Importantly, it can be trained purely from 2D images, without ground-truth pose annotations, and with a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to exploit shading information during training, …
Cadmatic, “Cadmatic 3D plant Design seat,” published 2015Q3, discloses Cadmatic 3D Plant Design seat is an integrated, database-driven design module and provides powerful tools for 3D layout-, piping-, HVAC-, cable tray- and structural design of plants in shaded and colored views. It produces information for installation and ordering materials.
Sfar US20190205485A1, discloses a computer-implemented method for generating a 3D model representing a building. The method comprises providing a 2D floor plan representing a layout of the building. The method also comprises determining a semantic segmentation of the 2D floor plan. The method also comprises determining the 3D model based on the semantic segmentation. Such a method provides an improved solution for processing a 2D floor plan.
S. Dodge et al., “Parsing Floor Plan Images,” published in May 2017, discloses analyzing floor plan images using wall segmentation, object detection, and optical character recognition. Introducing a challenging new real-estate floor plan dataset, R-FP, evaluate different wall segmentation methods, and propose fully convolutional networks (FCN) for this task.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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