DETAILED ACTION
This Office Action is sent in response to the Applicant’s Communication received on 04/14/2026 for application number 18/250,436. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, IDS, and Claims.
Claim 1, 10-13, 15, and 16 are amended.
Claims 1-16 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Objections
In view of the amendments to claims 10, 15, and 16, the corresponding objections are withdrawn.
35 USC 101
On page 9 of the remarks section, Applicant argues that amended claim 12 now recites "a database for managing a plurality of computer-aided design (CAD) knowledge models" and "one or more processors coupled to the database," with "the one or more processors" configured to cause the computer system to perform the recited operations. As amended, claim 13 now recites a computer program product in which "the computer program" is "stored at a memory," the memory is "coupled to one or more processors," and the computer program, when executed on the one or more processors, causes a computer system to perform the recited operations. These amendments provide the structural features identified as missing in the Office Action. Amended claim 12 is directed to a statutory computer system including expressly recited hardware components. Amended claim 13 is directed to a statutory computer program product implemented using memory and processors. Claims 12 and 13 therefore are not directed to software per se or data per se. Rather, claims 12 and 13 recite specific computer-based implementations using identified computing structure.
The Examiner finds the Applicant’s argument persuasive. Therefore, the 35 USC 101 rejection is withdrawn.
35 USC 103
On pages 11 and 12 of the remarks section, Applicant argues that De Keyser does not disclose obtaining training records for each input CAD-identifier, nor training a machine learning module for each input CAD-identifier, Kamiyama does not disclose machine learning training, much less "training a computer-implemented machine learning module for each input CAD-identifier based on the corresponding set of training records, and Shuiming does not disclose machine learning training at all. Accordingly, the cited references do not disclose or suggest the claimed per-CAD-identifier training arrangement. The Office Action does not identify any disclosure showing that a separate machine learning module is trained for each CAD-identifier. The claimed features cannot be reconstructed from the cited references without hindsight.
The Examiner respectfully disagrees. First, one reference alone was not cited as teaching the entirety of the limitations of claim 1. Rather, it would have been obvious for the combination of references to have suggested the methodology of claim. To clarify, for the limitation “obtaining a set of training records for each input CAD-identifier”, reference De Keyser (DK) teaches “obtaining a set of training records for each input” in paragraph 0026: “for each record of the set of records (for each input) the following input (a set of training records) is provided (obtaining) to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record” and reference Shuiming teaches “obtaining training records for each CAD-identifier” in paragraph 0023: “The attribute record update module is used to obtain the attribute record ID from the extended data of the CAD drawing entity after modification, query the record in the database (obtaining training records) attribute table based on the ID value (for each CAD-identifier), and update the database attribute table”. For the limitation “training a computer-implemented machine learning module for each input CAD-identifier based on the corresponding set of training records”, reference DK teaches “training a computer-implemented machine learning module for each input based on the corresponding set of training records” in paragraph 0013: “The insertion of recurring constructional connections in relation to pairs of construction elements is simplified by the computer-implemented machine learning module (a computer-implemented machine learning module). The module can be trained (training) via the set of records (for each input based on the corresponding set of training records)”, and reference Kamiyama teaches “training a module for each CAD-identifier based on set of records” in paragraph 0007: “a method for managing CAD knowledge, comprising:… associating the knowledge model with elements of a CAD model by adding association information (for each CAD-identifier) to the elements (based on set of records); linking, as a verification object, one or more elements in the CAD model with one or more corresponding elements in the knowledge model; and updating (training a module) substantially immediately, a change in value of the one or more elements in the knowledge model in both the CAD model and in the knowledge model”. Second, in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
On pages 12 of the remarks section, the Applicant argues that Kamiyama's verification objects are not the claimed CAD-identifiers. Kamiyama explains that constraint blocks in a SysML knowledge model may be designated as verification objects of corresponding elements in a CAD model. Kamiyama, paragraph 0019. That disclosure concerns linking CAD model elements to SysML knowledge model elements for verification and synchronization. It does not disclose a CAD-identifier corresponding to a parameter of a parametric CAD-model, as recited in claim 1.
The Examiner respectfully disagrees. Paragraph 0019 of Kamiyama clearly demonstrates that verification objects correspond to elements in a CAD model, “Constraint blocks in the SysML knowledge model may be designated as verification objects of corresponding elements in the CAD model”. Moreover, paragraph 0016 of Kamiyama clearly demonstrates that parameters from a CAD model may be instantiated as property values in the SysML knowledge model and applied to the evaluation and calculation equations in constraint blocks.
On pages 12 and 13 of the remarks section, the Applicant argues that Kamiyama's discussion of parameters instantiated as property values and applied to evaluation and calculation equations does not disclose the claimed database structure in which each data field comprises a data slot and a CAD-identifier corresponding to a parameter of a parametric CAD-model, and in which each calculation module couples a value of a corresponding input and output data slot via a formula. Shuiming also does not disclose these features. Shuiming stores attributes associated with CAD graphic entities using handles and record IDs. Shuiming's entity handles and database IDs are identifiers for graphic entities and records. They are not CAD-identifiers corresponding to parameters of a parametric CAD-model. Nor does Shuiming disclose that "each calculation module couples a value of a corresponding input and output data slot via a formula." De Keyser does not disclose these features either. De Keyser concerns desirability prediction for BIM details and does not disclose the claimed parameter indexed database structure.
The Examiner respectfully disagrees. As mentioned above, one reference was not cited as teaching the entirety of the limitations of claim 1. Rather, the combined methodologies of the cited references would have resulted in the methodology of the claim 1. To clarify, DK teaches “wherein each calculation module couples a value of a corresponding input and output data slot via a formula” in paragraph 0034, “the computer-implemented machine learning module (each calculation module) comprises a classification algorithm (via a formula) based on an artificial neural network, a support vector machine, or a decision tree” and in paragraphs 0036-0037, “training an artificial neural network upon receiving as input for each pair of one or more pairs (couples a value of a corresponding input) at least one further property of the corresponding BIM and a corresponding binary desirability; and outputting a predicted user desirability (and output data slot) upon receiving as input for a pair at least one further property of the corresponding BIM”. Kamiyama teaches “providing a database for managing a plurality of computer-aided design (CAD) knowledge models” in the abstract, “The SysML knowledge model may be stored in a knowledge repository (providing a database) coupled to a knowledge server” and in paragraph 0007, “According to an exemplary embodiment, the previously described problems may be solved by a method for managing CAD knowledge (managing a plurality of computer-aided design (CAD) knowledge models)”. Paragraph 0016 Kamiyama teaches “wherein the database comprises for each knowledge model a plurality of calculation modules”: “The knowledge repository 200 stores (wherein the database comprises) the SysML knowledge model 202, which may include, for example, evaluation and calculation equations as constraint blocks 204 (a plurality of calculation modules) in the knowledge model (for each knowledge model)… The results of the calculations are substantially immediately reflected in the CAD model”. Shuiming teaches “wherein each data field comprises a data slot” in paragraph 0009: “receive other input attributes, and store the attributes (each data field) in the database attribute table (a data slot)”.
On pages 13 and 14 of the remarks section, Applicant argues that neither De Keyser, Kamiyama, nor Shuiming teach "wherein each of said training records at least comprises the starting training input value and the corresponding desired training input value". The Office Action therefore appears to equate De Keyser's binary desirability label with the claimed "desired training input value." Applicant respectfully submits that these are different features. A binary desirability label for a BIM detail is not a desired input value for a parameter of a parametric CAD-model.
The Examiner respectfully disagrees. The term “desired training input value” is broad term recited with a level of generality. Therefore, under the Broadest Reasonable Interpretation, DK’s “binary desirability” reads on the claimed “desired training input value”.
On page 14 of the remarks section, Applicant argues that the claimed training record collection is a real-time, interactive workflow that is structurally absent from all three cited references. In the claimed system, an engineer modifies a starting input value, one or more calculation modules immediately derive a proposed corresponding output value via the formula-coupled data slots, and the engineer's acceptance or rejection of that proposed value registers the pair as a training record. This real-time feedback loop is tightly coupled to the parametric CAD model and its formula-based calculation modules. De Keyser captures only a static, post-hoc binary accept/decline signal applied to an entire BIM detail - not a per-parameter, formula-derived value pair generated interactively at the moment of modification. Kamiyama and Shuiming capture no interactive training feedback whatsoever. Additionally, the claimed system supports multi-user, multibranch workflows: parameter changes, merge conflicts, and knowledge-backed validation are handled across concurrent design branches, enabling consistent
parameter behavior even when multiple engineers modify the same parametric CAD model simultaneously. None of the cited references discloses or suggests such a realtime, multi-user training record structure, and the cited combination provides no
basis for arriving at it without impermissible hindsight.
The Examiner respectfully disagrees. Although appears to be disclosed invention, steps such as “the claimed training record collection is a real-time, interactive Workflow”, “an engineer modifies a starting input value”, “the engineer's acceptance or rejection of that proposed value registers the pair as a training record”, and “system supports multi-user, multibranch workflows: parameter changes, merge conflicts, and knowledge-backed validation are handled across concurrent design branches, enabling consistent parameter behavior even when multiple engineers modify the same parametric CAD model simultaneously” are explicitly recited the claimed limitation. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
On page 15 of the remarks section, Applicant argues that neither De Keyser, Kamiyama, nor Shuiming, even in combination, do not teach "obtaining a proposed desired input value for said starting input value by means of the trained computer-implemented machine learning module". The Office Action's mapping effectively changes the nature of the output from a desirability prediction to a parameter recommendation, but the cited references do not support that change.
The Examiner respectfully disagrees. The cited limitation uses generic terms recited at a high level of generality. Therefore, under BRI, DK does indeed teach “obtaining a proposed desired input value for said starting input value by means of the trained computer-implemented machine learning module”. Specifically, DK teaches “The module can be trained via the set of records (starting input value), thereby learning user desirability (desired input value)” in paragraph 0013, and “a computer-implemented machine learning module is trained based on said set of records for predicting user desirability of a detail. Thereby, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record” in paragraph 0026. It is noted that the rejection does cite paragraph 0029 of DK as teaching the limitation.
On page 15 of the remarks section, Applicant argues that the motivations to combine are generic objectives that do not explain why a person of ordinary skill in the art would have modified De Keyser's BIM desirability prediction system with Kamiyama's SysML knowledge management framework and Shuiming's CAD entity attribute table in a manner that yields the features recited in claim 1.
The Examiner respectfully disagrees. In response to applicant’s argument that “generic objectives that do not explain why a person of ordinary skill in the art would have modified De Keyser's BIM desirability prediction system with Kamiyama's SysML knowledge management framework and Shuiming's CAD entity attribute table in a manner that yields the features recited in claim 1”, specifically, Applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the abstract of Kamiyama summarizes the inventive steps as detailed in the Kamiyama specification to improve maintainability and re-usability of knowledge, thereby reducing workload, while the paragraph 0011 of Shuiming summarizes the inventive steps as detailed in the specification of Shuiming to provide a foundation for effective data processing and management.
On page 16 of the remarks section, Applicant argues that the three cited references address fundamentally different technical domains. A person of ordinary skill in the art would have no motivation to integrate these systems in the specific manner required to arrive at the claimed invention.
The Examiner respectfully disagrees. The Examiner is not suggesting a bodily incorporation of Kamiyama and Shuiming to DK, but rather that the methodology of Kamiyama and Shuiming to the teachings of DK to improve maintainability and re-usability of knowledge, and provide a foundation for effective data processing and management, respectively, would have been an obvious motivation to combine in order to arrive at the claimed limitation. The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
On page 16 of the remarks section, Applicant argues that the claimed invention delivers quantifiable technical gains that are absent from the cited combination: it reduces iterative design cycles by providing engineers with data-backed, near-optimal starting values for each CAD parameter at the outset of design, rather than requiring manual trial-and-error; it prevents recurrence of known design issues by persistently capturing and leveraging prior accepted and rejected parameter configurations; it provides real-time, dependency aware parameter guidance by detecting downstream impacts of changes across the parametric CAD model; and it improves efficiency in multi-user, multi-branch workflows by detecting merge conflicts and validating parameter changes with knowledge-backed insights. These concrete, quantifiable gains - including reduced rework, faster design convergence, and improved parameter consistency across projects - arise specifically from the per-parameter ML training architecture, the formula-coupled data structure, and the real-time interactive training record collection, none of which are disclosed or suggested by the cited references individually or in combination.
The Examiner respectfully disagrees. Although appears to be disclosed invention, features such as “reduces iterative design cycles by providing engineers with data-backed, near-optimal starting values for each CAD parameter at the outset of design, rather than requiring manual trial-and-error; it prevents recurrence of known design issues by persistently capturing and leveraging prior accepted and rejected parameter configurations; it provides real-time, dependency aware parameter guidance by detecting downstream impacts of changes across the parametric CAD model; and it improves efficiency in multi-user, multi-branch workflows by detecting merge conflicts and validating parameter changes with knowledge-backed insights” are not explicitly recited the claimed limitation. Moreover, the specific terms “per-parameter ML training architecture”, “the formula-coupled data structure”, and “real-time interactive training record collection” are not part of the explicit claim language. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
On page 17, in regards to independent claims 12 and 13, Applicant argues that for at least some of the reasons discussed above, Applicant respectfully submits that the applied references fail to disclose, teach, or suggest each and every feature of claims 12 and 13. In particular, the applied references fail to disclose, teach, or suggest "obtaining a set of training records for each input CAD-identifier," "training a computer-implemented machine learning module for each input CAD-identifier based on the corresponding set of training records," "wherein each data field comprises a data slot for receiving a value and a CAD-identifier corresponding to a parameter of a parametric CAD-model," "wherein each of said training records at least comprises the starting training input value and the corresponding desired training input value," and "obtaining a proposed desired input value for said starting input value by means of the trained computer-implemented machine learning module."
The Examiner respectfully disagrees. Applicant appears to have similar arguments regarding claims 12 and 13 as in claim 1. Therefore, Applicant’s arguments is not persuasive for similar reasons as claim 1 above.
Lastly, on page 17, in regards to dependent claims, Applicant argues that the other claims currently under consideration in this application are dependent from independent claim 1 or claim 16, discussed above, and are believed to be allowable for at least similar reasons. Because each dependent claim is deemed to define an additional aspect of the invention, the individual consideration of each on its own merits is respectfully requested. Accordingly, reconsideration and withdrawal of the rejections of the dependent claims are respectfully requested. Additionally, Applicant further addresses the rejections of several dependent claims below.
The Examiner respectfully disagrees. The Applicant has not presented any new additional arguments regarding the dependent claims. Therefore, the rejection of the dependent claims are sustained.
Therefore, 35 USC 103 rejection is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
At least, claims 12, 13, 15, and 16 are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In this instant the applicant AMENDED Claims 12 and 13 and introduced new subject matter. Independent claim 12 in-part recites: "one or more processors". Independent claim 13 in-part recites: "a memory" and “the memory coupled to one or more processers”. However, careful review of the specifications do not show sufficient support. Consistent with MPEP 2163.04, the (1) Applicant has not pointed out, and (2) it is not clear from reading the specification as articulated in the above mentioned paragraphs, where the amended claim limitations “one or more processors", "a memory" and “the memory coupled to one or more processers” are supported. In conclusion, here, the claim is an amended claim, the support for these limitations are not apparent, and applicant has not pointed out where the limitations are supported.
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 nonobviousness.
Claim(s) 1-3, 5, 6, 8, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over De Keyser et al. (US 20190026403 A1), hereinafter DK, in view of Kamiyama et al. (US 20100042658 A1), hereinafter Kamiyama, and Shuiming (CN102760171A, see attached translation), hereinafter Shuiming.
Regarding claim 1, DK teaches,
Computer-implemented method for providing insights on user desirability [Para 0024, the present invention provides for a computer-implemented method (CIM) for predicting user desirability], comprising the steps of:
managing a plurality of computer-aided design (CAD) knowledge models [Para 0023; Para 0024, a computer-implemented method (CIM) for predicting user desirability of a detail in a building information model (BIM), comprising several steps… the present invention provides for a computer system for predicting user desirability of a detail in a BIM],
a calculation module [Para 0024, a computer-implemented machine learning module],
wherein each calculation module (Para 0034, computer-implemented machine learning module) couples a value of a corresponding input and output data slot (Paras 0036-0037, outputting a predicted user desirability upon receiving as input) via a formula (Para 0034, classification algorithm) [Para 0034, said step of training the computer-implemented machine learning module comprises the step of updating the digital training data of the computer-implemented machine learning module, based on said at least one further property of the corresponding building information model and said binary desirability, Preferably, the trained computer-implemented machine learning module comprises digital training data and computer-executable instructions for: updating the digital training data based on at least one further property of a BIM and a binary desirability; and outputting a predicted user desirability upon inputting at least one further property of a BIM. Preferably, the computer-implemented machine learning module comprises a classification algorithm based on an artificial neural network, a support vector machine, or a decision tree; Paras 0036-0037, training an artificial neural network upon receiving as input for each pair of one or more pairs at least one further property of the corresponding BIM and a corresponding binary desirability; and outputting a predicted user desirability upon receiving as input for a pair at least one further property of the corresponding BIM];
obtaining a set of training records for each input [Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record],
wherein each of said training records is obtained by: receiving a starting training input value [Para 0025, The first construction elements of the set of records comprise at least one first element property, preferably geometrical property, in common. The second construction elements of the set of records comprise at least one second element property, preferably geometrical property, in common; Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record],
wherein for said starting training input value a corresponding starting training output value is obtained by means of one or more corresponding calculation modules [Paras 0035-0037, the computer-implemented machine learning module comprises computer-executable instructions for: training an artificial neural network upon receiving as input for each pair of one or more pairs at least one further property of the corresponding BIM and a corresponding binary desirability; and outputting a predicted user desirability upon receiving as input for a pair at least one further property of the corresponding BIM];
obtaining a modification to the starting training input or output value [Para 0033, user input is received about a suggested constructional connection, whereby the user input is one of accepting or declining. In this embodiment, the computer-implemented machine learning module is additionally trained based on said received user input. Hereby, the following input is provided to the computer-implemented machine learning module],
wherein for said modified value a proposed corresponding training input (Para 0034, updating the digital training data) or output value is obtained for said knowledge model by means of one or more corresponding calculation modules [Para 0013, The present invention also allows for continuous learning, as acceptance or decline of a suggestion can be used to further train the module; Para 0034, the trained computer-implemented machine learning module comprises digital training data… said step of training the computer-implemented machine learning module comprises the step of updating the digital training data of the computer-implemented machine learning module, based on said at least one further property of the corresponding building information model and said binary desirability],
wherein the modification to the starting training input or output value occurs due to a user action [Para 0013, The present invention also allows for continuous learning, as acceptance or decline of a suggestion can be used to further train the module; Para 0064, The module may be further trained after each acceptance or decline of a detail suggestion. The module may also be trained only after a part of the subset or the whole subset is suggested to the user. The subset may be updated after each acceptance or decline of detail suggestion. The subset may also be updated only after a part of the subset or the whole subset is suggested to the user];
obtaining an acceptation indicator (Para 0064, acceptance or decline of detail suggestion) for the proposed corresponding training input or output value [Para 0064, The module may be further trained after each acceptance or decline of a detail suggestion. The module may also be trained only after a part of the subset or the whole subset is suggested to the user. The subset may be updated after each acceptance or decline of detail suggestion. The subset may also be updated only after a part of the subset or the whole subset is suggested to the user],
wherein said input and output values are thereby registered as a desired training input or output value [Para 0026, The computer-implemented machine learning module may hereby be trained to categorize user desirability of a detail, whereby two categories are available, e.g. “positive” and “negative”, “desired” and “undesired”, “1” and “0”, and the like];
wherein each of said training records at least comprises the starting training input value and the corresponding desired training input value [Para 0025, The first construction elements of the set of records comprise at least one first element property, preferably geometrical property, in common. The second construction elements of the set of records comprise at least one second element property, preferably geometrical property, in common; Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record… The computer-implemented machine learning module may hereby be trained to categorize user desirability of a detail, whereby two categories are available, e.g. “positive” and “negative”, “desired” and “undesired”, “1” and “0”, and the like; Para 0029, for each pair of said set of pairs a user desirability is predicted, comprising the step of inputting at least one further property of the corresponding BIM to the trained computer-implemented machine learning module. A predicted user desirability may be a binary desirability… A predicted user desirability may… be a predicted percentage likelihood of (“positive”) user desirability];
training a computer-implemented machine learning module for each input based on the corresponding set of training records [Para 0013, The insertion of recurring constructional connections in relation to pairs of construction elements is simplified by the computer-implemented machine learning module. The module can be trained via the set of records];
providing a starting input value of a CAD-knowledge model [Para 0025, a set of records is obtained. Each record comprises digital data on user input about a detail in relation to a first and a second construction element in a BIM],
wherein for said starting input value a corresponding output value (Para 0026, predicting user desirability) is obtained by means of one or more corresponding calculation modules (Para 0026, computer-implemented machine learning module) [Para 0026, a computer-implemented machine learning module is trained based on said set of records for predicting user desirability of a detail. Thereby, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record]; and
obtaining a proposed desired input value for said starting input value by means of the trained computer-implemented machine learning module [Para 0013, The module can be trained via the set of records, thereby learning user desirability; Para 0026, a computer-implemented machine learning module is trained based on said set of records for predicting user desirability of a detail. Thereby, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record].
DK does not teach providing a database for managing a plurality of computer-aided design (CAD) knowledge models; wherein the database comprises for each knowledge model a plurality of input and output data fields and a plurality of calculation modules, wherein each data field comprises a data slot for receiving a value and a CAD-identifier corresponding to a parameter of a parametric CAD-model; obtaining training records for each CAD-identifier; receiving input in one of the input data slots of database; training a module for each CAD-identifier based on set of records; providing a input value in an input data slot.
Kamiyama teaches,
providing a database (Abstract, knowledge repository) for managing a plurality of computer-aided design (CAD) knowledge models [Abstract, A knowledge model of CAD knowledge may be created using a modeling language such as SysML to improve maintainability and re-usability of knowledge, thereby reducing workload. The SysML knowledge model may be stored in a knowledge repository coupled to a knowledge server. The SysML knowledge model may be accessed through the knowledge server; Para 0007, According to an exemplary embodiment, the previously described problems may be solved by a method for managing CAD knowledge];
wherein the database comprises for each knowledge model a plurality of calculation modules (Para 0016, evaluation and calculation equations as constraint blocks 204) [Para 0016, The knowledge repository 200 stores the SysML knowledge model 202, which may include, for example, evaluation and calculation equations as constraint blocks 204 in the knowledge model… The results of the calculations are substantially immediately reflected in the CAD model 222];
a CAD-identifier (Para 0019, verification objects) corresponding to a parameter of a parametric CAD-model (Para 0016, parameters may be instantiated as property values in the SysML knowledge model 202 and applied to… constraint blocks) [Para 0016, Knowledge server 210 may receive, from CAD application 220, parameters such as dimensions from a CAD model 222, and instantiate the SysML knowledge model 202 using the parameters received from the CAD model 222. The parameters may be instantiated as property values in the SysML knowledge model 202 and applied to the evaluation and calculation equations in constraint blocks 204; Para 0019, Constraint blocks in the SysML knowledge model may be designated as verification objects of corresponding elements in the CAD model];
training a module for each CAD-identifier based on set of records [Para 0007, a method for managing CAD knowledge, comprising:… associating the knowledge model with elements of a CAD model by adding association information to the elements; linking, as a verification object, one or more elements in the CAD model with one or more corresponding elements in the knowledge model; and updating substantially immediately, a change in value of the one or more elements in the knowledge model in both the CAD model and in the knowledge model]Kamiyana is analogous to the claimed invention as they both relate to CAD knowledge management. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK’s teachings to incorporate the teachings of Kamiyama and provide managing CAD models in order to [Kamiyama, Abstract] improve maintainability and re-usability of knowledge, thereby reducing workload.
DK-Kamiyama do not teach a plurality of input and output data fields, wherein each data field comprises a data slot for receiving a value; obtaining training records for each CAD-identifier; receiving input in one of the input data slots of database; providing a input value in an input data slot.
Shuiming teaches,
a plurality of input (Para 0009, input attributes) and output (Para 0013, attribute label) data fields, wherein each data field comprises a data slot for receiving a value (Para 0009, database attribute table) [Para 0009, receive other input attributes, and store the attributes in the database attribute table; Para 0013, after modifying the attribute record in the database attribute table, the graphic entity is found on the graphic based on the entity handle in the attribute table, and the attribute label associated with the graphic entity is modified].
obtaining training records (Para 0023, query the record in the database) for each CAD-identifier (Para 0023, based on the ID value) [Para 0023, The attribute record update module is used to obtain the attribute record ID from the extended data of the CAD drawing entity after modification, query the record in the database attribute table based on the ID value, and update the database attribute table];
receiving input in one of the input data slots of database [Para 0009, receive other input attributes, and store the attributes in the database attribute table;];
providing a input value in an input data slot [Para 0009, receive other input attributes, and store the attributes in the database attribute table;].
Shuiming is analogous to the claimed invention as they both relate to processing CAD information. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK and Kamiyama’s teachings to incorporate the teachings of Shuiming and provide training input data to [Shuiming, para 0011] provide a foundation for effective data processing and management.
Regarding claim 2, DK-Kamiyama-Shuiming teach the limitations of claim 1.
DK further teaches,
wherein the computer-implemented machine learning module comprises an algorithm based on machine learning and/or statistical learning [Para 0034, the computer-implemented machine learning module comprises a classification algorithm based on an artificial neural network, a support vector machine, or a decision tree].
Regarding claim 3, DK-Kamiyama-Shuiming teach the limitations of claim 1.
wherein the computer- implemented machine learning module comprises an algorithm based on an artificial neural network, a support vector machine, or decision tree [Para 0034, the computer-implemented machine learning module comprises a classification algorithm based on an artificial neural network, a support vector machine, or a decision tree].
Regarding claim 5, DK-Kamiyama-Shuiming teach the limitations of claim 1.
DK further teaches,
wherein obtaining an acceptation indicator for the proposed corresponding starting input or output value occurs by means of a binary indicator, which is positive in case of accepting and negative in case of declining [Para 0025, The first construction elements of the set of records comprise at least one first element property, preferably geometrical property, in common. The second construction elements of the set of records comprise at least one second element property, preferably geometrical property, in common; Para 0033, the computer-implemented machine learning module is additionally trained based on said received user input. Hereby, the following input is provided to the computer-implemented machine learning module:… the binary desirability, which is positive in case of accepting and negative in case of declining].
Regarding claim 6, DK-Kamiyama-Shuiming teach the limitations of claim 1.
DK further teaches,
wherein obtaining an acceptation indicator for the proposed corresponding starting input or output value occurs by means of a score [Para 0025, The first construction elements of the set of records comprise at least one first element property, preferably geometrical property, in common. The second construction elements of the set of records comprise at least one second element property, preferably geometrical property, in common; Para 0029, A predicted user desirability may… be a predicted percentage likelihood of (“positive”) user desirability].
Regarding claim 8, DK-Kamiyama-Shuiming teach the limitations of claim 1.
Shuiming further teaches,
wherein each of the input and output data fields further comprise one or more of a component indicator [Para 0011, the attributes of area entities are stored in the database using their graphic handles and other key information as keywords; Para 0013, the attribute label associated with the graphic entity], a package indicator, a project indicator and a unit associated to the value.
Shuiming is analogous to the claimed invention as they both relate to processing CAD information. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK and Kamiyama’s teachings to incorporate the teachings of Shuiming and provide training input data to [Shuiming, para 0011] provide a foundation for effective data processing and management.
Regarding claim 12, DK teaches,
Computer system for providing insights on user desirability [Abstract, The current invention concerns a method, a system, and a computer program product for suggesting a detail in a building information model; Para 0024, the present invention provides for a computer-implemented method (CIM) for predicting user desirability], the computer system comprising:
One or more processors coupled to database [Para 0024, The second aspect may in particular relate to a computer system comprising at least one processor, a visualization means, at least one user input device, and a computer-implemented machine learning module; Para 0068, the invention provides in storing analysis data on a tangible non-transitory computer-readable storage medium],
managing a plurality of computer-aided design (CAD) knowledge models [Para 0023; Para 0024, a computer-implemented method (CIM) for predicting user desirability of a detail in a building information model (BIM), comprising several steps… the present invention provides for a computer system for predicting user desirability of a detail in a BIM],
a calculation module [Para 0024, a computer-implemented machine learning module],
wherein each calculation module (Para 0034, computer-implemented machine learning module) couples a value of a corresponding input and output data slot (Paras 0036-0037, outputting a predicted user desirability upon receiving as input) via a formula (Para 0034, classification algorithm) [Para 0034, said step of training the computer-implemented machine learning module comprises the step of updating the digital training data of the computer-implemented machine learning module, based on said at least one further property of the corresponding building information model and said binary desirability, Preferably, the trained computer-implemented machine learning module comprises digital training data and computer-executable instructions for: updating the digital training data based on at least one further property of a BIM and a binary desirability; and outputting a predicted user desirability upon inputting at least one further property of a BIM. Preferably, the computer-implemented machine learning module comprises a classification algorithm based on an artificial neural network, a support vector machine, or a decision tree; Paras 0036-0037, training an artificial neural network upon receiving as input for each pair of one or more pairs at least one further property of the corresponding BIM and a corresponding binary desirability; and outputting a predicted user desirability upon receiving as input for a pair at least one further property of the corresponding BIM]; wherein the one or more processors are configured to cause computer system to perform [Para 0024, The second aspect may in particular relate to a computer system comprising at least one processor, a visualization means, at least one user input device, and a computer-implemented machine learning module]:
obtaining a set of training records for each input [Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record],
wherein each of said training records is obtained by: receiving a starting training input value [Para 0025, The first construction elements of the set of records comprise at least one first element property, preferably geometrical property, in common. The second construction elements of the set of records comprise at least one second element property, preferably geometrical property, in common; Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record],
wherein for said starting training input value a corresponding starting training output value is obtained by means of one or more corresponding calculation modules [Paras 0035-0037, the computer-implemented machine learning module comprises computer-executable instructions for: training an artificial neural network upon receiving as input for each pair of one or more pairs at least one further property of the corresponding BIM and a corresponding binary desirability; and outputting a predicted user desirability upon receiving as input for a pair at least one further property of the corresponding BIM];
obtaining a modification to the starting training input or output value [Para 0033, user input is received about a suggested constructional connection, whereby the user input is one of accepting or declining. In this embodiment, the computer-implemented machine learning module is additionally trained based on said received user input. Hereby, the following input is provided to the computer-implemented machine learning module],
wherein for said modified value a proposed corresponding training input (Para 0034, updating the digital training data) or output value is obtained for said knowledge model by means of one or more corresponding calculation modules [Para 0013, The present invention also allows for continuous learning, as acceptance or decline of a suggestion can be used to further train the module; Para 0034, the trained computer-implemented machine learning module comprises digital training data… said step of training the computer-implemented machine learning module comprises the step of updating the digital training data of the computer-implemented machine learning module, based on said at least one further property of the corresponding building information model and said binary desirability],
wherein the modification to the starting training input or output value occurs due to a user action [Para 0013, The present invention also allows for continuous learning, as acceptance or decline of a suggestion can be used to further train the module; Para 0064, The module may be further trained after each acceptance or decline of a detail suggestion. The module may also be trained only after a part of the subset or the whole subset is suggested to the user. The subset may be updated after each acceptance or decline of detail suggestion. The subset may also be updated only after a part of the subset or the whole subset is suggested to the user];
obtaining an acceptation indicator (Para 0064, acceptance or decline of detail suggestion) for the proposed corresponding training input or output value [Para 0064, The module may be further trained after each acceptance or decline of a detail suggestion. The module may also be trained only after a part of the subset or the whole subset is suggested to the user. The subset may be updated after each acceptance or decline of detail suggestion. The subset may also be updated only after a part of the subset or the whole subset is suggested to the user],
wherein said input and output values are thereby registered as a desired training input or output value [Para 0026, The computer-implemented machine learning module may hereby be trained to categorize user desirability of a detail, whereby two categories are available, e.g. “positive” and “negative”, “desired” and “undesired”, “1” and “0”, and the like];
wherein each of said training records at least comprises the starting training input value and the corresponding desired training input value [Para 0025, The first construction elements of the set of records comprise at least one first element property, preferably geometrical property, in common. The second construction elements of the set of records comprise at least one second element property, preferably geometrical property, in common; Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record… The computer-implemented machine learning module may hereby be trained to categorize user desirability of a detail, whereby two categories are available, e.g. “positive” and “negative”, “desired” and “undesired”, “1” and “0”, and the like; Para 0029, for each pair of said set of pairs a user desirability is predicted, comprising the step of inputting at least one further property of the corresponding BIM to the trained computer-implemented machine learning module. A predicted user desirability may be a binary desirability… A predicted user desirability may… be a predicted percentage likelihood of (“positive”) user desirability];
training a computer-implemented machine learning module for each input based on the corresponding set of training records [Para 0013, The insertion of recurring constructional connections in relation to pairs of construction elements is simplified by the computer-implemented machine learning module. The module can be trained via the set of records];
providing a starting input value of a CAD-knowledge model [Para 0025, a set of records is obtained. Each record comprises digital data on user input about a detail in relation to a first and a second construction element in a BIM],
wherein for said starting input value a corresponding output value (Para 0026, predicting user desirability) is obtained by means of one or more corresponding calculation modules (Para 0026, computer-implemented machine learning module) [Para 0026, a computer-implemented machine learning module is trained based on said set of records for predicting user desirability of a detail. Thereby, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record]; and
obtaining a proposed desired input value for said starting input value by means of the trained computer-implemented machine learning module [Para 0013, The module can be trained via the set of records, thereby learning user desirability; Para 0026, a computer-implemented machine learning module is trained based on said set of records for predicting user desirability of a detail. Thereby, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record].
DK does not teach providing a database for managing a plurality of computer-aided design (CAD) knowledge models; wherein the database comprises for each knowledge model a plurality of input and output data fields and a plurality of calculation modules, wherein each data field comprises a data slot for receiving a value and a CAD-identifier corresponding to a parameter of a parametric CAD-model; obtaining training records for each CAD-identifier; receiving input in one of the input data slots of database; training a module for each CAD-identifier based on set of records; providing a input value in an input data slot.
Kamiyama teaches,
providing a database (Abstract, knowledge repository) for managing a plurality of computer-aided design (CAD) knowledge models [Abstract, A knowledge model of CAD knowledge may be created using a modeling language such as SysML to improve maintainability and re-usability of knowledge, thereby reducing workload. The SysML knowledge model may be stored in a knowledge repository coupled to a knowledge server. The SysML knowledge model may be accessed through the knowledge server; Para 0007, According to an exemplary embodiment, the previously described problems may be solved by a method for managing CAD knowledge];
wherein the database comprises for each knowledge model a plurality of calculation modules (Para 0016, evaluation and calculation equations as constraint blocks 204) [Para 0016, The knowledge repository 200 stores the SysML knowledge model 202, which may include, for example, evaluation and calculation equations as constraint blocks 204 in the knowledge model… The results of the calculations are substantially immediately reflected in the CAD model 222];
a CAD-identifier (Para 0019, verification objects) corresponding to a parameter of a parametric CAD-model (Para 0016, parameters may be instantiated as property values in the SysML knowledge model 202 and applied to… constraint blocks) [Para 0016, Knowledge server 210 may receive, from CAD application 220, parameters such as dimensions from a CAD model 222, and instantiate the SysML knowledge model 202 using the parameters received from the CAD model 222. The parameters may be instantiated as property values in the SysML knowledge model 202 and applied to the evaluation and calculation equations in constraint blocks 204; Para 0019, Constraint blocks in the SysML knowledge model may be designated as verification objects of corresponding elements in the CAD model];
training a module for each CAD-identifier based on set of records [Para 0007, a method for managing CAD knowledge, comprising:… associating the knowledge model with elements of a CAD model by adding association information to the elements; linking, as a verification object, one or more elements in the CAD model with one or more corresponding elements in the knowledge model; and updating substantially immediately, a change in value of the one or more elements in the knowledge model in both the CAD model and in the knowledge model].
Kamiyana is analogous to the claimed invention as they both relate to CAD knowledge management. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK’s teachings to incorporate the teachings of Kamiyama and provide managing CAD models in order to [Kamiyama, Abstract] improve maintainability and re-usability of knowledge, thereby reducing workload.
DK-Kamiyama do not teach a plurality of input and output data fields, wherein each data field comprises a data slot for receiving a value; obtaining training records for each CAD-identifier; receiving input in one of the input data slots of database; providing a input value in an input data slot.
Shuiming teaches,
a plurality of input (Para 0009, input attributes) and output (Para 0013, attribute label) data fields, wherein each data field comprises a data slot for receiving a value (Para 0009, database attribute table) [Para 0009, receive other input attributes, and store the attributes in the database attribute table; Para 0013, after modifying the attribute record in the database attribute table, the graphic entity is found on the graphic based on the entity handle in the attribute table, and the attribute label associated with the graphic entity is modified].
obtaining training records (Para 0023, query the record in the database) for each CAD-identifier (Para 0023, based on the ID value) [Para 0023, The attribute record update module is used to obtain the attribute record ID from the extended data of the CAD drawing entity after modification, query the record in the database attribute table based on the ID value, and update the database attribute table];
receiving input in one of the input data slots of database [Para 0009, receive other input attributes, and store the attributes in the database attribute table;];
providing a input value in an input data slot [Para 0009, receive other input attributes, and store the attributes in the database attribute table;].
Shuiming is analogous to the claimed invention as they both relate to processing CAD information. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK and Kamiyama’s teachings to incorporate the teachings of Shuiming and provide training input data to [Shuiming, para 0011] provide a foundation for effective data processing and management.
Regarding claim 13, DK teaches,
Computer program product for providing insights on user desirability [Abstract, The current invention concerns a method, a system, and a computer program product for suggesting a detail in a building information model; Para 0024, the present invention provides for a computer-implemented method (CIM) for predicting user desirability], the computer program stored at a memory and the memory coupled to one or more processors, wherein the computer program, when executed on one or more processors, cause a computer system [Para 0024, The second aspect may in particular relate to a computer system comprising at least one processor, a visualization means, at least one user input device, and a computer-implemented machine learning module. In a third aspect, the present invention provides for a computer program product (CPP) for predicting user desirability of a detail in a BIM, whereby the CPP comprises instructions which, when the CPP is executed by a computer; Para 0050, a first tangible non-transitory computer-readable storage medium to a second tangible non-transitory computer-readable storage medium without intermediate visualization] to perform:
managing a plurality of computer-aided design (CAD) knowledge models [Para 0023; Para 0024, a computer-implemented method (CIM) for predicting user desirability of a detail in a building information model (BIM), comprising several steps… the present invention provides for a computer system for predicting user desirability of a detail in a BIM],
a calculation module [Para 0024, a computer-implemented machine learning module],
wherein each calculation module (Para 0034, computer-implemented machine learning module) couples a value of a corresponding input and output data slot (Paras 0036-0037, outputting a predicted user desirability upon receiving as input) via a formula (Para 0034, classification algorithm) [Para 0034, said step of training the computer-implemented machine learning module comprises the step of updating the digital training data of the computer-implemented machine learning module, based on said at least one further property of the corresponding building information model and said binary desirability, Preferably, the trained computer-implemented machine learning module comprises digital training data and computer-executable instructions for: updating the digital training data based on at least one further property of a BIM and a binary desirability; and outputting a predicted user desirability upon inputting at least one further property of a BIM. Preferably, the computer-implemented machine learning module comprises a classification algorithm based on an artificial neural network, a support vector machine, or a decision tree; Paras 0036-0037, training an artificial neural network upon receiving as input for each pair of one or more pairs at least one further property of the corresponding BIM and a corresponding binary desirability; and outputting a predicted user desirability upon receiving as input for a pair at least one further property of the corresponding BIM];
obtaining a set of training records for each input [Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record],
wherein each of said training records is obtained by: receiving a starting training input value [Para 0025, The first construction elements of the set of records comprise at least one first element property, preferably geometrical property, in common. The second construction elements of the set of records comprise at least one second element property, preferably geometrical property, in common; Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record],
wherein for said starting training input value a corresponding starting training output value is obtained by means of one or more corresponding calculation modules [Paras 0035-0037, the computer-implemented machine learning module comprises computer-executable instructions for: training an artificial neural network upon receiving as input for each pair of one or more pairs at least one further property of the corresponding BIM and a corresponding binary desirability; and outputting a predicted user desirability upon receiving as input for a pair at least one further property of the corresponding BIM];
obtaining a modification to the starting training input or output value [Para 0033, user input is received about a suggested constructional connection, whereby the user input is one of accepting or declining. In this embodiment, the computer-implemented machine learning module is additionally trained based on said received user input. Hereby, the following input is provided to the computer-implemented machine learning module],
wherein for said modified value a proposed corresponding training input (Para 0034, updating the digital training data) or output value is obtained for said knowledge model by means of one or more corresponding calculation modules [Para 0013, The present invention also allows for continuous learning, as acceptance or decline of a suggestion can be used to further train the module; Para 0034, the trained computer-implemented machine learning module comprises digital training data… said step of training the computer-implemented machine learning module comprises the step of updating the digital training data of the computer-implemented machine learning module, based on said at least one further property of the corresponding building information model and said binary desirability],
wherein the modification to the starting training input or output value occurs due to a user action [Para 0013, The present invention also allows for continuous learning, as acceptance or decline of a suggestion can be used to further train the module; Para 0064, The module may be further trained after each acceptance or decline of a detail suggestion. The module may also be trained only after a part of the subset or the whole subset is suggested to the user. The subset may be updated after each acceptance or decline of detail suggestion. The subset may also be updated only after a part of the subset or the whole subset is suggested to the user];
obtaining an acceptation indicator (Para 0064, acceptance or decline of detail suggestion) for the proposed corresponding training input or output value [Para 0064, The module may be further trained after each acceptance or decline of a detail suggestion. The module may also be trained only after a part of the subset or the whole subset is suggested to the user. The subset may be updated after each acceptance or decline of detail suggestion. The subset may also be updated only after a part of the subset or the whole subset is suggested to the user],
wherein said input and output values are thereby registered as a desired training input or output value [Para 0026, The computer-implemented machine learning module may hereby be trained to categorize user desirability of a detail, whereby two categories are available, e.g. “positive” and “negative”, “desired” and “undesired”, “1” and “0”, and the like];
wherein each of said training records at least comprises the starting training input value and the corresponding desired training input value [Para 0025, The first construction elements of the set of records comprise at least one first element property, preferably geometrical property, in common. The second construction elements of the set of records comprise at least one second element property, preferably geometrical property, in common; Para 0026, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record… The computer-implemented machine learning module may hereby be trained to categorize user desirability of a detail, whereby two categories are available, e.g. “positive” and “negative”, “desired” and “undesired”, “1” and “0”, and the like; Para 0029, for each pair of said set of pairs a user desirability is predicted, comprising the step of inputting at least one further property of the corresponding BIM to the trained computer-implemented machine learning module. A predicted user desirability may be a binary desirability… A predicted user desirability may… be a predicted percentage likelihood of (“positive”) user desirability];
training a computer-implemented machine learning module for each input based on the corresponding set of training records [Para 0013, The insertion of recurring constructional connections in relation to pairs of construction elements is simplified by the computer-implemented machine learning module. The module can be trained via the set of records];
providing a starting input value of a CAD-knowledge model [Para 0025, a set of records is obtained. Each record comprises digital data on user input about a detail in relation to a first and a second construction element in a BIM],
wherein for said starting input value a corresponding output value (Para 0026, predicting user desirability) is obtained by means of one or more corresponding calculation modules (Para 0026, computer-implemented machine learning module) [Para 0026, a computer-implemented machine learning module is trained based on said set of records for predicting user desirability of a detail. Thereby, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record];
obtaining a proposed desired input value for said starting input value by means of the trained computer-implemented machine learning module [Para 0013, The module can be trained via the set of records, thereby learning user desirability; Para 0026, a computer-implemented machine learning module is trained based on said set of records for predicting user desirability of a detail. Thereby, for each record of the set of records the following input is provided to the module: at least one further property of the corresponding BIM; and a binary desirability according to the record].
DK does not teach providing a database for managing a plurality of computer-aided design (CAD) knowledge models; wherein the database comprises for each knowledge model a plurality of input and output data fields and a plurality of calculation modules, wherein each data field comprises a data slot for receiving a value and a CAD-identifier corresponding to a parameter of a parametric CAD-model; obtaining training records for each CAD-identifier; receiving input in one of the input data slots of database; training a module for each CAD-identifier based on set of records; providing a input value in an input data slot.
Kamiyama teaches,
providing a database (Abstract, knowledge repository) for managing a plurality of computer-aided design (CAD) knowledge models [Abstract, A knowledge model of CAD knowledge may be created using a modeling language such as SysML to improve maintainability and re-usability of knowledge, thereby reducing workload. The SysML knowledge model may be stored in a knowledge repository coupled to a knowledge server. The SysML knowledge model may be accessed through the knowledge server; Para 0007, According to an exemplary embodiment, the previously described problems may be solved by a method for managing CAD knowledge];
wherein the database comprises for each knowledge model a plurality of calculation modules (Para 0016, evaluation and calculation equations as constraint blocks 204) [Para 0016, The knowledge repository 200 stores the SysML knowledge model 202, which may include, for example, evaluation and calculation equations as constraint blocks 204 in the knowledge model… The results of the calculations are substantially immediately reflected in the CAD model 222];
a CAD-identifier (Para 0019, verification objects) corresponding to a parameter of a parametric CAD-model (Para 0016, parameters may be instantiated as property values in the SysML knowledge model 202 and applied to… constraint blocks) [Para 0016, Knowledge server 210 may receive, from CAD application 220, parameters such as dimensions from a CAD model 222, and instantiate the SysML knowledge model 202 using the parameters received from the CAD model 222. The parameters may be instantiated as property values in the SysML knowledge model 202 and applied to the evaluation and calculation equations in constraint blocks 204; Para 0019, Constraint blocks in the SysML knowledge model may be designated as verification objects of corresponding elements in the CAD model];
training a module for each CAD-identifier based on set of records [Para 0007, a method for managing CAD knowledge, comprising:… associating the knowledge model with elements of a CAD model by adding association information to the elements; linking, as a verification object, one or more elements in the CAD model with one or more corresponding elements in the knowledge model; and updating substantially immediately, a change in value of the one or more elements in the knowledge model in both the CAD model and in the knowledge model].
Kamiyana is analogous to the claimed invention as they both relate to CAD knowledge management. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK’s teachings to incorporate the teachings of Kamiyama and provide managing CAD models in order to [Kamiyama, Abstract] improve maintainability and re-usability of knowledge, thereby reducing workload.
DK-Kamiyama do not teach a plurality of input and output data fields, wherein each data field comprises a data slot for receiving a value; obtaining training records for each CAD-identifier; receiving input in one of the input data slots of database; providing a input value in an input data slot.
Shuiming teaches,
a plurality of input (Para 0009, input attributes) and output (Para 0013, attribute label) data fields, wherein each data field comprises a data slot for receiving a value (Para 0009, database attribute table) [Para 0009, receive other input attributes, and store the attributes in the database attribute table; Para 0013, after modifying the attribute record in the database attribute table, the graphic entity is found on the graphic based on the entity handle in the attribute table, and the attribute label associated with the graphic entity is modified].
obtaining training records (Para 0023, query the record in the database) for each CAD-identifier (Para 0023, based on the ID value) [Para 0023, The attribute record update module is used to obtain the attribute record ID from the extended data of the CAD drawing entity after modification, query the record in the database attribute table based on the ID value, and update the database attribute table];
receiving input in one of the input data slots of database [Para 0009, receive other input attributes, and store the attributes in the database attribute table;];
providing a input value in an input data slot [Para 0009, receive other input attributes, and store the attributes in the database attribute table;].
Shuiming is analogous to the claimed invention as they both relate to processing CAD information. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK and Kamiyama’s teachings to incorporate the teachings of Shuiming and provide training input data to [Shuiming, para 0011] provide a foundation for effective data processing and management.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over DK in view of Kamiyama and Shuiming, and in further view of Mirabella et al. (WO 2020023811
A1), hereinafter Mirabella.
Regarding claim 4, DK-Kamiyama-Shuiming teach the limitations of claim 1 including said set of training records for each input CAD-identifier [DK, para 0026; Shuiming, para 0023].
DK-Kamiyama-Shuiming do not teach training records from two or more knowledge models.
Mirabella teaches,
training records (Para 0013, new designs) from two or more knowledge models (Para 0013, pre-existing designs) [Para 0013, Based on the pre- existing designs, CAD models representative of new designs are constructed; Para 0016, the pre-existing designs 103 may be each formatted as a 3D scan, a CAD model, or a topology optimized model; Para 0028, The existing designs may further include new designs that are based on the pre-existing designs; Para 0029, A GAN may be trained using the existing designs to construct the parameterized model].
Mirabella is analogous to the claimed invention as they both relate to CAD models. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK, Kamiyama, and Shuiming’s teachings to incorporate the teachings of Wang and provide training records from two or more knowledge models in order to improve model output by advancing the complexity of training records.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over DK in view of Kamiyama and Shuiming, and in further view of Coutts (US 20080036769 A1), hereinafter Coutts.
Regarding claim 7, DK-Kamiyama-Shuiming teach the limitations of claim 1 including the database (Kamiyama, Abstract), the input and output data fields (Shuiming, para 0408), and the data fields (Shuiming, para 0408).
DK-Kamiyama-Shuiming do not teach an interface module comprising a view selector module; a view- indicator; wherein depending on each of the view-indicators, the view selector module determines which data are shown by the interface module.
Coutts teaches,
an interface module (Para 0007, user interface) comprising a view selector module (Para 0007, means for updating a current reference point), a view- indicator (Para 0007, specification of a new endpoint), wherein depending on each of the view-indicators, the view selector module determines which data (Para 0007, direction) are shown by the interface module [Para 0007, a computer aided design (CAD) system is disclosed that includes a user interface comprising means for updating a current reference point for specifying a plurality of coordinate positions indicating endpoints of a plurality of graphical objects. The user interface can accept successive coordinate positions corresponding to the endpoints from a user, wherein any two endpoints define a direction. Upon specification of a new endpoint, the updating means updates the current reference point to be a penultimate endpoint if the new endpoint and the penultimate endpoint define a new direction. And if the new endpoint and the penultimate endpoint define the same direction as a current direction, the updating means maintains the current reference point (i.e., it does not change the current reference point)].
Coutts is analogous to the claimed invention as they both relate to interfaces for CAD systems. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK, Kamiyama, and Shuiming’s teachings to incorporate the teachings of Coutts and provide an interface module for a CAD system in order to [Coutts, para 0003] provide a user with more flexibility and efficiency in creating and/or modifying drawings.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over DK in view of Kamiyama and Shuiming, and in further view of Arrouye et al. (US 8060514 B2), hereinafter Arrouye.
Regarding claim 9, DK-Kamiyama-Shuiming teach the limitations of claim 1 including the input and output data fields (Shuiming, paras 0009, and 0013), a knowledge model (DK, para 0024), and the database (Kamiyama, Abstract).
DK-Kamiyama-Shuiming do not teach data stored as one or more of a flat file, a structured file, a relational table file or an XML data file.
Arrouye teaches,
data stored as one or more of a flat file, a structured file, a relational table file or an XML data file [Col 8, lines 42-44, the metadata database is maintained as a flat file format as described below, and the file system directory 417 maintains this flat file format].
Arrouye is analogous to the claimed invention as they both relate to data management systems. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK, Kamiyama, and Shuiming teachings to incorporate the teachings of Arrouye and provide data stored on a flat file in order to achieve [Arrouye, col 8, lines 49-50] faster retrieval of information from database.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over DK in view of Kamiyama and Shuiming, and in further view of Arrouye and Camiener et al. (US 20020130869 A1), hereinafter Camiener.
Regarding claim 10, DK-Kamiyama-Shuiming teach the limitations of claim 1 including the input and output data fields (Shuiming, paras 0009, and 0013), a knowledge model (DK, para 0024), and the database (Kamiyama, Abstract).
DK-Kamiyama-Shuiming do not teach wherein data are stored as a string in a flat file, wherein elements of data in a string of a flat file are separated by means of a separation symbol.
Arrouye further teaches,
wherein data are stored as a string in a flat file [Col 8, lines 42-46, the metadata database is maintained as a flat file format as described below, and the file system directory 417 maintains this flat file format. One advantage of a flat file format is that the data is laid out on a storage device as a string of data].
Arrouye is analogous to the claimed invention as they both relate to data management systems. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK, Kamiyama, and Shuiming’s teachings to incorporate the teachings of Arrouye and provide data stored on a flat file in order to achieve [Arrouye, col 8, lines 49-50] faster retrieval of information from database.
DK-Kamiyama-Shuiming-Arrouye teach the above limitations of claim 10 including a string of a flat file (Arrouye, col 8, lines 42-46).
DK-Kamiyama-Shuiming-Arrouye do not teach wherein elements of data of a flat file are separated by means of a separation symbol.
Camiener teaches,
wherein elements of data of a flat file are separated by means of a separation symbol [Para 0027, The neutral file format may be represented in… flat files containing… comma-separated values (CSV)].
Camiener is analogous to the claimed invention as they both relate to interfaces for CAD systems. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK, Kamiyama, and Shuiming’s teachings to incorporate the teachings of Camiener and provide elements of data of a flat file separated by separation symbols in order to improve portability of the model.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over DK in view of Kamiyama and Shuiming, and in further view of STEINBRECHER et al. (US 20230325745 A1), hereinafter Steinbrecher.
Regarding claim 11, DK-Kamiyama-Shuiming teach the limitations of claim 1 including the database (Kamiyama, Abstract).
DK-Kamiyama-Shuiming do not teach wherein a CAD-knowledge model can be exported as one or more of a flat file, a structured file, a relational table file or an XML data file.
Steinbrecher teaches,
wherein a CAD-knowledge model can be exported as one or more of a flat file, a structured file, a relational table file or an XML data file [Para 0383, Import of architectural model 1132; here the architect creates an architectural model in a CAD system (e.g. ArchiCAD), exports the model to a standard file format (e.g. ifc, csv, xml) and loads the file into the DBS].
Steinbrecher is analogous to the claimed invention as they both relate to design systems. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK, Kamiyama, and Shuiming teachings to incorporate the teachings of Steinbrecher and provide exporting a CAD model in order to provide an efficient method for transmission.
Claim(s) 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over DK in view of Kamiyama and Shuiming, and in further view of Greisser et al. (US 20160328945 A1), hereinafter Greisser.
Regarding claim 14, DK-Kamiyama-Shuiming teach the limitations of claim 1.
DK-Kamiyama-Shuiming do not teach providing insights on user desirability of cooling installations.
Greisser teaches,
providing insights on user desirability of cooling installations [Para 0002, The present invention relates to environmental control and monitoring systems such as… cooling; Para 0047, The threshold values noted above may be set and adjusted by the user U via the computers… by generating new rules or by editing old rules].
Greisser is analogous to the claimed invention as they both relate to cooling stations. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified DK, Kamiyama, and Shuiming’s teachings to incorporate the teachings of Greisser and provide providing insights on user desirability of cooling installations in order to enhance the functionality of cooling stations by adjusting cooling station operations to user preferences.
Claims 15 and 16 are computer system and computer program product claims, respectively, that recite identical limitations to claim 14. Therefore, claims 15 and 16 are rejected using the same rationale as claim 14.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SYED RAYHAN AHMED/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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