DETAILED ACTION
This Office Action is sent in response to the Applicant’s Communication received on 04/25/2023 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 2-11 and 14 are amended.
Claims 15 and 16 are added.
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 .
Claim Objections
Claims 11, 15, and 16 are objected to because of the following informalities:
Claim 11: The limitation “preferably” introduces optionality in the claim language and therefore objected to.
Claim 15 should read “Use of the computer system…”
Claim 16 should read “Use of the computer program product…”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 12 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claimed device does not demonstrate any structural recitations. Claim 12 recites a “computer system” claim comprising only software components such as “a database,” “CAD knowledge models,” “calculation modules,” “a parametric CAD-model,” and “a computer-implemented machine learning module” and do not comprise any physical or tangible structure. Claim 13 recites a “computer program product” claim comprising only software components such as “a database,” “CAD knowledge models,” “calculation modules,” “a parametric CAD-model,” and “a computer-implemented machine learning module” and do not comprise any physical or tangible structure. MPEP 2106.03 states: Non-limiting examples of claims that are not directed to any of the statutory categories include: Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural
recitations.
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];
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:
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 computer system is configured for:
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.
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], comprising instructions for:
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, preferably 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 preferably wherein elements of data of a flat file are separated by means of a separation symbol.
Camiener teaches,
preferably 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED RAYHAN AHMED whose telephone number is (571)270-0286. The examiner can normally be reached Mon-Fri ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SYED RAYHAN AHMED/Examiner, Art Unit 2126
/VAN C MANG/Primary Examiner, Art Unit 2126