DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
3. 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.
3.1 Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A- Prong One
The claim(s) recite(s) a computer-implemented method for generating design solutions to one or more design problems, the method comprising:
The step of: “generating a first multi-dimensional data point based on the first design model”; “mapping the first design model to a first node of a trained self-organizing map based on the first multi-dimensional data point, wherein the first node corresponds to a first location within a design space”; under the broadest reasonable interpretation fall under a mental process Therefore, the claims are directed to an abstract idea, by use of generic computer components and thus are clearly directed to an abstract idea, as constructed.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional limitation such as: “one or more non-transitory computer readable media”, storing “instructions”, “one or more processors”, “a memory”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0023], and fig.1) which can be of any other type of processing unit, including general-purpose computer (para [0165], these computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “receiving a first design model that is associated with a first design problem”, under the broadest reasonable interpretation, reasonable fall under data gathering and processing activities that are pre-solution activities” and ”displaying a visual representation of the first design model residing at the first location within the design space based on the first node” are also well-known, routine and conventional post-solution activities to display data and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as previously discussed above with reference to the integration of abstract idea into a practical application, the additional elements of: “one or more non-transitory computer readable media”, storing “instructions”, “one or more processors”, “a memory”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0023], and fig.1) which can be of any other type of processing unit, including general-purpose computer (para [0165], these computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “receiving a first design model that is associated with a first design problem”, under the broadest reasonable interpretation, reasonable fall under data gathering and processing activities that are pre-solution activities” and ”displaying a visual representation of the first design model residing at the first location within the design space based on the first node” are also well-known, routine and conventional post-solution activities to display data and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101. Therefore, using computer components amount to no more than mere instructions to perform the abstract, and thus are not sufficient to amount to significantly more than the recited abstract, as constructed.
3.2 Dependent claims 2-10, 12-19 merely include limitations pertaining to: (claims 2 and 12), “wherein the first multi-dimensional data point comprises a plurality of feature values, and wherein each feature value included in the plurality of feature values corresponds to a different feature associated with the first design model” (mental process); (claims 3 and 13) “wherein generating the first multi-dimensional data point comprises determining a plurality of features associated with the first design problem and determining, for each feature included in the plurality of features, a feature value based on the first design model” (mental process); (claims 4 and 14); “wherein mapping the first design model to the first node comprises determining, for each node included in the trained self-organizing map, a distance between the node and the first multi-dimensional data point, and determining that the first node has a shortest distance to the first multi-dimensional data point relative to all other nodes in the trained self-organizing map” (mathematical concept or otherwise mental process); (claims 5 and 15) “wherein displaying the visual representation of the first design model comprises determining a target location within a two-dimensional representation of the design space based on the first node” (mental process); (claim 6) “wherein the first design model corresponds to a first version of a design solution to the first design problem” (mental process), and “wherein displaying the visual representation of the first design model comprises displaying a visual indication of a relationship between the first design model and a second design model that corresponds to a second version of the design solution to the first design problem” (WURC post-solution activities); (claim 7) “modifying the first design model to generate a second design model”; “generating a second multi-dimensional data point based on the second design model”; “mapping the second design model to a second node of the trained self-organizing map based on the second multi-dimensional data point, wherein the second node corresponds to a second location within the design space that is different than the first location” (mental process); and “displaying a visual representation of the second design model residing at the second location within the design space based on the second node” (WURC post-solution activities), (claims 8 and 16) “generating a plurality of training data points based on the first design problem”, and “generating the trained self-organizing map by generating, for each node included in an untrained self-organizing map, a corresponding weight vector based on the plurality of training data points” (mental process or otherwise a mathematical concept); (claims 9 and 17) “wherein generating the trained self-organizing map comprises: determining, for a first training data point included in the plurality of training data points, that a first node included in the untrained self-organizing map has a shortest distance to the first training data point relative to all other nodes included in the untrained self-organizing map”, and “updating the corresponding weight vector based on the first training data point” (mental process or otherwise a mathematical concept); (claim 10) “generating a plurality of design solutions based on the first design problem”, and “generating, for each design solution included in the plurality of design solutions, generating a different training data point based on the design solution” (mental process); (claims 18-19) “18 “wherein generating the plurality of design solutions comprises receiving one or more parameters associated with the first design problem”; “19 “wherein generating the plurality of design solutions comprises receiving one or more initial design solutions associated with the first design problem” (pre-solution gathering activities), all of which further amount to further mathematical concept and/or mental process similar to that already recited by the independent claims and already addressed above and thus are further not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
4. 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.
4.0 Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schaffer et al. (U.S. Patent No. 11,429,758), in view of Hanna et al. (USPG_PUB No. 2021/0383261).
4.1 In considering claims 1, 11, and 20, Schaffer et al. teaches a computer-implemented method for generating design solutions to one or more design problems, the method comprising:
receiving a first design model that is associated with a first design problem (see col.3 lines 10-16, a method for modeling overhead line structures of electric railways is provided. Rail network design software identifies a reference line for an overhead line structure in a rail network model. The rail network design software accesses a structure template associated with the overhead line structure to obtain the first design model. The reference line has one or more key points. Col.7 lines 1-6, At step 730, the rail network design software 300 accesses a structure template from the template library 370 associated with each reference line. The associated structure template may be determined based on a user selection, a property of the reference line, or other indicia of association.); generating a first multi-dimensional data point based on the first design model (col.3 lines 14-28, The rail network design software updates coordinates of one or more additional points (regular points) of the structure template based on the adjusted coordinates of the one or more key points of the structure template and one or more constraints of the structure template. Based on the coordinates of each key point and additional points of the structure template, the software automatically generates a 3D model of the overhead line structure, that may be displayed or otherwise utilized. Col.7 lines 51-58, At sub-step 760, the stick representation generation process 340 generate a stick representation of each overhead line structure by extending line segments between the coordinates of selected key points and regular points. FIG. 9 is an example stick representation 900 of an overhead line structure that may be generated as part of sub-step 760 of FIG. 7.); mapping the first design model to a first node based on the first multi-dimensional data point, wherein the first node corresponds to a first location within a design space (col.3 lines 16-20, and then matches each key point of the reference line with a corresponding key point of the structure template and adjusts coordinates of the corresponding key point of the structure template to coincide with the key point of the reference line. See further col.5 lines 53-60, Dimensions of components may be constrained based on (e.g., to match) the points they extend between. The cell mappings may indicate mappings of components and points to cells of a cell library. Components are generally mapped to a type of cell while points are generally mapped to properties of that type of cell (e.g., its origin point, rotation, length, etc.; col.7 lines 37-43, At sub-step 765, the cell mapping process 350 generates a cell-based representation of each overhead line structure by using the cell mappings in the structure template to map each component to a corresponding cell of the cell-based representation and each point to a property of a corresponding cell.); and displaying a visual representation of the first design model residing at the first location within the design space based on the first node (see col.3 lines 26-28, the software automatically generates a 3D model of the overhead line structure, that may be displayed or otherwise utilized. Further col.8 lines 14-22, At step 770, the 3D model of each overhead line structure (e.g., as a stick representation and/or a cell-based representation) is output. Such output may take a number of different forms. For example, the display process 360 of the rail network design software 300 may displays the 3D model to a user on a display screen. Alternatively, or additionally, the rail network design software 300 may provide the 3D model (e.g., as a stick representation) to an internal or external process that automatically generates SEDs.). However, he does not specifically state that the said mapping is to a node of a trained self-organizing map.
Hanna et al. provides machine learning system and method for collaborative modeling and prediction (see title, abstract) that includes mapping/providing as input a generated metadata model to generating a trained map (see para [0017] the machine learning system could use one or more computing device is further configured to generate the trained machine learning model by: A) generating an initial machine learning model mapped as input to the machine learning system; B) training, with a training dataset, the initial machine learning model to generate one or more experimental collaboration predictions, wherein the training dataset comprises historical entity data associated with the position and one or more known collaboration outcomes associated with the historical entity data, wherein the trained machine learning model is the secondary machine learning model or mapped trained model). Hanna et al. further teaches the non-transitory computer-readable medium along with program instruction and the processor of claims 11 and 20 (see para 0031).
Schaffer et al. and Hanna et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Hanna et al. is similar to that of Schaffer et al. Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.2 Regarding claims 2 and 12, the combined teachings of Schaffer et al. and Hanna et al. teach that wherein the first multi-dimensional data point comprises a plurality of feature values, and wherein each feature value included in the plurality of feature values corresponds to a different feature associated with the first design model (see Schaffer et al. col.7 line 63-col..8 line 5, generates a cell-based representation of each overhead line structure by using the cell mappings in the structure template to map each component to a corresponding cell of the cell-based representation and each point to a property of a corresponding cell. Static components (e.g., wire clamps) may be mapped to static (i.e. non-parametric) cells. Variable components may be mapped to parametric cells whose dimension values are adjusted at placement time; Hanna et al. para [0019], the collaboration score. [0020] According to a further aspect, the machine learning system of the first aspect or any other aspect, wherein: A) the at least one computing device is configured to determine at least one physical proximity score from the plurality of physical proximity scores that most positively contributed to the collaboration score; and B) the report further comprises the at least one physical proximity score that most positively contributed to the collaboration score). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.3 As per claims 3 and 13, the combined teachings of Schaffer et al. and Hanna et al. teach determining a plurality of features associated with the first design problem (see Hanna et al. para [0022], E) determining, via the at least one computing device, a plurality of physical proximity scores for each of the plurality of skills and tasks based on the metadata and the distribution of capacity across the plurality of task locations) and determining, for each feature included in the plurality of features, a feature value based on the first design model (Hanna et al. [0022], F) generating, via the at least one computing device, a collaboration score for the position based on the plurality of physical proximity scores.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.4 With regards to claims 4 and 14, the combined teachings of Schaffer et al. and Hanna et al. teach that wherein mapping the first design model to the first node comprises determining, for each node included in the trained self-organizing map, a distance between the node and the first multi-dimensional data point and determining that the first node has a shortest distance to the first multi-dimensional data point relative to all other nodes in the trained self-organizing map (Hanna et al. [0050], As an example, the data service 107 can convert each of the skills and tasks into multi-dimensional vectors, and identify a closest bin based on a distance to multi-dimensional vector or areas corresponding to each bin. In some embodiments, the vectors for various bins can be tuned as new skills and tasks are assigned to the bin. Schaffer et al. col. Lines 18-28, As part of defining the structure template, the user may specify constraints (e.g., within a vertical plane), which may include horizontal offset, vertical offset, slope from a point, vector offset (i.e., a vector defined by two points with an offset perpendicular to the vector), angle distance (i.e. an angle based on two points that set a baseline and a distance from one of the two points), a horizontal offset maximum or minimum for two points, a vertical offset maximum or minimum from two points, or other horizontal, vertical or absolute distances, slopes, vectors or more complex relationships). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.5 With regards to claims 5 and 15, the combined teachings of Schaffer et al. and Hanna et al. teach determining a target location within a two-dimensional representation of the design space based on the first node (see Schaffer et al. col.2 lines 12-21, In a common industry workflow using existing software applications, engineers first perform spreadsheet based location design for overhead line structures. The engineers typically use a spreadsheet application to perform the necessary geometrical calculations, and produce numeric results. The numeric results are then provided to a draftsman who uses a computer aided design (CAD) application to manually, or with the assistance of custom macros, create a two-dimensional (2D) OLE layout showing the placement of overhead line structures. Hanna et al. para [0015], 3) identify a plurality of task locations for the entity; 4) determine a distribution of capacity across the plurality of task locations based on the entity data; 5) determine a plurality of physical proximity scores for each of the plurality of skills and tasks based on the metadata and the distribution of capacity across the plurality of task locations). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.6 Regarding claim 6, the combined teachings of Schaffer et al. and Hanna et al. teach that wherein the first design model corresponds to a first version of a design solution to the first design problem (see Hanna et al. para [0088], the system may generate a first training set including two subsets of labeled data (e.g., in instances of supervised training) or two subsets of unlabeled data (e.g., in instances of unsupervised training). According to one embodiment, a second subset of the training dataset includes historical entity data and metadata describing known job positions that do not require any collaboration.), and wherein displaying the visual representation of the first design model comprises displaying a visual indication of a relationship between the first design model and a second design model that corresponds to a second version of the design solution to the first design problem (see Schaffer et al. col.5 lines 41-53, The constraints may indicate relationships (e.g., within a plane extending vertically through the overhead line structure's foundation). For example, one point may be constrained to one or more other points to maintain horizontal, vertical or absolute distances, slopes, vectors or more complex relationships. Hanna et al. para [0052], the data service 107 can analyze the collaboration criteria by performing iterative regression analysis on the historical location data 117 and position data 119 to identify correlations in the data. Schaffer et al. [0070], In another example, the collaboration application 129 displays a collaboration classification of a position and a ranked list of position aspects that most heavily contributed to the classification of the position. [0106], For example, the computing environment 100 transmits a collaboration score to the collaboration application 129 and the collaboration application 129 renders the ranking on the display 125.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.7 As per claim 7, the combined teachings of Schaffer et al. and Hanna et al. teach modifying the first design model to generate a second design model (see Schaffer et al. col.7 lines 26-36, At step 745, for each reference line, the rail network design software 300 updates coordinates of regular points of the associated structure template based on the adjusted coordinates of key points in the structure template and the constraints defined in the template applicable to them. Thereafter, at step 750, for each reference line, the coordinate transform process 320 of the rail network design software 300 transforms the coordinates of each key point and regular point of the associated structure template from the coordinate system of the reference line back to the global coordinate system of the rail network model. further Hanna et al. para [0067], a work culture category may be used by the modeling service 109 to modify data that is input to and analyzed via one or more machine learning models. In one embodiment, a work culture category may be used by the modeling service 109 to modify outputs generated by one or more machine learning models); generating a second multi-dimensional data point based on the second design model; mapping the second design model to a second node of the trained self-organizing map based on the second multi-dimensional data point, wherein the second node corresponds to a second location within the design space that is different than the first location (see Hanna et al. para [0050], As an example, the data service 107 can convert each of the skills and tasks into multi-dimensional vectors, and identify a closest bin based on a distance to multi-dimensional vector or areas corresponding to each bin. In some embodiments, the vectors for various bins can be tuned as new skills and tasks are assigned to the bin. Further, based on the classification, the data service 107 can match the skills and tasks to historical job positions, and the data service 107 can determine additional position metadata based on the historical job positions,); and displaying a visual representation of the second design model residing at the second location within the design space based on the second node (see Hanna et al. [0070]-[0071], The computing device 105 can include a display 125 on which various user interfaces can be rendered by a collaboration application 129 to configure, monitor, and control various functions of the collaboration system 100. The collaboration application 129 can display information associated with processes of the collaboration system 100 and/or data stored thereby. In one example, the collaboration application 129 displays location profiles that are generated or retrieved from the data store 113. In another example, the collaboration application 129 displays a collaboration classification of a position and a ranked list of position aspects that most heavily contributed to the classification of the position. [0071], In one example, a first computing device 105 is associated with a company user account and the collaboration application 129 is configured to display position profiles, including collaboration metrics, and provide access to collaboration evaluation processes. See further Schaffer et al. col.8 lines 14-19, At step 770, the 3D model of each overhead line structure (e.g., as a stick representation and/or a cell-based representation) is output. Such output may take a number of different forms. For example, the display process 360 of the rail network design software 300 may displays the 3D model to a user on a display screen.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.8 With regards to claims 8 and 16, the combined teachings of Schaffer et al. and Hanna et al. teach that generating a plurality of training data points based on the first design problem (see Hanna et al. para [0110] In at least one embodiment, generating the machine learning model includes creating a plurality of parameters based on various factors that may influence a position's collaborative nature. In some embodiments, the model service 109 generates the plurality of parameters based on entity data and metadata derived therefrom (e.g., referring to entity data and metadata obtained via steps 203-209 of the process 200). In various embodiments, the model service 109 generates a machine learning model based on historical model data 123.), and generating the trained self-organizing map by generating, for each node included in an untrained self-organizing map, a corresponding weight vector based on the plurality of training data points (see Hanna et al. para [0110] In at least one embodiment, generating the machine learning model includes creating a plurality of parameters based on various factors that may influence a position's collaborative nature. In some embodiments, the model service 109 generates the plurality of parameters based on entity data and metadata derived therefrom (e.g., referring to entity data and metadata obtained via steps 203-209 of the process 200). In various embodiments, the model service 109 generates a machine learning model based on historical model data 123. For example, the model service 109 retrieves historical model data 123 that defines a trained machine learning model, and the model service 109 retrains the machine learning model using one or more training datasets (e.g., that may utilize more current data and/or may be specific to a particular position or set of positions). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.9 As per claims 9 and 17, the combined teachings of Schaffer et al. and Hanna et al. teach determining, for a first training data point included in the plurality of training data points, that a first node included in the untrained self-organizing map has a shortest distance to the first training data point relative to all other nodes included in the untrained self-organizing map (Hanna et al. [0050], As an example, the data service 107 can convert each of the skills and tasks into multi-dimensional vectors, and identify a closest bin based on a distance to multi-dimensional vector or areas corresponding to each bin. In some embodiments, the vectors for various bins can be tuned as new skills and tasks are assigned to the bin. Schaffer et al. col. Lines 18-28, As part of defining the structure template, the user may specify constraints (e.g., within a vertical plane), which may include horizontal offset, vertical offset, slope from a point, vector offset (i.e., a vector defined by two points with an offset perpendicular to the vector), angle distance (i.e. an angle based on two points that set a baseline and a distance from one of the two points), a horizontal offset maximum or minimum for two points, a vertical offset maximum or minimum from two points, or other horizontal, vertical or absolute distances, slopes, vectors or more complex relationships), and updating the corresponding weight vector based on the first training data point (see Hanna et al. para [0114], In at least one embodiment, the model service 109 determines one or more parameters, parameter weight values, or other model settings and properties that contributed to the model error or that, if adjusted, may improve performance of the model. In various embodiments, following step 312, the process 300 returns to step 303 and the model service 109 adjusts one or more parameters, parameter weight values, or other model settings and properties to reduce the model error. [0115] In one example, at step 312, the model service 109 determines that a weight value for a “estimated collaborative tasks” parameter is too low and, thereby, caused the machine learning model to generate inaccurate collaboration scores or classifications (e.g., based on comparisons between known and experimental outcomes). In this example, the process 300 proceeds to step 303 and the model service 109 generates a second iteration of the machine learning model in which the weight value for the “estimated collaborative tasks” parameter is reduced.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.10 With regards to claim 10, the combined teachings of Schaffer et al. and Hanna et al. teach generating a plurality of design solutions based on the first design problem, and generating, for each design solution included in the plurality of design solutions, generating a different training data point based on the design solution (see Hanna et al. para [0115], the model service 109 determines that a weight value for a “estimated collaborative tasks” parameter is too low and, thereby, caused the machine learning model to generate inaccurate collaboration scores or classifications (e.g., based on comparisons between known and experimental outcomes). In this example, the process 300 proceeds to step 303 and the model service 109 generates a second iteration of the machine learning model in which the weight value for the “estimated collaborative tasks” parameter is reduced. Continuing the example, the process 300 proceeds to steps 306 and the second iteration of the machine learning model generates additional experimental output for evaluation at step 309. [0116] The system can iteratively repeat steps 303-312, thereby continuously training and/or combining the one or more machine learning models until a particular machine learning model demonstrates one or more error metrics below a predefined threshold, or demonstrates an accuracy and/or precision at or above one or more predefined thresholds.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.11 With regards to claim 18, the combined teachings of Schaffer et al. and Hanna et al. teach receiving one or more parameters associated with the first design problem (see Schaffer et al. col.3 lines 10-16, a method for modeling overhead line structures of electric railways is provided. Rail network design software identifies a reference line for an overhead line structure in a rail network model. The rail network design software accesses a structure template associated with the overhead line structure to obtain the first design model. The reference line has one or more key points. Col.7 lines 1-6, At step 730, the rail network design software 300 accesses a structure template from the template library 370 associated with each reference line. The associated structure template may be determined based on a user selection, a property of the reference line, or other indicia of association). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
4.12 As per claim 19, the combined teachings of Schaffer et al. and Hanna et al. teach wherein generating the plurality of design solutions comprises receiving one or more initial design solutions associated with the first design problem (see Schaffer et al. col.3 lines 10-28, The rail network design software updates coordinates of one or more additional points (regular points) of the structure template based on the adjusted coordinates of the one or more key points of the structure template and one or more constraints of the structure template. Based on the coordinates of each key point and additional points of the structure template, the software automatically generates a 3D model of the overhead line structure, that may be displayed or otherwise utilized. Col.7 lines 51-58, At sub-step 760, the stick representation generation process 340 generate a stick representation of each overhead line structure by extending line segments between the coordinates of selected key points and regular points. FIG. 9 is an example stick representation 900 of an overhead line structure that may be generated as part of sub-step 760 of FIG. 7.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing pd the applicant’s invention to combine the method of Hanna et al. with that of Schaffer et al. because Hanna et al. teaches the improvement of performance of the model and reduction of error (see para [0114]).
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
5.1 Hakimi et al. (USPG_PUB No. 2023/0185997) teaches a method for machine-assisted collaborative product design that includes training a neural network to simulate a plurality of stakeholder personas in a product review process to provide a plurality of stakeholder models.
6. Claims 1-20 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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, EMERSON C PUENTE can be reached at 571-272-3652. 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.
/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 December 11, 2025