DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/12/2025 has been entered.
This action is in response to the arguments filed on 11/12/2025. Claims 1-15 are pending in the application and have been considered below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 4-5 recite the limitation “the drive system.” There is insufficient antecedent basis for this limitation in the claims.
The claims 2-15 are dependent claims of claim 1 respectively and inherit the deficiency of the claim they depend upon.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Englard et al. (US 2019/0178988 A1, hereinafter referred to as Englard), in view of Sustaeta et al. (US 2020/0162559 A1, hereinafter referred to as Sustaeta) and further in view of LI et al. (US 2018/0345496 A1, hereinafter referred to as LI).
As to claim 1, Englard teaches a computer-implemented method of training an artificial intelligence module (Al module) for selecting at least one component of an industrial drive system, the method comprising:
providing, on data storage, an initial training data set comprising a set of rules, wherein each rule is indicative of a selection decision [for selecting at least one component of the industrial drive system based on at least one usage requirement for the industrial drive system] (paragraphs [006]-[0007], “generating a first set of training data that includes (i) first sensor data indicative of real or simulated vehicle environments, the first sensor data corresponding to a first setting of one or more sensor parameters, and (ii) an indicator of the first setting,); [0038], the area of focus is determined using a heuristic approach, as represented by various rules, algorithms, criteria, etc.; [0052]--[0053], segmentation module 110 may use predetermined rules or algorithms to identify objects; [0056], the prediction component 120 may assume that an object that has been classified as another vehicle will follow rules of the road ( e.g., stop when approaching a red light), and will react in a certain way to other dynamic objects (e.g., attempt to maintain some safe distance from other vehicles); [0059]-[0060], the dynamic object detector 134 may access a locally-stored set of rules or algorithms that determine whether an object being tracked by the tracking module 114 is to be flagged as a "dynamic” object);
generating a [modified] training data set [based on supplementing the initial training data set] with at least one further selection decision for selecting the at least one component of the drive system based on simulation data relating to a simulation of at least a part of the drive system, wherein the simulation data includes at least one or more of simulation results of electrical, thermal and/or mechanical aspects or properties of the drive system, empiric data relating to a selection of the at least one component, and operational data indicative of at least one operational characteristic of at least one installed industrial drive system (paragraphs [0006]-[0008],” generating, by one or more processors, a second set of training data that includes (i) second sensor data indicative of real or simulated vehicle environments, the second sensor data corresponding to a second setting of the one or more sensor parameters, and (ii) an indicator of the second setting. The method also includes training, by one or more processors, the perception component, at least in part by training a single neural network of the perception component using the first and second sets of training data The trained perception component is configured to generate signals descriptive of a current state of the environment, as the vehicle moves through the environment, by processing (i) sensor data generated by the one or more sensors and (ii) one or more indicators indicating which setting of the one or more sensor parameter corresponds to which portions of the generated sensor data.” [0035]” capture vehicle environment information to improve the safety/performance of an autonomous vehicle, to generate alerts for a human driver, or simply to collect data relating to a particular driving trip (e.g., to record how many other vehicles or pedestrians were encountered during the trip, etc.)”; [0041] In some embodiments, a machine learning based model is trained to control one or more sensor parameters. The model may be trained using any of various types of learning, such as supervised learning, reinforcement learning, or evolutionary algorithms, and may be trained using real-world data and/or data generated in a simulated environment (interpreted by Examiner as simulation results). The model may be an attention model that is trained to direct the focus of one or more sensors to particular areas (e.g., by adjusting the size and/or center of a field of regard, a scan line distribution, etc.). For embodiments utilizing an attention model, the model may be trained to determine where to "look" within the environment. Such training may use sets of real or simulated sensor data that have been labeled according to "correct" outputs of one or more perception functions (e.g., segmentation and/or classification); [0077] "may receive or detect photons from the input beam 235 and generate one or more representative signals. For example, the receiver 240 may generate an output electrical signal 245 that is representative of the input beam 235. The receiver may send the electrical signal 245 to the controller 250." [0106]-[0107] The sensor data 402 may be generated by one or more types of sensors, such as one or more lidar devices, cameras, radar devices, thermal imaging units, IMUs, and/or other sensor types… the sensor data 402 may correspond to data generated by some or all of the sensors 102 of FIG. 1.” Examiner interprets “the generated signals by the trained perception component that are descriptive of a current state of the simulated environment” includes simulated electrical sensor signals ((e.g. electrical signal 245 ([0077]), lidar, radar, camera signals) This reads on the simulation results of electrical and/or mechanical aspects of the drive system; [0128]-[0129]”the attention model 634 may learn that an expert human driver tends to focus particular distance ahead on the road when the vehicle is turning, with that distance being affected in specific ways by factors such as tum radius, weather, visibility, distance to a leading vehicle, and so on (i.e., empiric data). Further described in paragraphs [0188] and [0194] …. According to the specification, in [0021], the empiric data is defined as a human selection decision of one or more components. Examiner interprets the expert human driver selection (e.g. turn radius factor) related to the steering component as empiric data; [0109] …operational parameters (e.g., braking, speed and steering parameters) (i.e., operational data)…; [0209]…operational parameter level ( e.g., “increase speed by 3 miles per hour and steer 5 degrees left) (i.e., operational data)…; [0199]-[0202]…According to paragraph [0015] of the specification, “the braking, speed and steering parameters” are operational characteristic of at least one installed industrial drive system); and
training the Al module based on the modified training data set (paragraphs [0006]-[0007] …training, by one or more processors, the perception component, at least in part by training a single neural network of the perception component using the first and second sets of training data…; [0202]…training a machine learning based model
(e.g., a single neural network) of the perception component using the first and second sets of training data generated at blocks 942 and 944… (i.e., modified training data set)).
However, Englard teaches decision making/selection but fails to explicitly teach:
generating a modified training data set based on supplementing the initial training data set; and
selecting at least one component of the industrial drive system based on at least one usage requirement for the industrial drive system;
However, Sustaeta, in combination with Englard, teaches:
selecting at least one component of the industrial drive system based on at least one usage requirement for the industrial drive system (paragraphs [0005], the pump is chosen according to the maximum and minimum flow and head required in the application, and the motor is selected based on the chosen pump hydraulic power requirements, and other electrical and mechanical considerations. The corresponding motor drive is selected according to the motor specifications; [0172] …Setup information 968 may be provided to the controller 966 (i.e., “component of the industrial drive system”), which may include operating limits (e.g., min/max speeds, min/max flows, min/max pump power levels, min/max pressures allowed, NPSHR values, and the like), such as are appropriate for a given pump 904, motor 906, piping and process conditions, and/or process dynamics and other system constraints. The control system 908 provides for operation within an allowable operating range about the setpoint 910, whereby the system 902 is operated at a desired operating point within the allowable range, in order to optimize one or more performance characteristics (e.g., such as life cycle cost, efficiency, life expectancy, safety, emissions, operational cost, MTBF, noise, vibration, and the like…; [0208]… Setup information 2068 may be provided to the controller 2066, which may include operating limits (e.g., min/max speeds, min/max flows, min/max pump power levels, min/max pressures allowed, NPSHR values, and the like), such as are appropriate for a given pump 2004, motor 2006, and piping and process conditions. The controller 2066 comprises a diagnostic component 2070, which is adapted to diagnose one or more operating conditions associated with the pump 2004, the motor 2006, the motor drive 2060, and/or other components of system 2002. In particular the controller 2066 may employ the diagnostic component 2070 to provide the control signal 2064 to the motor drive 2060 according to setpoint 2010 and a diagnostic signal (not shown) from the diagnostic component 2070 according to the diagnosed operating condition in the pump 2004 or system 2002. In this regard, the diagnosed operating condition may comprise motor or pump faults, pump cavitation, or failure and/or degradation in one or more system components. The controller 2066 may further comprise an optimization component 2070a, operating in similar fashion to the optimization component 70 illustrated and described above...; wherein Examiner interprets “operating limits,” “and process conditions, and/or process dynamics and other system constraints” and “allowable operating range” as usage requirement),
wherein the industrial drive system operates with the selected at least one component (paragraph [0004], an electric motor may be combined with a motor
drive providing variable electrical power to the motor, as well as with a pump, whereby the motor rotates the pump shaft to create a controllable pumping system), and wherein the at least one component of the industrial drive system is to be exchanged, adjusted, and/or modified within the industrial drive system (paragraphs [0026], operating a motorized system, wherein a controller operatively
associated with the system includes a diagnostic component to diagnose an operating condition associated with the pump. The operating conditions detected by the diagnostic component may include motor or pump faults, or failure and/or degradation, and/or failure prediction (e.g., prognostics) in one or more system components; [0091] Furthermore, some critical action such as turning off a pump, may be deemed particularly sensitive and potentially dangerous. Before this action is automatically invoked based on prognostics, it may be required that two or more, independent system components (e.g. agent clusters) may corroborate the expected or potential future state and independently establish that the optimum course of action is to turn off the pump or machinery…; [0092] Accordingly, by predicting future states as to such extrinsic factors and taking action in connection with controlling such factors, various components can be protected from entering into undesired future states. For example, many failures of machines can be attributed to environmental influences (e.g., contamination that can contribute to failure of the machine. By monitoring and controlling such influences in a dynamic and proactive manner, machine failure can be mitigated; Examiner interprets “a diagnostic component to diagnose an operating condition associated with the pump” to teach the limitation. Examiner’s interpretation is based on paragraph [0015] of the original disclosure(specification)).
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the system of to add usage requirement to the system of Englard, as taught by Sustaeta, above. The modification would have been obvious because one of ordinary skill would be motivated to optimize one or more performance characteristics (e.g., such as life cycle cost, efficiency, life expectancy, safety, emissions, operational cost, MTBF, noise, vibration, and the like), as suggested by Sustaeta ([0172]).
However, Englard and Sustaeta fail to explicitly teach:
generating a modified training data set based on supplementing the initial training data set;
wherein the AI module is configured to provide, as an output, the at least one component of the industrial drive system based on the at least one usage requirement that is provided to the trained AI module as an input.
LI, in combination with Englard and Sustaeta, teaches:
generating a modified training data set based on supplementing the initial training data set (paragraphs [0030]- [0035] “Since machine learning models 208
are trained using real-world training data 216 containing images collected from an environment that is identical or similar to the one in which physical process 202 operates, augmented images 220 may imitate the shading, lighting, noise, and/or other real-world conditions encountered by physical process 202 in performing the task.”
“Augmented images 220 and the corresponding labels (e.g., object positions, object orientations, object types, graspable points in each object, depth information and/or 3D locations of objects or features in augmented images 220, etc.) from simulation engine 120 may then be used as training data 212 for machine learning model 210. For example, augmented images 220 and the labels may allow a neural network and/or other type of machine learning model 210 to learn the positions, orientations, and types
of objects in augmented images 220”; Examiner interprets “real-world training data 216 “(interpreted by Examiner as “initial training data set”) and “Augmented images 220” (interpreted by Examiner as “supplemental training data set”) as modified training data set);
wherein the AI module is configured to provide, as an output, the at least one component of the industrial drive system based on the at least one usage requirement that is provided to the trained AI module as an input (paragraphs [0003] and [0024] In particular, simulation data from simulation engine 120 may be used to train, program, and/or execute the physical process. For example, the simulation data may include images that are generated from computer-aided design (CAD) models of physical objects. The images may be used to train industrial robots that use visual feedback and/or perception to perform grasping, sorting, and/or assembly tasks; [0029]-[0030] Simulation engine 120 may also generate labels that are used with training data 212 to train machine learning model 210 in physical process 202. For example, simulation engine 120 may output, in metadata that is embedded in each simulated image and/or stored separately from the simulated image).
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the combination system of Englard and Sustaeta to add one usage requirement that is provided to the trained AI module as an input to the combination system of Englard and Sustaeta, as taught by LI, above. The modification would have been obvious because one of ordinary skill would be motivated to improve the training to better reflect real-world conditions or manual collection of real-world data in a real-world environment, as suggested by LI ([0026]).
As to claim 2, which incorporates the rejection of claim 1, Englard discloses wherein providing the initial training data set comprises:
generating the initial training data set using a software tool for deterministically selecting one or more components of the at least one component of the industrial drive system based on one or more usage requirements of the at least one usage requirement for the industrial drive system (paragraphs [0006]-[0007]…The trained perception component is configured to generate signals descriptive of a current state of the environment, as the vehicle moves through the environment, by processing (i) sensor data generated by the one or more sensors and (ii) one or more indicators indicating which setting of the one or more sensor parameters corresponds to which portions of the generated sensor data...; [0007]…generating a first set of training data that includes (i) first sensor data indicative of real or simulated vehicle environments, the first sensor data corresponding to a first setting of one or more sensor parameters, and (ii) an indicator of the first setting…).
As to claim 3, which incorporates the rejection of claim 1, Englard discloses wherein each rule of the set of rules of the initial training data set comprises at least one pair of data entries with a first data element indicative of the at least one usage requirement and a second data element indicative of the at least one component of the industrial drive system fulfilling the at least one usage requirement (paragraphs [0006]-[0007], generating a first set of training data that includes (i) first sensor data indicative of real or simulated vehicle environments, the first sensor data corresponding to a first setting of one or more sensor parameters, and (ii) an indicator of the first setting).
As to claim 4, which incorporates the rejection of claim 1, Englard discloses wherein the at least one component of the industrial drive system is at least one of a drive, a motor, a load, a transformer, a gearbox, a pump, a ventilation device, a heating device, an air conditioning device, a controller, a motion control, and a machinery of the drive system (paragraphs [0048], a vehicle may include, may take the form of, or may be referred to as a car, automobile, motor vehicle; [0074]… a controller 250; [0096], vehicle controller 322).
As to claim 5, which incorporates the rejection of claim 1, Englard discloses wherein the at least one usage requirement for the industrial drive system is indicative of a specification of at least one of a drive, a motor, a load, a transformer, a gearbox, a pump, a ventilation device, a heating device, an air conditioning device, a controller, a motion control, and a machinery of the drive system (paragraphs [0046]-[0048], an "autonomous" or "self-driving" vehicle is a vehicle configured to sense its environment and navigate or drive with no human input, with little human input, with optional human input, and/or with circumstance-specific human input. For example, an autonomous vehicle may be configured to drive to any suitable location and control or perform all safety critical functions (e.g., driving, steering, braking, parking) for the entire trip, with the driver not being expected (or even able) to control the vehicle at any time. As another example, an autonomous vehicle may allow a driver to safely tum his or her attention away from driving tasks in particular environments (e.g., on freeways) and/or in particular driving modes.).
As to claim 6, which incorporates the rejection of claim 1, Englard discloses:
weighting the at least one further selection decision for selecting the at least one component of the industrial drive system relative to one or more rules of the set of rules of the initial training data set (paragraph [0008] The perception component is trained using a first set of training data that includes (i) first sensor data indicative of real or simulated vehicle environments, the first sensor data corresponding to a first setting of one or more sensor parameters, and (ii) an indicator of the first setting; wherein Examiners interprets (i) and (ii) as rules of the first set of training data).
14. A method of using an artificial intelligence module, the method comprising selecting at least one component of the industrial drive system, wherein the artificial intelligence module is trained according to the method of claim 1. (see rejection of claim 1).
15. An apparatus for selecting at least one component of an industrial drive system, the apparatus comprising:
an artificial intelligence module, Al module, wherein the Al module is trained according to the method of claim 1. (see rejection of claim 1).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Englard et al. (US 2019/0178988 A1, hereinafter referred to as Englard), in view of Sustaeta et al. (US 2020/0162559 A1, hereinafter referred to as Sustaeta) and further in view of LI et al. (US 2018/0345496 A1, hereinafter referred to as LI), and Fantana et al. (US 2006/0259277 A1, hereinafter referred to as Fantana).
As to claim 7, which incorporates the rejection of claim 1, Englard, Sustaeta and LI fail to explicitly teach:
weighting at least a part of the simulation data, the empiric data and the operational data with at least partly differing weights and/or weighting factors.
Fantana, in combination with Englard, Sustaeta and LI, teaches:
weighting at least a part of the simulation data, the empiric data and the operational data with at least partly differing weights and/or weighting factors (paragraphs [0034]-[0036], different weightings wt1 ... wtn of the different input parameters R1 ... Rn, among other things; [0036], weighting factors w 1 ... w n …and/or knowledge-based, i.e., based on empirical data; [0107], operational data; [0107]-[0108], ,operational data; historical transformer load data with a thermal simulation).
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the system of Englard, Sustaeta, Tripathi and LI to add weighting factors to the combination system of Englard, Sustaeta, Tripathi and LI, as taught by Fantana, above. The modification would have been obvious because one of ordinary skill would be motivated to use preference allocation and preference changes, as suggested by Fantana ([0036]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Englard et al. (US 2019/0178988 A1, hereinafter referred to as Englard), in view of Sustaeta et al. (US 2020/0162559 A1, hereinafter referred to as Sustaeta) and further in view of LI et al. (US 2018/0345496 A1, hereinafter referred to as LI), and Yao et al. (US 8.892496 B2, hereinafter referred to as Yao).
As to claim 8, which incorporates the rejection of claim 1, Englard, Sustaeta and LI fail to explicitly teach wherein supplementing the initial training data set comprises:
adjusting at least one rule of the set of rules of the initial training data set based on the empiric data, and the operational data, wherein the empiric data represents selections made by an expert obtained from using simulation tools, and wherein the empiric data comprises decisions made in all phases of the industrial drive system's lifecycle.
Yao, in combination with Englard, Sustaeta and LI, teaches:
adjusting at least one rule of the set of rules of the initial training data set based on the empiric data and the operational data (col. 7, lines 46-56 This inference system is then exposed to real world or simulation data on an iterative basis, the membership functions and rules being adjusted incrementally to maximize agreement between measured values and output of the fuzzy inference), wherein the
empiric data represents selections made by an expert obtained from using simulation tools, and wherein the empiric data comprises decisions made in all phases of the industrial drive system's lifecycle (col. 8, lines 49-67 In step 100-5b and 100-5c, initial membership functions and rule weightings are selected for each regime A, B and C, typically based on expert input. The initial MF s and rules may
be identical for the different regimes. If expert knowledge is available, however, training will be faster if initial values suitable to the individual regime are input.).
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the system of Englard, Sustaeta and LI to add weighting factors to the combination system of Englard, Sustaeta and LI, as taught by Yao, above. The modification would have been obvious because one of ordinary skill would be motivated to use a training that would be performed with sets of data representing all the expected operating conditions for the apparatus, to ensure that the fuzzy inference can respond appropriately in all circumstances., as suggested by Yao (col. 7, lines 57-60).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Englard et al. (US 2019/0178988 A1, hereinafter referred to as Englard), in view of Sustaeta et al. (US 2020/0162559 A1, hereinafter referred to as Sustaeta) and further in view of LI et al. (US 2018/0345496 A1, hereinafter referred to as LI), and Cao et al. (US 8,250,008 B1, hereinafter referred to as Cao).
As to claim 9, which incorporates the rejection of claim 1, Englard, Sustaeta and LI fail to explicitly teach wherein supplementing the initial training data set comprises:
replacing at least one rule of the set of rules of the initial training data set based on the at least one further selection decision and/or based on at least one of the simulation data, the empiric data, and the operational data
Cao, in combination with Englard, Sustaeta and LI, teaches wherein supplementing the initial training data set comprises:
replacing at least one rule of the set of rules of the initial training data set based on the at least one further selection decision and/or based on at least one of the simulation data, the empiric data, and the operational data (col.6, lines 1-28…Using the adjusted rules, the model refinement system 120 classifies pairs of data in the initial training set used to generate the decision tree 200. The model refinement system 120 then filters the initial training set to remove data records representing non-duplicate accounts that are incorrectly classified as duplicate accounts to generate a filtered training set, as described with reference to FIG. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the combination system Englard, Sustaeta and LI to add replacing rules to the combination system of Englard, Sustaeta and LI, as taught by Cao, above. The modification would have been obvious because one of ordinary skill would be motivated to remove data records representing non-duplicate accounts that are incorrectly classified as duplicate accounts to generate a filtered training set, as suggested by Cao (col.6, lines 1-6).
. Claims 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Englard et al. (US 2019/0178988 A1, hereinafter referred to as Englard), in view of Sustaeta et al. (US 2020/0162559 A1, hereinafter referred to as Sustaeta) and further in view of LI et al. (US 2018/0345496 A1, hereinafter referred to as LI), and Deo et al. (US 10,990,901 B2, hereinafter referred to as Deo).
As to claim 10, which incorporates the rejection of claim 1, Englard, Sustaeta and LI fail to explicitly teach wherein supplementing the initial training data set comprises:
deriving the at least one further selection decision from at least one of the simulation data, the empiric data, and the operational data.
Deo, in combination with Englard, Sustaeta and LI, teaches wherein supplementing the initial training data set comprises:
deriving the at least one further selection decision from at least one of the simulation data, the empiric data, and the operational data (col. 2, lines 55-67 col 3, lines 1-7…selecting a trained model, from the plurality of trained models, based on model metrics and the scores, and processing a training sample, with the trained model, to generate first results, wherein the training sample has been created based on the unbiased training data and production data (i.e., “at least one of the simulation data, the empiric data, and the operational data”) associated with a production environment in which the trained model is to be utilized .
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the combination system of Englard, Sustaeta and LI to add a selection decision to the combination system of Englard, Sustaeta and LI, as taught by Deo, above. The modification would have been obvious because one of ordinary skill would be motivated to remove data 10 records representing non-duplicate accounts that are incorrectly classified as duplicate accounts to generate a filtered training set, as suggested by Deo (col.6, lines 1-6).
As to claim 12, which incorporates the rejection of claim 1, Englard, Sustaeta and LI fail to explicitly teach wherein supplementing the initial training data set comprises:
identifying an inappropriate selection decision for selecting the at least one component of the industrial drive system based on at least one of the simulation data, the empiric data, and the operational data.
Deo, in combination with Englard, Sustaeta and LI, teaches wherein supplementing the initial training data set comprises:
identifying an inappropriate selection decision for selecting the at least one component of the industrial drive system based on at least one of the simulation data, the empiric data, and the operational data (col.6, lines 1-28…Using the adjusted rules, the model refinement system 120 classifies pairs of data in the initial training set used to generate the decision tree 200. The model refinement system 120 then filters the initial training set to remove data records representing non-duplicate accounts that are incorrectly classified as duplicate accounts to generate a filtered training set, as described with reference to FIG. 3); and
selecting the at least one further selection decision taking into account the identified inappropriate selection decision (col. 2, lines 55-67 col 3, lines 1-7…selecting a trained model, from the plurality of trained models, based on model metrics and the scores, and processing a training sample, with the trained model, to generate first results, wherein the training sample has been created based on the unbiased training data and production data (i.e., “at least one of the simulation data, the empiric data, and the operational data”) associated with a production environment in which the trained model is to be utilized).
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the combination system of Englard, Sustaeta and LI to add a selection decision to the combination system Englard, Sustaeta and LI, as taught by Deo, above. The modification would have been obvious because one of ordinary skill would be motivated to remove data 10 records representing non-duplicate accounts that are incorrectly classified as duplicate accounts to generate a filtered training set, as suggested by Deo (col.6, lines 1-6).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Englard et al. (US 2019/0178988 A1, hereinafter referred to as Englard), in view of Sustaeta et al. (US 2020/0162559 A1, hereinafter referred to as Sustaeta) and further in view of LI et al. (US 2018/0345496 A1, hereinafter referred to as LI), and Hosek et al. (US 7,882,394 B2, hereinafter referred to as Hosek).
As to claim 11, which incorporates the rejection of claim 1, Englard, Sustaeta and LI fail to explicitly teach wherein the operational data comprise at least one of maintenance data indicative of a maintenance procedure performed on the at least one installed drive system, sensor data of at least one senor of the at least one installed drive system, disturbance data indicative of a disturbance of the at least one installed drive system, time series data indicative of a temporal development of the at least one installed drive system, and operator data indicative of at least one operator action performed on the at least one installed drive system.
Hosek, in combination with Englard, Sustaeta and LI, teaches wherein the operational data comprise at least one of maintenance data indicative of a maintenance procedure performed on the at least one installed drive system, sensor data of at least one senor of the at least one installed drive system, disturbance data indicative of a disturbance of the at least one installed drive system, time series data indicative of a temporal development of the at least one installed drive system, and operator data indicative of at least one operator action performed on the at least one installed drive system (col. 47, lines 38-44, analyzing time series data )
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify the combination system of Englard, Sustaeta and LI to add a time series data to the combination system of Englard, Sustaeta and LI, as taught by Hosek, above. The modification would have been obvious because one of ordinary skill would be motivated to perform diagnostic, as suggested by Hosek (col. 47, lines 38-44).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Englard et al. (US 2019/0178988 A1, hereinafter referred to as Englard), in view of Sustaeta et al. (US 2020/0162559 A1, hereinafter referred to as Sustaeta) and further in view of ripathi et al. (US 2016/0063171 A1, hereinafter referred to as Tripathi), and LI et al. (US 2018/0345496 A1, hereinafter referred to as LI), and Holtham (US 2018/0247227 A1, hereinafter referred to as Holtham).
.
As to claim 13, which incorporates the rejection of claim 1 Englard, Sustaeta and LI fail to explicitly teach: generating the simulation data to supplement the initial training data.
Holtham, in combination with Englard, Sustaeta and LI, teaches:
generating the simulation data to supplement the initial training data set (paragraphs [0051]-[0052] Using Monte Carlo type simulations to produce a training data simulation model, as well as any at block 806 that are based on domain knowledge... Additional models can be generated in block 806 based on additional information not present in the initial training data…).
It would have been obvious to one of ordinary skill in the art before the effective filing of
the claimed invention to modify combination system of Englard, Sustaeta and LI to add a simulation data to the initial training data to the combination system of Englard, Sustaeta and LI, as taught by Holtham, above. The modification would have been obvious because one of ordinary skill would be motivated to create an additional set of M models generated from the original data, with the M models based on additional information, as suggested by Holtham ([0050]).
Response to Applicant’s arguments
The Applicant’s arguments filed on 11/12/2025 have been fully considered but are partially moot in view of new ground(s) of rejection.
Rejections under 35 U.S.C. § 103
Argument (page 8 of 12):
Applicant appears to assert that Li fails to describe their simulation engine selecting a component or having their physical process operate using a selected component based on the usage requirement that was provided to a trained AI module as input as required by amended claim 1.
Examiner response:
Examiner respectfully disagrees. Englard teaches the amended limitation “wherein the industrial drive system operates with the selected at least one component, and wherein the at least one component of the industrial drive system is to be exchanged, adjusted, and/or modified within the industrial drive system as explained ion the office action above.
Argument (page 8 of 12) - (page 9 of 12):
However, a robot performing a grasping task is not the same as or
suggestive of providing, as an output, the at least one component of the industrial drive
system based on the at least one usage requirement that is provided to the trained AI module as an input, wherein the industrial drive system operates with the selected at least one component and wherein the at least one component of the industrial drive system is to be exchanged, adjusted, and/or modified within the industrial drive system, as required by amended claim 1.
Applicant respectfully submitted that Li fails to disclose or suggest at least the amended feature of claim 1 noted above for at least these reasons.
Examiner response:
Examiner respectfully disagrees. Englard teaches the amended limitation “wherein the industrial drive system operates with the selected at least one component, and wherein the at least one component of the industrial drive system is to be exchanged, adjusted, and/or modified within the industrial drive system” as explained ion the office action above.
Argument (page 9 of 12):
Applicant appears to assert that Li fails to disclose or suggest at least the amended feature of claim 1 noted above for at least these reasons.
Examiner response:
Examiner respectfully disagrees. Englard teaches the amended limitations wherein the simulation data includes at least one or more of simulation results of electrical, thermal and/or mechanical aspects or properties of the drive system, and “wherein the industrial drive system operates with the selected at least one component, and wherein the at least one component of the industrial drive system is to be exchanged, adjusted, and/or modified within the industrial drive system” as explained in the rejection above.
Argument (page 9 of 12):
Applicant appears to assert that Englard is silent as to all three of simulation data, empiric data, and operational data, let alone where the simulation data clarifies simulation results of "electrical, thermal and/or mechanical aspects or properties of the drive system" as recited by amended claim 1.
Examiner response:
Examiner respectfully disagrees. Englard teaches all three of simulation data, empiric data, and operational data and the amended limitation “wherein the simulation data includes at least one or more of simulation results of electrical, thermal and/or mechanical aspects or properties of the drive system” as explained in the office action above.
Argument (page 10 of 12):
Applicant respectfully submitted that the Office has failed to provide a proper
motivation or reasoning for combining Englard, Sustaeta, and Li. Applicant submits
that the Office has failed to provide a plausible motivation to combine the references and instead is relying on impermissible hindsight gleaned from Applicant's disclosure to combine the cited references for at least the above noted reasons.
Examiner response:
Examiner respectfully disagrees. “One cannot show nonobviousness One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986)” (MPEP 2145 (IV).
Per MPEP, "[a]ny judgment on obviousness is in a sense necessarily a reconstruction based on hindsight reasoning, but so long as it takes into account only knowledge which was within the level of ordinary skill in the art at the time the claimed invention was made and does not include knowledge gleaned only from applicant's disclosure, such a reconstruction is proper."
Argument (page 11 of 12):
Applicant respectfully submitted that Englard, Sustaeta, Li, Cao, Yao, and Bernstein fail to disclose or suggest the features of amended claim 8 for at least these reasons.
Examiner response:
Examiner respectfully disagrees. Englard, Sustaeta, Li, Cao, Yao, and Bernstein fail to disclose or suggest the features of amended claim 8 as explained above in the office cation.
Argument (page 11 of 12):
Applicant appears to assert that because the combination of Englard, Sustaeta, and Li fails to disclose or suggest at least the above-recited features of amended independent claim 1, the combination of Englard, Sustaeta, and Li cannot render claims 1, 14, and 15 or any of their dependent claims as obvious. Accordingly, reconsideration and withdrawal of the rejection of claims 1-16 under 35 U.S.C. § 103 based on Englard, Sustaeta, and Li is respectfully requested.
Examiner response:
Examiner respectfully disagrees. Englard, Sustaeta and Li disclose or suggest at least the above-recited features of amended independent claim 1, and render claims 1, 14, and 15 or any of their dependent claims as obvious.
Accordingly, the rejection of claims 1-15 under 35 U.S.C. § 103 based on Englard, Sustaeta and Li is respectfully maintained.
Conclusion
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/ABABACAR SECK/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147