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 .
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.
Claim(s) 1-4, 6, 8, 12-13, 15-18 is/are rejected under 35 U.S.C. 103 as being anticipated by Robert Bielby et al. (hereinafter Bielby) (“US 20210049444 A1”, 02/18/2021) in view of Lukasz Masiukiewicx et al. (hereinafter Masiukiewicz) (“US 20230285832 A1”, 09/14/2023).
Regarding claim 1, Bielby teaches;
A computerized method for determining an operability status of a
([0039] sensors can be installed in an automotive system to collect data during its routine operations; and the sensor data can be used to predict whether and how soon a component needs replacement or maintenance. … AI system can be used to suggest a maintenance service and/or identify the component that likely needs replacement or maintenance.)
obtaining sensor data collected by a sensor during deployment of the
([0039] sensors can be installed in an automotive system to collect data during its routine operations)
deploying a trained machine learning model on the sensor data resulting in a classification of a current operability status of the
([0084] For example, the machine learning module can use an unsupervised machine learning (175) to train the ANN (125) to recognize/classify normal patterns of sensor data (121) and thus to have the capability to detect anomaly based on deviation from the normal patterns, as illustrated in FIG. 4. Alternatively, supervised machine learning (171) can be used, as illustrated in FIG. 3 or 5.)
and generating an alert when the classification indicates that the
([0228] For example, the artificial neural network (ANN) (125) can classify whether the noises recorded in the microphone(s) (353) is normal or abnormal. When the noises recorded in the microphone(s) (353) is abnormal, the data storage device (101) stores a copy of the abnormal noises; and the processors (133) of the vehicle (111) can generate an alert on the infotainment system (149) to suggest a schedule for a maintenance service.)
Bielby fails to teach but Masiukiewicz teaches;
A ball throwing machine
([Abstract] ball machine to launch balls)
ball-throwing machine to perform operations according to a training program, wherein the training program comprises a set of drills, commands, or instructions executed by the ball-throwing machine to launch one or more balls, ([0047] finite period of time may correspond to the duration of, for example, an instructional class or a training session … over the finite period of time, the controller may adjust settings of the machine 100 to increase the difficulty of balls launched (e.g., increased speed, increased spin, etc.))
OBVIOUSNESS TO COMBINE MASIUKIEWICZ WITH BIELBY:
Masiukiewicz is analogous art to the present disclosure as it pertains to collecting sensor data during operation of a ball launching machine. Bielby is analogous art to the present disclosure as it pertains to collecting sensor data during routine machine operation and analyzing it with an ANN to predict component failure or maintenance needs. Additionally, Masiukiewicz explains that; ([0021] the automatic ball machine 100 may use the first camera 121 as an environment sensor to monitor, via the controller 110, an area in a direction that the ball 101 is being launched, and in at least one configuration around the automatic ball machine 100 to ensure no person or unintended objects are struck by the balls 101 being launched by the automatic ball machine 100, or harmed by any automated mechanical movement of the automatic ball machine 100. The automatic ball machine 100 may establish a keep-out region, that if violated, will result in the automatic ball machine 100 stopping launching of the balls 101 and/or mechanical movement, such as the ball launching system 130), and that; ([0053] A common problem with conventional ball machines is that the spinner wheels attached to the motors as part of a ball launch system will wear over time and, as a result, flight of the balls will change over time… Such changes in trajectory may also be caused by … wear in any other components of the ball machine … [0054] According to aspects of the present invention, the controller 110 may further use the imaging system 120 to determine the location of the ball 101 after being launched and determine if the ball 101 does not end up at the desired location, to further determine a location error). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the operability determination methods of Bielby during deployment of the training program of Masiukiewicz’s ball throwing machine to determine if any components of the ball throwing machine require maintenance, thereby allowing preventative action to be taken before the any operational malfunctions occur. Such a combination amounts to using Bielby’s known operability determination methods to operate using data collected during deployment of the training program of Masiukiewicz’s known ball throwing machine to determine operational abnormalities of the ball throwing machine, predictably allowing the ball throwing machine to operate as intended and ensure no person or unintended objects are struck by launched balls.
Regarding claim 2,
Bielby teaches;
obtaining training sensor data from the sensor collected during deployment of the
([0071] Typically, a vehicle (111) can be assumed to be operating in a normal/healthy condition in a certain time period … the sensor data (121) obtained during this period of time can be pre-classified as “normal” to train the ANN (125) using an unsupervised learning (175) as in FIG. 4, or a supervised learning (171) as in FIG. 5.)
obtaining labels for the training sensor data;
([0073] Optionally, a supervised machine learning (171) can be used to train the ANN (125), as illustrated in FIG. 5. During the “normal” service period of the vehicle (111) or a component, an expected classification (177) can be used to label the sensor data (121).)
and processing the labeled training sensor data with a machine learning algorithm resulting in the trained machine learning model through adjustment of internal parameters of the trained machine learning model causing a mapping of the training sensor data to the labels.
([0073] Optionally, a supervised machine learning (171) can be used to train the ANN (125), as illustrated in FIG. 5. During the “normal” service period of the vehicle (111) or a component, an expected classification (177) can be used to label the sensor data (121). The supervised learning (171) can be used to minimize the classification differences between the predictions (179) made using the ANN (125) according to the sensor data (121) and the expected classification (177).)
NOTE: Teaches processing the labeled training sensor data with a machine learning algorithm resulting in the trained machine learning model (training the ANN using supervised learning using the labeled data being the sensor data labeled with the expected classifications) causing a mapping of the training sensor data to the labels (the predictions of the ANN are mapping classifications / labels to the training sensor data).
([0016] In general, an ANN may be trained using a supervised method where the parameters in the ANN are adjusted to minimize or reduce the error between known outputs resulted from respective inputs and computed outputs generated from applying the inputs to the ANN.)
NOTE: Teaches the resulting trained neural network being obtained through adjustment of internal parameters of the trained machine learning model.
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
perform the operations according to the training program
([0047] finite period of time may correspond to the duration of, for example, an instructional class or a training session … over the finite period of time, the controller may adjust settings of the machine 100 to increase the difficulty of balls launched (e.g., increased speed, increased spin, etc.))
OBVIOUSNESS:
Using the same reasoning from claim 1.
Regarding claim 3,
Bielby teaches;
wherein the machine learning algorithm is a classification algorithm such that the trained machine learning model resulting from processing the labeled training sensor data with the machine learning algorithm is configured to classify the current operability status of the
([0084] For example, the machine learning module can use an unsupervised machine learning (175) to train the ANN (125) to recognize/classify normal patterns of sensor data (121) and thus to have the capability to detect anomaly based on deviation from the normal patterns, as illustrated in FIG. 4. Alternatively, supervised machine learning (171) can be used, as illustrated in FIG. 3 or 5.)
NOTE: Teaches the machine learning algorithm being a classification algorithm (ANN used to classify) such that the trained machine learning model resulting from processing the labeled training sensor data with the machine learning algorithm (the aforementioned trained ANN resulting from the supervised learning using the labeled training sensor data) is configured to classify the current operability status of the machine (the classification used to detect abnormalities in the operability of the vehicle).
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
OBVIOUSNESS:
Using the same reasoning from claim 1.
Regarding claim 4,
Bielby teaches;
wherein the machine learning algorithm is a predictive algorithm such that the trained machine learning model resulting from the processing the labeled training sensor data with the machine learning algorithm is configured to predict the future operability status of the
([0066] For example, the maintenance service data (123) can identify the measured wear and tear of a component as a function of time to predict a time to a recommended service. The sensor data (121) can be used in the ANN (125) to generate a predicted time to the recommended service. The supervised machine learning module (171) can adjust the artificial neural network (125) to reduce/minimize the difference between the time predicted based on the sensor data (121) and the time computed from the measurement of wear and tear.)
NOTE: Teaches the machine learning algorithm being a predictive algorithm (ANN generates a prediction) such that the trained machine learning model resulting from the processing the labeled training sensor data with the machine learning algorithm (the ANN trained using the aforementioned supervised learning) is configured to predict the future operability status of the machine (predict when, in the future, the vehicle will require service, i.e., when in the future the operability status of the machine will be poor enough to need service).
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
OBVIOUSNESS:
Using the same reasoning from claim 1.
Regarding claim 6,
Bielby teaches;
wherein the sensor data includes at least one of audio data collected by a microphone, vibration data collected by an accelerometer, ball-presence data collected by a ball-sensor, temperature data collected by a temperature sensor, or current data collected by a current sensor.
([0059] Further, the sensors (103) can include audio sensors (e.g., microphone) configured to monitor noises from various components and locations in the vehicle (111), a vibration sensor, a pressure sensor, a force sensor, a stress sensor, and/or a deformation sensor configured to measure loads on a component of the vehicle (111), accelerometers and/or gyroscope sensors measuring the motions of some components of the vehicle (111), etc. Such sensors can be used to monitor the operating status and/or health of the components for predictive maintenance.)
NOTE: Teaches the sensor data including at least audio data (noises) from a microphone and vibration data (motions, which can include vibrations) from an accelerometer.
Regarding claim 8,
Claim 8 is a method claim that is substantially similar to method claim 1, with one different limitation, which is taught by Bielby in view of Masiukiewicz;
Bielby teaches;
deploying a neural network to process the sensor data resulting in a classification of a current operability status of the
([0084] For example, the machine learning module can use an unsupervised machine learning (175) to train the ANN (125) to recognize/classify normal patterns of sensor data (121) and thus to have the capability to detect anomaly based on deviation from the normal patterns, as illustrated in FIG. 4. Alternatively, supervised machine learning (171) can be used, as illustrated in FIG. 3 or 5.)
NOTE: Teaches deploying a neural network to process the sensor data resulting in a classification of a current operability status of the machine or a prediction of a future operability status of the ball-throwing machine (using an ANN to classify sensor data indicating the presence of an anomaly of the machine).
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
OBVIOUSNESS:
Using the same reasoning from claim 1.
Regarding claim 12,
Bielby teaches;
wherein generating the alert includes causing display of the alert on a display screen of a network device communicatively coupled to the
([0062] The infotainment system (149) can be used to present the predicted or schedule maintenance service. Optionally, the communication device (139) can establish a connection to a mobile device of the driver of the vehicle (111) to inform the driver of the recommended maintenance service and/or the recommended data of the service, to calendar the appointment, etc.)
NOTE: Teaches an alternative to generating an alert (presenting predicted maintenance service) via the infotainment system is generating an alert via the display screen of a network device (informing the user via mobile device, which would be communicated through the screen of the device) commutatively coupled to the machine (the communication device of the vehicle establishes a connection to a mobile device).
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
OBVIOUSNESS:
Using the same reasoning from claim 1.
Regarding claim 13,
Claim 13 is substantially similar to claim 6 and is rejected using the same reasoning.
Regarding claim 15,
Claim 15 is substantially similar to claim 1, and is rejected using the same reasoning.
Regarding claim 16,
Claim 16 is substantially similar to claim 2, and is rejected using the same reasoning.
Regarding claim 17,
Claim 17 is substantially similar to claim 3, and is rejected using the same reasoning.
Regarding claim 18,
Claim 18 is substantially similar to claim 4, and is rejected using the same reasoning.
Claim(s) 5, 7, 14 is/are rejected under 35 U.S.C. 103 as being anticipated by Bielby (“US 20210049444 A1”, 02/18/2021) in view of Masiukiewicz (“US 20230285832 A1”, 09/14/2023) further in view of Robert Allen Gee et al (hereinafter Gee) (“US 20220284470 A1”, 09/08/2022).
Regarding claim 5,
Bielby and Masiukiewicz fail to teach but Gee teaches;
wherein the machine learning algorithm is linear regression.
([0223] It will be appreciated that neural networks are one example of machine learning algorithms. Other examples include linear regression, logistic regression, decision tree, Bayer, and random forest algorithms. Still other examples are possible. These algorithms can be substituted for the neural networks described herein.)
NOTE: Teaches a plurality of ML algorithms which can be used interchangeably, one of which is a linear regression model.
OBVIOUSNESS TO COMBINE GEE WITH BIELBY AND MASIUKIEWICZ:
Gee is analogous art to the present disclosure as it pertains to monitoring operational states of a machine (a vehicle) by deploying a trained machine learning model. Additionally, Gee and Bielby addresses the same problem of using sensor data of a machine (vehicle) and machine learning to predict component condition, wear, or maintenance needs before failure. Gee states that it would be reasonable to implement this process using any machine learning algorithm, and even explicitly states a linear regression model. ([0223] It will be appreciated that neural networks are one example of machine learning algorithms. Other examples include linear regression, logistic regression, decision tree, Bayer, and random forest algorithms. Still other examples are possible. These algorithms can be substituted for the neural networks described herein.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to implement the ball machine predictive maintenance system of Bielby as modified by Masiukiewicz to use a linear regression model for tasks more suitable for linear regression as taught by Gee.
Regarding claim 7,
Bielby teaches;
Microphone and accelerometer
(Using the reasoning from claim 6)
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
OBVIOUSNESS:
Using the same reasoning from claim 1.
Bielby and Masiukiewicz fail to teach but Gee teaches;
wherein the [sensors] are disposed on a control unit of the
([0041] The sensors are deployed at a vehicle and may be configured to obtain first data, the vehicle being driven by a driver, the first data describing conditions of components of and specifying an individual driving pattern of the driver. The control circuit is coupled to the sensors and the neural network.)
NOTE: Gee teaches the sensors are disposed on a control unit of the machine.
OBVIOUSNESS:
Gee and Bielby addresses the same problem of using sensor data of a machine (vehicle) and machine learning to predict component condition, wear, or maintenance needs before failure. Bielby discloses sensors including a microphone and accelerometer, while Gee teaches the sensors disposed on a control unit of the machine. A microphone, accelerometer, and a control unit (control circuit) coupled to sensors are all known in the art, and each of these elements merely perform the same functions as they do separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, the integration of a microphone and accelerometer into the control unit would allow for sound and vibration/motion data to be integrated into the system, further improving the awareness of the system. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to integrate a control unit coupled to the microphone and accelerometer into the system, to allow the system to additionally operate based on sound and vibration/motion data, further improving the awareness of the system.
Regarding claim 14,
Claim 14 is a method claim substantially similar to method claim 7, and is rejected using the same reasoning.
Claim(s) 9-11, 19-20 is/are rejected under 35 U.S.C. 103 as being anticipated by Bielby (“US 20210049444 A1”, 02/18/2021) in view of Masiukiewicz (“US 20230285832 A1”, further in view of Colin Bruce Clement et al. (hereinafter Clement) (US 20230222334 A1, 07/13/2023).
Regarding claim 9,
Bielby teaches;
obtaining training sensor data collected by the sensor during deployment of the
([0071] Typically, a vehicle (111) can be assumed to be operating in a normal/healthy condition in a certain time period … Thus, the sensor data (121) obtained during this period of time can be pre-classified as “normal” to train the ANN (125) using an unsupervised learning (175) as in FIG. 4, or a supervised learning (171) as in FIG. 5.)
obtaining labels for the training sensor data;
([0073] Optionally, a supervised machine learning (171) can be used to train the ANN (125), as illustrated in FIG. 5. During the “normal” service period of the vehicle (111) or a component, an expected classification (177) can be used to label the sensor data (121).)
NOTE: Teaches obtaining labels for the training sensor data (expected classification used to label the sensor data).
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
perform the operations according to the training program
([0047] finite period of time may correspond to the duration of, for example, an instructional class or a training session … over the finite period of time, the controller may adjust settings of the machine 100 to increase the difficulty of balls launched (e.g., increased speed, increased spin, etc.))
OBVIOUSNESS:
Using the same reasoning from claim 1.
Bielby and Masiukiewicz fail to teach but Clement teaches;
and performing a neural network training process including multiple iterations of a feedforward pass,
([0117] wherein during training of the deep learning model: select a first portion of the at least one weight matrix to quantize with integer data types and selecting a second portion of the at least one weight matrix expressed as full-precision floating point values; determine values for weights of the at least one weight matrix through multiple iterations of a forward pass)
computing a loss function, a backpropagation step, and updating weights of the neural network resulting in adjustment of the weights of each layer of the neural network.
([0082] During training, an error loss is computed which is used to optimize the weights of the model (block 608). The loss is then used to adjust the model weights during training in order to minimize the loss function. The backward pass backpropagates the loss to each layer where a gradient is computed (block 610) and used to update the weights of that layer (block 612). The process of adjusting or updating the weights is considered model training and as the model keeps training and the loss is getting minimized, the model is learning.)
OBVIOUSNESS TO COMBINE CLEMENT, BIELBY, AND MASIUKIEWICZ:
Clement is analogous art to the present disclosure as it pertains to a backpropagation-based training process for a machine learning model. Bielby utilizes trains a neural network to monitor the operational status of a machine (vehicle), while Clement discloses a specific training process for a neural network. Additionally, Clement states that their disclosed training process allows the process to consume less resources; ([0004] A deep learning model is trained through quantization with noise training to learn to perform a target software engineering task. During the quantized with noise training, a portion of the weights of a weight matrix are quantized into integer data types. By reducing the bit-width of a portion of the weights during training makes the model more resilient to quantization and reduces the noise or discrepancy between the quantized and full-precision model outputs. Upon completion of the training, all the weights of a weight matrix are post quantized into integer data types resulting in a smaller model size that consumes less resources to train and deploy in an inference system. The smaller-sized model is beneficial for computing environments with limited computing resources, such as mobile and resource-constrained devices.) Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, for the neural network taught by Bielby to be trained using the process disclosed by Clement to decrease resource consumption during training.
Regarding claim 10,
Bielby teaches;
wherein the neural network
([0084] For example, the machine learning module can use an unsupervised machine learning (175) to train the ANN (125) to recognize/classify normal patterns of sensor data (121) and thus to have the capability to detect anomaly based on deviation from the normal patterns, as illustrated in FIG. 4. Alternatively, supervised machine learning (171) can be used, as illustrated in FIG. 3 or 5.)
NOTE: Teaches the neural network classifying a current operability status (classify normal patterns of sensor data to detect anomalies of the vehicle) of the machine.
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
OBVIOUSNESS:
Using the same reasoning from claim 1.
Bielby and Masiukiewicz fail to teach but Clement teaches;
the neural network utilizes an activation function and an output layer function
([0043] The output layer 352 includes a linear layer 354 and a softmax layer 356.)
NOTE: Teaches a neural network utilizing an activation function (softmax) and an output layer function for producing output.
OBVIOUSNESS:
Using the same reasoning from claim 9.
Regarding claim 11,
Bielby teaches;
wherein the neural network
([0066] For example, the maintenance service data (123) can identify the measured wear and tear of a component as a function of time to predict a time to a recommended service. The sensor data (121) can be used in the ANN (125) to generate a predicted time to the recommended service. The supervised machine learning module (171) can adjust the artificial neural network (125) to reduce/minimize the difference between the time predicted based on the sensor data (121) and the time computed from the measurement of wear and tear.)
NOTE: Teaches the neural network being configured to predict a future operability status of the machine (predict a time to recommend service, i.e., when in the future the wear and tear of the component renders the vehicle in need of service).
Bielby fails to teach but Masiukiewicz teaches;
Ball-throwing machine
([Abstract] ball machine to launch balls)
OBVIOUSNESS:
Using the same reasoning from claim 1.
Bielby and Masiukiewicz fail to teach but Clement teaches;
the neural network utilizes an activation function and an output layer function
([0043] The output layer 352 includes a linear layer 354 and a softmax layer 356.)
NOTE: Teaches a neural network utilizing an activation function (softmax) and an output layer function for producing output.
OBVIOUSNESS:
Using the same reasoning from claim 9.
Regarding claim 19,
Claim 19 is substantially similar to claim 9, and is therefore rejected using the same reasoning
Regarding claim 20,
Claim 20 is substantially similar to claim 10, and is therefore rejected using the same reasoning
Response to Arguments
Applicant's arguments filed 08/03/2026 regarding the claim rejections under 35 USC 103 have been fully considered but they are not persuasive. Starting on page 2, the applicant states that; “It is respectfully submitted that the amendment is intended to clarify the scope of the recited "training program," thereby further distinguishing the claimed invention from the cited references. Regarding "obtaining sensor data collected by a sensor during deployment of the ball-throwing machine to perform operations according to a training program, wherein the sensor is disposed on the ball-throwing machine," the Office Action has alleged that paragraph [0039] of Bielby teaches this limitation by mapping the claimed "training program" onto Bielby's use of the collected sensor data to train the artificial neural network, that is, the machine-learning training process. (Office Action, p. 3.) Applicant respectfully disagrees and submits that the cited art fails to teach or suggest the amended limitation.
Amended claim 1 clarifies that the "training program" is "a set of drills, commands, or instructions executed by the ball-throwing machine to launch one or more balls," which as understood by one of ordinary skill in the art is an operational ball-launching regimen that the machine executes, not the training of a machine-learning model. Bielby at paragraph [0039] discloses only that "sensors can be installed in an automotive system to collect data during its routine operations," and that this sensor data is then used to "train" the artificial neural network. (See Bielby, paragraph [0039].) Bielby's "training" is the machine-learning training process, and its data collection occurs during undefined "routine operations," not during the machine's execution of a set of drills, commands, or instructions to launch balls. Accordingly, one of ordinary skill in the art would understand that the claimed 'training program' refers to an operational ball-launching regimen executed by the ball- throwing machine, whereas Bielby's 'training' refers to the training of a machine-learning model. These are fundamentally different concepts directed to different aspects of system operation.”
Examiner respectfully disagrees. The present rejection does not rely upon Bielby’s training of an artificial neural network as the claimed “training program” as modified by the amendments. Rather, Bielby is relied upon for teaching collection of sensor data during routine machine operation and use of the sensor data by a neural network to determine abnormal operating conditions. Masiukiewicz is relied upon for teaching operation of a ball-throwing machine during a training session according to settings/instructions executed by the ball throwing machine to launch one or more balls, which reads on the amended “training program” limitation. See the rejection of amended claim 1 at pages 4-5 of the present office action. Accordingly, Applicant’s distinction between Bielby’s machine learning training and the claimed operational training program does not overcome the rejection based on the combined teachings of Bielby and Masiukiewicz.
The applicant further states that; “Even assuming the Office believes the combination of Bielby and Masiukiewicz establishes a prima facie case of obviousness for amended claim 1, Applicant respectfully submits that Bielby is non-analogous art and therefore cannot properly be relied upon in an obviousness analysis. A reference may be used in an obviousness rejection only if it is analogous art, meaning that it is either
(1) from the same field of endeavor as the claimed invention, regardless of the problem addressed, or
(2) reasonably pertinent to the particular problem with which the inventor was concerned. In re Bigio, 381 F.3d 1320, 1325 (Fed. Cir. 2004); MPEP § 2141.01(a). Applicant respectfully submits that Bielby satisfies neither prong.
With respect to the first prong, claim 1 is directed to "[a] computerized method for determining an operability status of a ball-throwing machine," and the present application concerns ball-throwing machines used to deliver balls to a player during athletic training. Bielby, by contrast, is directed to automotive systems and predictive maintenance of vehicles and automotive engines, disclosing that "sensors can be installed in an automotive system to collect data during its routine operations" and using that data to predict when a vehicle component requires replacement or maintenance. (Bielby, paragraph [0039].) Bielby further explains that its disclosed embodiments pertain to "predictive maintenance of automotive engines." (Bielby, Title; paragraph [0001]) A method for determining the operability of a ball-throwing machine and a system for predictive maintenance of automotive vehicles are plainly not within the same field of endeavor.
With respect to the second prong, the particular problem addressed by the present application is maintaining the reliability and consistency of a ball-throwing machine used in athletic training. For example, the specification explains that "improvements in reliability and consistency of the ball- throwing process can benefit the player and the training program" and that such machines "may operate for hours, days, or even months at a time without being checked for operational issues." (As- Filed Specification, paragraphs [0002]-[0003].) The Federal Circuit has explained that a reference is reasonably pertinent only if, because of the matter with which it deals, it would have "logically commended itself' to an inventor's attention in considering the problem faced by the inventor. In re Clay, 966 F.2d 656, 659 (Fed. Cir. 1992). Likewise, the Federal Circuit has emphasized that the analogous-art inquiry must focus on the particular problem addressed by the claimed invention and whether a skilled artisan would have consulted the reference in attempting to solve that problem. Donner Tech., LLC v. Pro Stage Gear, LLC, 979 F.3d 1353, 1361 (Fed. Cir. 2020).
Here, a person of ordinary skill seeking to improve the reliability and operational consistency of a ball-throwing machine used in athletic training would not have looked to Bielby's automotive predictive-maintenance system, which is concerned with vehicle operation, vehicle-component wear, maintenance scheduling, driving conditions, and the personalized operating habits of vehicle operators and occupants. Bielby explains that sensor data is collected from an automotive system and used to predict whether and when vehicle components require replacement or maintenance, and further explains that the disclosed neural network is customized based on the operating environments and habits of vehicle users. (Bielby, paragraphs [0038]-[0039].) The operational environment, objectives, constraints, and failure modes of an automotive system differ substantially from those of a ball-throwing machine used in athletic training. Consequently, Bielby is not reasonably pertinent to the particular problem with which the inventors were concerned.
The Examiner asserts that Bielby is analogous art because it pertains to "predicting abnormalities in the operations of a machine using neural networks with sensor data input." (Office Action, p. 5.) Applicant respectfully submits that this characterization defines both the field of endeavor and the problem at an impermissibly high level of abstraction. Under such a broad formulation, virtually any machine-learning-based monitoring system directed to any type of machine could qualify as analogous art regardless of the technology involved. Federal Circuit precedent, however, requires a more focused inquiry directed to the specific problem confronted by the inventors, not a generalized problem statement broad enough to encompass disparate technologies. See In re Clay, 966 F.2d at 659; Donner, 979 F.3d at 1361. When the inquiry is conducted at the appropriate level of specificity, Bielby is directed to predictive maintenance of automotive vehicles and engines, whereas the present application is directed to determining the operability status of ball-throwing machines used in athletic training. Because Bielby is neither from the same field of endeavor nor reasonably pertinent to the problem addressed by the present application, Bielby constitutes non- analogous art. As Bielby serves as the primary reference underlying each of the rejections under 35 U.S.C. § 103, its status as non-analogous art provides an independent basis for withdrawal of those rejections.
Indeed, the Examiner's own rationale illustrates the overgeneralization inherent in the rejection. The Office does not identify a problem specifically shared by Bielby and the present application. Rather, the Office states only that Bielby pertains to "predicting abnormalities in the operations of a machine using neural networks with sensor data input" and, on that basis alone, concludes that Bielby is analogous art. (Office Action, p. 5.) This characterization is so abstract that it effectively ignores the technological context of Bielby and the present application. If such a generalized description were sufficient, nearly any reference directed to machine-learning-based monitoring of any machine could be deemed analogous art, regardless of the industry, operating environment, failure modes, or problem being addressed. The analogous-art inquiry, however, requires more than identifying broad conceptual similarities. The Office has not explained why a person of ordinary skill seeking to improve the reliability and operational consistency of a ball- throwing machine used in athletic training would have consulted automotive predictive-maintenance systems, nor has the Office identified a particular problem common to both technologies that would have logically directed such a person to Bielby. The absence of such an explanation further demonstrates that the rejection is predicated on an impermissibly generalized characterization of the invention and the prior art rather than the specific problem addressed by the claimed invention.”
Examiner respectfully disagrees. Even assuming arguendo that Bielby is not within the same field of endeavor as the present invention, Bielby is reasonably pertinent to a problem with which the inventors were concerned. The present specification identifies the problem of components of a ball-throwing machine wearing or otherwise developing operability issues that may not be readily observable during operation (Applicant’s specification, [0004]). Bielby likewise addresses detecting deterioration or abnormal operating conditions of machine components that may not otherwise be readily apparent, based on sensor data collected during operation, and taking responsive action. Thus, despite Bielby’s automotive context, the matter with which Bielby deals would have logically commended itself to the attention of one of ordinary skill in the art, before the effective filing date, seeking to detect deterioration or abnormal operation of components of a ball-throwing machine.
Additionally, both the applicant’s specification and Masiukiewicz further indicate why the operability status determination methods of Bielby would have logically commended itself to the inventor’s attention in considering the problem faced by the inventor. The applicant states that “[0004] over time, a ball-throwing machine may wear down in certain areas or suffer minor operability issues that are not easily observed by the naked eye.” while Masiukiewicz states that “[0053] A common problem with conventional ball machines is that the spinner wheels attached to the motors as part of a ball launch system will wear over time and, as a result, flight of the balls will change over time… Such changes in trajectory may also be caused by … wear in any other components of the ball machine, [0054] According to aspects of the present invention, the controller 110 may further use the imaging system 120 to determine the location of the ball 101 after being launched and determine if the ball 101 does not end up at the desired location, to further determine a location error.” Accordingly, Masiukiewicz demonstrates that component wear and resulting degradation in ball-launch performance were known problems in ball-throwing machines and that sensor information was known to be used to identify resulting operational errors. Thus, one of ordinary skill in the art, before the effective filing date, would reasonably have consulted Bielby’s sensor-based operability status techniques when addressing deterioration of components of a ball-throwing machine resulting in degraded or abnormal operations.
The applicant further states; “For at least the foregoing reasons, none of the pending rejections can be sustained. Bielby does not teach obtaining sensor data collected "during deployment of the ball-throwing machine to perform operations according to a training program, the training program comprising a set of drills, commands, or instructions executed by the ball-throwing machine to launch one or more balls," as expressly recited in independent claims 1, 8, and 15. Nor does the Office identify any disclosure in Bielby directed to collecting sensor data during execution of drills or training routines performed by a ball- throwing machine. Instead, the rejection relies on an impermissibly generalized characterization of Bielby as merely involving machine-learning analysis of sensor data from a "machine." (Office Action, p. 5.) Such a broad characterization cannot substitute for an actual disclosure of the claimed limitation, nor can it establish that Bielby is analogous art. Masiukiewicz was cited only for teaching a ball-throwing machine and does not remedy Bielby's failure to disclose the claimed collection of sensor data during execution of a training program. Likewise, Gee and Clement were cited solely for additional limitations recited in certain dependent claims and do not cure the deficiencies of the combination of Bielby and Masiukiewicz. Accordingly, independent claims 1, 8, and 15 are patentable over the cited art, and dependent claims 2-7, 9-14, and 16-20 are likewise patentable at least by virtue of their dependency therefrom, as well as for the additional limitations recited therein.”
Examiner respectfully disagrees. Bielby teaches collecting sensor data from a machine during its routine operation, while Masiukiewicz teaches operating a ball-throwing machine during deployment of a training program according to ball-launch settings/instructions. In the proposed combination, Bielby’s sensor-based operability monitoring is applied to Masiukieqicz’s ball-throwing machine during such operation. Accordingly, sensor data would be collected while the ball-throwing machine is executing the claimed training program comprising drills, commands, or instructions for launching one or more balls. The rejection therefore does not rely upon Bielby alone to disclose the entire limitation, but upon the combined teachings of Bielby and Masiukiewicz. Accordingly, independent claims 1, 8, and 15 are not patentable over the cited art, and dependent claims 2-7, 9-14, and 16-20 are likewise not patentable at least by virtue of their dependency therefrom.
CONCLUSION
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MATTHEW ALAN CADY/ Examiner, Art Unit 2145
/CESAR B PAULA/ Supervisory Patent Examiner, Art Unit 2145
