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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/13/2026 has been entered.
Response to Arguments
103 Rejections
Applicant argues that Lavid fails to teach machine learning models that are specifically trained for each industrial machine; however, the examiner respectfully disagrees. As discussed below, Lavid teaches in para. [0037] a database 150 storing sensory inputs (raw, preprocessed, or both) collected from a plurality of other sensors (not shown) associated with other machines (also not shown). The database 150 may further store indicators, anomalous patterns, behavioral trends, failure predictions, machine learning models utilized for analyzing sensory input data, or a combination thereof. The disclosure defines an apparatus having access to machine learning models related to other machines and their respective sensory inputs. Therefore, the examiner considers the disclosure of Lavid to teach a plurality of machine-specific machine learning models trained for each machine.
Therefore, the combination, as a whole, teaches object models associated with devices that each comprise a trained machine learning model for that device. The proposed modification therefore results in scoring containers 150 of Faulhaber having placeholders that are filled with a particular trained machine learning model at runtime by accessing the particular trained machine learning model stored outside of the docker image within the associated object model of the particular device (see below).
Therefore, the combination, as a whole, teaches the claimed invention.
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.
Claim(s) 1-2, 5-6, 11-12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lavid et al. (WO 2020036818A1) in view of Faulhaber, JR. et al. (2019/0155633).
With a respect to claim 1, Lavid et al. teaches an industrial data monitoring apparatus (Fig. 1), comprising: an input unit (130); a processing unit (140); and an output unit (240); wherein, the input unit (130) is configured to receive industrial monitor data from a plurality of devices (as the apparatus of Fig. 1 is configured to receive data from other sensors, not shown, from other machines, not shown; [0031] [0037]; Lavid et al. discloses the apparatus of Fig. 1 collecting sensor data from a machine and other, not shown, machines; [0037]; therefore, the disclosure of Lavid et al. teaches the industrial data monitoring apparatus configured to receive data from a machine 170 and other machine devices, like a turbine, engine, welding machine, etc. [0033] and their respective sensory data), each of the plurality of devices (i.e. the disclosed machine devices and their respective machine sensory data) having an associated object model (as Lavid et al. teaches each device and its respective sensory data will contain a plurality of indicative data features that are used to autonomously select an appropriate object model that encapsulates that particular machine device using its sensory data from a plurality of machine learning models; [0013] [0030]) comprising a trained machine learning model for that device (as the object model contains the ML model for that machine and its respective sensory data [0030]), each of the trained machine learning models being associated with a difference device of the plurality of devices (as Lavid et al. teaches throughout the disclosure, the apparatus of Fig. 1 receives machine sent sensory data and the apparatus of Fig. 1 autonomously selects the best fitting machine learning model from a database of machine learning models based on the data features found in that machine particular data set [0037]; wherein, the processing unit (140) is configured to access a machine learning environment (250; [0046]); wherein, the processing unit (140) is configured to access the trained machine learning models of the object models of the plurality of devices (as Fig. 6 depicts selecting a machine learning model respective to the pre-processed sensory at s620 from a databased of stored machine learning models; [0037]), wherein the plurality of trained machine learning models (i.e. the models stored on the database 150 relative to the pre-processed sensor input; [0037] [0059]) are separate from machine learning environment (as the models are taught to be selected from a databased, separate from the sensor inputs and thereby considered to be separate from the machine learning environment, insofar as how “separate” is structurally defined), wherein each trained machine learning algorithm was trained on the basis of a plurality of training industrial monitoring data and associated ground truth information (as indirectly taught by the operation of the apparatus of Fig. 1, as the machine learning models, to operate correctly, must be trained using training data and ground truth data; Lavid et al. teaches using ML models for those machines, thereby inferring those models have been trained in the conventional manner, as supported by applicant’s remarks on page 5-6 of their filed response dated 5/12/2025), wherein the plurality of training industrial monitoring data and associated ground truth information was for the device or type of device associated with the trained machine learning algorithm (as indirectly taught by the operation of the apparatus of Fig. 1, as the machine learning models, to operate correctly, must be trained using training data and ground truth data for that specific machine; Lavid et al. teaches using ML models for those machines, thereby inferring those models have been trained in the conventional manner), wherein the plurality of training industrial monitoring data and associated ground truth information was for the device or type of device associated with the trained machine learning algorithm (as taught by the operation of the apparatus of Fig. 1; as the machine learning models, to operate correctly, must be trained using training data and ground truth data for that specific machine; Lavid et al. teaches using ML models for those machines, thereby inferring those models have been trained in the conventional manner), and wherein the ground truth information associated with the plurality of training industrial monitoring data comprised associated health score information for the device or type of device associated with specific training industrial monitoring data for the device or type of device (i.e. machine) associated with specific training industrial monitoring data (as indirectly taught by the operation of the apparatus of Fig. 1, as the machine learning models, to operate correctly, must be trained using training data and ground truth data for that specific machine, including predicted failure times using the training data that dictates those time; Lavid et al. teaches using ML models for those machines, thereby inferring those models have been trained in the conventional manner), wherein, with respect to industrial monitor data expected to be received (from the machine and its respective sensors), from a particular device of the plurality of devices (as sensed by the sensors 120 of that machine of the plurality of machines, considered by the examiner as not part of the claimed invention), the apparatus (Fig. 1) is configured to implement the machine learning environment (250) and run the trained machine learning model comprised within the object model (i.e. a learning model is based on that machine’s model and sensor input; [0074]) for the particular device to analyze industrial monitor data (for example, Lavid et al. discloses industrial data like sound, motion, energy, temperature; [0026]) received from the particular device (i.e. the machine and its respective sensors) of the plurality of devices (i.e. the other machines); and wherein, the output unit (240) is configured to output an analysis result (as 240 sends notification based on the anomalous activity; [0046]) associated with the analysis of industrial monitor data (via sensors 120) received from the particular device (i.e. the machine) of the plurality of devices (i.e. the other machines) and wherein the analysis result comprises a health score of the particular device of the plurality of devices (as S440 teaches outputting the health results of the machine of the plurality of other machines not analyzed; [0073]).
Lavid et al. remains silent regarding via a docker image, wherein the docker image includes a scoring script usable to analyze industrial monitor data of any one of the plurality of device, the scoring script having a placeholder for a particular trained machine learning model for a particular device of the plurality of devices, wherein the placeholder is filled by the particular trained machine learning model during runtime, by accessing the particular trained machine learning model stored outside of the docker image, wherein at runtime the docker image receives the industrial monitor data associated with the particular device of the plurality of devices and the particular trained machine learning model of the object model associated with the particular device to fill the placeholder.
Faulhaber, JR. et al. teaches a similar apparatus that includes using a docker image (i.e. a container image 150; [0052]), wherein the docker image (150) includes a scoring script (i.e. executable instructions; [0054]) usable to analyze industrial monitor data of any one of the plurality of device (as Faulhaber, JR et al. teaches an apparatus 100 that receives data from client(s); these clients are considered to be data sources from various devices, where the data of a particular device under analysis will contain a docker image and corresponding scoring script related to that particular device), the scoring script (i.e. executable instructions) having a placeholder (i.e. a layer that includes instructions that represent an algorithm that defines a machine learning model for that particular device; [0030]) for a particular trained machine learning model for a particular device of the plurality of devices (for example, Lavid et al. discloses a robotic device [0030] [0049]), wherein the placeholder (i.e. the layer that includes instructions that represent the algorithm that defines the ML model) is filled by the particular trained machine learning model (i.e. the particular trained machine learning model as defined by the docker image) during runtime, by accessing the particular trained machine learning model stored outside of the docker image (Faulhaber teaches an indication being used to identify the machine learning model at a data storage data location outside the docker image; [0057]), wherein at runtime the docker image (150) receives the industrial monitor data associated with the particular device of the plurality of devices and the particular trained machine learning model of the object model associated with the particular device to fill the placeholder (Faulhaber teaches in [0062] docker image 150 can include requests for a set of inputs for s particular machine learning model).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the apparatus of Lavid et al. to include docker image control logic and steps of Faulhaber, JR. et al. because Faulhaber, JR. et al. teaches such a modification improves a user’s ability to implement machine learning and training regardless of that user’s knowledge or experience in machine learning; Abstract.
The method of claim 11 is performed by the operation of the rejected structure of claim 1.
With a respect to claim 2, Lavid et al. teaches the industrial data monitoring apparatus (Fig. 1) wherein the processing unit (140) is configured to access and implement a data score algorithm or function (as Lavid et al. teaches in [0053] using a correlation function, which is known to utilize scoring mechanisms like the disclosed Pearson correlation coefficient, insofar as how the scoring function is structurally defined), and wherein the data score algorithm or function (i.e. the correlation function) is configured to process the analysis of the industrial monitor data (via the sensors) received from the processing unit (140) for the particular device (i.e. the machine) of the plurality of devices (i.e. the other machines) to determine the analysis result (i.e. predicated failure time; abstract).
The method of claim 12 is performed by the operation of the rejected structure of claim 2.
With a respect to claim 5, Lavid et al. teaches the industrial data monitoring apparatus (Fig. 1) wherein the machine learning environment (250) is located on a data storage (as indirectly taught in Fig. 2, as the unit is storing the machine learning environment) of the apparatus (Fig. 1).
With a respect to claim 6, Lavid et al. teaches the industrial data monitoring apparatus (Fig. 1) wherein the plurality of trained machine learning models (chosen from at s620, Fig. 6) are located on a data storage (as indirectly taught in Fig. 2, as the unit is storing the machine learning models selected at s620) of the apparatus (Fig. 1).
With a respect to claim 15, Lavid et al. teaches the method wherein the machine learning environment (250) is located on a memory of an industrial data monitoring apparatus (as seen in Fig. 2)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Drevo et al. (2016/0132787) teaches a similar apparatus that provides multiple machine learning platforms specific to a dataset.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Meier can be reached on 571-272-2149. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW G MARINI/Primary Examiner, Art Unit 2853