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 .
DETAILED ACTION
This non-final office action is responsive to the U.S. patent application no. 18/797,586 filed on August 8, 2024.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
The application claims priority under 35 U.S.C. 120 to U.S. non-provisional application No. 17/900,676 filed on August 31, 2022.
Information Disclosure Statement
The information disclosure statement (IDS) forms submitted on 1/6/2025 and 1/15/2026 are compliant with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it recites
“a system comprising:
one or more computers; and
one or more computer-readable media …”
Regarding the “one or more computers”, Applicant stated in paragraph [0039] of the application that
“A system of one or more computers can be so configured by virtue of software, firmware, hardware, or a combination of them installed on the system that in operation cause the system to perform the actions,”
which indicates to one of ordinary skill in the art that the one or more computers can be software per se. that is non-statutory under 35 U.S.C. 101.
Meanwhile, the “one or more computer-readable media” can be embodied in transitory signals when given their broadest reasonable interpretation. A transitory signal is non-statutory under 35 U.S.C. 101.
A system that comprises non-statutory subject matter is itself non-statutory. Therefore claim 8 is directed to non-statutory subject matter.
The dependent claims 9-14 do not recite limitations that would redirect the claimed invention to statutory subject matter, therefore inherit the issue of the independent claim 8.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 17 of U.S. Patent No. 11,689,944 in view of Mayer et al. (US 2022/0239367). Although the claims at issue are not identical, they are not patentably distinct from each other as shown below.
Present Application no. 18/797,586
Patent No. 11,689,944
1. A method performed by one or more computers, the method comprising:
obtaining, by the one or more computers, a set of performance indicators for a satellite terminal;
providing, by the one or more computers, input data that is based on the set of performance indicators to a machine learning model;
determining, by the one or more computers, a network condition classification for the satellite terminal based on output that the machine learning model generates in response to receiving the input data based on the set of performance indicators;
selecting, by the one or more computers, a change to operation or configuration of the satellite terminal based on the determined network condition classification for the satellite terminal; and
sending, by the one or more computers, an instruction to initiate the selected change for the satellite terminal.
17. A method comprising:
receiving, by a communication device, data traffic;
evaluating, by the communication device, the data traffic using an anomaly detector comprising a machine learning model trained to predict whether data traffic patterns differ from a set of observed traffic patterns present in a set of training data;
using, by the communication device, a traffic classifier comprising a machine learning model to predict a quality of service (QoS) class for network connections or data flows for traffic that the anomaly detector predicts to be similar to the observed traffic patterns; and
storing, by the communication device, data traffic that the anomaly detector predicts to be different from the observed traffic patterns.
The patent no. 11,689,944 might not have explicitly recited in its claims subject matter found in the last two clauses of claim 1 of the instant application. However, Mayer et al. disclosed such subject matter as shown below.
Mayer disclosed
selecting, by the one or more computers, a change to operation or configuration of the satellite terminal based on the determined network condition classification for the satellite terminal (Mayer, Fig. 3, step 306 and [0055], “modifying, 306, the satellite communication link based on the predicted event. In some embodiments, the predicted event may be a link impairing event, in which the modifying the satellite communication link may include configuring one or more back up satellite communication links and rerouting the traffic from the anticipated impaired satellite communication link to the one or more back up satellite communication links”); and
sending, by the one or more computers, an instruction to initiate the selected change for the satellite terminal (Mayer, [0040], “Depending on the circumstances, the need to remove a downlink entirely may be performed by directly modifying routing tables, either immediately, or if an almanac function is used, at the next planned update.”).
One of the ordinary skill in the art, before the effective filing date of the claimed invention (AIA ), would have been motivated to combine Patent no. 11,689,944 and Mayer because both references disclosed methods and systems for monitoring satellite terminals performance and managing their operation using machine learning. Therefore it would have been obvious back then to combine the teaching of Mayer et al. with patent no. 11,689,944 to arrive at the subject matter in claim 1 of the instant application.
Claim 1, 5, 6, 7, 8, 12, 13, 14, 15, 19 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3, 8, 9, 10 and 13 of U.S. Patent No. 12,107,742. Although the claims at issue are not identical, they are not patentably distinct from each other as shown below.
Present application no. 18/797,586
Patent No. 12,107,742
1. A method performed by one or more computers, the method comprising:
obtaining, by the one or more computers, a set of performance indicators for a satellite terminal;
providing, by the one or more computers, input data that is based on the set of performance indicators to a machine learning model;
determining, by the one or more computers, a network condition classification for the satellite terminal based on output that the machine learning model generates in response to receiving the input data based on the set of performance indicators;
selecting, by the one or more computers, a change to operation or configuration of the satellite terminal based on the determined network condition classification for the satellite terminal; and
sending, by the one or more computers, an instruction to initiate the selected change for the satellite terminal.
5. The method of claim 1, wherein the machine learning model comprises a deep neural network.
6. The method of claim 1, wherein the machine learning model comprises at least one of a neural network, a support vector machine, a classifier, a regression model, a clustering model, a decision tree, a random forest model, a genetic algorithm, a Bayesian model, or a Gaussian mixture model.
7. The method of claim 1, wherein the machine learning model is configured to output a set of scores including a score for each network condition classification in a predetermined set of network condition classifications, wherein the scores indicate relative likelihoods that the respective network condition classifications are appropriate given the input data provided to the machine learning model.
1. A method performed by one or more computers, the method comprising:
retrieving, by the one or more computers, data indicating labels for clusters of network performance anomalies, wherein the clusters respectively correspond to different network conditions experienced by satellite terminals of a satellite communication system, wherein the clusters each include multiple network performance anomalies grouped according to frequency of co-occurrence such that the clusters respectively represent different groups of network performance anomalies that are indicated by a data set to co-occur for individual satellite terminals of the satellite communication system, and wherein each label for a cluster indicates a network condition classification for a network condition associated with the network performance anomalies in the cluster;
generating, by the one or more computers, a set of training data to train a machine learning model, the set of training data being generated by assigning the labels for the clusters to sets of performance indicators used to generate the clusters, the labels being assigned based on levels of commonality among types of network performance anomalies indicated by respective sets of performance indicators and the types of network performance anomalies included in the respective clusters;
training, by the one or more computers, a machine learning model to predict network condition classifications for satellite terminals based on input of performance indicators for the satellite terminals, wherein the machine learning model is trained to evaluate satellite terminals with respect to a predetermined set of network condition classifications including the network condition classifications for the clusters, and wherein the machine learning model is trained using training data examples from the set of training data in which each training example includes a set of performance indicators and the label assigned for the set of performance indicators; and
for each of one or more satellite terminals, determining, by the one or more computers, a network condition classification for the satellite terminal based on output that the trained machine learning model generates based on input of performance indicators for the satellite terminal.
3. The method of claim 2, further comprising storing rules that indicate different operation or configuration changes corresponding to different network condition classifications in the predetermined set of network condition classifications;
wherein managing the one or more satellite terminals comprises, for a particular satellite terminal of the one or more satellite terminals:
using, by the one or more computers, an output of the trained machine learning model to determine a network condition classification for the particular satellite terminal;
selecting, by the one or more computers, a change to operation or configuration of the particular satellite terminal that the stored rules indicate for the determined network condition classification for the particular satellite terminal; and
sending, by the one or more computers, an instruction to initiate the selected change for the particular satellite terminal.
8. The method of claim 1, wherein the machine learning model comprises a deep neural network.
9. The method of claim 1, wherein the machine learning model comprises at least one of a neural network, a support vector machine, a classifier, a regression model, a clustering model, a decision tree, a random forest model, a genetic algorithm, a Bayesian model, or a Gaussian mixture model.
10. The method of claim 1, wherein the machine learning model is configured to output a set of scores including a score for each of the network condition classifications in the predetermined set of network condition classifications, wherein the scores indicate relative likelihoods that the respective network condition classifications are appropriate given the input provided to the machine learning model.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Vasudevan et al. (U.S. 2021/0204152).
Regarding claim 1, Vasudevan disclosed a method performed by one or more computers, the method comprising:
obtaining, by the one or more computers, a set of performance indicators for a satellite terminal (Vasudevan disclosed in [0056] that “The traffic classification system 170 can be configured to collect traffic statistics for data transferred in both directions (e.g., to and from the terminal 130) and to generate higher layer metrics and estimates”);
providing, by the one or more computers, input data that is based on the set of performance indicators to a machine learning model (Vasudevan disclosed in Abstract and [0007] that “The anomaly detector examines traffic (e.g., packet statistics) to identify traffic that appears to be different from the classes of traffic that the traffic classifier is trained to identify”; Vasudevan further disclosed in [0078] that “ The anomaly detector 174 typically does not receive actual packets of data streams 202a-202f, but instead receives values or features derived from the data streams 202a-202f. For example, application layer metrics can be estimated from the incoming traffic connections using TCP-layer statistics which can include packet-level information. The estimated application layer metrics include information about the objects exchanged during the flow or connection, which is then input into the ML-based anomaly detector 174.” Said anomaly detector anticipates the machine learning model in the claim);
determining, by the one or more computers, a network condition classification for the satellite terminal based on output that the machine learning model generates in response to receiving the input data based on the set of performance indicators (Vasudevan disclosed in [0012] that “an anomaly detector comprising a machine learning model trained to predict whether data traffic patterns differ from a set of observed traffic patterns present in a set of training data” in other words the anomaly detector determines a classification of the traffic as anomalous or non-anomalous);
selecting, by the one or more computers, a change to operation or configuration of the satellite terminal based on the determined network condition classification for the satellite terminal (Vasudevan disclosed in [0006, 0057, 0081] that “Traffic labeled as anomalous is also collected by data collection functionality 220, along with associated meta-data ... The collected information can be recorded for later analysis and labelling or use as training data for further training the machine learning models 174, 176.” Said “further training the machine learning models 174, 176” anticipates “a change to operation or configuration of the satellite terminal” in in the claim); and
sending, by the one or more computers, an instruction to initiate the selected change for the satellite terminal (Vasudevan disclosed in [0082] that “Terminals receive these model updates and begin using the new models (e.g., models 174, 176 having an improved or refined training state, such as the set of internal parameters such as neural network weights). After the update, the traffic classifier 176 can be configured to recognize a wider range of traffic patterns”).
Claim 8 recites substantially the same subject matter as claim 1, in system form rather than method form, therefore the rejection rationale for claim 1 applies equally as well to claim 8.
Claim 15 recites substantially the same subject matter as claim 1, in system form rather than method form, therefore the rejection rationale for claim 1 applies equally as well to claim 15.
Regarding claims 2, 9 and 16, Vasudevan disclosed the subject matter of claims 1, 8 and 15, respectively.
Vasudevan further disclosed wherein the satellite terminal comprises a very small aperture terminal (VSAT) (Vasudevan, [0075], “VSAT”).
Regarding claims 3, 10 and 17, Vasudevan disclosed the subject matter of claims 1, 8 and 15, respectively.
Vasudevan further disclosed storing rules that indicate different operation or configuration changes corresponding to different network condition classifications in a predetermined set of network condition classifications (Vasudevan disclosed in [0067] that “traffic classification can be performed based on other measured statistics, such as the size and timing of packets and/or objects in a data flow. Machine learning and rule-based models can be used” As a machine learning model is a way to store rule, the further training of models 174, 176 as disclosed in [0006, 0057, 0081] is in essence to store operation and configuration changes in the model); and
wherein selecting the change to operation or configuration of the satellite terminal comprises selecting a change to operation or configuration of the satellite terminal that the stored rules indicate for the determined network condition classification for the satellite terminal (Vasudevan disclosed in [0082] that “Terminals receive these model updates and begin using the new models (e.g., models 174, 176 having an improved or refined training state, such as the set of internal parameters such as neural network weights).” Said terminals refer to “satellite terminals” therefore using the new models comprises selecting and making a change to the operation/configuration of the satellite terminals).
Regarding claims 4, 11 and 18, Vasudevan disclosed the subject matter of claims 1, 8 and 15, respectively.
Vasudevan further disclosed wherein the set of performance indicators comprises values for multiple metrics that indicate, for a period of time, a presence or severity of anomalies in operation of the satellite terminal or in data transfer by the satellite terminal (Vasudevan disclosed in [0055] that “the traffic classification system 170 can estimate or derive application-layer metrics using TCP state machine statistics for a TCP connection. This can be done by analyzing the sequence of requests from the client device 140 and responses from the server 160, the sizes of messages in packets and bytes, the relative timestamps, and so on” and in [0057] that “Anomalous traffic information is stored in the cache 175 over a time window”); and
providing, as the input data, feature scores based on the performance indicators (Vasudevan disclosed in [0079] that “The anomaly detector 174 typically does not receive actual packets of data streams 202a-202f, but instead receives values or features derived from the data streams 202a-202f.”).
Regarding claims 5, 12 and 19, Vasudevan disclosed the subject matter of claims 1, 8 and 15, respectively.
Vasudevan further disclosed wherein the machine learning model comprises a deep neural network (Vasudevan disclosed in [0022] that “the machine learning model of the anomaly detector comprises a trained neural network, and wherein the machine learning model of the traffic classifier comprises a trained neural network”).
Regarding claims 6, 13 and 20, Vasudevan disclosed the subject matter of claims 1, 8 and 15, respectively.
Vasudevan further disclosed wherein the machine learning model comprises at least one of a neural network, a support vector machine, a classifier, a regression model, a clustering model, a decision tree, a random forest model, a genetic algorithm, a Bayesian model, or a Gaussian mixture model (Vasudevan disclosed in [0022] that “the machine learning model of the anomaly detector comprises a trained neural network, and wherein the machine learning model of the traffic classifier comprises a trained neural network”).
Regarding claims 7 and 14, Vasudevan disclosed the subject matter of claims 1 and 8.
Vasudevan further disclosed wherein the machine learning model is configured to output a set of scores including a score for each network condition classification in a predetermined set of network condition classifications, wherein the scores indicate relative likelihoods that the respective network condition classifications are appropriate given the input data provided to the machine learning model (Vasudevan disclosed in [0060] that “For new entries in the history table, or for entries with very few previous instances, a confidence score is output to reflect the confidence or level of uncertainty in the classification” and in [0090] that “a selector module 410 can be used to choose between the classification from the two classifiers 172, 176, based on factors such as (1) a confidence score from the machine learning classifier 176 (e.g., indicating how confident the classifier 176 is in its classification, or how well the current traffic pattern matches the predicted class)”.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 8, 10, 15 and 17 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Mayer (U.S. 2022/0239367).
Regarding claim 1, Mayer disclosed a method performed by one or more computers, the method comprising:
obtaining, by the one or more computers, a set of performance indicators for a satellite terminal (Mayer disclosed in Fig. 3, step 302 and [0053] of “collecting, 302, from a plurality of ground facing sensors, data indicative of one or more conditions, the one or more conditions at least in part influencing operational conditions of a satellite communication link”);
providing, by the one or more computers, input data that is based on the set of performance indicators to a machine learning model (Mayer disclosed in Fig. 3, step 304 and [0054] of “predicting 304, an event associated with the operational conditions of the satellite communication link, the predicting at least in part based on the data. … the processing may also include aggregating the data, where additional data is received from one or more other entities. The processing may include using artificial intelligence and machine learning to characterize the satellite communication link”);
determining, by the one or more computers, a network condition classification for the satellite terminal based on output that the machine learning model generates in response to receiving the input data based on the set of performance indicators (Mayer, Fig. 3 step 304 and [0054], “The processing of the data may indicate that the one or more conditions may impair the satellite communication link. Accordingly, a prediction of a link impairing event based on the one or more conditions may be made. The prediction may indicate the extent of impairment or event including a duration and the likely time when the impairment or event is expected to occur.”);
selecting, by the one or more computers, a change to operation or configuration of the satellite terminal based on the determined network condition classification for the satellite terminal (Mayer, Fig. 3, step 306 and [0055], “modifying, 306, the satellite communication link based on the predicted event. In some embodiments, the predicted event may be a link impairing event, in which the modifying the satellite communication link may include configuring one or more back up satellite communication links and rerouting the traffic from the anticipated impaired satellite communication link to the one or more back up satellite communication links”); and
sending, by the one or more computers, an instruction to initiate the selected change for the satellite terminal (Mayer, [0040], “Depending on the circumstances, the need to remove a downlink entirely may be performed by directly modifying routing tables, either immediately, or if an almanac function is used, at the next planned update.”).
Claim 8 recites substantially the same subject matter as claim 1, in system form rather than method form, therefore the rejection rationale for claim 1 applies equally as well to claim 8.
Claim 15 recites substantially the same subject matter as claim 1, in system form rather than method form, therefore the rejection rationale for claim 1 applies equally as well to claim 15.
Regarding claims 3, 10 and 17, Mayer disclosed the subject matter of claims 1, 8 and 15, respectively.
Mayer further disclosed storing rules that indicate different operation or configuration changes corresponding to different network condition classifications in a predetermined set of network condition classifications (Mayer disclosed in [0055] that “Depending on the nature of predicted event, the modification of the satellite communication link may be on a routing policy” meaning that the modification to the communication link is stored in and reflected by the routing policy); and
wherein selecting the change to operation or configuration of the satellite terminal comprises selecting a change to operation or configuration of the satellite terminal that the stored rules indicate for the determined network condition classification for the satellite terminal (Mayer, [0006, 0007, 0029, 0030], “the processing of the collected data may indicate that a link-impairing event is anticipated and thus the routing system may reroute the traffic to an alternative path before the link-impairing event occurs.” Said “rerouting” is a change to configuration that is selected in response to the link-impairing event that is predicted using machine learning as disclosed in Mayer paragraph [0054]).
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 of this title, 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.
Claims 2, 4-7, 9, 11-14, 16, and 18-20 are rejected under 35 U.S.C. 103 as obvious over Mayer (U.S. 2022/0239367), in view of Vasudevan et al. (U.S. 2021/2024152).
Regarding claims 2, 9 and 16, Mayer disclosed the subject matter of claims 1, 8 and 15, respectively.
Mayer might not have explicitly disclosed but Vasudevan disclosed wherein the satellite terminal comprises a very small aperture terminal (VSAT) (Vasudevan, [0075], “VSAT”).
One of the ordinary skill in the art, before the effective filing date of the claimed invention (AIA ), would have been motivated to combine Vasudevan and Mayer because both references disclosed methods and systems for monitoring satellite terminals performance and managing their operation using machine learning. Therefore it would have been obvious back then to integrate Vasudevan’s teaching of the satellites being very small aperture terminals into Mayer as such that the satellites in Mayer could also be VSATs as it would have expanded the use cases of Mayer’s methods.
Regarding claims 4, 11 and 18, Mayer disclosed the subject matter of claims 1, 8 and 15, respectively.
Mayer further disclosed wherein the set of performance indicators comprises values for multiple metrics that indicate, for a period of time, a presence or severity of anomalies in operation of the satellite terminal or in data transfer by the satellite terminal (Mayer disclosed in [0042] that “A satellite routing system may be augmented to periodically check the list of satellites and then …. The primary gateway may provide periodic or continuous monitoring on a local basis and then, in the case of an anticipated impairment or event (e.g. proximity of a cloud), the primary gateway may perform a local rerouting action to an alternate path by communicating with the satellite responsible for the alternate path.” Said disclosure means that in Mayer the data collection and event prediction is done for a period of time repeatedly); and
Mayer might not have explicitly disclosed
providing, as the input data, feature scores based on the performance indicators.
However, in the same field of endeavor, Vasudevan disclosed
providing, as the input data, feature scores based on the performance indicators (Vasudevan disclosed in [0079] that “The anomaly detector 174 typically does not receive actual packets of data streams 202a-202f, but instead receives values or features derived from the data streams 202a-202f.” In Vasudevan the anomaly detector uses machine learning models).
One of the ordinary skill in the art, before the effective filing date of the claimed invention (AIA ), would have been motivated to combine Vasudevan and Mayer because both references disclosed methods and systems for monitoring satellite terminals performance and managing their operation using machine learning. Therefore it would have been obvious back then to combine Vasudevan’s teaching of using feature scores based on the performance indicators as input data with Mayer’s teaching of using machine learning to process the collected satellite communication link data as such that in Mayer the collected data would also be processed into feature scores then provided as input data, as in machine learning it is a commonly used technique to obtain feature scores from collected data first the provide the feature scores as input to the machine learning models.
Regarding claims 5, 12 and 19, Mayer disclosed the subject matter of claims 1, 8 and 15, respectively.
Mayer might not have explicitly disclosed but Vasudevan further disclosed wherein the machine learning model comprises a deep neural network (Vasudevan disclosed in [0022] that “the machine learning model of the anomaly detector comprises a trained neural network, and wherein the machine learning model of the traffic classifier comprises a trained neural network”).
The motivation for combining Mayer and Vasudevan is the same as that provided for the rejection of claims 2 and 4.
Regarding claims 6, 13 and 20, Mayer disclosed the subject matter of claims 1, 8 and 15, respectively.
Mayer might not have explicitly disclosed but Vasudevan further disclosed wherein the machine learning model comprises at least one of a neural network, a support vector machine, a classifier, a regression model, a clustering model, a decision tree, a random forest model, a genetic algorithm, a Bayesian model, or a Gaussian mixture model (Vasudevan disclosed in [0022] that “the machine learning model of the anomaly detector comprises a trained neural network, and wherein the machine learning model of the traffic classifier comprises a trained neural network”).
The motivation for combining Mayer and Vasudevan is the same as that provided for the rejection of claims 2 and 4.
Regarding claims 7 and 14, Mayer disclosed the subject matter of claims 1 and 8 respectively.
Mayer might not have explicitly disclosed but Vasudevan further disclosed wherein the machine learning model is configured to output a set of scores including a score for each network condition classification in a predetermined set of network condition classifications, wherein the scores indicate relative likelihoods that the respective network condition classifications are appropriate given the input data provided to the machine learning model (Vasudevan disclosed in [0060] that “For new entries in the history table, or for entries with very few previous instances, a confidence score is output to reflect the confidence or level of uncertainty in the classification” and in [0090] that “a selector module 410 can be used to choose between the classification from the two classifiers 172, 176, based on factors such as (1) a confidence score from the machine learning classifier 176 (e.g., indicating how confident the classifier 176 is in its classification, or how well the current traffic pattern matches the predicted class)”.
The motivation for combining Mayer and Vasudevan is the same as that provided for the rejection of claims 2 and 4.
Related Prior Art
Bode et al. (US 2024/0129027) is directed to techniques for proactively monitoring and detecting failures associated with downlinking data during satellite passes.
Reimer et al. (US 11,733,397) is directed to systems and methods for computing position protection levels for satellite terminals using machine learning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY X ZHANG whose telephone number is (571)270-5012. The examiner can normally be reached 8:30am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joon H Hwang can be reached at 571-272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHIRLEY X ZHANG/Primary Examiner, Art Unit 2447