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 action is in response to application filed 03/12/2025.
Claims 2-21 are pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/10/2025 has been placed in record and considered by the examiner.
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.
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Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,149,417 B2 . Although the claims at issue are not identical, they are not patentably distinct from each other because claims 2-21 of the current application perform the similar steps or limitations recited by claims 1-20 of U.S. Patent No. 12,149,417 B2 as detailed below by the examiner.
Claim 2-21
Current Application
Claim 1-20
Patent Case No. 12,149,417 B2
Claim 2. A method performed by a communication device, the method comprising:
receiving, by the communication device, an idle period forecasting model from one or more computers over a communication network, wherein the idle period forecasting model is configured to predict occurrences of future communication idle periods in which network traffic of the communication device over the communication network is below a threshold;
generating, by the communication device, feature data based on communication data indicating prior communication activity involving the communication device, wherein the feature data indicates amounts of network traffic over the communication network over time;
providing, by the communication device, the generated feature data as input to the idle period forecasting model;
obtaining, by the communication device, output of the idle period forecasting model that is generated in response to input of the generated feature data, wherein the output of the idle period forecasting model includes an idle period prediction that indicates a predicted future communication idle period for the communication device; and
performing, by the communication device, a maintenance operation for the communication device with timing of the maintenance operation being determined based on the idle period prediction
Claim 1. A method performed by one or more computers, the method comprising:
obtaining, by the one or more computers, communication data from a plurality of devices in a satellite communication network, wherein the communication data from each device indicates levels of network traffic for the device over the satellite communication network over time;
generating, by the one or more computers, an idle period forecasting model using the obtained communication data, wherein the idle period forecasting model is configured to predict occurrence of future communication idle periods in which communication activity is predicted to be below a threshold; and
providing, by the one or more computers, the idle period forecasting model via multicast over the satellite communication network to the plurality of devices to enable each device of the plurality of devices to generate, using the idle period forecasting model, predictions of future communication idle periods for the device.
Claim 11. The method of claim 1, wherein the devices are configured to delay automatically performing maintenance that involves at least one of rebooting, restarting or applying a software update until a communication idle period is predicted to occur, and the idle period forecasting model enables the devices to each use the same idle period forecasting model to generate different predictions of future communication idle periods based on respective patterns of communication over the satellite communication network of the devices.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 5, 8, 12, 15, 18, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dou et al. (US 2020/0285503 A1) in view of Zhang et al. (US 2023/0146912 A1) .
Regarding claim 2, Dou discloses a method performed by a communication device, the method comprising: wherein the idle period forecasting model is configured to predict occurrences of future communication idle periods in which network traffic of the communication device over the communication network is below a threshold ([0051]: when the virtual machine is operational by 8 AM on one day and operational during the previous 24 hours, the time series model may be used predict when in the next 24 hours the CPU usage, disk I/O usage, and/or network usage may fall below an idle threshold), generating, by the communication device, feature data based on communication data indicating prior communication activity involving the communication device, wherein the feature data indicates amounts of network traffic over the communication network over time ([0067]: forecast achieved through use of a time series forecasting model trained on historical metric data and usage data of the virtual machine, the historical metric data including a time series of equally-spaced data points representing a CPU usage of the virtual machine, the historical usage data including physical dimensions of resources consumed by the virtual machine, the forecast being below an idle threshold for the virtual machine);
providing, by the communication device, the generated feature data as input to the idle period forecasting model ([0048]: The production metric data is a time series that is then input to the time series forecasting model to forecast when the CPU usage of the virtual machine will be below an idle threshold, such as below 5% of the CPU usage for a future time period);
obtaining, by the communication device, output of the idle period forecasting model that is generated in response to input of the generated feature data, wherein the output of the idle period forecasting model includes an idle period prediction that indicates a predicted future communication idle period for the communication device ([0012]: Several machine learning models are trained on historical metric data of a virtual machine over a continuous time period. The models are tested and at least one model is selected for use in a production run. [0038]: The selected model is used by the forecast engine 126 with production usage data 142 and production metric data 144 to forecast the time when the CPU usage will be below the idle threshold (block 132); and
performing, by the communication device, a maintenance operation for the communication device with timing of the maintenance operation being determined based on the idle period prediction ([0049]: when an idle time is forecasted, the cloud resource management system 104 may take one of several actions (block 306). If the user of the virtual machine has configured the virtual machine for an automatic shutdown, the system may initiate actions to automatically shut down the virtual machine for a predetermined length of time. The user may be informed of the idle time and provided with a cost estimate of the savings in shutting down the virtual machine. The user may initiate actions to shut down the virtual machine, reduce resources provisioned to the virtual machine, ignore the idle time forecast, or take any other action).
However, Dou does not disclose receiving, by the communication device, an idle period forecasting model from one or more computers over a communication network.
In an analogous art, Zhang discloses receiving, by the communication device, an idle period forecasting model from one or more computers over a communication network ([0075]: the construction device provides, for a user, an interface for entering the scenario information of the target prediction scenario. The user enters, in the interface, the scenario information of the target prediction scenario into the construction device, so that the construction device obtains the scenario information of the target prediction scenario. [0126]: the construction device generates the model deployment message, where the model deployment message carries the constructed prediction model. Then, the construction device sends the model deployment message to the terminal device. The terminal device receives the model deployment message, and parses the model deployment message to obtain the constructed prediction model. The terminal device stores the prediction model).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Dou to comprise “receiving, by the communication device, an idle period forecasting model from one or more computers over a communication network” taught by Zhang.
One of ordinary skilled in the art would have been motivated because it would have enabled a terminal device to deploy the constructed prediction model to perform network predictions (Zhang, [0066]).
Regarding claim 5, Dou-Zhang discloses the method of claim 2, wherein performing the maintenance operation for the communication device comprises initiating, by the communication device, the maintenance operation in a time range that the idle period prediction of the idle period forecasting model indicates that a communication channel is likely to be idle (Dou, [0049]: when an idle time is forecasted, the cloud resource management system 104 may take one of several actions (block 306). If the user of the virtual machine has configured the virtual machine for an automatic shutdown, the system may initiate actions to automatically shut down the virtual machine for a predetermined length of time. The user may be informed of the idle time and provided with a cost estimate of the savings in shutting down the virtual machine. The user may initiate actions to shut down the virtual machine, reduce resources provisioned to the virtual machine (i.e. maintenance).
Regarding claim 8, Dou-Zhang discloses the method of claim 2, wherein the maintenance operation comprises at least one of a software update for the communication device or a reboot of the communication device (Dou, [0018]: [0018] The model selected for a target virtual machine is then used in a subsequent production run to forecast when the target virtual machine will be idle. The virtual machine may be shut down temporarily at the forecasted idle time and restarted thereafter).
Regarding claims 12 and 21; the claims are interpreted and rejected for the same reason as set forth in claim 2.
Regarding claim 15; the claim is interpreted and rejected for the same reason as set forth in claim 5.
Regarding claim 18; the claim is interpreted and rejected for the same reason as set forth in claim 8.
Claims 3, 6, 13, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dou in view of Zhang, as applied to claim 2, in further view of Huberman et al. (US 2020/0092176 A1).
Regarding claim 3, Dou-Zhang discloses the method of claim 2.
However, Dou-Zhang discloses wherein the communication device is a satellite terminal.
In analogous art, Huberman discloses wherein the communication device is a satellite terminal ([0027]: predicting a probability of successful transmission from a device connected to a common node comprises determining a state of each of a plurality of devices connected to a common node. [0054]: the term “network” refers generally to any type of telecommunications or data network including, without limitation, hybrid fiber coaxial (HFC) networks, satellite networks, telco networks, and data networks (including MANs, WANs, LANs, WLANs, internets, and intranets).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Dou-Zhang to comprise “wherein the communication device is a satellite terminal” taught by Huberman.
One of ordinary skilled in the art would have been motivated because it would have enabled for predicting future network traffic further comprises ensuring a minimum quality of service is maintained when at least a portion of the future idle capacity of the network is reallocated (Huberman,[0016]).
Regarding claim 6, Dou-Zhang discloses the method of claim 2.
However, Dou-Zhang does not disclose wherein receiving the idle period forecasting model comprises receiving the idle period forecasting model over a satellite network.
In analogous art, Huberman discloses wherein the communication device is a satellite terminal ([0014]: a predicted future traffic rate is subtracted from a maximum network capacity to determine a future idle capacity during the second time window. For example, when the measured future traffic rate is a measured maximum future traffic rate, a predicted maximum future traffic rate can be subtracted from a maximum network capacity to determine a future idle capacity during the second time window. [0054]: the term “network” refers generally to any type of telecommunications or data network including, without limitation, hybrid fiber coaxial (HFC) networks, satellite networks, telco networks, and data networks (including MANs, WANs, LANs, WLANs, internets, and intranets).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Dou-Zhang to comprise “wherein receiving the idle period forecasting model comprises receiving the idle period forecasting model over a satellite network” taught by Huberman.
One of ordinary skilled in the art would have been motivated because it would have enabled for predicting future network traffic further comprises ensuring a minimum quality of service is maintained when at least a portion of the future idle capacity of the network is reallocated (Huberman,[0016]).
Regarding claim 13; the claim is interpreted and rejected for the same reason as set forth in claim 3.
Regarding claim 16; the claim is interpreted and rejected for the same reason as set forth in claim 6.
Claims 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dou in view of Zhang, as applied to claim 2, in further view of Gardner et al. (US 2021/0064388 A1).
Regarding claim 4, Dou-Zhang discloses the method of claim 2.
However, Dou-Zhang does not disclose wherein the communication device provides network connectivity to one or more client devices, such that the one or more client devices send and receive network traffic over a network through the communication device; wherein the method comprises obtaining communication data that describes network traffic over one or more network connections provided by the communication device for the one or more client devices; wherein generating the feature data comprises generating feature data that indicates amounts of network traffic over time for the one or more network connections provided by the communication device to the one or more client devices; wherein obtaining the output of the idle period forecasting model comprises obtaining output that indicates a predicted future communication idle period in which network traffic from connected client devices is predicted to be below the threshold.
In analogous art, Gardner discloses wherein the communication device provides network connectivity to one or more client devices, such that the one or more client devices send and receive network traffic over a network through the communication device ([0049]: . For example, the computing system 110 can determine from the activity data 114 which applications executing on the server environment 106 communicates back to client device 102. For example, the computing system 110 can determine from the activity data 114 that an application corresponding to an online transaction will communicate back to the client device 102 for verification of the transaction. [0126]: The mobile computing device 550 may communicate wirelessly through the communication interface 566, which may include digital signal processing circuitry where necessary); wherein the method comprises obtaining communication data that describes network traffic over one or more network connections provided by the communication device for the one or more client devices ([0072]: The computing system 110 polls the server environment 106 at periodic intervals to retrieve activity data 114 from the server environment 106, e.g., data indicating current load levels, numbers of users who are active, numbers of tasks being processed, and so on); wherein generating the feature data comprises generating feature data that indicates amounts of network traffic over time for the one or more network connections provided by the communication device to the one or more client devices ([0056]: the computing system 110 provides the analyzed activity data 120 (e.g., generated from the activity data 114 with the embedded metadata and other analyzations) and the historical data 122 to the machine learning model 124. The machine learning model 124 may include one or more neural network layers that are trained to predict a usage likelihood 126 of usage of the server environment 106. The usage likelihood 126 can be a percentage or a number indicating how likely the server environment will be used over a predetermined time period); wherein obtaining the output of the idle period forecasting model comprises obtaining output that indicates a predicted future communication idle period in which network traffic from connected client devices is predicted to be below the threshold ([0080]: T0 to time T1, the total utilization and the user activity utilization of the server environment 106 is greater than the user activity threshold. Once the user activity utilization dips below the user activity threshold, as shown at time T1, the computing system 110 instructs the server environment 106 to shut down from time T1 to time T2. The computing system 110 instructs the server environment 106 to shut down based on the indication that the user activity utilization is below the user activity threshold during time T1 to time T2).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Dou-Zhang to comprise “wherein the communication device provides network connectivity to one or more client devices, such that the one or more client devices send and receive network traffic over a network through the communication device; wherein the method comprises obtaining communication data that describes network traffic over one or more network connections provided by the communication device for the one or more client devices; wherein generating the feature data comprises generating feature data that indicates amounts of network traffic over time for the one or more network connections provided by the communication device to the one or more client devices; wherein obtaining the output of the idle period forecasting model comprises obtaining output that indicates a predicted future communication idle period in which network traffic from connected client devices is predicted to be below the threshold” taught by Gardner.
One of ordinary skilled in the art would have been motivated because it would have enabled to predict low usage of an environment in order to send instructions causing the environment to be automatically shut down, which can help to conserve or reallocate computing resources and increase power efficiency (Gardner, [0003]).
Regarding claim 14; the claim is interpreted and rejected for the same reason as set forth in claim 4.
Claims 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dou in view of Zhang, as applied to claim 2, in further view of Paulraj et al. (US 2022/0329328 A1).
Regarding claim 7, Dou-Zhang discloses the method of claim 2.
However, Dou-Zhang does not disclose wherein receiving the idle period forecasting model comprises receiving the idle period forecasting model by multicast over a satellite network.
In analogous art, Paulraj discloses wherein receiving the idle period forecasting model comprises receiving the idle period forecasting model by multicast over a satellite network ([0027]: telecommunication service provider network 150 may also include one or more servers 155. In one example, the servers 155 may each comprise a computing device or system. [0075]: the processing system may deploy the retrained first machine learning model to the telecommunication network when the prediction accuracy exceeds a threshold).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Dou-Zhang to comprise “wherein receiving the idle period forecasting model comprises receiving the idle period forecasting model by multicast over a satellite network” taught by Paulraj.
One of ordinary skilled in the art would have been motivated because it would have enabled for predicting network traffic volumes for backbone links in a nationwide Multi-Protocol Label Switching (MPLS) network, which may be used for link load balancing (Paulraj,[0012]).
Regarding claim 17; the claim is interpreted and rejected for the same reason as set forth in claim 7.
Claims 9-10, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dou in view of Zhang, as applied to claim 2, in further view of Bhatnagar et al. (US 2022/0052933 A1).
Regarding claim 9, Dou-Zhang discloses the method of claim 2.
However, Dou-Zhang does not disclose further comprising determining, by the communication device, an expected duration for completion of the maintenance operation; wherein the maintenance operation for the communication device is initiated in response to the communication device determining that the predicted future communication idle period is at least as long as the expected duration for completion of the maintenance operation.
In analogous art, Bhatnagar discloses comprising determining, by the communication device, an expected duration for completion of the maintenance operation ([0055]: the method can include generating a recommendation. The recommendation can be to perform maintenance for the particular service during the widow with the least score); wherein the maintenance operation for the communication device is initiated in response to the communication device determining that the predicted future communication idle period is at least as long as the expected duration for completion of the maintenance operation ([0056]: the recommendation 930 is for a two hour maintenance duration at a relatively idle point in the resource utilization plot. [0074]: projecting the identified intervals to the maintenance deadline. At 1386, the method can include recommending maintenance for the particular service during a time slot, equal to a maintenance duration, within the projected idle intervals).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Dou-Zhang to comprise “further comprising determining, by the communication device, an expected duration for completion of the maintenance operation; wherein the maintenance operation for the communication device is initiated in response to the communication device determining that the predicted future communication idle period is at least as long as the expected duration for completion of the maintenance operation” taught by Bhatnagar.
One of ordinary skilled in the art would have been motivated because it would have enabled to recommending maintenance for the particular service during a time slot (Bhatnagar,[0074]).
Regarding claim 10, Dou-Zhang discloses the method of claim 2.
However, Dou-Zhang does not disclose further comprising: based on the output of the idle period forecasting model, determining, by the communication device, a start time for the predicted future communication idle period and a duration of the predicted future communication idle period; selecting, by the communication device, the maintenance operation to be performed and completed within the duration of the predicted future communication idle period; and performing, by the communication device, the maintenance operation within the predicted future communication idle period.
In analogous art, Bhatnagar discloses further comprising: based on the output of the idle period forecasting model, determining, by the communication device, a start time for the predicted future communication idle period and a duration of the predicted future communication idle period ([0056]: the recommendation 930 is for a two hour maintenance duration at a relatively idle point in the resource utilization plot. [0074]: projecting the identified intervals to the maintenance deadline. At 1386, the method can include recommending maintenance for the particular service during a time slot, equal to a maintenance duration, within the projected idle intervals); selecting, by the communication device, the maintenance operation to be performed and completed within the duration of the predicted future communication idle period; and performing, by the communication device, the maintenance operation within the predicted future communication idle period ([0055]: the method can include generating a recommendation. The recommendation can be to perform maintenance for the particular service during the widow with the least score)
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Dou-Zhang to comprise “further comprising: based on the output of the idle period forecasting model, determining, by the communication device, a start time for the predicted future communication idle period and a duration of the predicted future communication idle period; selecting, by the communication device, the maintenance operation to be performed and completed within the duration of the predicted future communication idle period; and performing, by the communication device, the maintenance operation within the predicted future communication idle period” taught by Bhatnagar.
One of ordinary skilled in the art would have been motivated because it would have enabled to recommending maintenance for the particular service during a time slot (Bhatnagar,[0074]).
Regarding claim 19; the claim is interpreted and rejected for the same reason as set forth in claim 9.
Regarding claim 20; the claim is interpreted and rejected for the same reason as set forth in claim 10.
Claim 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dou in view of Zhang, as applied to claim 2, in further view of Khaligh et al. (US 2022/0261661 A1).
Regarding claim 11, Dou-Zhang discloses the method of claim 2, wherein the idle period forecasting model comprises a vector autoregressive (VAR) statistical model (([0017]: the models are trained using different machine learning techniques such as: autoregressive integrated moving average (ARIMA).
However, Dou-Zhang does not discloses wherein the idle period forecasting model comprises a long short-term memory recurrent neural network (LSTM RNN).
In analogous art, Khaligh discloses wherein the idle period forecasting model comprises a long short-term memory recurrent neural network (LSTM RNN) ([0031]: scheduling framework 202, which utilizes regression models, neural networks (NNs), recurrent NNs (e.g., long short-term memory (LSTMs)) and other types of models to improve prediction accuracy (e.g., prediction of which resources (e.g., boards) will be idle, which resources will be consumed per unit of time)).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Dou-Zhang to comprise “wherein the idle period forecasting model comprises a long short-term memory recurrent neural network (LSTM RNN)” taught by Khaligh.
One of ordinary skilled in the art would have been motivated because it would have enabled to generate models to be evaluated for their ability to predict idle resources (Khaligh, [0032]).
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Sethi et al., US 2022/0391722 A1: Reducing Impact of Collecting System State Information.
Penar et al., US 2019/0334785 A1: Forecasting Underutilization of a Computing Resource.
Conclusion
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/J.C.T/Examiner, Art Unit 2446
/BRIAN J. GILLIS/Supervisory Patent Examiner, Art Unit 2446