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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/06/2026 has been entered.
Response to Amendment
This office action is in response to amendment/reconsideration filed on 07/06/2026, the amendment/reconsideration has been considered. Claims 1, 11 and 16 are independent claims and are amended. Claims 1-20 are pending for examination as cited below.
Interview Summary
Examiner contacted the attorney of record on 03/30/2026, 07/29/2026 “Nicholas Martin” Reg. no.: 60,926 and requested a terminal disclaimer against the parent application which is a U.S. Patent No.: 11,855,859. Mr. Nicholas requested an office action.
Response to Arguments
Applicant’s arguments with respect to the amended claim(s) 1, 11 and 16 have been considered but are moot in view of the new grounds of rejection necessitated by claim amendments.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poornachandran et al. (Pub. No.: US 2021/0105226 A1) hereinafter “Poor” in view of Hu et al. (Pub. No.: US 2021/0204300 A1), hereinafter “Hu” and further in view of Raguraman et at. Pub. No.: US 2012/0195324 A1), hereinafter “Ragu”.
As to claim 1, A method (Poor, Abstract, discloses a network compute device that collects context data, determines device priorities, determines bandwidth allocations, updates a network policy, and manages network traffic) comprising:
identifying, by a device, a set of applications, the set of applications executing on a network (Poor, [0016], Poor identifies context for multiple connected compute devices based on application data, device activity, network traffic information, and workload/flow types. The applications executing at the devices supply input to the context identification and prioritization process),
each of the applications having a corresponding set of patterns related to known network usage (Poor, [0016], The network compute device 104 is additionally configured to identify patterns based on usage. Such usage patterns may be identified based on historically allocated bandwidth and may be device, user, and/or location specific, for example. Poor identifies usage pattern over time from context/network data, including historically allocated bandwidth.),
wherein the corresponding set of patterns are specific to at least one of a user, an application, a device, or a location (Poor, [0049], the context priority determiner 418 would assign a higher priority to the parent's smartphone, as device ownership serves as a tie-breaker and/or is given a higher weight relative to the activity being performed on the devices. Similarly, in another example in which two televisions are streaming digital content (e.g., movies), the deciding priority factor between the two televisions may be the location in which the television resides (e.g., the basement versus a bedroom), the time of day during, a detected presence of a viewer, etc.);
collecting, by the device, network activity data for each of the set of applications (Poor, [0042]-[0043], Poor teaches, collecting network traffic, packets, flows, source/destination identifiers, flow identifications, workload identification, and application provided data for connected devices.);
analyzing, by the device, for each application, the network activity data based on a respective set of patterns (Poor, [0046], analyzes collected context inputs including network traffic and application data to identify device/application context and usage patterns over time.) using a trained artificial intelligence / machine learning model (AI/ML) (Poor, [0048], expressly states that the context priority determination may rely on training through machine learning models);
determining, by the device, based on the analysis, a ranking for the set of applications, the ranking corresponding to a priority of execution on the network (Poor, [0048], Poor discloses, determining a priority for the context associated with each network device/application. The disclosed higher versus-lower priority hierarchy corresponds to the claimed application ranking);
modifying, by the device, network parameters of the network based on the determined ranking, the modified network parameters implementing an operational configuration (Poor, [0051], determines a bandwidth allocation and updates a moderated bandwidth allocation policty that governs how much total bandwidth is allocated to each connected device) that allocates specific portions of the network to specific applications based on the ranking (Poor, [0071], the determined bandwidth portion for each connected compute device is based on its associated context priority. Applications executing at the device provide the relevant context inputs.),
the modification causing a priority of network usage for a higher-ranking application over network usage of a lower ranking application (Poor, [0017], device 108 can be allocated based on the priority associated with that the context for each compute device 108. For example, the network compute device 104 may be configured to allocate a higher amount of bandwidth to one compute device 108 having a context with a higher priority level relative to another compute device 108 having a context with a lower priority level.);
automatically, by the device, configuring the network based on the modified network parameters of the network (Poor, [0045], is configured to manage the configuration for the dynamic allocation of bandwidth across connected compute devices 108 at a given time. To do so, the illustrative dynamic bandwidth allocation manager 414 includes a context identifier 416, a context priority determiner 418, a context priority adjuster 420, and a bandwidth moderator 422.); and
enabling, by the device, the set of applications to operate at the time period based on the configuration of the network (Poor, [0044], the network traffic ingress/egress manager 406 is configured to enforce the network policy and other policies, such as the moderated bandwidth allocation policy described herein. In some embodiments, the policy related rules may be stored in the policy data 412.).
Poor however is silent to disclose explicitly, by assigning the higher-ranking application a minimum portion of bandwidth for the time period the modification corresponding to changes as to which network channels are used for the set of applications.
Hu however discloses a similar concept in the same field of endeavor including, by assigning the higher-ranking application a minimum portion of bandwidth for the time period the modification corresponding to changes as to which network channels are used for the set of applications, (Hu, [0050-0051], To allocate bandwidth at the priority level, the algorithms 126 can use two loops. The IGM uses a first loop that uses preconfigured priority weights to pre-allocate bandwidth for each of multiple queues representing different priority levels. The IGM uses a second loop to adjust the bandwidth for each queue based on an aggregated measure of backlog among active terminals.).
Therefore, before the effective filing date of the instant application it would have been obvious to incorporate the teachings of “Hu” into those of “Poor” to provide a system for bandwidth allocation using machine learning. In some implementations, a request for bandwidth in a communications system is received. Data indicative of a measure of bandwidth requested and a status of the communication system are provided as input to a machine learning model. One or more outputs from the machine learning model indicate an amount of bandwidth to allocate to the terminal, and bandwidth is allocated to the terminal based on the one or more outputs from the machine learning model.
Poor and Hu however are silent to disclose explicitly, the modification further limiting network activity of the lower ranking application to a throttled range of bandwidth for the time period during an operation of the higher-ranking application.
Ragu however discloses a similar concept in the same field of endeavor including, the modification further limiting network activity of the lower ranking application to a throttled range of bandwidth for the time period during an operation of the higher-ranking application (Ragu, [0026-0027], The network transmission information may also designate each transmission with different priorities. For example, transmissions that are latency tolerant may be designated as low priority, while transmissions that are latency sensitive may be designated as high priority.).
Therefore, before the effective filing date of the instant application it would have been obvious to one of the ordinary skilled in the art to incorporate the teachings of “Ragu” into those of “Poor and Hu” to provide a system that allocate bandwidth on a global large scale network. Bandwidth allocation is based on the predicted bandwidth demands of the network users. Each user may be assigned one of a plurality of different values that determines the amount of bandwidth allocated to that user. In instances where an application runs on behalf of a plurality of remote clients, a system and method is provided that allows for the allocation of bandwidth based each individual remote client.
As to claim 2. The combine system of Poor, Hu and Ragu discloses the invention as in parent claims above discloses, wherein the network activity data corresponds to current activity of each of the set of applications (Poor, [0016]), wherein the network activity data relates to at least one of network usage at a location, a type of application (Poor, [0042]), type of device associated with each application and user identity associated with the application (Poor, [0046]), wherein the network usage corresponds to at least one of downloads, uploads, and network resources accessed by a respective application (Poor, [0042]), which can be specific to at least one of a location, a device, an application and a user (Poor, [0016]).
As to claim 3. The combine system of Poor, Hu and Ragu discloses the invention as in parent claims above discloses, wherein the configuration further comprises: allocating specific portions of the network to specific applications based on the ranking, wherein the portion of the network corresponds to at least one of a channel and an antenna of an access point device at a location (Poor, [0056], bandwidth priorities.).
As to claim 4. The combine system of Poor, Hu and Ragu discloses the invention as in parent claims above discloses, wherein the specific applications are assigned a minimum portion of bandwidth for a time period based on an associated ranking to each specific application (Poor, [0045]).
As to claim 5. The combine system of Poor, Hu and Ragu discloses the invention as in parent claims above discloses, further comprising: analyzing information related to each of the set of applications (Poor, [0043]); and
determining, based on the information analysis, a type of each application (Poor, [0043]).
As to claim 6. The combine system of Poor, Hu and Ragu discloses the invention as in parent claims above discloses, wherein the ranking of the set of applications is based on the determined type of each application, wherein a respective application is weighted based on its respective type (Poor, [0046] and Hu, [0050]).
As to claim 7, The combine system of Poor, Hu and Ragu discloses the invention as in parent claims above discloses, further comprising:
collecting activity data from a plurality of applications operating on the network (Poor, [0073-0076]);
analyzing the activity data (Poor, [0016]);
determining a plurality of patterns of behavior for the network (Poor, [0016]); and
storing the determined plurality of patterns of behavior, wherein the set of patterns are identified from the stored plurality of patterns of behavior (Poor, [0063]).
As to claim 8. The combine system of Poor, Hu and Ragu discloses the invention as in parent claims above discloses, wherein the network is a location-specific network, wherein the network is a Wi-Fi network (Poor, [0026]).
As to claim 9. The combine system of Dhan and Nguyen discloses the invention as in parent claims above discloses, wherein the device is a user device (Poor, abstract.).
As to claim 10. The combine system of Dhan and Nguyen discloses the invention as in parent claims above discloses, wherein the device is an access point for a location (Poor, [0026]).
As to claim 11. Is rejected for same rationale as applied to claim 1 above.
As to claim 12. Is rejected for same rationale as applied to claim 2 above.
As to claim 13. Is rejected for same rationale as applied to claim 3 above.
As to claim 14. Is rejected for same rationale as applied to claim 4 above.
As to claim 15. Is rejected for same rationale as applied to claim 6 above.
As to claim 16. Is rejected for same rationale as applied for claims 1 and 11 above.
As to claim 17. Is rejected for same rationale as applied to claim 2 and 12 above.
As to claim 18. Is rejected for same rationale as applied to claims 3 and 13 above.
As to claim 19. Is rejected for same rationale as applied to claims 4 and 14 above.
As to claim 20. Is rejected for same rationale as applied to claim 15 above.
Double Patenting
The non-statutory 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 non-statutory 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 non-statutory 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 non-statutory 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.
Claims 1, 11 and 16 are rejected on the ground of non-statutory double patenting as being unpatentable over claim 1, 10 and 14 of U.S. Patent No. 11855859 in view of Raguraman et at. Pub. No.: US 2012/0195324 A), hereinafter “Ragu “. Although the claims at issue are not identical, they are not patentably distinct from each other because see the table below:
Instant application No.: 18/509,509 U.S. Patent No.: 11855859
A method comprising:
identifying, by a device, a set of applications, the set of applications executing on a network, each of the applications having a corresponding set of patterns related to known network usage,
wherein corresponding set of patterns are specific to at least one of a user, an application, a device, or a location;
collecting, by the device, network activity data for each of the set of applications;
analyzing, by the device, for each application, the network activity data based on a respective set of patterns,
using a trained artificial intelligence / machine learning model (AI/ML);
determining, by the device, based on the analysis, a ranking for the set of applications, the ranking corresponding to a priority of execution on the network;
modifying, by the device, network parameters of the network based on the determined ranking, the modified network parameters implementing an operational configuration that allocates specific portions of the network to specific application based on the ranking, the modification causing a priority of network usage for a higher ranking application over network usage of a lower ranking application by assigning the higher ranking application a minimum portion of bandwidth for the time period, the modification corresponding to changes as to which network channels are used for the set of applications, the modification further limiting network activity of the lower ranking application to a throttled range of bandwidth for the time period during an operation of the higher ranking application;
automatically, by the device, configuring the network based on the modified network parameters of the network; and
enabling, by the device, the set of applications to operate at the time via the configured network parameters of the network.
A method comprising:
identifying, by a device, a set of applications, the set of applications executing at a time on a network, each of the applications having a corresponding set of patterns related to known network usage;
collecting, by the device, current network activity data for each of the set of applications;
analyzing, by the device, for each application, the current network activity data based on a respective set of patterns;
determining, by the device, a ranking for the set of applications based on the QoE value for each application in the set of applications, the ranking corresponding to a priority of execution on the network;
determining, by the device, based on the analysis of the current network activity, a Quality of Experience (QoE) value for each application, the QoE value comprising information indicating a required usage of the network by a respective application;
automatically, by the device, configuring network parameters of the network based on the determined ranking, the automatic configuration comprising modifications to the network parameters so as to prioritize network usage of a higher ranking application over a lower ranking application; and
enabling, by the device, the set of applications to operate at the time via the configured network parameters of the network.
11. A device comprising: a processor configured to:
identifying, by a device, a set of applications, the set of applications executing on a network, each of the applications having a corresponding set of patterns related to known network usage;
collecting, by the device, network activity data for each of the set of applications;
analyzing, by the device, for each application, the network activity data based on a respective set of patterns;
determining, by the device, a ranking for the set of applications, the ranking corresponding to a priority of execution on the network;
modifying, by the device, network parameters of the network based on the determined ranking, the modification causing a priority of network usage for a higher ranking application over network usage of a lower ranking application, the modification corresponding to changes as to which network channels are used for the set of applications, the modification further preventing operation of the lower ranking application from functioning on the network for a time period during an operation of the higher ranking application;
automatically, by the device, configuring the network based on the modified network parameters of the network; and
enabling, by the device, the set of applications to operate at the time via the configured network parameters of the network.
10. A device comprising: a processor configured to:
identify a set of applications, the set of applications executing at a time on a network, each of the applications having a corresponding set of patterns related to known network usage;
collect current network activity data for each of the set of applications;
analyze, for each application, the current network activity data based on a respective set of patterns;
determine a ranking for the set of applications based on the QoE value for each application in the set of applications, the ranking corresponding to a priority of execution on the network;
determine, based on the analysis of the current network activity, a Quality of Experience (QoE) value for each application, the QoE value comprising information indicating a required usage of the network by a respective application;
automatically configure network parameters of the network based on the determined ranking, the automatic configuration comprising modifications to the network parameters so as to prioritize network usage of a higher-ranking application over a lower ranking application; and
enable the set of applications to operate at the time via the configured network parameters of the network.
16. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by the device, perform a method comprising:
identifying, by a device, a set of applications, the set of applications executing on a network, each of the applications having a corresponding set of patterns related to known network usage;
collecting, by the device, network activity data for each of the set of applications;
analyzing, by the device, for each application, the network activity data based on a respective set of patterns;
determining, by the device, a ranking for the set of applications, the ranking corresponding to a priority of execution on the network;
modifying, by the device, network parameters of the network based on the determined ranking, the modification causing a priority of network usage for a higher ranking application over network usage of a lower ranking application, the modification corresponding to changes as to which network channels are used for the set of applications, the modification further preventing operation of the lower ranking application from functioning on the network for a time period during an operation of the higher ranking application;
automatically, by the device, configuring the network based on the modified network parameters of the network; and
enabling, by the device, the set of applications to operate at the time via the configured network parameters of the network.
14. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by the device, perform a method comprising:
identifying, by the device, a set of applications, the set of applications executing at a time on a network, each of the applications having a corresponding set of patterns related to known network usage;
collecting, by the device, current network activity data for each of the set of applications;
analyzing, by the device, for each application, the current network activity data based on a respective set of patterns;
determining, by the device, a ranking for the set of applications based on the QoE value for each application in the set of applications, the ranking corresponding to a priority of execution on the network;
determining, by the device, based on the analysis of the current network activity, a Quality of Experience (QoE) value for each application, the QoE value comprising information indicating a required usage of the network by a respective application;
automatically, by the device, configuring network parameters of the network based on the determined ranking, the automatic configuration comprising modifications to the network parameters so as to prioritize network usage of a higher ranking application over a lower ranking application; and
enabling, by the device, the set of applications to operate at the time via the configured network parameters of the network.
3. The method of claim 1, wherein the configuration further comprises: allocating specific portions of the network to specific applications based on the ranking, wherein the portion of the network corresponds to at least one of a channel and an antenna of an access point device at a location.
2. The method of claim 1, wherein the configuration further comprises: allocating specific portions of the network to specific applications based on the ranking, wherein the portion of the network corresponds to at least one of a channel and an antenna of an access point device at a location.
4. The method of claim 3, wherein the specific applications are assigned a minimum portion of bandwidth for a time period based on an associated ranking to each specific application.
3. The method of claim 2, wherein the specific applications are assigned a minimum portion of bandwidth for a time period based on an associated ranking to each specific application.
5. The method of claim 1, further comprising: analyzing information related to each of the set of applications; and determining, based on the information analysis, a type of each application.
4. The method of claim 1, further comprising: analyzing information related to each of the set of applications; and determining, based on the information analysis, a type of each application.
6. The method of claim 5, wherein the ranking of the set of applications is based on the determined type of each application, wherein a respective application is weighted based on its respective type.
5. The method of claim 4, wherein the ranking of the set of applications is further based on the determined type of each application, wherein a respective application is weighted based on its respective type.
7. The method of claim 1, further comprising: collecting activity data from a plurality of applications operating on the network; analyzing the activity data; determining a plurality of patterns of behavior for the network; and storing the determined plurality of patterns of behavior, wherein the set of patterns are identified from the stored plurality of patterns of behavior.
6. The method of claim 1, further comprising: collecting activity data from a plurality of applications operating on the network; analyzing the activity data; determining a plurality of patterns of behavior for the network; and storing the determined plurality of patterns of behavior, wherein the set of patterns are identified from the stored plurality of patterns of behavior.
8. The method of claim 1, wherein the network is a location-specific network, wherein the network is a Wi-Fi network.
7. The method of claim 1, wherein the network is a location-specific network, wherein the network is a Wi-Fi network.
9. The method of claim 1, wherein the device is a user device.
8. The method of claim 1, wherein the device is a user device.
10. The method of claim 1, wherein the device is an access point for a location.
9. The method of claim 1, wherein the device is an access point for a location.
The main difference between the instant application’s independent claims 1, 11 and 16 and that of Patent applications independent claims 1, 10 and 14 is “throttled range of bandwidth for a time period during an operation of the higher-ranking application.”
Ragu however in the same field of endeavor discloses a similar concept including, throttled range of bandwidth for a time period during an operation of the higher-ranking application (Ragu, (Ragu, [0026-0027], The network transmission information may also designate each transmission with different priorities. For example, transmissions that are latency tolerant may be designated as low priority, while transmissions that are latency sensitive may be designated as high priority.).
Therefore, before the effective filing date of the instant application it would have been obvious to one of the ordinary skilled in the art to incorporate the teachings of “Ragu” into those of “Poor and Hu” to provide a system that allocate bandwidth on a global large scale network. Bandwidth allocation is based on the predicted bandwidth demands of the network users. Each user may be assigned one of a plurality of different values that determines the amount of bandwidth allocated to that user. In instances where an application runs on behalf of a plurality of remote clients, a system and method is provided that allows for the allocation of bandwidth based each individual remote client.
Similarly, the dependent claims 12-15 and 17-20 and rejected for the same rationale as applied to claims 3-10 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached PTO-892.
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/Tauqir Hussain/Primary Examiner, Art Unit 2446
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