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 .
Response to Amendment
The amendments filed on 6/2/2026 have been entered.
Claims 1-2, and 7 have been amended.
Claims 6, and 11 are cancelled.
Response to Arguments
Applicant’s arguments filed 6/2/2026 have been fully considered but moot in light of amendments introduced by the applicant with the new ground(s) of rejection presented in this Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Regarding claims 1-2, and 7: the instant claims recite the term “three potential QUIC streams”. The term “potential” in the instant claims is a relative term which renders the claim indefinite. The term “potential” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. This would render the claims indefinite. For purpose of examination, examiner interprets this term as “three
Regarding claims 3-5, 8-10, and 12-20: dependent claims are rejected as they depend on claims 1-2, and 7.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 1-2, 4, 7, 9, 12, 15 are rejected under 35 U.S.C. 103 as being un-patentable by Eriksson et al. (“Eriksson”, US 20220255816 A1) hereinafter Eriksson, in view of Xia et al. (“Xia”, US 20220368765 A1) hereinafter Xia.
Regarding claim 1, Eriksson teaches a method for selecting quick user datagram protocol (UDP) internet connection (QUIC) ([0032-0034] QUIC packet attributes is being used by the AccessGW in the network node)(Fig. 5 QUIC connection) streams by a network node in a wireless communication system ([0011] network node operative in a mobile communication network, and implementing an AccessGw operative to estimate a quality metric for a packet flow associated with an application and carrying latency sensitive connection)([0023, 0090] wireless network), comprising:
receiving, by the network node, current values associated with one or more network parameters related to a data session in real-time (Fig. 7, monitor network traffic)([0010-0011] network traffic parameters for the packets belonging to the packet flow)([0008] monitoring IP flows carrying latency sensitive content passing the apparatus, and information about the application behavior and target Quality of Experience (QoE) or target connectivity characteristics such as Quality of Service (QoS) from the application, to provide ongoing predictions of QoE/QoS), wherein the one or more network parameters comprise at least one of: connection metrics, network condition and a type of service ([0048-0049, 0053, 0064-0065] throughput, RTT, packet loss)(claim 59, network parameters include RTT); and
determining, by the network node, a plurality of QUIC streams related to the data session based on the current values associated with the one or more network parameters ([0033-0045] he AccessGW predicts the QoE for a tenant speech, leveraging information visible outside an encryption envelope and packet size temporal signatures, such as inter arrival time and payload size variation, etc. In the case of QUIC, this also means unencrypted standard and proprietary headers, frames, and in particular the spin bit that is a reflection of the RTT as experienced in the application or transport layer of the device, he AccessGW includes a trusted QUIC proxy, which has been authoritatively included in the QUIC data stream by the Application provider, or tenant, in the WebRTC session establishment. This advantageously allows the AccessGw to access information in encrypted QUIC headers, to perform the same late loss and QoE predictions.) ([0011] the network node is then operative to iteratively monitor network traffic; classify packets belonging to the packet flow; and analyze network traffic parameters for the packets belonging to the packet flow. Based on the obtained late loss algorithm, the network node is operative to predict a late loss for the packet flow. Based on the obtained quality metric algorithm, the network node is operative to predict a quality metric for the packet flow. The network node is further operative to report the predicted quality metric)(Fig. 7 predict a late loss for the packet flow based on the obtained parameters)([0024] The AccessGw monitors network traffic and predicts the QoE of speech sessions in real-time, outputting the result to different receivers.)(Fig. 2, Fig. 1)(Claim 51, obtain one or more algorithms for estimating late loss and quality metric for the packet flow by training one or more machine learning functions based on network traffic metrics including measured latency, throughput, and packet loss.), and
wherein the plurality of QUIC streams are streamed in the wireless communication system ([0023, 0090] wireless network).
Eriksson does not explicitly teach, but Xia teaches
on a basis of at least one of the one or more network parameters ([0046-0065] connection manager, loss detector, congestion control, Ack Manager, ), selecting for each QUIC stream of the plurality of QUIC streams a QUIC stream type from among at least three potential QUIC stream types, the at least three potential QUIC stream types comprising: a reliable QUIC stream type, a semi- reliable QUIC stream type, and an unreliable QUIC stream type, wherein the semi- reliable QUIC stream type in the data session initiates an acknowledgement (ACK) when at least one data packet is dropped, the ACK serving to adapt a transmission rate, ([0134-0135] whether the stream is a reliable stream, an unreliable stream, or a partially reliable stream)([0135] in the case of a reliable stream or a partially reliable stream, the application may determine to retransmit the lost frame. As indicated above, in the case of an unreliable stream, the application can determine to retransmit a lost frame (at least for a predetermined number of times))([0080] Assume that the sender received acknowledgment frames for the packets numbered 1-70 and 76-78. As such, the sender determines that the largest_acked packet number is, for example, 78. In the case of a reliable stream, the sender can continue to transmit the packets numbered 71-75 until they are acknowledged. In the case of an unreliable stream, the sender does not retransmit the packets numbered 71-75. In another example, the UTF can retransmit the packets numbered 71-75 of an unreliable stream (or a partially reliable stream) a predetermined number of times (e.g., 2, 3, 5, or some other number of times). The receiver stops including acknowledgments of packet numbers smaller or equal to the largest_ack.),
It would have been obvious to a person skilled in the art, before the effective filing date of the invention, to modify Eriksson in view of Xia in order to select QUIC data and traffic from reliable or unreliable traffic or semi reliable because it would improve the ack performance in the various application scenarios and be able to satisfy a complete set of communication requirements for a complex system architectures and network management (Xia [0003, 0102]).
Regarding claim 2, Eriksson teaches a method for selecting quick user datagram protocol (UDP) internet connection (QUIC) ([0032-0034] QUIC packet attributes is being used by the AccessGW in the network node)(Fig. 5 QUIC connection) streams by a network node in wireless communication ([0011] network node operative in a mobile communication network, and implementing an AccessGw operative to estimate a quality metric for a packet flow associated with an application and carrying latency sensitive connection)([0023, 0090] wireless network), comprising:
receiving, by the network node, current values associated with one or more network parameters related to a data session in real-time (Fig. 7, monitor network traffic)([0010-0011] network traffic parameters for the packets belonging to the packet flow)([0008] monitoring IP flows carrying latency sensitive content passing the apparatus, and information about the application behavior and target Quality of Experience (QoE) or target connectivity characteristics such as Quality of Service (QoS) from the application, to provide ongoing predictions of QoE/QoS), wherein the one or more network parameters comprise at least one of: connection metrics, network condition and a type of service ([0048-0049, 0053, 0064-0065] throughput, RTT, packet loss)(claim 59, network parameters include RTT);
predicting, by the network node using an artificial intelligence (AI) model ([0027, 0048, 0096] machine learning algorithm, machine learning models), new values associated with the one or more network parameters for the data session based on the current values associated with the one or more network parameters ([0011] the network node is then operative to iteratively monitor network traffic; classify packets belonging to the packet flow; and analyze network traffic parameters for the packets belonging to the packet flow. Based on the obtained late loss algorithm, the network node is operative to predict a late loss for the packet flow. Based on the obtained quality metric algorithm, the network node is operative to predict a quality metric for the packet flow. The network node is further operative to report the predicted quality metric)(Fig. 7 predict a late loss for the packet flow based on the obtained parameters)([0024] The AccessGw monitors network traffic and predicts the QoE of speech sessions in real-time, outputting the result to different receivers.)(Fig. 2, Fig. 1)(Claim 51, obtain one or more algorithms for estimating late loss and quality metric for the packet flow by training one or more machine learning functions based on network traffic metrics including measured latency, throughput, and packet loss.); and
determining, by the network node, a plurality of QUIC streams related to the data session based on the new values associated with the one or more network parameters ([0033-0045] he AccessGW predicts the QoE for a tenant speech, leveraging information visible outside an encryption envelope and packet size temporal signatures, such as inter arrival time and payload size variation, etc. In the case of QUIC, this also means unencrypted standard and proprietary headers, frames, and in particular the spin bit that is a reflection of the RTT as experienced in the application or transport layer of the device, he AccessGW includes a trusted QUIC proxy, which has been authoritatively included in the QUIC data stream by the Application provider, or tenant, in the WebRTC session establishment. This advantageously allows the AccessGw to access information in encrypted QUIC headers, to perform the same late loss and QoE predictions.), and
wherein the plurality of QUIC streams are streamed in the wireless communication system ([0023, 0090] wireless network).
Eriksson does not explicitly teach, but Xia teaches
on a basis of at least one of the one or more network parameters ([0046-0065] connection manager, loss detector, congestion control, Ack Manager, ), selecting for each QUIC stream of the plurality of QUIC streams a QUIC stream type from among at least three potential QUIC stream types, the at least three potential QUIC stream types comprising: a reliable QUIC stream type, a semi- reliable QUIC stream type, and an unreliable QUIC stream type, wherein the semi- reliable QUIC stream type in the data session initiates an acknowledgement (ACK) when at least one data packet is dropped, the ACK serving to adapt a transmission rate, ([0134-0135] whether the stream is a reliable stream, an unreliable stream, or a partially reliable stream)([0135] in the case of a reliable stream or a partially reliable stream, the application may determine to retransmit the lost frame. As indicated above, in the case of an unreliable stream, the application can determine to retransmit a lost frame (at least for a predetermined number of times))([0080] Assume that the sender received acknowledgment frames for the packets numbered 1-70 and 76-78. As such, the sender determines that the largest_acked packet number is, for example, 78. In the case of a reliable stream, the sender can continue to transmit the packets numbered 71-75 until they are acknowledged. In the case of an unreliable stream, the sender does not retransmit the packets numbered 71-75. In another example, the UTF can retransmit the packets numbered 71-75 of an unreliable stream (or a partially reliable stream) a predetermined number of times (e.g., 2, 3, 5, or some other number of times). The receiver stops including acknowledgments of packet numbers smaller or equal to the largest_ack.),
It would have been obvious to a person skilled in the art, before the effective filing date of the invention, to modify Eriksson in view of Xia in order to select QUIC data and traffic from reliable or unreliable traffic or semi reliable because it would improve the ack performance in the various application scenarios and be able to satisfy a complete set of communication requirements for a complex system architectures and network management (Xia [0003, 0102]).
Regarding claim 4, Eriksson and Xia teach the method as claimed in claim 2,
Eriksson teaches monitoring, by the network node, one or more performance parameters of the data session ([0010-0011] The network node is then operative to iteratively monitor network traffic; classify packets belonging to the packet flow; and analyze network traffic parameters for the packets belonging to the packet flow)(Fig. 7 iterative process); and
updating, by the network node, the AI model based on the one or more performance parameters and the current values associated with the one or more network parameters ([0010-0011] The network node is then operative to iteratively monitor network traffic; classify packets belonging to the packet flow; and analyze network traffic parameters for the packets belonging to the packet flow)(Fig. 7 iterative process).
Regarding claim 7, claim 7 is rejected with the same reasoning as claim 2.
Regarding claim 9, claim 9 is rejected with the same reasoning as claim 4.
Regarding claim 15, Eriksson and Xia teach the method as claimed in claim 2,
Eriksson teaches wherein the connection metrics comprise information related to at least one of: packet loss, round trip time (RTT) ([0048-0049, 0053, 0064-0065] throughput, RTT, packet loss)(claim 59, network parameters include RTT), packet arrival time ([0033] packet arrival time), per packet interval, and size of packet ([0033] packet size, payload size, inter arrival time).
Regarding claim 12, claim 12 is rejected with the same reasoning as claim 15.
Claims 5, 10, 18 are rejected under 35 U.S.C. 103 as being un-patentable by Eriksson et al. (“Eriksson”, US 20220255816 A1) hereinafter Eriksson, and Xia et al. (“Xia”, US 20220368765 A1) hereinafter Xia, in view of Biasio et al. (“Biasio” , AQUICImplementation for ns-3 :2019) hereinafter Biasio.
Regarding claim 5, Eriksson and Xia teach the method as claimed in claim 2,
Eriksson and Xia do not explicitly teach, but Biasio teaches
wherein the plurality of QUIC streams related to the data session are multiplexed into a single socket ([page 3] AQuicSocketBase object receives and transmits QUIC packets and acknowledgments, accounts for retransmissions, performs flow and congestion control at a connection level, takes care of the initial handshake and exchange of transport parameters, and handles the life cycle and the state machine of a QUIC connection).
It would have been obvious to a person skilled in the art, before the effective filing date of the invention, to modify Eriksson and Xia in view of Biasio in order to create a socket for QUIC packets and streams because QUIC socket performs flow control and congestion control and takes care of handshake and exchange for transport parameter, and QUIC supports a connection-level identifier that provides robustness against updates in the underlying layers of the protocol stack, e.g., IP address updates caused by Network Address Translation(NAT) and/or mobility in cellular and Wi-Fi networks (Biasio [Page 2, Page 3]).
Regarding claim 10, claim 10 is rejected with the same reasoning as claim 5.
Regarding claim 18, Eriksson, Xia, and Biasio teach the network node as claimed in claim 10,
Eriksson teaches wherein the connection metrics comprise information related to at least one of: packet loss, round trip time (RTT) ([0048-0049, 0053, 0064-0065] throughput, RTT, packet loss)(claim 59, network parameters include RTT), packet arrival time ([0033] packet arrival time), per packet interval, and size of packet ([0033] packet size, payload size, inter arrival time).
Claims 16 and 13 are rejected under 35 U.S.C. 103 as being un-patentable by Eriksson et al. (“Eriksson”, US 20220255816 A1) hereinafter Eriksson, and Xia et al. (“Xia”, US 20220368765 A1) hereinafter Xia, in view of BLASCO et al. (“BLASCO“, US 20240244504 A1”) hereinafter BLASCO.
Regarding claim 16, Eriksson and Xia teach the method as claimed in claim 2,
Eriksson and Xia do not explicitly teach, but BLASCO teaches
wherein the network condition comprises at least one of: received signal strength indicator (RSSI), signal interference noise ratio (SINR), radio access technology (RAT) type, carrier aggregation (CA) or non-CA, and downlink radio blocks (DRB) availability ([0133, 0138-0140, 0150] The value of the measurement or parameter may be compared to previously reported measurements or parameters, or compared to a model (e.g. a propagation model), to determine the candidate start area(s) Sm start and/or assign them an area metric p(Sm start). For example, any of the following measurements may be indicative of the proximity to a cell center or cell edge (and therefore indicative of where a candidate start area might be): a signal quality measurement such as a received power or strength (e.g. RSSI, RSRP, etc.)([0200] The set of measurements and/or parameters can comprise any one or more of: signal quality measurements by the terminal device and/or the communication network (e.g. RSRP, RSSI);)([0066] QUIC may be used).
It would have been obvious to a person skilled in the art, before the effective filing date of the invention, to modify Eriksson and Xia in view of BLASCO in order to have RSSI indicator when positioning a network node because RSSI provides a rough indication about the path loss and the packet loss between different network nodes and will provide more details about the area where a network node is located considering poor coverage leading to path and packet loss (BLASCO [0138-0140]).
Regarding claim 13, claim 13 is rejected with the same reasoning as claim 16
Claims 17 and 14 are rejected under 35 U.S.C. 103 as being un-patentable by Eriksson et al. (“Eriksson”, US 20220255816 A1) hereinafter Eriksson, and Xia et al. (“Xia”, US 20220368765 A1) hereinafter Xia, in view of KARAPANTELAKIS et al. (“KARAPANTELAKIS “, US 20240154852 A1) hereinafter KARAPANTELAKIS.
Regarding claim 17, Eriksson and Xia teach the method as claimed in claim 2,
Eriksson and Xia do not explicitly teach, but KARAPANTELAKIS teaches
wherein the type of service comprises at least one of: enhanced mobile broad band (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communication (mMTC) ([0068-0070] improvement in the computation and communication technologies can enable a 5G network to classify a set of services into three different categories: (1) ultra-reliable low-latency communication (uRLLC), (2) enhanced mobile broadband (eMBB), and (3) massive machine-type communication (mMTC)).
It would have been obvious to a person skilled in the art, before the effective filing date of the invention, to modify Eriksson and Xia in view of KARAPANTELAKIS in order to have different types of traffic such as eMMB, URLLC because these types of services such as uRLLC may be characterized with a requirement of high reliability for handling latency-intolerant applications and the service type mMTC-type applications may have a wide range of use cases, including vehicular communication and Internet of Things (IoT) environments with a massive number of gadgets generating small-sized sensory data. (KARAPANTELAKIS [0068-0070]).
Regarding claim 14, claim 14 is rejected with the same reasoning as claim 17.
Claim 19 is rejected under 35 U.S.C. 103 as being un-patentable by Eriksson et al. (“Eriksson”, US 20220255816 A1) hereinafter Eriksson, and Xia et al. (“Xia”, US 20220368765 A1) hereinafter Xia, and Biasio et al. (“Biasio” , AQUICImplementation for ns-3 :2019) hereinafter Biasio, in view of BLASCO et al. (“BLASCO“, US 20240244504 A1”) hereinafter BLASCO.
Regarding claim 19, Eriksson, Xia, and Biasio teach the network node as claimed in claim 10,
Eriksson, Xia, and Biasio do not explicitly teach, but BLASCO teaches
wherein the network condition comprises at least one of: received signal strength indicator (RSSI), signal interference noise ratio (SINR), radio access technology (RAT) type, carrier aggregation (CA) or non-CA, and downlink radio blocks (DRB) availability ([0133, 0138-0140, 0150] The value of the measurement or parameter may be compared to previously reported measurements or parameters, or compared to a model (e.g. a propagation model), to determine the candidate start area(s) Sm start and/or assign them an area metric p(Sm start). For example, any of the following measurements may be indicative of the proximity to a cell center or cell edge (and therefore indicative of where a candidate start area might be): a signal quality measurement such as a received power or strength (e.g. RSSI, RSRP, etc.)([0200] The set of measurements and/or parameters can comprise any one or more of: signal quality measurements by the terminal device and/or the communication network (e.g. RSRP, RSSI);)([0066] QUIC may be used).
It would have been obvious to a person skilled in the art, before the effective filing date of the invention, to modify Eriksson, Xia, and Biasio in view of BLASCO in order to have RSSI indicator when positioning a network node because RSSI provides a rough indication about the path loss and the packet loss between different network nodes and will provide more details about the area where a network node is located considering poor coverage leading to path and packet loss (BLASCO [0138-0140]).
Claim 20 is rejected under 35 U.S.C. 103 as being un-patentable by Eriksson et al. (“Eriksson”, US 20220255816 A1) hereinafter Eriksson, and Xia et al. (“Xia”, US 20220368765 A1) hereinafter Xia, and Biasio et al. (“Biasio” , AQUICImplementation for ns-3 :2019) hereinafter Biasio, in view of KARAPANTELAKIS et al. (“KARAPANTELAKIS “, US 20240154852 A1 hereinafter KARAPANTELAKIS.
Regarding claim 20, Eriksson, Xia, and Biasio teach the network node as claimed in claim 10,
Eriksson, Xia, and Biasio do not explicitly teach, but KARAPANTELAKIS teaches
wherein the type of service comprises at least one of: enhanced mobile broad band (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communication (mMTC) ([0068-0070] improvement in the computation and communication technologies can enable a 5G network to classify a set of services into three different categories: (1) ultra-reliable low-latency communication (uRLLC), (2) enhanced mobile broadband (eMBB), and (3) massive machine-type communication (mMTC)).
It would have been obvious to a person skilled in the art, before the effective filing date of the invention, to modify Eriksson, Xia, and Biasio in view of KARAPANTELAKIS in order to have different types of traffic such as eMMB, URLLC because these types of services such as uRLLC may be characterized with a requirement of high reliability for handling latency-intolerant applications and the service type mMTC-type applications may have a wide range of use cases, including vehicular communication and Internet of Things (IoT) environments with a massive number of gadgets generating small-sized sensory data (KARAPANTELAKIS [0068-0070]).
Allowable Subject Matter
Claims 3 and 8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and overcome the 112(b) rejection set forth in this office action.
The following is the reason for allowable subject matter in claims 3 and 8.
The prior arts (“Eriksson”, US 20220255816 A1) and (“Xia”, US 20220368765 A1) fail to teach or suggest fairly the machine learning model is deep reinforcement learning model and training this model comprises providing historical data of the one or more network parameters corresponding to each data session of a plurality of training data sessions; configuring the DRL model to identify a plurality of patterns from the historical data and corresponding training data session of the plurality of training data sessions, wherein a performance metric associated with each training data session of the plurality of training data sessions is evaluated; and providing the performance metric as feedback to the DRL model.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FADI HAJ SAID whose telephone number is (571)272-2833. The examiner can normally be reached on 8:00 AM - 5:00 PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached on 571-272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FADI HAJ SAID/Primary Examiner, Art Unit 2444
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