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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/28/2026 has been entered.
EXAMINER’S COMMENT
Response to Arguments
Regarding claim 1 rejections under 35 USC § 103:
Applicant's arguments filed 07/28/2026 have been fully considered but they are not persuasive.
The Applicant contends:
“Claim 1 recites, inter alia, "determine cross-layer characteristics of a plurality of network protocol layers used for transmission of intended signals for the radio," and "classify the interference signal using one or more features in the interference signal and the cross-layer characteristics." On page 6 of the Office Action and in support of rejecting "determine one or more layers characteristics of one or more network layers used for transmission of intended signals for the radio," as previously recited in claim 1, the Examiner points to paragraphs [0035], and [0078]- [0080] of Safavi. Paragraph [0035] describes the contextual information that a particular layer may provide such as RF environmental factors, RSSI, BER, etc. However, Safavi, in these paragraphs or elsewhere, does not provide any description related to cross-layer sensing to determine cross- layer characteristics of multiple network protocol layers, as recited in claim 1, Balakrishnan suffers from the same deficiencies as Safavi. Therefore, a hypothetical combination of Safavi and Balakrishnan fails to render obvious the features recited in claim 1. Claims 8 and 15 recite features that are somewhat similar to those recited in claim 1. Therefore, a hypothetical combination of Safavi and Balakrishan also fails to render obvious the features recited in claims 8 and 15 as well as claims 2-7, 9-14, and 16-20 that depend from one of claims 1, 8, and 15. For the foregoing reasons, the undersigned representative respectfully requests reconsideration and withdrawal of the rejection of claims 1-20 under 35 U.S.C. 103” (emphasis in original)
The Examiner disagrees, and asserts that, Safavi discloses, "determine cross-layer characteristics of a plurality of network protocol layers used for transmission of intended signals for the radio," (see paragraphs [0034]-[0038], [0042], [0048]-[0050], [0054]-[0056], [0083]-[0088], [0104]-[0106], [0134], [0136], [0142]-[0143][0152]-[0155])
Safavi specifically discloses “[0034] In an embodiment, cross-layer contextual information may enable direct access to, or accurate inference of, a device's radio resource control (RRC) state machine and an application's interaction with it. In this way, transparency to lower layer protocols may be improved. Such cross-layer contextual information collection may encompass both device and network related information. Cross-layer contextual information may comprise information or parameters from two or more open systems interconnection (OSI) layers such as user input/preferences layer, protocol layers (such as HTTP, transport, network, radio link, radio resource control states, or other similar layers), data link layer, session layer, presentation layer, application layer, MAC, and physical (PHY), or other similar layers. In some embodiments the protocol layers may be those affected or influenced by application behavior.”
“[0035] The layers providing contextual information may include various parameters. For example, physical layer may comprise parameters which may affect wireless or radio connectivity, such as radio frequency (RF) environmental factors (e.g., interference levels, noise, co-channel interference, adjacent interference, etc.), spectrum contamination, received signal strength indication (RSSI), bit error rate (BER), cell coverage, noise signal ratio (NSR), committed information rate (CIR), signal to interference ratio (SIR), etc. Exemplary parameters for the MAC/link layer may include channel access delay, number of retransmissions, clear channel assessment (CCA), threshold (in WiFi), etc. Exemplary parameters for a network layer may comprise types of network protocols supported (e.g., IP, mIP, mIPv6, etc.), detailed information for points of attachment (PoAs) such as supported network interfaces, max bandwidth, network preferences, billing information, handover rate, handover policies, etc. Exemplary parameters in a transport layer may include supported protocols (e.g., UDP, TCP, etc.). Exemplary parameters for an application layer may include required quality of service (QoS) parameters (e.g., delay, jitter, packet error rate, etc.), application type (e.g., switching between applications), sender bit rate, or other similar information. Exemplary parameters of user preferences may comprise price/cost of connection, quality of experience (QoE), battery life, privacy, security, preferred applications or services, or other similar information.”
“[0036] The contextual information may enable the selection of an optimal candidate network for handoff, by factoring true tradeoffs between important parameters such as QoS and energy consumption. A handoff may be performed based on cross-layer contextual information (e.g., a physical layer parameter and a parameter from at least one other OSI layer).”
“[0037] Device models may be implemented that factor various contexts from multiple layers of protocol (e.g., cross-layer) to create a network-related performance prediction platform that may reduce the gap between applications and lower layer protocols. Device optimization may be provided by proactively exploiting channel conditions in multiple radio connectivity choices”
“[0038] The cross-layer contextual information may expose interactions among different protocol layers. In an embodiment, the protocol layers may include an application layer, a transport layer, RRC defining multiple channel states, and user input and interactions. The user input and interactions may, for instance, enable verification of a level of efficiency of resource usage for various client device applications. In this regard, accurately inferring lower layer RRC states and accurate quantification of resources energy consumptions may provide significant benefits to finding an optimal connectivity. For example, once an RRC state machine's interaction with a specific application is determined (or defined) and predicted, an optimal (e.g., best) available network connectivity candidate may be selected for a vertical (or horizontal) handoff.”
“[0042] In certain embodiments, systems and methods may be based on a cross-layer optimization architecture that defines a fine grained device model and selects the optimal network connectivity for the existing application given user preferences and existing contextual information extracted from multiple layers. User experience may be maintained or enhanced during a VHO by seamlessly maintaining the application and session connectivity using the mobile device.”
“[0043] In certain embodiments, the mobile device may be included within an ecosystem with other devices, such as network edge devices. The network edge devices may, for example, cooperate with the mobile device to provide the seamless VHO. In an embodiment, a seamless VHO may include monitoring and collecting contextual information. The contextual information may include cross layer contextual information associated with an application, user preferences, radio frequency (RF) channel sensing, and neighboring device detection. The seamless VHO may include ecosystem communications, device and network state modeling, context management (e.g., metric computations), handoff prediction and network selection, and handoff execution and management.”
“[0049] The cross-layer information may include both device specific and network specific data to provide device optimization. The optimization may include various criteria. For example, in one embodiment, the optimization may include the device battery power optimization that maintains a QoS of an application that may be currently running on a client device. Multiple choices of network connectivity or radio access technology (RAT) PoAs may be searched to achieve an optimal choice that maximizes resource usage or minimizes the unnecessary overheads that may exist due to the lack of transparency in the lower layer behavior with respect to the application layer.”
“[0050] In certain embodiments, network awareness may be based on interference awareness. For example, real time RF monitoring may be implemented to enhance the accuracy and speed of the channel scanning required in ambient network sensing. A network model may be implemented with real time contextual information that may predict the device resource allocation changes due to existing interferences. In this way, the accuracy of network based context used in the radio resource enhancement process may be increased. In addition, interference awareness may significantly help with horizontal and vertical handoff by speeding up the RF monitoring phase (e.g., avoiding channels in an interface) required in a handoff process.
“[0084] In certain embodiments, cross-layer contextual information may be used to enhance the effectiveness of context-awareness in ambient network sensing. By accurately and efficiently exposing cross-layer interactions across various layers, inefficient resource usage for smartphone applications may be detected. As indicated above, cross-layer contextual information may comprise information or parameters from two or more OSI layers….”
See also paragraphs [0050],[0054], [0068], [0078]-[0080], [0083], [0088] “Once interference is detected, time-based scanning may help characterize the interference source and its behavior to make a more energy and time efficient channel. In this way, smart scanning may be provided. Analog and digital beam forming may also be used to locate sources of interference, as well as to furnish a better link budget for interference measurement device. As a result, measurement accuracy may be improved” … “The scan data may be processed to establish a real time visual interference classification map of existing energy at different frequency bands. The map may cover multiple RATs of interest as captured by a scanner. Moreover, interference classification may also be performed by a remote server or appliance on the backbone, such as by transmitting the raw spectral data captured by WNICs in a network device, such as an AP, and forwarded by a network CPU. The interference classification may also be performed by a network CPU within a network edge device that sends consolidated spectrum data and interference event notifications across the network to the remote server or appliance”)
For these reasons and the reasons of the previous Office action the rejections of claim 1 is maintained.
Regarding claims 8, 15, 2-7, 9-14 and 16-20 rejections under 35 USC § 103:
Applicant's arguments filed 07/28/2026 have been fully considered but they are not persuasive.
The Applicant contends:
“Claims 8 and 15 recite features that are somewhat similar to those recited in claim 1. Therefore, a hypothetical combination of Safavi and Balakrishnan also fails to render obvious the features recited in claims 8 and 15 as well as claims 2-7, 9-14, and 16-20 that depend from one of claims 1, 8, and 15. For the foregoing reasons, the undersigned representative respectfully requests reconsideration and withdrawal of the rejection of claims 1-20 under 35 U.S.C. 103”
The Examiner disagrees, and asserts that, because the rejection of claim 1 is maintained, for the same reasons the rejections of claim 8, 15, 2-7, 9-14 and 16-20 are also maintained.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Safavi (US 20160050589 A1) in view of Balakrishnan (US 20210258988 A1).
Regarding claims 1, 8 and 15, Safavi discloses memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions (abstract figure 2 block 202 smartphone “Computer implemented methods, systems, and computer readable media provided herein may collect contextual information including parameter from a physical layer and a parameter from at least one other OSI layer. A handoff may be initiated based on the physical layer parameter and the at least one other OSI layer parameter”) to receive an interference signal via an antenna of the radio (paragraph [0035], [0078]-[0080] “The layers providing contextual information may include various parameters. For example, physical layer may comprise parameters which may affect wireless or radio connectivity, such as radio frequency (RF) environmental factors (e.g., interference levels, noise, co-channel interference, adjacent interference, etc.), spectrum contamination, received signal strength indication (RSSI), bit error rate (BER), cell coverage, noise signal ratio (NSR), committed information rate (CIR), signal to interference ratio (SIR), etc. Exemplary parameters for the MAC/link layer may include channel access delay, number of retransmissions, clear channel assessment (CCA), threshold (in WiFi), etc.” … “The scan data may be processed to establish a real time visual interference classification map of existing energy at different frequency bands. The map may cover multiple RATs of interest as captured by a scanner. Moreover, interference classification may also be performed by a remote server or appliance on the backbone, such as by transmitting the raw spectral data captured by WNICs in a network device, such as an AP, and forwarded by a network CPU. The interference classification may also be performed by a network CPU within a network edge device that sends consolidated spectrum data and interference event notifications across the network to the remote server or appliance”); determine cross-layer layers characteristics of a plurality of network protocol layers used for transmission of intended signals for the radio (paragraph [0034]-[0038], [0048]-[0056] “In an embodiment, cross-layer contextual information may enable direct access to, or accurate inference of, a device's radio resource control (RRC) state machine and an application's interaction with it. In this way, transparency to lower layer protocols may be improved. Such cross-layer contextual information collection may encompass both device and network related information. Cross-layer contextual information may comprise information or parameters from two or more open systems interconnection (OSI) layers such as user input/preferences layer, protocol layers (such as HTTP, transport, network, radio link, radio resource control states, or other similar layers), data link layer, session layer, presentation layer, application layer, MAC, and physical (PHY), or other similar layers. In some embodiments the protocol layers may be those affected or influenced by application behavior.” … "The layers providing contextual information may include various parameters. For example, physical layer may comprise parameters which may affect wireless or radio connectivity, such as radio frequency (RF) environmental factors (e.g., interference levels, noise, co-channel interference, adjacent interference, etc.), spectrum contamination, received signal strength indication (RSSI), bit error rate (BER), cell coverage, noise signal ratio (NSR), committed information rate (CIR), signal to interference ratio (SIR), etc. Exemplary parameters for the MAC/link layer may include channel access delay, number of retransmissions, clear channel assessment (CCA), threshold (in WiFi), etc.”).
PNG
media_image1.png
427
719
media_image1.png
Greyscale
Safavi doesn’t disclose classify the interference signal using one or more features in the interference signal and cross-layer characteristics; and determine an interference mitigation scheme for countering the interference signal based on a classification of the interference signal. Balakrishnan discloses classify the interference signal using one or more features in the interference signal and the one or more layers characteristics (figure 10-11 block 1004 figure 14 block 1402 paragraphs [0153]-[0154], [0185] “Blind estimation may use an underlying feature/energy discovery/detection approach from a spectrum sensing unit to identify traffic of a neighbor that overlaps with the transmission of the CIRN node. One or more techniques can be utilized to estimate the interference. For example, the CIRN node may detect a particular modulation or signal feature from a node or network at a time overlapping the transmission of the CIRN node. After detection, the CIRN node may detect a different modulation (e.g., a lower modulation order) or different feature from the same node or network” … “The PHY features information includes features such as the bandwidth of operation, multiple access scheme such as OFDMA, FDMA or TDMA, maximum transmit power, length of preamble, and subcarrier spacing, among others. In addition, the vector st may include a MAC level feature vector (fmt), which may contain MAC feature information including backoff parameters, frame length, and duty cycle, among others”); and determine an interference mitigation scheme for countering the interference signal based on a classification of the interference signal (figure 10 block 1006 “Interference mitigation/avoidance methods can then be utilized by the CIRN node at operation 1006. For example, the model may reduce the transmission power, engage frequency hopping, and/or delay transmissions through the use of a backoff procedure, which may be analogous to the WiFi backoff procedure, to reduce the possibility of collisions”).
PNG
media_image2.png
294
228
media_image2.png
Greyscale
PNG
media_image3.png
387
788
media_image3.png
Greyscale
Safavi and Balakrishnan are analogous art because they are from the same field of communications. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate in the technique disclosed by Safavi the interference avoidance disclosed by Balakrishnan. The suggestion/motivation for doing so would have been to improve communications (Balakrishnan abstract). See also KSR. In the KSR case, the Court stated that in certain circumstances what is obvious to try is also obvious, such as where "there is a design need or market pressure to solve a problem, and there are a finite number of identified, predictable solutions, a person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense." Regarding hindsight, the Court found that "[r]igid preventive rules that deny fact finders recourse to common sense . . . are neither necessary under our case law nor consistent with it." The Court stated that "familiar items may have obvious uses beyond their primary purposes," analogizing an obvious invention to the fitting together of pieces to a puzzle. The Court in this regard further stated that the person of ordinary skill is also a person of ordinary creativity, and not "an automaton."
Regarding claims 2, 9 and 16, Safavi and Balakrishnan disclose claims 1, 8 and 15, Safavi also discloses also discloses determine a feature matrix based on a combination of the one or more features and the cross-layer characteristics (paragraphs [0034]-[0038] “Cross-layer contextual information may comprise information or parameters from two or more open systems interconnection (OSI) layers such as user input/preferences layer, protocol layers (such as HTTP, transport, network, radio link, radio resource control states, or other similar layers), data link layer, session layer, presentation layer, application layer, MAC, and physical (PHY), or other similar layers. In some embodiments the protocol layers may be those affected or influenced by application behavior.”). Balakrishnan also discloses determine a feature matrix based on a combination of the one or more features and the cross-layer characteristics (figure 11 paragraphs [0154]-[0157] “Alternatively, the CIRN node may maintain a running average of the traffic and observe the manner in which the averages changes after transmission starts. Interference may be estimated at the CIRN node using a covariance matrix. In particular, a time-averaged correlation matrix E may be calculated where y is the received signal and E is the time-averaged function” … “Alternatively, or in addition, the physical layer transmission parameters, such as the modulation scheme and other signal information that can act as signature for each node transmission, may be shared by the node in the collaboration channel. The receiver (e.g., CIRN node) can therefore effectively associate each signal type received with a particular node and ultimately estimate the interference caused to that node” [0184]-[0185] “The PHY features information includes features such as the bandwidth of operation, multiple access scheme such as OFDMA, FDMA or TDMA, maximum transmit power, length of preamble, and subcarrier spacing, among others. In addition, the vector st may include a MAC level feature vector (fmt), which may contain MAC feature information including backoff parameters, frame length, and duty cycle, among others”) and classify the interference signal using the feature matrix (abstract “A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model” paragraphs [0152] “The interference estimation methods may take one or more of several possible approaches, ranging from explicitly signaled information about the interference to methods for detecting and estimating the interference based upon varying amounts of side information. The latter methods can be broadly divided into model-based approaches, which rely on algorithm features extracted from raw I/Q signals, and deep learning-based approaches, which use supervised learning to train neural networks to perform signal classification tasks.”)
Regarding claims 3, 10 and 17, Safavi and Balakrishnan disclose claims 2,9 and 16, Balakrishnan also discloses classify the interference signal using a trained neural network, the trained neural network being configured to receive the feature matrix as an input and provide a classification of the interference signal as an output (abstract “A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model” paragraphs [0152] “The interference estimation methods may take one or more of several possible approaches, ranging from explicitly signaled information about the interference to methods for detecting and estimating the interference based upon varying amounts of side information. The latter methods can be broadly divided into model-based approaches, which rely on algorithm features extracted from raw I/Q signals, and deep learning-based approaches, which use supervised learning to train neural networks to perform signal classification tasks” figures 11-14)
Regarding claims 4, 11 and 18, Safavi and Balakrishnan disclose claims 1, 8 and 15, Balakrishnan also discloses determine the interference mitigation scheme using a trained neural network, the trained neural network being configured to receive the interference signal that is classified as an input and provide as output the interference mitigation scheme (abstract “A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model” paragraphs [0152] “The interference estimation methods may take one or more of several possible approaches, ranging from explicitly signaled information about the interference to methods for detecting and estimating the interference based upon varying amounts of side information. The latter methods can be broadly divided into model-based approaches, which rely on algorithm features extracted from raw I/Q signals, and deep learning-based approaches, which use supervised learning to train neural networks to perform signal classification tasks” figures 11-14)
Regarding claims 5, 12 and 19, Safavi and Balakrishnan disclose claims 1, 8 and 15, Balakrishnan also discloses implement the interference mitigation scheme by modifying at least one parameter associated with signal transmission using the radio (paragraph [0182] “After determining and training the model at operation 1004, the interference caused by the CIRN node to nodes in the neighboring network can be detected. Interference mitigation/avoidance methods can then be utilized by the CIRN node at operation 1006. For example, the model may reduce the transmission power, engage frequency hopping, and/or delay transmissions through the use of a backoff procedure, which may be analogous to the WiFi backoff procedure, to reduce the possibility of collisions”)
Regarding claims 6, 13 and 20, Safavi and Balakrishnan disclose claims 5, 12 and 19, Safavi also discloses configuration of one or more network protocol layers of the plurality of network protocol layers (paragraphs [0034]-[0038] “Cross-layer contextual information may comprise information or parameters from two or more open systems interconnection (OSI) layers such as user input/preferences layer, protocol layers (such as HTTP, transport, network, radio link, radio resource control states, or other similar layers), data link layer, session layer, presentation layer, application layer, MAC, and physical (PHY), or other similar layers. In some embodiments the protocol layers may be those affected or influenced by application behavior.”) Balakrishnan also discloses configuration of one or more network protocol layers of the plurality of network protocol layers (paragraph [0182] “After determining and training the model at operation 1004, the interference caused by the CIRN node to nodes in the neighboring network can be detected. Interference mitigation/avoidance methods can then be utilized by the CIRN node at operation 1006. For example, the model may reduce the transmission power, engage frequency hopping, and/or delay transmissions through the use of a backoff procedure, which may be analogous to the WiFi backoff procedure, to reduce the possibility of collisions”)
Regarding claims 7 and 14, Safavi and Balakrishnan disclose claims 1 and 8, Safavi also discloses plurality of network protocol layers including a physical layer, a MAC layer and a network layer of a modem of the radio (paragraphs [0034]-[0038] “Cross-layer contextual information may comprise information or parameters from two or more open systems interconnection (OSI) layers such as user input/preferences layer, protocol layers (such as HTTP, transport, network, radio link, radio resource control states, or other similar layers), data link layer, session layer, presentation layer, application layer, MAC, and physical (PHY), or other similar layers. In some embodiments the protocol layers may be those affected or influenced by application behavior.”) Balakrishnan also discloses plurality of network protocol layers including a physical layer, a MAC layer and a network layer of a modem of the radio (“The PHY features information includes features such as the bandwidth of operation, multiple access scheme such as OFDMA, FDMA or TDMA, maximum transmit power, length of preamble, and subcarrier spacing, among others. In addition, the vector st may include a MAC level feature vector (fmt), which may contain MAC feature information including backoff parameters, frame length, and duty cycle, among others”)
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18069192 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1-20 of the present application are anticipated by claims 1-20 of copending Application No. 18069192 (reference application).
Claim 1-20 of the present application are a combination of claims 1-20 of copending Application No. 18069192 (reference application) with inherent common-sense features to the 5G radio. See KSR case above.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Present Application
Copending Application No. 18069192
1. A radio comprising: at least one memory having computer-readable instructions stored therein; and one or more processors configured to execute the computer-readable instructions to:
receive an interference signal via an antenna of the radio;
determine cross-layer characteristics of a plurality of network protocol layers used for transmission of intended signals for the radio;
classify the interference signal using one or more features in the interference signal and the cross-layer characteristics; and
determine an interference mitigation scheme for countering the interference signal based on at least a classification of the interference signal
1. A device comprising: a memory having computer-readable instructions stored therein; and one or more processors configured to execute the computer-readable instructions stored on the memory to operate as a virtualized radio access network to:
receive at least one interference signal via an antenna; detect one or more features of the at least one interference signal;
determine one or more inter-layer characteristics of a plurality of network layers of a modem of the device; generate a feature set including the one or more features and the one or more inter- layer characteristics;
classify, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set; and
select, using a decision engine, an interference mitigation scheme from a plurality of interference mitigation schemes associated with the plurality of interference classes to counter the at least one interference signal based on classification of the at least one interference signal
2. The radio of claim 1, wherein the one or more processors are further configured to:
determine a feature matrix based on a combination of the one or more features and the cross-layer characteristics; and
classify the interference signal using the feature matrix
1…
determine one or more inter-layer characteristics of a plurality of network layers of a modem of the device; generate a feature set including the one or more features and the one or more inter- layer characteristics;
classify, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set….
3. The radio of claim 2, wherein the one or more processors are configured to classify the interference signal using a trained neural network, the trained neural network being configured to receive the feature matrix as an input and provide a classification of the interference signal as an output
1….
classify, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set and
select, using a decision engine, an interference mitigation scheme from a plurality of interference mitigation schemes ….
4. The radio of claim 1, wherein one or more processors are configured to
determine the interference mitigation scheme using a trained neural network, the trained neural network being configured to receive the interference signal that is classified as an input and provide as output the interference mitigation scheme
1…..
and
select, using a decision engine, an interference mitigation scheme from a plurality of interference mitigation schemes associated with the plurality of interference classes to counter the at least one interference signal based on classification of the at least one interference signal
5. The radio of claim 1, wherein the one or more processors are further configured to implement
the interference mitigation scheme by modifying at least one parameter associated with signal transmission using the radio
5. The device of claim 1, wherein
the interference mitigation scheme includes one or more of modifying a utilized modulation and coding scheme or increasing a transmit power of the device
6. The radio of claim 5, wherein the at least one parameter is a configuration of one or more network protocol layers of the plurality of network protocol layers
1…. and one or more processors
determine one or more inter-layer characteristics of a plurality of network layers of a modem of the device; generate a feature set including the one or more features and the one or more inter- layer characteristics
7. The radio of claim 1, wherein the plurality of network protocol layers including a physical layer, a MAC layer and a network layer of a modem of the radio
1…. and one or more processors
determine one or more inter-layer characteristics of a plurality of network layers of a modem of the device; generate a feature set including the one or more features and the one or more inter- layer characteristics
8. One or more non-transitory computer-readable media comprising computer-readable instructions, which when executed by one or more processors of a radio, cause the radio to:
receive an interference signal via an antenna of the radio;
determine cross-layer characteristics of a plurality of network protocol layers used for transmission of intended signals for the radio;
classify the interference signal using one or more features in the interference signal and the cross-layer characteristics; and
determine an interference mitigation scheme for countering the interference signal based on at least a classification of the interference signal
8. One or more non-transitory computer-readable media comprising computer-readable instructions, which when executed by one or more processors configured to operate as a virtualized radio access network, cause the virtualized radio access network to:
receive at least one interference signal via an antenna; detect one or more features of the at least one interference signal;
determine one or more inter-layer characteristics of a plurality of network layers of a modem associated with the virtualized radio access network; generate a feature set including the one or more features and the one or more inter-layer characteristics;
classify, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set; and
select, using a decision engine, an interference mitigation scheme from a plurality of interference mitigation schemes associated with the plurality of interference classes to counter the at least one interference signal based on classification of the at least one interference signal
9. The one or more non-transitory computer-readable media of claim 8, wherein execution of the computer-readable instructions further causes the radio to:
determine a feature matrix based on a combination of the one or more features and the cross-layer characteristics; and classify the interference signal using the feature matrix
8….
generate a feature set including the one or more features and the one or more inter-layer characteristics;
classify, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set…
10. The one or more non-transitory computer-readable media of claim 9, wherein execution of the computer-readable instructions further cause the radio to
classify the interference signal using a trained neural network, the trained neural network being configured to receive the feature matrix as an input and provide a classification of the interference signal as an output
1…
determine one or more inter-layer characteristics of a plurality of network layers of a modem associated with the virtualized radio access network; generate a feature set including the one or more features and the one or more inter-layer characteristics;
classify, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set…
11. The one or more non-transitory computer-readable media of claim 8, wherein execution of the computer-readable instructions further causes the radio to
determine the interference mitigation scheme using a trained neural network, the trained neural network being configured to receive the interference signal that is classified as an input and provide as output the interference mitigation scheme
1….
classify, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set; and select, using a decision engine, an interference mitigation scheme from a plurality of interference mitigation schemes associated with the plurality of interference classes to counter the at least one interference signal based on classification of the at least one interference signal
12. The one or more non-transitory computer-readable media of claim 8, wherein execution of the computer-readable instructions further causes the radio to implement the interference mitigation scheme by modifying at least one parameter associated with signal transmission using the radio
12. The one or more non-transitory computer-readable media of claim 8, wherein the interference mitigation scheme includes one or more of modifying a utilized modulation and coding scheme or increasing a transmit power of a device associated with the virtualized radio access network
13. The one or more non-transitory computer-readable media of claim 12, wherein the at least one parameter is a configuration of one or more network protocol layers, of the plurality of network protocol layers
1… determine one or more inter-layer characteristics of a plurality of network layers of a modem associated with the virtualized radio access network; generate a feature set including the one or more features and the one or more inter-layer characteristics
14. The one or more non-transitory computer-readable media of claim 8, wherein the plurality of network protocol layers including a physical layer, a MAC layer and a network layer of a modem of the radio
1… determine one or more inter-layer characteristics of a plurality of network layers of a modem associated with the virtualized radio access network; generate a feature set including the one or more features and the one or more inter-layer characteristics
15. A method comprising:
receiving, at a controller of a radio, an interference signal via an antenna of the radio;
determining, by the controller, cross-layer characteristics of a plurality of network protocol layers used for transmission of intended signals for the radio;
classifying, by the controller, the interference signal using one or more features in the interference signal and the cross-layer characteristics; and
determining, by the controller, an interference mitigation scheme for countering the interference signal based on at least a classification of the interference signal
15. A method of interference mitigation by a virtualized radio access network, the method comprising:
receiving at least one interference signal via an antenna; detecting one or more features of the at least one interference signal;
determining one or more inter-layer characteristics of a plurality of network layers of a modem associated with the virtualized radio access network; generating a feature set including the one or more features and the one or more inter-layer characteristics;
determining one or more inter-layer characteristics of a plurality of network layers of a modem associated with the virtualized radio access network; generating a feature set including the one or more features and the one or more inter-layer characteristics;
classifying, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set; and
selecting, using a decision engine, an interference mitigation scheme from a plurality of interference mitigation schemes associated with the plurality of interference classes to counter the at least one interference signal based on classification of the at least one interference signal
16. The method of claim 15, further comprising:
determining a feature matrix based on a combination of the one or more features and the cross-layer characteristics; and classifying the interference signal using the feature matrix
15….
determining one or more inter-layer characteristics of a plurality of network layers of a modem associated with the virtualized radio access network; generating a feature set including the one or more features and the one or more inter-layer characteristics…
17. The method of claim 16, wherein the interference signal is classified using a trained neural network, the trained neural network being configured to receive the feature matrix as an input and provide a classification of the interference signal as an output
15…. generating a feature set including the one or more features and the one or more inter-layer characteristics…
classifying, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set
18. The method of claim 15, wherein the interference mitigation scheme is determined using a trained neural network, the trained neural network being configured to receive the interference signal that is classified as an input and provide as output the interference mitigation scheme
15….classifying, using a trained neural network, the at least one interference signal as one of a plurality of interference classes based on the feature set; and selecting, using a decision engine, an interference mitigation scheme from a plurality of interference mitigation schemes associated with the plurality of interference classes to counter the at least one interference signal based on classification of the at least one interference signal
19. The method of claim 15, wherein the interference mitigation scheme is implemented by modifying at least one parameter associated with signal transmission using the radio
19. The method of claim 15, wherein the interference mitigation scheme includes one or more of modifying a utilized modulation and coding scheme or increasing a transmit power of a device associated with the virtualized radio access network
20. The method of claim 19, wherein the at least one parameter is a configuration of one or more network protocol layers of the plurality of network protocol layers
15… determining one or more inter-layer characteristics of a plurality of network layers of a modem associated with the virtualized radio access network; generating a feature set including the one or more features and the one or more inter-layer characteristics
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN A TORRES whose telephone number is (571) 272-3119. The examiner can normally be reached M-F 9-5.
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, Kenneth N Vanderpuye can be reached at (571) 272-3078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JUAN A TORRES/Primary Examiner, Art Unit 2634
Prosecution Insights