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 .
EXAMINER’S COMMENT
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/22/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The modifications to the claims were received on 02/12/2026. These modifications are accepted by the Examiner.
In view of the amendment filed on 02/12/2026, the Examiner withdraws claim rejections under 35 USC § 112(b) to claims 1-20 of the previous Office action.
Response to Arguments
Regarding claim rejections under 35 USC § 103:
Applicant's arguments filed 02/12/2026 have been fully considered but they are not persuasive.
The Applicant contends:
“First, claim 1 recites, inter alia, "receive an interference signal via an antenna of the radio." On pages 4 and 5 of the Office Action, the examiner points to paragraph [0032] of Safavi to reject this feature. There is no discussion of receiving an interference signal by a network device in paragraph [0032] or any other paragraph of Safavi. For example, paragraph [0032] states that "The inherent diversity across multiple network resources and access technologies may be used for intra and inter technology switching, also known as horizontal and vertical handoff (or handover), even when the user is not on the move. More specifically, combined with traditional handoffs triggered by user movements, performance and efficiency may be significantly enhanced, both at the device level and the network level." There is no discussion of receiving an interference signal by any device. Safavi is directed to obtaining contextual information in a network including physical layer and OSI layer context in order to determine when to perform device handoff/handover from one network node/base station to another. Therefore, Safavi does not teach "receive an interference signal via an antenna of the radio," as recited in claim 1. Second, claim 1 further recites, inter alia, "classify the interference signal using one or more features in the interference signal and the one or more layers characteristics," and "determine an interference mitigation scheme for countering the interference signal based on at least a classification of the interference signal." On page 6 of the Office Action, the examiner correctly recognizes that Safavi fails to teach or suggest the above recited features of claim 1. However, it is alleged that Balakrishnan teaches the same. Specifically, the examiner cites to figure 10 and 11 (block 1004), figure 14 (block 1402) and paragraphs [0153], [0154], and [0185] of Balakrishnan to support this rejection. The cited paragraphs of Balakrishnan describe a process whereby a Collaborative Intelligent Radio Network (CIRN) node detects interference caused by the CIRN node to neighbor nodes due to transmissions from the CIRN node, using blind and non-blind estimation methods. In other words, according to Balakrishnan, a node estimates its own interference on neighboring nodes. Therefore, because Safavi does not teach receiving an interference signal at a radio, any alleged interference classification and interference mitigation scheme determination performed by Balakrishnan, cannot teach or suggest classifying of an interference signal followed by determining an interference mitigation scheme, as defined by and recited in claim 1. Therefore, a hypothetical combination of Safavi and Balakrishan 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”
The Examiner disagrees, and asserts that, as indicated in the previous Office action Safavi discloses to receive at least one interference signal via an antenna of the radio (paragraph [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.”)
See also paragraphs [0050], [0078]-[0080], [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”)
See also “Supplementary European Search Report issued November 24, 2025 in corresponding European Application No. 22912673.5.” filed with the Information Disclosure Statement (IDS) Form (SB08) dated 12/22/2025 the specifically indicated that this limitation is discloses by Safavi in paragraphs [0079]-[0081] and [0103]-[0106].
Because Safavi discloses specifically an interference classification, necessarily have to be an interference received.
See also Balakrishnan figure 10 block 1002 presented in the previous Office action.
For these reasons and the reasons of the previous Office action the rejections of claim 1 is maintained.
Because the rejection of claim 1 is maintained, for the same reasons the rejections of claim 2-10 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 one or more layers characteristics of one or network layers used for transmission of intended signals for the radio (paragraph [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.”).
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Safavi doesn’t disclose classify the interference signal using one or more features in the interference signal and the one or more layers 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”).
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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, Balakrishnan also discloses determine a feature matrix based on a combination of the one or more features and the one or more layers 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, Balakrishnan also discloses configuration of one or more network 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, Balakrishnan also discloses one or more network 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”)
Conclusion
THIS ACTION IS MADE FINAL. 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 JUAN A TORRES whose telephone number is (571) 272-3119. The examiner can normally be reached M-F 9-5.
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/JUAN A TORRES/Primary Examiner, Art Unit 2634