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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/23/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1-3, 7, 10, 11, 19 and 24 are objected to because of the following informalities:
In claim 1, lines 4-5, the occurrence of "the wireless nodes" should be "--- the plurality of wireless nodes ----"
In claim 1, lines 6, the occurrence of "the steps" should be "--- steps ----"
In claim 2, lines 12, the occurrence of "at least one wireless connection" should be "--- the at least one wireless connection ----"
In claim 3, lines 2-3, the occurrence of "wire connections" should be "--- the wireless connections ----"
In claim 7, lines 4-5, the occurrence of "at least one wireless connection" should be "--- the at least one wireless connection ----"
In claim 10, lines 5, the occurrence of "at least one wireless connection" should be "--- the at least one wireless connection ----"
In claim 11, lines 3-4, the occurrence of "the further wireless connection" should be "--- the further wireless connections ----"
In claim 19, lines 1, the occurrence of "a data transmission" should be "--- the data transmission ----"
In claim 24, lines 1, the occurrence of "a wireless node" should be "--- the wireless node ----"
Appropriate corrections are required.
Claim Objections
Claim 24 is also objected to because of the following informalities:
The step a communication device of claim 24 is depending on claim 1. It is improper to have one independent claim reciting the same steps from another independent claim. See MPEP 608.01. Appropriate correction is required.
For the purpose of examinations, the examiner will interpret the claim(s) as best understood.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 7, 17, 20, 22, 23 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Ottersten et al. [hereinafter as Ottersten], US 2021/0345134 A1 in view of Xiao [hereinafter as Xiao], CN 115829467 A in view of Chung et al. [hereinafter as Chung], US 2023/0275728 A1 further in view of Uusitalo et al. [hereinafter as Uusitalo], US 2022/0361011 A1.
Regarding claim 1, Ottersten discloses wherein a method for adapting wireless parameters within a bidirectional wireless network containing a plurality of wireless nodes (Fig.1 [0066]-[0067], a method for adapting wireless parameters within a bidirectional wireless communications network 100 comprising a plurality of wireless devices 120, 122/wireless nodes and Fig.1&2A [0088]) and at least one gateway (Fig.1 [0066]-[0067], gateway e.g., first radio network node 110 and second radio network node 111),
wherein wireless connections are provided between the at least one gateway and the wireless nodes (Fig.1 [0066]-[0067], wireless connections e.g., downlink beam 115a, 115b, 116, uplink beam 117 are provided between the first radio network node 110 and second radio network node 111/gateway and the plurality of wireless devices 120, 122/wireless nodes),
wherein the bidirectional wireless network is provided as a first digital twin (Fig.1 [0076][0082]-[0083], the bidirectional wireless communications network 100 comprises machine learning units 300 and provided as a digital twin or Intelligent Agent (AI) used for various purposes and Fig.2A-B&3 [0102], the machine learning model relates to one or more of the network nodes 110, 110, 120, 122, 130. a machine learning model describing e.g. the network environment of the network node and how the network node may interact with other network nodes in the network)
in a wireless node of the wireless nodes or the at least one gateway or in a wireless-network external head-end (Fig.1 [0076][0082]-[0083], the machine learning units 300 comprised in a node operating within the wireless communications network 100 and /or the external network 200, the machine learning unit 300 comprised in the radio network node 110/gateway, the machine learning unit 300 comprised in the core network 102 e.g., the central node 130), which comprises the steps of:
determining or estimating a received signal strength indicator (RSSI) of each of the wireless connections (Fig.2A-B&3 [0102]-[0103], determining/estimating an input data parameters such as received signal strength indicator (RSSI) relating to the one or more of the network nodes 110, 110, 120, 122, 130 of the wireless connections e.g., downlink beam 115a, 115b, 116, uplink beam 117).
Even though Ottersten discloses wherein performing one or more measurements by means of the at least one network node 110, 111, 120, 122, 130, and by means of the machine learning unit 300, using information relating to the performed one or more measurements as input data to the machine learning model in order to determine the prediction of the performance of the one network node 110, 111, 120, 122, 130, wherein the prediction is based on output data from the machine learning model, in the same field of endeavor, Xiao teaches wherein determining or estimating a path loss of each of the wireless connections from an associated said RSSI (Fig.1-2 page 3 lines 18-29, determining or calculating/estimating path loss model by using digital twin technology with the specific formula RSSI = -(10nlgd+A) for each of the wireless connections from an associated RSSI received signal strength indicator and Fig.1 page 1 last paragraph to page 2 lines 1-5);
assigning the path loss of each of the wireless connections to respective ones of the wireless connections of the first digital twin (Fig.1-3 page 4 lines 13-48, updating the digital twin model/assigning the path loss model based on the RSSI for the wireless connection of the first digital twin in real-time monitoring through the digital twin technology).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Ottersten to incorporate the teaching of Xiao in order to provide for a management and monitoring system.
It would have been beneficial to update the digital twin model/assign the path loss model based on the RSSI for the wireless connection of the first digital twin in real-time monitoring through the digital twin technology as taught by Xiao to have incorporated in the system of Ottersten to provide for improving the applicability and portability of the system. (Xiao, Fig.1-3 page 4 lines 13-48 and Fig.1-3 page 5 lines 6-15).
Even though Ottersten and Xiao disclose wherein assigning the path loss of each of the wireless connections to respective ones of the wireless connections of the first digital twin, in the same field of endeavor, Chung teaches wherein assigning the path loss of each of the wireless connections to respective ones of the wireless connections of the first digital twin (Fig.1&9a-b [0034][0342], a pathloss RS for the uplink signal is assigned/configured/updated/indicated by the configuration information to respective RF connection/wireless connection of the artificial intelligence system i.e., first digital twin based on the estimated pathloss value calculation and Fig.1&8 [0006][0164], bi-directional two panels in 3GPP UE antenna modeling for configuring transmission parameters of an uplink signal e.g., a transmission beam, a panel, a path loss reference signal etc.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Ottersten and Xiao to incorporate the teaching of Chung in order to provide for an improved mobile broadband communication.
It would have been beneficial to assign/configure/update/indicate a pathloss RS for the uplink signal by the configuration information to respective RF connection/ wireless connection of the artificial intelligence system i.e., first digital twin based on the estimated pathloss value calculation and, bi-directional two panels in 3GPP UE antenna modeling for configuring transmission parameters of an uplink signal e.g., a transmission beam, a panel, a path loss reference signal etc. as taught by Chung to have incorporated in the system of Ottersten and Xiao to provide a higher capacity and an improved reliability and latency. (Chung, Fig.1 [0074], Fig.1&8 [0006][0164] and Fig.1&9a-b [0034][0342]).
Even though Ottersten, Xiao and Chung disclose wherein the output data from the machine learning model may be a prediction of modulation and coding scheme, transmitter beam or receiver beam to use and, the wireless communications system 10 may perform a change of transmit beam and/or receive beam, change of MCS selection operation based on the determined prediction of the performance of the at least one network node 110, 111, 120, 122, 130 and, in order to determine a beam prediction, another ML model may be trained to monitor the link quality to perform prediction of link adaptation, in the same field of endeavor, Uusitalo teaches wherein assigning the path loss of each of the wireless connections to respective ones of the wireless connections of the first digital twin (Fig.8&12 [0088][0106], a radio-based procedure for updating the digital twin, updating/assigning the path loss experienced by the radio signal in the radio link/each of the wireless connections to the respective the wireless connections of the first digital twin using machine learning algorithm); and
adapting, on a basis of the path loss of each of the wireless connections of the first digital twin, at least one wireless parameter for a future data transmission for at least one wireless connection of the wireless connections of the bidirectional wireless network (Fig.1&8 [0088]-[0090][0106], updating/adapting the radio parameters for a predicted data transmission for the radio link/radio interfaces/wireless connections of the wireless network based on the path loss experienced by the radio signal of the wireless connection for the digital twin, e.g., the updated machine learning ML algorithm is providing a prediction/future data transmission based on the digital twin is above or below a threshold and Fig.16a-b [0148], a plurality of radio interfaces are coupled to the antennas 25).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Ottersten, Xiao and Chung to incorporate the teaching of Uusitalo in order to provide for optimizing radio performance.
It would have been beneficial to update/adapt the radio parameters for a predicted data transmission for the wireless connections of the wireless network based on the path loss experience by the radio signal of the wireless connection for the digital twin, e.g., the updated machine learning ML algorithm is providing a prediction/future data transmission based on the digital twin is above or below a threshold and a plurality of radio interfaces are coupled to the antennas 25 as taught by Uusitalo to have incorporated in the system of Ottersten, Xiao and Chung to provide a prediction about the expected radio reliability in the corresponding location of the environment. (Uusitalo, Fig.1&8 [0088]-[0090][0106], Fig.16a-b [0148] and Fig.15 [0156]).
Regarding claim 2, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 1 as stated above wherein Ottersten further discloses the bidirectional wireless network additionally comprises further wireless connections between the wireless nodes (Fig.1 [0066]-[0068][0095], the bidirectional wireless communication network 100 additionally comprises further wireless connections e.g., Internet of Things (IoT), Narrow band IoT (NB-IoT), eMTC device, CAT-M device, MBB device, Wi-Fi device, LTE device, Device to Device (D2D) device connections between peers/ between the wireless nodes and Fig.11 [0246], connections 3221, 3222), the method further comprises the steps of:
determining or estimating the RSSI of each of the further wireless connections (Fig.2A-B&3 [0102]-[0103], determining/estimating an input data parameters such as received signal strength indicator (RSSI) relating to the one or more of the network nodes 110, 110, 120, 122, 130 of the wireless connections e.g., downlink beam 115a, 115b, 116, uplink beam 117).
Additionally, Xiao discloses wherein determining or estimating the path loss of associated ones of the further wireless connections from associated said RSSI (Fig.1-2 page 3 lines 21-29, determining or calculating/estimating path loss model with the specific formula RSSI = -(10nlgd+A) for each of the wireless connections from an associated RSSI received signal strength indicator); assigning the path loss of each of the further wireless connections to the further wireless connections of the first digital twin (Fig.1-3 page 4 lines 13-48, updating the digital twin model/assigning the path loss model based on the RSSI for the wireless connection of the first digital twin in real-time monitoring through the digital twin technology).
Additionally, Uusitalo discloses wherein using the path loss of each of the further wireless connections of the first digital twin to adapt the at least one wireless parameter for the future data transmission for at least one wireless connection of the bidirectional wireless network (Fig.1&8 [0088]-[0090][0106], updating/adapting the radio parameters for a predicted data transmission for the radio link/radio interfaces/the wireless connections of the wireless network based on the path loss experienced by the radio signal of the wireless connection for the digital twin, e.g., the updated machine learning ML algorithm is providing a prediction/future data transmission based on the digital twin is above or below a threshold and Fig.16a-b [0148], a plurality of radio interfaces are coupled to the antennas 25).
Regarding claim 7, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 2 as stated above wherein Uusitalo further discloses signal parameters of individual ones of the wireless connections and the further wireless connections are determined and are used additionally to adapt the at least one wireless parameter for the future data transmission for at least one wireless connection of the bidirectional wireless network (Fig.1&5&11 [0053][0101][0114], determining or estimating signal parameters of the wireless connections and the further wireless connections and Fig.1&8 [0088]-[0090], updating/adapting the radio parameters for a predicted data transmission for the wireless connections of the wireless network based on the path loss experience by the radio signal of the wireless connection for the digital twin).
Regarding claim 17, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 7 as stated above wherein Ottersten further discloses selecting the signal parameters from the group consisting of: the received signal strength indicator (RSSI); a packet error rate (PER); and a bit error rate (BER) (Fig.1-3 [0103], the received signal strength indicator (RSSI) and bit error rates).
Additionally, Uusitalo discloses a signal-to-noise ratio (SNR); a packet error rate (PER) (Fig.7 [0074][0079], signal-to-noise ratio (SNR); a packet error rate (PER)/ block error rate (BLER)).
Regarding claim 20, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 1 as stated above wherein Uusitalo further discloses adapting the at least one wireless parameter for the future data transmission for the at least one wireless connection of the bidirectional wireless network such that a reception probability is increased for a data transmission via the wireless connection and/or the further wireless connection having a high path loss (Fig.1&7-8 [0040][0088]-[0090], updating/adapting the radio parameters for a predicted data transmission for the wireless connections of the bidirectional wireless network such that a success probability/reception probability is increased for a data transmission via the wireless connection).
Regarding claim 22, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 1 as stated above wherein Ottersten further discloses the wireless node is a sensor device and/or an actuator device (Fig.1 [0068], the wireless node is a sensor
device).
Regarding claim 23, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 1 as stated above wherein Ottersten further discloses the wireless node contains a neuromorphic processor unit (Fig.1-3 [0114], the wireless node contains a
neuromorphic processor unit, and each artificial neuron has an activation function).
Regarding claim 24, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 1 as stated above wherein Ottersten further discloses a communication device being a wireless node, a gateway or a head-end (Fig.1 [0066]-[0067], a communication device is a wireless node of a plurality of wireless devices 120, 122 and a gateway e.g., first radio network node 110 and second radio network node 111 and Fig.1 [0076][0082]-[0083], the machine learning units 300 comprised in a node operating within the wireless communications network 100 and /or the external network 200, the machine learning unit 300 comprised in the radio network node 110/gateway, the machine learning unit 300 comprised in the core network 102 e.g., the central node 130), wherein the communication device is configured to implement the method according to claim 1 (Fig.1 [0066]-[0067], the communication device/ plurality of wireless devices 120, 122 are configured to implement the method according to claim 1).
Claims 3-4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Ottersten et al. [hereinafter as Ottersten], US 2021/0345134 A1 in view of Xiao [hereinafter as Xiao], CN 115829467 A in view of Chung et al. [hereinafter as Chung], US 2023/0275728 A1 in view of Uusitalo et al. [hereinafter as Uusitalo], US 2022/0361011 A1 further in view of Feng et al. [hereinafter as Feng], US 2017/0251382 A1.
Regarding claim 3, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 2 as stated above wherein Uusitalo further discloses determining or estimating a path loss exponent of the wireless connections and the further wire connections (Fig.8&16a-b [0088][0137][0143], determining or estimating a path loss exponent experienced by the radio signal for the wireless connections and the further wired connections link 70).
Even though Ottersten, Xiao, Chung and Uusitalo disclose determining or estimating a path loss exponent of the wireless connections and the further wire connections, in the same field of endeavor, Feng teaches wherein determining or estimating a path loss exponent of the wireless connections and the further wire connections (Fig.1&5&11 [0053][0101][0114], determining or estimating a path loss exponent ƞ=3.5 of the wireless connections and the further wired connections of backbone networks).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Ottersten, Xiao, Chung and Uusitalo to incorporate the teaching of Feng in order to provide for improving the efficiency of the topology convergency check.
It would have been beneficial to determine or estimate a path loss exponent ƞ=3.5 of the wireless connections and the further wired connections of backbone networks as taught by Feng to have incorporated in the system of Ottersten, Xiao, Chung and Uusitalo to provide the dramatic success rate improvement for the inauguration over neighbor discovery. (Feng, Fig.1&5&11 [0053][0101][0114] and Fig.12-14 [0116]).
Regarding claim 4, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 3 as stated above wherein Ottersten further discloses adapting the at least one wireless parameter for the future data transmission for at least one of the wireless connections additionally according to the path loss exponent (Fig.1&8 [0088]-[0090][0105], updating/adapting the radio parameters for a predicted data transmission for the wireless connections of the wireless network according to the path loss exponent).
Additionally, Feng discloses wherein adapting the at least one wireless parameter for the future data transmission for at least one of the wireless connections additionally according to the path loss exponent (Fig.11 [0114], a path loss exponent for updating the performance).
Regarding claim 5, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 3 as stated above wherein Xiao further discloses continuously updating the path loss and/or the path loss exponent on a basis of a latest RSSI of associated ones of the wireless connections and the further wireless connections (Fig.1-3 page 4 lines 13-48, updating the path loss model based on the RSSI for the wireless connection associated ones of the wireless connections and Fig.1-2 page 3 lines 21-29, the further wireless connections from the associated RSSI).
Regarding claim 6, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 3 as stated above wherein Xiao further discloses continuously assigning the path loss and/or the path loss exponent of each of the wireless connections and the further wireless connections to the respective wireless connections of the first digital twin (Fig.1-3 page 4 lines 13-48, assigning the path loss model based on the RSSI for the wireless connection in real-time through the digital twin technology and Fig.1-2 page 3 lines 21-29, the further wireless connections of the first digital twin).
Regarding claim 8, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 3 as stated above wherein Ottersten further discloses artificial intelligence is provided for predicting or estimating a future path loss and/or a future path loss exponent and/or a future RSSI for a respective one of the wireless connections and the further wireless connections (Fig.1-2A&B [0007][0103]-[0105], artificial intelligence is provided for predicting or estimating a future path loss and/or a further received signal strength).
Regarding claim 9, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 8 as stated above wherein Ottersten further discloses the artificial intelligence additionally predicts or estimates a future signal power and/or a future reception probability (Fig.1-2A&B [0007][0010]-[0011], the artificial intelligence additionally predicts or computes/estimates a future signal power).
Regarding claim 10, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 9 as stated above wherein Feng further discloses the future path loss and/or the future path loss exponent and/or the future signal power and/or the future reception probability of the respective wireless connections (Fig.1-11 [0069][0101]-[0114], the future path loss and/or the future path loss exponent and/or the future signal power and/or the future reception probability of the respective wireless connections).
Additionally, Uusitalo discloses the further wireless connections are used to adapt the at least one wireless parameter for the future data transmission for at least one wireless connection of the bidirectional wireless network (Fig.1&8 [0088]-[0090], the further wireless connections are used to update/adapt the radio parameters for a predicted data transmission for the wireless connections of the bidirectional wireless network based on the path loss experience by the radio signal of the wireless connection for the digital twin).
Regarding claim 11, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 8 as stated above wherein Ottersten further discloses the artificial intelligence is trained by the path loss and/or the path loss exponent and/or signal parameters of an associated one of the wireless connection and/or of the further wireless connection (Fig.1-2A&B [0007][0103]-[0105], artificial intelligence is trained for predicting or estimating a future path loss and/or signal parameters of an associated one of the wireless connection and/or of the further wireless connection).
Regarding claim 12, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 11 as stated above wherein Ottersten further discloses the signal parameters are filtered and/or clustered before a training of the artificial intelligence (Fig.1-2A&B [0007][0093]-[0095], the signal parameters are clustered before a training the artificial intelligence, and Fig.1&6 [0221]).
Regarding claim 13, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 8 as stated above wherein Ottersten further discloses a prediction or estimate by the artificial intelligence is based on a method of maximum likelihood or a method of minimum mean square error (Fig.1&7 [0007][0232], a prediction or estimate by the artificial intelligence is based on a method of minimum mean square error (MSE)).
Regarding claim 14, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 8 as stated above wherein Feng further discloses the artificial intelligence can determine a time at which the wireless node has a lowest path loss (Fig.1&7 [0069][0101][0114], determining a time at which the wireless node has a lower path loss between closer BNs).
Regarding claim 15, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 8 as stated above wherein Ottersten further discloses a second digital twin is provided in the wireless node or the at least one gateway or the wireless-network external head-end, and the artificial intelligence includes the second digital twin (Fig.1 [0076][0082]-[0083], a second digital twin is provided in the machine learning units 300 in the wireless node of the wireless communications network 100 and /or the external network 200, the machine learning unit 300 comprised in the radio network node 110/gateway, the machine learning unit 300 comprised in the core network 102 e.g., the central node 130 and Fig.1&7 [0007][0081]-[0083], the artificial intelligence includes the digital twins/second digital twin).
Regarding claim 16, Ottersten, Xiao, Chung, Uusitalo and Feng disclose all the elements of claim 15 as stated above wherein Uusitalo further discloses the second digital twin obtains information about the bidirectional wireless network from the first digital twin (Fig.1-3&5 [0043]-[0044][0057][0059], the digital twins/second digital twin obtains signal information about radio communication links 90 between the mobile devices 88 and a base station 84/the bidirectional wireless network from the first digital twin and Fig.1-3 [0046], updating relevant information among the digital twins).
Regarding claim 21, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 1 as stated above wherein Ottersten further discloses an unsynchronized data
transmission takes place via the wireless connections (Fig.6 [0220]-[0221], data
transmission takes place via the wireless connections).
Even though Ottersten, Xiao, Chung and Uusitalo disclose an unsynchronized data transmission takes place via the wireless connections, in the same field of endeavor, Feng teaches wherein an unsynchronized data transmission takes place via the wireless connections (Fig.12&17 [0119], asynchronous MACs/unsynchronized data transmission takes place via the wireless connections).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Ottersten, Xiao, Chung and Uusitalo to incorporate the teaching of Feng in order to provide for improving the efficiency of the topology convergency check.
It would have been beneficial to use asynchronous MACs/unsynchronized data transmission which takes place via the wireless connections as taught by Feng to have incorporated in the system of Ottersten, Xiao, Chung and Uusitalo to provide the dramatic success rate improvement for the inauguration over neighbor discovery. (Feng, Fig.12-14 [0116] and Fig.12&17 [0119]).
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over ids&gd Ottersten et al. [hereinafter as Ottersten], US 2021/0345134 A1 in view of Xiao [hereinafter as Xiao], CN 115829467 A in view of Chung et al. [hereinafter as Chung], US 2023/0275728 A1 in view of Uusitalo et al. [hereinafter as Uusitalo], US 2022/0361011 A1 further in view of Ganusov et al. [hereinafter as Ganusov], US 2022/0197855 A1.
Regarding claim 18, Ottersten, Xiao, Chung and Uusitalo disclose all the elements of claim 1 as stated above.
However, Ottersten, Xiao, Chung and Uusitalo do not explicitly disclose the wireless node or the at least one gateway sends a test transmission before a data transmission.
In the same field of endeavor, Ganusov teaches wherein the wireless node or the at least one gateway sends a test transmission before a data transmission (Fig.18 [0138], the CM 132 transmits a test transmission before sending all of the data transmission).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Ottersten, Xiao, Chung and Uusitalo to incorporate the teaching of Ganusov in order to provide a high amount of performance improvement.
It would have been beneficial to use the CM 132 which transmits a test transmission before sending all of the data transmission as taught by Ganusov to have incorporated in the system of Ottersten, Xiao, Chung and Uusitalo to provide for improving system throughput and reduced execution time. (Ganusov, Fig.13 [0113] and Fig.18 [0138]).
Regarding claim 19, Ottersten, Xiao, Chung, Uusitalo and Ganusov disclose all the elements of claim 18 as stated above wherein Ganusov further discloses a data transmission takes place once the test transmission has sufficient quality (Fig.18 [0138], the CM 132 proceeds to exchange data/a data transmission takes place once the test transmission has sufficient; Fig.14 [0121] and Fig.10 [0090], large amounts of data occur in machine learning applications, artificial intelligence applications).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Petkov et al. (U.S Patent No.: US 10064132 B2) teaches Bidirectional Wireless Data Transmission Method.
Sun et al. (U.S Patent No.: US 12294872 B2) teaches Wireless Access Point Group-based Calibration Method and Device, and Computer Storage Medium.
Hehn et al. (Pub. No.: US 2024/0364437 A1) teaches Pathloss Prediction Using a Machin Learning Component.
Sun et al. (Pub. No.: US 2025/0021861 A1) teaches Digital Twin for AI/ML Training and Testing.
Aldossari et al. (U.S Patent No.: US 11128391 B2) teaches System and Method for Predicting Wireless Channel Path Loss.
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/V.L/Examiner, Art Unit 2414
/EDAN ORGAD/Supervisory Patent Examiner, Art Unit 2414
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