CTNF 18/038,260 CTNF 81237 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Election/Restrictions 08-25-01 AIA Applicant’s election without traverse of Group I (claims 1-9 and 27-32) in the reply filed on 5/11/2026 is acknowledged. 08-06 AIA Claim s 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group II , there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 5/11/2026 . Claim Rejections - 35 USC § 112 07-36 AIA The following is a quotation of 35 U.S.C. 112(d): (d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph: Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers. 07-36-01 AIA Claim s 27-32 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 27 has the same scope as its parent claim 9 . Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. Claims 28-32 are rejected for their dependency of claim 27. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1 and 2 are rejected under 35 U.S.C. 103 as being unpatentable over Koudouridis (WO 2012/072445 A1) in view of Kuehbeck (US Pub. No. 2022/0163977) in view of ITU-T, FG-ML5G-ARC5G, “Focus group on Machine Learning for Future Networks including 5G (FG-ML5G) Unified architecture for machine learning in 5G and future networks”, hereinafter FG-ML5G . Regarding claim 1 , Koudouridis discloses a computer-implemented method performed by a first network node ( figure 1: cognitive engine/node ) in a communication network, the method comprising: determine, from a machine learning model ( see figures 1 and 2: cognitive learning model/engine ) at the first network node, a value for a network parameter for the cellular network for operation of a communication device based on a set of observations of the communication device, and a key performance indicator (KPI) of the cellular network ( see figure 3: parameters, performance metrics, and KPI state are received at the Controller. The Controller using cognitive learning model/prediction configures radio resource parameter allocation to radio/cellular network. Page 4 line 19 to page 5 line 12 disclose communication device and data from environment are collected for the cognitive engine. Page 5, line 13- page 6, line 7 teaches cognitive node further determines a value for a network parameter variable based on its model of the local environment and performance metric dynamics. Page 6, line 10-page 9, line 14 discloses using a machine learning model to learn from KPls and observations of the environment, monitoring parameters, and configuration settings and discovery parameters of neighboring nodes and devices ); and signal the value of the network parameter to a network node in the cellular network for a resource allocation of the cellular network ( see figure 3: parameters, performance metrics, and KPI state are received at the Controller. The Controller using cognitive learning model/prediction configures radio resource parameter allocation to radio/cellular network. P age 15, line 14-page 17, line 20 discloses the cognitive node determines the network parameter value to configure a remote device of cellular network and allocate resources in the network). Koudourisdis does not teach a safety level of the communication device. However, Kuehbeck discloses safety level of a communication device (autonomous vehicle) is determined based on the communication device’s sensing parameters. The parameters/safety level are inputted in a machine swarm intelligence algorithm [0050] for a value of network parameter (calculated trajectory). Resource is allocated to transmit the calculated trajectory to the communication device ( see figure 3, paragraphs 14, 22, 25, 43, and 50 ). Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to implement in Koudourisdis a safety level of the communication device. The motivation would have been for high safety mode in a safe, reliable, and efficient manner ( paragraph 7 ). Koudourisdis and Kuehbeck does not teach a core network node. However, FG-ML5G discloses a core network node is part of multi-level machine learning pipeline in 3GPP and Mobile Edge Computing where at least inputs from UE and RANs are used to make predictions using machine learning ( figure 3 and section 8.2, pages 16-17 ). Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to implement in Koudourisdis and Kuehbeck a core network node. The motivation would have been for logical architecture in 3GPP system ( section 8.2 ). Regarding claim 2 , all limitations of claims 1 are disclosed above. Koudourisdis does not teach but Kuehbeck discloses the safety level comprises a state of the communication device and is calculated from the set of observations of the communication device, and wherein the safety level has a value in a range of values between a minimum safety level value and a maximum safety level value (see figure 3, paragraphs 14, 22, 25, 43, and 50: Level 2 and level 4. Observations of communication device include sensor’s parameters). Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to implement in Koudourisdis the safety level comprises a state of the communication device and is calculated from the set of observations of the communication device, and wherein the safety level has a value in a range of values between a minimum safety level value and a maximum safety level value. The motivation would have been for having a high safety level while ensuring reliability and efficiency . 07-21-aia AIA Claim (s) 8, 9, 27, and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koudouridis (WO 2012/072445 A1) in view of Kuehbeck (US Pub. No. 2022/0163977) in view of ITU-T, FG-ML5G-ARC5G, “Focus group on Machine Learning for Future Networks including 5G (FG-ML5G) Unified architecture for machine learning in 5G and future networks”, hereinafter FG-ML5G, in further view of Yea et al. (US Pat. No. 6,829,491) . Regarding claim 8 , Koudourisdis discloses a first network node ( figure 1: cognitive engine/node ) in a communication network, the first node comprising: determine, from a machine learning model ( see figures 1 and 2: cognitive learning model/engine ) at the first network node, a value for a network parameter for the cellular network for operation of a communication device based on a set of observations of the communication device, and a key performance indicator (KPI) of the cellular network ( see figure 3: parameters, performance metrics, and KPI state are received at the Controller. The Controller using cognitive learning model/prediction configures radio resource parameter allocation to radio/cellular network. Page 4 line 19 to page 5 line 12 disclose communication device and data from environment are collected for the cognitive engine. Page 5, line 13- page 6, line 7 teaches cognitive node further determines a value for a network parameter variable based on its model of the local environment and performance metric dynamics. Page 6, line 10-page 9, line 14 discloses using a machine learning model to learn from KPls and observations of the environment, monitoring parameters, and configuration settings and discovery parameters of neighboring nodes and devices ); and signal the value of the network parameter to a network node in the cellular network for a resource allocation of the cellular network ( see figure 3: parameters, performance metrics, and KPI state are received at the Controller. The Controller using cognitive learning model/prediction configures radio resource parameter allocation to radio/cellular network. P age 15, line 14-page 17, line 20 discloses the cognitive node determines the network parameter value to configure a remote device of cellular network and allocate resources in the network). Koudourisdis does not teach a safety level of the communication device. However, Kuehbeck discloses safety level of a communication device (autonomous vehicle) is determined based on the communication device’s sensing parameters. The parameters/safety level are inputted in a machine swarm intelligence algorithm [0050] for a value of network parameter (calculated trajectory). Resource is allocated to transmit the calculated trajectory to the communication device ( see figure 3, paragraphs 14, 22, 25, 43, and 50 ). Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to implement in Koudourisdis a safety level of the communication device. The motivation would have been for high safety mode in a safe, reliable, and efficient manner ( paragraph 7 ). Koudourisdis and Kuehbeck does not teach a core network node. However, FG-ML5G discloses a core network node is part of multi-level machine learning pipeline in 3GPP and Mobile Edge Computing where at least inputs from UE and RANs are used to make predictions using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to implement in Koudourisdis and Kuehbeck a core network node. The motivation would have been for logical architecture in 3GPP system (section 8.2). Koudourisdis does not explicitly teach the first node comprising: at least one processor; at least one memory connected to the at least one processor and storing program code that is executed by the at least one processor to perform operations. However, in the same field of optimizing/smart network, Yea discloses a smart node/system comprising: at least one processor; at least one memory connected to the at least one processor and storing program code that is executed by the at least one processor to perform operations ( see figure 2B smart network server 250 and col. 6 lines 17-24 ). Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to implement in Koudourisdis and Kuehbeck the first node comprising: at least one processor; at least one memory connected to the at least one processor and storing program code that is executed by the at least one processor to perform operations. The motivation would have been for hardware/software needed to execute the claimed invention. Regarding claims 9 and 27 , all limitations of claims 8 and 9 are disclosed above. Koudourisdis does not teach but Kuehbeck discloses the safety level comprises a state of the communication device and is calculated from the set of observations of the communication device, and wherein the safety level has a value in a range of values between a minimum safety level value and a maximum safety level value (see figure 3, paragraphs 14, 22, 25, 43, and 50: Level 2 and level 4. Observations of communication device include sensor’s parameters). Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to implement in Koudourisdis the safety level comprises a state of the communication device and is calculated from the set of observations of the communication device, and wherein the safety level has a value in a range of values between a minimum safety level value and a maximum safety level value. The motivation would have been for having a high safety level while ensuring reliability and efficiency. Regarding claim 29 , all limitations of claim 27 are disclosed above. Koudourisdis does not teach but FG-ML5G discloses the machine learning model comprises a plurality of device machine learning models with each device machine learning model in the plurality of machine learning models corresponding to a communication device in the at least one communication device (figures 2 and 3; section 8.2, pages 16-17: machine learning model runs algorithm for each UE separately, thus each UE has its corresponding machine learning model). Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to implement in Koudourisdis the machine learning model comprises a plurality of device machine learning models with each device machine learning model in the plurality of machine learning models corresponding to a communication device in the at least one communication device. The motivation would have been for individual solution . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 3-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. 07-43-02 Claims 28 and 30-32 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(d) or 35 U.S.C. 112 (pre-AIA), fourth paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: Regarding dependent claims 3 and 28, elements of collecting a plurality of sets of observations, safety levels, and KPIs into an aggregated dataset, training the machine learning model from the aggregated dataset to predict a maximum value, and predicting the value for the network parameter from the machine learning model are not found in the art. Even if they are found, it would invariably require impermissible hindsight reasoning due to the numbers and verities of references required and the content in which the limitations are recited in combination with the independent claims. It would not have been obvious to combine the several references to teach the limitations of the dependent claims 3 and 28. Claims 4-7 are allowable for the same reason set forth in claim 3. Claims 30-32 have similar allowable reason as claims 3 and 28. it would invariably require impermissible hindsight reasoning due to the numbers and verities of references required and the content in which the limitations are recited in combination with the independent claims . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Rosales et al. (US Pub. No. 2020/0249683) discloses controller for autonomous vehicle. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TITO Q PHAM/Examiner, Art Unit 2466 Application/Control Number: 18/038,260 Page 2 Art Unit: 2466 Application/Control Number: 18/038,260 Page 3 Art Unit: 2466 Application/Control Number: 18/038,260 Page 4 Art Unit: 2466 Application/Control Number: 18/038,260 Page 5 Art Unit: 2466 Application/Control Number: 18/038,260 Page 6 Art Unit: 2466 Application/Control Number: 18/038,260 Page 7 Art Unit: 2466 Application/Control Number: 18/038,260 Page 8 Art Unit: 2466 Application/Control Number: 18/038,260 Page 9 Art Unit: 2466 Application/Control Number: 18/038,260 Page 10 Art Unit: 2466 Application/Control Number: 18/038,260 Page 11 Art Unit: 2466 Application/Control Number: 18/038,260 Page 12 Art Unit: 2466 Application/Control Number: 18/038,260 Page 13 Art Unit: 2466
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