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 .
Applicant amended claims 1-15, 17-18, 21, and 22 and canceled claims 19-20 and 23-29 in the preliminary amendment filed on 07/05/2024.
The claims 1-18 and 21-22 are pending.
Claim Objections
Claims 7 and 12 are objected to because of the following informalities:
Claim 7 recites the limitation "the end of each iteration of the iterative learning process" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the acceptable level of interference…" in lines 5-6. There is insufficient antecedent basis for this limitation in the claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims lack the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 U.S.C. 101. They are clearly not a series of steps or acts to be a process nor are they a combination of chemical compounds to be a composition of matter. As such, they fail to fall within a statutory category. As per paragraph 0149 of the current publication, the “obtain module”, “select module”, “configure module”, and “process module” may be implemented in software. Therefore, the “server entity” fails the definition of a machine within the meaning of 35 U.S.C. 101 (A machine is a "concrete thing, consisting of parts, or of certain devices and combination of devices”, MPEP 210603(i)).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-18 and 21-22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Challita et al (WO 2021242166 A1).
With respect to claim 1, Challita teaches a method for performing an iterative learning process with agent entities (Fig. 1: communication device 105), the method being performed by a server entity (Abstract: "network node"; paragraph 0036 disclose "URLLC node/s within the factory"), the server entity communicating with the agent entities over a radio propagation channel (Fig. 1: communication device 105 served by the non-public network 101; par. 41: "the same, frequency band"; paragraph 0047 disclose "communicate wirelessly with network nodes and/or other wireless devices"), the method comprising: obtaining an estimate of a level of interference of the radio propagation channel (Abstract: "estimating a level of subsequent interference from the first communication device for the subsequent position");
obtaining an estimate of an acceptable level of interference (paragraph 0007: "a second defined threshold") for performing at least one iteration of the iterative learning process with the agent entities (paragraph 0007 disclose "updating the machine learning model for each of a defined time interval until when the interference level is equal to or less than a second defined threshold"; par. 36: "A URLLC node/s within the factory determines if an eMBB user/s is approaching or is inside the factory"); selecting an interference mitigating network operation from a set of available interference mitigating network operations (Abstract: "initiating control of radio resources to reduce the estimated interference level from the first communication device"; paragraph 0036 disclose "The URLLC network node/s takes actions to proactively minimize the interference level from the eMBB user/s proactive radio resource management based on future locations of the eMBB user/s. An eMBB network node can also take actions to proactively minimize the interference its users are causing towards the URLLC network"), wherein the interference mitigating network operation is selected as a function of the estimate of the acceptable level of interference and the estimate of the level of interference (paragraph 0008 disclose "performing an action to reduce the interference from the communication device. The action is based on at least one or more of a scheduling limitation, a transmission power limitation, a handover, HO, a split of resource block"); configuring, in accordance with the interference mitigating network operation, at least one of: the server entity, the agent entities, a network node causing the level of interference level (Abstract: "initiating control of radio resources to reduce the estimated interference level from the first communication device"); and
performing at least one iteration of the iterative learning process with the agent entities (paragraph 0007 disclose "further operations performed by the network node include updating the machine learning model for each of a defined time interval until when the interference level is equal to or less than a second defined threshold").
With respect to claim 2, Challita teaches wherein the interference mitigating network operation is associated with which time/frequency resources are to be used by one of: the server entity, the agent entities, the network node when the at least one iteration of the iterative learning process is performed (paragraph 0070; 0071; 0073; 0076; 0114 disclose performing an action to reduce the interference from the communication device. The action is based on at least one or more of a scheduling limitation, a transmission power limitation, a handover, a split of resource block, an alternative route, and a non-allowed location), and wherein, according to the interference mitigating network operation, a smallest set of time/frequency resources as required for maintaining the acceptable level of interference is selected (paragraph 0070; 0071; 0073; 0076; 0114 disclose performing an action to reduce the interference from the communication device. The action is based on at least one or more of a scheduling limitation, a transmission power limitation, a handover, a split of resource block, an alternative route, and a non-allowed location).
With respect to claim 3, Challita teaches wherein the interference mitigating network operation pertains to transmission power used by the network node, and wherein the network node is configured with the interference mitigating network operation by being requested to reduce transmission power in time/frequency resources causing the level of interference (paragraph 0070; 0071; 0073; 0076; 0114 disclose performing an action to reduce the interference from the communication device. The action is based on at least one or more of a scheduling limitation, a transmission power limitation, a handover, a split of resource block, an alternative route, and a non-allowed location).
With respect to claim 4, Challita teaches wherein performing one iteration of the iterative learning process involves each of the agent entities to send a respective model update of the iterative learning process to the server entity, and wherein the interference mitigating network operation pertains to repetitive transmission of the model update from the agent entities per said at least one iteration of the iterative learning process (paragraph 0052; 0064 disclose the URLLC users reporting their SINR to the URLLC base station. If the SINR is understood as a "model"; based on the estimated future location of a MS.sub.m, the URLLC network node estimates the corresponding level of the inter-network interference from MS.sub.m towards the factory base station and the mobile stations. This estimate can be based on, but not limited to, the following input: a map illustrating the factory layout, a map illustrating the radio propagation within the factory, and/or historical signal strength measurements performed by the URLLC network (URLLC base stations and URLLC mobile stations) combined with the locations of the mobile stations).
With respect to claim 5, Challita teaches wherein performing one iteration of the iterative learning process involves each of the agent entities to send a respective model update of the iterative learning process to the server entity, and wherein the interference mitigating network operation pertains to applying interference suppression when receiving the model updates from the agent entities during said at least one iteration of the iterative learning process (paragraph 0052; 0064 disclose the URLLC users reporting their SINR to the URLLC base station. If the SINR is understood as a "model"; based on the estimated future location of a MS.sub.m, the URLLC network node estimates the corresponding level of the inter-network interference from MS.sub.m towards the factory base station and the mobile stations. This estimate can be based on, but not limited to, the following input: a map illustrating the factory layout, a map illustrating the radio propagation within the factory, and/or historical signal strength measurements performed by the URLLC network (URLLC base stations and URLLC mobile stations) combined with the locations of the mobile stations.).
With respect to claim 6, Challita teaches wherein the level of interference is estimated as a function of any of: scheduling information received from the network node, measurements on signals received from the network node, a historically experienced level of interference of the radio propagation channel (paragraph 0064 disclose based on the estimated future location of a MS.sub.m, the URLLC network node estimates the corresponding level of the inter-network interference from MS.sub.m towards the factory base station and the mobile stations. This estimate can be based on, but not limited to, the following input: a map illustrating the factory layout, a map illustrating the radio propagation within the factory, and/or historical signal strength measurements performed by the URLLC network (URLLC base stations and URLLC mobile stations) combined with the locations of the mobile stations).
With respect to claim 7, Challita teaches wherein the level of interference is estimated as a function of measurements on test data communicated between the server entity and the agent entities at the end of each iteration of the iterative learning process (paragraph 0070; 0071; 0073; 0076; 0114 disclose performing an action to reduce the interference from the communication device. The action is based on at least one or more of a scheduling limitation, a transmission power limitation, a handover, a split of resource block, an alternative route, and a non-allowed location; paragraph 0064 disclose based on the estimated future location of a MS.sub.m, the URLLC network node estimates the corresponding level of the inter-network interference from MS.sub.m towards the factory base station and the mobile stations. This estimate can be based on, but not limited to, the following input: a map illustrating the factory layout, a map illustrating the radio propagation within the factory, and/or historical signal strength measurements performed by the URLLC network (URLLC base stations and URLLC mobile stations) combined with the locations of the mobile stations).
With respect to claim 8, Challita teaches wherein the estimate of the acceptable level of interference is obtained either by the acceptable level of interference being estimated by the server entity or by the estimate of the acceptable level of interference being received from the agent entities (Abstract: "estimating a level of subsequent interference from the first communication device for the subsequent position").
With respect to claim 9, Challita teaches wherein the method further comprises:
sending instructions to the agent entities to estimate the acceptable level of interference for the iterative learning process, and wherein the estimate of the acceptable level of interference is received from the agent entities in response thereto (paragraph 0072 disclose in case of critical URLLC activity at a certain location and at particular time along the predicted trajectory of the eMBB user, the URLLC network action can inform the macro network (e.g., by sending a special warning message) that the eMBB user is expected to interfere the URLLC network. The macro network can then take this warning into account and take actions to limit the level of the uplink interference towards the URLLC network, as described herein. Once the predicted location of the eMBB user is such that the resulting interference is estimated to be sufficiently low (below a certain predefined threshold), the URLLC network node action can inform the macro network that actions to limit the interference are no longer required).
With respect to claim 10, Challita teaches wherein the acceptable level of interference is estimated as a function of any of: convergence rate of the performed iterative learning process, measurements on test data inserted in the performed iterative learning process, current number of iteration of the performed iterative learning process, variation in output between consecutive iterations of the performed iterative learning process, convergence rate of a historically performed iterative learning process (paragraph 0007 disclose updating the machine learning model for each of a defined time interval until the first communication device is localized to a non-proximate position to the non-public communication network or when the interference level is equal to or less than a second defined threshold).
With respect to claim 11, Challita teaches wherein the iterative learning process involves the use of a neural network, wherein the neural network has layers, wherein at least one layer is activated during each iteration of the iterative learning process, and wherein the acceptable level of interference is estimated as a function of which of the layers the at least one iteration of the iterative learning process pertains to (paragraph 0080-0085 disclose the neural network circuit 400 is a feed-forward neural network 517 that includes the input layer 410 having a plurality of input nodes, the sequence of neural network hidden layers 420 each including a plurality of weight nodes, and the output layer 430 including an output node. In the particular non-limiting example of this illustrative embodiment, the input layer 410 includes input nodes 11 to IN (where N is any plural integer). Input information for use in performing the detecting includes, without limitation: interference level on the plurality of network nodes in the non-public communication network, a signal to interference plus noise ratio, SI NR, value of at least one second communication device served by the non-public communication network, a traffic load level of neighboring network nodes from the plurality of network nodes in the non-public communication network and/or a traffic load level of at least one second communication device served by the non-public communication network, a historical time division duplex pattern received from the public communication network, wherein the non-public communication network and the public communication network are operated by a common operator etc.).
With respect to claim 12, Challita teaches a method for performing an iterative learning process with a server entity, the method being performed by an agent entity, the agent entity communicating with the server entity over a radio propagation channel (Fig. 1: communication device 105 served by the non-public network 101; par. 41: "the same, frequency band"; paragraph 0047 disclose "communicate wirelessly with network nodes and/or other wireless devices"), the method comprising: receiving instructions from the server entity to estimate the acceptable level of interference for the iterative learning process (Abstract: "estimating a level of subsequent interference from the first communication device for the subsequent position"; paragraph 0007: "a second defined threshold") for performing at least one iteration of the iterative learning process with the agent entities (paragraph 0007 disclose "updating the machine learning model for each of a defined time interval until when the interference level is equal to or less than a second defined threshold"; par. 36: "A URLLC node/s within the factory determines if an eMBB user/s is approaching or is inside the factory"); estimating the acceptable level of interference for the iterative learning process (paragraph 0007: "a second defined threshold") for performing at least one iteration of the iterative learning process with the agent entities (paragraph 0007 disclose "updating the machine learning model for each of a defined time interval until when the interference level is equal to or less than a second defined threshold"; paragraph 36 disclose "A URLLC node/s within the factory determines if an eMBB user/s is approaching or is inside the factory"); reporting the estimated acceptable level of interference to the server entity (paragraph 0007: "a second defined threshold") for performing at least one iteration of the iterative learning process with the agent entities (paragraph 0007 disclose "updating the machine learning model for each of a defined time interval until when the interference level is equal to or less than a second defined threshold"; paragraph 36 disclose "A URLLC node/s within the factory determines if an eMBB user/s is approaching or is inside the factory"); and performing at least one iteration of the iterative learning process with the server entity (paragraph 0007 disclose "further operations performed by the network node include updating the machine learning model for each of a defined time interval until when the interference level is equal to or less than a second defined threshold"), wherein said at least one iteration is performed based on an interference mitigating network operation as determined as a function of the estimated acceptable level of interference (Abstract: "initiating control of radio resources to reduce the estimated interference level from the first communication device"; paragraph 0007 disclose "further operations performed by the network node include updating the machine learning model for each of a defined time interval until when the interference level is equal to or less than a second defined threshold"; paragraph 0008 disclose "performing an action to reduce the interference from the communication device. The action is based on at least one or more of a scheduling limitation, a transmission power limitation, a handover, HO, a split of resource block").
With respect to claim 13, Challita teaches wherein performing one iteration of the iterative learning process involves the agent entity to send a model update of the iterative learning process to the server entity (paragraph 0007 disclose In some embodiments, further operations performed by the network node include updating the machine learning model for each of a defined time interval until the first communication device is localized to a non-proximate position to the non-public communication network or when the interference level is equal to or less than a second defined threshold).
With respect to claim 14, Challita teaches wherein the interference mitigating network operation pertains to repetitive transmission of the model update from the agent entity per said at least one iteration of the iterative learning process (paragraph 0007 disclose In some embodiments, further operations performed by the network node include updating the machine learning model for each of a defined time interval until the first communication device is localized to a non-proximate position to the non-public communication network or when the interference level is equal to or less than a second defined threshold).
With respect to claim 15, Challita teaches wherein the acceptable level of interference is estimated by adding a noise component to weights of the model update and estimating how much the noise component impacts the model update (paragraph 0127 disclose wherein the indication is an output from a machine learning model for the detecting, wherein the measurement is an input to the machine learning model and comprises at least one of: the interference level on the plurality of network nodes in the non-public communication network; a signal to interference plus noise ratio, SINR, value of at least one second communication device served by the non-public communication network; a traffic load level of neighboring network nodes from the plurality of network nodes in the nonpublic communication network; a traffic load level of at least one second communication device served by the non-public communication network; and a historical time division duplex pattern received from the public communication network, wherein the non-public communication network and the public communication network are operated by a common operator).
With respect to claim 16, Challita teaches wherein how much the noise component impacts the model update is determined using a model performance metric as used when a model to which the model update pertains is trained (paragraph 0127 disclose wherein the indication is an output from a machine learning model for the detecting, wherein the measurement is an input to the machine learning model and comprises at least one of: the interference level on the plurality of network nodes in the non-public communication network; a signal to interference plus noise ratio, SINR, value of at least one second communication device served by the non-public communication network; a traffic load level of neighboring network nodes from the plurality of network nodes in the nonpublic communication network; a traffic load level of at least one second communication device served by the non-public communication network; and a historical time division duplex pattern received from the public communication network, wherein the non-public communication network and the public communication network are operated by a common operator).
With respect to claim 17, Challita teaches wherein the acceptable level of interference is estimated as a function of any of: convergence rate of the performed iterative learning process, measurements on test data inserted in the performed iterative learning process, current number of iteration of the performed iterative learning process, variation in output between consecutive iterations of the performed iterative learning process, convergence rate of a historically performed iterative learning process (paragraph 0007 disclose updating the machine learning model for each of a defined time interval until the first communication device is localized to a non-proximate position to the non-public communication network or when the interference level is equal to or less than a second defined threshold).
With respect to claim 18, Challita teaches wherein one value of the acceptable level of interference is estimated per each iteration of the iterative learning process (paragraph 0103 disclose the machine learning model is iteratively trained before deploying the machine learning model at the network node and wherein the training uses a plurality of the inputs to the machine learning model from a plurality of network nodes in the non-public communication network to iteratively calculate an accuracy of an expected output associated with the plurality of the inputs that indicates whether a communication device is proximate the non-public communication network).
The limitations of claim 21 are rejected in the analysis of claim 1 above, and the claim is rejected on that basis.
The limitations of claim 22 are rejected in the analysis of claim 1 above, and the claim is rejected on that basis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHEIKH T NDIAYE whose telephone number is (571)270-3914. The examiner can normally be reached Monday-Friday 8:00am-5:30pm.
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/CHEIKH T NDIAYE/Primary Examiner, Art Unit 2447
6/27/2026
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