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 .
Claim Rejections - 35 USC § 102
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 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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Zhu et al. (U.S. Patent Application Number: 2023/0413152).
Consider claim 1; Zhu discloses a computer-implemented method comprising:
determining one or more parameters associated with roaming activity of a client station (STA) within a wireless network [“…handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction.” (par. 199, lines 6-8)];
evaluating the one or more parameters with a machine learning model to predict a sliding boundary associated with the client STA, a first access point (AP) within the wireless network, and a second AP within the wireless network [“…the AI/ML based mobility predicting component 199 of a source network node is configured to obtain at least one mobility related prediction associated with a UE or at least one target network node, the at least one mobility related prediction being derived by at least one neural network, and handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction.” (par. 199, lines 1-8)]; and
transmitting information associated with the sliding boundary to at least one of the first AP or the second AP [“…the network entity 1602 further includes means for receiving an RRM measurement report including at least one flag indicating that the RRM measurement is based on the prediction of the mobility or a confidence level of the prediction. In one configuration, the network entity 1602 further includes means for transmitting at least one parameter for a machine learning model for the UE to decide RRM prediction and the at least one parameter of the RRM measurement. In another configuration, the network entity 1602 may be a target network entity, and the network entity 1602 includes means for receiving a handover request to handover a UE from a source network node, means for obtaining at least one mobility prediction associated with the UE or the target network node, the at least one mobility related prediction being derived by at least one neural network, and the means for transmitting a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.” (par. 199, lines 102-120)].
Consider claim 2; Zhu discloses the sliding boundary indicates, for each position of the client STA between the first AP and the second AP (par. 199, lines 1-8, 36-43, 102-106), one or more respective predicted radio frequency (RF) parameters of the client STA at the second AP (par. 199, lines 1-8, 36-43, 102-106).
Consider claim 3; Zhu discloses the one or more predicted RF parameters comprise a predicted signal strength [e.g. handover (par. 199, lines 1-8, 36-43)], a predicted modulation and coding scheme (MCS), or a combination thereof.
Consider claim 4; Zhu discloses each position of the client STA between the first AP and the second AP is based on a respective signal strength of the client STA at the first AP (par. 199, lines 1-8).
Consider claim 5; Zhu discloses the sliding boundary indicates, for each position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8), (i) a predicted likelihood of successful association of the client STA with the second AP and (ii) a confidence level associated with the predicted likelihood of successful association of the client STA with the second AP (e.g. base station) (par. 199, lines 1-8, 36-43, 102-120).
Consider claim 6; Zhu discloses determining that the sliding boundary (par. 199, lines 1-8, 36-43, 102-120) satisfies a predetermined condition (par. 75, lines 4-8), wherein the information associated with the sliding boundary is transmitted (par. 199, lines 106-110) after determining that the sliding boundary (par. 199, lines 1-8, 36-43, 102-120) satisfies a predetermined condition (par. 75, lines 4-8).
Consider claim 7; Zhu discloses for a position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8), a radio frequency (RF) parameter of the client STA at the second AP that is predicted by the sliding boundary (par. 199, lines 1-8, 36-43, 102-120) being greater than a threshold (par. 75, lines 30-35).
Consider claim 8; Zhu discloses the position of the client STA is predicted position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8).
Consider claim 9; Zhu discloses the predetermined condition comprises, for a position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8), (i) a likelihood of successful association of the client STA with the second AP that is predicted by the sliding boundary (par. 199, lines 1-8, 36-43, 102-120) being greater than a first threshold (par. 75, lines 30-35) and (ii) a confidence level associated with the likelihood of successful association of the client STA that is predicted by the sliding boundary (par. 199, lines 1-8, 36-43, 102-120) being greater than a second threshold (par. 75, lines 30-35).
Consider claim 10; Zhu discloses the position of the client STA is predicted position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8).
Consider claim 11; Zhu discloses the information associated with the sliding boundary comprises at least one of (i) a predicted radio frequency (RF) parameter of the client STA at the second AP corresponding to a position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8, 36-43), (ii) a predicted likelihood of successful association of the client STA at the second AP corresponding to the position of the client STA between the first AP and the second AP, (iii) a confidence level associated with the predicted likelihood of successful association of the client STA at the second AP, or (iv) an indication that the client STA has a status of a sticky client at the position of the client STA between the first AP and the second AP.
Consider claim 12; Zhu discloses detecting a coverage gap between the first AP and the second AP based on the sliding boundary [e.g. based on the trajectory prediction (par. 199, lines 1-8, 36-43, 102-120)]; and transmitting an indication of the coverage gap to a computing system [e.g. via the handover message (par. 199, lines 1-8, 36-43)].
Consider claim 13; Zhu discloses a computer-implemented method comprising:
determining, by a first access point (AP), that a sliding boundary [“…the network entity 1602 further includes means for receiving an RRM measurement report including at least one flag indicating that the RRM measurement is based on the prediction of the mobility or a confidence level of the prediction. In one configuration, the network entity 1602 further includes means for transmitting at least one parameter for a machine learning model for the UE to decide RRM prediction and the at least one parameter of the RRM measurement. In another configuration, the network entity 1602 may be a target network entity, and the network entity 1602 includes means for receiving a handover request to handover a UE from a source network node, means for obtaining at least one mobility prediction associated with the UE or the target network node, the at least one mobility related prediction being derived by at least one neural network, and the means for transmitting a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.” (par. 199, lines 102-120)] associated with a client station (STA), the first AP, and a second AP in a wireless network satisfies a predetermined condition [“…handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction.” (par. 199, lines 1-8)]; and
responsive to the determination [“…handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction.” (par. 199, lines 1-8)], transmitting, by the first AP, a frame to the client STA comprising (i) a request for the client STA to roam to the second AP (e.g. base station) and (ii) information associated with the sliding boundary [“…the network entity 1602 further includes means for receiving an RRM measurement report including at least one flag indicating that the RRM measurement is based on the prediction of the mobility or a confidence level of the prediction. In one configuration, the network entity 1602 further includes means for transmitting at least one parameter for a machine learning model for the UE to decide RRM prediction and the at least one parameter of the RRM measurement. In another configuration, the network entity 1602 may be a target network entity, and the network entity 1602 includes means for receiving a handover request to handover a UE from a source network node, means for obtaining at least one mobility prediction associated with the UE or the target network node, the at least one mobility related prediction being derived by at least one neural network, and the means for transmitting a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.” (par. 199, lines 102-120)].
Consider claim 14; Zhu discloses the sliding boundary indicates, for each position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8, 36-43), one or more respective predicted radio frequency (RF) parameters of the client STA at the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8, 36-43, 102-120); and the predetermined condition comprises, for a first position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8), at least one of the one or more predicted RF parameters of the client STA at the second AP being greater than a threshold for the at least one of the one or more predicted RF parameters (par. 75, lines 30-35; par. 199, lines 1-8, 36-43).
Consider claim 15; Zhu discloses the information associated with the sliding boundary comprises an indication of the first position and the at least one of the one or more predicted RF parameters of the client STA at the first position (par. 199, lines 1-8, 36-43, 102-106).
Consider claim 16; Zhu discloses the sliding boundary indicates, for each position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8, 36-43), a respective predicted likelihood of successful association of the client STA with the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8, 36-43); and the predetermined condition comprises, for a first position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8), the predicted likelihood of successful association of the client STA with the second AP being greater than a threshold (par. 75, lines 30-35; par. 199, lines 1-8, 36-43).
Consider claim 17; Zhu discloses the information associated with the sliding boundary comprises an indication of the first position and the predicted likelihood of successful association of the client STA with the second AP at the first position [e.g. UE location (par. 199, lines 1-8, 36-43)].
Consider claim 18; Zhu discloses the sliding boundary indicates, for each position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8, 36-43), an indication of whether the client STA has a status of a sticky client [e.g. handover is canceled (par. 75, lines 1-4)]; and the predetermined condition comprises, for a first position of the client STA between the first AP and the second AP (e.g. base station) (par. 82, lines 1-6; par. 199, lines 1-8), the client STA having the status of the sticky client [e.g. handover is canceled (par. 75, lines 1-4)].
Consider claim 19; Zhu discloses the information associated with the sliding boundary comprises an indication of the first position and an indication that the client STA has the status of the sticky client (e.g. handover is canceled) at the first position (e.g. UE location) (par. 75, lines 1-4; par. 199, lines 1-8, 36-43).
Consider claim 20; Zhu discloses a first network device comprising:
one or more memories collectively storing instructions [“The on-chip memory 1612, 1632, 1642 and the additional memory modules 1614, 1634, 1644 may each be considered a computer-readable medium/memory.” (par. 198, lines 28-30) “The computer-readable medium/memory may also be used for storing data that is manipulated by the processor(s) when executing software.” (par. 19, lines 37-39)]; and
one or more processors communicatively coupled to the one or more memories [“Each of the processors 1612, 1632, 1642 is responsible for general processing, including the execution of software stored on the computer-readable medium/memory. The software, when executed by the corresponding processor(s) causes the processor(s) to perform the various functions described supra.” (par. 198, lines 32-37)], the one or more processors being collectively configured to execute the instructions to cause the first network device to perform an operation [“Each of the processors 1612, 1632, 1642 is responsible for general processing, including the execution of software stored on the computer-readable medium/memory. The software, when executed by the corresponding processor(s) causes the processor(s) to perform the various functions described supra.” (par. 198, lines 32-37)]. Claim 20 reflects the method comprising computer executable instructions for implementing the article of manufacture as claimed in claim 13, and is rejected along the same rationale.
Sun is another reference that discloses the inventive concept.
Conclusion
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/JOEL AJAYI/
Primary Examiner, Art Unit 2646
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