DETAILED ACTION
Response to Amendment
This Action is in response to the amendment dated 1/22/2026, for which the amendment and corresponding arguments filed on the same date have been entered. Claims 32-51 are currently pending in this application, with claims 32, 50 and 51 being independent. No claims have been cancelled, amended or added. This Action is made Non-FINAL.
Response to Arguments
Applicant’s arguments, with respect to the rejection of the claims filed 32-51, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of the new references used in the below.
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 32, 33, 50 and 51 are rejected under 35 U.S.C. 102(a2) as being anticipated by Lovelekar, et al (US PG Publication 2021/0360495), hereafter Lovelekar.
Regarding claim 32, Lovelekar teaches
a method for use in a conditional handover for a user equipment in a wireless communication network, wherein the method comprises:
predicting, based on a known first location of the user equipment in a first cell of the wireless communication network at a first point in time, a second location of the user equipment at a second point in time by applying a movement prediction to the user equipment relative to the known first location at the first point in time to obtain a probability for the second location to be located in at least one of the first cell and any one of one or more second cells in the wireless communication network, wherein the one or more second cells are different from the first cell
([0103] If a wireless device may be travelling along a known route such as in the illustrated scenario of FIG. 7, the network may be pre-aware of possible handover target cells along with the usual handover points (where devices usually handoff from one serving cell to the next target cell) along the known/fixed route, and so may configure early conditional handover for one or more cells along the predicted route(s)
[0104] Machine learning (e.g., using minimization of drive test (MDT) or self-organizing network (SON) reports from devices, and/or any of various other possible data sources) may be used to create probabilistic models to determine potential routes of devices from certain locations, possibly associated with certain times of day and/or other considerations); and
determining, based on the obtained probability, for which one or more of the one or more second cells the conditional handover is to be configured
([0103] the network may be pre-aware of possible handover target cells along with the usual handover points (where devices usually handoff from one serving cell to the next target cell) along the known/fixed route, and so may configure early conditional handover for one or more cells along the predicted route(s)
[0104] Machine learning (e.g., using minimization of drive test (MDT) or self-organizing network (SON) reports from devices, and/or any of various other possible data sources) may be used to create probabilistic models to determine potential routes of devices from certain locations, possibly associated with certain times of day and/or other considerations).
Regarding claim 33, Lovelekar teaches the method of claim 32,
wherein said applying of the movement prediction to the user equipment to obtain the probability is based at least in part on one or both of:
a speed of movement of the user equipment
([0080] Typical characteristics of such travel along a known route (e.g., typical distance travelled on the train, typical train speeds, etc.) may further be considered, e.g., as determined based on previously gathered data, route analysis, and/or any of various other techniques); and
a direction of movement of the user equipment.
Regarding claim 50, Lovelekar teaches an apparatus adapted to operate in a wireless communication network for at least partially controlling a conditional handover for a user equipment, wherein the apparatus is configured to:
predict, based on a known first location of the user equipment in a first cell of the wireless communication network at a first point in time, a second location of the user equipment at a second point in time by applying a movement prediction to the user equipment relative to the known first location at the first point in time to obtain a probability for the second location to be located in at least one of the first cell and any one of one or more second cells in the wireless communication network, wherein the one or more second cells are different from the first cell
([0103] If a wireless device may be travelling along a known route such as in the illustrated scenario of FIG. 7, the network may be pre-aware of possible handover target cells along with the usual handover points (where devices usually handoff from one serving cell to the next target cell) along the known/fixed route, and so may configure early conditional handover for one or more cells along the predicted route(s)
[0104] Machine learning (e.g., using minimization of drive test (MDT) or self-organizing network (SON) reports from devices, and/or any of various other possible data sources) may be used to create probabilistic models to determine potential routes of devices from certain locations, possibly associated with certain times of day and/or other considerations); and
determine, based on the obtained probability, for which one or more of the one or more second cells the conditional handover is to be configured
([0103] the network may be pre-aware of possible handover target cells along with the usual handover points (where devices usually handoff from one serving cell to the next target cell) along the known/fixed route, and so may configure early conditional handover for one or more cells along the predicted route(s)
[0104] Machine learning (e.g., using minimization of drive test (MDT) or self-organizing network (SON) reports from devices, and/or any of various other possible data sources) may be used to create probabilistic models to determine potential routes of devices from certain locations, possibly associated with certain times of day and/or other considerations).
Regarding claim 51, Lovelekar teaches
a non-transitory computer readable medium having a computer program product stored thereon, the computer program product comprising program code portions that, when executed on processing circuitry of an apparatus configured to at least partially control a conditional handover for a user equipment
([0044] The memory medium may comprise other types of non-transitory memory as well or combinations thereof. In addition, the memory medium may be located in a first computer system in which the programs are executed),
configures the processing circuitry to:
predict, based on a known first location of the user equipment in a first cell of the wireless communication network at a first point in time, a second location of the user equipment at a second point in time by applying a movement prediction to the user equipment relative to the known first location at the first point in time to obtain a probability for the second location to be located in at least one of the first cell and any one of one or more second cells in the wireless communication network, wherein the one or more second cells are different from the first cell
([0103] If a wireless device may be travelling along a known route such as in the illustrated scenario of FIG. 7, the network may be pre-aware of possible handover target cells along with the usual handover points (where devices usually handoff from one serving cell to the next target cell) along the known/fixed route, and so may configure early conditional handover for one or more cells along the predicted route(s)
[0104] Machine learning (e.g., using minimization of drive test (MDT) or self-organizing network (SON) reports from devices, and/or any of various other possible data sources) may be used to create probabilistic models to determine potential routes of devices from certain locations, possibly associated with certain times of day and/or other considerations); and
determine, based on the obtained probability, for which one or more of the one or more second cells the conditional handover is to be configured
([0103] the network may be pre-aware of possible handover target cells along with the usual handover points (where devices usually handoff from one serving cell to the next target cell) along the known/fixed route, and so may configure early conditional handover for one or more cells along the predicted route(s)
[0104] Machine learning (e.g., using minimization of drive test (MDT) or self-organizing network (SON) reports from devices, and/or any of various other possible data sources) may be used to create probabilistic models to determine potential routes of devices from certain locations, possibly associated with certain times of day and/or other considerations).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 34, 35 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Lovelekar, in view of Chang, et al (US PG Publication 2022/0322173), hereafter Chang.
Regarding claim 34, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
further comprising determining whether there is a difference in service quality between the conditional handover and a non-conditional handover, wherein the conditional handover comprises configuring a plurality of the second cells for the conditional handover and wherein the non-conditional handover comprises configuring a single one of the second cells for the non-conditional handover.
In the same field of endeavor, Chang teaches
([0036] When an RLF occurs, if the conditional handover candidate cell stored by the UE has good signal quality, then a handover to this cell can be performed to recover connection to a network side instead of initiating an RRC connection re-establishment procedure; otherwise, the UE selects other non-conditional handover candidate cell to initiate an RRC connection re-establishment procedure to recover the connection to the network side
(When good signal quality, conditional handover performed, and otherwise (not good signal quality, so difference), UE selects other non-conditional handover candidate cell)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes conditional handover, to include Chang’s teaching of conditional handover, for the benefit of providing solutions to some problems encountered in implementation of the CHO (see [0003]).
Regarding claim 35, Lovelekar, in view of Chang, teaches the method of claim 34.
Chang further teaches
wherein:
if there is a difference in service quality, a predefined number of the second cells is configured for the conditional handover, wherein the predefined number of the second cells is less than a total number of cells neighboring the first cell
([0026] The UE performs, on the basis of the measurement configuration, measurement on a radio link corresponding to a neighboring cell; when a configured measurement reporting condition is met, the UE transmits a measurement report to the base station
[0036] When an RLF occurs, if the conditional handover candidate cell stored by the UE has good signal quality, then a handover to this cell can be performed to recover connection to a network side instead of initiating an RRC connection re-establishment procedure; otherwise, the UE selects other non-conditional handover candidate cell to initiate an RRC connection re-establishment procedure to recover the connection to the network side. The conditional handover candidate cell refers to a (target) cell in the conditional handover configuration in the RRC message for configuring the conditional handover received by the UE. The network side may configure one or more handover execution condition candidate cells for the UE at the same time
(When good signal quality, conditional handover performed to configured one or more (number of) candidate cells of target/neighbor cells)); and
if there is no difference in service quality, only the single one of the second cells is configured for the non-conditional handover
([0036] When an RLF occurs, if the conditional handover candidate cell stored by the UE has good signal quality, then a handover to this cell can be performed to recover connection to a network side instead of initiating an RRC connection re-establishment procedure; otherwise, the UE selects other non-conditional handover candidate cell to initiate an RRC connection re-establishment procedure to recover the connection to the network side
(When good signal quality, conditional handover performed, and otherwise (not good signal quality, so difference), UE selects other non-conditional handover candidate cell (other/single cell))).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, in view of Chang, which includes conditional handover, to include Chang’s teaching of conditional handover, for the benefit of providing solutions to some problems encountered in implementation of the CHO (see [0003]).
Regarding claim 38, Lovelekar, in view of Chang, teaches the method of claim 34.
Lovelekar further teaches
wherein the conditional handover is configured for one or more of the second cells only when a service used by the user equipment at the first point in time is predefined to be a handover-sensitive service comprising one or more of ultra-low latency traffic
([0190] The cell transition predictions 903 may also be based on other parameters besides travel speed and direction. For example, UEs 111, 121 could be handed over to cells/NANs 131-133 experiencing better radio conditions than other cells/NANs 131-133, or cells/NANs 131-133 having less load characteristics than other cells/NANs 131-133. The edges between the nodes may represent a probability of handover between the nodes representing those cells
(Determining parameters associated with conditions (load (traffic) characteristics) to be used for handover of UE to the other cells)),
real-time video, and
voice service.
Claims 36, 37 and 47-49 are rejected under 35 U.S.C. 103 as being unpatentable over Lovelekar, in view of Yang, et al (US PG Publication 2022/0264390), hereafter Yang.
Regarding claim 36, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
wherein the conditional handover is configured for two of the second cells with two highest probabilities for the second location to be located in the respective one of the two cells amongst the second cells.
In the same field of endeavor, Yang teaches
wherein the conditional handover is configured for two of the second cells with two highest probabilities for the second location to be located in the respective one of the two cells amongst the second cells
([0074] The source base station may also determine the network quality after the UE is switched to each CHO target cell according to various pieces of measurement information fed back by the UE, and thereby determining the expected probability factor associated with each CHO target cell. After the CHO resource change request indication information is received, the CHO target base station determines whether to change the resource of each CHO target cell according to the expected probability factor associated with each CHO target cell. When the expected probability factor is 100%, the expectation is the highest
(Determination made to change/configure resources for each of cells with highest expected probability)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes conditional handover, to include Yang’s teaching of conditional handover, for the benefit of improving the resource utilization rate of the system, reducing the processing burden of a UE and improving the use experience of a user (see [0007]).
Regarding claim 37, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
wherein the conditional handover is configured only for those of the second cells for which the probability is above a predefined threshold probability.
In the same field of endeavor, Yang teaches
wherein the conditional handover is configured only for those of the second cells for which the probability is above a predefined threshold probability
([0074] The source base station may also determine the network quality after the UE is switched to each CHO target cell according to various pieces of measurement information fed back by the UE, and thereby determining the expected probability factor associated with each CHO target cell. After the CHO resource change request indication information is received, the CHO target base station determines whether to change the resource of each CHO target cell according to the expected probability factor associated with each CHO target cell. If the expected probability factor is lower than a certain threshold value, the resources of CHO target cells with the expected probability factor lower than a certain threshold value may be released. When the expected probability factor is 100%, the expectation is the highest
(Determination made to change/configure and not release resources for each of cells with highest expected probability above threshold)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes conditional handover, to include Yang’s teaching of conditional handover, for the benefit of improving the resource utilization rate of the system, reducing the processing burden of a UE and improving the use experience of a user (see [0007]).
Regarding claim 47, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
wherein a number of cells for which the probability is predicted is chosen so that a summed probability for the second location to be located in any one of the number of cells is higher than a predefined probability threshold of 90%.
In the same field of endeavor, Yang teaches
wherein a number of cells for which the probability is predicted is chosen so that a summed probability for the second location to be located in any one of the number of cells is higher than a predefined probability threshold of 90%
([0074] The expected probability factor associated with each CHO target cell is determined according to the predicted probability of the UE switching to each CHO target cell. When the expected probability factor is 100%, the expectation is the highest, and when the expected probability factor is 1%, the expectation is the lowest
(Each of cells for which the probability is predicted is determined so that a probability for CHO handover to other/second cell location is 100% (higher than 90%)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes conditional handover, to include Yang’s teaching of conditional handover, for the benefit of improving the resource utilization rate of the system, reducing the processing burden of a UE and improving the use experience of a user (see [0007]).
Regarding claim 48, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
further comprising configuring the conditional handover for those one or more cells of the one or more second cells which neighbor the first cell.
In the same field of endeavor, Yang teaches
further comprising configuring the conditional handover for those one or more cells of the one or more second cells which neighbor the first cell
([0058] Conditional handover according to an embodiment. As shown in FIG. 3, a certain source base station has local cells including Cell1/2/3/4/5/6/7, where the Cell1 is a current main service cell of a certain terminal UE1. With the undirected movement of the UE1, neighbor cells including Cell2/3/4/5/6/7 may become potential/candidate target cells of the UE1. Therefore, in order to enhance mobile robustness and improve user experience, the source base station can pre-configure the Cell2/3/4/5/6/7 cells as the potential/candidate target base station cells of the UE through the CHO mechanism
(CHO conditional handover configured for neighbor cells)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes conditional handover, to include Yang’s teaching of conditional handover, for the benefit of improving the resource utilization rate of the system, reducing the processing burden of a UE and improving the use experience of a user (see [0007]).
Regarding claim 49, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
wherein the determining for which of the one or more second cells the conditional handover is to be configured is further based on one or more of:
a neighbor cell relation between the first cell and a corresponding, respective one of the one or more second cells;
a handover success rate and/or handover failure rate for the neighbor cell relation;
a service quality degradation during handover for the neighbor cell relation and/or for one or more service types;
a service quality per cell and/or per the neighbor cell relation and/or per service; and
a radio condition per cell and/or per the neighbor cell relation.
In the same field of endeavor, Yang teaches
wherein the determining for which of the one or more second cells the conditional handover is to be configured is further based on one or more of:
a neighbor cell relation between the first cell and a corresponding, respective one of the one or more second cells
([0058] Conditional handover according to an embodiment. As shown in FIG. 3, a certain source base station has local cells including Cell1/2/3/4/5/6/7, where the Cell1 is a current main service cell of a certain terminal UE1. With the undirected movement of the UE1, neighbor cells including Cell2/3/4/5/6/7 may become potential/candidate target cells of the UE1. Therefore, in order to enhance mobile robustness and improve user experience, the source base station can pre-configure the Cell2/3/4/5/6/7 cells as the potential/candidate target base station cells of the UE through the CHO mechanism
(CHO conditional handover configured for neighbor cells, where Cell2/3/4/5/6/7 are neighbor cells in relation to Cell 1));
a handover success rate and/or handover failure rate for the neighbor cell relation;
a service quality degradation during handover for the neighbor cell relation and/or for one or more service types;
a service quality per cell and/or per the neighbor cell relation and/or per service; and
a radio condition per cell and/or per the neighbor cell relation.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes conditional handover, to include Yang’s teaching of conditional handover, for the benefit of improving the resource utilization rate of the system, reducing the processing burden of a UE and improving the use experience of a user (see [0007]).
Claim 39 is rejected under 35 U.S.C. 103 as being unpatentable over Lovelekar, in view of Chang, and further in view of Futaki, et al (US PG Publication 2025/0024342), hereafter Chang.
Regarding claim 39, Lovelekar, in view of Chang, teaches the method of claim 34.
Lovelekar, in view of Chang, does not teach
wherein the non-conditional handover is configured when a service used by the user equipment at the first point in time is predefined to be a handover-non-sensitive service comprising one or both of
a data file download and
web browsing.
In the same field of endeavor, Futaki teaches
wherein the non-conditional handover is configured when a service used by the user equipment at the first point in time is predefined to be a handover-non-sensitive service comprising one or both of
a data file download and
web browsing
([0101] The source DU 2A may send a DDDS frame as used in a normal (i.e., non-conditional) handover
[0169] UE 3 Fig. 19
Fig. 19, UE includes application processor 1904
[0174] The application processor 1904 loads a system software program (Operating System (OS)) and various application programs (e.g., a call application, a WEB browser
(Non-conditional handover configured, where UE application processor uses web browsing)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, in view of Chang, which includes conditional handover, to include Futaki’s teaching of non-conditional handover, for the benefit of contributing to allowing a device to be aware of satisfaction of an execution condition of a conditional mobility (or initiation of the conditional mobility) (see [0016]).
Claims 40-43 are rejected under 35 U.S.C. 103 as being unpatentable over Lovelekar, in view of Bullock (US PG Publication 2021/0168217).
Regarding claim 40, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
wherein the applying of the movement prediction comprises applying a movement prediction model to the user equipment, and wherein the method further comprises:
training the movement prediction model based on a user identity relative to the user equipment, a first timestamp (T1), a first position of the user equipment at the first timestamp T1, a second timestamp (T2), and a second position of the user equipment at the second timestamp T2; and
wherein said training of the movement prediction model is further based on information relative to a service used by the user equipment at T1.
In the same field of endeavor, Bullock teaches
wherein the applying of the movement prediction comprises applying a movement prediction model to the user equipment, and wherein the method further comprises:
training the movement prediction model based on a user identity relative to the user equipment, a first timestamp (T1), a first position of the user equipment at the first timestamp T1, a second timestamp (T2), and a second position of the user equipment at the second timestamp T2
([0005] The predicted locations and associated weights may be determined at least in part by a machine-learning-based prediction model, which may be trained on data such as the location features, time stamps, and the known true locations of particular client systems
[0041] Each location prediction 156 may include a time stamp 157, a predicted location 158 of the network address 154, and a weight 159. Each predicted location 158 may be copied from or otherwise based on a known location 124 received in known location data 120. The time stamp 157 may represent a time at which a known location 124 containing the predicted location 158 was received, an age of the predicted location 158, or other time-based value related to the age of the predicted location 158. A predicted location 158 may be understood as an estimate or approximation of a device's current location based on available information, e.g., the prediction model's determination of where the device associated with the network address is physically located at the time the prediction is made. A user identifier (“ID”) 186 may optionally be included in each prediction table entry 153 to identify the user associated with the network address 154. The user ID 186 may be based on the user ID 126 included in the known location data 120); and
wherein said training of the movement prediction model is further based on information relative to a service used by the user equipment at T1
([0035] The time stamp 157 may represent a time at which the corresponding predicted location 158 was determined based on, for example, previous known locations 124. The predicted locations 158 and associated weights 159 may be determined at least in part by a machine-learning-based prediction model 151, which may be trained on data such as the location features 171, time stamps 157, and the known true locations 135b or particular client systems 130b. The true location 135 known at each client system 130 may be latitude and longitude coordinates determined by the client system 130 using, e.g., a GPS
(Trained, based on client using GPS service)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes prediction used for configuring a conditional handover, to include Bullock’s teaching of prediction using a location prediction model, for the benefit of determining geographic locations of devices or users based on network addresses of computing devices (see [0002]).
Regarding claim 41, Lovelekar, in view of Bullock, teaches method of claim 40.
Bullock further teaches
further comprising determining an accuracy of the movement prediction model by comparing a predicted position of the user equipment at the second timestamp T2 with the second position, wherein the predicted position of the user equipment at the second timestamp T2 is based on applying the movement prediction model to the user equipment being at the first position at T1
(FIG. 2D prediction table 203
[0044] The predicted locations 158 may be sorted by weight to produce a list of the predicted locations 158 in order of likelihood of being the true location of the network address 154. As an example, if the model 151 generates more than one predicted location 158, the associated weights 159 may be used to determine a ranking of the predicted locations 158 in order of confidence
[0105] The weight 1.0 may be determined by the learning algorithm 180. Confidence in the accuracy of the predicted country Scotland may be high when the data specifying the location has recently been received. In row 281B, the date associated with Iteration 1b, 1/6/2018 (in the Iteration column), indicates that the row was generated on 1/6/2018, which is one day after the row's time stamp of 1/5/2018.
[0106] The example City Level columns in row 281B include Predicted Cities Edinburgh=1.0, indicating 100% confidence that address 128.1.2.2 is located in Edinburgh as of the time and date row 281B is generated (1/6/2018)
(Location accuracy for Scotland location with time stamp of 1/5/2018 is determined by the learning algorithm model, based on a weight and an associated confidence level that is higher when compared to other locations in FIG. 2D prediction table 203)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, in view of Bullock, which includes prediction used for configuring a conditional handover, to include Bullock’s teaching of prediction using a location prediction model, for the benefit of determining geographic locations of devices or users based on network addresses of computing devices (see [0002]).
Regarding claim 42, Lovelekar, in view of Bullock, teaches method of claim 40.
Bullock further teaches
wherein the second location is predicted after:
the training has been performed for at least a predefined time period
([0042] The location prediction system may receive known locations at any time, though the prediction model 151 may be updated at periodic intervals based on known location data 120 received during each time interval); and/or
an amount of input feature samples for the training is above a predefined threshold amount.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, in view of Bullock, which includes prediction used for configuring a conditional handover, to include Bullock’s teaching of prediction using a location prediction model, for the benefit of determining geographic locations of devices or users based on network addresses of computing devices (see [0002]).
Regarding claim 43, Lovelekar, in view of Bullock, teaches Lovelekar teaches the method of claim 41.
Bullock further teaches
wherein the second location is predicted after the accuracy is above a predefined threshold accuracy
([0048] The learning algorithm 180 may refine the location prediction(s) 156 associated with each network address 154 by, for example, changing the values of the weights 159 to more accurately rank the location predictions 156. The weights 159 may be based at least in part on a weighting factor 177. The weighting factor 177 may be a value between 0 and 1
[0049] As an example, each weight may be decreased by exponential decay using a daily reduction factor such as 0.5, 0.75, 0.8, or the like. Location predictions 156 may be deleted when their age (based on, e.g., their included time stamp 157) exceeds a threshold, e.g., 20 days, 28 days, 30 days, 2 months, or the like
(Location accuracy above threshold, based on days)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, in view of Bullock, which includes prediction used for configuring a conditional handover, to include Bullock’s teaching of Bullock’s teaching of prediction using a location prediction model, for the benefit of determining geographic locations of devices or users based on network addresses of computing devices (see [0002]).
Claim 45 is rejected under 35 U.S.C. 103 as being unpatentable over Lovelekar, in view of Kim, et al (US PG Publication 2022/0070752), hereafter Kim.
Regarding claim 45, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
wherein a frequency for the determining for which of the one or more second cells the conditional handover is to be configured is dependent on a moving speed of the user equipment.
In the same field of endeavor, Kim teaches
wherein a frequency for the determining for which of the one or more second cells the conditional handover is to be configured is dependent on a moving speed of the user equipment
([0148] The UE's velocity (<=500 km/h), may be relatively much less than that of LEO satellites e.g. 7-10 km/s. This may imply that a UE in the coverage of a LEO satellite cell/spot beam will encounter more frequent handovers and it could mean that a handover takes place every few seconds. That is, a UE in the coverage of a LEO satellite cell/spot beam will encounter more frequent handovers than in a terrestrial cell).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes conditional handover, to include Kim’s teaching of conditional handover based on UE velocity, for the benefit of performing an actual handover after a handover condition is met, and, as a result, significantly reducing the extra handover signaling overhead associated with frequent cell change and potentially frequent measurement reporting (see [0150]).
Claim 46 is rejected under 35 U.S.C. 103 as being unpatentable over Lovelekar, in view of Shrestha, et al (US PG Publication 2021/0368407), hereafter Shrestha.
The equivalent citations from US Provisional Application# 63/027,225, to which US PG Publication 2021/0368407 has priority, are shown below.
Citation Used Equivalent Citation from US Provisional Application
[0140] [0052]
[0141] [0053]
Regarding claim 46, Lovelekar teaches the method of claim 32.
Lovelekar does not teach
wherein the second location is predicted continuously, and wherein the determining for which of the one or more second cells the conditional handover is to be configured is performed at one or more predefined times in a time window starting with the first point in time and ending with the second point in time.
In the same field of endeavor, Shrestha teaches
wherein the second location is predicted continuously, and wherein the determining for which of the one or more second cells the conditional handover is to be configured is performed at one or more predefined times in a time window starting with the first point in time and ending with the second point in time
([0140] After performing the handover to the target satellite 160-c, it may be appropriate for the UE 115-a to perform another handover to the future target satellite 160-b at a later time. For instance, it may be appropriate for the UE 115-a to be handed over to a new cell every 13.2 seconds
[0141] Thus, the NTN may know the exact time (e.g., t1 in the future) for the UE 115-a to be handed over to a next cell (e.g., on the predictable path of the UE 115-a)
(Path predicted for handover to new cell every 13.2 seconds continuously, where t1 first point in time and every 13.2 seconds thereafter includes at least a second point in time)).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Lovelekar, which includes conditional handover, to include Shrestha’s teaching of conditional handover, for the benefit of limiting overhead and latency in an non-terrestrial network (NTN) and allow the UE to perform handovers more efficiently (see [0141]).
Allowable Subject Matter
Claim 44 is 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.
Conclusion
Citation of Pertinent Prior Art not Applied
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hong (US PG Publication 2024/0205783) teaches a base station configures conditional handover for one or more candidate target cells in the UE, an expected conditional handover execution order is expressed as (expected/predicted/estimated) candidate target cell order.
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/FRANK E DONADO/Examiner, Art Unit 2641
/CHARLES N APPIAH/Supervisory Patent Examiner, Art Unit 2641
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