Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 7, 2026 has been entered.
Claims 1, 3-4, 9, 11-12, 17, and 19-20 have been amended. Claim 2, 10, and 18 have been cancelled. Claims 1, 3-9, 11-17, and 19-20 are subject to examination and have been examined.
Response to Arguments
Applicant's arguments, with respect to the amended claims, filed November 25, 2025 have been fully considered but they are not persuasive for the following reasons:
Applicant's Argument:
Regarding Claims 1, 9 and 17 (which now includes the features from claim 2), the Applicant argues in substance that (pages 10-11): "Furthermore, on page 16 of the Office Action, the Examiner contends, "based on the accuracy of the predicted time of arrival, and based on the known route as recited by Zou, a person skilled in the art at the time of invention can easily discern the remaining time until arrival at a destination."
However, even if one skilled in the art could "easily discern the remaining time until arrival at a destination," which Applicant does not concede, Zou still fails to disclose "determining the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region."''
Examiner's Response:
The Examiner respectfully disagrees.
Regarding the amended limitations in Claim 1 (similar limitations are in independent claims 9 and 17), Zou, in view of Zielinski and Altman, teaches the claimed limitations as written. Zou further teaches, in ¶ [0075] and ¶ [0134], making adjustment to the prediction (which is equated to the claimed "determining the prediction mechanism adopted for predicting") to predict the quality of service (beam coverage level) at any point on the known route; this is a part of the closed loop prediction adjustment, where the UE makes measurements at a predicted location.
¶ [0075]: "Embodiments of the present disclosure provide closed loop prediction adjustment with UE feedback for accurate prediction of an inactive UE's location. In an aspect, the UE based on its measurement of the beam determines whether current cell/beam is as the network predicted. If not, the UE sends a location update message or signal to the network. The network will send update mobility prediction information to the UE."
And ¶ [0134]: "Optionally, in any of the preceding aspects, the mobility prediction information includes actions scheduled to take at each of the cells, and/or TRPs, and/or beams on the predicted route and at the predicted time duration. All above cells/TRPs/beams entering time and the time duration of stay included in the mobility prediction information message and delivered to the UE allows the UE to compare the predicted time and actual time at the current cell/TRP/beam coverage. The metrics for the offset can be other than the time difference (e.g. number of the beams from the predicted beam coverage, distance from the predicted location at this moment). If the difference is larger than a threshold, the UE report to the network and the network adjust the prediction. The UE notification to the network can be pre-defined signal pattern or uplink message. This forms a closed tracking loop to ensure the accuracy of the prediction." By the above rationale, Zou, in view of Zielinski and Altman teaches the amended limitations. See updated rejection below.
Regarding all other arguments presented by Applicant, the arguments are substantially the same as those that have already been addressed above; and in the interest of brevity, the Examiner directs Applicant to those responses above.
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.
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.
Claims 1, 3-5, 7-9, 11-13, 15-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zielinski et.al. (US Patent Application Publication, 20200107212, hereinafter, “Zielinski”) in view of Altman (US Patent Application Publication, 20210114616, hereinafter, “Altman”), further in view of Patent Application Publication, 20210092584, hereinafter, “Zou”).
Regarding claim 1, Zielinski teaches:
A communication method for automatic driving, performed by a computer device, comprising (Zielinski: [0073] ... The backend server 320 to which the vehicles 10 send messages to and receive messages from is also connected to the Internet 300. In the field of cooperative and autonomous driving the backend server 320 typically is located in a traffic control center ... Fig. 1):
acquiring driving information of a vehicle (Zielinski: [0085] Both blocks, traffic flow prediction block 1640 and surroundings modelling block 1650 receive information about the planned route of a vehicle and the corresponding time or a time period in which the route will be started ... Figs. 1, 3);
determining a network cell of a path which the vehicle needs to pass according to the driving information (Zielinski: [0086] Reference number 1630 denotes a cells locating block. In this block the mobile communication cells along the path following the planned route of the requesting vehicle will be determined. The determined cell identifiers will be forwarded to the prediction function block 1610 on line 1632. Also this information in one embodiment could be taken from a corresponding detailed map ... Figs. 1, 3);
determining, among a plurality of prediction mechanisms, a prediction mechanism adopted for predicting quality of service (QoS) of the network cell (Zielinski: [0083] Reference number 1610 denotes the QoS prediction function block. It is the task of the QoS prediction function block 1610 to forecast the QoS parameters for a planned V2V or V2X or V2N communication and inform the requesting vehicle accordingly … [0084] Reference number 1620 denotes a channel modelling block which performs a function of calculating a predicted channel model. The predicted channel model is an important input to the QoS prediction function block 1610. The predicted channel model could be generated in the form of a channel model profile, i.e. a plurality of channel models is calculated for different times and places ... Figs. 1, 3); and
acquiring a QoS prediction result of the network cell according to the prediction mechanism (Zielinski: [0086] ... Moreover, this cells location block further includes a scheduling function block which typically comprises the same type of scheduling algorithms which are also applied in the scheduler of an eNodeB 210. The result of the scheduling operation will be forwarded to the prediction function block 1610 on line 1631. Also the assigned resources, i.e. the resource blocks which the scheduling algorithm in the cells locating block 1630 assigned for the requested communication type will be forwarded to the prediction function block 1610. This type of information will be transported over line 1633. Via line 1634 the information from the requesting vehicle about the communication type which is planned, the QoS requirements for the respective planned communication type, the requested prediction horizon and the requested prediction reliability is forwarded to the prediction function block 1610 … [0087] In summary, the prediction function block 1610 may perform the prediction function on different levels, one may be the link level where it will be determined which communication link provides which quality of service level. Second, the prediction function block 1610 may perform the prediction function on the system level, where resource blocks are pre-allocated and where it will be determined what the estimated QoS parameter values such as block error rate/packet error rate, end to end latency, throughput, etc. are... Figs. 1, 3).
Although Zielinski teaches calculating a predicted channel model as an important input to the QoS prediction function, Zielinski does not explicitly teach:
the plurality of prediction mechanisms comprising a first prediction mechanism comprising statistics of historical data on QoS characteristics of the network cell and a second prediction mechanism comprising the statistics of historical data and prediction of a future trend on QoS characteristics of the network cell,
wherein the determine the prediction mechanism adopted for predicting the QoS of the network cell comprises:
determining a time remaining until the vehicle arrives at a first position region according to the driving information; and
determining the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region.
However, in the same field of endeavor, Altman teaches:
the plurality of prediction mechanisms comprising a first prediction mechanism comprising statistics of historical data on QoS characteristics of the network cell and a second prediction mechanism comprising the statistics of historical data and prediction of a future trend on QoS characteristics of the network cell (Altman: [0117] In some embodiments a processor or module may display the bonded communication map, correlated or synchronized to geo-location coordinates or to communication infrastructure identifiers or elements, or independently, including experienced data, current data, estimated or predicted data, historical data, or other data … [0119] Optionally, the map … may also show the calculated or estimated or predicted or determined metrics, the matching of the performance or QoS or QoE or cost or battery consumption or other parameter types correlation to specific or types of applications (such as: “The communication in Route Segment 1 would be adequate for HD streaming video”, or “The communication in Route Segment 2 would be inadequate for high-quality streaming audio but would be adequate for Voice Over IP communications”, or “The communication in Route Segment 3 is expected to be unreliable or spotty and may be sufficient for performing a Google search or for cursory Twitter posting but not for consuming Facebook streaming videos”, or “The communication in Route Segment 4 is expected to not be reliable or sufficient enough for safe remote-assisted driving option or support if needed” or the like), the different levels of service that are expected in different route segments or in an entire route, expected communication congestion periods and expected congested route segments when using bonded communications or when using each or any combination of the potential cellular or other networks and operators or links in the devices' modems or transceivers, or in another form … [0121] ... The map generated by the present invention provides application level communication data (e.g., predicted, estimated, historical), or IP performance level view of such data, that allows the user to understand what types of communication(s) the user and his various application can expect now or soon or in the future or were experienced in the past, in the vehicular route or route-segment or in other potential routes or modified routes or in replacement routes), when using this bonded communications link or another one or a modified one, as well as its expected Quality of Experience (QoE) and quality of ride in terms of passing the time by the driver and/or occupants of the vehicle…).
Therefore, 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 Zielinski to include the features as taught by Altman above in order to produce bonded wireless communication. (Altman, ¶ [0021]).
Zielinski-Altman does not explicitly teach:
wherein the determine the prediction mechanism adopted for predicting the QoS of the network cell comprises:
determining a time remaining until the vehicle arrives at a first position region according to the driving information; and
determining the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region.
However, in the same field of endeavor, Zou teaches:
wherein the determine the prediction mechanism adopted for predicting the QoS of the network cell comprises:
determining a time remaining until the vehicle arrives at a first position region according to the driving information (Zou: [0072] ... If a user knows the route, then it is likely that the route falls into the user's travel pattern Thus, the intelligent network may be able to predict the devices location at any time fairly accurately based on the habits of the user. For example, the user's commute to and from work may be observed and determined over time by the intelligent network, thereby allowing the network to predict the travel route and schedule accurately. If the route is not well known by the user, the user will likely use a map application to generate the deterministic travel route. These mapping applications can fairly accurately predict the time of arrival at the destination after the traveling route is determined [based on the accuracy of the predicted time of arrival, and based on the known route as recited by Zou, a person skilled in the art at the time of invention can easily discern the remaining time until arrival at a destination] ...); and
determining the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region (Zou: [0072] The capabilities in an intelligent network support very accurate UE mobility prediction. That is, the location of UE at any given time may be predicted down to, in some cases, beam coverage level accuracy. A beam refers to directional signal transmission and/or reception … [Zou further teaches, in ¶ [0075] and ¶ [0134], making adjustment to the prediction mechanism (which is equated to the claimed "determining the prediction mechanism adopted for predicting") to predict the quality of service (beam coverage level) at any point on the known route; this is a part of the closed loop prediction adjustment, where the UE makes measurements at a predicted location]: [0075] Embodiments of the present disclosure provide closed loop prediction adjustment with UE feedback for accurate prediction of an inactive UE's location. In an aspect, the UE based on its measurement of the beam determines whether current cell/beam is as the network predicted. If not, the UE sends a location update message or signal to the network. The network will send update mobility prediction information to the UE. And, [0134] Optionally, in any of the preceding aspects, the mobility prediction information includes actions scheduled to take at each of the cells, and/or TRPs, and/or beams on the predicted route and at the predicted time duration. All above cells/TRPs/beams entering time and the time duration of stay included in the mobility prediction information message and delivered to the UE allows the UE to compare the predicted time and actual time at the current cell/TRP/beam coverage. The metrics for the offset can be other than the time difference (e.g. number of the beams from the predicted beam coverage, distance from the predicted location at this moment). If the difference is larger than a threshold, the UE report to the network and the network adjust the prediction. The UE notification to the network can be pre-defined signal pattern or uplink message. This forms a closed tracking loop to ensure the accuracy of the prediction.).
Therefore, 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 Zielinski-Altman to include the features as taught by Zou above in order to provide signaling overhead and delay reduction. (Zou, ¶ [0058]).
Regarding claim 9, Zielinski teaches:
A communication apparatus applied to automatic driving of a vehicle, comprising (Zielinski: [0073] ... The backend server 320 to which the vehicles 10 send messages to and receive messages from is also connected to the Internet 300. In the field of cooperative and autonomous driving the backend server 320 typically is located in a traffic control center ... Fig. 1):
at least one memory configured to store program code (Zielinski: [0060] The functions of the various elements shown in the figures may be provided by the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, read only memory (ROM) for storing software, random access memory (RAM), and nonvolatile storage. Fig. 1); and
at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising (Zielinski: [0060] The functions of the various elements shown in the figures may be provided by the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, read only memory (ROM) for storing software, random access memory (RAM), and nonvolatile storage. Fig. 1):
acquisition code configured to cause the at least one processor to acquire driving information of the vehicle Zielinski: [0085] Both blocks, traffic flow prediction block 1640 and surroundings modelling block 1650 receive information about the planned route of a vehicle and the corresponding time or a time period in which the route will be started ... Figs. 1, 3); and
processing code configured to cause the at least one processor to determine a network cell of a path which the vehicle needs to pass according to the driving information (Zielinski: [0086] Reference number 1630 denotes a cells locating block. In this block the mobile communication cells along the path following the planned route of the requesting vehicle will be determined. The determined cell identifiers will be forwarded to the prediction function block 1610 on line 1632. Also this information in one embodiment could be taken from a corresponding detailed map ... Figs. 1, 3), wherein:
the processing code is further configured to cause the at least one processor to determine, among a plurality of prediction mechanisms, a prediction mechanism adopted for predicting quality of service (QoS) of the network cell (Zielinski: [0083] Reference number 1610 denotes the QoS prediction function block. It is the task of the QoS prediction function block 1610 to forecast the QoS parameters for a planned V2V or V2X or V2N communication and inform the requesting vehicle accordingly … [0084] Reference number 1620 denotes a channel modelling block which performs a function of calculating a predicted channel model. The predicted channel model is an important input to the QoS prediction function block 1610. The predicted channel model could be generated in the form of a channel model profile, i.e. a plurality of channel models is calculated for different times and places ... Figs. 1, 3);
the acquisition code is further configured to cause the at least one processor to acquire a QoS prediction result of the network cell according to the prediction mechanism (Zielinski: [0086] ... Moreover, this cells location block further includes a scheduling function block which typically comprises the same type of scheduling algorithms which are also applied in the scheduler of an eNodeB 210. The result of the scheduling operation will be forwarded to the prediction function block 1610 on line 1631. Also the assigned resources, i.e. the resource blocks which the scheduling algorithm in the cells locating block 1630 assigned for the requested communication type will be forwarded to the prediction function block 1610. This type of information will be transported over line 1633. Via line 1634 the information from the requesting vehicle about the communication type which is planned, the QoS requirements for the respective planned communication type, the requested prediction horizon and the requested prediction reliability is forwarded to the prediction function block 1610 … [0087] In summary, the prediction function block 1610 may perform the prediction function on different levels, one may be the link level where it will be determined which communication link provides which quality of service level. Second, the prediction function block 1610 may perform the prediction function on the system level, where resource blocks are pre-allocated and where it will be determined what the estimated QoS parameter values such as block error rate/packet error rate, end to end latency, throughput, etc. are... Figs. 1, 3).
Although Zielinski teaches calculating a predicted channel model as an important input to the QoS prediction function, Zielinski does not explicitly teach:
the plurality of prediction mechanisms comprising a first prediction mechanism comprising statistics of historical data on QoS characteristics of the network cell and a second prediction mechanism comprising the statistics of historical data and prediction of a future trend on QoS characteristics of the network cell; and
the processing code is further configured to cause the at least one processor to determine a time remaining until the vehicle arrives at a first position region according to the driving information, and determine the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region.
However, in the same field of endeavor, Altman teaches:
the plurality of prediction mechanisms comprising a first prediction mechanism comprising statistics of historical data on QoS characteristics of the network cell and a second prediction mechanism comprising the statistics of historical data and prediction of a future trend on QoS characteristics of the network cell (Altman: [0117] In some embodiments a processor or module may display the bonded communication map, correlated or synchronized to geo-location coordinates or to communication infrastructure identifiers or elements, or independently, including experienced data, current data, estimated or predicted data, historical data, or other data … [0119] Optionally, the map … may also show the calculated or estimated or predicted or determined metrics, the matching of the performance or QoS or QoE or cost or battery consumption or other parameter types correlation to specific or types of applications (such as: “The communication in Route Segment 1 would be adequate for HD streaming video”, or “The communication in Route Segment 2 would be inadequate for high-quality streaming audio but would be adequate for Voice Over IP communications”, or “The communication in Route Segment 3 is expected to be unreliable or spotty and may be sufficient for performing a Google search or for cursory Twitter posting but not for consuming Facebook streaming videos”, or “The communication in Route Segment 4 is expected to not be reliable or sufficient enough for safe remote-assisted driving option or support if needed” or the like), the different levels of service that are expected in different route segments or in an entire route, expected communication congestion periods and expected congested route segments when using bonded communications or when using each or any combination of the potential cellular or other networks and operators or links in the devices' modems or transceivers, or in another form … [0121] ... The map generated by the present invention provides application level communication data (e.g., predicted, estimated, historical), or IP performance level view of such data, that allows the user to understand what types of communication(s) the user and his various application can expect now or soon or in the future or were experienced in the past, in the vehicular route or route-segment or in other potential routes or modified routes or in replacement routes), when using this bonded communications link or another one or a modified one, as well as its expected Quality of Experience (QoE) and quality of ride in terms of passing the time by the driver and/or occupants of the vehicle…).
Therefore, 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 Zielinski to include the features as taught by Altman above in order to produce bonded wireless communication. (Altman, ¶ [0021]).
Zielinski-Altman does not explicitly teach:
the processing code is further configured to cause the at least one processor to determine a time remaining until the vehicle arrives at a first position region according to the driving information, and determine the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region.
However, in the same field of endeavor, Zou teaches:
the processing code is further configured to cause the at least one processor to determine a time remaining until the vehicle arrives at a first position region according to the driving information (Zou: [0072] ... If a user knows the route, then it is likely that the route falls into the user's travel pattern Thus, the intelligent network may be able to predict the devices location at any time fairly accurately based on the habits of the user. For example, the user's commute to and from work may be observed and determined over time by the intelligent network, thereby allowing the network to predict the travel route and schedule accurately. If the route is not well known by the user, the user will likely use a map application to generate the deterministic travel route. These mapping applications can fairly accurately predict the time of arrival at the destination after the traveling route is determined [based on the accuracy of the predicted time of arrival, and based on the known route as recited by Zou, a person skilled in the art at the time of invention can easily discern the remaining time until arrival at a destination] ...), and determine the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region (Zou: [0072] The capabilities in an intelligent network support very accurate UE mobility prediction. That is, the location of UE at any given time may be predicted down to, in some cases, beam coverage level accuracy. A beam refers to directional signal transmission and/or reception … [Zou further teaches, in ¶ [0075] and ¶ [0134], making adjustment to the prediction mechanism (which is equated to the claimed "determining the prediction mechanism adopted for predicting") to predict the quality of service (beam coverage level) at any point on the known route; this is a part of the closed loop prediction adjustment, where the UE makes measurements at a predicted location]: [0075] Embodiments of the present disclosure provide closed loop prediction adjustment with UE feedback for accurate prediction of an inactive UE's location. In an aspect, the UE based on its measurement of the beam determines whether current cell/beam is as the network predicted. If not, the UE sends a location update message or signal to the network. The network will send update mobility prediction information to the UE. And, [0134] Optionally, in any of the preceding aspects, the mobility prediction information includes actions scheduled to take at each of the cells, and/or TRPs, and/or beams on the predicted route and at the predicted time duration. All above cells/TRPs/beams entering time and the time duration of stay included in the mobility prediction information message and delivered to the UE allows the UE to compare the predicted time and actual time at the current cell/TRP/beam coverage. The metrics for the offset can be other than the time difference (e.g. number of the beams from the predicted beam coverage, distance from the predicted location at this moment). If the difference is larger than a threshold, the UE report to the network and the network adjust the prediction. The UE notification to the network can be pre-defined signal pattern or uplink message. This forms a closed tracking loop to ensure the accuracy of the prediction.).
Therefore, 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 Zielinski-Altman to include the features as taught by Zou above in order to provide signaling overhead and delay reduction. (Zou, ¶ [0058]).
Regarding claim 17, Zielinski teaches:
A non-transitory computer-readable storage medium, storing computer code that when executed by at least one processor causes the at least one processor to:
acquire driving information of a vehicle Zielinski: [0085] Both blocks, traffic flow prediction block 1640 and surroundings modelling block 1650 receive information about the planned route of a vehicle and the corresponding time or a time period in which the route will be started ... Figs. 1, 3);
determine a network cell of a path which the vehicle needs to pass according to the driving information (Zielinski: [0086] Reference number 1630 denotes a cells locating block. In this block the mobile communication cells along the path following the planned route of the requesting vehicle will be determined. The determined cell identifiers will be forwarded to the prediction function block 1610 on line 1632. Also this information in one embodiment could be taken from a corresponding detailed map ... Figs. 1, 3);
determine, among a plurality of prediction mechanisms, a prediction mechanism adopted for predicting quality of service (QoS) of the network cell (Zielinski: [0083] Reference number 1610 denotes the QoS prediction function block. It is the task of the QoS prediction function block 1610 to forecast the QoS parameters for a planned V2V or V2X or V2N communication and inform the requesting vehicle accordingly … [0084] Reference number 1620 denotes a channel modelling block which performs a function of calculating a predicted channel model. The predicted channel model is an important input to the QoS prediction function block 1610. The predicted channel model could be generated in the form of a channel model profile, i.e. a plurality of channel models is calculated for different times and places ... Figs. 1, 3); and
acquire a QoS prediction result of the network cell according to the prediction mechanism (Zielinski: [0086] ... Moreover, this cells location block further includes a scheduling function block which typically comprises the same type of scheduling algorithms which are also applied in the scheduler of an eNodeB 210. The result of the scheduling operation will be forwarded to the prediction function block 1610 on line 1631. Also the assigned resources, i.e. the resource blocks which the scheduling algorithm in the cells locating block 1630 assigned for the requested communication type will be forwarded to the prediction function block 1610. This type of information will be transported over line 1633. Via line 1634 the information from the requesting vehicle about the communication type which is planned, the QoS requirements for the respective planned communication type, the requested prediction horizon and the requested prediction reliability is forwarded to the prediction function block 1610 … [0087] In summary, the prediction function block 1610 may perform the prediction function on different levels, one may be the link level where it will be determined which communication link provides which quality of service level. Second, the prediction function block 1610 may perform the prediction function on the system level, where resource blocks are pre-allocated and where it will be determined what the estimated QoS parameter values such as block error rate/packet error rate, end to end latency, throughput, etc. are... Figs. 1, 3).
Although Zielinski teaches calculating a predicted channel model as an important input to the QoS prediction function, Zielinski does not explicitly teach:
the plurality of prediction mechanisms comprising a first prediction mechanism comprising statistics of historical data on QoS characteristics of the network cell and a second prediction mechanism comprising the statistics of historical data and prediction of a future trend on QoS characteristics of the network cell,
wherein the determine the prediction mechanism adopted for predicting the QoS of the network cell comprises:
determining a time remaining until the vehicle arrives at a first position region according to the driving information; and
determining the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region.
However, in the same field of endeavor, Altman teaches:
the plurality of prediction mechanisms comprising a first prediction mechanism comprising statistics of historical data on QoS characteristics of the network cell and a second prediction mechanism comprising the statistics of historical data and prediction of a future trend on QoS characteristics of the network cell (Altman: [0117] In some embodiments a processor or module may display the bonded communication map, correlated or synchronized to geo-location coordinates or to communication infrastructure identifiers or elements, or independently, including experienced data, current data, estimated or predicted data, historical data, or other data … [0119] Optionally, the map … may also show the calculated or estimated or predicted or determined metrics, the matching of the performance or QoS or QoE or cost or battery consumption or other parameter types correlation to specific or types of applications (such as: “The communication in Route Segment 1 would be adequate for HD streaming video”, or “The communication in Route Segment 2 would be inadequate for high-quality streaming audio but would be adequate for Voice Over IP communications”, or “The communication in Route Segment 3 is expected to be unreliable or spotty and may be sufficient for performing a Google search or for cursory Twitter posting but not for consuming Facebook streaming videos”, or “The communication in Route Segment 4 is expected to not be reliable or sufficient enough for safe remote-assisted driving option or support if needed” or the like), the different levels of service that are expected in different route segments or in an entire route, expected communication congestion periods and expected congested route segments when using bonded communications or when using each or any combination of the potential cellular or other networks and operators or links in the devices' modems or transceivers, or in another form … [0121] ... The map generated by the present invention provides application level communication data (e.g., predicted, estimated, historical), or IP performance level view of such data, that allows the user to understand what types of communication(s) the user and his various application can expect now or soon or in the future or were experienced in the past, in the vehicular route or route-segment or in other potential routes or modified routes or in replacement routes), when using this bonded communications link or another one or a modified one, as well as its expected Quality of Experience (QoE) and quality of ride in terms of passing the time by the driver and/or occupants of the vehicle…).
Therefore, 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 Zielinski to include the features as taught by Altman above in order to produce bonded wireless communication. (Altman, ¶ [0021]).
Zielinski-Altman does not explicitly teach:
wherein the determine the prediction mechanism adopted for predicting the QoS of the network cell comprises:
determining a time remaining until the vehicle arrives at a first position region according to the driving information; and
determining the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region.
However, in the same field of endeavor, Zou teaches:
wherein the determine the prediction mechanism adopted for predicting the QoS of the network cell comprises:
determining a time remaining until the vehicle arrives at a first position region according to the driving information (Zou: [0072] ... If a user knows the route, then it is likely that the route falls into the user's travel pattern Thus, the intelligent network may be able to predict the devices location at any time fairly accurately based on the habits of the user. For example, the user's commute to and from work may be observed and determined over time by the intelligent network, thereby allowing the network to predict the travel route and schedule accurately. If the route is not well known by the user, the user will likely use a map application to generate the deterministic travel route. These mapping applications can fairly accurately predict the time of arrival at the destination after the traveling route is determined [based on the accuracy of the predicted time of arrival, and based on the known route as recited by Zou, a person skilled in the art at the time of invention can easily discern the remaining time until arrival at a destination] ...); and
determining the prediction mechanism adopted for predicting the QoS of the network cell corresponding to the first position region based on the time remaining until the vehicle arrives at the first position region (Zou: [0072] The capabilities in an intelligent network support very accurate UE mobility prediction. That is, the location of UE at any given time may be predicted down to, in some cases, beam coverage level accuracy. A beam refers to directional signal transmission and/or reception … [Zou further teaches, in ¶ [0075] and ¶ [0134], making adjustment to the prediction mechanism (which is equated to the claimed "determining the prediction mechanism adopted for predicting") to predict the quality of service (beam coverage level) at any point on the known route; this is a part of the closed loop prediction adjustment, where the UE makes measurements at a predicted location]: [0075] Embodiments of the present disclosure provide closed loop prediction adjustment with UE feedback for accurate prediction of an inactive UE's location. In an aspect, the UE based on its measurement of the beam determines whether current cell/beam is as the network predicted. If not, the UE sends a location update message or signal to the network. The network will send update mobility prediction information to the UE. And, [0134] Optionally, in any of the preceding aspects, the mobility prediction information includes actions scheduled to take at each of the cells, and/or TRPs, and/or beams on the predicted route and at the predicted time duration. All above cells/TRPs/beams entering time and the time duration of stay included in the mobility prediction information message and delivered to the UE allows the UE to compare the predicted time and actual time at the current cell/TRP/beam coverage. The metrics for the offset can be other than the time difference (e.g. number of the beams from the predicted beam coverage, distance from the predicted location at this moment). If the difference is larger than a threshold, the UE report to the network and the network adjust the prediction. The UE notification to the network can be pre-defined signal pattern or uplink message. This forms a closed tracking loop to ensure the accuracy of the prediction.).
Therefore, 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 Zielinski-Altman to include the features as taught by Zou above in order to provide signaling overhead and delay reduction. (Zou, ¶ [0058]).
Regarding claim 3, 11, and 19, Zielinski-Altman-Zou discloses on the features with respect to claims 1 , 9, and 17.
Zou further teaches:
determining, based on the time being greater than a first threshold value, that the prediction mechanism is the first prediction mechanism (Zou: [0028] …. All above cells/TRPs/beams entering time and the time duration of stay included in the mobility prediction information message and delivered to the UE allows the UE to compare the predicted time and actual time at the current cell/TRP/beam coverage … If the difference is larger than a threshold, the UE report to the network and the network adjust the prediction [i.e., change to first prediction mechanism]. The UE notification to the network can be pre-defined signal pattern or uplink message. This forms a closed tracking loop to ensure the accuracy of the prediction.).
The rationale and motivation for adding this teaching of Zou is the same as the rationale and motivation for Claims 1 , 9, and 17.
Regarding claim 4, 12, and 20, Zielinski-Altman-Zou discloses on the features with respect to claims 1, 9, and 17 as outlined above.
Zou further teaches:
determining, based on the time being less than or equal to a second threshold value, that the prediction mechanism is the second prediction mechanism (Zou: [0028] …. All above cells/TRPs/beams entering time and the time duration of stay included in the mobility prediction information message and delivered to the UE allows the UE to compare the predicted time and actual time at the current cell/TRP/beam coverage … If the difference is larger than a threshold, the UE report to the network and the network adjust the prediction [i.e., if less than or equal to, adjustment to the prediction is NOT needed, hence the second prediction mechanism]. The UE notification to the network can be pre-defined signal pattern or uplink message. This forms a closed tracking loop to ensure the accuracy of the prediction.).
The rationale and motivation for adding this teaching of Zou is the same as the rationale and motivation for Claims 1 , 9, and 17.
Regarding claims 5 and 13, Zielinski-Altman-Zou discloses on the features with respect to claims 1 and 9 as outlined above.
Zielinski further teaches:
wherein the QoS prediction result of the network cell comprises at least one of a bandwidth, delay, reliability of the network cell (Zielinski: [0087] In summary, the prediction function block 1610 may perform the prediction function on different levels, one may be the link level where it will be determined which communication link provides which quality of service level. Second, the prediction function block 1610 may perform the prediction function on the system level, where resource blocks are pre-allocated and where it will be determined what the estimated QoS parameter values such as block error rate/packet error rate, end to end latency, throughput, etc. are... Figs. 1, 3).
Regarding claims 7 and 15, Zielinski-Altman-Zou discloses on the features with respect to claims 1 and 9 as outlined above.
Zielinski further teaches:
wherein the driving information comprises at least one of driving intention information or driving trajectory information (Zielinski: [0085] … Both blocks, traffic flow prediction block 1640 and surroundings modelling block 1650 receive information about the planned route of a vehicle and the corresponding time or a time period in which the route will be started …).
Regarding claims 8 and 16, Zielinski-Altman-Zou discloses on the features with respect to claims 1 and 9 as outlined above.
Zielinski further teaches:
acquiring inputted first information from a user application as the driving information, the first information comprising at least one of the driving intention information or the driving trajectory information (Zielinski: [0091] ... In the field TR the planned travel route will be entered. In one embodiment it could be in the form of a GPS track. In the following ST field an entry about the start time is transported, i.e. the time or time period which the user has entered when he wants to start the route.).
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zielinski-Altman-Zou in view of Jeon et.al. (US Patent Application Publication, 20220060942, hereinafter, “Jeon”).
Regarding claim 6 and 14, Zielinski-Altman-Zou discloses on the features with respect to claims 1 and 9 as outlined above.
Zielinski-Altman-Zou does not explicitly teach:
transmitting a QoS analytics subscribing request to a network data analytics function NWDAF according to the prediction mechanism; and
acquiring a QoS analytics subscribing notice transmitted by the NWDAF, the QoS analytics subscribing notice comprising the QoS prediction result.
However, in the same field of endeavor, Jeon teaches:
transmitting a QoS analytics subscribing request to a network data analytics function NWDAF according to the prediction mechanism (Jeon: [0095] At step 1 in FIG. 10, a SMF and a PF are subscribed to NWDAF for NWDAF analytics information service, as NWDAF consumers. The PF can request to get collected data from NWDAF or OAM, or can collect necessary data from NFs that store and manage the data the PF needs to collect for generating the QoS predictions.); and
acquiring a QoS analytics subscribing notice transmitted by the NWDAF, the QoS analytics subscribing notice comprising the QoS prediction result (Jeon: [0096] At step 2 in FIG. 10, the SMF is also subscribed to the PF for in-advance QoS notification service, as a PF consumer. The PF can e.g. collect Internal Group ID, application function (AF) Transaction ID, and Data Network Name (DNN) as well as congestion information on the current traffic path from NWDAF. In addition, the PF can request to get collected data of specific NF or AF collected from NWDAF. The PF as a logical entity generates predictions based on collected data from … NWDAF ...).
Therefore, 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 Zielinski-Altman-Zou to include the features as taught by Jeon above in order to improve the overall system reliability. (Jeon, ¶ [0006]).
Conclusion
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/LIEM H. NGUYEN/Primary Examiner, Art Unit 2416