DETAILED ACTION
This action is responsive to remarks filed on 26 May 2026. Claims 1-15 are pending examination.
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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-15 have been considered and are persuasive with respect to the prior rejection. Accordingly, the prior rejection is withdrawn. Claims 1-15 are rejected under 35 U.S.C. 103 for the reasons set forth below.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 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 nonobviousness.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 20250300749 A1) (hereinafter Kim) in view of Gopal Reddy et al. (US 20240397374 A1) (hereinafter Gop).
In regards to claim 1, Kim-Gop teaches a method for data transmission between a data server device and a data-receiving client device in a communication network, the server device comprising (Kim, fig. 1):
a memory including software code and a processor which, when it executes the software code, causes the server device to implement the method, the method comprising (Kim, fig. 3, [0097]- [0115]: [0099] Referring to FIG. 3, wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 2 and may be configured by various elements, components, units/portions, and/or modules. For example, each of the wireless devices 100 and 200 may include a communication unit 110, a control unit 120, a memory unit 130, and additional components 140.):
- obtaining, over a first time period, a plurality of measurements characterizing a quality of transmission between the client device and the server device (Kim, fig. 21-22, [0538]- [0641], [0643]- [0668]: [0561] For example, a wireless device may configure a prediction time window. At the first time point, the wireless device may start the measurement prediction within the prediction time window. The wireless device may derive one or more measurement results for the measurement object within the prediction time window. [0578] However, the predicted results may be inaccurate. In wireless channels, prediction of channel quality necessarily introduces prediction errors. The prediction error tends to increase if the prediction task aims to predict far future (i.e., higher error at longer prediction horizon). Conversely, the prediction error tends decrease if the prediction task aims to predict near future (i.e., smaller error at shorter prediction horizon). If the predicted results provided to network are inaccurate, the network may make incorrect decisions for mobility and/or radio resource management.);
- transmitting the information to the client device (Kim, fig. 11-12, [0224]- [0233], [0241]- [0284]: [0245] Step 3. UE sends measurement report message to NG-RAN node1 including the required measurement.);
- receiving, during a second future period with respect to the first period, a data transmission request from the client device, the time of transmission of the request by the client device depending on said information transmitted by the server device to the client device (Kim, fig. 21, fig. 23, [0538]- [0641], [0646]- [0668]: [0659] The second measurement report may include at least one of the following. [0660] Information to indicate that the measurement report is complimentary measurement report, which may comprise: [0661] t1: the time at which the predictive measurement result of the measurement target in the first measurement report satisfies the reporting event. [0662] Measurement ID of the first measurement report. [0663] Indicator indicating that the measurement report is the second (complimentary) measurement report associated with the first measurement report. [0664] M(t1): measurement result of the measurement target at t1. [0665] The error in prediction quality, if this error type is used for triggering the second measurement report. [0666] The error in prediction time, if this error type is used for triggering the second measurement report. [0667] The accuracy information of prediction model calculated in UE side [0668] The threshold value configured by network.) and (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0042] The logic step 1 illustrates a table including performance parameters CQI (e.g., having ranges A, B, and C) and SINR (e.g., having ranges X, Y, Z) received, for example, from three different user devices at different locations. The table includes a quality characterization of the wireless connection associated with the user device that provided the parameters at different levels (e.g., poor, moderate, excellent). [0043] The quality factors from the logic step 3 can further be compared to historical quality factors associated with respective geobins. The historical quality factors are collected at different time intervals (1 min, 5 min, 10 min, 30 min, etc.) during a pre-defined time period (e.g., 6 hours, 12 hours, 24 hours, or 48 hours). For example, the quality factors for geobin 1, geobin 2, through geobin N are normalized across time intervals (n+1), (n+2) through (n+3) to receive updated quality factors.); and
- transmitting the data to the client device in response to the request (Kim, fig. 26-27, [0695]- [0696], [0697]- [0708]: [0698] 1) The network configures a measurement configuration applicable for a cell, including measurement object(s) and measurement report configurations and a threshold to detect an erroneous predictive measurement result. [0699] 2) The UE derives the first measurement result of the cell for a certain future time at which the predictive measurement result satisfies the reporting condition. [0700] The first measurement result is a predictive measurement result. [0701] 3) The UE sends the first measurement report of the cell if the first measurement result satisfies at the current time t.sub.a. [0702] At t.sub.a, the UE sends the first measurement report in both FIG. 26 and FIG. 27. [0703] The first measurement report includes time information t1 and measurement results for time t1. [0704] 4) The UE performs the measurements of the cell at time t1 and derive the second measurement result. [0705] The second measurement result is a non-predictive measurement result. [0706] 5) The UE determines the prediction error (gap) between the first measurement result and the second measurement result for the future time t1. [0707] 6-1) The UE sends the second measurement report of the cell at time t.sub.1 if the prediction error (gap) is larger than the threshold. (FIG. 26) [0708] 6-2) The UE does not send the second measurement report of the cell if the prediction error (gap) is smaller than the threshold. (FIG. 27)).
Thus, Kim does not teach determining, depending on the measurements, information characterizing the evolution over time of a relative level of quality of transmission during the first period and receiving, during a second future period with respect to the first period.
Similar to the system of Kim, Gop teaches determining quality factors based on performance parameters (CQI and SINR) and comparing quality factors collected at different time intervals, which can be seen as, determining, depending on the measurements, information characterizing the evolution over time of a relative level of quality of transmission during the first period (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0042] FIG. 4 is a diagram illustrating an exemplary logic 400 for transforming performance parameters to quality factors. The logic step 1 illustrates a table including performance parameters CQI (e.g., having ranges A, B, and C) and SINR (e.g., having ranges X, Y, Z) received, for example, from three different user devices at different locations. The table includes a quality characterization of the wireless connection associated with the user device that provided the parameters at different levels (e.g., poor, moderate, excellent). The characterization is performed based on a combination of the CQI and SINR ranges. The logic step 2 illustrates a determination of the geobin (e.g., geobin 1, 2, and N) based on latitude and longitude coordinates associated with the location of the user device. The logic step 3 illustrates a correlation of the quality characterization with the respective geobins. The correlation is associated with a particular time interval. The time interval can include a real-time (or near real-time) time interval. The logic step 3 also includes a quality factor (e.g., QoS factor) associated with the geobins. For example, the geobins determined to have an excellent or moderate wireless network quality based on the performance parameters are assigned for non-GBR resources, while the geobin determined to have a poor wireless network quality is assigned for GBR resources. [0043] The quality factors from the logic step 3 can further be compared to historical quality factors associated with respective geobins. The historical quality factors are collected at different time intervals (1 min, 5 min, 10 min, 30 min, etc.) during a pre-defined time period (e.g., 6 hours, 12 hours, 24 hours, or 48 hours). For example, the quality factors for geobin 1, geobin 2, through geobin N are normalized across time intervals (n+1), (n+2) through (n+3) to receive updated quality factors.).
Similar to the system of Kim, Gop teaches collecting historical performance parameters during a first time period and updating quality factors using information collected during later time intervals (n+1, n+2, and n+3), which can be seen as, receiving, during a second future period with respect to the first period (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0052] At 508, the AMF can temporarily store the quality factor correlated with the geographical space to the database (e.g., the table of logic step 3 stored in the database). The AMF can further update the historical quality factors with the quality factor correlated with the geographical space associated with the real-time time interval (e.g., as described with respect to the normalization function in FIG. 4). [0053] In some implementations, the database includes historical performance parameters associated with multiple geographical spaces collected during multiple time intervals within a pre-defined time period (e.g., the table for normalization function in FIG. 4). The system can be caused to determine historical quality factors based on the historical performance parameters associated with the multiple time intervals across the pre-defined time period. For example, the historical performance parameters at time intervals (n+1), (n+2), and (n+3) can be collected over a pre-defined time period (e.g., 6 hours, 12 hours, 24hours, or 48 hours). The system can be further caused to normalize the historical quality factors associated with the multiple time intervals across the pre-defined time period (e.g., the updated quality factor (QF) in FIG. 4). In some implementations, the historical quality factors are determined based on historical performance parameters collected from one or more user devices located within the geographical space during a pre-defined time period.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim with Gop to utilize quality information collected over multiple time intervals, to improve the accuracy of transmission quality assessments and network resource management decisions (Gop, [0039]).
In regards to claim 2, Kim teaches the method of claim 1:
the first period being sub-divided into time intervals, wherein determining the information comprises determining a relative level of quality for each interval on the basis of measurements taken for this interval with respect to one or more thresholds determined from the measurements taken over the entire first period (Kim, fig. 8, fig. 21, fig. 23, [0125]-[0135], [0538]-[0641], [0646]-[0668]: [0127] Referring to FIG. 8, downlink and uplink transmissions are organized into frames. Each frame has T.sub.f=10 ms duration. Each frame is divided into two half-frames, where each of the half-frames has 5 ms duration. Each half-frame consists of 5 subframes, where the duration T.sub.sf per subframe is 1 ms. Each subframe is divided into slots and the number of slots in a subframe depends on a subcarrier spacing. Each slot includes 14 or 12 OFDM symbols based on a cyclic prefix (CP). In a normal CP, each slot includes 14 OFDM symbols and, in an extended CP, each slot includes 12 OFDM symbols. The numerology is based on exponentially scalable subcarrier spacing Δf=2u*15 kHz.).
In regards to claim 3, Kim teaches the method according to claim 2:
wherein the one or more thresholds are chosen between the highest and the lowest measurement value of the first period (Kim, fig. 21, fig. 23, [0538]-[0641], [0646]-[0668]: [0544] Event A1: Serving becomes better than threshold [0545] Event A2: Serving becomes worse than threshold [0546] Event A3: Neighbour becomes offset better than SpCell [0547] Event A4: Neighbour becomes better than threshold [0548] Event A5: SpCell becomes worse than threshold1 and neighbour becomes better than threshold2 [0549] Event A6: Neighbour becomes offset better than SCell [0550] Event B1: Inter RAT neighbour becomes better than threshold [0551] Event B2: PCell becomes worse than threshold1 and inter RAT neighbour becomes better than threshold2 [0552] Event I1: Interference becomes higher than threshold [0553] Event C1: The NR sidelink channel busy ratio is above a threshold [0554] Event C2: The NR sidelink channel busy ratio is below a threshold [0555] Event X1: Serving L2 U2N Relay UE becomes worse than threshold1 and NR Cell becomes better than threshold2 [0556] Event X2: Serving L2 U2N Relay UE becomes worse than threshold [0557] Event Y1: PCell becomes worse than threshold1 and candidate L2 U2N Relay UE becomes better than threshold2 [0558] Event Y2: Candidate L2 U2N Relay UE becomes better than threshold).
In regards to claim 4, Gop teaches the method according to claim 1:
Thus, Kim does not explicitly teach wherein the determination of the information and the transmission of this information are carried out periodically.
Similar to the system of Kim, Gop teaches collecting historical performance parameters and determining updated quality factors at multiple time intervales during a predefined time period, which can be seen as, wherein the determination of the information and the transmission of this information are carried out periodically (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0052] At 508, the AMF can temporarily store the quality factor correlated with the geographical space to the database (e.g., the table of logic step 3 stored in the database). The AMF can further update the historical quality factors with the quality factor correlated with the geographical space associated with the real-time time interval (e.g., as described with respect to the normalization function in FIG. 4). [0053] In some implementations, the database includes historical performance parameters associated with multiple geographical spaces collected during multiple time intervals within a pre-defined time period (e.g., the table for normalization function in FIG. 4). The system can be caused to determine historical quality factors based on the historical performance parameters associated with the multiple time intervals across the pre-defined time period. For example, the historical performance parameters at time intervals (n+1), (n+2), and (n+3) can be collected over a pre-defined time period (e.g., 6 hours, 12 hours, 24hours, or 48 hours). The system can be further caused to normalize the historical quality factors associated with the multiple time intervals across the pre-defined time period (e.g., the updated quality factor (QF) in FIG. 4). In some implementations, the historical quality factors are determined based on historical performance parameters collected from one or more user devices located within the geographical space during a pre-defined time period.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim with Gop to utilize quality information collected over multiple time intervals, to improve the accuracy of transmission quality assessments and network resource management decisions (Gop, [0039]).
In regards to claim 5, Kim teaches the method according to claim 1:
wherein the first period is one among: a time range of a day, a day (Kim, fig. 21-22, [0538]-[0641]: [0564] For example, the wireless device may transmit, to the network, information on the prediction time window. For example, the wireless device may transmit, to the network, information on the prediction time window along with the information about the predictive measurement result. For example, the information on the prediction time window may include a start point and/or an end point of the prediction time window.).
In regards to claim 6, Kim teaches the method according to claim 1:
wherein the communication network is a cellular network (Kim, fig. 1, [0070]-[0096]: [0073] In the present disclosure, the wireless devices 100a to 100f may be called user equipments (UEs). A UE may include, for example, a cellular phone, a smartphone, a laptop computer, a digital broadcast terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a slate personal computer (PC), a tablet PC, an ultrabook, a vehicle, a vehicle having an autonomous traveling function, a connected car, an UAV, an AI module, a robot, an AR device, a VR device, an MR device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a FinTech device (or a financial device), a security device, a weather/environment device, a device related to a 5G service, or a device related to a fourth industrial revolution field.).
In regards to claim 7, Kim-Gop teaches a method for data transmission from at least one data server device to a client device in a communication network, the client device comprising:
a memory including software code and a processor which, when it executes the software code, causes the receiving device to implement the method, the method comprising (Kim, fig. 3, [0097]-[0101]: [0099] Referring to FIG. 3, wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 2 and may be configured by various elements, components, units/portions, and/or modules. For example, each of the wireless devices 100 and 200 may include a communication unit 110, a control unit 120, a memory unit 130, and additional components 140.):
- receiving, by the client device, from a given server, information transmitted by a given server device characterizing an evolution of a relative level of quality of transmission between the client device and said given server device during a first time period (Kim, fig. 21-22, [0538]-[0641], [0643]-[0668]: [0644] For example, error is measured as the difference of measurement quality between a predictive measurement result and an actual measurement result (t.sub.a and t.sub.1 respectively) for a particular future time t1 as shown in FIG. 22, where the particular future time t1 is when the measurement reporting event is expected to be met based on the predictive measurement result of the measurement target derived at t.sub.a. That is, error is derived based on the value M(t.sub.1)−P.sup.t1 (t.sub.a), where M(x) indicates a non-predictive measurement result of the measurement target derived at time x, and P.sup.y(x) indicates the predictive measurement result of the measurement target derived at time x for the concerned future time y. That is,) and (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0042] FIG. 4 is a diagram illustrating an exemplary logic 400 for transforming performance parameters to quality factors. The logic step 1 illustrates a table including performance parameters CQI (e.g., having ranges A, B, and C) and SINR (e.g., having ranges X, Y, Z) received, for example, from three different user devices at different locations. The table includes a quality characterization of the wireless connection associated with the user device that provided the parameters at different levels (e.g., poor, moderate, excellent). The characterization is performed based on a combination of the CQI and SINR ranges. The logic step 2 illustrates a determination of the geobin (e.g., geobin 1, 2, and N) based on latitude and longitude coordinates associated with the location of the user device. The logic step 3 illustrates a correlation of the quality characterization with the respective geobins. The correlation is associated with a particular time interval. The time interval can include a real-time (or near real-time) time interval. The logic step 3 also includes a quality factor (e.g., QoS factor) associated with the geobins. For example, the geobins determined to have an excellent or moderate wireless network quality based on the performance parameters are assigned for non-GBR resources, while the geobin determined to have a poor wireless network quality is assigned for GBR resources. [0043] The quality factors from the logic step 3 can further be compared to historical quality factors associated with respective geobins. The historical quality factors are collected at different time intervals (1 min, 5 min, 10 min, 30 min, etc.) during a pre-defined time period (e.g., 6 hours, 12 hours, 24 hours, or 48 hours). For example, the quality factors for geobin 1, geobin 2, through geobin N are normalized across time intervals (n+1), (n+2) through (n+3) to receive updated quality factors.);
- estimating the relative level of quality of transmission during a given time interval of the second period on the basis of said information corresponding to the same time interval of the first period, the time range covered by the first period including at least the time range of a second period (Kim, fig. 12, fig. 21-22, [0241]-[0284], [0538]-[0641], [0643]-[0668]: [0241] FIG. 12 shows an example of Model Training and Model Inference both located in RAN node. [0242] Step 0. NG-RAN node 2 is assumed to optionally have an AI/ML model, which can generate required input such as resource status and utilization prediction/estimation etc. [0248] Step 6. NG-RAN node1 obtains the measurement report as inference data for real-time UE mobility optimization. [0249] Step 7. The NG-RAN node 1 obtains the input data for inference from the NG-RAN node 2 for UE mobility optimization, where the input data for inference includes the required input information from the NG-RAN node 2. If the NG-RAN node 2 executes the AI/ML model, the input data for inference can include the corresponding inference result from the NG-RAN node 2.) and (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0052] At 508, the AMF can temporarily store the quality factor correlated with the geographical space to the database (e.g., the table of logic step 3 stored in the database). The AMF can further update the historical quality factors with the quality factor correlated with the geographical space associated with the real-time time interval (e.g., as described with respect to the normalization function in FIG. 4). [0053] In some implementations, the database includes historical performance parameters associated with multiple geographical spaces collected during multiple time intervals within a pre-defined time period (e.g., the table for normalization function in FIG. 4). The system can be caused to determine historical quality factors based on the historical performance parameters associated with the multiple time intervals across the pre-defined time period. For example, the historical performance parameters at time intervals (n+1), (n+2), and (n+3) can be collected over a pre-defined time period (e.g., 6 hours, 12 hours, 24hours, or 48 hours). The system can be further caused to normalize the historical quality factors associated with the multiple time intervals across the pre-defined time period (e.g., the updated quality factor (QF) in FIG. 4). In some implementations, the historical quality factors are determined based on historical performance parameters collected from one or more user devices located within the geographical space during a pre-defined time period.); and
- deciding to send a data transmission request to the given server device during the given interval depending on the comparison of the estimation for this interval with a quality threshold (Kim, fig. 21-22, [0538]-[0641], [0643]-[0668]: [0647] For example, errors are measured as the difference of measurement quality between predictive measurement result and an actual measurement result at difference times (t.sub.a and t.sub.2 respectively) for a certain time (t2) as shown in FIG. 23. That is, error is derived based on the value M(t.sub.2)−P.sup.t2(t.sub.a), where M(x) indicates a measurement result of the measurement target at time x, and P.sup.y(x) indicates the predictive measurement result of).
Thus, Kim does not teach information transmitted by a given server device characterizing an evolution of a relative level of quality of transmission between the client device and said given server device during a first time period and estimating the relative level of quality of transmission during a given time interval of the second period on the basis of said information corresponding to the same time interval of the first period.
Similar to the system of Kim, Gop teaches determining quality factors based on performance parameters (CQI and SINR), tracking the quality factors over different time intervals, and comparing the quality factors with historical quality factors collected at different time intervals during a predefined time period, which can be seen as, information transmitted by a given server device characterizing an evolution of a relative level of quality of transmission between the client device and said given server device during a first time period (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0042] FIG. 4 is a diagram illustrating an exemplary logic 400 for transforming performance parameters to quality factors. The logic step 1 illustrates a table including performance parameters CQI (e.g., having ranges A, B, and C) and SINR (e.g., having ranges X, Y, Z) received, for example, from three different user devices at different locations. The table includes a quality characterization of the wireless connection associated with the user device that provided the parameters at different levels (e.g., poor, moderate, excellent). The characterization is performed based on a combination of the CQI and SINR ranges. The logic step 2 illustrates a determination of the geobin (e.g., geobin 1, 2, and N) based on latitude and longitude coordinates associated with the location of the user device. The logic step 3 illustrates a correlation of the quality characterization with the respective geobins. The correlation is associated with a particular time interval. The time interval can include a real-time (or near real-time) time interval. The logic step 3 also includes a quality factor (e.g., QoS factor) associated with the geobins. For example, the geobins determined to have an excellent or moderate wireless network quality based on the performance parameters are assigned for non-GBR resources, while the geobin determined to have a poor wireless network quality is assigned for GBR resources. [0043] The quality factors from the logic step 3 can further be compared to historical quality factors associated with respective geobins. The historical quality factors are collected at different time intervals (1 min, 5 min, 10 min, 30 min, etc.) during a pre-defined time period (e.g., 6 hours, 12 hours, 24 hours, or 48 hours). For example, the quality factors for geobin 1, geobin 2, through geobin N are normalized across time intervals (n+1), (n+2) through (n+3) to receive updated quality factors.).
Similar to the system of Kim, Gop teaches collecting historical performance parameters during a predefined time period and determining updated quality factors based on the historical performance parameters, which can be seen as, estimating the relative level of quality of transmission during a given time interval of the second period on the basis of said information corresponding to the same time interval of the first period (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0052] At 508, the AMF can temporarily store the quality factor correlated with the geographical space to the database (e.g., the table of logic step 3 stored in the database). The AMF can further update the historical quality factors with the quality factor correlated with the geographical space associated with the real-time time interval (e.g., as described with respect to the normalization function in FIG. 4). [0053] In some implementations, the database includes historical performance parameters associated with multiple geographical spaces collected during multiple time intervals within a pre-defined time period (e.g., the table for normalization function in FIG. 4). The system can be caused to determine historical quality factors based on the historical performance parameters associated with the multiple time intervals across the pre-defined time period. For example, the historical performance parameters at time intervals (n+1), (n+2), and (n+3) can be collected over a pre-defined time period (e.g., 6 hours, 12 hours, 24hours, or 48 hours). The system can be further caused to normalize the historical quality factors associated with the multiple time intervals across the pre-defined time period (e.g., the updated quality factor (QF) in FIG. 4). In some implementations, the historical quality factors are determined based on historical performance parameters collected from one or more user devices located within the geographical space during a pre-defined time period.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim with Gop to utilize quality information collected over multiple time intervals, to improve the accuracy of transmission quality assessments and network resource management decisions (Gop, [0039]).
In regards to claim 8, Kim teaches the method according to claim 7:
wherein the sending decision is taken as time advances during the second period, until sending results in successfully receiving said data (Kim, fig. 21-22, [0301]-[0491], [0538]-[0641], [0643]-[0668]: [0303] Hereinafter, technical features related to measurement report are described. Parts of section 5.5.4 and section 5.5.5 of 3GPP TS 38.331 v17.0.0 may be refer. If AS Security has been Activated Successfully, the UE Shall: [0304] 1> for each measId included in the measIdList within VarMeasConfig: [0305] 2> if the corresponding reportConfig includes a reportType set to eventTriggered or periodical: [0306] 3> if the corresponding measObject concerns NR: [0307] 4> if the corresponding reportConfig includes measRSSI-ReportConfig: [0308] 5> consider the resource indicated by the rmtc-Config on the associated frequency to be applicable; [0309] 4> if the eventA1 or eventA2 is configured in the corresponding reportConfig: [0310] 5> consider only the serving cell to be applicable; [0311] 4> if the eventA3 or eventA5 is configured in the corresponding reportConfig: [0312] 5> if a serving cell is associated with a measObjectNR and neighbours are associated with another measObjectNR, consider any serving cell associated with the other measObjectNR to be a neighbouring cell as well.).
In regards to claim 9, Kim teaches the method according to claim 7:
wherein said quality threshold depends on the placement of the given interval in the second period, the quality threshold having a maximum value at the start of the second period and decreasing as time advances in the second period toward a minimum value (Kim, fig. 21-22, [0301]-[0491], [0538]-[0641], [0643]-[0668]: [0476] Event A1: Serving becomes better than threshold [0477] Event A2: Serving becomes worse than threshold [0478] Event A3: Neighbor becomes offset better than SpCell [0479] Event A4: Neighbor becomes better than threshold [0480] Event A5: SpCell becomes worse than threshold1 and neighbor becomes better than threshold2 [0481] Event A6: Neighbour becomes offset better than SCell [0482] Event B1: Inter RAT neighbor becomes better than threshold [0483] Event B2: PCell becomes worse than threshold1 and inter RAT neighbor becomes better than threshold2 [0484] Event I1: Interference becomes higher than threshold [0485] Event C1: The NR sidelink channel busy ratio is above a threshold [0486] Event C2: The NR sidelink channel busy ratio is below a threshold [0487] Event X1: Serving L2 U2N Relay UE becomes worse than threshold1 and NR Cell becomes better than threshold2 [0488] Event X2: Serving L2 U2N Relay UE becomes worse than threshold [0489] Event Y1: PCell becomes worse than threshold1 and candidate L2 U2N Relay UE becomes better than threshold2 [0490] Event Y2: Candidate L2 U2N Relay UE becomes better than threshold).
In regards to claim 10, Kim teaches the method according to claim 7:
wherein the second period is one among:
- a part of a day corresponding to the same part of the day covered by the first day during a previous day (Kim, fig. 21-22, [0538]-[0641]: [0564] For example, the wireless device may transmit, to the network, information on the prediction time window. For example, the wireless device may transmit, to the network, information on the prediction time window along with the information about the predictive measurement result. For example, the information on the prediction time window may include a start point and/or an end point of the prediction time window.);
- the day following a day covered by the first period (Kim, fig. 21-22, [0538]-[0641]: [0613] The UE may derive predictive measurement results of cells associated with the concerned measurement objects for a certain future time. [0614] The UE may derive a future time at which predictive measurement results satisfy the reporting conditions within the prediction time window T2. [0615] To derive predictive measurement results, the UE may apply the configured prediction model.).
In regards to claim 11, Kim-Gop teaches the method according to claim 7:
which comprises receiving information from a plurality of server devices, the decision to send a request to a server device depending on the information received from this server device (Kim, fig. 21, fig. 23, [0538]-[0641], [0646]-[0668], [0669]-[0693]: [0675] 3) The UE sends the first measurement report of the cell if the first measurement result for the future time satisfies the reporting condition. [0676] At t.sub.a, the UE sends the first measurement report in both FIG. 24 and FIG. 25. [0677] The first measurement report includes measurement results for time t.sub.a+T1. [0678] 4) The UE performs the measurements of the cell at time t.sub.a+T1 and derive the second measurement result. [0679] 5) The second measurement result is a non-predictive measurement result. The UE determines the prediction error (gap) between the first measurement result and the second measurement result for the time t.sub.a+T1. [0680] 6-1) The UE sends the second measurement report of the cell at the time t.sub.a+T1 if the gap is larger than the threshold (FIG. 24) [0681] 6-2) The UE does not send the second measurement report of the cell if the gap is smaller than the threshold. (FIG. 25).).
Thus, Kim does not explicitly teach plurality of server devices.
Similar to the system of Kim, Gop teaches multiple application servers (or application functions), which can be seen as, plurality of server devices (Gop, fig. 2, fig. 3, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0040] The AMF is in communication with the application servers (or application functions). At 310, the application servers can receive a request to establish voice or video calls (e.g., CORE services request) to a user device within a particular geobin (e.g., the wireless device 202 in FIG. 3). In response to the request, the application servers also request or retrieve the real-time quality factor associated with the particular geobin. At 312, the application servers apply a logic to determine whether the quality factor associated with the geobin has changed compared to historical quality factors for the particular geobin. In response to the determination, the application servers generate a message (e.g., R-msg) that indicates whether the PCF should deploy an existing or a new GBR service type for the wireless device 202. For example, when the quality factor for the particular geobin has changed above a pre-defined threshold, a new non-GBR service type is sent to the PCF at 314. When the quality factor for the particular geobin has not changed above the pre-defined threshold, an existing GBR service type is sent to the PCF at 316. At 318, a base station (e.g., a base station 102 such as gNB NAN) performs a default non-GBR/GBR resource (e.g., bearer) setup process in accordance with the service type to establish the voice or video call terminating at the wireless device 202.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim with Gop to utilize quality information collected over multiple time intervals, to improve the accuracy of transmission quality assessments and network resource management decisions (Gop, [0039]).
In regards to claim 12, Kim teaches the method according to claim 7:
wherein the communication network is a cellular network (Kim, fig. 1, [0070]-[0096]: See above for paragraph [0073].).
In regards to claim 13, Kim teaches a server device comprising:
a memory including software code and a processor which, when it executes the software code, causes the server device to implement the method according to claim 1 (Kim, [0070]-[0096]: [0072] The wireless devices 100a to 100f represent devices performing communication using radio access technology (RAT) (e.g., 5G new RAT (NR)) or LTE) and may be referred to as communication/radio/5G devices. The wireless devices 100a to 100f may include, without being limited to, a robot 100a, vehicles 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, a home appliance 100e, an IoT device 100f, and an artificial intelligence (AI) device/server 400. [0087] The first wireless device 100 may include one or more processors 102 and one or more memories 104 and additionally further include one or more transceivers 106 and/or one or more antennas 108. The processor(s) 102 may control the memory(s) 104 and/or the transceiver(s) 106 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure. For example, the processor(s) 102 may process information within the memory(s) 104 to generate first information/signals and then transmit radio signals including the first information/signals through the transceiver(s) 106. The processor(s) 102 may receive radio signals including second information/signals through the transceiver(s) 106 and then store information obtained by processing the second information/signals in the memory(s) 104. The memory(s) 104 may be connected to the processor(s) 102 and may store a variety of information related to operations of the processor(s) 102. For example, the memory(s) 104 may store software code including commands for performing a part or the entirety of processes controlled by the processor(s) 102 or for performing the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts described in the present disclosure. Herein, the processor(s) 102 and the memory(s) 104 may be a part of a communication modem/circuit/chip designed to implement RAT (e.g., LTE or NR). The transceiver(s) 106 may be connected to the processor(s) 102 and transmit and/or receive radio signals through one or more antennas 108. Each of the transceiver(s) 106 may include a transmitter and/or a receiver. The transceiver(s) 106 may be interchangeably used with radio frequency (RF) unit(s). In the present disclosure, the first wireless device 100 may represent a communication modem/circuit/chip.).
In regards to claim 14, Kim teaches a client device comprising:
a memory including software code and a processor which, when it executes the software code, causes the client device to implement the method according to claim 7 (Kim, fig. 5, [0102]-[0124]: [0110] The memory 104 is operatively coupled with the processor 102 and stores a variety of information to operate the processor 102. The memory 104 may include ROM, RAM, flash memory, memory card, storage medium and/or other storage device. When the embodiments are implemented in software, the techniques described herein can be implemented with modules (e.g., procedures, functions, etc.) that perform the descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in the present disclosure. The modules can be stored in the memory 104 and executed by the processor 102. The memory 104 can be implemented within the processor 102 or external to the processor 102 in which case those can be communicatively coupled to the processor 102 via various means as is known in the art.).
In regards to claim 15, Kim-Gop teach a system comprising a client device according to claim 14 and at least one server device comprising:
a memory including software code and a processor which, when it executes the software code, causes the server device to (Kim, fig. 3, [0097]-[0101]: [0099] Referring to FIG. 3, wireless devices 100 and 200 may correspond to the wireless devices 100 and 200 of FIG. 2 and may be configured by various elements, components, units/portions, and/or modules. For example, each of the wireless devices 100 and 200 may include a communication unit 110, a control unit 120, a memory unit 130, and additional components 140.):
obtain, over a first time period, a plurality of measurements characterizing a quality of transmission between the client device and the server device (Kim, fig. 21-22, [0538]- [0641], [0643]- [0668]: [0561] For example, a wireless device may configure a prediction time window. At the first time point, the wireless device may start the measurement prediction within the prediction time window. The wireless device may derive one or more measurement results for the measurement object within the prediction time window. [0578] However, the predicted results may be inaccurate. In wireless channels, prediction of channel quality necessarily introduces prediction errors. The prediction error tends to increase if the prediction task aims to predict far future (i.e., higher error at longer prediction horizon). Conversely, the prediction error tends decrease if the prediction task aims to predict near future (i.e., smaller error at shorter prediction horizon). If the predicted results provided to network are inaccurate, the network may make incorrect decisions for mobility and/or radio resource management.);
transmit the information to the client device (Kim, fig. 11-12, [0224]- [0233], [0241]- [0284]: [0245] Step 3. UE sends measurement report message to NG-RAN node1 including the required measurement.);
receive, during a second future period with respect to the first period, a data transmission request from the client device, the time of transmission of the request by the client device depending on said information transmitted by the server device to the client device (Kim, fig. 21, fig. 23, [0538]- [0641], [0646]- [0668]: [0659] The second measurement report may include at least one of the following. [0660] Information to indicate that the measurement report is complimentary measurement report, which may comprise: [0661] t1: the time at which the predictive measurement result of the measurement target in the first measurement report satisfies the reporting event. [0662] Measurement ID of the first measurement report. [0663] Indicator indicating that the measurement report is the second (complimentary) measurement report associated with the first measurement report. [0664] M(t1): measurement result of the measurement target at t1. [0665] The error in prediction quality, if this error type is used for triggering the second measurement report. [0666] The error in prediction time, if this error type is used for triggering the second measurement report. [0667] The accuracy information of prediction model calculated in UE side [0668] The threshold value configured by network.) and (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0042] The logic step 1 illustrates a table including performance parameters CQI (e.g., having ranges A, B, and C) and SINR (e.g., having ranges X, Y, Z) received, for example, from three different user devices at different locations. The table includes a quality characterization of the wireless connection associated with the user device that provided the parameters at different levels (e.g., poor, moderate, excellent). [0043] The quality factors from the logic step 3 can further be compared to historical quality factors associated with respective geobins. The historical quality factors are collected at different time intervals (1 min, 5 min, 10 min, 30 min, etc.) during a pre-defined time period (e.g., 6 hours, 12 hours, 24 hours, or 48 hours). For example, the quality factors for geobin 1, geobin 2, through geobin N are normalized across time intervals (n+1), (n+2) through (n+3) to receive updated quality factors.); and
transmit the data to the client device in response to the request (Kim, fig. 26-27, [0695]- [0696], [0697]- [0708]: [0698] 1) The network configures a measurement configuration applicable for a cell, including measurement object(s) and measurement report configurations and a threshold to detect an erroneous predictive measurement result. [0699] 2) The UE derives the first measurement result of the cell for a certain future time at which the predictive measurement result satisfies the reporting condition. [0700] The first measurement result is a predictive measurement result. [0701] 3) The UE sends the first measurement report of the cell if the first measurement result satisfies at the current time t.sub.a. [0702] At t.sub.a, the UE sends the first measurement report in both FIG. 26 and FIG. 27. [0703] The first measurement report includes time information t1 and measurement results for time t1. [0704] 4) The UE performs the measurements of the cell at time t1 and derive the second measurement result. [0705] The second measurement result is a non-predictive measurement result. [0706] 5) The UE determines the prediction error (gap) between the first measurement result and the second measurement result for the future time t1. [0707] 6-1) The UE sends the second measurement report of the cell at time t.sub.1 if the prediction error (gap) is larger than the threshold. (FIG. 26) [0708] 6-2) The UE does not send the second measurement report of the cell if the prediction error (gap) is smaller than the threshold. (FIG. 27)).
Thus, Kim does not teach determine, depending on the measurements, information characterizing the evolution over time of a relative level of quality of transmission during the first period and receive, during a second future period with respect to the first period.
Similar to the system of Kim, Gop teaches determining quality factors based on performance parameters (CQI and SINR) and comparing quality factors collected at different time intervals, which can be seen as, determine, depending on the measurements, information characterizing the evolution over time of a relative level of quality of transmission during the first period (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0042] FIG. 4 is a diagram illustrating an exemplary logic 400 for transforming performance parameters to quality factors. The logic step 1 illustrates a table including performance parameters CQI (e.g., having ranges A, B, and C) and SINR (e.g., having ranges X, Y, Z) received, for example, from three different user devices at different locations. The table includes a quality characterization of the wireless connection associated with the user device that provided the parameters at different levels (e.g., poor, moderate, excellent). The characterization is performed based on a combination of the CQI and SINR ranges. The logic step 2 illustrates a determination of the geobin (e.g., geobin 1, 2, and N) based on latitude and longitude coordinates associated with the location of the user device. The logic step 3 illustrates a correlation of the quality characterization with the respective geobins. The correlation is associated with a particular time interval. The time interval can include a real-time (or near real-time) time interval. The logic step 3 also includes a quality factor (e.g., QoS factor) associated with the geobins. For example, the geobins determined to have an excellent or moderate wireless network quality based on the performance parameters are assigned for non-GBR resources, while the geobin determined to have a poor wireless network quality is assigned for GBR resources. [0043] The quality factors from the logic step 3 can further be compared to historical quality factors associated with respective geobins. The historical quality factors are collected at different time intervals (1 min, 5 min, 10 min, 30 min, etc.) during a pre-defined time period (e.g., 6 hours, 12 hours, 24 hours, or 48 hours). For example, the quality factors for geobin 1, geobin 2, through geobin N are normalized across time intervals (n+1), (n+2) through (n+3) to receive updated quality factors.).
Similar to the system of Kim, Gop teaches collecting historical performance parameters during a first time period and updating quality factors using information collected during later time intervals (n+1, n+2, and n+3), which can be seen as, receive, during a second future period with respect to the first period (Gop, fig. 4, [0011]-[0029], [0030]-[0041], [0042]-[0058], [0059]-[0064]: [0052] At 508, the AMF can temporarily store the quality factor correlated with the geographical space to the database (e.g., the table of logic step 3 stored in the database). The AMF can further update the historical quality factors with the quality factor correlated with the geographical space associated with the real-time time interval (e.g., as described with respect to the normalization function in FIG. 4). [0053] In some implementations, the database includes historical performance parameters associated with multiple geographical spaces collected during multiple time intervals within a pre-defined time period (e.g., the table for normalization function in FIG. 4). The system can be caused to determine historical quality factors based on the historical performance parameters associated with the multiple time intervals across the pre-defined time period. For example, the historical performance parameters at time intervals (n+1), (n+2), and (n+3) can be collected over a pre-defined time period (e.g., 6 hours, 12 hours, 24hours, or 48 hours). The system can be further caused to normalize the historical quality factors associated with the multiple time intervals across the pre-defined time period (e.g., the updated quality factor (QF) in FIG. 4). In some implementations, the historical quality factors are determined based on historical performance parameters collected from one or more user devices located within the geographical space during a pre-defined time period.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim with Gop to utilize quality information collected over multiple time intervals, to improve the accuracy of transmission quality assessments and network resource management decisions (Gop, [0039]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Keranen et al. (US 20250202626 A1) abstract discloses Method, network device and a computer program in a communication network for the assisting of an application server using the Constrained Application Protocol, CoAP, in the communication of a data object to client devices, whereby the data object comprises data blocks (See fig. 2-3).
Khanka (US 20240414575 A1) summary discloses the methods and systems preserve network capacity by serially sharing content from user device to user device based on nearness of the next device to share the content. A method begins with monitoring the RF signal conditions for user devices at an event (See fig. 1).
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/FRANCESCA LIMA SANTOS/Examiner, Art Unit 2468
/MARCUS SMITH/Supervisory Patent Examiner, Art Unit 2468
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