DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 05/06/2026 has been entered. Applicant’s amendment to the specification have overcome each and every objection to the specification as previously set forth in the Non-Final Office action mailed 02/24/2026.
Response to Arguments
Applicant's arguments filed 05/06/2026 have been fully considered but they are not persuasive.
Regarding Claim 1, rejected under 35 U.S.C. 103 as being unpatentable over Jheng et al. (US-2017/0041872-A1), hereinafter Jheng in view of Pica et al. (US-9538439-B2), hereinafter Pica and further in view of O’Connell et al. (US-11882013-B2), hereinafter O’Connell. Applicant argues, the combination of references fail to teach the determination of throughput demand. Examiner respectfully disagrees, the combination of references teach both an estimation of throughput supply and demand. Pica, discloses a method to estimate link capacity by estimating available bandwidth (i.e. supply) and achievable throughput (i.e. demand) (Pica, figs. 11-12, par. 135; the UE 11 can measure one or more parameters of a plurality of networks, some of which can be measured along with assistance information received from a network, to determine an estimated bandwidth or throughput achievable over the networks (which can be referred to herein as an available bandwidth, available bandwidth/achievable throughput, etc.)).
Applicant further argues, the combination of references fail to disclose a processor configured to determine, based on the estimated throughput demand and the estimated throughput supply whether an anomalous traffic condition has occurred. Examiner respectfully disagrees, the combination of references teach a method to estimate throughput demand and supply (Pica, figs. 11-12, par. 135; the UE 11 can measure one or more parameters of a plurality of networks, some of which can be measured along with assistance information received from a network, to determine an estimated bandwidth or throughput achievable over the networks (which can be referred to herein as an available bandwidth, available bandwidth/achievable throughput, etc.)) to determine if an anomalous traffic condition has occurred (O’Connell, figs. 1 and 5, par. 68; At block 504, a traffic pattern deviation may be determined from the monitored traffic pattern data. For example, a monitored IoT device may exhibit an unexpected traffic pattern). In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant further argues the combination of references fail to teach a processor configured to (i) if an anomalous traffic condition has not occurred, cause the transceiver to transmit a request to the NW device to configure the UE with the second set of RF parameters and (ii) if an anomalous traffic condition has occurred, cause the transceiver to transmit a request to the NW device to configure the UE with a third set of RF parameters selected to alleviate the anomalous traffic condition. Examiner respectfully disagrees, the combination of reference teach a method to estimate throughput demand and supply (Pica, figs. 11-12, par. 135; the UE 11 can measure one or more parameters of a plurality of networks, some of which can be measured along with assistance information received from a network, to determine an estimated bandwidth or throughput achievable over the networks (which can be referred to herein as an available bandwidth, available bandwidth/achievable throughput, etc.)) to determine if an anomalous traffic condition has occurred (O’Connell, figs. 1 and 5, par. 68; At block 504, a traffic pattern deviation may be determined from the monitored traffic pattern data. For example, a monitored IoT device may exhibit an unexpected traffic pattern) and configure the UE based on a set of parameters (Jheng, par. 31; Traffic characteristic module 222 determines a traffic characteristic for the UE. Traffic characteristic module 222 evaluates UE conditions and determines traffic characteristic of the UE. The traffic characteristics can be a preconfigured set and/or can be dynamically configured/updated by the system), see also par.47 where Jheng further discloses the UE is configured based on the received traffic condition. Examiner further notes, Jheng discloses the UE configuration can be dynamic, which under the broadest most reasonable interpretation means the updates are being constantly produced with a first, second or third set parameters as the traffic conditions are updated. In response to applicant's argument that Jheng, Pica or O’Connell fail to teach a procedure where the UE requests a specific configuration in one reference, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Examiner further reminds applicant, The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, see MPEP 2111.04.
For the reasons previously stated above independent claims 1 and 12 and their respective dependent claims are rejected.
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.
Claim(s) 1-6, 12-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jheng et al. (US-2017/0041872-A1) hereinafter Jheng in view of Pica et al. (US-9538439-B2) hereinafter Pica and further in view of O’Connell et al. (US-11882013-B2) hereinafter O’Connell .
Regarding Claim 1, Jheng discloses a user equipment (UE) comprising (Jheng, par. 6; A user equipment (UE) detects one or more predefined UE conditions in a mobile communication network): a transceiver configured to receive and transmit traffic (Jheng, fig. 2, par. 30; The UE has RF transceiver module 211), over a time step, via a wireless network (NW) (Jheng, par. 37; Such prescheduled application normally transmits data package periodically at certain prescheduled time interval), based on a first set of radio frequency (RF) parameters received from a NW device (Jheng, par. 34; UE 301 can configure a predefined data size threshold, or a bandwidth threshold); and a processor operably coupled to the transceiver (Jheng, fig. 2, par. 30; The UE has RF transceiver module 211, coupled with antenna 201 receives RF signals from antenna 201, converts them to baseband signals and sends them to processor 212), the processor configured to: classify the traffic received and transmitted over the time step into a traffic class (Jheng, fig. 1, par. 29; UE 101 identifies the traffic characteristic of the data request); select a second set of RF parameters based on the traffic class (Jheng, fig. 2, par. 31; The traffic characteristics can be a preconfigured set and/or can be dynamically configured/updated by the system); estimate a throughput demand and a throughput supply (Jheng, fig. 1, par. 29; UE 101 and eNB 102 also exchange messages to perform RRC re-configuration according to the parameters from the attach procedure). determine, based on the estimated throughput demand and the estimated throughput supply, whether an anomalous traffic condition has occurred; if an anomalous traffic condition has not occurred, cause the transceiver to transmit a request to the NW device to configure the UE with the second set of RF parameters; and if an anomalous traffic condition has occurred, cause the transceiver to transmit a request to the NW device to configure the UE with a third set of RF parameters selected to alleviate the anomalous traffic condition (Jheng, fig. 13, par. 23; the UE sends UE conditions to the network and the network configures UE based on the received UE conditions).
Jheng does not explicitly disclose a method to determine and estimate throughout demand or supply, however, Pica discloses method and apparatus for estimating link throughput based on assistance information (Pica, par. 157; bandwidth estimating component 17 can estimate the achievable bandwidth or throughput in the network based at least in part on the measured channel quality, or determined spectral efficiency). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine Jheng’s method for network traffic characterization with Pica’s methods for throughput estimation to enhance the transmission power consumption in a network.
The combination of Jheng and Pica does not explicitly teach a method for detecting anomalous traffic conditions, however, O’Connell discloses a method of network traffic monitoring for anomalous behavior detection (O’Connell, fig. 5, par. 68; a method 500 for changing the status of a device responsive to a device anomaly determination). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Jheng and Pica, with O’Connell’s method of network anomaly determination to enhance the network throughput prediction and improve bandwidth resource allocation.
Regarding Claim 2, the combination of Jheng, Pica and O’Connell further teaches, the UE of Claim 1, wherein: to determine that an anomalous traffic condition has not occurred, the processor is further configured to determine that the estimated throughput supply exceeds the estimated throughput demand by a threshold (O’Connell, par. 15; These anomalies may include; a volume of data transmitted by the device above or below a threshold; a frequency above or below a threshold at which the device establishes a session; the establishment of one or more sessions for a duration above or below a threshold; etc.); the processor is further configured to: determine whether the first set of RF parameters are identical to the second set of RF parameters (Jheng, par. 31; The traffic characteristics can be a preconfigured set and/or can be dynamically configured/updated by the system. The determination algorithms can be preconfigured and/or can be dynamically updated.); and determine whether a UAI timer has expired; and the request to the NW device to configure the UE with the second set of RF parameters is transmitted based on: a determination that the first set of RF parameters are not identical to the second set of RF parameters (Pica, par. 148; The UE 11 can measure one or more parameters of a plurality of networks, some of which can be measured along with assistance information received from a network, to determine an estimated bandwidth or throughput achievable over the networks); and a determination that the UAI timer has expired (Pica, par 59; Resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds) when the traffic volume was above a configurable threshold (e.g., traffic volume was at least X bits during time window T)) Examiners note, in addition, Jheng discloses the use of timers with the ability to determining when the time window expires (par. 50).
Regarding Claim 3, the combination of Jheng, Pica and O’Connell further teaches, the UE of Claim 1, wherein: to determine that an anomalous traffic condition has occurred (O’Connell, par. 39; PCF 114 may begin monitoring the packet data of device 102 responsive to an alert generated by CHF 112 indicating a traffic pattern deviation has occurred), the processor is further configured to determine that the estimated throughput supply fails to exceed the estimated throughput demand by a threshold (O’Connell, par. 39; an indication that the traffic pattern exceeded a threshold maximum or minimum, or responsive to a determination by PCF 114 that a number of usage counters at CHF 112 has deviated from a threshold); the processor is further configured to: determine whether the first set of RF parameters are identical to the third set of RF parameters (Pica, par. 148; The UE 11 can measure one or more parameters of a plurality of networks, some of which can be measured along with assistance information received from a network, to determine an estimated bandwidth or throughput achievable over the networks); and determine whether an anomaly timer has expired (Pica, par 59; Resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds) when the traffic volume was above a configurable threshold (e.g., traffic volume was at least X bits during time window T)).; and the request to the NW device to configure the UE with the third set of RF parameters is transmitted based on: a determination that the first set of RF parameters are not identical to the third set of RF parameters; and a determination that the anomaly timer has expired (Jheng, fig. 6, par 39; At step 605, the UE determines a timer interval for the prescheduled data transmission. At step 606, the UE transmits the data. After the data transmission is done, at step 607, the UE switches off the RF module. At step 608, the UE starts a timer with the timer interval for the prescheduled traffic. At step 609, the timer expired).
Regarding Claim 4, the combination of Jheng, Pica and O’Connell further teaches, the UE of Claim 1, wherein the processor is further configured to: determine a channel quality indicator (CQI) (Pica, par. 48; the received assistance information can include a TBS, a channel quality indicator (CQI)-to-TBS mapping table, a CQI offset, a power headroom offset, etc.); and estimate, based on the CQI, a modulation coding scheme (MCS), wherein, the MCS is estimated based on past observations of MCS for a given CQI, and the throughput supply is estimated based on the estimated MCS (Pica, par 162, power headroom determining component 77 can determine the power headroom as a SNR or SINR margin relative to a base modulation and coding scheme (MCS) remaining at UE 11 (via network communications component 31 ) after accounting for transmit power limitations).
Regarding Claim 5, the combination of Jheng, Pica and O’Connell further teaches, the UE of Claim 1 wherein to estimate the throughput demand, the processor is further configured to: determine a present throughput observation corresponding to the traffic; determine if a throughput observation database comprises more than one throughput observation (Pica, par 138; connected mode throughput determining component 33 configured to observe throughput for UE 11 per scheduled event over one or more past scheduling events); if the throughput observation database fails to comprise more than one observation: update the throughput observation database with the present throughput observation (O’Connell, par. 50; CHF 112 may periodically monitor a subset of devices and may update the expected traffic pattern periodically. As an example, CHF 112 may monitor 1-2% of the devices of a group over a one month time period. CHF 112 may derive the expected traffic pattern from the results and may refresh the expected traffic pattern annually); and if the throughput observation database comprises more than one throughput observation: determine, based on a statistical analysis of the throughput observation database, a z score for the present throughput observation (Pica, par. 59; an estimated fraction of available cell resources (a) may be derived based on a function of a resource block fraction (RB) (alpha_RB) and a TDM fraction (alpha_TDM). In an aspect, the number of resource blocks (alpha_RB) may be determined based on historical data: resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds) when the traffic volume was above a configurable threshold (e.g., traffic volume was at least X bits during time window T)); update, based on the z score, a value of a throughput observation counter (O’Connell, par. 41; To check for anomalies in volume data, the value of a counter used to collect session data usage over a predefined duration is compared periodically with the expected value of that counter over the same duration. For instance, the value of the counter may be compared with the expected value, e.g. when a first session is established each day, and an alert is raised if the usage deviated from the traffic pattern range by a threshold); and update the throughput observation database based on the updated value of the throughput observation counter (O’Connell, par. 61; ePCF 314 may initialize or otherwise update usage counters for tracking session establishments associated with device 102); wherein the throughput demand is estimated based on the updated throughput observation database (Jheng, par. 31; The traffic characteristics can be a preconfigured set and/or can be dynamically configured/updated by the system. The determination algorithms can be preconfigured and/or can be dynamically updated).
Regarding Claim 6, the combination of Jheng, Pica and O’Connell further teaches, the UE of Claim 5, wherein the processor is further configured to: if an absolute value of the updated value of the throughput observation counter is N, update the throughput observation database to retain only N most recent throughput observations (O’Connell, par. 61; ePCF 314 may initialize or otherwise update usage counters for tracking session establishments associated with device 102); and if an absolute value of the updated value of the throughput observation counter is less than N: update the throughput observation database to include the present throughput observation; and if the throughput observation database comprises at least K observations, update the throughput observation database to remove an oldest throughput observation, wherein N indicates how many throughput observations should deviate from a result of the statistical analysis before determining that a present throughput has substantially changed from a previous throughput, and wherein K is a large enough number to obtain a statistical analysis (O’Connell, par. 35;Different usage counters may be used to track the usage of different network services, e.g. telephony, messaging, mobile data, etc. In an example, PCF 114 may maintain usage thresholds or other policies specified by a subscription, user agreement, and/or specific network configuration. Policies maintained by PCF 114 may be modified by a user or in accordance with a configuration of PCF 114. When a threshold is exceeded or a trigger event occurs as governed by a policy of PCF 114, PCF 114 may generate an alert, restrict access to a network, change the status of a device, or take other action as governed by network policies specified at PCF 114). Examiners note, the usage of counters can be used and updated based on a user specified configuration, removing or adding counters based of data samples from throughput observation.
Regarding Claim 9, the combination of Jheng, Pica and O’Connell further teaches the UE of Claim 1, wherein to estimate the throughput demand, the processor is further configured to: determine a transport block size (TBS) for all transport blocks transmitted over the time step (Pica, par. 48; The estimated available fraction of cell resources may relate to cell resources available as indicated by the cell, such as a transport block size (TBS), power headroom offset, etc., and can be determined based on received assistance information); normalize a total of the TBS for the transport blocks transmitted over the time step based on a size of the time step (Pica, par. 59; the number of resource blocks (alpha_RB) may be determined based on historical data: resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds)); and normalize a total of the TBS for the transport blocks received over the time step based on the size of the time step, wherein the throughput demand is estimated based on the normalized total of the TBS for the transport blocks transmitted over the time step and the normalized total of the TBS for the transport blocks received over the time step (Pica par. 154; the standard TBS can be based on a default network configuration (e.g., in HSDPA). In this regard, bandwidth estimating component 17 can compute an estimated available bandwidth as an achievable throughput for the link).
Regarding Claim 10, the combination of Jheng, Pica and O’Connell further teach the UE of Claim 1, wherein to estimate the throughput demand, the processor is further configured to: determine a packet size for all packets (Jheng. Par. 34; UE 301 can determine traffic characteristics by categorizing APN. Such categorization can be preconfigured or dynamically updated. Further, UE 301 can configure a predefined data size threshold, or a bandwidth threshold.) comprising a five tuple including a process ID (Jheng, par. 33; UE 301 connects with eNB 302, which connects with MME 303. UE 301 has a subscriber identity module (SIM) card 320. Most SIM card for a UE contains subscriber information, such as International Mobile Subscriber Identity (IMSI) and service feature set) transmitted over the time step (Pica, par. 59; the number of resource blocks (alpha_RB) may be determined based on historical data: resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds)); determine a packet size for all packets comprising a five tuple including a process ID received over the time step (Pica, fig. 25, Par. 257; diagram 2500 illustrating an example of a DL frame structure in LTE, which may be received by UE 11 (discussed above). A frame (10 ms) may be divided into 10 equally sized sub-frames. Each sub-frame may include two consecutive time slots. A resource grid may be used to represent two time slots, each time slot including a resource block); normalize a total of the packet sizes for the packets comprising a five tuple including a process ID transmitted over the time step based on a size of the time step; and normalize a total of the packet sizes for the packets comprising a five tuple including a process ID received over the time step based on the size of the time step, wherein the throughput demand is estimated based on the normalized total of the packet sizes for the packets comprising a five tuple including a process ID transmitted over the time step and the normalized total of the packet sizes for the packets comprising a five tuple including a process ID received over the time step (Pica, par. 58; The estimated fraction of available cell resources can be determined from assistance information related to the cell, and an available bandwidth/achievable throughput can be estimated based on the assistance information and determined link capacity.) Examiners note, Pica, par. 251 further expands on some of the data packets that can be used, to include packet data network and all user IP-packets, which inherently encompasses a 5-tuple data packet.
Regarding Claim 11, the combination of Jheng, Pica and O’Connell further teaches the UE of Claim 1, wherein to estimate the throughput demand the processor is further configured to: segregate all packets comprising a five tuple including a process ID transmitted over the time step into real time (RT) uplink (UL) packets and non-real time (NRT) UL packets ; segregate all packets comprising a five tuple including a process ID received over the time step into RT downlink (DL) packets and NRT DL packets; determine a packet size for all RT UL packets; determine a packet size for all NRT UL packets; determine a packet size for all RT DL packets; determine a packet size for all NRT DL packets (Jheng. Par. 34; UE 301 can determine traffic characteristics by categorizing APN. Such categorization can be preconfigured or dynamically updated. Further, UE 301 can configure a predefined data size threshold, or a bandwidth threshold.); normalize a total of the packet sizes for the RT UL packets based on a size of the time step; and normalize a total of the packet sizes for the NRT UL packets based on the size of the time step; normalize a total of the packet sizes for the RT DL packets based on the size of the time step (Pica, par. 48; the received assistance information can include a TBS, a channel quality indicator (CQI)-to-TBS mapping table, a CQI offset, a power headroom offset, etc. Thus, available bandwidth or an achievable throughput over an uplink or downlink at the cell can be determined based at least in part on using the assistance information along with the estimated link capacity); and normalize a total of the packet sizes for the NRT DL packets based on the size of the time step, wherein the throughput demand is estimated based on the normalized totals of the packet sizes for the RT UL packets, the NRT UL packets, the RT DL packets, and the NRT DL packets. (Jheng, par. 37 the UE determines if the data transmission request is for a prescheduled application. Such prescheduled application normally transmits data package periodically at certain prescheduled time interval. If the traffic is not prescheduled and no other related traffic characteristic is detected, the UE may send the traffic regularly.) Examiners note, it is inherent that the data package is being transmitted in real time and non-real time.
Regarding Claim 12, Jheng discloses a method of operating a user equipment (UE) (Jheng, par. 6; A user equipment (UE) detects one or more predefined UE conditions in a mobile communication network):, the method comprising: receiving and transmitting traffic, over a time step, via a wireless network (NW) (Jheng, par. 37; Such prescheduled application normally transmits data package periodically at certain prescheduled time interval), based on a first set of radio frequency (RF) parameters received from a NW device (Jheng, par. 34; UE 301 can configure a predefined data size threshold, or a bandwidth threshold); classifying the traffic received and transmitted over the time step into a traffic class (Jheng, fig. 1, par. 29; UE 101 identifies the traffic characteristic of the data request); selecting a second set of RF parameters based on the traffic class (Jheng, fig. 2, par. 31; The traffic characteristics can be a preconfigured set and/or can be dynamically configured/updated by the system); estimating a throughput demand and a throughput supply (Jheng, fig. 1, par. 29; UE 101 and eNB 102 also exchange messages to perform RRC re-configuration according to the parameters from the attach procedure); determining, based on the estimated throughput demand and the estimated throughput supply, whether an anomalous traffic condition has occurred; if an anomalous traffic condition has not occurred, transmitting a request to the NW device to configure the UE with the second set of RF parameters; and if an anomalous traffic condition has occurred, transmitting a request to the NW device to configure the UE with a third set of RF parameters selected to alleviate the anomalous traffic condition (Jheng, fig. 13, par. 23; the UE sends UE conditions to the network and the network configures UE based on the received UE conditions).
Jheng does not explicitly disclose a method to determine and estimate throughout demand or supply, however, Pica discloses method and apparatus for estimating link throughput based on assistance information (Pica, par. 157; bandwidth estimating component 17 can estimate the achievable bandwidth or throughput in the network based at least in part on the measured channel quality, or determined spectral efficiency). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date to combine Jheng’s method for network traffic characterization with Pica’s methods for throughput estimation to enhance the transmission power consumption in a network.
The combination of Jheng and Pica does not explicitly teach a method for detecting anomalous traffic conditions, however, O’Connell discloses a method of network traffic monitoring for anomalous behavior detection (O’Connell, fig. 5, par. 68; a method 500 for changing the status of a device responsive to a device anomaly determination). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date to combine the teachings of Jheng and Pica, with O’Connell’s method of network anomaly determination to enhance the network throughput prediction and improve bandwidth resource allocation.
Regarding Claim 13, the combination of Jheng, Pica and O’Connell further teaches the method of claim 12, wherein: to determine that an anomalous traffic condition has not occurred, the method further comprises: determining that the estimated throughput supply exceeds the estimated throughput demand by a threshold (O’Connell, par. 15; These anomalies may include; a volume of data transmitted by the device above or below a threshold; a frequency above or below a threshold at which the device establishes a session; the establishment of one or more sessions for a duration above or below a threshold; etc.); the method further comprises: determining whether the first set of RF parameters are identical to the second set of RF parameters (Jheng, par. 31; The traffic characteristics can be a preconfigured set and/or can be dynamically configured/updated by the system. The determination algorithms can be preconfigured and/or can be dynamically updated.); and determining whether a UAI timer has expired; and the request to the NW device to configure the UE with the second set of RF parameters is transmitted based on: determining that the first set of RF parameters are not identical to the second set of RF parameters (Pica, par. 148; The UE 11 can measure one or more parameters of a plurality of networks, some of which can be measured along with assistance information received from a network, to determine an estimated bandwidth or throughput achievable over the networks); and determining that the UAI timer has expired (Pica, par 59; Resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds) when the traffic volume was above a configurable threshold (e.g., traffic volume was at least X bits during time window T)) Examiners note, in addition, Jheng discloses the use of timers with the ability to determining when the time window expires (par. 50).
Regarding Claim 14, the combination of Jheng, Pica and O’Connell further teaches the method of claim 12, wherein: to determine that an anomalous traffic condition has occurred (O’Connell, par. 39; PCF 114 may begin monitoring the packet data of device 102 responsive to an alert generated by CHF 112 indicating a traffic pattern deviation has occurred), the method further comprises: determining that the estimated throughput supply fails to exceed the estimated throughput demand by a threshold (O’Connell, par. 39; an indication that the traffic pattern exceeded a threshold maximum or minimum, or responsive to a determination by PCF 114 that a number of usage counters at CHF 112 has deviated from a threshold); the method further comprises: determining whether the first set of RF parameters (Pica, par. 148; The UE 11 can measure one or more parameters of a plurality of networks, some of which can be measured along with assistance information received from a network, to determine an estimated bandwidth or throughput achievable over the networks) are identical to the third set of RF parameters (Pica, par 59; Resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds) when the traffic volume was above a configurable threshold (e.g., traffic volume was at least X bits during time window T)); and determining whether an anomaly timer has expired; and the request to the NW device to configure the UE with the third set of RF parameters is transmitted based on: determining that the first set of RF parameters are not identical to the third set of RF parameters; and determining that the anomaly timer has expired (Jheng, fig. 6, par 39; At step 605, the UE determines a timer interval for the prescheduled data transmission. At step 606, the UE transmits the data. After the data transmission is done, at step 607, the UE switches off the RF module. At step 608, the UE starts a timer with the timer interval for the prescheduled traffic. At step 609, the timer expired).
Regarding Claim 15, the combination of Jheng, Pica and O’Connell further teaches the method of claim 12, further comprising: determining a channel quality indicator (CQI) (Pica, par. 48; the received assistance information can include a TBS, a channel quality indicator (CQI)-to-TBS mapping table, a CQI offset, a power headroom offset, etc.); and estimating, based on the CQI, a modulation coding scheme (MCS), wherein, the MCS is estimated based on past observations of MCS for a given CQI, and the throughput supply is estimated based on the estimated MCS (Pica, par 162, power headroom determining component 77 can determine the power headroom as a SNR or SINR margin relative to a base modulation and coding scheme (MCS) remaining at UE 11 (via network communications component 31 ) after accounting for transmit power limitations).
Regarding Claim 16, the combination of Jheng, Pica, O’Connell and Nokia further teaches wherein estimating the throughput demand comprises: determining a present throughput observation corresponding to the traffic; determining if a throughput observation database comprises more than one throughput observation (Pica, par 138; connected mode throughput determining component 33 configured to observe throughput for UE 11 per scheduled event over one or more past scheduling events); if the throughput observation database fails to comprise more than one observation: updating the throughput observation database with the present throughput observation (O’Connell, par. 50; CHF 112 may periodically monitor a subset of devices and may update the expected traffic pattern periodically. As an example, CHF 112 may monitor 1-2% of the devices of a group over a one month time period. CHF 112 may derive the expected traffic pattern from the results and may refresh the expected traffic pattern annually); if the throughput observation database comprises more than one throughput observation: determining, based on a statistical analysis of the throughput observation database, a z score for the present throughput observation (Pica, par. 59; an estimated fraction of available cell resources (a) may be derived based on a function of a resource block fraction (RB) (alpha_RB) and a TDM fraction (alpha_TDM). In an aspect, the number of resource blocks (alpha_RB) may be determined based on historical data: resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds) when the traffic volume was above a configurable threshold (e.g., traffic volume was at least X bits during time window T)); updating, based on the z score, a value of a throughput observation counter(O’Connell, par. 41; To check for anomalies in volume data, the value of a counter used to collect session data usage over a predefined duration is compared periodically with the expected value of that counter over the same duration. For instance, the value of the counter may be compared with the expected value, e.g. when a first session is established each day, and an alert is raised if the usage deviated from the traffic pattern range by a threshold); and updating the throughput observation database based on the updated value of the throughput observation counter (O’Connell, par. 61; ePCF 314 may initialize or otherwise update usage counters for tracking session establishments associated with device 102); if an absolute value of the updated value of the throughput observation counter is N, updating the throughput observation database to retain only N most recent throughput observations; if an absolute value of the updated value of the throughput observation counter is less than N:updating the throughput observation database to include the present throughput observation; and if the throughput observation database comprises at least K observations, updating the throughput observation database to remove an oldest throughput observation, wherein N indicates how many throughput observations should deviate from a result of the statistical analysis before determining that a present throughput has substantially changed from a previous throughput; wherein K is a large enough number to obtain a statistical analysis; and wherein the throughput demand is estimated based on the updated throughput observation database. (O’Connell, par. 35; Different usage counters may be used to track the usage of different network services, e.g. telephony, messaging, mobile data, etc. In an example, PCF 114 may maintain usage thresholds or other policies specified by a subscription, user agreement, and/or specific network configuration. Policies maintained by PCF 114 may be modified by a user or in accordance with a configuration of PCF 114. When a threshold is exceeded or a trigger event occurs as governed by a policy of PCF 114, PCF 114 may generate an alert, restrict access to a network, change the status of a device, or take other action as governed by network policies specified at PCF 114). Examiners note, the usage of counters can be used and updated based on a user specified configuration, removing or adding counters based of data samples from throughput observation)
Regarding Claim 19, the combination of Jheng, Pica, O’Connell and Nokia further teaches the method of Claim 12, wherein estimating the throughput demand comprises: determining a transport block size (TBS) for all transport blocks transmitted over the time step (Pica, par. 48; The estimated available fraction of cell resources may relate to cell resources available as indicated by the cell, such as a transport block size (TBS), power headroom offset, etc., and can be determined based on received assistance information); normalizing a total of the TBS for the transport blocks transmitted over the time step based on a size of the time step (Pica, par. 59; the number of resource blocks (alpha_RB) may be determined based on historical data: resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds)); and normalizing a total of the TBS for the transport blocks received over the time step based on the size of the time step, wherein the throughput demand is estimated based on the normalized total of the TBS for the transport blocks transmitted over the time step and the normalized total of the TBS for the transport blocks received over the time step (Pica par. 154; the standard TBS can be based on a default network configuration (e.g., in HSDPA). In this regard, bandwidth estimating component 17 can compute an estimated available bandwidth as an achievable throughput for the link).
Regarding Claim 20, the combination of Jheng, Pica, O’Connell and Nokia further teach the method of Claim 12, wherein estimating the throughput demand comprises: determining a packet size for all packets (Jheng. Par. 34; UE 301 can determine traffic characteristics by categorizing APN. Such categorization can be preconfigured or dynamically updated. Further, UE 301 can configure a predefined data size threshold, or a bandwidth threshold.) comprising a five tuple including a process ID (Jheng, par. 33; UE 301 connects with eNB 302, which connects with MME 303. UE 301 has a subscriber identity module (SIM) card 320. Most SIM card for a UE contains subscriber information, such as International Mobile Subscriber Identity (IMSI) and service feature set) transmitted over the time step (Pica, par. 59; the number of resource blocks (alpha_RB) may be determined based on historical data: resource blocks that were allocated to the UE in the connected mode in the recent past (e.g., over a configurable time window of T seconds)); determining a packet size for all packets comprising a five tuple including a process ID received over the time step (Pica, fig. 25, Par. 257; diagram 2500 illustrating an example of a DL frame structure in LTE, which may be received by UE 11 (discussed above). A frame (10 ms) may be divided into 10 equally sized sub-frames. Each sub-frame may include two consecutive time slots. A resource grid may be used to represent two time slots, each time slot including a resource block); normalizing a total of the packet sizes for the packets comprising a five tuple including a process ID transmitted over the time step based on a size of the time step; and normalizing a total of the packet sizes for the packets comprising a five tuple including a process ID received over the time step based on the size of the time step, wherein the throughput demand is estimated based on the normalized total of the packet sizes for the packets comprising a five tuple including a process ID transmitted over the time step and the normalized total of the packet sizes for the packets comprising a five tuple including a process ID received over the time step (Pica, par. 58; The estimated fraction of available cell resources can be determined from assistance information related to the cell, and an available bandwidth/achievable throughput can be estimated based on the assistance information and determined link capacity.) Examiners note, Pica, par. 251 further expands on some of the data packets that can be used, to include packet data network and all user IP-packets, which inherently encompasses a 5-tuple data packet.
Claim(s) 7- 8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Jheng et al. (US-2017/0041872-A1) hereinafter Jheng in view of Pica et al. (US-9538439-B2) hereinafter Pica, in view of O’Connell et al. (US-11882013-B2) hereinafter O’Connell and further in view of 3GPP RAN TSG-RAN WG1 #20 meeting (TSGR1#20(01)0548), hereinafter Nokia.
The combination of Jheng, Pica and O’Connell does not explicitly teach a method to determine throughput demand based of using standard deviation, mean or a percentile. However, Nokia discloses a method to calculate throughput using mean, standard deviation and a percentage difference to compare between data sets. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date to combine the teachings of Jheng, Pica and O’Connell with Nokia’s method of calculating throughput using mean, standard deviation and percentage differences since it was it was conventional and common knowledge in the art to use this method to calculate throughput.
Regarding Claim 7, the combination of Jheng, Pica, O’Connell and Nokia further teach, the UE of Claim 1, wherein to estimate the throughput demand, the processor is further configured to: determine a mean and a scaled standard deviation of a plurality of throughput observations comprised by a throughput observation database (Nokia, pg. 1, 2.1 simulation outputs; cell throughput, DSCH throughput and downlink mean user throughput are measured. Also the corresponding standard deviations are recorded), wherein the throughput demand is estimated based on the mean and the scaled standard deviation (Pica, par. 55; The available link capacity may be estimated by determining a supportable rate based on the channel quality index, determining a partial bandwidth corresponding to a total number of codes used by the cell for the supportable rate, scaling the partial bandwidth based on at least one of an average served number of codes, a time-division multiplexing (TDM) fraction, and a ratio of T2Pavailable/MPO to determine the partial available bandwidth, and combining the partial bandwidth to an observed connected mode throughput over one or more past scheduling events to determine the total available bandwidth).
Regarding Claim 8, the combination of Jheng, Pica, O’Connell and Nokia further teaches the UE of Claim 1, wherein to estimate the throughput demand the processor is further configured to: determine a mean and a percentile of a plurality of throughput observations comprised by a throughput observation database, wherein the throughput demand is estimated based on the percentile (Nokia, pgs. 4-7, Simulation Results, tables 4.1 and 4.2; Table 4.1 The percentage difference of the system performance when FCS cases are compared to those without FCS).
Regarding Claim 17, the combination of Jheng, Pica, O’Connell and Nokia further teach the method of Claim 12, wherein estimating the throughput demand comprises: determining a mean and a scaled standard deviation of a plurality of throughput observations comprised by a throughput observation database (Nokia, pg. 1, 2.1 simulation outputs; cell throughput, DSCH throughput and downlink mean user throughput are measured. Also the corresponding standard deviations are recorded), wherein the throughput demand is estimated based on the mean and the scaled standard deviation (Pica, par. 55; The available link capacity may be estimated by determining a supportable rate based on the channel quality index, determining a partial bandwidth corresponding to a total number of codes used by the cell for the supportable rate, scaling the partial bandwidth based on at least one of an average served number of codes, a time-division multiplexing (TDM) fraction, and a ratio of T2Pavailable/MPO to determine the partial available bandwidth, and combining the partial bandwidth to an observed connected mode throughput over one or more past scheduling events to determine the total available bandwidth).
Regarding Claim 18, the combination of Jheng, Pica, O’Connell and Nokia further teaches The method of Claim 12, wherein estimating the throughput demand comprises: determining a mean and a percentile of a plurality of throughput observations comprised by a throughput observation database, wherein the throughput demand is estimated based on the percentile (Nokia, pgs. 4-7, Simulation Results, tables 4.1 and 4.2; Table 4.1 The percentage difference of the system performance when FCS cases are compared to those without FCS).
It is noted that any citations to specific pages, columns, lines or figures in the prior art
references and any interpretation of the reference should not be considered limiting in any way. A
reference is relevant for all it contains and may be relied upon for all that it would have reasonably
suggested to a person of ordinary skill in the art. See MPEP 2123
Conclusion
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/MRCM/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649
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