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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/26/2025 has been entered.
Response to Arguments
Applicant’s Argument: Applicant argues the prior art fails to teach determining long or short OOS and RAT-related operation includes “holding a call session” for short-period OOS.
Examiner’s Response: Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The amended portions incorporate elements from the specification not previously claimed.
Examiner notes that claim 1 does not clearly specify a difference between long and short OOS, thus it is not clear the reference to which one determines whether OOS is short or long. A new secondary reference is applied after an updated search in which OOS is detected, and this may be considered long-period OOS, in which case the contingent limitation regarding the response to short-term OOS does not have patentable weight. Regarding claim 19, Examiner has applied a new primary reference in response to the amendment that changes the scope of the invention of the apparatus claim 19.
Claim Rejections - 35 USC § 103
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) 1-2, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshki et al. (“Pezeshki”) (US 20220038861 A1) in view of Singh et al. (“Singh”) (US 20220286994 A1, effective filing date of provisional application 63/156,765 filed March 4, 2021).
Regarding claim 1, Pezeshki teaches:
A method, comprising: extracting, by a processor of a user equipment (UE) [¶0113, UE, processor shown in Figure 5 510], one or more feature metrics regarding a wireless network environment [¶0113, determine feature metrics, e.g. “position and velocity of the UE 500 in relation to one or more transitions” regarding wireless network using machine learning algorithm] based at least partially on sensor information received from one or more sensors of the UE and radio frequency (RF) signal information from a RF circuit of the UE [¶0113 Within the UE, “the indoor/outdoor unit 560 uses one or more sensor measurements and/or one or more sensor captures (e.g., image captures) and/or one or more base station signal measurements” see further ¶0023 signal measurements include incoming communication signal measurements, RF circuit shown in Figure 2 215];
identifying, by the processor, a scenario with respect to a current status of the wireless network environment according to the one or more feature metrics [¶0113 identify position status, as well as future status corresponding to “a scenario with respect to a current status of the wireless network environment” wherein the environment being the wireless network coverage as well as expected movement of the device and future status, based on position and velocity of UE in relation to transitions and historical information];
and performing, by the processor, a radio access technology (RAT)-related operation responsive to the identifying of the scenario [¶0114 send report to the base station, further ¶0119 950 in Figure 9, UE may change a parameter considered RAT-related see ¶0026. ¶0030 indicates 5g considered RAT thus the change in beam or handover is RAT-related].
Pezeshki teaches classifying scenario but not the OOS scenario.
Singh teaches wherein a scenario with respect to a current status of the wireless network environment comprises a long-period out-of-service (OOS) or a short-period OOS [¶0160, machine learning to determine coverage scenarios including handovers and out of service events based on historical data, wherein out of service detection may be a long period out of service, Examiner noting that the claim does not clearly define how OOS is determined to be “short” or “long” thus any OOS may be determined as being “long” as in Singh, see ¶0159 where out of service indication causes switching thus considered long as there is no reference claimed], and wherein the RAT-related operation comprises holding on a call session in an event that the scenario is the short-period OOS [Examiner notes that the detection is considered a long period OOS in Singh, thus the limitations for this method claim regarding response to the “Short” OOS does not have patentable weight as it would not occur, this response being a contingent limitation see MPEP 2111.04 II].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify the step of updating the model using a result. Pezeshki teaches implementing a machine learning algorithm for determining RAT-related operations and it would have been obvious to specify detecting OOS events as in Singh as this detection mitigates negative impacts ¶0160.
Regarding claim 2, Pezeshki-Singh teaches:
The method of Claim 1, wherein the extracting of the one or more feature metrics comprises: training a machine-learning model with previous sensor information and previous RF signal information [Pezeshki ¶0104 machine learning algorithm classifies current indoor/outdoor status based on sensor and signal inputs, and the machine learning model “may provide an indoor/outdoor status classification output based on the one or more inputs. The classification accuracy [of the machine learning algorithm] may improve over time,” wherein the “inputs” are sensor and RF signal information, thus the machine learning model improves over time and is repeatedly run with current inputs, see ¶0109, Examiner noting that previous iterations use previous sensor and RF signal information and improve the accuracy of the machine learning model corresponding to “training” using “previous” information]; and extracting the one or more feature metrics by utilizing the machine-learning model based on the received sensor information and RF signal information [Pezeshki ¶0113, implement machine learning to extract position and velocity of UE in relation to one or more indoor/outdoor transitions (corresponding to extracting feature metrics) to determine indoor/outdoor status, based on sensor and received signal information as inputs].
Regarding claim 9, Pezeshki-Singh teaches:
The method of Claim 1, wherein the sensor information comprises information received from one or more of an ambient light sensor, a proximity sensor, a G-sensor, an accelerometer sensor, a magnetism sensor, a gyroscope, and a global positioning system (GPS) sensor [Pezeshki ¶0056 teaches several sensors including the ones claimed].
Claim(s) 3-5, 11-13, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshki et al. (“Pezeshki”) (US 20220038861 A1) in view of Singh et al. (“Singh”) (US 20220286994 A1, effective filing date of provisional application 63/156,765 filed March 4, 2021) and Anchan et al. (“Anchan”) (US 20160302128 A1).
Regarding claim 3, Pezeshki-Singh teaches:
The method of Claim 2, wherein the identifying of the scenario comprises: performing environment classification on the extracted one or more feature metrics [Pezeshki ¶0113 indicates metrics used to classify environment e.g. indoor outdoor]; determining that the scenario is a recurrent scenario according to a result of the environment classification [Pezeshki ¶0107 wherein the indoor status may be a recurrent scenario see where the machine learning algorithm takes into account “historical status of the UE 630 (e.g., history of being indoors at the present time of day)” indicating this may be determined to be recurring at this time of day, and further ¶0108, a period of validity of scenario being indoor/outdoor based on historical information about the UE being in particular building at particular time, considered determining it is recurrent as the UE determines it is again in indoor/outdoor scenario with a learned validity time].
Pezeshki teaches a model that improves over time but not expressly updating the model with the result. Anchan teaches updating the machine-learning model with a result of the environment classification, as current status, via an on-device learning mechanism [¶0120, machine learning algorithm using sensor and signal information to determine potential handoff decision, and update the model based on result i.e. whether handoff occurred corresponding to result of environment classification].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify the step of updating the model using a result. Pezeshki teaches implementing a machine learning algorithm for determining RAT-related operations and it would have been obvious to specify updating the machine learning model using the current result as in Anchan who teaches this allows for “improving rate adaptation reaction time during handoffs” as in ¶0003.
Regarding claim 4, Pezeshki-Singh-Anchan teaches:
The method of Claim 3, wherein the on-device learning mechanism involves: comparing feature similarity metrics to map the scenario to one or more recurrent scenarios [Pezeshki ¶0108 “determine that the UE 500 is indoors and is likely to remain indoors for a period of time. The determination of the period of time may depend on one or more factors, and may be determined by the processor 510 executing a machine-learning algorithm” factors including historical time and building considered similarity metrics, to map to recurrent indoor/outdoor status, recurrent as the validity time is learned over time based on the scenario recurring].
Pezeshki teaches comparing metrics but does not teach updating a database.
Anchan shows that it is conventional in the art for machine learning algorithms to include a step of updating a database with a result of the comparing, wherein the database is utilized by the machine-learning model in extracting the one or more feature metrics [¶0120, machine learning model uses information on potential handoffs in the past, and ¶0133 UE stores (updates) information (corresponding to database) regarding potential handoffs in data structure, see ¶0121 wherein handoff occurrence used by machine learning model].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify the step of updating a database based on a machine learning operation such as a comparison. Pezeshki teaches implementing a machine learning algorithm for determining RAT-related operations and it would have been obvious to specify updating a database after a comparison as in Anchan who teaches this allows for “improving rate adaptation reaction time during handoffs” as in ¶0003.
Regarding claim 5, Pezeshki-Singh-Anchan teaches:
The method of claim 4, wherein the performing of the RAT-related operation comprises performing steering based on one or more RAT selection strategies [Pezeshki, ¶0119 UE may perform handover based on feedback from network, considered steering, Figure 9 950, feedback considered selection strategy ].
Pezeshki teaches steering but does not teach it is inter-RAT according to the database.
Anchan teaches inter-RAT steering based on one or more RAT selection strategies according to the database [¶0121-123, UE uses machine learning model to determine inter-RAT handoff ¶0123, thus based on information used as inputs to machine learning model ¶0120-121 including stored (database) information].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify the step of inter-RAT steering based on a database. Pezeshki teaches handover based on machine learning results and it would have been obvious to specify inter-RAT steering based on machine learning results as in Anchan for improved experience during a handoff ¶0059.
Regarding claim 11, Pezeshki teaches:
A method, comprising: training, by a processor of a user equipment (UE) [¶0113, UE, processor shown in Figure 5 510], a machine-learning model for radio frequency (RF) feature extraction [¶0104 machine learning algorithm trained based on sensor and signal measurement inputs]; utilizing, by the processor, the machine-learning model to extract one or more feature metrics regarding a wireless network environment [¶0104 machine learning updated over time “The classification accuracy may improve over time, and may be particularly useful in predicting future status” and ¶0113 “For example, […] unit 560 may implement a machine-learning algorithm to determine (e.g., classify) the […] status of the UE 500 (e.g., based on a combination of sensor information and/or received signal information (e.g., measurement(s)))”] based at least partially on sensor information and RF signal information [¶0113 Within the UE, “ 560 uses one or more sensor measurements and/or one or more sensor captures (e.g., image captures) and/or one or more base station signal measurements” see further ¶0023 includes incoming communication signal measurements, RF circuit shown in Figure 2 215]; performing, by the processor, environment classification of the wireless network environment according to the one or more feature metrics [¶0113 classify indoor/outdoor status corresponding to environment classification according to metrics wherein the environment being the wireless network coverage as well as expected movement of the device]; and determining, by the processor, an action to undertake based on the result of the environment classification and UE information [¶0114 send report to the base station, further ¶0119 950 in Figure 9, UE may change a parameter considered RAT-related see ¶0026. ¶0030 indicates 5g considered RAT thus the change in beam or handover is RAT-related],
Pezeshki teaches classifying scenario but not the OOS scenario.
Singh teaches wherein a scenario with respect to a current status of the wireless network environment comprises a long-period out-of-service (OOS) or a short-period OOS [¶0160, machine learning to determine coverage scenarios including handovers and out of service events based on historical data, considered long-period OOS as the claim does not define a reference for determining long vs. short]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify the step of updating the model using a result. Pezeshki teaches implementing a machine learning algorithm for determining RAT-related operations and it would have been obvious to specify detecting OOS events as in Singh as this detection mitigates negative impacts ¶0160.
Pezeshki teaches a model that improves over time but not expressly updating the model with the
result. Anchan teaches updating the machine-learning model with a result of the environment
classification, as current status, via an on-device learning mechanism [¶0120, machine learning
algorithm using sensor and signal information to determine potential handoff decision, and
update the model based on result i.e. whether handoff occurred corresponding to result of
environment classification].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to specify the step of updating the model using a result. Pezeshki teaches
implementing a machine learning algorithm for determining RA T-related operations and it would have
been obvious to specify updating the machine learning model using the current result as in Anchan who
teaches this allows for "improving rate adaptation reaction time during handoffs" as in 110003.
Regarding claim 12, Pezeshki-Singh-Anchan teaches:
The method of Claim 11, wherein the on-device learning mechanism involves: comparing feature similarity metrics to map the scenario to one or more recurrent scenarios [Pezeshki ¶0108 “determine that the UE 500 is indoors and is likely to remain indoors for a period of time. The determination of the period of time may depend on one or more factors, and may be determined by the processor 510 executing a machine-learning algorithm” using factors including historical time and building location considered similarity metrics, to map to recurrent indoor/outdoor status, recurrent as the validity time is learned over time based on the scenario recurring].
Pezeshki teaches comparing metrics but does not teach updating a database.
Anchan shows that it is conventional in the art for machine learning algorithms to include a step of updating a database with a result of the comparing, wherein the database is utilized by the machine-learning model in extracting the one or more feature metrics [¶0120, machine learning model uses information on potential handoffs in the past, and ¶0133 UE stores (updates) information (corresponding to database) regarding potential handoffs in data structure, see ¶0121 wherein handoff occurrence used by machine learning model].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify the step of updating a database based on a machine learning operation such as a comparison. Pezeshki teaches implementing a machine learning algorithm for determining RAT-related operations and it would have been obvious to specify updating a database after a comparison as in Anchan who teaches this allows for “improving rate adaptation reaction time during handoffs” as in ¶0003.
Regarding claim 13, Pezeshki-Singh-Anchan teaches:
The method of claim 12, wherein the performing of the RAT-related operation comprises performing steering based on one or more RAT selection strategies [Pezeshki, ¶0119 UE may perform handover based on feedback from network, considered steering, Figure 9 950, considered selection strategy ].
Pezeshki teaches steering but does not teach it is inter-RAT according to the database.
Anchan teaches inter-RAT steering based on one or more RAT selection strategies according to the database [¶0121-123, UE uses machine learning model to determine inter-RAT handoff ¶0123, thus based on information used as inputs to machine learning model ¶0120-121 including stored (database) information].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify the step of inter-RAT steering based on a database. Pezeshki teaches handover based on machine learning results and it would have been obvious to specify inter-RAT steering based on machine learning results as in Anchan for improved experience during a handoff ¶0059.
Regarding claim 17, Pezeshki-Singh-Anchan teaches:
The method of Claim 11, wherein the sensor information comprises information received from one or more of an ambient light sensor, a proximity sensor, a G-sensor, an accelerometer sensor, a magnetism sensor, a gyroscope, and a global positioning system (GPS) sensor [Pezeshki ¶0056 teaches several sensors including the ones claimed].
Claim(s) 6, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshki et al. (“Pezeshki”) (US 20220038861 A1) in view of Singh et al. (“Singh”) (US 20220286994 A1, effective filing date of provisional application 63/156,765 filed March 4, 2021), Anchan et al. (“Anchan”) (US 20160302128 A1) and Yerrabommanahalli et al. (US 20140051449 A1, hereinafter ‘449).
Regarding claim 6, Pezeshki-Singh-Anchan teaches:
The method of Claim 5.
Pezeshki-Anchan teaches RAT selection strategies but not altering trigger conditions.
‘449 teaches wherein the one or more RAT selection strategies comprise an adjustment in a trigger condition of a measurement report to trigger network-leading inter-RAT steering when the UE is in a connected mode [¶0043, user device may detect reduction in frequency link performance indicating it is in connected mode with a network node, and adjusts trigger thresholds based on detection and measurements of radio frequency link].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify that the device may adjust trigger conditions during steering as in ‘449. Pezeshki-Anchan teaches a device in connected mode performing inter-RAT steering based on machine learning results and it would have been obvious to specify modifying the trigger conditions as in ‘449 to teach cell reselection before performance being significantly impacted ¶0043.
Regarding claim 14, Pezeshki-Singh-Anchan teaches:
The method of Claim 13.
Pezeshki-Anchan teaches RAT selection strategies but not altering trigger conditions.
‘449 teaches wherein the one or more RAT selection strategies comprise an adjustment in a trigger condition of a measurement report to trigger network-leading inter-RAT steering when the UE is in a connected mode [¶0043, user device may detect reduction in frequency link performance indicating it is in connected mode, and adjusts trigger thresholds based on detection and measurements of radio frequency link].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify that the device may adjust trigger conditions during steering as in ‘449. Pezeshki-Anchan teaches a device in connected mode performing inter-RAT steering based on machine learning results and it would have been obvious to specify modifying the trigger conditions as in ‘449 to teach cell reselection before performance being significantly impacted ¶0043.
Claim(s) 7, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshki et al. (“Pezeshki”) (US 20220038861 A1) in view of Singh et al. (“Singh”) (US 20220286994 A1, effective filing date of provisional application 63/156,765 filed March 4, 2021), Anchan et al. (“Anchan”) (US 20160302128 A1) and Yang et al. (“Yang”) (WO 2013019288 A1).
Regarding claim 7, Pezeshki-Singh-Anchan teaches:
The method of Claim 5.
Pezeshki-Anchan teaches inter-RAT steering but does not specify a scan in idle mode however Yang teaches wherein the one or more RAT selection strategies comprise initiating a UE-based inter-RAT selection via a frequency scan involving a local radio resource control (RRC) connection release when the UE is in an idle mode [¶0057, connection release message with RAT in redirection information, UE performs frequency scan after release].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify that the device performs a frequency scan based on a release message. Pezeshki-Anchan teaches a device in connected mode performing inter-RAT steering based on machine learning results and it would have been obvious to specify a frequency scan as in Yang as this is an essential part of finding an alternate cell ¶0056.
Regarding claim 15, Pezeshki-Singh-Anchan teaches:
The method of Claim 13.
Pezeshki-Freda-Anchan teaches inter-RAT steering but does not specify a scan in idle mode however Yang teaches wherein the one or more RAT selection strategies comprise initiating a UE-based inter-RAT selection via a frequency scan involving a local radio resource control (RRC) connection release when the UE is in an idle mode [¶0057, connection release message with RAT in redirection, UE performs frequency scan].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify that the device performs a frequency scan based on a release message. Pezeshki-Anchan teaches a device in connected mode performing inter-RAT steering based on machine learning results and it would have been obvious to specify a frequency scan as in Yang as this is an essential part of finding an alternate cell ¶0056
Claim(s) 8, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshki et al. (“Pezeshki”) (US 20220038861 A1) in view of Singh et al. (“Singh”) (US 20220286994 A1, effective filing date of provisional application 63/156,765 filed March 4, 2021), Anchan et al. (“Anchan”) (US 20160302128 A1) and Kuppelur et al. (“Kuppelur”) (US 20230047063 A1).
Regarding claim 8, Pezeshki-Singh-Anchan teaches:
The method of Claim 5, wherein the performing of the inter-RAT steering comprises: transitioning from a first RAT to camp on a second RAT responsive to degradation of a signal strength of the first RAT to less than a threshold [Anchan, ¶0091 teaches handover based on signal strength below a threshold, ¶0049 transition to another cell when radio condition lower than threshold, further in ¶0050, ¶0123 handover may be inter-RAT, see rationale for combination as in claim 5].
Pezeshki-Anchan teaches inter-RAT steering and wherein a radio condition for determining handover is signal strength but does not specifically teach transitioning back to the first RAT based on the same radio condition. Kuppelur teaches wherein the performing of the inter-RAT steering comprises: transitioning from a first RAT to camp on a second RAT responsive to degradation of a radio condition of the first RAT to less than a threshold [¶0049 transition to another cell when radio condition lower than threshold, further in ¶0050]; and transitioning back to camp on the first RAT from the second RAT responsive to the condition of the first RAT resuming to equal to or greater than the threshold [¶0051, may measure former RAT and transition back if the conditions are above a threshold], wherein a first priority level associated with the first RAT is higher than a second priority level associated with the second RAT [¶0050-51 shows the first RAT is higher priority].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach inter-RAT steering wherein a device may transition to a second RAT and then back to the first RAT in response to improved conditions as in Kuppelur. Pezeshki-Anchan already teaches inter-RAT steering based on signal strength measurements as the radio condition falling below a threshold. It would have been obvious to modify this process by transitioning back to a first RAT as Kuppelur teaches this allows for the UE to avoid remaining connected to a lower priority RAT even when the higher priority RAT is improved ¶0007.
Regarding claim 16, Pezeshki-Singh-Anchan teaches:
The method of Claim 13, wherein the performing of the inter-RAT steering comprises: transitioning from a first RAT to camp on a second RAT responsive to degradation of a radio condition of the first RAT to less than a threshold [Anchan, ¶0091 teaches handover based on signal strength below a threshold, ¶0049 transition to another cell when radio condition lower than threshold, further in ¶0050, ¶0123 handover may be inter-RAT, see rationale for combination as in claim 13].
Pezeshki-Anchan teaches inter-RAT steering and wherein a radio condition for determining handover is signal strength but does not specifically teach transitioning back to the first RAT. Kuppelur teaches wherein the performing of the inter-RAT steering comprises: transitioning from a first RAT to camp on a second RAT responsive to degradation of a radio condition of the first RAT to less than a threshold [¶0049 transition to another cell when radio condition lower than threshold, further in ¶0050]; and transitioning back to camp on the first RAT from the second RAT responsive to the condition of the first RAT resuming to equal to or greater than the threshold [¶0051, may measure former RAT and transition back if the conditions are above a threshold], wherein a first priority level associated with the first RAT is higher than a second priority level associated with the second RAT [¶0050-51].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach inter-RAT steering wherein a device may transition to a second RAT and then back to the first RAT in response to improved conditions as in Kuppelur. Pezeshki-Anchan already teaches inter-RAT steering based on signal strength measurements as the radio condition falling below a threshold. It would have been obvious to modify this process by transitioning back to a first RAT as Kuppelur teaches this allows for the UE to avoid remaining connected to a lower priority RAT even when the higher priority RAT is improved ¶0007.
Claim(s) 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nakano (US 20150271627 A1) in view of JP 2018170551 A (hereinafter ‘551).
Regarding claim 19, Nakano teaches:
An apparatus implementable in a user equipment (UE), comprising: one or more sensors; a radio frequency (RF) circuit configured to communicate wirelessly; and a processor coupled to the one or more sensors and the RF circuit, the processor configured to perform operations comprising [¶0044 portable radio device 20 comprising components, GPS unit]: extracting one or more feature metrics regarding a wireless network environment [¶0044 obtains metrics including RSSI and GPS] based at least partially on sensor information received from one or more sensors of the UE and radio frequency (RF) signal information from a RF circuit [¶0044, received signal strength from stand-by signals being RF signal information, and GSP data corresponding to GPS sensor data];
identifying a scenario with respect to a current status of the wireless network environment according to the one or more feature metrics, wherein the scenario comprises a long-period out-of-service (OOS) or a short-period OOS; [¶0044, monitor signal strength and GPS, identify a scenario in which device has entered out-of-service area, corresponding to a short-period OOS, see ¶0050 where it is monitored that the device may reenter the service area];
and performing, via the RF circuit, a radio access technology (RAT)-related operation responsive to the identifying of the scenario [the portable radio device 20 has entered an area outside the radio communication enabled area 1 of the base station (see FIG. 1) (i.e., an out-of-service area), the microcomputer 21 carries out a power save processing of the speaker-microphone, wherein ¶0049, send request via Bluetooth to device 30, Figure 1, to disconnect, thus RAT-related operation as it is Bluetooth].
Nakano teaches detecting OOS but does not teach holding a call.
‘551 teaches wherein the RAT-related operation comprises holding on a call session in an event that the scenario is the short-period OOS [“The second mobile terminal according to the present invention has a method for holding a call when moving out of service area, […] A process in which the IP-PBX transmits to the mobile terminal via the public wireless network when a mobile terminal performing wireless communication in the IP network goes out of the IP network; When the incoming call from the wireless network to the mobile terminal is an incoming call for continuation of an in-range call, a process of switching the path between the mobile terminal and the IP terminal using the public wireless network is executed.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to specify the response of holding a call in the event of moving into an out of service area as in ‘551. Nakano teaches detecting OOS and it would have been obvious to specify holding a call in order that the device may be able to move while holding a call on a mobile terminal see Background.
Allowable Subject Matter
Claim 20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Examiner notes this is an apparatus claim, as opposed to the method claim 1, thus all the contingent limitations in claim 19 must be incorporated in the claimed structure.
Conclusion
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/JAY L VOGEL/ Primary Examiner, Art Unit 2478