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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/5/2026 has been entered.
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.
Claim 1-20 is/are rejected under 35 U.S.C. 103 as being obvious over Azizi (US 20190364492) in view of Smith (US 6721572)
Regarding claim 1, 10 and 19, Azizi teaches a method for accessing, by a mobile terminal, a network cell, comprising:
obtaining location information of the mobile terminal ([1011] FIG. 96, prediction engine 9600 may collect a variety of high-level context information, including ..location information (e.g., from a GNSS such as a GPS));
predicting a first area without network service or with weak network service (poor radio coverage), and a second area with normal network service (strong/improved radio coverage) that the mobile terminal is predicted to enter after passing through the first area, based on the location information of the terminal, ([1001] “a terminal device may utilize context information to optimize power consumption... terminal device may predict when or where the poor and strong radio coverage will occur and schedule radio activity such as cell scans and/or data transfers based on the predictions”; [1003] “by anticipating when or where a user will be in poor radio coverage along a known route; a terminal device may, for example, suspend cell scans and/or data transfer until improved radio coverage is expected”. Thus, the system predicts/anticipates when and where the weak/poor signal service area is and suspends any scanning until the radio coverage is expected/predicted to be improved/strong.)
wherein the first area and the second area are on a travel route of the mobile terminal ([1003] this disclosure may apply high-level context information to optimize radio activity on predicted radio conditions... to predict user travel routes and subsequently optimize radio activity such as cell scans and data transfer along predicted routes... terminal devices may predict which network access nodes will be available along a predicted travel route)
obtaining second area information including information about at least one network cell in the second area (predicted travel routes including where strong radio coverage is) before the mobile terminal arrives at a first area ([1001] “a terminal device may utilize context information to optimize power consumption... terminal device may predict when or where the poor and strong radio coverage will occur and schedule radio activity such as cell scans and/or data transfers based on the predictions”; [1003] this disclosure may apply high-level context information to optimize radio activity on predicted radio conditions... to predict user travel routes and subsequently optimize radio activity such as cell scans and data transfer along predicted routes. For example, by anticipating when or where a user will be in poor radio coverage along a known route (e.g., depending on base station or access point coverage..”), a terminal device may, for example, suspend cell scans and/or data transfer until improved radio coverage is expected… terminal devices may predict which network access nodes will be available along a predicted travel route and may utilize such information to make radio and radio access selections, such as selecting certain cells, certain networks”)
Thus, this means the terminal has to anticipate/predict and obtain the information regarding the first area/bad coverage and second area/improved/strong coverage areas before the terminal actually arrives at the second area. In other word, the terminal first anticipates and predicts/obtains information regarding the first and second area at an initial time at an initial area before the terminal travel to the first and second area. Using the obtained information from the prediction, then the terminal schedule the radio activity and finally the terminal would arrive at the first area where the cell scanning or data transfer are suspended until the terminal moves to second area. Therefore, the terminal obtains/predict the information regarding the second information before the mobile terminal arrives at a first area.)
performing a network cell search (cell scans) and accessing a searched network cell (radio access selections) based on the obtained second area information, in response to detecting that the mobile terminal enters the second area ([1003] this disclosure may apply high-level context information to optimize radio activity on predicted radio conditions... to predict user travel routes and subsequently optimize radio activity such as cell scans and data transfer along predicted routes... terminal devices may predict which network access nodes will be available along a predicted travel route and may utilize such information to make radio and radio access selections, such as selecting certain cells, certain networks [1011] “terminal device 9102 may utilize context information... to evaluate context information to predict user travel routes and radio conditions and to subsequently control radio activity based thereon” [1003] “by anticipating when or where a user will be in poor radio coverage along a known route; a terminal device may, for example, suspend cell scans and/or data transfer until improved radio coverage is expected”).
With further regard to claims 10 and 19, Azizi teaches a processor; and a memory ([0313] As shown in FIG. 3, terminal device 200 may include...memory 312 and one or more processors).
However, Azizi does not explicitly teach “detecting that the mobile terminal enters the second area based on time elapsed since the mobile terminal arrives at the first area”.
In an analogous art, Smith teaches detecting that the mobile terminal enters the second area (Second area is a strong-signal area, Fig. 3 shows area 306 which is “the end of the dead zone” where a call can be reestablished; Note: “the end of deadzone” is the same as the beginning of the strong-signal/second area)
based on time elapsed (time calculated for passing through the dead zone)
since the mobile terminal arrives at the first area (Fig. 3 shows area 304/first area/bad-signal area which is wireless deadzone where a call is dropped)
(Col 16; L46-51; “the time calculated for passing through the dead zone can be based on the dead zone size and the estimated speed of the vehicle. Based on these parameters, the end of the dead zone can be predicted. This prediction can be sent to the mobile unit by the base station prior to entry of the dead zone.” Col 15 L44-52; “You are approaching a dead zone in the next 2 minutes. I will be dropping the call in 1 minute and will reconnect within the next 10 minutes after passage through the dead zone." As previously disclosed in copending application Ser. No. 09/238,854, the time calculated for passing through the dead zone can be based on the dead zone size and the estimated speed of the vehicle.”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify Azizi’s teaching of predicting a second area to be based on a time elapse as taught by Smith because making the position determination based on time lapse is a simpler method than other more involved and complicated positioning methods.
Regarding claim 2, 11 and 20, Azizi and Smith teach the method according to claim 1, wherein the predicting of the first area (Azizi, predict when or where the poor coverage) and the second area and obtaining of first area information and the second area information (Azizi, predict when or where the strong coverage), are based on historical trip feature data of the mobile terminal and the location information of the mobile terminal (Azizi [1001] “a terminal device may utilize context information to optimize power consumption .. a terminal device may predict when or where the poor and strong radio coverage will occur and schedule radio activity such as cell scans and/or data transfers based on the predictions”; [1036] “prediction engine 9600 may monitor radio-related information over a windowed time period ... Prediction engine 9600 may also obtain other context information, such as one or more of location information, user-generated movement information, or time/sensory information, and utilize the historical sequence of radio conditions along with the other context information (such as current location... ) in order to predict a future sequence of radio conditions (e.g., in the order of milliseconds or seconds in the future).”).
Regarding claim 3 and 12, Azizi and Smith teach the method according to claim 2, wherein the historical trip feature data comprises at least one of a route area (Azizi, [1118] “various aspects may utilize context information such as location information, velocity information, route information, etc., from vehicles 11902-11906 at roadside network access node 11900 in order to predict vehicle trajectories”),
time information corresponding to the route area (Azizi [1036] “terminal device 9102 may implement these aspects on a more fine-grained scale. For example, in addition or alternative to applications related to controlling radio activity during travel on roads or other longer paths (which may be in the order of minutes or hours), terminal device 9102 may control radio activity over much smaller durations of time (e.g., milliseconds or seconds). For example, prediction engine 9600 may monitor radio-related information over a windowed time period (e.g., in the order of seconds or milliseconds) to obtain a historical sequence of radio conditions, which may be a sequence of signal strength measurements, signal quality measurements, or other radio-related context information. Prediction engine 9600 may also obtain other context information, such as one or more of location information, user-generated movement information, or time/sensory information, and utilize the historical sequence of radio conditions along with the other context information (such as current location, accelerometer or gyroscope information, etc.) in order to predict a future sequence of radio conditions (e.g., in the order of milliseconds or seconds in the future”),
route network information corresponding to the route area ([1118] “various aspects may utilize context information such as location information, velocity information, route information, etc., from vehicles 11902-11906 at roadside network access node 11900 in order to predict vehicle trajectories” [1026] “decision module 9612 may continue receiving prediction results from learning engine 9702 and may continually evaluate predicted route information in 9712 to determine if the predicted route has changed. For example, while prediction engine 9600 may anticipate that a user will continue on a regular or planned route, a user may make other decisions that affect the predicted route, such as by stopping a car, taking a detour, being stuck in traffic, speeding up or down”),
user feature information (Azizi [1036] “Prediction engine 9600 may also obtain other context information, such as one or more of location information, user-generated movement information, or time/sensory information, and utilize the historical sequence of radio conditions along with the other context information (such as current location, accelerometer or gyroscope information, etc.) in order to predict a future sequence of radio conditions”. Since the claim and the specification does not define what “user feature” means, the examiner interprets “user feature” as “user-generated movement information” [1264] manager application 14102 may also determine the service profile key based on a usage profile, such as how often and/or at what times or days a particular application is used, of dedicated applications 14104-14106.”), and
traffic feature information (Azizi [1036] “Prediction engine 9600 may also obtain other context information, such as one or more of location information, user-generated movement information, or time/sensory information, and utilize the historical sequence of radio conditions along with the other context information (such as current location, accelerometer or gyroscope information, etc.) in order to predict a future sequence of radio conditions (e.g., in the order of milliseconds or seconds in the future”
[1026] “decision module 9612 may continue receiving prediction results from learning engine 9702 and may continually evaluate predicted route information in 9712 to determine if the predicted route has changed. For example, while prediction engine 9600 may anticipate that a user will continue on a regular or planned route, a user may make other decisions that affect the predicted route, such as by stopping a car, taking a detour, being stuck in traffic, speeding up or down”).
Regarding claim 4 and 13, Azizi and Smith teach the teaches the method according to claim 2, wherein the first area and the second area are predicted, and the first area information and the second area are obtained, when a condition for triggering is detected, and wherein the condition for triggering comprises at least one of an expiration of a timing with a predetermined prediction period (expiry of the backoff timer), a location change (route has changed in 9712), a deviation from a trajectory (route has changed in 9712), and a change in current network connection status (In other low signal conditions.. decision module 9612 may utilize the prediction results)
(Azizi [1026] “decision module 9612 may continue receiving prediction results from learning engine 9702 and may continually evaluate predicted route information in 9712 to determine if the predicted route has changed. For example, while prediction engine 9600 may anticipate that a user will continue on a regular or planned route, a user may make other decisions that affect the predicted route, such as by stopping a car, taking a detour, being stuck in traffic, speeding up or down... Decision module 9612 may thus continuously monitor the prediction results in 9712 to identify whether the predicted route has changed. If decision module 9612 determines that the predicted route has changed in 9712, decision module 9612 may update the expected poor radio condition time in 9714 and re-set the backoff timer at baseband modem 9206 in 9710. Decision module 9612 may continue monitoring prediction results and updating the backoff timer if necessary. Eventually, terminal device 9102 may reach the end of section 9504 and thus leave the expected poor radio condition area, which may coincide with the expiry of the backoff timer. Baseband modem 9206 may then switch to normal operation modes in 9716 and restart performing cell scans (e.g., according to cell scan triggering conditions). As opposed to section 9504 in which no cells may be available, baseband modem 9206 may re-detect network access node 9110 within range of terminal device 9102 and may subsequently re-establish a connection with network access node 9110. In other low signal conditions, such as when terminal device 9102 is at a cell edge and only a single cell is detectable, decision module 9612 may utilize the prediction results to set the backoff timer to coincide with an expected time when terminal device 9102 enters the coverage area of a stronger cell”; [1026] “decision module 9612 may continue receiving prediction results from learning engine 9702 and may continually evaluate predicted route information in 9712 to determine if the predicted route has changed. For example, while prediction engine 9600 may anticipate that a user will continue on a regular or planned route, a user may make other decisions that affect the predicted route, such as by stopping a car, taking a detour, being stuck in traffic, speeding up or down.”)
Furthermore, the examiner notes that this limitation " the first area and the second area are predicted, and the first area information and the second area information are obtained, when a condition for triggering is detected" is a contingent limitation.
The word “when” indicates that the limitation “the first area and the second area are predicted” occurs only when one of “a condition for triggering is detected” is met. However, the present claims never affirmatively require such “a condition for triggering is detected” to occur. The broadest reasonable interpretation of these limitations does not require these conditional steps to be performed. See Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential) where the board held that when method steps are to be carried out only upon the occurrence of a condition precedent, the broadest reasonable interpretation holds that those steps are not required to be performed. See MPEP 2111.04; See also Application 14/231802 where the PTAB determined that there is no meaningful distinction between “if” and “when”.
As such, the whole limitation " the first area and the second area are predicted, and the first area information and the second area information are obtained, when a condition for triggering is detected, and wherein the condition for triggering comprises at least one of an expiration of a timing with a predetermined prediction period, a location change, a deviation from a trajectory, and a change in current network connection status" do not appear to have patentable weight since they are contingent upon a condition occurring.
The Examiner suggests positively reciting one of criteria is met. For example:
“determining that a condition for triggering is detected;
in response to determining that the condition for triggering is detected, predicting the first area and the second area.”
Regarding claim 5 and 14, Azizi and Smith teach the method according to claim 2, wherein the first area and the second area are predicted, and the first area information and the second area information are obtained in a server or locally (Prediction engine is part of the terminal device) in the mobile terminal (Azizi [1010] “FIG. 96 shows a functional diagram of terminal device 9102 in accordance with some aspects. As shown in FIG. 96, prediction engine 9600 may include preprocessing module 9602, local repository 9604, and local learning module 9606 while decision engine 9610 may include decision module 9612. As will be described in detail, prediction engine 9600 may receive context information as input, which prediction engine 9600 may, for example, process, store, and evaluate in order to make predictions about expected user behavior including in particular user travel routes”; Prediction engine is part of the terminal device, thus the predicting occurs locally).
Regarding claim 6 and 15, Azizi and Smith teach the method according to claim 2, wherein the second area information comprises at least one of network cell information,
network signal strength of the network cell ([1036] “prediction engine 9600 may monitor radio-related information over a windowed time period (e.g., in the order of seconds or milliseconds) to obtain a historical sequence of radio conditions; [1030] decision module 9612 may receive prediction results from prediction engine 9600 that indicate that terminal device 9102 will be in low signal conditions while traveling on a predicted route for an expected duration of time.. In a scenario where terminal device 9102 is expected to move out of low signal conditions to higher signal conditions at a later point on the predicted route (e.g., according to higher Received Signal Strength Indicator (RSSI) measurements)”),
network coverage of the network cell ([1030] “decision module 9612 instructs baseband modem 9206 to delay and reschedule data transfer for a duration of time until improved radio coverage is expected, baseband modem 9206 may reschedule some data transfer (e.g., for latency-tolerant data) but not for other data (e.g., for latency-critical data). Such smart scheduling of data transfer may dramatically reduce power consumption as data transfer will occur in more efficient conditions. Similarly, prediction engine 9600 may identify that a desired network such as a home Wi-Fi network will soon be available along the predicted route. Depending on the latency-sensitivity of data, decision module 9612 may decide to suspend data transfer until the desired network is available (e.g., in order to reduce cellular data usage).”)
network connection speed ([1615] loading network node 18212 may be a network access node such as a base station or an access point. In some aspects, interface 18214 may provide a high-speed or high-speed/capacity/reliability wireless connection between loading network node 18212 and vehicle network access node 18204, which may enable vehicle network access node 18204 to quickly download large amounts of data via loading network node 18212 via a wireless connection. [1030] decision module 9612 may receive prediction results from prediction engine 9600 that indicate that terminal device 9102 will be in low signal conditions while traveling on a predicted route for an expected duration of time. As such low signal conditions may limit data transfer speeds .. decision module 9612 may instruct baseband modem 9206 to delay data transfer for the expected duration of time until terminal device 9102 is expected to move into higher signal conditions, thus causing baseband modem 9206 to delay data transfer until terminal device 9102 transitions to the higher signal conditions that may offer higher data transfer speeds and more power-efficient data transfer... Prediction engine 9600 and decision engine 9610 may continue this process along the predicted route by identifying areas that are expected to have strong radio conditions and scheduling data transfer by baseband modem 9206 to occur during the expected strong radio conditions.),
network type ([0307] A terminal device configured to operate on a plurality of radio access technologies (e.g., the first and second RATs) can be configured in accordance with the wireless protocols of both the first and second RATs (and likewise for operation on additional RATs). For example, LTE network access nodes (e.g., eNodeBs) may transmit discovery and control information in a different format (including the type/contents of information”), and
network access status of the second area ([0332] After RAT connections are established, controller 308 may periodically trigger discovery at one or more of communication modules 306a-306d based on the current radio access status of the respective RAT connections. For example, controller 308 may establish a first RAT connection with a cell of the first RAT via first communication module 306a that was discovered during initial discovery. However, if the first RAT connection becomes poor (e.g., weak signal strength or low signal quality, or when the radio link fails and should be reestablished), controller 308 may trigger a fresh discovery procedure at first communication module 306a in order to detect other proximate cells of the first RAT to measure and potentially switch to (either via handover or reselection) another cell of the first RAT.)
(The examiner notes that the above limitation holds little weight since it does not positively recite an active step being performed or specific structure performing a function. However, for compact prosecution purpose, this limitation is also addressed. Azizi, [1118] “various aspects may utilize context information such as location information, velocity information, route information, etc., from vehicles 11902-11906 at roadside network access node 11900 in order to predict vehicle trajectories” [1036] “prediction engine 9600 may monitor radio-related information over a windowed time period (e.g., in the order of seconds or milliseconds) to obtain a historical sequence of radio conditions, which may be a sequence of signal strength measurements, signal quality measurements, or other radio-related context information. Prediction engine 9600 may also obtain other context information, such as one or more of location information, user-generated movement information, or time/sensory information, and utilize the historical sequence of radio conditions along with the other context information (such as current location, accelerometer or gyroscope information, etc.) in order to predict a future sequence of radio conditions (e.g., in the order of milliseconds or seconds in the future). Prediction engine 9600 may then provide the future sequence of radio conditions to decision engine 9610, which may control radio activity based on the future sequence of radio conditions”).
Smith teaches “the first area information comprises time required to pass through the first area under a preset condition” (time calculated for passing through the dead zone) (Fig. 3 shows area 304/first area/bad-signal area which is wireless deadzone where a call is dropped; Col 16; L46-51; “the time calculated for passing through the dead zone can be based on the dead zone size and the estimated speed of the vehicle. Based on these parameters, the end of the dead zone can be predicted. This prediction can be sent to the mobile unit by the base station prior to entry of the dead zone.” Col 15 L44-52; “You are approaching a dead zone in the next 2 minutes. I will be dropping the call in 1 minute and will reconnect within the next 10 minutes after passage through the dead zone." As previously disclosed in copending application Ser. No. 09/238,854, the time calculated for passing through the dead zone can be based on the dead zone size and the estimated speed of the vehicle.”) Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify Azizi’s teaching of predicting a second area to be based on a time elapse as taught by Smith because making the position determination based on time lapse is a simpler method than other more involved and complicated positioning methods.
Regarding claim 7 and 16, Azizi and Smith teach the method according to claim 6, wherein the network cell information of the second area is information on a network cell which the mobile terminal prefers to access in the second area (The examiner notes that the wherein limitation holds little weight as it does not positively recite an active step nor does it recite any specific structure performing a specific function. Nonetheless, this limitation was addressed for compact prosecution purpose. Azizi, [3607] “In Example 1293, the subject matter of Example 1280 can optionally include wherein the predicted radio conditions indicate predicted radio conditions of one or more first network access nodes of the one or more first areas and predicted radio conditions of one or more second network access nodes of the one or more second areas, and wherein controlling the radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas includes .. and selecting a second serving access node from the one or more second network access nodes while traveling in the one or more second areas based on the predicted radio conditions”).
Regarding claim 8 and 17, Azizi and Smith teach the method according to claim 1, further comprising: after the obtaining the second area information, detecting whether the mobile terminal enters the second area (Azizi [3597] “In Example 1283, the subject matter of Example 1282 can optionally include wherein controlling the radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas further includes triggering cell scans after entering the one or more second areas. [3598] In Example 1284, the subject matter of Example 1281 can optionally further include determining that a user is currently traveling in the one or more first areas, and determining that the predicted route runs through the one or more second areas, wherein controlling the radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas includes suspending cell scans while traveling in the one or more first areas until the one or more second areas is reached.”) However, Azizi does not explicitly teach that the detecting is based on time elapsed since the mobile terminal arrives at the first area.
Smith teaches detecting that the mobile terminal enters the second area (Second area is a strong-signal area, Fig. 3 shows area 306 which is “the end of the dead zone” where a call can be reestablished; Note: “the end of deadzone” is the same as the beginning of the strong-signal/second area) based on time elapsed (time calculated for passing through the dead zone) since the mobile terminal arrives at the first area (Fig. 3 shows area 304/first area/bad-signal area which is wireless deadzone where a call is dropped)
(Col 16; L46-51; “the time calculated for passing through the dead zone can be based on the dead zone size and the estimated speed of the vehicle. Based on these parameters, the end of the dead zone can be predicted. This prediction can be sent to the mobile unit by the base station prior to entry of the dead zone.” Col 15 L44-52; “You are approaching a dead zone in the next 2 minutes. I will be dropping the call in 1 minute and will reconnect within the next 10 minutes after passage through the dead zone." As previously disclosed in copending application Ser. No. 09/238,854, the time calculated for passing through the dead zone can be based on the dead zone size and the estimated speed of the vehicle.”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify Azizi’s teaching of predicting a second area to be based on a time elapse as taught by Smith because making the position determination based on time lapse is a simpler method than other more involved and complicated positioning methods.
Regarding claim 9 and 18, Azizi and Smith teach the method according to claim 8, further comprising: determining whether the mobile terminal arrives at the first area based on the positioning result of the mobile terminal (Azizi [3597] “In Example 1283, the subject matter of Example 1282 can optionally include wherein controlling the radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas further includes triggering cell scans after entering the one or more second areas” Fig. 97 discloses “Monitor location and set backoff timer when poor radio condition area/{first area} is reached” See step 9708.)
Response to Arguments
Applicant's arguments filed 06/05/26 have been fully considered but they are not persuasive.
Applicants argues that,
“Applicants submit that Azizi fails to disclose or render obvious "obtaining second area information including information about at least one network cell in the second area before the mobile terminal arrives at the first area;”
The examiner respectfully disagrees. As shown in Azizi’s disclosure, paragraph [1003] below, the terminal first predicts and obtains a first area without/weak network service and a second area with normal network service. Since the terminal can predict/obtain the first and second areas ahead of time, the terminal is able to anticipate and schedule when to suspend its own cell scanning when the mobile terminal is in the weak area and to stop scanning or perform data transfer until the coverage is expected/predicted to be improved or strong. Thus, in this scenario, Azizi’s teaching encompass the alleged missing limitation. In order for the mobile to anticipate and schedule the suspend scanning in weak-signal area UNTIL the coverage area expected to be improved/strong, the information of where second/strong-signal area has to be obtained before the terminal actually arrived at the first/weak coverage area.
Azizi teaches the following paragraphs.
[1001] “terminal device may predict when or where the poor and strong radio coverage will occur and schedule radio activity such as cell scans and/or data transfers based on the predictions”;
[1003] “by anticipating when or where a user will be in poor radio coverage along a known route; a terminal device may, for example, suspend cell scans and/or data transfer until improved radio coverage is expected”.
Applicants argues that,
Paragraphs [1001], [1003], and [1011] of Azizi at most teach that a terminal device may predict where poor or strong radio coverage will occur and may predict which network access nodes will be available along a travel route. Such teachings amount only to general prediction of coverage conditions or network availability, and do not satisfy the claim's requirement of a specific temporal relationship. In particular, the claim requires that second area information be obtained before the mobile terminal arrives at the first area, which imposes a clear ordering constraint tied to two distinct areas along a route.
The examiner respectfully disagrees. By definition, “predict” means an estimate that a specified event will happen in the future. “Schedule” means in a plan to be carried out some time in the future. Azizi teaches the terminal first predicts/obtains information that these first/weak and second/strong coverage areas will happen in the future along an expected travel route first.
In other word, the terminal first anticipates and predicts/obtains information regarding the first and second area at an initial time at an initial area before the terminal travel to the first and second area. Next, using the obtained information from the prediction, the terminal then schedules the radio activity and finally the terminal would arrive at the first area where the cell scanning or data transfer are suspended until the terminal moves to second area. Therefore, the terminal obtains/predict the information regarding the second information before the mobile terminal arrives at a first area.)
Citation of Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Sasabuchi (US 20150291159) teaches predicting location (future location) of the second vehicle B or the person H when a predetermined time elapses based on the identified travel direction and travel speed and calculates the first information based on the relation among the future location ([0043]).
Awad (US 20090247137) teaches notifications of impending call drops, impending loss of connectivity to a wireless network or a particular wireless service, or traversing an area with other specific characteristics are provided to a user of a mobile device. The determination of an impending call drop or service loss may be made based on network statistics collected on areas around the predicted path or current location of the user and/or the mobile device ([0003]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUNG L LAM whose telephone number is (571)272-6497. The examiner can normally be reached Monday -Thursday 9-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeanette Parker can be reached at 571-270-3647. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DUNG L LAM/Examiner, Art Unit 2646
Prosecution Insights