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 .
This action is responsive to communication received on 02/11/2026. Claims 1-2, 4-13 and 21 -28 are pending of which claim 1, 10 are amended and claim 21-28 newly added.
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.
Claims 1-2, 4-8 and 10-13 and 21-27 are rejected under 35 U.S.C. 103 as being unpatentable over Egner US 2017/0272972, and further in view of Senarath US 2018/0270073.
Regarding claims 1, 21 Egner teaches a system, and a non-transitory CRM comprising instruction executed by a processor to implement the system comprising: at least one processor; at least one memory coupled to the at least one processor, the memory having computer-executable instructions stored thereon that, when executed by the at least one processor, cause the system to: receive user device data from a user device, the user device data comprising data describing a location of the user device(smart connection manager determines current and predicted location an user to assign/recommend connections to use, ¶48);
[0048] In yet another example embodiment, a smart connect manager application may be run at a smart vehicle gateway 135 to determine wireless connection access options based on vehicle location and relevant context and anticipated communication and data needs in accordance with embodiments disclosed herein. Smart connection manager at a smart vehicle gateway 135 may request or send data objects or information to or from an application running at a remote data center 186 or another location such as a context aware radio resource management system remote server 190. In an aspect of the present disclosure, smart connection manager may determine scheduling of data transmissions based on wireless conditions at current and future locations and based on priority levels assigned to various types of data by criticality classification. Context aware radio resource management system remote server 190 may operate a context aware radio resource management system application according to the present disclosure. Similarly, remote access may be available to a remote database with wireless intelligence reports 195.
The user data being transmitted on a rate for the user device representing a cost to a user associated with the user device to use the networking services(the system considers both the performance/qos and the cost per byte for the user to use the particular link by a particular carrier/network provider, ¶s54,81, 172)
[0054]… In the case of a network broker server system, billing and other coordination of SIM profile options may be managed by a broker such as an MVNO. The context aware radio resource management system is described further below.
[0081] With a smart filtering or screening of the IMSIs requested from a network broker system, the network broker system may be able to carry a fewer number of IMSIs to supply the pools for checkout by users. This may in turn reduce costs at the network broker by requiring fewer licenses to the IMSIs. That cost savings may also reduce costs for a user.
[0172] In yet another embodiment, wireless conditions may also be rated according to some embodiments herein according to cost of data or communication transfers. For example, roaming charges may be incurred for some wireless links. In other examples, some carriers may be substantially more expensive on a cost per byte basis. In some embodiments, link ratings may be weighted by factors including cost of data transfers or communications and accordingly determination of wireless condition classification will similarly be affected. These factors may be dependent on user selection or determination by IT management of mobile information handling systems through user settings and the like.
identify a cost for networking services to be provided to the user device based on an indication of a location within which the networking services are provided and a time at which the networking services are to be provided(along a predicted path the cost for data transmission is determined for each geographic sector on a map, ¶72,77 fic 10c);
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.
[0077] In one example embodiment, smart vehicle gateway 235 may have a second WWAN wireless adapter with a second eSIM 276. In some embodiments, it is also understood that more than two wireless adapters with separate eSIMs is contemplated. With at least two WWAN wireless adapters each with switchable eSIMs 275 and 276, the smart connection manager 201 of a smart vehicle gateway 235 may establish two or more wireless links to a WWAN for efficient and seamless communication to the WWAN depending on the wireless conditions of a location of vehicle 236. The two or more wireless links may thus be established across the same wireless service carrier or across different wireless service carriers. Each wireless carrier may be utilized as a home network. Further, plural wireless protocols may be established for the plurality of wireless links to improve options for vehicle communication with the WWAN. This may be beneficial for vehicle travel across borders or between ranges of service provider network systems to avoid roaming which may cause increased costs and potential delays. In this way, the smart connection manager 201 of the smart vehicle gateway 235 may leverage the context aware radio resource management system with mobile wireless traffic reports and wireless usage trend data to opportunistically select from established wireless links to a WWAN.
the cost for networking services to be provided to the user device being the cost incurred by the network to provide the networking services to the user device(costs include cost per gigabyte for particular service provider that the provider charges the user, ¶s81-83)
[0081] With a smart filtering or screening of the IMSIs requested from a network broker system, the network broker system may be able to carry a fewer number of IMSIs to supply the pools for checkout by users. This may in turn reduce costs at the network broker by requiring fewer licenses to the IMSIs. That cost savings may also reduce costs for a user.
[0082] In addition, the context aware radio resource management system will request only IMSIs for optimal wireless link options as determined by link ratings described in the present disclosure. Thus, when a smart vehicle gateway or mobile information handling system switches to an optimal WWAN wireless link, an IMSI corresponding to the WWAN wireless link provides for that communication to be on a “home” network. This avoids roaming connections. It is understood that roaming connections may be more expensive to operate on a wireless service provider. Additionally, roaming connections may be substantially less efficient. In some cases, a roaming connection from a home network of an IMSI must be routed back to a home network link with the desired alternative wireless service provider. This can require additional communication links to achieve. A direct connection via an access to a home wireless network may be more efficient and less costly. Thus, selecting or switching between optimal wireless service providers by switching IMSIs may yield cost savings on a cost per gigabyte basis.
[0083] It is understood that cost per gigabyte may also vary between wireless links available from the list of optimal wireless links determined via a context aware radio resource management system as described herein. Cost per gigabyte on wireless service carriers may vary among WWAN links. For example, a vehicle travelling across borders may be subject to substantial cost fluctuations among WWAN carriers. In another example embodiment, some wireless links, such as non-WWAN links may be less expensive as well. Settings for a smart vehicle gateway or a mobile information handling system may serve to prioritize cost per gigabyte based on location when selection from optimal wireless links is made. Moreover, a smart connection manager may select between a plurality of simultaneous wireless links established for a smart vehicle gateway. The basis of selection may be on quality of the links available, traffic levels, or suitability to expected data needs. However, the basis of selection may also be based on a cost per gigabyte basis to select the most cost efficient option when available.
including: identify a network sector based on the indication of the location(connectivity condition determined from crowd source data of grad boxes(i.e. sectors) on a map, ¶s169,170)
[0169] In the example embodiment of FIG. 10C, an example embodiment of a bin map is shown with a predicted future path 1022 overlaid on grid boxes 1015 showing levels of wireless connectivity, including wireless conditions, for locations along the predicted future path. Legend 1025 depicts an example embodiment of crowd-sourced data available for the predicted future path. Bold grid boxes 1026 indicate locations along the predicted future path 1022 determined by the mobile path prediction system described in embodiments herein. Grid boxes 1015 may also indicate wireless connectivity conditions.
[0170] Great wireless conditions are indicated for locations with highlighting such as 1027. Good wireless connectivity conditions are indicated for locations with highlighting such as 1028. Poor wireless connectivity conditions are indicated for locations with highlighting such as 1029. These classifications of wireless connectivity conditions are only an example embodiment of classification of wireless conditions along a predicted future path. It is understood that multiple or fewer classification levels may be used instead. Wireless connectivity levels may be determined based on link ratings for wireless links as in the present disclosure. Thresholds of connectivity conditions may be set according to link rating levels. For example, link ratings at locations such as those depicted in FIG. 10B or 10C, will be drawn from crowd-sourced wireless traffic reports. Descriptions for determination of link ratings according to the present disclosure will rely in many embodiments on one or more QoS parameters as disclosed in several embodiments herein. The link ratings in the presently described embodiments appear as a percentage out of 100%, although any scale will work. A threshold level of wireless link rating may be used in determining a classification of the wireless link based on its expected wireless conditions. Multiple wireless link rating thresholds may define several classifications for wireless conditions at locations such as depicted in FIG. 10B and FIG. 10C.
predict a future network load for the network sector at the time at which networking services are to be provided to the user device(desired priorities and cost are calculated for a predicted path …. Predicting the load along the path as user travels through each sectors(home, work ) in a cells(sectors ) of the map, ¶146, fig 10C)
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines an optimal wireless link selection for a mobile information handling system in terms of cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
and determine the cost for the networking services based on the predicted future network load for the network sector(priority and cost of data usage in the sectors along the future path are predicted so as to control when to reschedule sending of data and/or which networks to use to minimize cost, ¶s72, 146)
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines an optimal wireless link selection for a mobile information handling system in terms of cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
and cause the user device to transmit data based on a priority of a type of the data transmitted by the user device and the dynamic rate for the user device to user the networking services provided to the user device by the network service provider instead of the rate for the user device to use the networking services provide to the user by the(user’s mobile device transmits and receives data on a selected link rating , such ratings include cost considerations, where such higher priority data may require and higher quality link which imply higher cost per byte, and for lower priority data may choose to delay transmission of such lower quality data for a low cost network such as, ¶s72, 204-208.
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.
[0204] In accordance with several disclosures herein, link ratings that define wireless conditions as locations along a predicted future path may be impacted by several additional factors in addition to the QoS parameters described in embodiments above. Other embodiments discussed in the present disclosure include the effect of wireless link energy consumption on link ratings as well. If a mobile information handling system relies on battery power, a state of charge may determine whether link energy consumption by wireless links is factored in link rating determinations. As explained in examples above, weighting may be applied to lower power consuming wireless links when determining link ratings. In another example embodiment, cost of data transfer or communication may be established as a weighting factor in determining a link rating for a wireless link at a location. For example, an additional roaming charge at a location may reduce the link rating of a wireless link at a location. These factors are discussed in embodiments above. Link rating determinations relate to wireless condition assessment and classification in the present embodiment. As described above, these wireless condition assessments and classifications may include the factors including link energy consumption or cost of data or communication transfers.
[0205] Proceeding to 1404, the smart connection manager may receive data to be transmitted. In a mobile information handling system embodiment, data to be transmitted is queued and prepared for transmission by the wireless adapter. In a further embodiment where the mobile information handling system is a mobile gateway device, data from IoT devices and information handling systems in a local network are transmitted to the mobile gateway device and received their for pass-through transmission controlled by the smart connection manager on one or more established optimal wireless links.
[0206] Proceeding to 1406, the smart connection manager may determine classification of data to be transmitted. In an aspect of the present disclosure, data to be transmitted must be classified with respect to criticality. Transmission policy may be established for the smart connection manager to receive an indication of the type of data to be transmitted and to classify that data according to the indicated type. Examples of data to be transmitted are discussed in several embodiments herein.
[0207] Transmission policy for data classification by the smart connection manager may generally follow certain rules of classification in an example embodiment. Further, policy may follow some guidelines with respect to criticality classification in some embodiments. For example, data that involves ongoing communication or highly critical data for ongoing operations to proceed may be classified as high priority to avoid undue interruption of the users. Data involving burst data transfers or substantial queuing capability may have a lesser priority. Data that involves extremely high throughput requirements may be of lesser priority in some cases due to its effect on other data transmission. Periodically transmitted data, such as background data transfers, may be an example of lowest priority data. For example, IoT data involving monitoring of sensors and the like and that may periodically transmit data to a remotely-located IoT big data tracking software application system may often be of low priority.
[0208] In several aspects, data content, such as tracked IoT data content, may determine or impact classification levels of priority assigned to data to be transmitted at 1406. In an example, tracked IoT relating to mission critical key performance indicators for critical aspects of a system's operation, safety monitoring, or health monitoring key performance indicators for user biometrics, may be assigned a higher priority classification. In some example embodiments, data classification may even include certain types of data whereby a smart connection manager may elect not to transmit the data and instead store the data locally to avoid undue burden on the established wireless link when wireless conditions at a location are poor.
Egner does not teach generate a dynamic rate for the user device to use the networking services provided to the user by the network service provider based on the user device data and the cost for the networking services. Senarath in the same field of endeavor as the invention teaches a system for an adjustable rate charging system. Senarath teaches generate a dynamic rate for the user device to use the networking services provided to the user by the network service provider based on the user device data and the cost for the networking services(charging information based on dynamic charges for congestion pricing is received so the user or devices of the user based on preferences can automatically change the behavior of the devices such as disconnecting in higher price times, congestion pricing is different for different geographical regions, ¶s91,210 233) .
[0091] To supplement the conventional charging data collection embodiments of the present invention allow for the placement of monitoring functions at different locations of the network. The charging data collected by the monitoring functions can vary, so that one instance of a monitoring function can track the number of transactions, while another can track the volume of data. A single data flow associated with a UE may be monitored by more than one function. Services may be charged on a per-use basis (e.g. a per-transaction basis), based on traffic (e.g. a per-bit basis), etc. The collected charging data may also include information not used in 3G/4G networks. In addition to a time of day charging structure that is applied across a network, next generation networks may employ geographically differentiated charging. This may allow a network to charge more for data in a geographic region of the network that is particularly congested. To do this, the location of the UE, either in absolute terms, or in relation to the topology of the network would need to be included in the collected data. Furthermore, the time and traffic loads may need to be available to correlate to this charging record if not recorded in the charging data. If the UE location is based on a UE reported location, the placement of the monitoring function can be varied. If the location data is not based on, for example, a GPS location reported by the UE, then the placement of the charging function either at a basestation/access node or at an anchor point serving a plurality of basestations can be used to collect this information. To facilitate charging customization, charging data collection function (or a monitoring function) can be instantiated at a selected location in the core network in order to extract network activity information. This collected information can be provided to the OSS/BSS or to a customer for use in a given billing scenario. Charging can vary for example based on a geographic location of the network usage, traffic or congestion considerations, or time of day considerations, for example.
[0210] An example of a bidding process is described as follows, with reference to FIG. 11. At step 2000 UEs may send requests to an operator for a particular service to be provided for a given charging rate, and these requests are received. If the service is relevant to an important or popular event (e.g., live event or disaster), or the bidding process occurs at a peak service consumption time, a significant number of UEs may be requesting the service. The UE request for service can be handled by the CSP, or by the NO on behalf of the CSP. This can effectively provide congestion pricing incentives so that during events that create congestion, users will willingly (or UEs will automatically, based on user preferences) disconnect or reduce their service expectations. The changes in charging rate and charging rules can by dynamic until a desired effect is achieved.
[0233] In various embodiments, the UE may be executing an application that automatically performs load balancing and/or switching between two available systems. For example, a type of access via a Wi-Fi network may be selected dynamically based on charging information. Multiple types of access may be used concurrently, with different access types receiving different proportions of the user's traffic in accordance with a load balancing operation. The UE may also include a function that allows for scheduling of transmissions based on fore-knowledge of charging rates and the differences in rates based on time, location and/or access technology, and optionally based on fore-knowledge of UE mobility and route (e.g. knowledge of the UE location). Such a function may also be resident in the CSP and provide instructions to the UE to effect the same function. There may also be coordinating functions in the UE and CSP to effect this functionality. Such a function may allow for transmission of data for select applications or services and defer the access of other applications and services based on the above described factors.
It would have been obvious to a person of ordinary skill in the art at the time of the effective filing of the instant application to modify Egner’s prediction of cost for providing services based on crow-source data along predicted travel with information on adaptive data rate charged for users as taught by Senarath. The reason for this modification would be to affect user data behavior that save resource cost to the service provider for providing transmission resource and provide monetary saving cost to the user for using lower cost data connections or delay transmission until devices are located withing lower cost data connections.
Regarding claims 2 and 22, Egner teaches wherein the user device data further comprises an indication of a priority of a type of data that the user device transmits.
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines an optimal wireless link selection for a mobile information handling system in terms of cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
Regarding claims 4 and 23, Egner teaches wherein the computer-executable instructions, when executed by the at least one processor, further cause the system to: identify a path of the user device;
[0147] The context aware radio resource management system utilizes a path prediction system 805 to determine a predicted future path of travel for the mobile device such as a mobile gateway device during a set of future time intervals. The mobile path prediction system uses position data for the mobile information handling system. Velocity and acceleration data are detected by motion sensors the mobile device or on the vehicle of a smart vehicle gateway in one aspect. Velocity and acceleration may also be determined from position data or changes in position data in other aspects. Position data may be determined via a global positioning system. Alternatively, a mobile positioning system via the wireless network may determine position and movement of the mobile information handling system. Using this position data, the mobile path prediction system estimates a predicted future path of travel for mobile information handling system. In an example embodiment, the mobile information handling system is a smart vehicle gateway or other mobile gateway device travelling in a user area.
identify one or more network sectors within which the user device is likely to be located based on the path of the user device(path is predicted across a map grid of sectors, ¶158)
[0158] The context aware radio resource management system proceeds to 935. At 935, the path prediction system portion correlates the preliminary predicted path with the visitation history location matrix. The preliminary predicted path begins as a selected mobile device trajectory. In the example embodiment, this may be done via overlay of bin maps containing both preliminary predicted path and the visitation history information for locations near the preliminary predicted path. An example visitation history matrix bin map is shown in FIG. 9B below. Nearby locations for the visitation history matrix may be limited to those locations that fall within a certain number of bin map grid boxes from the preliminary predicted path. How many bin map grid boxes are used as nearby locations will depend on the physical size of each bin map grid box and factors such as how many future time intervals are used to determine the predicted future path.
for each respective network sector of the one or more network sectors:
identify a respective cost for networking services to be provided to the user device within the respective network sector based on the path of the user device(cost of service, signal quality etc analyze for each grid location on the predicted path of the user device, ¶s72,146,158)
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines
[0158] The context aware radio resource management system proceeds to 935. At 935, the path prediction system portion correlates the preliminary predicted path with the visitation history location matrix. The preliminary predicted path begins as a selected mobile device trajectory. In the example embodiment, this may be done via overlay of bin maps containing both preliminary predicted path and the visitation history information for locations near the preliminary predicted path. An example visitation history matrix bin map is shown in FIG. 9B below. Nearby locations for the visitation history matrix may be limited to those locations that fall within a certain number of bin map grid boxes from the preliminary predicted path. How many bin map grid boxes are used as nearby locations will depend on the physical size of each bin map grid box and factors such as how many future time intervals are used to determine the predicted future path.
determine a respective dynamic rate for the user device to use the networking services based on the user device data and the respective cost for networking services;
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.
and cause the user device to transmit data based on the respective dynamic rate when the user device is located within the respective network sector.
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.
Regarding claims 5 and 24, Egner teaches wherein the computer-executable instructions, when executed by the at least one processor, further cause the system to: identify one or more speeds of the user device as the user device travels along the identified path.
[0147] The context aware radio resource management system utilizes a path prediction system 805 to determine a predicted future path of travel for the mobile device such as a mobile gateway device during a set of future time intervals. The mobile path prediction system uses position data for the mobile information handling system. Velocity and acceleration data are detected by motion sensors the mobile device or on the vehicle of a smart vehicle gateway in one aspect. Velocity and acceleration may also be determined from position data or changes in position data in other aspects. Position data may be determined via a global positioning system. Alternatively, a mobile positioning system via the wireless network may determine position and movement of the mobile information handling system. Using this position data, the mobile path prediction system estimates a predicted future path of travel for mobile information handling system. In an example embodiment, the mobile information handling system is a smart vehicle gateway or other mobile gateway device travelling in a user area.
Regarding claims 6 ands 25, Egner teaches wherein the computer-executable instructions, when executed by the at least on processor to determine the respective dynamic rate, further cause the processor to: determine the respective dynamic rate for the user device based on the user device data, the respective cost for networking services, and the one or more speeds of the user device(adaptive rate to schedule data transmission based on velocity of user device, cost of wireless links and data priority, ¶s146,147, 210 ).
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines an optimal wireless link selection for a mobile information handling system in terms of cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
[0147] The context aware radio resource management system utilizes a path prediction system 805 to determine a predicted future path of travel for the mobile device such as a mobile gateway device during a set of future time intervals. The mobile path prediction system uses position data for the mobile information handling system. Velocity and acceleration data are detected by motion sensors the mobile device or on the vehicle of a smart vehicle gateway in one aspect. Velocity and acceleration may also be determined from position data or changes in position data in other aspects. Position data may be determined via a global positioning system. Alternatively, a mobile positioning system via the wireless network may determine position and movement of the mobile information handling system. Using this position data, the mobile path prediction system estimates a predicted future path of travel for mobile information handling system. In an example embodiment, the mobile information handling system is a smart vehicle gateway or other mobile gateway device travelling in a user area.
[0210] Flow may proceed to 1408 where the smart connection manager may determine if data to be transmitted is high-critical or of high priority. Examples shown above in Table 1 include voice data, video conferencing data, mission critical key performance indicators and the like. If the data to be transmitted is determined to be highly critical, then flow may proceed to 1410 where the data will be transmitted regardless of the wireless conditions if transmission is possible. In an example embodiment, the smart connection manager may seek to switch wireless links, for example when multiple wireless links are simultaneously established for a mobile information handling system. According to embodiments described, a plurality of wireless links may be available. The smart connection manager will seek to connect to the best option when wireless conditions are poor for the wireless link prepared for the predicted future path. The smart connection manager may then select from among the available options at the location along the predicted future path to transmit the highly critical data. In yet another example embodiment, a new wireless link may be established by the smart connection manager despite previous selection for a wireless link for a predicted future path. For highly critical data, the process of ranking and selecting an optimal wireless link for a location may be started anew to ensure transmission of such data in some embodiments. When transmission does occur, data will be transmitted according to regular queuing and transmission procedures by the wireless adapter. At this point, the flow may end.
Regarding claims 7 and 26, Egner teaches, wherein the computer-executable instructions, when executed by the at least one processor to cause the user device to transmit data based on the respective dynamic rate, further cause the processor to: provision one or more resources within the respective network sector for the user device(scheduling, i.e. radio resource management provisions i.e schedules transmission resources based on cost, priority and other factors, ¶84)
[0084] FIG. 3 illustrates a context aware radio resource management method for use in selecting a network and technology within wireless network 100 at a given location. Several factors are assessed by the context aware radio resource management method in selecting a radio technology and a service provider. A software agent is deployed at a mobile information handling system or elsewhere in the network for executing the context aware radio resource management method. In one example embodiment, the context aware radio resource manager may reside at a mobile information handling system or smart vehicle gateway such as 135 and may interface with the cloud based context aware resource management system server such as 190. At step 310, the context aware radio resource management system software agent obtains user profile data. The user profile data establishes an approximate cyclostationary usage pattern of the mobile information handling system. The time of day, location, types of usage, and usage percentages during a sample time interval are example factors included in the user profile data.
Regarding claims 8 and 27, Egner teaches wherein the computer-executable instructions, when executed by the at least on processor to provision the one or more resources further causes the processor to: allocate one or more network slices for the user device(time slices are allocated for device to transmit/receive data in a mobile system, such time slices also referred to a transmission scheduling, ¶99,198)
[0099] A previous user data profile collected for the operation of the mobile information handling system, smart vehicle gateway, or IoT sensors/devices may serve as the baseline device profile for the respective device. Such a profile is specific to the location of the device and to a time slice during which operation is being optimized. Locations may be assigned to geographic zones such as a campus, city, borough, county, etc. Time may be assigned to defined time periods during a day but may differ across days of the week. This zoning and time definition is optional but will help control the number of different user profiles generated.
[0198] FIG. 14A and FIG. 14B show an example method embodiment for operation of a context aware radio resource management system for scheduling transmission of data based on critical level classification of the data and wireless link radio conditions. In particular, a wireless adapter may schedule transmission of data across a wireless link based on relative predicted QoS link ratings for locations along a predicted future pathway of the mobile information handling system. In an example embodiment, the mobile information handling system may be a mobile gateway device such as a smart vehicle gateway.
Regarding claim 10, Egner teaches a method comprising: identifying one or more sectors of a network, each sector representing a geographic area within which the network is able to provide networking services to user devices(path is predicted across a map grid of sectors, ¶158)
[0158] The context aware radio resource management system proceeds to 935. At 935, the path prediction system portion correlates the preliminary predicted path with the visitation history location matrix. The preliminary predicted path begins as a selected mobile device trajectory. In the example embodiment, this may be done via overlay of bin maps containing both preliminary predicted path and the visitation history information for locations near the preliminary predicted path. An example visitation history matrix bin map is shown in FIG. 9B below. Nearby locations for the visitation history matrix may be limited to those locations that fall within a certain number of bin map grid boxes from the preliminary predicted path. How many bin map grid boxes are used as nearby locations will depend on the physical size of each bin map grid box and factors such as how many future time intervals are used to determine the predicted future path.
for each sector of the one or more sectors: identifying a cost for providing networking services to user devices located within the sector;(cost of service, signal quality etc analyze for each grid location on the predicted path of the user device, ¶s72,146,158)
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines
[0158] The context aware radio resource management system proceeds to 935. At 935, the path prediction system portion correlates the preliminary predicted path with the visitation history location matrix. The preliminary predicted path begins as a selected mobile device trajectory. In the example embodiment, this may be done via overlay of bin maps containing both preliminary predicted path and the visitation history information for locations near the preliminary predicted path. An example visitation history matrix bin map is shown in FIG. 9B below. Nearby locations for the visitation history matrix may be limited to those locations that fall within a certain number of bin map grid boxes from the preliminary predicted path. How many bin map grid boxes are used as nearby locations will depend on the physical size of each bin map grid box and factors such as how many future time intervals are used to determine the predicted future path.
predicting a future network load for the sector at a time at which the networking services are to be provided to the user devices and(desired priorities and cost are calculated for a predicted path …. Predicting the load along the path as user travels through each sectors(home, work ) in a cells(sectors ) of the map, ¶146, fig 10C)
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines an optimal wireless link selection for a mobile information handling system in terms of cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
the cost for networking services to be provided to the user device being the cost incurred by the network to provide the networking services to the user device(costs include cost per gigabyte for particular service provider that the provider charges the user, ¶s81-83)
[0081] With a smart filtering or screening of the IMSIs requested from a network broker system, the network broker system may be able to carry a fewer number of IMSIs to supply the pools for checkout by users. This may in turn reduce costs at the network broker by requiring fewer licenses to the IMSIs. That cost savings may also reduce costs for a user.
[0082] In addition, the context aware radio resource management system will request only IMSIs for optimal wireless link options as determined by link ratings described in the present disclosure. Thus, when a smart vehicle gateway or mobile information handling system switches to an optimal WWAN wireless link, an IMSI corresponding to the WWAN wireless link provides for that communication to be on a “home” network. This avoids roaming connections. It is understood that roaming connections may be more expensive to operate on a wireless service provider. Additionally, roaming connections may be substantially less efficient. In some cases, a roaming connection from a home network of an IMSI must be routed back to a home network link with the desired alternative wireless service provider. This can require additional communication links to achieve. A direct connection via an access to a home wireless network may be more efficient and less costly. Thus, selecting or switching between optimal wireless service providers by switching IMSIs may yield cost savings on a cost per gigabyte basis.
[0083] It is understood that cost per gigabyte may also vary between wireless links available from the list of optimal wireless links determined via a context aware radio resource management system as described herein. Cost per gigabyte on wireless service carriers may vary among WWAN links. For example, a vehicle travelling across borders may be subject to substantial cost fluctuations among WWAN carriers. In another example embodiment, some wireless links, such as non-WWAN links may be less expensive as well. Settings for a smart vehicle gateway or a mobile information handling system may serve to prioritize cost per gigabyte based on location when selection from optimal wireless links is made. Moreover, a smart connection manager may select between a plurality of simultaneous wireless links established for a smart vehicle gateway. The basis of selection may be on quality of the links available, traffic levels, or suitability to expected data needs. However, the basis of selection may also be based on a cost per gigabyte basis to select the most cost efficient option when available.
determining the cost for networking services based on the predicted future network load for the network sector(priority and cost of data usage in the sectors along the future path are predicted so as to control when to reschedule sending of data and/or which networks to use to minimize cost, ¶s72, 146)
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines an optimal wireless link selection for a mobile information handling system in terms of cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
and identifying a change in the cost for providing networking services over time(QOS, cost and other condition evolve over time thus assessment is performed repeatedly at regular intervals, ¶s58) ;
[0058] Wireless links 115 may connect to a macro-cellular wireless network 150 via one of the service providers 160 or 170 or satellite provider 172. In the depicted example, service provider A 160 may provide wireless data connectivity via a 3G, 4G, or 5G protocol. Service provider B 170 may offer connectivity via a 2.5G, 3G, 4G, or 5G protocol. Any combination of macro-cellular wireless connectivity is possible for each or both of the service providers. The connection quality of service (QOS) and speed of wireless links 115 may vary widely depending on several factors including the service provider bandwidth, the number of mobile information handling systems and users in a location, and other factors. Quality of service impacts energy consumption and efficiency of a mobile information handling system communicating wirelessly. Thus, selection of a wireless link may depend on assessment of the link radio frequency conditions. Radio frequency conditions for wireless links will evolve over time. Differences in wireless link QOS or efficiency will also vary minute-by-minute, hourly, daily, weekly or monthly or during even longer periods. Thus, assessment may need to be regular. This is particularly true for a smart vehicle gateway 135 where vehicle travel may alter conditions depending on location.
receiving data from a user device, the user device data the user data being transmitted based on a predetermined rate for networking services provided by the network to the user device and comprising data describing a location of the user device(location with respect o geographical sector t), data describing one or more types of data for which the user device requires networking services(data can be identified as video data or sensor data), and data describing a priority measure for each type of data of the one or more types of data(video data may be higher priority and need high quality/cost links, sensor data type may be low priority and can be delay to use a lower cost link i.e. wifi) the rate representing a cost to a used associated with the user device to use the networking services(user’s mobile device transmits and receives data on a selected link rating , such ratings include cost considerations, where such higher priority data may require and higher quality link which imply higher cost per byte, and for lower priority data may choose to delay transmission of such lower quality data for a low cost network such as, ¶s204-208)
[0168] The example embodiment of FIG. 10B may show a bin map 1000 having estimated QoS levels for the variety of wireless links available at grid box locations 1015. As with the other bin maps, colors, patterns or a third dimension on the grid boxes 1015 may be used on the bin map 1000 to show QoS ranges or energy consumption data for wireless link sources at depicted grid box locations. In the shown example embodiment, hatching is depicted highlighting certain grid box locations. In some example aspects, this hatching may indicate great wireless connectivity. On other example aspects, the hatching may instead represent medium or poor wireless QoS or wireless connectivity levels. Similarly in other aspects, the hatching may represent wireless link energy consumption levels predicted. The measured QoS data and energy link consumption data is from a plurality of wireless intelligence reports for locations in the user area. Grid boxes 1015 may indicate in some aspects the locations in a user area where sufficient crowd-sourced mobile wireless traffic report data is available at the context aware radiofrequency resource management system.
[0204] In accordance with several disclosures herein, link ratings that define wireless conditions as locations along a predicted future path may be impacted by several additional factors in addition to the QoS parameters described in embodiments above. Other embodiments discussed in the present disclosure include the effect of wireless link energy consumption on link ratings as well. If a mobile information handling system relies on battery power, a state of charge may determine whether link energy consumption by wireless links is factored in link rating determinations. As explained in examples above, weighting may be applied to lower power consuming wireless links when determining link ratings. In another example embodiment, cost of data transfer or communication may be established as a weighting factor in determining a link rating for a wireless link at a location. For example, an additional roaming charge at a location may reduce the link rating of a wireless link at a location. These factors are discussed in embodiments above. Link rating determinations relate to wireless condition assessment and classification in the present embodiment. As described above, these wireless condition assessments and classifications may include the factors including link energy consumption or cost of data or communication transfers.
[0205] Proceeding to 1404, the smart connection manager may receive data to be transmitted. In a mobile information handling system embodiment, data to be transmitted is queued and prepared for transmission by the wireless adapter. In a further embodiment where the mobile information handling system is a mobile gateway device, data from IoT devices and information handling systems in a local network are transmitted to the mobile gateway device and received their for pass-through transmission controlled by the smart connection manager on one or more established optimal wireless links.
[0206] Proceeding to 1406, the smart connection manager may determine classification of data to be transmitted. In an aspect of the present disclosure, data to be transmitted must be classified with respect to criticality. Transmission policy may be established for the smart connection manager to receive an indication of the type of data to be transmitted and to classify that data according to the indicated type. Examples of data to be transmitted are discussed in several embodiments herein.
[0207] Transmission policy for data classification by the smart connection manager may generally follow certain rules of classification in an example embodiment. Further, policy may follow some guidelines with respect to criticality classification in some embodiments. For example, data that involves ongoing communication or highly critical data for ongoing operations to proceed may be classified as high priority to avoid undue interruption of the users. Data involving burst data transfers or substantial queuing capability may have a lesser priority. Data that involves extremely high throughput requirements may be of lesser priority in some cases due to its effect on other data transmission. Periodically transmitted data, such as background data transfers, may be an example of lowest priority data. For example, IoT data involving monitoring of sensors and the like and that may periodically transmit data to a remotely-located IoT big data tracking software application system may often be of low priority.
[0208] In several aspects, data content, such as tracked IoT data content, may determine or impact classification levels of priority assigned to data to be transmitted at 1406. In an example, tracked IoT relating to mission critical key performance indicators for critical aspects of a system's operation, safety monitoring, or health monitoring key performance indicators for user biometrics, may be assigned a higher priority classification. In some example embodiments, data classification may even include certain types of data whereby a smart connection manager may elect not to transmit the data and instead store the data locally to avoid undue burden on the established wireless link when wireless conditions at a location are poor.
determining a sector within which the user device is located based on the location of the user device and the identified one or more sectors(user location determined within a map grid box, ¶169);
[0169] In the example embodiment of FIG. 10C, an example embodiment of a bin map is shown with a predicted future path 1022 overlaid on grid boxes 1015 showing levels of wireless connectivity, including wireless conditions, for locations along the predicted future path. Legend 1025 depicts an example embodiment of crowd-sourced data available for the predicted future path. Bold grid boxes 1026 indicate locations along the predicted future path 1022 determined by the mobile path prediction system described in embodiments herein. Grid boxes 1015 may also indicate wireless connectivity conditions.
and the change in the cost for providing networking services over time within the determined sector(QOS, cost and other condition evolve over time thus assessment is performed repeatedly at regular intervals, ¶s58) ;
[0058] Wireless links 115 may connect to a macro-cellular wireless network 150 via one of the service providers 160 or 170 or satellite provider 172. In the depicted example, service provider A 160 may provide wireless data connectivity via a 3G, 4G, or 5G protocol. Service provider B 170 may offer connectivity via a 2.5G, 3G, 4G, or 5G protocol. Any combination of macro-cellular wireless connectivity is possible for each or both of the service providers. The connection quality of service (QOS) and speed of wireless links 115 may vary widely depending on several factors including the service provider bandwidth, the number of mobile information handling systems and users in a location, and other factors. Quality of service impacts energy consumption and efficiency of a mobile information handling system communicating wirelessly. Thus, selection of a wireless link may depend on assessment of the link radio frequency conditions. Radio frequency conditions for wireless links will evolve over time. Differences in wireless link QOS or efficiency will also vary minute-by-minute, hourly, daily, weekly or monthly or during even longer periods. Thus, assessment may need to be regular. This is particularly true for a smart vehicle gateway 135 where vehicle travel may alter conditions depending on location.
determine whether the user device should not transmit at least one type of data of the one or more types of data based on the priority measure for each type of data and the determined dynamic rate for providing networking services to the user device by the network(bases on analysis data transmission of a certain type/priority can be delay or scheduled based on connection cost/quality over predicted path, ¶199)
[0199] Data may be scheduled for transmission by a smart connection manager or a wireless adapter depending on predicted wireless link connectivity conditions for locations along the predicted future path determined for the mobile information handling system. In a further aspect, data of lower criticality may be rescheduled for transmission when a location has poor wireless conditions. The scheduled transmission may be delayed until a location on the predicted future path is encountered with improved wireless connectivity conditions. However, highly critical data may still be transmitted, if possible, regardless of the wireless conditions at a location. In yet another aspect of the present disclosure, data transmission may be scheduled for accelerated transmission if future locations upcoming on a predicted future path will be of poor wireless quality.
and causing the user device to not transmit the at least one type of data(user’s mobile device transmits and received data at a speed in accordance with QOS of the wireless link selected, ¶s63,93).
[0063] Factors impacting energy consumption include switching and signaling during communication access, setup, and authentication. Additional factors that impact energy consumption include control communications, latencies, transmission/reception, and switching for the wireless link. As described above, these factors can be specific to the type of wireless service being requested, whether voice, messaging, SMTP, Audio, Video, HTTP or other service types. It can also be specific to the mobile information handling system used. Certain protocols may not be available on some mobile information handling systems. In each instance, radio frequency transmission subsystems and controllers operate and consume device power. Based on these numerous factors, the system of the present embodiment may automatically switch between radio network technologies or service providers to optimize radio frequency conditions, traffic conditions, device power consumption, cost, or any of the above. Selection of a wireless service provider and technology protocol may generally depend on the optimal wireless technology used for a service requested, the radio frequency conditions of a link, traffic conditions for the wireless link, and availability of a link. Wireless service provider may also be referred to as wireless service carrier herein. Technology protocol is also referred to as wireless protocol in some instances herein as well.
[0093] The selection of a wireless link by the context aware radio resource management system may depend on the factors and settings described above. For example, if optimal speed of connection is the goal with less consideration of power consumption, the weight assigned by the context aware system to input data may be influenced. This may be the case if the context aware resource management system detects a connection to an AC power source. User profile data 310 showing usage and the wireless link radio frequency wireless traffic reports 320 indicating link quality and capacity will be more heavily weighted. Energy consumption data may be less heavily weighed. If on the other hand, lower power consumption and long battery life are optimal considerations, battery power level data 330 and the energy link reports 340 may be more heavily weighted. Any combination of weighting involving anticipated usage, radio frequency channel quality, battery power levels, or efficient power consumption may be used in the present embodiment.
Egner does not teach generating a dynamic rate for providing networking services to the user device based on a current time, the cost for providing networking services to user devices located within the determined sector.
Senarath in the same field of endeavor as the invention teaches a system for an adjustable rate charging system. Senarath teaches generating a dynamic rate for providing networking services to the user device based on a current time, the cost for providing networking services to user devices located within the determined sector(charging information based on dynamic charges for congestion pricing is received so the user or devices of the user based on preferences can automatically change the behavior of the devices such as disconnecting in higher price times, congestion pricing is different for different geographical regions, ¶s91,210 233) .
[0091] To supplement the conventional charging data collection embodiments of the present invention allow for the placement of monitoring functions at different locations of the network. The charging data collected by the monitoring functions can vary, so that one instance of a monitoring function can track the number of transactions, while another can track the volume of data. A single data flow associated with a UE may be monitored by more than one function. Services may be charged on a per-use basis (e.g. a per-transaction basis), based on traffic (e.g. a per-bit basis), etc. The collected charging data may also include information not used in 3G/4G networks. In addition to a time of day charging structure that is applied across a network, next generation networks may employ geographically differentiated charging. This may allow a network to charge more for data in a geographic region of the network that is particularly congested. To do this, the location of the UE, either in absolute terms, or in relation to the topology of the network would need to be included in the collected data. Furthermore, the time and traffic loads may need to be available to correlate to this charging record if not recorded in the charging data. If the UE location is based on a UE reported location, the placement of the monitoring function can be varied. If the location data is not based on, for example, a GPS location reported by the UE, then the placement of the charging function either at a basestation/access node or at an anchor point serving a plurality of basestations can be used to collect this information. To facilitate charging customization, charging data collection function (or a monitoring function) can be instantiated at a selected location in the core network in order to extract network activity information. This collected information can be provided to the OSS/BSS or to a customer for use in a given billing scenario. Charging can vary for example based on a geographic location of the network usage, traffic or congestion considerations, or time of day considerations, for example.
[0210] An example of a bidding process is described as follows, with reference to FIG. 11. At step 2000 UEs may send requests to an operator for a particular service to be provided for a given charging rate, and these requests are received. If the service is relevant to an important or popular event (e.g., live event or disaster), or the bidding process occurs at a peak service consumption time, a significant number of UEs may be requesting the service. The UE request for service can be handled by the CSP, or by the NO on behalf of the CSP. This can effectively provide congestion pricing incentives so that during events that create congestion, users will willingly (or UEs will automatically, based on user preferences) disconnect or reduce their service expectations. The changes in charging rate and charging rules can by dynamic until a desired effect is achieved.
[0233] In various embodiments, the UE may be executing an application that automatically performs load balancing and/or switching between two available systems. For example, a type of access via a Wi-Fi network may be selected dynamically based on charging information. Multiple types of access may be used concurrently, with different access types receiving different proportions of the user's traffic in accordance with a load balancing operation. The UE may also include a function that allows for scheduling of transmissions based on fore-knowledge of charging rates and the differences in rates based on time, location and/or access technology, and optionally based on fore-knowledge of UE mobility and route (e.g. knowledge of the UE location). Such a function may also be resident in the CSP and provide instructions to the UE to effect the same function. There may also be coordinating functions in the UE and CSP to effect this functionality. Such a function may allow for transmission of data for select applications or services and defer the access of other applications and services based on the above described factors.
It would have been obvious to a person of ordinary skill in the art at the time of the effective filing of the instant application to modify Egner with adaptive data rate charging subscription profiles as taught by Senarath. The reason for this modification would be to affect user data behavior that save resource cost to the service provider for providing transmission resource and provide monetary saving cost to the user for using lower cost data connections or delay transmission until devices are located withing lower cost data connections.
Regarding claim 11, Egner teaches receiving, over a period of time, one or more updates regarding the location of the user device( mobile device report current location to the system , ¶s151, 211)
[0151] Sensors may provide additional automatic inputs into the optimized radio link selection at 820 as well. Sensors may detect mobile device current state information at 830 which may include battery levels, the current radio wireless link operating on the mobile device, and consideration of the most current mobile device location. Such incumbent current state information will be considered to determine if making a change to different radio wireless connection is worthwhile. Additionally, the incumbent current state will be weighed with the risk of service interruption or other factors such as whether the improvement is worth the change. For example, if the currently active radio link is determined to be one of the top few optimal radio links on the predicted path, the context aware radio management system will elect not to switch radio links even if the current link is not the most optimal from a cost, QoS, or power perspective. Alternatively, if the weighted QoS parameters of the currently operating link are meet the minimum requirements or are within a threshold level of deviation from the most optimal radio link, then no switch of radio link will occur.
[0211] If some of the data to be transmitted is not classified as highly critical, flow proceeds to 1412. At 1412, the smart connection manager will determine if the priority level is medium critical for the data to be transferred. If so, the smart connection manager will determine if the wireless link rating for one or more established optimal links meets a first level of minimum QoS at 1414. In other words, an assessment of the classification of wireless conditions for the established wireless link at a location will be made by the smart connection manager. In an example embodiment, if the wireless conditions for a current location along the predicted future path are good, then transmission may be scheduled currently or in the near future. Flow will proceed to 1416 to assess future locations along the predicted future path for wireless condition classification.
and for each respective update of the one or more updates: determining a new sector within which the user device is located based on at least the respective update;
(location of user mobile device identified with respect to a map grid box ¶158);
[0158] The context aware radio resource management system proceeds to 935. At 935, the path prediction system portion correlates the preliminary predicted path with the visitation history location matrix. The preliminary predicted path begins as a selected mobile device trajectory. In the example embodiment, this may be done via overlay of bin maps containing both preliminary predicted path and the visitation history information for locations near the preliminary predicted path. An example visitation history matrix bin map is shown in FIG. 9B below. Nearby locations for the visitation history matrix may be limited to those locations that fall within a certain number of bin map grid boxes from the preliminary predicted path. How many bin map grid boxes are used as nearby locations will depend on the physical size of each bin map grid box and factors such as how many future time intervals are used to determine the predicted future path.
determining a new current time(current condition determine with respect to current timestamps, ¶48)
[0048] In yet another example embodiment, a smart connect manager application may be run at a smart vehicle gateway 135 to determine wireless connection access options based on vehicle location and relevant context and anticipated communication and data needs in accordance with embodiments disclosed herein. Smart connection manager at a smart vehicle gateway 135 may request or send data objects or information to or from an application running at a remote data center 186 or another location such as a context aware radio resource management system remote server 190. In an aspect of the present disclosure, smart connection manager may determine scheduling of data transmissions based on wireless conditions at current and future locations and based on priority levels assigned to various types of data by criticality classification. Context aware radio resource management system remote server 190 may operate a context aware radio resource management system application according to the present disclosure. Similarly, remote access may be available to a remote database with wireless intelligence reports 195
[0123] Once a radio frequency channel performance profile is submitted to the context aware radio resource management system and a wireless link selected, the mobile information handling system may periodically scan multiple wireless links or measure the selected wireless link at step 670. The system may conduct testing to determine the capacity of a link during operation. In order to minimize radio communication and use of resources, the network broker may be used to proactively notify a mobile information handling system if a wireless link selection was made using an obsolete crowd-sourced data source. This network broker server system may compare time stamps of crowd-sourced data used for wireless link selection or ranking with current time stamps of network-stored crowd-sourced material.
and determining a new dynamic rate for providing networking services to the user device based on the new current time, the cost for providing networking services to user devices located within the new sector and the change in the cost for providing networking services over time within the new sector(as user travel over a predicted path adaptive rate is allocated to the mobile device in each grid box as the user travels, ¶s 72, 159) ,
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.
[0159] The flow proceeds to 940 where the path prediction system modifies the preliminary predicted path locations based on the visitation history location matrix data. The path prediction system modifies the preliminary predicted path to include a location on the visitation history location matrix depending on the frequency of visitation to that location. Additional factors in modifying the preliminary predicted path may include the nearness of the frequently visited grid map box location to the preliminary predicted path. For example, a highly visited location one grid box away from the preliminary predicted path will cause a modification of the preliminary predicted path. However, a less visited location three or more grid boxes away from a preliminary predicted path location will unlikely cause a modification to the preliminary predicted path. The mobile path prediction system sets a threshold of factors to determine at what point the modification to the preliminary path will occur. Application of a set of conditional probabilities, such as with Bayesian classifier statistics, may take into account several variables such as proximity to trajectory and frequency of visitation to determine where to predict future path locations. Another factor having impact on modifying the preliminary predicted path includes the time of day. Time of day takes into account cyclostationary considerations such as daily routines of the mobile device user. The modification of the preliminary predicted path may occur in a recursive fashion to correlate additional probability estimation of a location along the preliminary predicted path until a predicted future path is determined.
Regarding claim 12, Egner teaches receiving an indication of a route of the user device, the route including one or more potential future locations of the user device(future path of the user device is predicted , ¶72);
one or more times that the user device will be located at the one or more future locations based on the indication of the route(path prediction based on also user habits, i.e a user device a history of visiting a particular location, ¶157) ;
[0157] Proceeding to 930, the context aware radio resource management system accesses a location matrix having historic visitation data for the mobile information handling system. The history of visitation is recorded from user profile data for mobile devices as described above. The visitation history location matrix may be also mapped to a bin map of user area. The visitation history location matrix contains data about the frequency and time spent at locations and may also include temporal information relating to times during the day when such visitation is made. In this way, the visitation history location matrix will contain information relating to cyclostationary daily habits of a mobile device user's visitation.
and determining the dynamic rate based on the one or more future locations included in the route and the one or more times that the user device will be located at the one or more future locations(wireless link will be selected based on optimal link in each map grid, ¶72).
[0072] As a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel, wireless link characteristics will vary based on time and location. For example, wireless conditions for the wireless link will vary with changes in location and time. In an aspect of the present disclosure, the smart connection manager may better utilize wireless link capacity limited during poor wireless conditions by prediction of wireless conditions along a predicted future mobile path. Wireless conditions may be predicted for a location and time from crowd-sourced data and wireless traffic reports provided to the context aware radio resource management system. Predicting a future path for a mobile information handling system 110, 120, 130 or a mobile gateway device 135 travel is described further below in connection to a path prediction system. Additionally, the context aware radio resource management system of the present disclosure may predict wireless link loading based on historical usage trends for a mobile device. With this data, a smart connection manager may adaptively schedule data transmission to avoid overextending limited capacity available on a wireless link during expected poor wireless conditions. The smart connection manager may assign criticality classifications to data before transmission and determine to reschedule transmission of lower priority data. Rescheduling data transmissions may include delaying transmission until a future location is reached with good wireless conditions in one aspect. Rescheduling data transmissions may also include accelerating transmission when future locations are predicted to have poor wireless conditions.
Regarding claim 13, Egner teaches identifying a speed of the user device; and determining the dynamic rate based on the current time, the cost for providing networking services, the change in the cost for providing networking services over time, and the speed of the user device(adaptive rate to schedule data transmission based on velocity of user device, cost of wireless links and data priority, ¶s146,147, 210, adaptive rate is assess ar regular intervals as wireless condition change over time, ¶58 ).
[0146] FIG. 8 shows an embodiment example method for determining an optimized wireless link selection for a mobile information handling system during future movement. The mobile information handling system may be a mobile gateway device such as a smart vehicle gateway or smart personal connect gateway in some examples Multiple estimations and data inputs may be analyzed by the context aware radio resource management system. The context aware radio resource management system determines an optimal wireless link selection for a mobile information handling system in terms of cost, power consumption, or quality of wireless channel links for connecting voice or data. FIG. 8 illustrates input data generated via a path prediction system 805, a usage trend determination system to determine predicted path radio usage requirements 810 from user profiles, and a radio link QoS assessment system to determine estimated QoS scoring for radio channels 815 at future locations along the predicted path. Additional inputs may also influence the context aware radio resource management system determination of optimized wireless link selection. Data inputs may include goals or priorities 825 indicating desired priorities of cost, radio link profiles (such as QoS), or power usage from energy link consumption profiles. Additional data analyzed may include current operating states of the mobile device 830. Limitations may also be placed on the context aware radio resource management system to prevent the benefits of the optimized wireless link selection system from intruding too much on user experience. For example, switching too much may impact the efficiency benefits of the method. An example embodiment includes inhibitors 845 that limit changes to wireless links to prevent switching.
[0147] The context aware radio resource management system utilizes a path prediction system 805 to determine a predicted future path of travel for the mobile device such as a mobile gateway device during a set of future time intervals. The mobile path prediction system uses position data for the mobile information handling system. Velocity and acceleration data are detected by motion sensors the mobile device or on the vehicle of a smart vehicle gateway in one aspect. Velocity and acceleration may also be determined from position data or changes in position data in other aspects. Position data may be determined via a global positioning system. Alternatively, a mobile positioning system via the wireless network may determine position and movement of the mobile information handling system. Using this position data, the mobile path prediction system estimates a predicted future path of travel for mobile information handling system. In an example embodiment, the mobile information handling system is a smart vehicle gateway or other mobile gateway device travelling in a user area.
[0210] Flow may proceed to 1408 where the smart connection manager may determine if data to be transmitted is high-critical or of high priority. Examples shown above in Table 1 include voice data, video conferencing data, mission critical key performance indicators and the like. If the data to be transmitted is determined to be highly critical, then flow may proceed to 1410 where the data will be transmitted regardless of the wireless conditions if transmission is possible. In an example embodiment, the smart connection manager may seek to switch wireless links, for example when multiple wireless links are simultaneously established for a mobile information handling system. According to embodiments described, a plurality of wireless links may be available. The smart connection manager will seek to connect to the best option when wireless conditions are poor for the wireless link prepared for the predicted future path. The smart connection manager may then select from among the available options at the location along the predicted future path to transmit the highly critical data. In yet another example embodiment, a new wireless link may be established by the smart connection manager despite previous selection for a wireless link for a predicted future path. For highly critical data, the process of ranking and selecting an optimal wireless link for a location may be started anew to ensure transmission of such data in some embodiments. When transmission does occur, data will be transmitted according to regular queuing and transmission procedures by the wireless adapter. At this point, the flow may end.
[0058] Wireless links 115 may connect to a macro-cellular wireless network 150 via one of the service providers 160 or 170 or satellite provider 172. In the depicted example, service provider A 160 may provide wireless data connectivity via a 3G, 4G, or 5G protocol. Service provider B 170 may offer connectivity via a 2.5G, 3G, 4G, or 5G protocol. Any combination of macro-cellular wireless connectivity is possible for each or both of the service providers. The connection quality of service (QOS) and speed of wireless links 115 may vary widely depending on several factors including the service provider bandwidth, the number of mobile information handling systems and users in a location, and other factors. Quality of service impacts energy consumption and efficiency of a mobile information handling system communicating wirelessly. Thus, selection of a wireless link may depend on assessment of the link radio frequency conditions. Radio frequency conditions for wireless links will evolve over time. Differences in wireless link QOS or efficiency will also vary minute-by-minute, hourly, daily, weekly or monthly or during even longer periods. Thus, assessment may need to be regular. This is particularly true for a smart vehicle gateway 135 where vehicle travel may alter conditions depending on location.
Claims 9 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Egner/Senarath as applied to claims 1 and 27 above, and further in view of Bevan US 2021/0212017.
Regarding claims 9 and 28, Egner does not teach wherein the location within which networking services are provided includes an indication of the altitude of the user device. Bevan in the same field of endeavor teaches a system, for support access to wireless networks by mobile devices. Bevan teaches wherein the location within which networking services are provided includes an indication of the altitude of the user device.
[0048] In some embodiments, communications access point 120, local office 103 or both may dynamically extend or reallocate the coverage area of a wireless network based on respective location information. Location information may indicate, for example, the geographic coordinates (e.g., latitude, longitude, altitude) of the device, the address of the user of the device, the direction of travel of the device, the speed of travel of the device, predicted locations of the device at future points in time, location accuracy, or any other suitable information. In certain implementations, location information may be determined by processing information received from or associated with the device, such as Global Positioning System (GPS) information, cellular tower triangulation information, wireless signal strength, and time of arrival of a wireless signal. In certain implementations, location information may include data determined from historical location information. For example, location information may include geographic locations at which the device is most frequently located (e.g., a list of the most common geographic locations over the past year), average geographic locations at particular times of the day or year, average travel speeds and routes (e.g., to differentiate between walking and driving). In another example, radio-frequency identification (RFID) information may be used. For example, a vehicle may include an RFID receiver for reading RFID information associated with roadside mile markers. The RFID information may then be transmitted to the appropriate processor for use in determining location information. Example location information data structures are discussed in further detail with reference to FIGS. 5 and 6.
It would have been obvious to a person of ordinary skill in the art at the time of the effective filing of the instant application to modify Egner’a selection of wireless links based on tracking user grid location(i.e. lat long) with inclusion of altitude in addition to latitude and longitude. The reason for this modification would be to optimize wireless link selection for drones and other unmanned aerial device.
Applicant Remarks
The applicant argues on page 9 and 10 of the remarks alleging that the cited references Egner in view of Senerath do not teach the claims 1, 10 and 21 and amended. The applicant alleges that Egner in view of Senerath do not teach the cost for networking services to be provided to the user device being the cost incurred by the network to provide the networking services to the user device. The examiner contends Egner teaches such amendments as presented in the rejection. That is the examiner contends the teaching in ¶s81-83 of cost per gigiabyte considerations that are charge by the service provider to the user/subscriber using such wireless data services teaches the cost for networking services incurred to provide networking services to the user device as claimed.
Prior Art Cited But Not Used IN A Rejection
US 2014/0003404 - Determining Suitability Of An Access Network.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tom Y. Chang whose telephone number is 571-270-5938. The examiner can normally be reached on Monday-Friday from 9am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emmanuel Moise, can be reached on (571)272-3865. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TOM Y CHANG/
Primary Examiner, Art Unit 2455
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