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 .
Priority
Examiner acknowledges the following data:
Applicant has no priority data on file.
Information Disclosure statements
Applicant has no Information Disclosure statements, (IDS) on file.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (US 2022/0046075) in view of (US 2020/0401399).
Regarding claim 1, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1) for dynamically transferring user data between edge servers based on predicted need, the system comprising (Device 104a may access the internet via an access point, as described previously. In this example, a user in Hangzhou, China would like to talk to another user in New York. The app within the device 104a gets some edge servers in Shanghai, Guangzhou, Wuxi, etc. from access controller 312 via the aforementioned general optimization. Then, the app sends the best (predicted need) edge server (in the present example the Shanghai edge server 344c) voice packets (user data). The Shanghai edge server 344c then routes the packets to an edge server in California and then one in Virginia (edge servers), [0087], lines 1-8):
two or more radio access network nodes (access networks, [0100], line 3); and
one or more computer processing components configured to perform operations comprising (Processor(s) 1722 (also referred to as central processing units, or CPUs), [0104], line 2):
receiving a request to start an edge instance (The app within the device 104a gets (requests) some edge servers in Shanghai, Guangzhou, Wuxi, etc. from access controller 312 via the aforementioned general optimization. The app then tests (start testing) these several servers for the very best ones, and determines which algorithms to employ (start); thus is seen as the APP within the device 104a gets/ receives a requests to test the edge servers and determine which algorithm to employ (start) on the edge servers (start an edge instance), [0087], lin3w 2-4);
storing context data (network label system 1210 includes a number of subcomponents: a network information collector 1212 pulls temporal, identification and quality metrics (context data) from initiated real-time data (context data) transfers; a network history database (context data) 1214 saves (storing) this collected information and operates in conjunction with a network modeling module 1216 to generate and store predictions (context data) of network, [0090], lines 3-6);
determining the location of a User Equipment (UE) within an edge geographic area (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (area), [0057], line 3, [0058], lines 1-5);
determining the location of the UE is within a periphery of the edge geographic area (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (periphery), [0057], line 3, [0058], lines 1-5);
querying a database of historical location (Querying users’ IP address database (database of historical location), [0059], line 2) and duration data for the UE (network information collector 1212 pulls real-time data (duration data) transfers (querying) network history database 1214, saves this collected information and store predictions of network, [0090], lines 3-6);
identifying geographically adjacent edge servers (Determine (identifying) the best edge servers 120a-x based purely upon regional and geographic variables. Servers may also be narrowed (identified) by their regional location, [0058], lines 6-7, [0078], line 6);
preparing a priority list of geographically adjacent edge servers based on the historical location (Access controller 312 also receives feedback from a regional optimizer 316 which returns (prepares) a listing of edge servers 120a-x (priority list) located in the same region (historical location) as the device 104 running the app 220, [0060], lines 1-2) and duration data (maintaining this list of backup servers allows for very rapid (duration data) switching to a secondary server, [0064], lines 4-5); and
Li et al does not specifically disclose concept of caching context data on the geographically adjacent edge servers in accordance with the priority list.
However, Matsuo et al specifically teaches concept of caching context data on the geographically adjacent edge servers in accordance with the priority list (arrangement determination unit 1252 determines arrangement of logics (functions) in which required performance is maximized on the basis of the measured performance (context data) and attribute information (context data) including information (context data) representing an arrangement position, processing performance (context data) and the like of each edge server stored (cached) in the edge server information storage 132; thus is seen as storing (caching) measured performance (context data), attribute information (context data) representing/on an arrangement position (geographically) and processing performance (context data) of each edge server stored (cached) in the edge server information storage 132, [0052], lines 1-4).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Li et al with concept of caching context data on the geographically adjacent edge servers in accordance with the priority list of Matsuo et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve efficiency of information collection and fast response time, anticipation for a distributed application in which a plurality of application logics (a component that performs some kind of process on input data and outputs the data is referred to as “logic” below) are arranged and executed in geographically distributed machines in a vertical distribution environment, (Matsuo et al, [0002], lines 3-6)
Regarding claim 2, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising identifying a home location of the UE is located in a geographic area of a third adjacent edge server, wherein the third adjacent edge server is given additional priority on the priority list (access controller 312 also receives feedback from a regional optimizer 316 which returns a listing of edge servers 120a-x (priority list) located in the same region (geographic area) as the device 104 running the app 220. For example, in some embodiments there are multiple edge servers 120a-x (priority list) in Nanjing (geographic area) and Hangzhou (geographic area) with the ISP China (geographic area) Telecom. For a user from Wuxi, the regional optimizer 316 would recommend Nanjing before Hangzhou, because Wuxi and Nanjing are both in Jiangsu province, [0060], lines 1-8).
Regarding claim 3, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising identifying the UE is likely utilizing a transportation infrastructure element (Much of the call quality improvements provided herein requires interplay between the devices 104a-n (UE) and the associated software stored thereon, and the servers located in the control layer 130 and transport layer 140, ).
Regarding claim 4, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising determining the a transportation infrastructure element communicates to a third adjacent edge server and prioritizing the third adjacent edge server over other servers (Within the transport layer 140, there are tens of edge servers 120a-x. Each edge IDC 120a-x includes a last mile optimizer 342a-x, and a server relay 344a-x. The last mile optimizer 342a-x runs in the background before the initiation of a call. The apps 220a-n measure the quality of transmissions between various last mile optimizers 342a-x, a selected by the access controller 312. This selection of the ‘several best’ servers for testing by the access controller 312 is performed based upon the applications IP address and geographic location information by the general optimizer 310, in some embodiments. For a user from Wuxi, the regional optimizer 316 would recommend Nanjing before (prioritizing) Hangzhou, because Wuxi and Nanjing are both in Jiangsu province, [0058], lines 1-6).
Regarding claim 5, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising identifying the UE is likely to be blocked by topography into moving into a geographic edge area of a fourth edge server and lowering the priority of the fourth edge server (Within the transport layer 140, there are tens of edge servers 120a-x. Each edge IDC 120a-x includes a last mile optimizer 342a-x, and a server relay 344a-x. The last mile optimizer 342a-x runs in the background before the initiation of a call. The apps 220a-n measure the quality of transmissions between various last mile optimizers 342a-x, a selected by the access controller 312. This selection of the ‘several best’ servers for testing by the access controller 312 is performed based upon the applications IP address and geographic location information by the general optimizer 310, in some embodiments. For a user from Wuxi, the regional optimizer 316 would recommend Nanjing before (prioritizing) Hangzhou, because Wuxi and Nanjing are both in Jiangsu province, [0058], lines 1-6).
Regarding claim 6, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising:
querying traffic data (Querying users’ IP address database (database of historical location), [0059], line 2);
determining the UE is likely to deviate from the historical location and duration data (network information collector 1212 pulls real-time data (duration data) transfers (querying) network history database 1214, saves this collected information and store predictions of network, [0090], lines 3-6); and
adjusting the priority list of geographically adjacent edge servers (First, just before a call is initiated, a last mile optimization is performed in order to select the best server for device connection and algorithm selection. Secondly, during a call over the network, a real time optimization may be employed to dynamically reroute transmission pathways based upon changing network conditions, [0054], lines 1-5).
Regarding claim 7, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising, predicting a destination for the UE based on historical location (Querying users’ IP address database (database of historical location), [0059], line 2) and duration data for the UE (network information collector 1212 pulls real-time data (duration data) transfers (querying) network history database 1214, saves this collected information and store predictions of network, [0090], lines 3-6).
Regarding claim 8, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising, querying a topography and transportation infrastructure database, predicting a travel path to the predicted destination, and adjusting the priority list according to the travel path (The transmission optimizations employed by the current system are twofold in order to address each possible source of packet degradation. First, just before a call is initiated, a last mile optimization is performed in order to select the best server for device connection and algorithm selection. Secondly, during a call over the network, a real time optimization may be employed to dynamically reroute transmission pathways based upon changing network conditions. These systems and methods will be described in more detail below, [0054], lines 1-5).
Regarding claim 9, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising, receiving from the UE an indication of the UE’s destination is in a third adjacent server and adjusting the priority list to favor the third adjacent server (access controller 312 also receives feedback from a regional optimizer 316 which returns a listing of edge servers 120a-x (priority list) located in the same region (geographic area) as the device 104 running the app 220. For example, in some embodiments there are multiple edge servers 120a-x (priority list) in Nanjing (geographic area) and Hangzhou (geographic area) with the ISP China (geographic area) Telecom. For a user from Wuxi, the regional optimizer 316 would recommend Nanjing before Hangzhou, because Wuxi and Nanjing are both in Jiangsu province, [0060], lines 1-8).
Regarding claim 10, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), wherein the number of adjacent edge servers is greater than the number of adjacent edge servers on the priority list (Within the transport layer 140, there are tens of edge servers 120a-x. Each edge IDC 120a-x includes a last mile optimizer 342a-x, and a server relay 344a-x. The last mile optimizer 342a-x runs in the background before the initiation of a call. The apps 220a-n measure the quality of transmissions between various last mile optimizers 342a-x, a selected by the access controller 312. This selection of the ‘several best’ servers for testing by the access controller 312 is performed based upon the applications IP address and geographic location information by the general optimizer 310, in some embodiments. For a user from Wuxi, the regional optimizer 316 would recommend Nanjing before (prioritizing) Hangzhou, because Wuxi and Nanjing are both in Jiangsu province, [0058], lines 1-6).
Regarding claim 11, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising, determining which category of a plurality of device categories the UE belongs (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (area), [0057], line 3, [0058], lines 1-5).
Regarding claim 12, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), wherein the UE is a connected vehicle category of devices and a current navigation instruction for the UE adjusts the priority list (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (periphery), [0057], line 3, [0058], lines 1-5).
Regarding claim 13, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), wherein the UE is an augmented reality device (alternate embodiments the systems and methods of communication packet delivery improvement disclosed herein are equally able to be implemented on a wide array of devices (i.e., laptop and desktop computers, game consoles (augmented reality device), dedicated teleconferencing equipment, etc.), [0047], lines 4-6).
Regarding claim 14, Li et al discloses system (FIG. 1 is an example block diagram of a system, [0021], line 1), further comprising, removing edge servers from the priority list based on (Determine (identifying) the best edge servers 120a-x based purely upon regional and geographic variables. Servers may also be narrowed (identified) by their regional location; thus is seen as excluding (removing) any edge servers that are not regional and geographic variables, [0058], lines 6-7, [0078], line 6):
querying a topography and transportation infrastructure database (Querying users’ IP address database (database of historical location), [0059], line 2),
determining which category of a plurality of device categories the UE belongs (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (area), [0057], line 3, [0058], lines 1-5), and
identifying a status for a set of conditions including weather, traffic, and current events (Once the possible server candidates are identified, a load balancer 320 looks at the relative loads each server is currently subjected to in order to identify the most likely server to be best suited to handle the data traffic. The access controller 312 then communicates this short list of the ‘several best’ servers to the app 220 for testing, [0060], lines 5-8).
Regarding claim 15, Li et al discloses Computer-readable storage media having computer-executable instructions embodied thereon that, when executed by one or more processors, cause the processors to (instrumentation and interface 210 couples, physically or logically, with a network optimization application 220 (“app”), which typically includes computer readable instructions on non-volatile device storage 230, and executed via one or more processors, [0056], lines 1-3):
receive a request from a user equipment (UE) to utilize edge services (The app within the device 104a gets (requests) some edge servers in Shanghai, Guangzhou, Wuxi, etc. from access controller 312 via the aforementioned general optimization. The app then tests (start testing) these several servers for the very best ones, and determines which algorithms to employ (start); thus is seen as the APP within the device 104a gets/ receives a requests to test the edge servers and determine which algorithm to employ (start) on the edge servers (start an edge instance), [0087], lin3w 2-4);
store context data on a first edge server (network label system 1210 includes a number of subcomponents: a network information collector 1212 pulls temporal, identification and quality metrics (context data) from initiated real-time data (context data) transfers; a network history database (context data) 1214 saves (storing) this collected information and operates in conjunction with a network modeling module 1216 to generate and store predictions (context data) of network, [0090], lines 3-6);
determine the geographical location of the UE is proximal to a perimeter of a first geographical area (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (area), [0057], line 3, [0058], lines 1-5);
query a stored dataset of the UE’s historical location (Querying users’ IP address database (database of historical location), [0059], line 2) and duration history (network information collector 1212 pulls real-time data (duration data) transfers (querying) network history database 1214, saves this collected information and store predictions of network, [0090], lines 3-6);
develop a list of adjacent edge servers (Access controller 312 also receives feedback from a regional optimizer 316 which returns (prepares) a listing of edge servers 120a-x located in the same region (historical location) as the device 104 running the app 220, [0060], lines 1-2);
determine a priority list of edge servers by analyzing the stored dataset to refine the list of adjacent edge servers (Access controller 312 also receives feedback from a regional optimizer 316 which returns (determine) a listing of edge servers 120a-x (priority list) located in the same region (historical location) as the device 104 running the app 220, [0060], lines 1-2); and
Li et al does not specifically disclose concept of cache a portion of the context data on at least one edge server from the priority list.
However, Matsuo et al specifically teaches concept of cache a portion of the context data on at least one edge server from the priority list (arrangement determination unit 1252 determines arrangement of logics (functions) in which required performance is maximized on the basis of the measured performance (context data) and attribute information (context data) including information (context data) representing an arrangement position, processing performance (context data) and the like of each edge server stored (cached) in the edge server information storage 132; thus is seen as storing (caching) measured performance (context data), attribute information (context data) representing/on an arrangement position (geographically) and processing performance (context data) of each edge server stored (cached) in the edge server information storage 132, [0052], lines 1-4)..
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Li et al with concept of cache a portion of the context data on at least one edge server from the priority list of Matsuo et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve efficiency of information collection and fast response time, anticipation for a distributed application in which a plurality of application logics (a component that performs some kind of process on input data and outputs the data is referred to as “logic” below) are arranged and executed in geographically distributed machines in a vertical distribution environment, (Matsuo et al, [0002], lines 3-6)
Regarding claim 16, Li et al discloses computer-readable storage media (instrumentation and interface 210 couples, physically or logically, with a network optimization application 220 (“app”), which typically includes computer readable instructions on non-volatile device storage 230, and executed via one or more processors, [0056], lines 1-3), wherein one or more of the processors is located on a remote server on a network connected to the first edge server (The instrumentation and interface 210 couples, physically or logically, with a network optimization application 220 (“app”), which typically includes computer readable instructions on non-volatile device storage 230, and executed via one or more processors. In the instant example, the app 220 is responsible for communicating with the last mile optimizer (found in the transport layer 140), and an access controller (found in the controller layer 130). The app 220 receives information from the access controller regarding last-mile server, and engages the last mile optimizers to measure packet transmission quality, [0056], lines 1-6).
Regarding claim 17, Li et al discloses computer-readable storage media (instrumentation and interface 210 couples, physically or logically, with a network optimization application 220 (“app”), which typically includes computer readable instructions on non-volatile device storage 230, and executed via one or more processors, [0056], lines 1-3), further comprising determining the velocity of the UE exceeds a certain limit and increasing the frequency of determining the location of the UE (FIG. 1 provides an example schematic block diagram for a system for improved communication packet delivery for enhanced call over network (CON) quality, shown generally at 100. In this example block diagram, a number of participants 102a-n are illustrated engaging a plurality of devices 104a-n. Note that for a successful call, only two devices, and a minimum of two participants 102a-n are required. However, as will be elucidated below, in conjunction with examples and embodiments, call quality improvements are especially helpful as the number of participants 102a-n increases due to the increased number of data pathways being relied upon, [0045], lines 1-6).
Regarding claim 18, Li et al discloses computer-readable storage media (instrumentation and interface 210 couples, physically or logically, with a network optimization application 220 (“app”), which typically includes computer readable instructions on non-volatile device storage 230, and executed via one or more processors, [0056], lines 1-3), wherein a UE navigation destination is received and the priority list is reduced to the edge server indicated by the UE navigation destination (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (periphery), [0057], line 3, [0058], lines 1-5).
Regarding claim 19, Li et al discloses method for performing predictive and dynamic user context transference, comprising (Device 104a may access the internet via an access point, as described previously. In this example, a user in Hangzhou, China would like to talk to another user in New York. The app within the device 104a gets some edge servers in Shanghai, Guangzhou, Wuxi, etc. from access controller 312 via the aforementioned general optimization. Then, the app sends the best (predicted need) edge server (in the present example the Shanghai edge server 344c) voice packets (user data). The Shanghai edge server 344c then routes the packets to an edge server in California and then one in Virginia (edge servers), [0087], lines 1-8):
receiving a request for edge services on a first edge server from a user equipment (UE) (The app within the device 104a gets (requests) some edge servers in Shanghai, Guangzhou, Wuxi, etc. from access controller 312 via the aforementioned general optimization. The app then tests (start testing) these several servers for the very best ones, and determines which algorithms to employ (start); thus is seen as the APP within the device 104a gets/ receives a requests to test the edge servers and determine which algorithm to employ (start) on the edge servers (start an edge instance), [0087], lin3w 2-4);
storing context data on the first edge server (network label system 1210 includes a number of subcomponents: a network information collector 1212 pulls temporal, identification and quality metrics (context data) from initiated real-time data (context data) transfers; a network history database (context data) 1214 saves (storing) this collected information and operates in conjunction with a network modeling module 1216 to generate and store predictions (context data) of network, [0090], lines 3-6);
determining the UE’s location within a first geographic area serviced by the first edge server (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (area), [0057], line 3, [0058], lines 1-5);
determining the UE’s location is within a periphery of the first geographic area (Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)). Selection of the ‘several best’ edge servers 120a-x for testing by the access controller 312 is performed based upon the applications 220a-n geographic location information; thus is seen as Apps 220a-n correspond to their respective devices 104a-n (User Equipment (UE)) geographic location information is determined within the edge servers’ geographic location (periphery), [0057], line 3, [0058], lines 1-5);
compiling a data set of historical location (Querying users’ IP address database (database of historical location), [0059], line 2) and duration data for the UE (network information collector 1212 pulls real-time data (duration data) transfers (querying) network history database 1214, saves this collected information and store predictions of network, [0090], lines 3-6);
compiling a list of adjacent edge servers (prepares) a listing of edge servers 120a-x located in the same region (historical location) as the device 104 running the app 220, [0060], lines 1-2);
computing a priority list of qualified edge servers by applying the data set to refine the list of adjacent edge servers (Access controller 312 also receives feedback from a regional optimizer 316 which returns (computing) a listing of edge servers 120a-x (priority list) located in the same region (historical location) as the device 104 running the app 220, [0060], lines 1-2); and
Li et al does not specifically disclose concept of caching a portion of the context data on at least one adjacent edge server based on the priority list.
However, Matsuo et al specifically teaches concept of caching a portion of the context data on at least one adjacent edge server based on the priority list (arrangement determination unit 1252 determines arrangement of logics (functions) in which required performance is maximized on the basis of the measured performance (context data) and attribute information (context data) including information (context data) representing an arrangement position, processing performance (context data) and the like of each edge server stored (cached) in the edge server information storage 132; thus is seen as storing (caching) measured performance (context data), attribute information (context data) representing/on an arrangement position (geographically) and processing performance (context data) of each edge server stored (cached) in the edge server information storage 132, [0052], lines 1-4).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Li et al with concept of caching a portion of the context data on at least one adjacent edge server based on the priority list of Matsuo et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve efficiency of information collection and fast response time, anticipation for a distributed application in which a plurality of application logics (a component that performs some kind of process on input data and outputs the data is referred to as “logic” below) are arranged and executed in geographically distributed machines in a vertical distribution environment, (Matsuo et al, [0002], lines 3-6).
Regarding claim 20, Li et al discloses method further comprising, determining the UE is a connected vehicle running a navigation application routing a set of directions which direct the UE to travel into a geographic area of a third adjacent edge server, wherein the third adjacent edge server is given additional priority on the priority list (access controller 312 also receives feedback from a regional optimizer 316 which returns a listing of edge servers 120a-x (priority list) located in the same region (geographic area) as the device 104 running the app 220. For example, in some embodiments there are multiple edge servers 120a-x (priority list) in Nanjing (geographic area) and Hangzhou (geographic area) with the ISP China (geographic area) Telecom. For a user from Wuxi, the regional optimizer 316 would recommend Nanjing before Hangzhou, because Wuxi and Nanjing are both in Jiangsu province, [0060], lines 1-8).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANTZ BATAILLE whose telephone number is (571)270-7286. The examiner can normally be reached Monday-Friday 9:00 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached on 571-270-3438. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FRANTZ BATAILLE/ Primary Examiner, Art Unit 2681