DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1–13 are pending for examination the response filed on 10/21/2024. Claims 14–19 are WITHDRAWN.
Examiner’s Remarks
3. Examiner refers to and explicitly cites particular pages, sections, figures, paragraphs or columns and lines in the references as applied to Applicant’s claims to the extent practicable to streamline prosecution.
Although the cited portions of the references are representative of the best teachings in the art and are applied to meet the specific limitations of the claims, other uncited but related teachings of the references may be equally applicable as well. It is respectfully requested that, in preparing responses to the rejections, the Applicant fully considers not only the cited portions of the references, but also the references in their entirety, as potentially teaching, suggesting or rendering obvious all or one or more aspects of the claimed invention.
Abbreviations
4. Where appropriate, the following abbreviations will be used when referencing Applicant’s submissions and specific teachings of the reference(s):
i. figure / figures: Fig. / Figs.
ii. column / columns: Col. / Cols.
iii. page / pages: p. / pp.
References Cited
5. (A) McNamara et al., US 10,608,433 B1 (“McNamara”).
(B) Chan et al., US 2012/0117399 A1 (“Chan”).
(C) Cencini et al., US 2017/0264493 A1 (“Cencini”).
(D) Miriyala et al., US 10,742,557 B1 (“Miriyala”).
Notice re prior art available under both pre-AIA and AIA
6. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
A.
7. Claims 1–10 are rejected under 35 U.S.C. 103 as being unpatentable over (A) McNamara in view of (B) Chan.
See “References Cited” section, above, for full citations of references.
8. Regarding claim 1, (A) McNamara teaches/suggests the invention substantially as claimed, including:
“A flexible datacenter comprising:
a plurality of computing systems, wherein the plurality of computing systems are configured to receive power from at least one power source”
(Fig. 5 and Col. 29, lines 38–45: in FIG. 5, the flexible datacenter 500 includes a power input system 502, a communication interface 503, a datacenter control system 504, a power distribution system 506, a climate control system 508, one or more sets of computing systems 512;
Fig. 2 and Col. 14, lines 28–34: As illustrated in FIG. 2, generation station 202 is configured to connect with BTM equipment which may function as BTM loads. In the illustrated embodiment of FIG. 2, the BTM equipment includes flexible datacenters 220. Various configurations to supply BTM power to flexible datacenters);
“a data control system configured to control data flow to the plurality of computing
systems based at least in part on a change in economic feasibility”
(Col. 20, lines 20–55: remote master control system 262 or another component may manage distribution and execution of computational operations at one or more traditional datacenters 260 and/or flexible datacenters 220 via one or more information-processing algorithms … Information used to make decisions may include economic and/or power-related information;
Col. 21, line 45–55: information may help a component ( e.g., the remote master control system 262 or a control system at a flexible datacenter 220) determine when to ramp up or ramp down power use at a flexible datacenter 220 or when to switch one or more computing systems at a flexible datacenter 220 into a low power mode or to operate at a different frequency, among other operational adjustments. The information can additionally or alternatively help a component within the arrangement of FIG. 2 to determine when to transfer computational operations between computing systems or between datacenters based on various factors;
Col. 22, lines 1–12: remote master control system 262 may be one or more computing systems configured to process all, or a subset of, the information described above, such as power, environment, computational characterization, and economic factors to assist with the distribution and execution of computing operations among one or more datacenters. For instance, the remote master control system 262 may be configured to obtain and delegate computational operations 10 among one or more datacenters;
Col. 22, lines 60–67: the remote master control system 262 may be combined with another component in other embodiments. To illustrate an example, the remote master control system 262 may operate as part of a flexible datacenter (e.g., a computing system or a datacenter control system of the flexible datacenter 220;
Col. 25, lines 1–20: The remote master control system 300 may subsequently use information to distribute and assign the computational operations to one or more datacenters (e.g., the flexible datacenters 220) that have the resources ( e.g., particular types of computing systems and available power) available to complete the computational operations … Although the remote master control system 300 is shown as a single entity, a network of computing systems may perform the operations of the remote master control system 300 in some examples. For example, the remote master control system 300 may exist in the form of computing systems (e.g., datacenter control systems) distributed across multiple datacenters;
Col. 33, lines 42–50: may assign computational operations based on various factors, such as the types of computing systems available and the type of computing systems required by each computing operation, the availability of the computing systems, whether computing systems can operate in a low power mode, and/or power consumption and/or costs associated with operating the computing systems, among others;
Col. 38, lines 1–10: Similarly, the flexible datacenter 500 may ramp up power consumption based on various conditions. For instance, the datacenter control system 504 may determine, or the generation control system 414, the remote master control system 300, or the grid operator 702 may communicate, that a change in local conditions may result in greater power generation, availability, or economic feasibility;
Col. 47, lines 48–52: monitor and analyze a set of conditions (including the power option data) to determine strategies for assigning, transferring, and otherwise managing computational operations using the one or more datacenters 1102-1106;
Col. 49, lines 1–25: performance strategy can also involve aspects related to the assignment, transfer, and performance of computational operations at the computing systems. For instance, the performance strategy may specify computational operations to be performed at the computing systems, an order for completing computational operations based on priorities associated with the computational operations, and an identification of which computing systems should perform which computational operations. In some instances, priorities may depend on revenue associated with completing each computational operation and deadlines for each computational operation. The monitored conditions may enable efficient distribution and performance of computational operations among computing systems at one or more datacenters (e.g., datacenters 1102-1106) in ways that can reduce costs and/or time to perform computational operations, take advantage of availability and abilities of computing systems at the datacenters 1102-1106, and/or take advantage in changes in the cost for power at the datacenters 1102-1106;
Col. 33, lines 20–25: operations. Performance of computational operations include a variety of tasks that one or more computing systems may perform, such as data storage, calculations, application processing, parallel processing, data manipulation, cryptocurrency mining, and maintenance of a distributed ledger, among others).
McNamara further teaches operating the plurality of computing systems in “a low power mode”
(Col. 21, lines 45–50: determine when to ramp up or ramp down power use at a flexible datacenter 220 or when to switch one or more computing systems at a flexible datacenter 220 into a low power mode or to operate 50 at a different frequency,).
McNamara does not teach: “wherein the plurality of computing systems are in a low power sleep mode when not receiving data.”
(B) Chan, in the context of McNamara’s teachings, however teaches or suggests implementing:
“wherein the plurality of computing systems are in a low power sleep mode when not receiving data.”
(¶ 43: e.g., a server, in the cluster may be in one of the following activation states with respect to a given application or set of applications that are intended to run on that server: 0) active mode, in which it is fully on and operational and processing workload; 1) idle mode, in which the server is idle but ready to accept workload nearly instantaneously; 2) standby mode, in which the server architectural state is saved in memory and the processors and part of the system are put into low-power state; 3) hibernate mode, in which the server architectural and memory state are saved into disk, and various components of the server such as the memory and CPU are in low power consumption mode).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of (B) Chan with those of (A) McNamara to operate the plurality of computing systems in a lower power mode such as standby or hibernate (based on configured or intended level of readiness to receive, accept, and ramp-up to perform computation operations). The motivation or advantage to do so is to reduce the power consumption of computing systems when not performing assigned operations.
9. Regarding claim 2, McNamara teaches or suggests:
“wherein the change in economic feasibility comprises the cost of power to power the plurality of computing systems of the flexible datacenter”
(Col. 20, lines 20–55: remote master control system 262 or another component may manage distribution and execution of computational operations at one or more traditional datacenters 260 and/or flexible datacenters 220 via one or more information-processing algorithms … Information used to make decisions may include economic and/or power-related information;
Col. 22, lines 1–12: remote master control system 262 may be one or more computing systems configured to process all, or a subset of, the information described above, such as power, environment, computational characterization, and economic factors to assist with the distribution and execution of computing operations among one or more datacenters. For instance, the remote master control system 262 may be configured to obtain and delegate computational operations 10 among one or more datacenters;
Col. 33, lines 42–50: may assign computational operations based on various factors, such as the types of computing systems available and the type of computing systems required by each computing operation, the availability of the computing systems, whether computing systems can operate in a low power mode, and/or power consumption and/or costs associated with operating the computing systems, among others;
10. Regarding claim 3, McNamara teaches or suggests:
“wherein the data control system controls the transmission of data to one or more computing systems of the plurality of computing systems”
(Col. 22, lines 1–12: remote master control system 262 may be one or more computing systems configured to process all, or a subset of, the information described above, such as power, environment, computational characterization, and economic factors to assist with the distribution and execution of computing operations among one or more datacenters. For instance, the remote master control system 262 may be configured to obtain and delegate computational operations 10 among one or more datacenters;
Col. 22, lines 60–67: the remote master control system 262 may be combined with another component in other embodiments. To illustrate an example, the remote master control system 262 may operate as part of a flexible datacenter (e.g., a computing system or a datacenter control system of the flexible datacenter 220;
Col. 25, lines 1–20: The remote master control system 300 may subsequently use information to distribute and assign the computational operations to one or more datacenters (e.g., the flexible datacenters 220) that have the resources ( e.g., particular types of computing systems and available power) available to complete the computational operations … Although the remote master control system 300 is shown as a single entity, a network of computing systems may perform the operations of the remote master control system 300 in some examples. For example, the remote master control system 300 may exist in the form of computing systems (e.g., datacenter control systems) distributed across multiple datacenters;
Col. 33, lines 42–50: may assign computational operations based on various factors, such as the types of computing systems available and the type of computing systems required by each computing operation, the availability of the computing systems, whether computing systems can operate in a low power mode, and/or power consumption and/or costs associated with operating the computing systems, among others;
Col. 47, lines 48–52: monitor and analyze a set of conditions (including the power option data) to determine strategies for assigning, transferring, and otherwise managing computational operations using the one or more datacenters 1102-1106;
Col. 49, lines 1–25: performance strategy can also involve aspects related to the assignment, transfer, and performance of computational operations at the computing systems. For instance, the performance strategy may specify computational operations to be performed at the computing systems, an order for completing computational operations based on priorities associated with the computational operations, and an identification of which computing systems should perform which computational operations. In some instances, priorities may depend on revenue associated with completing each computational operation and deadlines for each computational operation. The monitored conditions may enable efficient distribution and performance of computational operations among computing systems at one or more datacenters (e.g., datacenters 1102-1106) in ways that can reduce costs and/or time to perform computational operations, take advantage of availability and abilities of computing systems at the datacenters 1102-1106, and/or take advantage in changes in the cost for power at the datacenters 1102-1106).
11. Regarding claim 4, McNamara teaches or suggests:
“wherein the data control system controls the transmission of data to a select group of computing systems of the plurality of computing systems”
(Col. 22, lines 1–12: remote master control system 262 may be one or more computing systems configured to process all, or a subset of, the information described above, such as power, environment, computational characterization, and economic factors to assist with the distribution and execution of computing operations among one or more datacenters. For instance, the remote master control system 262 may be configured to obtain and delegate computational operations 10 among one or more datacenters;
Col. 22, lines 60–67: the remote master control system 262 may be combined with another component in other embodiments. To illustrate an example, the remote master control system 262 may operate as part of a flexible datacenter (e.g., a computing system or a datacenter control system of the flexible datacenter 220;
Col. 25, lines 1–20: The remote master control system 300 may subsequently use information to distribute and assign the computational operations to one or more datacenters (e.g., the flexible datacenters 220) that have the resources ( e.g., particular types of computing systems and available power) available to complete the computational operations … Although the remote master control system 300 is shown as a single entity, a network of computing systems may perform the operations of the remote master control system 300 in some examples. For example, the remote master control system 300 may exist in the form of computing systems (e.g., datacenter control systems) distributed across multiple datacenters;
Col. 33, lines 42–50: may assign computational operations based on various factors, such as the types of computing systems available and the type of computing systems required by each computing operation, the availability of the computing systems, whether computing systems can operate in a low power mode, and/or power consumption and/or costs associated with operating the computing systems, among others;
Col. 47, lines 48–52: monitor and analyze a set of conditions (including the power option data) to determine strategies for assigning, transferring, and otherwise managing computational operations using the one or more datacenters 1102-1106;
Col. 49, lines 1–25: performance strategy can also involve aspects related to the assignment, transfer, and performance of computational operations at the computing systems. For instance, the performance strategy may specify computational operations to be performed at the computing systems, an order for completing computational operations based on priorities associated with the computational operations, and an identification of which computing systems should perform which computational operations. In some instances, priorities may depend on revenue associated with completing each computational operation and deadlines for each computational operation. The monitored conditions may enable efficient distribution and performance of computational operations among computing systems at one or more datacenters (e.g., datacenters 1102-1106) in ways that can reduce costs and/or time to perform computational operations, take advantage of availability and abilities of computing systems at the datacenters 1102-1106, and/or take advantage in changes in the cost for power at the datacenters 1102-1106).
12. Regarding claim 5, McNamara teaches or suggests:
“wherein the data control system is further configured to determine the change in economic feasibility”
(Col. 37, lines 42–50: While the flexible datacenter 500 is online and operational, changed conditions or an operational directive may cause the datacenter control system 504 to modulate power consumption by the flexible datacenter 500. The datacenter control system 504 may determine, or the generation station control system 414, the remote master control system 300, or the grid operator 702 may communicate, that a change in local conditions may result in less power generation, availability, or economic feasibility, than would be necessary to fully power the flexible datacenter 500;
Col. 47, lines 48–52: monitor and analyze a set of conditions (including the power option data) to determine strategies for assigning, transferring, and otherwise managing computational operations using the one or more datacenters 1102-1106;
Col. 49, lines 1–25: performance strategy can also involve aspects related to the assignment, transfer, and performance of computational operations at the computing systems. For instance, the performance strategy may specify computational operations to be performed at the computing systems, an order for completing computational operations based on priorities associated with the computational operations, and an identification of which computing systems should perform which computational operations. In some instances, priorities may depend on revenue associated with completing each computational operation and deadlines for each computational operation. The monitored conditions may enable efficient distribution and performance of computational operations among computing systems at one or more datacenters (e.g., datacenters 1102-1106) in ways that can reduce costs and/or time to perform computational operations, take advantage of availability and abilities of computing systems at the datacenters 1102-1106, and/or take advantage in changes in the cost for power at the datacenters 1102-1106).
13. Regarding claim 6, McNamara teaches or suggests:
“wherein the change in economic feasibility is communicated to the data control system by an operator associated with the flexible datacenter”
(Col. 37, lines 42–50: While the flexible datacenter 500 is online and operational, changed conditions or an operational directive may cause the datacenter control system 504 to modulate power consumption by the flexible datacenter 500. The datacenter control system 504 may determine, or the generation station control system 414, the remote master control system 300, or the grid operator 702 may communicate, that a change in local conditions may result in less power generation, availability, or economic feasibility, than would be necessary to fully power the flexible datacenter 500).
14. Regarding claim 7, McNamara teaches or suggests:
“wherein the change in economic feasibility is communicated to the data control system by a remote master control system”
(Col. 37, lines 42–50: While the flexible datacenter 500 is online and operational, changed conditions or an operational directive may cause the datacenter control system 504 to modulate power consumption by the flexible datacenter 500. The datacenter control system 504 may determine, or the generation station control system 414, the remote master control system 300, or the grid operator 702 may communicate, that a change in local conditions may result in less power generation, availability, or economic feasibility, than would be necessary to fully power the flexible datacenter 500).
15. Regarding claim 8, McNamara teaches or suggests:
“wherein the data control system is collocated with the plurality of computing systems”
(Fig. 5 and Col. 29, lines 38–45: in FIG. 5, the flexible datacenter 500 includes a power input system 502, a communication interface 503, a datacenter control system 504, a power distribution system 506, a climate control system 508, one or more sets of computing systems 512;
Col. 13, lines 18–27: a control system may correspond to a specialized computing system or may be a computing system within a datacenter serving in the role of the control system. The location of the control system can vary within examples as well. For instance, the control system may be located at a datacenter or physically separate from the datacenter. In some examples, the control system may be part of a network of control systems that manage computational operations).
16. Regarding claim 9, McNamara teaches or suggests:
“wherein the data control system is located remotely from the plurality of computing systems.”
(Fig. 5 and Col. 29, lines 38–45: in FIG. 5, the flexible datacenter 500 includes a power input system 502, a communication interface 503, a datacenter control system 504, a power distribution system 506, a climate control system 508, one or more sets of computing systems 512;
Col. 13, lines 18–27: a control system may correspond to a specialized computing system or may be a computing system within a datacenter serving in the role of the control system. The location of the control system can vary within examples as well. For instance, the control system may be located at a datacenter or physically separate from the datacenter. In some examples, the control system may be part of a network of control systems that manage computational operations).
17. Regarding claim 10, McNamara teaches or suggests:
“wherein the change in economic feasibility is based on the cost of power to power the plurality of computing systems of the flexible datacenter compared to the price of a crypto coin”
(Col. 48, lines 30–40: Monitoring cryptocurrency prices 1126 may involve monitoring the current price of one or more cryptocurrencies, the hash rate and/or estimated power consumption associated with mining each cryptocurrency, and other factors associated with the cryptocurrencies. The remote master control system 262 may use data related to monitoring cryptocurrency prices 1126 to determine whether using computing systems to mine a cryptocurrency generates more revenue than the cost of power required for performance of the mining operations;
Col. 52, lines 12–25: When determining the power consumption strategy for a load, a computing system ( e.g., the remote master control system 262) may consider various conditions in addition to the power option data received based on one or more power option agreements. Particularly, the computing system may consider and weigh different conditions in addition to the power option data to determine power consumption targets and/or other control instructions for a load. The conditions may include, but are not limited to, the price of grid power, the price of alternative power sources ( e.g., BTM power, stored energy), the revenue associated with mining for one or more cryptocurrencies).
B.
18. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over (A) McNamara in view of (B) Chan, as applied to claim 1 above, and further in view of (C) Cencini.
19. Regarding claim 11, McNamara teaches that the flexible datacenter may include a plurality of racks, each of which may include one or more computing systems disposed therein
(Figs. 6A, 6B, Col. 34, lines 43–45: the datacenter control system 504, and the computing systems 512 arranged on one or more racks 604;
Col. 35, lines 20–23).
McNamara does not teach: “wherein the data control system comprises at least one primary controller and at least one lead controller.”
(C) Cencini, in the context of McNamara’s teachings, however teaches or suggests implementing:
“wherein the data control system comprises at least one primary controller and at least one lead controller.”
(Fig. 7 and ¶ 162: In the illustrated example, the rack controllers are arranged in a hierarchical tree in the topology of the management system 70. In FIG. 7, the differing modifiers of “primary” and “lead” should not be taken to indicate that, at least in some embodiments, the devices have a different architecture. Rather, in some embodiments, each of the devices illustrated in FIG. 7, in some embodiments, may be a ( e.g., identical) instance of a peer rack controller, each controlling a rack in the fashion described above. The lead and primary controllers may be simply designated rack controllers that perform additional tasks based on their role.
The topology may be determined by the rack controllers themselves, dynamically, by executing the routines described below with reference to FIG. 8, in some cases, without a human assigning the roles and arrangement shown in FIG. 7, and with the topology self-evolving to heal from the failure of devices. In this example, there are three levels to the topology. At the highest level is a primary rack controller 72. At a next lower level, adjacent the primary rack controller, and therefore in direct communication with the primary rack 72 are lead rack controller 74. Three rack controllers 74 are illustrated, but embodiments are consistent with substantially more, for instance on the order of more than 50 or more than 500. At the next level of the hierarchy, there are a plurality of rack controller 76).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of (C) Cencini with those of McNamara and Chan to operate a plurality of different types or roles of rack controllers (capable of performing different tasks/functions) to manage the operations of the computing systems arranged on one or more racks. The motivation or advantage to do so is to provide for the efficient, hierarchical (rack) control of computing systems capable of dynamic re-configuration.
C.
20. Claims 12–13 are rejected under 35 U.S.C. 103 as being unpatentable over (A) McNamara in view of (B) Chan, as applied to claim 1 above, and further in view of (D) Miriyala.
21. Regarding claim 12, McNamara teaches or suggests:
“wherein the data control system comprises … at least one monitoring server”
(Col. 19, lines 20–23: flexible datacenters 220 control system or the remote master control system 262 may monitor power conditions and other factors;
Col. 40, lines 33–35: The datacenter control system 936 may monitor activity of the computing systems 934 and obtain computational operations to perform from the queue system 312).
McNamara does not teach: “the data control system comprises at least one firewall, … and at least one switch.”
(D) Miriyala, in the context of McNamara’s teachings, however teaches or suggests implementing:
“the data control system comprises at least one firewall, … and at least one switch.”
(Figs. 1 and 2, Col. 3, line 53 to Col. 4, line 16: In this example, each of data centers 10 includes a set of storage systems and application servers 12A-12X (herein, “servers 12”) interconnected via high-speed switch fabric 14 provided by one or more tiers of physical network switches and routers. Switch fabric 14 is provided by a set of interconnected top-of-rack (TOR) switches 16A-16Z (collectively, “TOR switches 16”) coupled to a distribution layer of chassis switches 18A-18M ( collectively, "chassis switches 18"). Although not shown, each of data centers 10 may also include, for example, one or more non-edge switches, routers, hubs, gateways, security devices such as firewalls, intrusion detection, and/or intrusion prevention devices, servers, computer terminals, laptops, printers, databases, ….
In this example, TOR switches 16 and chassis switches 18 provide servers 12 with redundant (multi-homed) connectivity to IP fabric 20 and service provider network 7. Chassis switches 18 aggregate traffic flows and provides high-speed connectivity between TOR switches 16. TOR switches 16 may be network devices that provide layer two ( e.g., MAC) 10 and/or layer 3 (e.g., IP) routing and/or switching functionality. TOR switches 16 and chassis switches 18 may each include one or more processors and a memory, and that are capable of executing one or more software processes. Chassis switches 18 are coupled to IP fabric 20, which performs 15 layer 3 routing to route network traffic between data centers 10 and customers 11 by service provider network).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of (D) Miriyala with those of McNamara and Chan to provide a tiered, high speed switching network/configuration between computing systems of one or more datacenters. The motivation or advantage to do so is to support flexible control and management of network traffic among workloads executing in one or more computing systems across data centers.
22. Regarding claim 13, McNamara and Miriyala teach or suggest:
“wherein the data control system comprises at least one firewall, at least one monitoring server, at least one lead switch and at least one rack switch”
(McNamara, Col. 19, lines 20–23: flexible datacenters 220 control system or the remote master control system 262 may monitor power conditions and other factors;
Col. 40, lines 33–35: The datacenter control system 936 may monitor activity of the computing systems 934 and obtain computational operations to perform from the queue system 312).
Miriyala, Figs. 1 and 2, Col. 3, line 53 to Col. 4, line 16: In this example, each of data centers 10 includes a set of storage systems and application servers 12A-12X (herein, “servers 12”) interconnected via high-speed switch fabric 14 provided by one or more tiers of physical network switches and routers. Switch fabric 14 is provided by a set of interconnected top-of-rack (TOR) switches 16A-16Z (collectively, “TOR switches 16”) coupled to a distribution layer of chassis switches 18A-18M ( collectively, "chassis switches 18"). Although not shown, each of data centers 10 may also include, for example, one or more non-edge switches, routers, hubs, gateways, security devices such as firewalls, intrusion detection, and/or intrusion prevention devices, servers, computer terminals, laptops, printers, databases, ….
In this example, TOR switches 16 and chassis switches 18 provide servers 12 with redundant (multi-homed) connectivity to IP fabric 20 and service provider network 7. Chassis switches 18 aggregate traffic flows and provides high-speed connectivity between TOR switches 16. TOR switches 16 may be network devices that provide layer two ( e.g., MAC) 10 and/or layer 3 (e.g., IP) routing and/or switching functionality. TOR switches 16 and chassis switches 18 may each include one or more processors and a memory, and that are capable of executing one or more software processes. Chassis switches 18 are coupled to IP fabric 20, which performs 15 layer 3 routing to route network traffic between data centers 10 and customers 11 by service provider network).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN C WU whose telephone number is (571)270-5906. The examiner can normally be reached Monday through Friday, 8:30 A.M. to 5:00 P.M..
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, Meng-Ai An can be reached on (571)272-3756. 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.
/BENJAMIN C WU/Primary Examiner, Art Unit 2195
December 13, 2024