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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-15 are have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-15 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. [ 11,954,528, hereinafter refers as '528].
Regarding claim 1, the claim limitation of “network communication circuitry for use in the network communication with the at least one remote computer network via the at least one network; and processing circuitry configurable to perform operations comprising: based upon machine learning and request data, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node, the request data to be based upon one or more requests to be provided from the at least one remote computer node via the at least one network; wherein: the service request processing is configurable to comprise: accessing the one or more resources in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; and/or providing to the at least one remote computer node, via the at least one network, other data in response, at least in part, to the one or more requests; and the machine learning is configurable to be based upon one or more of: resource usage monitoring data; resource demand history data; dynamic condition data; and/or application requirement data” corresponds the claim limitation of “network communication circuitry for use in the network communication with the at least one remote computer network via the at least one network; and processing circuitry to execute instructions, the instructions, when executed by the processing circuitry, resulting in the server system being configurable to perform operations comprising: receiving one or more requests from the at least one remote computer node via the at least one network; based upon machine learning and the one or more requests, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node; accessing the one or more resources for use in the service request processing; and providing to the at least one remote computer node, via the at least one network, data resulting from processing of the one or more requests; wherein: the one or more resources are accessible in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; the machine learning is configurable to be based upon one or more of: resource usage monitoring data; resource demand history data; dynamic condition data; and/or application requirement data” of claim 1 of ‘528. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitation of claim 1 is met by the claim limitation of claim 1 of ‘528.
Regarding claim 2, the claim limitation is similar to claim 6 of ‘528.
Regarding claim 3, the claim limitation is similar to claim 3 of ‘528.
Regarding claim 4, the claim limitation is similar to claim 4 of ‘528.
Regarding claim 5, the claim limitation is similar to claim 5 of ‘528.
Regarding claim 6, the claim limitation of “network communication circuitry for use in the network communication with the at least one remote computer network via the at least one network; and processing circuitry configurable to perform operations comprising: based upon machine learning and request data, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node, the request data to be based upon one or more requests to be provided from the at least one remote computer node via the at least one network; wherein: the service request processing is configurable to comprise: accessing the one or more resources in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; and/or providing to the at least one remote computer node, via the at least one network, other data in response, at least in part, to the one or more requests; and the machine learning is configurable to be based upon one or more of: resource usage monitoring data; resource demand history data; dynamic condition data; and/or application requirement data” corresponds the claim limitation of “network communication circuitry for use in the network communication with the at least one remote computer network via the at least one network; and processing circuitry to execute instructions, the instructions, when executed by the processing circuitry, resulting in the server system being configurable to perform operations comprising: receiving one or more requests from the at least one remote computer node via the at least one network; based upon machine learning and the one or more requests, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node; accessing the one or more resources for use in the service request processing; and providing to the at least one remote computer node, via the at least one network, data resulting from processing of the one or more requests; wherein: the one or more resources are accessible in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; the machine learning is configurable to be based upon one or more of: resource usage monitoring data; resource demand history data; dynamic condition data; and/or application requirement data” of claim 8 of ‘528. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitation of claim 6 is met by the claim limitation of claim 8 of ‘528.
Regarding claim 7, the claim limitation is similar to claim 13 of ‘528.
Regarding claim 8, the claim limitation is similar to claim 10 of ‘528.
Regarding claim 9, the claim limitation is similar to claim 11 of ‘528.
Regarding claim 10, the claim limitation is similar to claim 12 of ‘528.
Regarding claim 11, the claim limitation of “network communication circuitry for use in the network communication with the at least one remote computer network via the at least one network; and processing circuitry configurable to perform operations comprising: based upon machine learning and request data, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node, the request data to be based upon one or more requests to be provided from the at least one remote computer node via the at least one network; wherein: the service request processing is configurable to comprise: accessing the one or more resources in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; and/or providing to the at least one remote computer node, via the at least one network, other data in response, at least in part, to the one or more requests; and the machine learning is configurable to be based upon one or more of: resource usage monitoring data; resource demand history data; dynamic condition data; and/or application requirement data” corresponds the claim limitation of “network communication circuitry for use in the network communication with the at least one remote computer network via the at least one network; and processing circuitry to execute instructions, the instructions, when executed by the processing circuitry, resulting in the server system being configurable to perform operations comprising: receiving one or more requests from the at least one remote computer node via the at least one network; based upon machine learning and the one or more requests, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node; accessing the one or more resources for use in the service request processing; and providing to the at least one remote computer node, via the at least one network, data resulting from processing of the one or more requests; wherein: the one or more resources are accessible in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; the machine learning is configurable to be based upon one or more of: resource usage monitoring data; resource demand history data; dynamic condition data; and/or application requirement data” of claim 15 of ‘528. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitation of claim 11 is met by the claim limitation of claim 15 of ‘528.
Regarding claim 12, the claim limitation is similar to claim 20 of ‘528.
Regarding claim 13, the claim limitation is similar to claim 17 of ‘528.
Regarding claim 14, the claim limitation is similar to claim 18 of ‘528.
Regarding claim 15, the claim limitation is similar to claim 18 of ‘528.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Iyer in view of Hughes (US 2007/0115812 A1) and further view of Al Farugue et al. (US 2013/0159461 A1, hereinafter refers as Al Faruque) an further in view of Chew (US 2018/0285767 A1) and further in view of Halpern et al. (US 2017/0126792 A1, hereinafter refers as Halpnern).
Regarding claim 1, Iyer discloses a server system for use in network communication with at least one remote computer node via at least one network, the server system comprising:
network communication circuitry for use in the network communication with the at least one remote computer network via the at least one network (Fig. 1, Fig. 3);
and processing circuitry configurable to perform operations comprising:
based upon machine learning and request data, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node, the request data to be based upon one or more requests to be provided from the at least one remote computer node via the at least one network (para. 28-30, each peer to peer device is acting as a remote node, to be assigned to record the program upon the request);
wherein: the service request processing is configurable to comprise: accessing the one or more resources in accordance with mapping data for use in identifying the one or more resources in association with the service request processing (para. 30, the status management module to maintain the status data [refers as mapping data] to associate each peer to peer device’s availability to perform the request of recording);
and/or providing to the at least one remote computer node, via the at least one network, other data in response, at least in part, to the one or more requests (para. 30-31); and the machine learning is configurable to be based upon one or more of: resource usage monitoring data; resource demand history data; dynamic condition data; and/or application requirement data (para. 30-31, automatic process of identifying a peer to peer device to perform a recording is based on its historical data or its availability data, para. 37, a software module);
Iyer does not explicitly disclose the machine learning is configurable to be based upon service level data;
Hughes teaches the machine learning is configurable to be based upon service level data (claim 18, 21, the software to direct the processor to transmit the data….based on….service level);
It would be obvious for one ordinary skill in the art to modify Iyer to include Hughes in order allow a system to utilize and provide more accurate data from using the machine learning algorithms.
Iyer in view of Hughes does not explicitly disclose the machine learning is configurable to be based application requirement data;
Al Farugue teaches the machine learning is configurable to be based application requirement data (para. 34, claim 6, cost analysis algorithm is based on the resource availability requirement data);
It would be obvious for one ordinary skill in the art to modify Iyer and Hughes to include Al Farugue in order allow a system to utilize and provide more accurate data from using the machine learning algorithms.
Iyer in view of Hughes in view of Al Farugue does not explicitly disclose the machine learning is configurable to be upon machine learning training data; and machine learning is configurable to generate resource usage recommendation data associated with the dynamically assigning of one or more resource;
Chew teaches the machine learning is configurable to be upon machine learning training data (para. 84);
It would be obvious for one ordinary skill in the art to modify Iyer and Hughes in view of Al Farugue to include Chew in order allow a system to train a more accurate machine learning algorithm.
Iyer in view of Hughes in view of Al Farugue in view of view of Chew does not explicitly disclose and machine learning is configurable to generate resource usage recommendation data associated with the dynamically assigning of one or more resource;
Halpnern teaches machine learning is configurable to generate resource usage recommendation data associated with the dynamically assigning of one or more resource (para. 69);
It would be obvious for one ordinary skill in the art to modify Iyer and Hughes in view of Al Farugue in view of Chew in view of Halpnern in order to allow a system to save the system resource.
Regarding claim 2, Iyer in view of Hughes in view of Al Farugue in view of Chew in view of Helpern discloses wherein: the one or more resources comprise a plurality of resources that are comprised in multiple computer nodes (Fig. 1); and the machine learning is configurable to be based upon: service level data; and/or policy data (Iyer para. 34-37, access control, authentication, and integrity).
Regarding claim 3, Iyer in view of Hughes in view of Al Farugue in view of Chew in view of Helpern discloses wherein: the operations further comprise: after the dynamically assigning of the one or more resources: releasing, based upon the machine learning, assignment of the one or more resources (Iyer para. 37, para. 31).
Regarding claim 4, Iyer in view of Hughes in view of Al Farugue in view of Chew in view of Helpern discloses wherein: after the dynamically assigning of the one or more resources: the mapping data is to be reconfigured to reflect releasing of assignment of the one or more resources (Iyer para. 31).
Regarding claim 5, Iyer in view of Hughes in view of Al Farugue in view of Chew in view of Helpern discloses wherein: the mapping data comprises address mapping data (Iyer para. 29-30, the status data of a peer to peer device that is assigned to perform the recording).
Regarding claim 6, the instant claim is met by the rejection of claim 1.
Regarding claim 7, the instant claim is met by the rejection of claim 2.
Regarding claim 8, the instant claim is met by the rejection of claim 3.
Regarding claim 9, the instant claim is met by the rejection of claim 4.
Regarding claim 10, the instant claim is met by the rejection of claim 5.
Regarding claim 11, the instant claim is met by the rejection of claim 1.
Regarding claim 12, the instant claim is met by the rejection of claim 2.
Regarding claim 13, the instant claim is met by the rejection of claim 3.
Regarding claim 14, the instant claim is met by the rejection of claim 4.
Regarding claim 15, the instant claim is met by the rejection of claim 5.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/CAI Y CHEN/Primary Examiner, Art Unit 2425