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 .
DETAILED ACTION
This action is responsive to the application filed on 07/10/2024 has a total of 20 claims pending in the application; there are 3 independent claims and 17 dependent claims, all of which are ready for examination by the examiner.
Remarks
The claims were presented as follow:
Claims 1-2, 4, 6-8, 11, 14-17, 19, 21, 24-25, 27, 29-31, and 34 are pending.
Claims 3, 5, 9-10, 12-13, 18, 20, 22-23, 26, 28, 32-33, 35-36 are cancelled.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 4, 6-8, 11 24-25, 27, 29-31, and 34 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 1-2, 4, 6-8, and 11 the following limitations are recited:
- “at least one baseband unit...” as recited in claim 1.
- “one or more radio units…” as recited in claim 1.
Dependent claims 2, 4, 6-8, 11 are also rejected since they are depended upon rejection claims set forth above.
As per claims 24-25, 27, 29-31, and 34 the following limitations are recited:
- “a master unit...” as recited in claim 24.
- “one or more remote antenna units…” as recited in claim 24.
Dependent claims 25, 27, 29-31, and 34 are also rejected since they are depended upon rejection claims set forth above.
The limitations noted immediately above are means-plus-function limitations that invoke 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for the claimed function. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Applicant may add a memory and processor or:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4, 6-8, 11, 14-17, 19, 21, 24-25, 27, 29-31, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Bisaria et al. Publication No. (US 2021/0274512 A1) in view of O`Sheaet al. Publication No. (US 2020/0343985 A1).
Regarding claim 1, Bisaria teaches a system, comprising:
at least one baseband unit (BBU) (base band units (vBBUs) 560A-C [0051] FIG.5);
one or more radio units communicatively coupled to the at least one BBU (RRUs 520A-C coupled to vBBUs 560A-C via fronthaul connection 545. ([0051] FIG.5);
one or more antennas communicatively coupled to the one or more radio units (distributed antenna system (DAS) [0016] FIG.5), wherein each respective radio unit of the one or more radio units is communicatively coupled to a respective subset of the one or more antennas (RRUs 520A-C coupled to vBBUs 560A-C and wirelessly coupled to user devices 540A-C inherently have coupled antennas [0051] FIG.5);
wherein the at least one BBU, the one or more radio units, and the one or more antennas are configured to implement a base station (radio network node include, but are not limited to, base stations (BS), multi-standard radio (MSR) nodes such as MSR BS, gNodeB, eNode B, network controllers, radio network controllers (RNC), base station controllers (BSC), relay, donor node controlling relay, base transceiver stations (BTS), access points (AP), transmission points, transmission nodes, remote radio units (RRU) (also termed radio units herein), remote ratio heads (RRH), and nodes in distributed antenna system (DAS) [0016] FIG.5) for wirelessly communicating with user equipment (edge network device 510, and RRUs 520A-C wirelessly coupled to user devices 540A-C [0052] FIG.5); and
a computing system (network device 150 can be used to advantageously distribute and replicate the monitoring of slice utilization data 495, evaluation of usage data (e.g., by capacity prediction component analyzing real time data store 490, and modifying capacity assigning of resources based on usage data, for the different levels. [0043]) configured to:
receive time data, traffic data, and quality of service (QoS) data (historical data store 310 storing usage data including bandwidth utilization by monitoring usage of network slices 195A-B allocated to devices that are being monitored and other utilization measures that effect predictions as to future utilization of the network slices [0038-42] FIG.8); and
determine a predicted radio resource usage of the base station based on the time data, the traffic data, and the QoS data (Usage data can broadly include, but is not limited to, bandwidth utilization by slices allocated to monitored devices and other utilization measures that can affect predictions as to future utilization of the network slice, data collection and analysis can result in predictions (e.g., projections) that can be used to modify capacity assigning for network slices before any service degradation for the network slice occurs [0042-43] FIG.6);
wherein the system is configured to dynamically modify, add, or delete a network slice based on the predicted radio resource usage of the base station (approaches to data collection and analysis can result in predictions (e.g., projections) that can be used to modify capacity assigning for network slices before any service degradation for the network slice occurs, the elements of network device 150 can be used at one or more levels of RAN 400, e.g., to advantageously distribute and replicate the monitoring of slice utilization data 495, evaluation of usage data (e.g., by capacity prediction component analyzing real time data store 490, and modifying capacity assigning of resources based on usage data, for the different levels [0042-43] e.g., dynamically change aspects of network slice 195A [0049-50] based on the monitored slice performance, facilitate recalibration of the resource profile in accordance with a condition of the network service type, resulting in a modification of the capacity of the resource assigned to the network slice [0059] FIG.7).
Bisaria does not explicitly teach wherein the computing system is a machine learning system.
O`Sheaet teaches a machine learning system (O`Sheaet: a machine-learning network such as the machine-learning network 605 within a system such as the system 600. The machine-learning network approach scales from small numbers of antennas, e.g., 1, 2, or 4, up to larger Massive MIMO systems, e.g., with 32, 64, 256 or more antennas [0132] a machine-learning network can learn to equalize many different channel response from different user equipment (UE) but which all draw from some distribution for the full sector. This distribution may change or be conditioned on other aspects over time, such as time of day, day of week, event activity, or other physical phenomena which can change the overall distribution of channel statistics for all user allocations within the cell [0122-124] FIG.5).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filling date of the claimed invention to have modified Bisaria by the teaching of O`Sheaet to use a machine learning system in order to learn to equalize many different channel response from different user equipment (O`Sheaet: [0122-124] FIG.5).
Regarding claim 2, the modified Bisaria teaches the system of claim 1, wherein the time data, the traffic data, and the QoS data includes: time of day; day of week; a number of user equipment wirelessly communicating with the base station (O`Sheaet: The machine-learning network can learn to equalize many different channel response from different UEs which all draw from some distribution for the full sector. This distribution may change or be conditioned on other aspects over time, such as time of day, day of week, event activity, or other physical phenomena which can change the overall distribution of channel statistics for all user allocations within the cell. [0124-125] FIG.5); and active quality of service identifiers (historical data store 310 storing usage history to maintain quality of services to respective user devices [0052] FIG.3).
Claim 3. (Cancelled).
Regarding claim 4, Bisaria teaches the system of claim 1, wherein the system is configured to dynamically add or delete a network slice based on the predicted radio resource usage of the base station (approaches to data collection and analysis can result in predictions (e.g., projections) that can be used to modify capacity assigning for network slices before any service degradation for the network slice occurs, the elements of network device 150 can be used at one or more levels of RAN 400, e.g., to advantageously distribute and replicate the monitoring of slice utilization data 495, evaluation of usage data (e.g., by capacity prediction component analyzing real time data store 490, and modifying capacity assigning of resources based on usage data, for the different levels [0042-43] e.g., dynamically change aspects of network slice 195A [0049-50] based on the monitored slice performance, facilitate recalibration of the resource profile in accordance with a condition of the network service type, resulting in a modification of the capacity of the resource assigned to the network slice [0059] FIG.7).
Claim 5. (Cancelled).
Regarding claim 6, Bisaria teaches the system of claim 1, wherein the system is configured to dynamically modify a network slice based on the predicted radio resource usage of the base station (dynamically change aspects of network slice 195A that can include, but are not limited to, backhaul network resources 455, edge network resources 445, and base station resources 435. Examples of aspects that can be changes can comprise, bandwidth capacity (e.g., noted above as assigned for network slice 195A at the time of setup), processing power allocated for stream processing at both RAN backhaul device 450 and edge network device 440 [0049-50] FIG.5).
Regarding claim 7, Bisaria teaches the system of claim 1, wherein the network slice includes a share of transport resources, core network resources, and radio access network resources (a single slice 272A can have shared use 570A-C by user devices 540A-C. In this example, slice 272A can have high-bandwidth resource profile 350 assigned, and this profile can affect the allocation of resources to the operation of the network slice [0054-55] FIG.5).
Regarding claim 8, the modified Bisaria teaches the system of claim 1, wherein the machine learning computing system is configured to utilize the time data, the traffic data, and the QoS data as inputs to a plurality of machine learning models (O'Shea: data inputs into the machine-learning network operating at each of the tow digital units (Dus) to obtain predictions related to the inputs [0093-95] FIG.6), wherein each machine learning model of the plurality of machine learning models is directed to a respective one or more quality of service identifiers, a respective frequency band, and/or a respective operator (O'Shea: machine-learning network 605 is used by two Dus 604 and 606, a DU performs High-PHY processing for a number of sectors. Therefore, each machine-learning network in the respective DU is directed to a specific number of sectors [i.e. specific sub-area of a service area] [0129-131] FIG.6).
Claims 9-10. (Cancelled).
Regarding claim 11, Bisaria teaches the system of claim 1, wherein the one or more radio units includes a plurality of radio units (RRUs 520A-C coupled to vBBUs 560A-C via fronthaul connection 545. ([0051] FIG.5), wherein the one or more antennas includes a plurality of antennas (nodes in distributed antenna system (DAS) [0016] FIG.5)
Claims 12-13. (Cancelled).
Regarding claims 14-17, 19 and 21, the independent claim and each dependent claim are related to the same limitation set for hereinabove in claims 1-2,4,6-8 and 11, where the difference used is the limitations were presented from a “method” side and the wordings of the claims were interchanged within the claim itself or some of the claims were presented as a combination of two or more previously presented limitations. This change does not affect the limitation of the above treated claims. Adding these phrases to the claims and interchanging the wording did not introduce new limitations to these claims. Therefore, these claims were rejected for similar reasons as stated above.
Claims 18,20, 22-23. (Cancelled).
Regarding claims 24-25,27,29-31 and 34, the independent claim and each dependent claim are related to the same limitation set for hereinabove in claims 1-2,4,6-8 and 11, where the difference used is the limitations were presented from a “system” side with a distributed antenna system (Bisaria: distributed antenna system (DAS) [0016] FIG.5) and the wordings of the claims were interchanged within the claim itself or some of the claims were presented as a combination of two or more previously presented limitations. This change does not affect the limitation of the above treated claims. Adding these phrases to the claims and interchanging the wording did not introduce new limitations to these claims. Therefore, these claims were rejected for similar reasons as stated above.
Claims 26,28,32-33, and 35-36. (Cancelled).
Conclusion
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDELNABI O MUSA whose telephone number is (571)270-1901, and email address is abdelnabi.musa@uspto.gov ‘preferred’. The examiner can normally be reached on M-F 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kevin Bates, can be reached on 571-2723980. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDELNABI O MUSA/Primary Examiner, Art Unit 2472
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