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 .
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 1-20 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.
Claims 1, 8 and 15 recite the steps of generating, determining and providing, however it is unclear which entity of a system is performing these steps. Dependent claims are also rejected for the same reason.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-4, 8-11, 15-18 provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 12-15 of co-pending Application No. 18/542,462 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented. See comparison table below for the method claims which are identical and the same rationale applies to the computer-readable storage media claims 8-11 and the system claims 15-18 of the instant application.
Instant Application 18/542,465
Co-pending Application 18/542,462
1. A method comprising: generating a data fabric from data from a plurality of probes of a cellular network; determining a first subset of the data fabric that is associated with a target key performance indicator of the cellular network; determining a key performance value associated with the target key performance indicator based on the first subset of the data fabric; and providing the key performance value to a northbound application associated with the cellular network.
12. A method comprising: generating a data fabric from data from a plurality of probes of a cellular network; determining a first subset of the data fabric that is associated with a target key performance indicator of the cellular network; determining a key performance value associated with the target key performance indicator based on the first subset of the data fabric; and providing the key performance value to a northbound application associated with the cellular network.
2. The method of claim 1, wherein determining the key performance value comprises: providing the first subset of the data fabric as input to a trained machine learning model; and obtaining the key performance value as output from the trained machine learning model based on the first subset of the data fabric.
13. The method of claim 12, wherein determining the key performance value comprises: providing the first subset of the data fabric as input to a trained machine learning model; and obtaining the key performance value as output from the trained machine learning model based on the first subset of the data fabric.
3. The method of claim 2, wherein determining the key performance value further comprises providing historical data associated with the first subset of the data fabric to the trained machine learning model as input, wherein the output from the trained machine learning model is further based on the historical data.
14. The method of claim 13, wherein determining the key performance value further comprises providing historical data associated with the first subset of the data fabric to the trained machine learning model as input, wherein the output from the trained machine learning model is further based on the historical data.
4. The method of claim 1, further comprising identifying the first subset of the data fabric, wherein identifying the first subset of the data fabric comprises identifying probe data from a first subset of the plurality of probes of the cellular network based on data tags provided by the first subset of the plurality of probes.
15. The method of claim 12, further comprising identifying the first subset of the data fabric, wherein identifying the first subset of the data fabric comprises identifying probe data from a first subset of the plurality of probes of the cellular network based on data tags provided by the first subset of the plurality of probes.
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.
Claim(s) 1, 2, 6-9, 13-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Boyle, III et al. (US 2024/0291709, hereinafter “Boyle”) in view of “Sommers” (US 2024/0205129), and in further view of Aktas et al. (US 2020/0067792, hereinafter “Aktas”).
For claims 1, 8 and 15, Boyle discloses A method comprising:
generating a data fabric from data from a plurality of probes of a cellular network (In step 8014, network devices, probes, sensors, Element Management Systems, Network Management Systems, and/or Operations Support Systems generate event data records (XDRs) based on the captured event data… In step 8026, the captured event data and/or the generated event data records are stored in a storage device, e.g., a database storage system, data lake system, or a data warehouse…; see Boyle par. 0248-0254);
determining a first subset of the data fabric that is associated with a target (see Boyle par. 0292 for feature of target) key performance indicator of the cellular network (In sub-step 8028, with respect to event data and event/or event records indicating a success condition corresponding to or for dependent protocols is stored. In sub-step 8030, with respect to event data and/or event data records indicating a success condition corresponding to or for dependent protocols, the devices performing the storing refrain from that is do not store such event data or event data records…. While data for both success and failure conditions are being stored system processing resources can be minimized for example when generating cross-layer KPIs but not processing the event data records indicating dependent protocol successes; see Boyle par. 0255-0258);
determining a key performance value associated with the target key performance indicator based on the first subset of the data fabric (In sub-step 8046, the cross-layer analytics system divides said first failure count by a value which is based on a sum of the first failure count and a count of successes of the first base protocol (Z) for said first period of time. In sub-step 8047, the cross-layer analytics system generates the plurality of different cross-layer key performance indicators using and/or based on the following equation: cross-layer KPIa = (f1+ f2+ ... + fN)/az; see Boyle par. 0264-0265); and
Boyle does not explicitly disclose a data fabric. Sommers discloses a data fabric (FIG. 3 shows a test controller 102, e.g., the test controller 102 of FIG. 1, a data center fabric 302, an active DPU 304, and a standby DPU 306… Test packets traverse the fabric 302 (which can be physical or emulated or a hybrid of physical and emulated components), where the test packets are routed and sent to one of DPUs 304 and 306 depending upon datacenter configuration; see Sommers par. 0084-0085 and Fig. 3). It would have been obvious to the ordinary skilled in the art before the effective filing date to use Sommers's arrangement in Boyle's invention to test data processing units in high availability configurations (see Sommers par. 0004).
Boyle does not explicitly disclose providing the key performance value to a northbound application associated with the cellular network. Aktas discloses providing the key performance value to a northbound application associated with the cellular network (TSF 400 receives KPI violations from Network Monitoring Function 350 on interface 422, which is a simple API such as the REST APL TSF 400 receives routing tables and network map from Controller 207 on interface 423, which is the 'Northbound' API provided by the Controller. This is the same API that all controller Applications 287 uses; see Aktas par. 0056-0057, see also par. 0046 and 0063). It would have been obvious to the ordinary skilled in the art before the effective filing date to use Aktas's arrangement in Boyle's invention to monitor the health of traffic flows directly from the data plane (see Aktas par. 0001).
Specifically for claim 8, Boyle discloses One or more non-transitory, computer-readable storage media having computer-readable instructions thereon which, when executed by one or more processing devices, cause the one or more processing devices to perform operations (A non-transitory computer readable medium including a first set of computer executable instructions which when executed by a processor of a node cause the node to:; see Boyle par. 0376).
Specifically for claim 15, Boyle discloses A system, comprising memory and a processing device coupled to the memory (FIG. 9 illustrates an exemplary computing device/node, e.g., an analytics system, monitoring device, network device/element/entity, probe, sensor, database system in accordance with an embodiment of the present invention. In some embodiments, one or more of the elements, nodes or components of the above mentioned system 100 shown in FIG. 1 and system 1000 shown in FIG. 10 are implemented in accordance with the exemplary computing device/node 900 illustrated in FIG. 9. Exemplary computing device/node 900 includes an optional display 902, an input device 904, a processor 906, e.g., a CPU, I/O interfaces 908 and 909, which couple the computing device/node 900 to networks or communications links and/or various other nodes/devices, memory 910; see Boyle par. 0221-0223).
For claims 2, 9 and 16, Boyle discloses The method of claim 1, wherein determining the key performance value comprises:
providing the first subset of the data fabric as input to a trained machine learning model (the filters are generated by the cross-layer analytics system utilizing/implementing a clustering algorithm on a large plurality of event data records captured for a particular failure call scenario, e.g., debug scenario. The clustering algorithm identifying and/or correlating the dependent protocols and event data relationships in the plurality of event data records, e.g., patterns in the event data of the plurality of event data records. In some embodiments, the filters are generated and/or determined using automated pattern recognition learning and/or machine learning to determine patterns in the event data records corresponding to a failure call scenario; see Boyle par. 0275); and
obtaining the key performance value as output from the trained machine learning model based on the first subset of the data fabric (In step 8060, the cross layer analytics system shows on a display the generated ranking of the different failure cause scenarios (e.g., debug scenarios) for the first base protocol (Z) for the first period of time. Operation proceeds from step 8060 to optional step 8062. In step 8062, the cross-layer analytics system determines a ranking of the most likely failure cause scenarios of the different failure cause scenarios for the first base protocol (Z) for the first period of time based on the ranking of the different failure cause scenarios; see Boyle par. 0278-0279).
For claims 6, 13 and 20, Boyle discloses The method of claim 1, further comprising causing performance of a corrective action based on the key performance value (In step 8078, the cross-layer analytics system performs one or more of the following actions in response to determining a most likely failure cause scenario for the first base protocol (Z) to be the failure cause scenario associated with the identified CL-KPI having the highest CL-KPI value: (i) take a mitigation action (e.g., implementing a policy change in the network regarding authentication during registration of user equipment devices), (ii) sending a notification message to a device of a system operator or to a threat detection system regarding the determined most likely failure call scenario; (iii) activating an alarm, e.g., making an audio sound indicating an alarm or displaying on a display device a visual indication of an alarm condition e.g., when the CL-KPI value corresponding to the determined most likely failure cause scenario is above a threshold alarm value; see Boyle par. 0287).
For claims 7 and 14, Boyle discloses The method of claim 1, further comprising obtaining a request from the northbound application for the key performance value, wherein determining the key performance value is performed responsive to the request (In step 8038, receive, e.g., by the cross layer analytics system 110, event data records 8036 corresponding to a first period of time. The event data records may be received from storage (e.g., in response to a database request from the cross-layer analytics system) or directly from the devices/entities that generated the event data records. Operation proceeds from step 8038 to step 8040. In step 8040, the cross-layer analytics system uses common elements in the received event detail records to correlate event detail records corresponding to the same event or having a high probability that they correspond to the same event. Operation proceeds from step 8040 via connection node B 8042 to step 8044 shown on FIG. 8C; see Boyle par. 0260-0261).
Claim(s) 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Boyle, Sommers and Aktas, and further in view of Coudert et al. (US 2025/0141760, hereinafter “Coudert”).
For claims 3, 10 and 17, the combination of Boyle, Sommers and Aktas does not explicitly disclose The method of claim 2, wherein determining the key performance value further comprises providing historical data associated with the first subset of the data fabric to the trained machine learning model as input, wherein the output from the trained machine learning model is further based on the historical data. Coudert discloses The method of claim 2, wherein determining the key performance value further comprises providing historical data associated with the first subset of the data fabric to the trained machine learning model as input (Analytics system 323 receives and filters the network KPIs based on an intended function of model 331. In this example, the intended function of machine learning model 331 is to generate synthetic KPIs that realistically depict the status of network 300 without exposing actual network data. Analytics system 323 determines the set of synthetic KPIs model 331 is trained to generate and selects corresponding ones of the KPIs reported by RAN 311, control plane 321, and user plane 322; see Coudert par. 0041), wherein the output from the trained machine learning model is further based on the historical data (Machine learning model 331 comprises any machine learning model implemented within network 300 to generate synthetic KPI streams, predict network conditions, and/or perform some other type of machine learning assisted task. A machine learning model comprises one or more machine learning algorithms that are trained based on historical data and/or other types of training data. A machine learning model may employ one or more machine learning algorithms through which data can be analyzed to identify patterns, make decisions, make predictions, or similarly produce output; see Coudert par. 0037). It would have been obvious to the ordinary skilled in the art before the effective filing date to use Coudert's arrangement in Boyle's invention to effectively generate network KPIs to train machine learning models (see Coudert par. 0034).
Claim(s) 4, 5, 11, 12, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Boyle, Sommers and Aktas, and further in view of Che et al. (US 2017/0153935, hereinafter “Che”).
For claims 4, 11 and 18, the combination of Boyle, Sommers and Aktas does not explicitly disclose The method of claim 1, further comprising identifying the first subset of the data fabric, wherein identifying the first subset of the data fabric comprises identifying probe data from a first subset of the plurality of probes of the cellular network based on data tags provided by the first subset of the plurality of probes. Che discloses The method of claim 1, further comprising identifying the first subset of the data fabric, wherein identifying the first subset of the data fabric comprises identifying probe data from a first subset of the plurality of probes of the cellular network based on data tags provided by the first subset of the plurality of probes (an inquiry is made as to whether any values in the reports are associated with at least a subset of the data detected by the particular probe (Operation 228). A value is associated with a subset of the data detected by the particular probe if the value is generated based on the subset of the data detected by the particular probe. In an example, a first probe detects a response time of a database, and a second probe detects a usage of a CPU by input/output (I/O) devices; see Che par. 0083-0087). It would have been obvious to the ordinary skilled in the art before the effective filing date to use Che's arrangement in Boyle's invention to perform testing to determine performance metrics of one or more target systems (see Che par. 0001).
For claims 5, 12 and 19, the combination of Boyle, Sommers and Aktas does not explicitly disclose The method of claim 1, wherein determining the first subset of the data fabric comprises identifying probe data of the data fabric based on data tags of the probe data. Che discloses The method of claim 1, wherein determining the first subset of the data fabric comprises identifying probe data of the data fabric based on data tags of the probe data (the association between the particular searchable tag and the particular probe is received after the particular probe has been used to detect data from a target system (using, for example, the operations described with reference to FIG. 2A). The association is received as a modification or update of a report specification and/or a probe description associated with the particular probe. Alternatively, the association is received as part of a new report specification and/or a new probe description associated with the particular probe; see Che par. 0082). It would have been obvious to the ordinary skilled in the art before the effective filing date to use Che's arrangement in Boyle's invention to perform testing to determine performance metrics of one or more target systems (see Che par. 0001).
Conclusion
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/CHAE S LEE/Primary Examiner, Art Unit 2415