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 .
Formal Matters
Applicant's response, filed 13 August 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 3-8, and 11-22 are currently pending and have been examined.
Claims 1 and 3 have been amended.
Claims 21 and 22 have been added.
Claims 1, 3-8, and 11-22 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for Application No. FI20215700, filed on 11/29/2023.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 15 June 2021 claiming benefit to FI20215700.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-8, and 11-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1, 3-8, and 11-22 are drawn to a method or apparatus, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for analyzing measurement results of a communication network.
This independent claim recites the following steps best characterized as a mental process under MPEP § 2106.04(a)(2)(III) citing the abstract idea grouping for mental processes in general:
obtaining measurement results, wherein the measurement results comprise multiple data entries, and wherein each data entry comprises multiple data values on a lowest hierarchy level and hierarchy information defining with which entities the entry is related to on different hierarchy levels
determining an aggregated anomaly score for each data entry by calculating anomaly score for the data values of the data entry and aggregating the anomaly scores to determine the aggregated anomaly score for the data entry
choosing data entries, wherein the aggregated anomaly score fulfils predefined criteria, for further analysis
performing hierarchical clustering on the chosen entries based on dissimilarity of the chosen entries to combine at least some of the chosen entries together; and
using the hierarchically clustered entries to identify one or more anomalous entities on hierarchy levels above the lowest hierarchy level
Under the broadest reasonable interpretation of the limitations, these limitations are best characterized as applying a mental process to a generic computing environment - see MPEP § 2106.04(a)(2)(III)(c)(2).
Furthermore, the steps of calculating an anomaly score and performing hierarchical clustering, while at a certain level of generality may be performed mentally, can also be characterized as representing mathematical relationships - see MPEP § 2106.04(a)(2)(I)(A).
Dependent claim 3 recites, in part, wherein calculating anomaly score for the data values of the data entry is performed using robust principal component analysis, RPCA.
Dependent claim 4 recites, in part, wherein top n highest aggregated anomaly scores fulfil the predefined criteria.
Dependent claim 5 recites, in part, wherein aggregated anomaly scores that exceed a predefined threshold fulfil the predefined criteria.
Dependent claim 6 recites, in part, wherein the data values comprise observed data values aggregated over a predefined period of time.
Dependent claim 7 recites, in part, wherein the hierarchy levels relate to subscription types and/or network devices and/or technology types and/or logical network entities.
Dependent claim 8 recites, in part, wherein the data values represent network performance.
Dependent claim 11 recites, in part, further comprising using the hierarchically clustered entries for making decisions on controlling the communications network.
Dependent claim 12 recites, in part, perform the method of claim 1.
Dependent claim 13 recites, in part, perform the method of claim 1.
Dependent claim 16 recites, in part, wherein the hierarchy levels relate to network devices and/or technology types and/or logical network entities.
Dependent claim 17 recites, in part, wherein the hierarchy levels relate to subscription types and/or network devices and/or technology types and/or logical network entities.
Dependent claim 18 recites, in part, wherein the hierarchy levels relate to network devices and/or technology types and/or logical network entities.
Dependent claim 19 recites, in part, wherein the data values represent network performance.
Dependent claim 21 recites, in part, wherein the hierarchically clustering entries identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level by mapping clustered lowest-level entries to at least one higher hierarchy level including a technology type or subscription type.
Dependent claim 22 recites, using the hierarchically clustered entries for making decisions on controlling the communications network by deciding where to target network maintenance actions based on anomalous entities identified on hierarchy levels above the lowest hierarchy level by adjusting parameters or settings, repairing or changing components, rolling out new functionalities or new components, or restarting devices in the communications network.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 12 accordingly, and hence are nonetheless directed towards fundamentally the same mathematical concept abstract idea grouping as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 12 recites an apparatus comprising: a processor, and a memory including computer program code. Claim 13 recites a non-transitory computer readable medium, having stored thereon a computer program comprising computer executable program code. The specification provides that the computer and corresponding hardware is a general purpose computer (see the instant specification on p. 6 lines 14-19). The use of a computer and corresponding hardware and software, in this case to perform the method of claim 1, only recites the computer as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 14 recites an automation system. The specification does not provide additional detail regarding the structure or algorithm design for the automation system. The use of an automation system, in this case to performing the method, only recites the automation system as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 1 recites obtaining measurements results from an operations support system of the communications network. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Claim 1 recites using the hierarchically clustered entries…to automatically adjust network parameters or restart network devices for controlling the communications network. Claims 15 and 20 recite use the identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level for making decisions on controlling the communications network. Claim 22 recites making decisions on controlling the communications network by deciding where to target network maintenance actions based on anomalous entities identified on hierarchy levels above the lowest hierarchy level by adjusting parameters or settings, repairing or changing components, rolling out new functionalities or new components, or restarting devices in the communications network. The specification provides no additional detail regarding algorithmic or hardware design for the decisions on controlling the network (see the instant specification on p. 6 lines 5-7 offering general exemplarity embodiments for the possible outcomes) and the claims do not positively recite the steps of performing the network maintenance actions merely “making decisions” regarding said actions. Therefore, the limitations amount to a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself (MPEP § 2106.05(h) similar to example vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) wherein the additional elements do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use). Additionally, the limitations would amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, and does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it" (MPEP 2106.05(f)(I) see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer. Claim 14 recites an automation system.
Claim 12 recites an apparatus comprising: a processor, and a memory including computer program code. Claim 13 recites a non-transitory computer readable medium, having stored thereon a computer program comprising computer executable program code. Claim 1 recites using the hierarchically clustered entries…to automatically adjust network parameters or restart network devices for controlling the communications network. Claims 15 and 20 recite use the identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level for making decisions on controlling the communications network. Claim 22 recites making decisions on controlling the communications network by deciding where to target network maintenance actions based on anomalous entities identified on hierarchy levels above the lowest hierarchy level by adjusting parameters or settings, repairing or changing components, rolling out new functionalities or new components, or restarting devices in the communications network.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claim 1 recites obtaining measurements results from an operations support system of the communications network. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1, 3-8, and 11-22 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-8, and 11-22 rejected under 35 U.S.C. 103 as being unpatentable over Rezaei et al., Automatic fault detection and diagnosis in cellular networks using operations support systems data, IEEE Network Operations and Management Symposium 468-473 (April 2016)[hereinafter Rezaei] in view of Mdini et al. (US Patent Application No. 2020/0136891)[hereinafter Mdini].
As per claim 1, Rezaei teaches on the following limitations of the claim:
method for analyzing measurement results of a communications network, the method comprising is taught in the § Abstract on p. 468 (teaching on a hierarchical clustering cellular network performance monitoring system)
obtaining measurement results obtaining measurement results from an operations support system of the communications network wherein the measurement results comprise multiple data entries, and is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators (treated as synonymous to a data value with a measurement result) including measured traffic (TCH values) and signaling (SDCCH values) data for a plurality of monitored network over a time period (treated as synonymous to a data entry) )
wherein each data entry comprises multiple data values on a lowest hierarchy level and hierarchy information defining with which entities the entry is related to on different hierarchy levels is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on KPIs measured TCH and SDCCH values (treated as synonymous to a data value) each having categorical hierarchy breakdowns for the measured values; specifically c. Signal Quality (RxQuality) with 8 levels from 0 (best - treated as synonymous to "a lowest hierarchy level") to 7 (worst))
determining an aggregated anomaly score for each data entry by calculating anomaly score for the data values of the data entry and aggregating the anomaly scores to determine the aggregated anomaly score for the data entry is taught in the § III. Fault Detection on p. 470 (teaching on determining a value for each KPI (treated as synonymous to calculating anomaly score) and determining an overall Complete Correct Call (CCC) rate (treated as synonymous to an aggregated anomaly score) by aggregating each KPI score)
choosing data entries, wherein the aggregated anomaly score fulfils predefined criteria, for further analysis is taught in the § III. Fault Detection on p. 470 and Fig. 2 on p. 470 (teaching on categorizing the CCC rate for each network based on a CCC rate threshold (treated as fulfilling a predefined threshold))
performing hierarchical clustering on the chosen entries based on dissimilarity of the chosen entries to combine at least some of the chosen entries together; and is taught in the § IV. Root Cause Analysis on p. 471 col 1 (teaching on performing applying unsupervised learning algorithms including agglomerative hierarchical clustering to assign specific fault clusters associated with a root cause wherein hierarchical clustering requires linkage criterion determining a dissimilarity distance between sets of observations)
using the hierarchically clustered entries to identify one or more anomalous entities on hierarchy levels to automatically adjust network parameters or restart network devices above the lowest hierarchy level for controlling the communications network is taught in the § A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements including adjusting network parameters (here - a dynamic "half rate" switching feature for the traffic channel) to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
Rezaei fails to teach the following limitation of claim 1. Mdini, however, does teach the following:
a computer implemented method is taught in the Abstract, in the Background in ¶ 0016, and in the Detailed Description in ¶ 0139-141 (teaching on computer and corresponding hardware and software for executing a hierarchical clustering cellular network performance monitoring system)
One of ordinary skill in the art would have recognized that applying the known technique of implemented the method on a general purpose computer would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of a Mdini to the network performance evaluation and correction method of Rezaei would have yielded predictable results of utilizing software for efficient advantages. Further, the combination would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow computer automation of the process.
As per claim 3, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei fails to teach the following; Mdini, however, does disclose:
the method of claim 1, wherein calculating anomaly score for the data values of the data entry is performed using robust principal component analysis, RPCA is taught in the Summary in ¶ 0008 (teaching on utilizing Robust Principal Component Analysis (RPCA) to detect performance anomalies)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the RPCA preprocessing algorithm of Mdini for the abnormality detection algorithm of Rezaei. Thus, the simple substitution of one known element for another producing a predictable result of identifying abnormal groups via a known algorithm renders the claim obvious.
As per claim 4, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein top n highest aggregated anomaly scores fulfil the predefined criteria is taught in the § III. Fault Detection on p. 470 and Fig. 2 on p. 470 (teaching on categorizing the CCC rate for each network based on a CCC rate threshold (treated as fulfilling a predefined threshold) wherein CCC less than 85 are labeled as fault and over 85 (treated as synonymous to top n highest) are labeled not fault - Examiner notes utilization of the "n lowest" for fault v "n highest" CCC scores is merely a function of the unclaimed mathematical calculation for the aggregated anomaly score and wherein the instant specification in ¶ 0060-61 is consistent with the prior art teaching of further analyzing the "most anomalous entries")
As per claim 5, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein aggregated anomaly scores that exceed a predefined threshold fulfil the predefined criteria is taught in the § III. Fault Detection on p. 470 and Fig. 2 on p. 470 (teaching on categorizing the CCC rate for each network based on a CCC rate threshold (treated as fulfilling a predefined threshold) wherein CCC less than 85 are labeled as fault and over 85 (treated as synonymous to top n highest) are labeled not fault - Examiner notes utilization of the "n lowest" for fault v "n highest" CCC scores is merely a function of the unclaimed mathematical calculation for the aggregated anomaly score and wherein the instant specification in ¶ 0060-61 is consistent with the prior art teaching of further analyzing the "most anomalous entries")
As per claim 6, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the data values comprise observed data values aggregated over a predefined period of time is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators (treated as synonymous to a data value with a measurement result) including measured traffic (TCH values) and signaling (SDCCH values) data for a plurality of monitored network over a time period (treated as synonymous to a data entry))
As per claim 7, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the hierarchy levels relate to subscription types and/or network devices and/or technology types and/or logical network entities is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters)
As per claim 8, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the data values represent network performance is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on key performance indicators measured TCH and SDCCH values (treated as synonymous to network performance a data value) each having categorical hierarchy breakdowns)
As per claim 11, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, further comprising using the hierarchically clustered entries for making decisions on controlling the communications network is taught in the § A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
As per claim 12, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei fails to teach the following; Mdini, however, does disclose:
an apparatus comprising: a processor, and a memory including computer program code; the memory and the computer program code configured to, with the processor, cause the apparatus to perform the method of claim 1 is taught in the Abstract, in the Background in ¶ 0016, and in the Detailed Description in ¶ 0139-141 (teaching on computer and corresponding hardware and software for executing a hierarchical clustering cellular network performance monitoring system)
One of ordinary skill in the art would have recognized that applying the known technique of implemented the method on a general purpose computer would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of a Mdini to the network performance evaluation and correction method of Rezaei would have yielded predictable results of utilizing software for efficient advantages. Further, the combination would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow computer automation of the process.
As per claim 13, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei fails to teach the following; Mdini, however, does disclose:
a non-transitory computer readable medium, having stored thereon a computer program comprising computer executable program code which, when executed by a processor, causes an apparatus to perform the method of claim 1 is taught in the Abstract, in the Background in ¶ 0016, and in the Detailed Description in ¶ 0139-141 (teaching on computer and corresponding hardware and software for executing a hierarchical clustering cellular network performance monitoring system)
One of ordinary skill in the art would have recognized that applying the known technique of implemented the method on a general purpose computer would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of a Mdini to the network performance evaluation and correction method of Rezaei would have yielded predictable results of utilizing software for efficient advantages. Further, the combination would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow computer automation of the process.
As per claim 14, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the method is performed by an automation system is taught in the § Abstract on p. 468 (teaching on a hierarchical clustering cellular network performance monitoring system being "automatic")
As per claim 15, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, further comprising using the identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level for making decisions on controlling the communications network is taught in the A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
As per claim 16, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the hierarchy levels relate to network devices and/or technology types and/or logical network entities is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters)
As per claim 17, the combination of Rezaei and Mdini discloses all of the limitations of claim 12. Rezaei also discloses the following:
the apparatus of claim 12, wherein the hierarchy levels relate to subscription types and/or network devices and/or technology types and/or logical network entities is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters)
As per claim 18, the combination of Rezaei and Mdini discloses all of the limitations of claim 12. Rezaei also discloses the following:
the apparatus of claim 12, wherein the hierarchy levels relate to network devices and/or technology types and/or logical network entities is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters)
As per claim 19, the combination of Rezaei and Mdini discloses all of the limitations of claim 12. Rezaei also discloses the following:
the apparatus of claim 12, wherein the data values represent network performance is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on key performance indicators measured TCH and SDCCH values (treated as synonymous to network performance a data value) each having categorical hierarchy breakdowns)
As per claim 20, the combination of Rezaei and Mdini discloses all of the limitations of claim 12. Rezaei also discloses the following:
the apparatus of claim 12, wherein the memory and the computer program code are further configured to, with the processor, cause the apparatus to use the identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level for making decisions on controlling the communications network is taught in the § A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
As per claim 21, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the hierarchically clustered entries identify one or more anomalous entities on hierarchy levels above the lowest hierarchy level by mapping clustered lowest-level entries to at least one higher hierarchy level including a technology type or subscription type is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 and § A. Traffic Channel KPIs on p. 471-472 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters and implementing different channel improvements to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
As per claim 22, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, further comprising using the hierarchically clustered entries for making decisions on controlling the communications network by deciding where to target network maintenance actions based on anomalous entities identified on hierarchy levels above the lowest hierarchy level by adjusting parameters or settings, repairing or changing components, rolling out new functionalities or new components, or restarting devices in the communications network is taught in the § A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements including adjusting network parameters (here - a dynamic "half rate" switching feature for the traffic channel) to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
Response to Arguments
Examiner acknowledges that appropriate corrections to the claims for the previous 35 U.S.C. 112(b) rejections has been made and withdraws the rejections accordingly.
Applicant's arguments filed with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the claims are not directed to a judicial exception as the claimed invention does not merely recite network control at a high level, but instead recited concrete technical control operations and receiving data from a communication network/ OSS infrastructure. Examiner disagrees. While the claims do recite receiving data over a network (Examiner has analyzed the step as an additional element under Step 2A and Step 2B above), the claims do not positively recite controlling the network devices, but instead identifying entities to then adjust or restart the network parameters/devices. The adjustment or restart steps are not positively claimed but instead recited as the intended use of the identification step. Newly added claim 22 similarly recites “making decisions on” the control step, but never positively recites the steps. Examiner notes the instant specification merely recites “the automatic controlling of the communication network 101 may be performed in the automation system 111 or in some other logically or physically separate entity. Controlling of the communications network 101 may involve adjusting parameters or settings, repairing or changing components, rolling out new functionalities or new components, restarting devices etc.” (instant specification on. 6 lines 3-7). Examiner notes that one of ordinary skill in the art would not recognize the Application has possession of the algorithm for performing said functions via a computer program product with the generic disclosure of said merely the intended outcome of said functions.
Applicant's arguments filed with respect to 35 USC § 103 have been fully considered but they are not persuasive. Applicant asserts that the RxQuality levels of Rezaei are not analogous to the hierarchy levels of the instant claims. Applicant asserts that Rezaei's RxQuality scale is just a quality rating. It is not hierarchy information identifying different related network entities across hierarchy levels. A quality level from 0 to 7 is not the same as a hierarchy involving, for example, technology types, subscription types, network devices, and logical network entities. Examiner notes that the BRI of a hierarchy (i.e. a ranking) does not require the level subtypes as asserted by Applicant. Furthermore, the example, technology types, subscription types, network devices, and logical network entities are never required by the claims together – instead each of the “subgroupings” for each hierarchal level are claimed as optional – see at least dependent claim 7. Therefore, it is noted that the features upon which applicant relies (i.e subtypes within each hierarchal level) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant then asserts that Rezaei discusses clustering fault records and associating clusters with root causes, which Applicant asserts is distinguishable from using clustered lowest-level entries to identify anomalous entities at a higher hierarchy level. For example site level entities identifying Prepaid or 3G entities in the example of Figure 3. Examiner is unpersuaded. The claim as currently drafted requires only generically “using” the higher v lower levels to determine the control operation. The method of “using” is not clearly defined in the claim and therefore the BRI merely requires any consideration of the “higher” level at any point during the method wherein the conclusionary step of “identifying” the operational change is affected by the higher v lower hierarchy. Therefore the rejection is sustained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hussain et al., Hierarchical Cluster-based Routing in Wireless Sensor Networks, Jodrey School of Computer Science, Acadia University (March 27, 2006) teaching on hierarchical clustering for continuous monitoring of wireless sensor networks in the § C. Genetic Algorithm (GA) for Hierarchical Clusters on p. 2 and § III. RESULTS on pg. 2
Yang et al., A Centralized Cluster-Based Hierarchical Approach for Green Communication in a Smart Healthcare System, 8 IEEE Access 101464-101475 (June 10, 2020) teaching on a cluster-based hierarchical model for monitoring and responding to sensor resource needs in the § III. A Centralized Cluster-Based Routing Approach for Patient Monitoring 101468 col 2 last paragraph to pg. 101469 col 2 and § V. Conclusion and Future Work on pg. 101474
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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