DETAILED ACTION
This Office Action is in response to the Application Ser. No. 19/039,213 filed on January 28, 2025. Claims 1-18 are pending and are examined.
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Priority
Acknowledgment is made of applicant’s claim for domestic priority under 35 U.S.C. 119 (e) based on Provisional Application Ser. No. 63/558,401 filed on February 27, 2024.
Information Disclosure Statement
Applicant’s submission of the Information Disclosure Statements dated April 11, 2025 and September 16, 2025, respectively, is acknowledged by the Examiner and the cited references have been considered in the examination of the claims now pending (see attached PTO-1449).
Drawings
Drawings were received on January 28, 2025. These drawings are accepted.
Specification
The disclosure is objected to because of the following informalities:
based upon the presence of a similar patent numbers in the specification and review of the disclosure of the associated references, there are typos in the patent number recited in paragraph [0060], i.e., “US 11, 416, 404 B2” should be “US 11,416,504 B2”, and in the publication number recited in paragraph [0142], i.e., “US2023/0188400 A1” should “US 2023/0188440 A1”; and
the patent numbers recited in paragraphs [0066] and [0107] should be properly recited as “US 11,132,109 B2” and “US 11,522,766 B2, respectively. Appropriate correction is required.
The attempt to incorporate subject matter into this application by reference to US 11,522,766 B2, US 2023/0188440 A1, and US 11,416,504 B2, respectively, is ineffective because the root words “incorporate” and/or “reference” have been omitted.
The incorporation by reference will not be effective until correction is made to comply with 37 CFR 1.57(c), (d), or (e). If the incorporated material is relied upon to meet any outstanding objection, rejection, or other requirement imposed by the Office, the correction must be made within any time period set by the Office for responding to the objection, rejection, or other requirement for the incorporation to be effective. Compliance will not be held in abeyance with respect to responding to the objection, rejection, or other requirement for the incorporation to be effective. In no case may the correction be made later than the close of prosecution as defined in 37 CFR 1.114(b), or abandonment of the application, whichever occurs earlier.
Any correction inserting material by amendment that was previously incorporated by reference must be accompanied by a statement that the material being inserted is the material incorporated by reference and the amendment contains no new matter. 37 CFR 1.57(g).
Claim Interpretation
6. “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” See MPEP § 2111.04 II.
7. Method Claims 1 and 14 recite contingent limitations that are not required to be performed as they are subject to conditions that may not be met.
8. Regarding method Claim 1, the following limitations recite steps that are performed only upon certain conditions being met:
“assigning tags among previously configured tags to groups of correlated anomalies, comprising automatic assigning of conditional tags if the associated condition or conditions is validated for said groups of correlated anomalies (emphasis added).”
Given its broadest reasonable interpretation, Claim 1 does not require the action “assigning tags among previously configured tags to groups of correlated anomalies, comprising automatic assigning of conditional tags” to be performed as it is subject to a condition, i.e., the associated condition or conditions are validated, that is not required by the claim to occur.
16. Regarding method Claim 14, the following limitations recite steps that are performed only upon certain conditions being met:
“applying the reconfigured values of parameters used for determining the anomaly threshold computed on a following observation period if the accuracy estimate is higher than an accuracy threshold (emphasis added).”
Given its broadest reasonable interpretation, Claim 14 does not require the action “applying the reconfigured values of parameters used for determining the anomaly threshold computed on a following observation period” to be performed as it is subject to a condition, i.e., the accuracy estimate is higher than an accuracy threshold, that is not required by the claim to occur.
While the broadest reasonable interpretation of Claim 1 does not require the performance of steps which are contingent upon conditions that are not required to occur, for the purposes of compact prosecution, insofar as the recited limitations are definite, prior art has been applied to each limitation in this Office Action as if each step is required to be performed.
Claim Objections
The claims are objected to because of the following informalities:
regarding claim 1, the term “one or several associated condition(s)” recited in line 8 should be “one or several associated conditions”;
regarding Claim 5, the phrase “automatic clustering applied” recited in line 1 should be “applying automatic clustering”;
regarding Claim 6, the term “backend system function(s)” recited in lines 2-3 should be “one or more backend system functions”;
regarding Claim 7, the quotation marks in lines 1 and 2 should be deleted;
regarding Claim 8, the quotation marks in line 1 should be deleted;
regarding Claim 9, the term” an anomaly threshold signal” recited in line 4 should be “an anomaly threshold”, the term “smoothed signal” recited in line 5 should be “the smoothed signal”, and the term “anomaly threshold” recited in line 5 should be “the anomaly threshold”, respectively;
regarding Claim 10, the braces in lines 2 and 3 should be deleted, and word “and” recited in line 2 should be replaced with “or”;
regarding Claim 11, the phrase “computations of statistics” recited in line 1 should be “computing statistics”; and
regarding Claim 15, the word “dimension” recited in line 2 should be “dimensions”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1-18 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.
Claim 1 recites the limitation “processing the received real-time network data to detect anomalies, and post-processing anomalies to compute groups of correlated anomalies” in lines 4-5. The relationship between the term “anomalies” recited in line 5 and “anomalies” recited in line 4 is unclear, rendering the claim indefinite. Are the “anomalies” that are post-processed the same “anomalies” that are detected?
Additionally, Claim 1 recites the limitation “assigning tags among previously configured tags to groups of correlated anomalies, comprising automatic assigning of conditional tags if the associated condition or conditions is validated for said groups of correlated anomalies” in lines 9-11. The relationship between the term “groups of correlated anomalies” recited in line 9 and “groups of correlated anomalies” recited in line 5 is unclear, rendering the claim indefinite. Are the “groups of correlated anomalies” that are tagged the same “groups of correlated anomalies” that are computed by the post-processing? Additionally, there is insufficient antecedent basis for the term “the associated condition or conditions” in the claims.
Dependent Claims 2-16 are rejected for the reasons presented above with respect to rejected Claim 1 in view of their dependence thereon.
For examination purposes, the term “anomalies” recited in line 5 is interpreted as “the anomalies”, the term “groups of correlated anomalies” recited in line 9 is interpreted as “the groups of correlated anomalies”, and the term “the associated condition or conditions” is interpreted as “the one or several associated conditions”.
Additionally, Claim 6 recites the limitation “wherein the tags categories comprise system-related categories, each system-related tag being either associated with backend system function(s) or having an associated condition, and wherein the method comprises automatically assigning the system-related tags” in lines 1-4. There is insufficient antecedent basis for the term “the tags categories”, “each system-related tag” and “the system-related tags” in the claims.
Dependent Claims 7-14 are rejected for the reasons presented above with respect to rejected Claim 6 in view of their dependence thereon.
Additionally, Claim 7 recites the limitation “wherein the system-related tags comprise a "data source unavailability" tag indicating whether a source of network data relating to a monitored metric of a group of correlated anomalies is cut-off” in lines 1-3. There is insufficient antecedent basis for the term “the system-related tags” in the claims.
Additionally, Claim 8 recites the limitation “wherein the system-related tags comprise an "external event" tag relative to a planned external event, said external event tag comprising conditions with rules related to one or several network dimensions related to the planned external event” in lines 1-3. There is insufficient antecedent basis for the term “the system-related tags” in the claims.
Additionally, Claim 9 recites the limitation “wherein the system-related tags comprise a conditional tag identifying weak results, said weak result tag being assigned based on conditions verified on anomaly detection, an anomaly associated to a rule being detected on a comparison between a smoothed signal and an anomaly threshold signal, weak result conditions including a maximal deviation percentage between smoothed signal and anomaly threshold being lower than a predetermined percentage of an anomaly threshold value” in lines 1-6. There is insufficient antecedent basis for the terms “the system-related tags” and “said weak result tag” in the claims. Additionally, the relationship between the term “weak result conditions” recited in line 4 and “conditions verified on anomaly detection” recited in lines 2-3 is unclear, rendering the claim indefinite.
Dependent Claims 10-14 are rejected for the reasons presented above with respect to rejected Claim 9 in view of their dependence thereon.
Additionally, Claim 15 recites the limitation “wherein the network data includes one or several metrics and dimension linked to said group of correlated anomalies, among group duration, group impacted subscribers, severity, user flags, number of anomalies contained in group, root cause diagnosis, presence of approximate periodicities, nature of rules linked to anomalies, nature of root cause diagnosis elements targeted, nature of root cause diagnosis 3GPP cause and geographic data linked to dimensions” in lines 1-6. There is insufficient antecedent basis for the terms “the network data” and “said group of correlated anomalies” in the claims.
For examination purposes, the term “the network data” recited in line 1 is interpreted as “the real-time network data” and the term “said group of correlated anomalies” recited in line 2 is interpreted as “said groups of correlated anomalies”.
Additionally, Claim 16 recites the limitation “wherein the processing comprises filtering groups of anomalies based on associated tags, automatically analyzing anomalies, triggering automatically exports or advanced statistics computation or group deletion or automatically reconfiguring anomaly detection” in lines 1-4. The scope of the limitation is unclear, rendering the claim indefinite. While the term “the processing” has antecedent basis in Claim 1, it is unclear how “the processing” comprises , e.g., “filtering groups of anomalies” or “group deletion”, when “the processing” recited in Claim 1 only involves detecting anomalies and occurs before the groups of correlated anomalies are computed in the post-processing. Additionally, the meaning of “triggering automatically exports or advanced statistics computation or group deletion or automatically reconfiguring anomaly detection” is unclear, rendering the claim indefinite. Specifically, the number and nature of the alternatives represented is unclear, e.g., does “triggering automatically” apply to all of “exports”, “advanced statistics computation” and “group deletion”, wherein the exports, advanced statistics computation and group deletion are alternatives of the triggering, or are “advanced statistics computation” and “group deletion” separate and independent alternatives to “triggering automatically exports”?
Insofar as they recite similar claim elements, Claims 17 and 18 are rejected for substantially the same reasons presented above with respect to Claim 1.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a series of steps for automatized monitoring of anomalies during operation of a network (see specification paragraph [0012]). The limitations, processing the received real-time network data to detect anomalies, and post-processing anomalies to compute groups of correlated anomalies, and assigning tags among previously configured tags to groups of correlated anomalies, comprising automatic assigning of conditional tags if the associated condition or conditions is validated for said groups of correlated anomalies, when given their broadest reasonable interpretation in view of the specification, are steps that can be performed in the human mind, or by a human using a pen and paper, i.e., the recited limitations are mental processes. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a)(2) III. Accordingly, the claim recites an abstract idea (Step 2A – Prong One Analysis).
As currently recited, the abstract idea is not integrated into a practical application. In particular, the additional limitations, receiving real-time network data describing operation of the network, and configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label, a condition field allowing to define one or several associated condition(s), are recited at a high-level of generality as they do not describe specific implementations for receiving the real-time network data or for configuring tags, respectively, and therefore merely add insignificant extra-solution activity, e.g., data gathering, to the abstract idea that does not add meaningful limitations to the practicing of the abstract idea. See MPEP § 2106.05(g). Further, while the specification itself may be directed to the improvement of a technological field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., the claim includes the components or steps of the invention that provide the improvement described in the specification. See MPEP § 2106.05(a). In the present case, as the additional limitations are recited at a high level of generality and do not adequately disclose the components or steps that provide the improvement, the additional limitations cannot integrate the abstract idea into a practical application. See MPEP § 2106.04(d). Therefore, the claim is directed to an abstract idea (Step 2A – Prong Two Analysis).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As the limitation, receiving real-time network data describing operation of the network, is recited at a high-level of generality as it does describe a specific implementation for receiving the real-time network data, it merely represents a function, i.e., receiving data over a network, which is well-understood, routine and conventional activity in the field. Likewise, the limitation configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label, a condition field allowing to define one or several associated condition(s), is recited at a high-level of generality as it does not describe a specific implementation for configuring tags, it merely represents a function, i.e., configuring tags or labels, which is well-understood, routine and conventional activity in the field. See Kumaran, US 2024/0230062 A1. Therefore, the additional limitations are insufficient to transform the abstract idea into a patent-eligible application of the abstract idea. See MPEP § 2106.05(d). Further, while the specification itself may be directed to the improvement of a technological field, the additional limitations are recited at a high level of generality and do not adequately disclose the components or steps that provide the improvement, and therefore do not amount to significantly more than the abstract idea. See MPEP 2106.05(a). Considering the claim limitations as an ordered combination does not add anything more than when considering them individually, nor do they describe a non-conventional or non-generic arrangement of conventional elements. See MPEP 2106.05(a). Therefore, the additional limitations do not amount to significantly more than the abstract idea (Step 2B Analysis). Accordingly, Claim 1 is ineligible.
Dependent Claim 2 recites a limitation that further defines an additional information element of the tag (i.e., an action field), and defines an additional step (i.e., applying at least one action) which is recited at a high level of generality, and therefore does not meaningfully limit the practicing of the abstract idea. See MPEP § 2106.04(d) and MPEP § 2106.05. Therefore, the additional limitation neither integrates the abstract idea into a practical application nor amounts to significantly more than the abstract idea. Accordingly, Claim 2 is ineligible.
15. Dependent Claims 6-10 recite limitations that further defines tag categories and tag types within the tag categories that may be assigned to the groups of correlated anomalies (i.e. system-related tags including data source unavailability tag, external event tag and weak result tag), which serve to narrow the abstract idea but do not meaningfully limit the practicing of the abstract idea. See MPEP § 2106.04(d) and MPEP § 2106.05. Therefore, the additional limitations neither integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. Accordingly, Claims 6-10 are ineligible.
Dependent Claims 11-13 recite limitations that define additional steps (i.e., computing statistics, selecting rules, computing reconfigured values of parameters and computing an accuracy estimate), which, given their broadest reasonable interpretation, can be performed with the human mind, i.e., the recited limitations are mental processes, and additional steps (i.e., applying reconfiguration of anomaly detection for selected rules and applying reconfigured values of parameters to groups of anomalies) which are recited at a high level of generality, respectively, and therefore do not meaningfully limit the practicing of the abstract idea. See MPEP § 2106.04(d) and MPEP § 2106.05. Therefore, the additional limitations neither integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. Accordingly, Claims 11-13 are ineligible.
Dependent Claims 14 recites a limitation that defines a step (i.e., applying the reconfigured values of parameters) that is not required to be performed under broadest reasonable interpretation. See “Claim Interpretation” section above. Therefore, the additional limitation neither integrates the abstract idea into a practical application nor amounts to significantly more than the abstract idea. Accordingly, Claim 14 is ineligible.
Dependent Claims 15 recites limitations that further defines the types of network data that are received (i.e., group duration, group impacted subscribers, severity, user flags, etc.), which serves to narrow the abstract idea but does not comprise an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application of that abstract idea. Therefore, the additional limitation does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. Accordingly, Claim 15 is ineligible.
Dependent Claim 16 recites a limitation that further defines the processing as being one of a set of alternatives (i.e., filtering groups of anomalies, automatically analyzing anomalies, etc.) which are recited at a high level of generality, and describe functions that are well-understood, routine and conventional in the art, and therefore do not meaningfully limit the practicing of the abstract idea. See MPEP § 2106.05(g). Therefore, the additional limitation does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. Accordingly, Claim 16 is ineligible.
Claim 17 recites a series of steps for automatized monitoring of anomalies during operation of a network (see specification paragraph [0012]). The limitations, processing the received real-time network data to detect anomalies, and post-processing anomalies to compute groups of correlated anomalies, and assigning tags among previously configured tags to groups of correlated anomalies, comprising automatic assigning of conditional tags if the associated condition or conditions is validated for said groups of correlated anomalies, when given their broadest reasonable interpretation in view of the specification, are steps that can be performed in the human mind, or by a human using a pen and paper, i.e., the recited limitations are mental processes. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a)(2) III. Accordingly, the claim recites an abstract idea (Step 2A – Prong One Analysis).
As currently recited, the abstract idea is not integrated into a practical application. In particular, the additional limitations, receiving real-time network data describing operation of the network, and configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label, a condition field allowing to define one or several associated condition(s), are recited at a high-level of generality as they do not describe specific implementations for receiving the real-time network data or for configuring tags, respectively, and therefore merely add insignificant extra-solution activity, e.g., data gathering, to the abstract idea that does not add meaningful limitations to the practicing of the abstract idea. See MPEP § 2106.05(g). The additional element, a non-transitory computer-readable storage medium comprising instructions, recited by the claim as implementing the recited steps merely informs the practitioner to apply the abstract idea using a generic computer system. See MPEP § 2106.05(f). Further, while the specification itself may be directed to the improvement of a technological field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., the claim includes the components or steps of the invention that provide the improvement described in the specification. See MPEP § 2106.05(a). In the present case, as the additional limitations are recited at a high level of generality and do not adequately disclose the components or steps that provide the improvement, the additional limitations cannot integrate the abstract idea into a practical application. See MPEP § 2106.04(d). Therefore, the claim is directed to an abstract idea (Step 2A – Prong Two Analysis).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As the limitation, receiving real-time network data describing operation of the network, is recited at a high-level of generality as it does describe a specific implementation for receiving the real-time network data, it merely represents a function, i.e., receiving data over a network, which is well-understood, routine and conventional activity in the field. Likewise, the limitation configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label, a condition field allowing to define one or several associated condition(s), is recited at a high-level of generality as it does not describe a specific implementation for configuring tags, it merely represents a function, i.e., configuring tags or labels, which is well-understood, routine and conventional activity in the field. See Kumaran, US 2024/0230062 A1. Therefore, the additional limitations are insufficient to transform the abstract idea into a patent-eligible application of the abstract idea. See MPEP § 2106.05(d). The additional element, a non-transitory computer-readable storage medium comprising instructions, recited by the claim as implementing the recited steps merely informs the practitioner to apply the abstract idea using a generic computer system and is therefore insufficient to transform the abstract idea into a patent-eligible application of the abstract idea. See MPEP § 2106.05(f). Further, while the specification itself may be directed to the improvement of a technological field, the additional limitations are recited at a high level of generality and do not adequately disclose the components or steps that provide the improvement, and therefore do not amount to significantly more than the abstract idea. See MPEP 2106.05(a). Considering the claim limitations as an ordered combination does not add anything more than when considering them individually, nor do they describe a non-conventional or non-generic arrangement of conventional elements. See MPEP 2106.05(a). Therefore, the additional limitations do not amount to significantly more than the abstract idea (Step 2B Analysis). Accordingly, Claim 17 is ineligible.
Claim 18 recites a series of steps for automatized monitoring of anomalies during operation of a network (see specification paragraph [0012]). The limitations, processing the received real-time network data to detect anomalies, and post-processing anomalies to compute groups of correlated anomalies, and assigning tags among previously configured tags to groups of correlated anomalies, comprising automatic assigning of conditional tags if the associated condition or conditions is validated for said groups of correlated anomalies, when given their broadest reasonable interpretation in view of the specification, are steps that can be performed in the human mind, or by a human using a pen and paper, i.e., the recited limitations are mental processes. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a)(2) III. Accordingly, the claim recites an abstract idea (Step 2A – Prong One Analysis).
As currently recited, the abstract idea is not integrated into a practical application. In particular, the additional limitations, receiving real-time network data describing operation of the network, and configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label, a condition field allowing to define one or several associated condition(s), are recited at a high-level of generality as they do not describe specific implementations for receiving the real-time network data or for configuring tags, respectively, and therefore merely add insignificant extra-solution activity, e.g., data gathering, to the abstract idea that does not add meaningful limitations to the practicing of the abstract idea. See MPEP § 2106.05(g). The additional elements, one or more processors, a receptor module, a processing module, a configuration module and an assigning module, recited by the claim as implementing the recited steps merely informs the practitioner to apply the abstract idea using a generic computer system. See MPEP § 2106.05(f). Further, while the specification itself may be directed to the improvement of a technological field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., the claim includes the components or steps of the invention that provide the improvement described in the specification. See MPEP § 2106.05(a). In the present case, as the additional limitations are recited at a high level of generality and do not adequately disclose the components or steps that provide the improvement, the additional limitations cannot integrate the abstract idea into a practical application. See MPEP § 2106.04(d). Therefore, the claim is directed to an abstract idea (Step 2A – Prong Two Analysis).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As the limitation, receiving real-time network data describing operation of the network, is recited at a high-level of generality as it does describe a specific implementation for receiving the real-time network data, it merely represents a function, i.e., receiving data over a network, which is well-understood, routine and conventional activity in the field. Likewise, the limitation configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label, a condition field allowing to define one or several associated condition(s), is recited at a high-level of generality as it does not describe a specific implementation for configuring tags, it merely represents a function, i.e., configuring tags or labels, which is well-understood, routine and conventional activity in the field. See Kumaran, US 2024/0230062 A1. Therefore, the additional limitations are insufficient to transform the abstract idea into a patent-eligible application of the abstract idea. See MPEP § 2106.05(d). The additional elements, one or more processors, a receptor module, a processing module, a configuration module and an assigning module, recited by the claim as implementing the recited steps merely informs the practitioner to apply the abstract idea using a generic computer system and are therefore insufficient to transform the abstract idea into a patent-eligible application of the abstract idea. See MPEP § 2106.05(f). Further, while the specification itself may be directed to the improvement of a technological field, the additional limitations are recited at a high level of generality and do not adequately disclose the components or steps that provide the improvement, and therefore do not amount to significantly more than the abstract idea. See MPEP 2106.05(a). Considering the claim limitations as an ordered combination does not add anything more than when considering them individually, nor do they describe a non-conventional or non-generic arrangement of conventional elements. See MPEP 2106.05(a). Therefore, the additional limitations do not amount to significantly more than the abstract idea (Step 2B Analysis). Accordingly, Claim 18 is ineligible.
Examiner’s Note
Regarding the rejection of dependent Claim 14 under 35 U.S.C. 101 presented above, the Examiner notes that the claim is ineligible as the features recited in the claim are not required under the broadest reasonable interpretation. See “Claim Interpretation” section above. However, Examiner acknowledges that the features of Claim 14 (when included with the feature of intervening Claims 6, 9 and 11-13) would overcome the rejection should all the limitations become required under the broadest reasonable interpretation.
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 1-6 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Arora et al., Pub. No. US 2024/0073113 A1, hereby “Arora”, in view of Kumaran, Pub. No. US 2014/0230062 A1.
Regarding Claim 1, Arora discloses “A method for automatized monitoring of anomalies during operation of a network (Arora figs. 1 and 2 and paragraphs 12, 45-46 and 69: a process for using machine learning to detect and correct performance limitations in a communication system), the method comprising:
receiving real-time network data describing operation of the network (Arora figs. 1 and 2 and paragraphs 33, 47-48, 54-55 and 70: computer system 110 receives performance indicator reports, i.e., sets of network performance metrics, from terminals 130),
processing the received real-time network data to detect anomalies, and post-processing anomalies to compute groups of correlated anomalies (Arora figs. 1 and 2 and paragraphs 47, 56-61, 70 and 78: computer system 110 processes the performance indicator reports using clustering techniques to identify clusters of anomaly types that occur together most frequently),
configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label... (Arora figs. 1-4, Table 2, and paragraphs 46-47, 62, 67, 70-71, 79-80 and 98-100: computer system 110 receives a label, i.e., a tag, for each of the clusters of correlated anomaly types, wherein each label is associated with a certain type of problem or performance limitation, i.e., a category, and comprises an identifier, e.g., '1' identifying a first group of correlated anomaly types, and a cluster label, e.g., 'severe transport problems'),
assigning tags among previously configured tags to groups of correlated anomalies... (Arora figs. 1 and 2 and paragraphs 28, 48, 63, 81 and 101: using machine learning model 111 trained using the labeled clusters of correlated anomaly types, computer system 110 automatically assigns labels to performance indicator reports based on how similar the anomalies in the performance indicator reports are to the anomalies of the clusters of correlated anomaly types associated with the labels).”
However, while Arora discloses assigning labels to performance indicator reports based on how similar the anomalies in the performance indicator reports are to the anomalies of the clusters of correlated anomaly types associated with the labels (Arora paragraphs 28, 48, 63, 81 and 101), Arora does not explicitly disclose “configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label, a condition field allowing to define one or several associated condition(s) (emphasis added),
assigning tags among previously configured tags to groups of correlated anomalies, comprising automatic assigning of conditional tags if the associated condition or conditions is validated for said groups of correlated anomalies (emphasis added).”
In the same field of endeavor, Kumaran discloses configuring one or more labels for identifying events included in one or more data streams, the labels having associated conditions that determine whether the label is applied to the data stream (Kumaran figs. 1, 3 and 5 and paragraphs 43, 56-58 and 107: labels and associated conditions, e.g., threshold-based triggers, used by event labeler 140 to tag events included in one or more data streams may be defined in advance), and further discloses automatically assigning one or more labels to the data stream when characteristics of the data stream satisfy the conditions associated with the labels (Kumaran figs. 1, 3 and 5 and paragraphs 43, 57-59 and 104-110: “In an embodiment, event labeler 140 may determine one or more tags for a data stream. Determining a tag may comprise analyzing the data stream and the characteristics associated with the data stream, and testing whether the characteristics satisfy one or more conditions. If the characteristics of the data stream satisfy a particular condition, then a tag associated with the particular condition may be associated with the data stream.”).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the method of Arora to assign labels to the performance indicator reports when the anomalies in the indicator reports satisfy conditions associated with the clusters of correlated anomaly types as taught by Kumaran because doing so constitutes a simple substitution of one known element (assigning a label based on similarity to labeled examples) for another (assigning a label based on satisfaction of one or more conditions associated with the label) to obtain predictable and desirable results (assigning labels to the set of anomalies present in performance indicator reports). See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding Claim 2, the combination of Arora and Kumaran discloses all of the limitations of Claim 1.
Additionally, Arora discloses “wherein each tag further comprises an action field allowing to define at least one action to be carried out automatically, the method comprising, for each group of correlated anomalies tagged with a tag having at least one associated action, applying said at least one action (Arora figs. 1 and 2, Table 2, and paragraphs 21, 48, 66, 71, 79 and 81: each label corresponding to a cluster of correlated anomaly types may be associated with an adjustment action that can be performed automatically).”
Regarding Claim 3, the combination of Arora and Kumaran discloses all of the limitations of Claim 1.
Additionally, Arora discloses “wherein at least some of the tags are non-conditional tags, the method further comprising, for at least one non-conditional tag, supervised automatic learning of a prediction model to predict the non-conditional tag, the prediction model being a classification decision tree trained by a tree-learning on a learning set of network data representative of groups of correlated anomalies tagged with said non-conditional tag (Arora figs. 1-3 and paragraphs 8-9, 18, 20, 47, 50, 62, 67, 70 and 82-83: “One of the techniques that the computer system 110 can employ is to cluster various types of anomalies indicated by performance indicator reports and then use labels for the clusters to create a labeled dataset for training a machine learning model 111. This machine learning model 111 is able to predict, given the raw indicator values of a terminal 130a-130c, which cluster best describes the situation that a terminal 130a-130c is experiencing, and thus which situation or problem is applicable to that terminal.”).”
Regarding Claim 4, the combination of Arora and Kumaran discloses all of the limitations of Claim 3.
Additionally, Arora discloses “wherein the non-conditional tag is a user-defined tag, and wherein the learning set is obtained from network data initially labelled with user-defined tag assignment (Arora figs. 1-3 and paragraphs 8-9, 18, 20, 47, 50, 62, 67, 70, 82-83 and 89-90: “The computer system 110 then receives labels for the clusters that indicate the network problem or situation each cluster represents (step 176), for example, based on input of an expert user provided a visualization of the clusters.”).”
Regarding Claim 5, the combination of Arora and Kumaran discloses all of the limitations of Claim 3.
Additionally, Arora discloses “comprising automatic clustering applied on incoming network data to obtain clusters of groups of correlated anomalies, each cluster forming a learning set for an associated tag category (Arora figs. 1-3 and paragraphs 8-9, 18, 20, 47, 50, 67, 70, 82-83 and 89-90: computer system 110 processes the performance indicator reports using clustering techniques to identify clusters of anomaly types that occur together most frequently, receives labels for the clusters that indicate the network problem or situation each cluster represents from an expert user, and uses the labeled clusters as training data to train machine learning model 111 to predict the labels or classifications based on indicator data received from terminals 130).”
Regarding Claim 6, the combination of Arora and Kumaran discloses all of the limitations of Claim 1.
Additionally, Kumaran discloses “wherein the tags categories comprise system-related categories, each system-related tag being either associated with backend system function(s) or having an associated condition, and wherein the method comprises automatically assigning the system-related tags (Kumaran figs. 1, 3 and 5 and paragraphs 43, 57-59 and 104-110: “In an embodiment, event labeler 140 may determine one or more tags for a data stream. Determining a tag may comprise analyzing the data stream and the characteristics associated with the data stream, and testing whether the characteristics satisfy one or more conditions. If the characteristics of the data stream satisfy a particular condition, then a tag associated with the particular condition may be associated with the data stream.”).”
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the method of Arora to assign labels to the performance indicator reports when the anomalies in the indicator reports satisfy conditions associated with the clusters of correlated anomaly types as taught by Kumaran for the reasons set forth in the rejection of Claim 1.
Regarding Claim 15, the combination of Arora and Kumaran discloses all of the limitations of Claim 1.
Additionally, Arora discloses “wherein the network data includes one or several metrics and dimension linked to said group of correlated anomalies, among group duration, group impacted subscribers, severity, user flags, number of anomalies contained in group, root cause diagnosis, presence of approximate periodicities, nature of rules linked to anomalies, nature of root cause diagnosis elements targeted, nature of root cause diagnosis 3GPP cause and geographic data linked to dimensions (Arora paragraphs 33, 57-59, 65, 73 and 78-79: “In some implementations, the performance indicators comprise values indicating one or more of an error count, an error type, an amount of delay, an amount of data received, an amount of data discarded, an amount of data retransmitted, an amount of bandwidth allocated, or an amount of a particular type of network message sent or received.”).”
Regarding Claim 16, the combination of Arora and Kumaran discloses all of the limitations of Claim 1.
Additionally, Arora discloses “wherein the processing comprises filtering groups of anomalies based on associated tags, automatically analyzing anomalies, triggering automatically exports or advanced statistics computation or group deletion or automatically reconfiguring anomaly detection (Arora paragraphs 58-60: processing performed by computer system 110 includes identification of the frequency of co-occurrence of anomalies, and generation of visualization of clusters of correlated anomaly types).”
Insofar as it recites similar claim elements, Claim 17 is rejected for substantially the same reasons presented above with respect to Claim 1.
Additionally, Arora discloses “A non-transitory computer-readable storage medium comprising instructions that, when executed, cause a processor to perform a method for automatized monitoring of anomalies during operation of a network... (Arora figs. 1 and 2 and paragraphs 12, 45-46, 69, 103 and 105-106: computer-readable media storing program instructions for using machine learning to detect and correct performance limitations in a communication system)”.
Insofar as it recites similar claim elements, Claim 18 is rejected for substantially the same reasons presented above with respect to Claim 1.
Additionally, Arora discloses “A non-transitory computer-readable storage medium comprising instructions that, when executed, cause a processor to perform a method for automatized monitoring of anomalies during operation of a network... (Arora figs. 1 and 2 and paragraphs 2, 45-46 and 69: system 100 comprising computer system 110 for using machine learning to detect and correct performance limitations in a communication system)”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Krishna et al., Pub. No. US 2017/0230449 A1, discloses a method for monitoring elements of a distributed computing system wherein tags may be applied to a monitored element based on a quality of trait of the element, wherein the tags may be human readable labels indicating a property of a monitored element, a type of monitored element, or a trait of the monitored element;
Côté et al. Pub. No. US 2019/0303726 A1, discloses machine learning systems and methods to predict abnormal behavior in networks wherein labeled datasets are utilized to train the ML model to recognize patterns associated with the labels;
Ford et al., Pub. No. US 2021/0341973 A1, discloses apparatuses and methods for identifying network anomalies wherein an operations engineer labels a set of anomaly events with a root cause category and provides the labels to an AI engine that utilizes the labels to classify the root cause of new anomaly events.
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