Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
This action is in response to the claims filed 5/30/2024:
Claims 1 – 20 are pending.
Claims 1, 10, and 11 are independent.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: “the plurality of network function transaction events comprises a plurality of network function event failures” does not appear in the instant specification. The instant specification introduces “NF failure events,” “failure event messages,” and network-function transaction events that are failures but never uses the terminology “network function event failures.”
Drawings
The drawings are objected to because FIG. 9 is a low quality scan containing illegible elements . Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim 5 is 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.
Regarding claim 5, “the plurality of network function transaction events comprises a plurality of network function event failures” is indefinite. The instant specification introduces “NF failure events,” “failure event messages,” and network-function transaction events that are failures but never uses the terminology “network function event failures.” Such that it would be unclear to one of ordinary skill in the art what network function event failures corresponds to. In the interest of further examination network function event failures is interpreted as “NF failure events”.
Claim Rejections - 35 USC § 101
101 Rejection
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 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 1: Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 1 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass machine learning processing, including the following:
identifying, by the processing system, that the first rule is contained in the first rule set and the second rule set (observation, evaluation, and judgement),
adding, by the processing system, the first rule to a set of active rules for generating alerts in the communication network, in response to identifying that the first rule is contained in the first rule set and the second rule set (observation, evaluation, and judgement)
Therefore, claim 1 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 1 recites additional elements “A processing system including at least one processor”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Claim 1 also recites additional elements “obtaining, by a processing system including at least one processor, a plurality of sequences of network function transaction events, each of the plurality of sequences comprising a plurality of network function transaction events in a communication network”, “applying, by the processing system, the plurality of sequences as inputs to a sequential rule mining module implemented by the processing system to obtain a first rule set comprising at least a first rule, wherein the first rule indicates that a consequent network function transaction event follows an antecedent comprising one or more prior network function transaction events”, and “applying, by the processing system, the plurality of sequences as inputs to a generative model to obtain a second rule set” which amounts to gathering and outputting data, which is insignificant extra-solution activity (See MPEP 2106.05(g)). Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 1 amount to no more than mere instructions to apply the judicial exception using a generic computer component and insignificant extra-solution activity. The gathering and outputting data is considered well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i)).
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claim 10 which recites a computer program product, as well as to dependent claims 2-9. The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 2 recites additional instructions to apply the judicial exception using generic computer components insignificant extra-solution activity of gathering and outputting data “applying the set of active rules to a stream of network function transaction events in the communication network;” (See MPEP 2106.05(f)) as well as additional observation, evaluation, and judgement “detecting at least one of: an antecedent or a consequent for at least one rule of the set of active rules in the stream of network function transaction events; and generating an alert indicating at least one of: the antecedent or the consequent for the at least one rule”
Dependent claim 3 recites additional observation, evaluation, and judgement based on mathematical calculations and relationships “wherein the first rule indicates a probability that the consequent network function transaction event follows the antecedent”
Dependent claim 4 recites additional observation, evaluation, and judgement based on mathematical calculations and relationships “the probability comprises a probability of 1”
Dependent claim 5 recites additional observation, evaluation, and judgement “wherein the plurality of network function transaction events comprises a plurality of network function event failures”
Dependent claim 6 recites additional observation, evaluation, and judgement “wherein the sequential rule mining module comprises: a prefix-projected sequential pattern mining algorithm; a generalized sequential pattern algorithm; a sequential pattern discovery using equivalence classes algorithm; an efficient rule miner algorithm; a rulegrowth algorithm; a class association rules for sequential patterns algorithm; or a rulegen algorithm”
Dependent claim 7 recites additional observation, evaluation, and judgement “the plurality of network function transaction events is associated with a plurality of cellular network function instances of the communication network”
Dependent claim 8 recites additional instructions to apply the judicial exception using generic computer components “the generative model comprises a large language model-based machine learning model”
Dependent claim 9 recites additional instructions to apply the judicial exception using generic computer components “the generative model comprises a generative pre-trained transformer model”
Regarding Claim 11: Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 11 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass machine learning processing, including the following:
adding, […], the at least one rule to a set of active rules for generating alerts in a communication network (observation, evaluation, and judgement)
to generate a rule set comprising at least one rule indicating a probability that a consequent network function transaction event follows an antecedent comprising one or more prior network function transaction events (observation, evaluation, and judgement)
Therefore, claim 11 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 11 recites additional elements “A processing system including at least one processor”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Claim 11 also recites additional elements “obtaining, by a processing system including at least one processor, a plurality of sequences of network function transaction events, each of the plurality of sequences comprising a plurality of network function transaction events” and “applying, by the processing system, the plurality of sequences as inputs to a generative model” which amounts to gathering and outputting data, which is insignificant extra-solution activity (See MPEP 2106.05(g)). Therefore, claim 11 is directed to a judicial exception.
Step 2B Analysis: Claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 11 amount to no more than mere instructions to apply the judicial exception using a generic computer component and insignificant extra-solution activity. The gathering and outputting data is considered well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i)).
For the reasons above, claim 11 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to dependent claims 12-20. The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 12 recites additional insignificant extra-solution activity of gathering and outputting data “obtaining a prompt associated with the plurality of sequences, wherein the applying of the plurality of sequences as inputs to the generative model is in response to the prompt” (See MPEP 2106.05(g))
Dependent claim 13 recites additional observation, evaluation, and judgement based on mathematical calculations and relationships “selecting one or more vectors from a vector database that are relevant to the prompt, wherein the one or more vectors comprise vectorized text from one or more data sources, wherein the applying of the plurality of sequences as inputs to the generative model to obtain the rule set includes applying the one or more vectors as supplemental prompt content to the generative model”
Dependent claim 14 recites additional observation, evaluation, and judgement based on mathematical calculations and relationships “the selecting of the one or more vectors and the applying of the one or more vectors as the supplemental prompt content to the generative model comprise a retrieval augmented generation process”
Dependent claim 15 recites additional observation, evaluation, and judgement “the prompt includes a request for an interpretation of at least one aspect of the rule set, and wherein the applying is further to generate the interpretation of the at least one aspect of the rule set”
Dependent claim 16 recites additional insignificant extra-solution activity of gathering and outputting data (See MPEP 2106.05(g)) “presenting the interpretation of the at least one aspect of the rule set” which is well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i))
Dependent claim 17 recites additional insignificant extra-solution activity of gathering and outputting data (See MPEP 2106.05(g)) “the prompt is obtained from a client system, and wherein the interpretation is presented to the client system” which is well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i))
Dependent claim 8 recites additional observation, evaluation, and judgement “the applying is further to generate an interpretation of at least one aspect of the rule set” as well as additional insignificant extra-solution activity of gathering and outputting data (See MPEP 2106.05(g)) “presenting the interpretation of the at least one aspect of the rule set” which is well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i))
Dependent claim 19 recites additional instructions to apply the judicial exception using generic computer components “the generative model comprises a large language model-based machine learning model”
Dependent claim 20 recites additional instructions to apply the judicial exception using generic computer components “the generative model comprises a generative pre-trained transformer model”
Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 1-20 are rejected under 35 U.S.C. § 101.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 3, 5, 6, 7, and 10 are rejected under U.S.C. §103 as being unpatentable over the combination of Osuala (US20220382784A1) and Solmaz (“ALACA: APlatform for Dynamic Alarm Collection and Alert Notification in Network Management Systems”, 2010).
Regarding claim 1, Osuala teaches A method comprising: obtaining, by a processing system including at least one processor, ([¶0111] "the computer 1701 may include a processor 1705, memory (main memory) 1710 coupled to a memory controller 1715, and one or more input and/or output (I/O) devices (or peripherals) 10, 1745 that may be communicatively coupled via a local input/output controller 1735. The input/output controller 1735 may be, but is not limited to, one or more buses or other wired or wireless connections")
a plurality of sequences of network function transaction events,([¶0123] "receiving a dataset comprising records, wherein each record of the records comprises information descriptive of an event corresponding to an entity" [¶0100] "A frequent itemset may be found if, in at least two different time buckets, the same sequence of at least two status reports from at least two different cluster categories is found." [¶0061] "the dataset may comprise records of chat logs, IT ticketing systems, electronic health records, web, search engine search results, system error logs, delivery notes in supply chain data, etc" sequence of event records interpreted as sequence of network function transaction events)
each of the plurality of sequences comprising a plurality of network function transaction events in a communication network;([¶0060] "The dataset may be received from a local database of the computing system or from a remote database system that is remotely connected to the computing system 100. The dataset may, automatically, be received or may be received upon request, e.g., the computing system 100 may query the remote database system to receive the dataset" [¶0061] "the dataset may comprise records of chat logs, IT ticketing systems, electronic health records, web, search engine search results, system error logs, delivery notes in supply chain data, etc")
applying, by the processing system, the plurality of sequences as inputs to a sequential rule mining module implemented by the processing system([¶0090] "Using this as input data to the association rule mining system, sequential patterns may be extracted" [¶0100] " the association rule learning component 109 may also use an association rule mining algorithm to determine frequent itemsets across time buckets. A frequent itemset may be found if, in at least two different time buckets, the same sequence of at least two status reports from at least two different cluster categories is found" Osuala's association rule component mines sequential patterns across time buckets)
to obtain a first rule set comprising at least a first rule, ([¶0057] "Such mined sequential patterns may be defined as association rules and may be returned to the orchestration component 101.")
wherein the first rule indicates that a consequent network function transaction event follows an antecedent comprising one or more prior network function transaction events;([¶0031] "Using the patterns, the association rules may be determined or inferred. Following the above example, it may be determined that the occurrence of the event Y is caused by the occurrence of the event X, if the event Y always occurs after event X." Osuala strongly discloses antecedent / consequent temporal-causal rules.)
applying, by the processing system, the plurality of sequences as inputs to a generative model to obtain a second rule set;([¶0154] "training a machine learning model using the set of rows with the first column as an independent variable and the second column as a dependent variable respectively, the machine learning model is trained to predict an event category based on input event categories;" [¶0157] "adding a sub-sequence of records to a list of synthetic rules if the trained machine learning model predicts the last event category of the sub-sequence with a prediction probability exceeding a predefined threshold" Osuala explicitly generates a synthetic/second rule set by applying the sequences as inputs to a trained machine learning model).
However, Osuala does not explicitly teach identifying, by the processing system, that the first rule is contained in the first rule set and the second rule set; and
adding, by the processing system, the first rule to a set of active rules for generating alerts in the communication network, in response to identifying that the first rule is contained in the first rule set and the second rule set.
Solmaz, in the same field of endeavor, teaches identifying, by the processing system, that the first rule is contained in the first rule set and the second rule set; and ([p. 6] "As we iterate through the anchor alarms and generate candidate rules, we use a map, M in the pseudo-code, to keep a count of the number of times each candidate rule has appeared. Two candidate rules returned by different calls to GETCANDIDATERULES are considered the same if their antecedents are the same (their consequent are the same by construction)" rules from different calls interpreted as rules in different rule sets)
adding, by the processing system, the first rule to a set of active rules for generating alerts in the communication network, in response to identifying that the first rule is contained in the first rule set and the second rule set.([p. 10] "Rule Data Type includes rules that are pre-calculated and pre-registered into the ALACA's CEP module" [p. 12] "Alarm Catcher, which runs pre-registered rules over alarm data coming from AlarmLog Splitter in Event data format […] these warnings are sent in the Alert data format to the Alert Sender module for further processing").
Osuala as well as Solmaz are directed towards machine learning rule mining. Therefore, Osuala as well as Solmaz are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Osuala with the teachings of Solmaz by identifying matching rules in different sets and adding rules to a rule set for generating alerts. Solmaz provides as additional motivation for combination ([p. 1] “It helps operators to enhance the design of their alarm management systems by allowing continuous analysis of data and event streams and predict network behavior with respect to potential failures by using the results of root cause analysis”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 2, the combination of Osuala and Solmaz teaches The method of claim 1, further comprising: applying the set of active rules to a stream of network function transaction events in the communication network;(Osuala [¶0013] "FIG. 5 is a flowchart of a method for controlling an entity" [¶0074] "receive as input at least one association rule of the entity which are determined by the method of FIG. 2" [¶0039] "a system can monitor and automatically detect new emerging patterns in real-time" Osuala applies at least one learned rule during monitoring. Monitoring/real-time detection data interpreted as stream of network function transaction events)
detecting at least one of: an antecedent or a consequent for at least one rule of the set of active rules in the stream of network function transaction events; and(Osuala [¶0074] "The association rule is a relationship between a subset of event categories and one outcome event category." [¶0139] "detecting a group of monitoring status records that have the subset of event categories respectively, the detected group fulfilling the grouping criterion;")
generating an alert indicating at least one of: the antecedent or the consequent for the at least one rule. (Osuala [¶0052] "Having mined such associations, such as the co-occurrence of these three symptoms that result in the diagnosis “heart disease”, one can implement an early warning system").
Regarding claim 3, the combination of Osuala and Solmaz teaches The method of claim 1, wherein the first rule indicates a probability that the consequent network function transaction event follows the antecedent.(Osuala [¶0033] "The predicted event category may be used for the association rule if the predicted event category is one of the mi−ki remaining non-selected event categories of the group GRPi and if the probability of the prediction is higher than the threshold value.").
Regarding claim 5, the combination of Osuala and Solmaz teaches The method of claim 1, wherein the plurality of network function transaction events comprises a plurality of network function event failures.(Osuala [¶0061] "the dataset may comprise records of […] system error logs").
Regarding claim 6, the combination of Osuala and Solmaz teaches The method of claim 1, wherein the sequential rule mining module comprises:
a prefix-projected sequential pattern mining algorithm; a generalized sequential pattern algorithm; a sequential pattern discovery using equivalence classes algorithm; an efficient rule miner algorithm; a rulegrowth algorithm;
a class association rules for sequential patterns algorithm; or a rulegen algorithm. (Osuala [¶0090] "Using this as input data to the association rule mining system, sequential patterns may be extracted").
Regarding claim 7, the combination of Osuala and Solmaz teaches The method of claim 1, wherein the plurality of network function transaction events is associated with a plurality of cellular network function instances of the communication network. (Solmaz [p. 1] "ALACA is used in the network operation center of a major mobile telecom provider" [p. 2] "we propose a platform solution to analyze millions of alarms in real time, produce alerts and analyze the root causes for a large-scale network operation data center. Our system includes methods for Complex Event Processing (CEP) and root cause analysis, where millions of alarms are captured, correlated, mined for root-causes in real time. We have developed our system inside a major mobile operator with 16 million subscribers to analyze the root causes of alarms in the network operation center. As a major mobile telecom provider, it is running a nation-wide network of Global System for Mobile Communications(GSM)/Universal Mobile Telecommunications System (UMTS) / Long Term Evolution (LTE) infrastructure, consisting of different types of nodes").
Regarding claim 10, claim 10 is directed towards a processing system to perform the method of claim 1. Therefore, the rejection applied to claim 1 also applies to claim 10. Claim 10 also recites additional elements A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising: (Osuala [¶0121] "When the systems and methods described herein are implemented in software 1712, as is shown in FIG. 15 , the methods may be stored on any computer readable medium, such as storage 1720, for use by or in connection with any computer related system or method. The storage 1720 may comprise a disk storage such as HDD storage.").
Claims 4, 8, and 9 are rejected under U.S.C. §103 as being unpatentable over the combination of Osuala and Solmaz and in further view of Bhattacharjya (“Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models”, 2023).
Regarding claim 4, the combination of Osuala and Solmaz teaches The method of claim 3.
However, the combination of Osuala and Solmaz doesn't explicitly teach, wherein the probability comprises a probability of 1.
Bhattacharjya, in the same field of endeavor, teaches The method of claim 3, wherein the probability comprises a probability of 1. ([p. 5669] "using probabilities Θ = {ΘX}, ΘX = {θx|h} s.t. X∈Lθx|h = 1").
The combination of Osuala and Solmaz as well as Bhattacharjya are directed towards machine learning rule mining. Therefore, the combination of Osuala and Solmaz as well as Bhattacharjya are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Osuala and Solmaz with the teachings of Bhattacharjya by using an LLM and having the probability of 1. Bhattacharjya provides as additional motivation for combination ([p. 5669] “The global dynamics of the sequence is assumed to be governed by a conditionally homogeneous sequential process over labels in L” [p. 5673] “Experiments on real-world datasets show the improved performance of these models for event prediction compared with relevant baselines”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 8, the combination of Osuala and Solmaz teaches The method of claim 1.
However, the combination of Osuala and Solmaz doesn't explicitly teach wherein the generative model comprises a large language model-based machine learning model.
Bhattacharjya, in the same field of endeavor, teaches The method of claim 1, wherein the generative model comprises a large language model-based machine learning model. ([p. 5673] "Our goal is to use an LSuMM to propose a potential next event label X (chosen from event-related Wikidata concepts, similar to the Timelines dataset) and then to guide an LLM to generate its textual description by providing additional context using the LSuMM's influencing set U for X.").
The combination of Osuala and Solmaz as well as Bhattacharjya are directed towards machine learning rule mining. Therefore, the combination of Osuala and Solmaz as well as Bhattacharjya are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Osuala and Solmaz with the teachings of Bhattacharjya by using an LLM and having the probability of 1. Bhattacharjya provides as additional motivation for combination ([p. 5669] “The global dynamics of the sequence is assumed to be governed by a conditionally homogeneous sequential process over labels in L” [p. 5673] “Experiments on real-world datasets show the improved performance of these models for event prediction compared with relevant baselines”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 9, the combination of Osuala and Solmaz teaches The method of claim 1.
However, the combination of Osuala and Solmaz doesn't explicitly teach wherein the generative model comprises a generative pre-trained transformer model.
Bhattacharjya, in the same field of endeavor, teaches The method of claim 1, wherein the generative model comprises a generative pre-trained transformer model. ([p. 5673] "Our goal is to use an LSuMM to propose a potential next event label X (chosen from event-related Wikidata concepts, similar to the Timelines dataset) and then to guide an LLM to generate its textual description by providing additional context using the LSuMM's influencing set U for X." [p. 5673] "Sample textual output generated by GPT3 based on influencing sets as context from SOSuMM vs. the other 3 LSuMMs" GPT3 (General Purpose Transformer 3) is pre-trained).
The combination of Osuala and Solmaz as well as Bhattacharjya are directed towards machine learning rule mining. Therefore, the combination of Osuala and Solmaz as well as Bhattacharjya are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Osuala and Solmaz with the teachings of Bhattacharjya by using an LLM and having the probability of 1. Bhattacharjya provides as additional motivation for combination ([p. 5669] “The global dynamics of the sequence is assumed to be governed by a conditionally homogeneous sequential process over labels in L” [p. 5673] “Experiments on real-world datasets show the improved performance of these models for event prediction compared with relevant baselines”). This motivation for combination also applies to the remaining claims which depend on this combination.
Claims 11, 12, 15, 16, 18, 19, and 20 are rejected under U.S.C. §103 as being unpatentable over the combination of Bhattacharjya and Solmaz.
Regarding claim 11, Bhattacharjya teaches A method comprising: obtaining, by a processing system including at least one processor, a plurality of sequences of network function transaction events, each of the plurality of sequences comprising a plurality of network function transaction events;([p. 5668] "There are K sequences of events in the dataset with a total of N = K k=1 Nk instances of events.")
applying, by the processing system, the plurality of sequences as inputs to a generative model([p. 5673] "We conduct a qualitative investigation to explore the effect of using influencing sets from LSuMMs as "context" in Large Language Models (LLMs).")
to generate a rule set comprising at least one rule ([p. 5670] "Definition 3. A logical summary Markov model (LSuMM) for event label set X ⊆ L (and corresponding random variable X) with |X| = J is a probabilistic logic program with a set of rules, each of the form: (pP(X1) : θ1) ∨···∨(pP(XJ) : θJ) ← f (p1,p2,··· ,pn)")
indicating a probability ([p. 5670] "Definition 3. A logical summary Markov model (LSuMM) for event label set X ⊆ L (and corresponding random variable X) with |X| = J is a probabilistic logic program with a set of rules, each of the form: (pP(X1) : θ1) ∨···∨(pP(XJ) : θJ) ← f (p1,p2,··· ,pn) [...] these disjunctions are annotated by the probabilities of their occurrence θj. The probabilities θj in each rule head sum to 1 if X = L or sum to ≤ 1 if X L")
that a consequent network function transaction event follows an antecedent comprising one or more prior network function transaction events; and([p. 5670] "the body is a logical formula f(·) (involving conjunctions, disjunctions and negations) of predicates pi that are either historical predicates or temporal relations that only in volve labels from an influencing set U. The head is a disjunction over positional occurrence predicates pP(Xj) indicating whether event label Xj ∈ X occurs at any arbitrary position in an event sequence" [p. 5671] "In the situation modeled by this program, failure event type C is most likely to occur when both A and B occur" The LSuMM rule body is the antecedent: predicates over historical events. The head is the consequent: an event label occurring at the prediction position. Example 5 even models failure event C as more likely after A then B within a look-back window.).
However, Bhattacharjya does not explicitly teach adding, by the processing system, the at least one rule to a set of active rules for generating alerts in a communication network.
Solmaz, in the same field of endeavor, teaches adding, by the processing system, the at least one rule to a set of active rules for generating alerts in a communication network.([p. 10] "Rule Data Type includes rules that are pre-calculated and pre-registered into the ALACA's CEP module" [p. 12] "Alarm Catcher, which runs pre-registered rules over alarm data coming from AlarmLog Splitter in Event data format […] these warnings are sent in the Alert data format to the Alert Sender module for further processing").
Bhattacharjya as well as Solmaz are directed towards machine learning rule mining. Therefore, Bhattacharjya as well as Solmaz are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Bhattacharjya with the teachings of Solmaz by identifying matching rules in different sets and adding rules to a rule set for generating alerts. Solmaz provides as additional motivation for combination ([p. 1] “It helps operators to enhance the design of their alarm management systems by allowing continuous analysis of data and event streams and predict network behavior with respect to potential failures by using the results of root cause analysis”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 12, the combination of Bhattacharjya and Solmaz teaches The method of claim 11, further comprising: obtaining a prompt associated with the plurality of sequences, wherein the applying of the plurality of sequences as inputs to the generative model is in response to the prompt.(Bhattacharjya [p. 5673] "Figure 2: Sample textual output generated by GPT3 based on influencing sets as context from SOSuMM vs. the other 3 LSuMMs […] Figure 2 shows the sample textual output for the next event generated by GPT3 based on the prompt text (including the current event textual description) and different influencing event types as additional context. See FIG. 2 which shows that GPT3 uses prompt text plus applies LSuMM-derived context to generate the next event textual output).
Regarding claim 15, the combination of Bhattacharjya and Solmaz teaches The method of claim 12, wherein the prompt includes a request for an interpretation of at least one aspect of the rule set, and wherein the applying is further to generate the interpretation of the at least one aspect of the rule set.(Bhattacharjya [p. 5673] "Figure 2: Sample textual output generated by GPT3 based on influencing sets as context from SOSuMM vs. the other 3 LSuMMs […] Figure 2 shows the sample textual output for the next event generated by GPT3 based on the prompt text (including the current event textual description) and different influencing event types as additional context. See FIG. 2 which shows that GPT3 uses prompt text plus applies LSuMM-derived context to generate the next interpretation of at least one aspect of the rule set).
Regarding claim 16, the combination of Bhattacharjya and Solmaz teaches The method of claim 15, further comprising: presenting the interpretation of the at least one aspect of the rule set.(Bhattacharjya [p. 5673] "Figure 2: Sample textual output generated by GPT3 based on influencing sets as context from SOSuMM vs. the other 3 LSuMMs […] Figure 2 shows the sample textual output for the next event generated by GPT3 based on the prompt text (including the current event textual description) and different influencing event types as additional context. See FIG. 2 which shows that GPT3 uses prompt text plus applies LSuMM-derived context to generate the next interpretation of at least one aspect of the rule set).
Regarding claim 18, the combination of Bhattacharjya and Solmaz teaches The method of claim 11, wherein the applying is further to generate an interpretation of at least one aspect of the rule set, the method further comprising: presenting the interpretation of the at least one aspect of the rule set.(Bhattacharjya [p. 5673] "Figure 2: Sample textual output generated by GPT3 based on influencing sets as context from SOSuMM vs. the other 3 LSuMMs […] Figure 2 shows the sample textual output for the next event generated by GPT3 based on the prompt text (including the current event textual description) and different influencing event types as additional context. See FIG. 2 (presentation) which shows that GPT3 uses prompt text plus applies LSuMM-derived context to generate the next interpretation of at least one aspect of the rule set).
Regarding claim 19, the combination of Bhattacharjya and Solmaz teaches The method of claim 11, wherein the generative model comprises a large language model-based machine learning model.(Bhattacharjya [p. 5673] "Our goal is to use an LSuMM to propose a potential next event label X (chosen from event-related Wikidata concepts, similar to the Timelines dataset) and then to guide an LLM to generate its textual description by providing additional context using the LSuMM's influencing set U for X.").
Regarding claim 20, the combination of Bhattacharjya and Solmaz teaches The method of claim 11, wherein the generative model comprises a generative pre-trained transformer model.(Bhattacharjya [p. 5673] "Our goal is to use an LSuMM to propose a potential next event label X (chosen from event-related Wikidata concepts, similar to the Timelines dataset) and then to guide an LLM to generate its textual description by providing additional context using the LSuMM's influencing set U for X." [p. 5673] "Sample textual output generated by GPT3 based on influencing sets as context from SOSuMM vs. the other 3 LSuMMs" GPT3 (General Purpose Transformer 3) is pre-trained).
Claims 13, 14, and 17 are rejected under U.S.C. §103 as being unpatentable over the combination of Bhattacharjya and Solmaz and in further view of Karapantelakis (“Using Large Language Models to Understand Telecom Standards”, 2024).
Regarding claim 13, the combination of Bhattacharjya and Solmaz teaches The method of claim 12.
However, the combination of Bhattacharjya and Solmaz doesn't explicitly teach, further comprising: selecting one or more vectors from a vector database that are relevant to the prompt,
wherein the one or more vectors comprise vectorized text from one or more data sources,
wherein the applying of the plurality of sequences as inputs to the generative model to obtain the rule set includes applying the one or more vectors as supplemental prompt content to the generative model.
Karapantelakis, in the same field of endeavor, teaches The method of claim 12, further comprising: selecting one or more vectors from a vector database that are relevant to the prompt, ([p. 3] "an embedding model that creates vector representations of the document, which capture the semantic meaning of text […] Another component is a type of database that can efficiently store and search for these embeddings. This database is called the vectorstore [...] a retriever takes the user’s query, which might have context, and semantically searches the vectorstore for similar results")
wherein the one or more vectors comprise vectorized text from one or more data sources, ([p. 3] "TeleRoBERTa [17], [18] was introduced as adaptation of the RoBERTa base model to the characteristics the telecommunications domain, trained on a large corpus of text collected from in-domain sources such as 3GPP specification" The system loads 3gPP Word/PDF documents, splits them into chunks, and embeds those chunks. The selected vectors therefore comprise vectorized text from telecom data sources)
wherein the applying of the plurality of sequences as inputs to the generative model to obtain the rule set includes applying the one or more vectors as supplemental prompt content to the generative model.([p. 3] "TeleRoBERTa [17], [18] was introduced as adaptation of the RoBERTa base model to the characteristics the telecommunications domain, trained on a large corpus of text collected from in-domain sources such as 3GPP specification").
The combination of Bhattacharjya and Solmaz as well as Karapantelakis are directed towards large language models. Therefore, the combination of Bhattacharjya and Solmaz as well as Karapantelakis are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Bhattacharjya and Solmaz with the teachings of Karapantelakis by applying LSuMM to the TeleROBERTa LLM model (simple substitution of GPT3 for TeleROBERTa). Karapantelakis provides as additional motivation for combination ([p. 1] “we provide a model of our own, TeleRoBERTa, that performs on par with foundation LLMs but with an order of magnitude less number of parameters”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 14, the combination of Bhattacharjya, Solmaz, and Karapantelakis teaches The method of claim 13, wherein the selecting of the one or more vectors and the applying of the one or more vectors as the supplemental prompt content to the generative model comprise a retrieval augmented generation process.(Karapantelakis [p. 3] "A. Retrieval Augmented Generation […] Another key component is an embedding model that creates vector representations of the document, which capture the semantic meaning of text, allowing to quickly and efficiently find other pieces of text with similar content. This vector representation created by the embedding model is simply called embedding").
Regarding claim 17, the combination of Bhattacharjya and Solmaz teaches
However, the combination of Bhattacharjya and Solmaz doesn't explicitly teach The method of claim 16, wherein the prompt is obtained from a client system, and wherein the interpretation is presented to the client system..
Karapantelakis, in the same field of endeavor, teaches The method of claim 16, wherein the prompt is obtained from a client system, and wherein the interpretation is presented to the client system.([p. 3 §III] "the user can feed its own dataset to the foundation model and guide the model response in real time through queries (prompts), enhancing the overall model response").
The combination of Bhattacharjya and Solmaz as well as Karapantelakis are directed towards large language models. Therefore, the combination of Bhattacharjya and Solmaz as well as Karapantelakis are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Bhattacharjya and Solmaz with the teachings of Karapantelakis by applying LSuMM to the TeleROBERTa LLM model (simple substitution of GPT3 for TeleROBERTa). Karapantelakis provides as additional motivation for combination ([p. 1] “we provide a model of our own, TeleRoBERTa, that performs on par with foundation LLMs but with an order of magnitude less number of parameters”). This motivation for combination also applies to the remaining claims which depend on this combination.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Roy (“Correlating contexts and NFR conflicts from event logs”, 2023) is directed towards rule mining in BERT based communication systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIDNEY VINCENT BOSTWICK whose telephone number is (571)272-4720. The examiner can normally be reached M-F 7:30am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
Prosecution Insights