CTNF 17/934,583 CTNF 100815 DETAILED ACTION This Office Action is responsive to Request for Continued Examination filed on February 27 th , 2026. Claims 1, 4, 7-8, 11, 14-15, 18, 21-23 and 25-31 are pending, with claims 27-31 being added and claims 5-6, 12-13, 19-20 and 24 cancelled. Any objections/rejections not mentioned in this Office Action have been withdrawn by the Examiner. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS) submitted on September 21 st , 2022, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Response to Amendments and Arguments Regarding rejections made under 35 U.S.C. 101, Applicant argues, "In light of the recent August 4th Memo regarding Subject Matter Eligibility Guidance Applicant's amended claims are patent eligible subject matter. The Memo guided examiner's specifically with respect to the the [ sic ] Step 2A Prong 2 improvements consideration under MPEP 2106.05(a) stating that the 'examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement' and going on to clarify that the 'claim itself does not need to explicitly recite the improvement described in the specification.' Applicant has specifically pointed out the improvements described by the specification and amended the independent claims to directly tie the claim limitations to the improvements detailed above." (page 10 of Remarks). Applicant’s arguments are not persuasive. While the Specification may make the assertion that compression ratios and system performance are improved with the claimed invention, the claims fail to illustrate any particular mechanism for this improvement. Compression is already used to reduce storage demands, but the method outlined in the limitations appears to provide only those known improvements, relying on known techniques and algorithms and machine learning models, amounting to merely using a computer as an automation tool. Accordingly, the rejections under 35 U.S.C. 101 are maintained. Further details are provided below. Regarding rejections made under 35 U.S.C. 103, Applicant argues, "[T]he Liu and Arye references fail to disclose amended independent claim 1. Therefore, for at least the above reasons, independent claim 1 is patentable over Liu and Arye. For similar reasons, independent claims 8 and 15 are also patentable over Liu and Arye." (page 12 of Remarks). Applicant’s argument is moot, as new grounds of rejection are raised in view of U.S. Patent 12,101,232 to Li et al. Further details are provided below. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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, 4, 7-8, 11, 14-15, 18, 21-23 and 25-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process that can be performed in the human mind or with the aid of pen and paper. This judicial exception is not integrated into a practical application because a computer is invoked merely as a tool to execute an abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because an abstract idea is merely applied on a generic computer without any element that would otherwise preclude performance of the abstrac. Regarding claim 1, the claim recites “A method for data compression, the method comprising:receiving operational data directly from one or more applications in real time, wherein the operational data is comprised of time-stamped process event logs produced by an execution of one or more processes by the one or more applications including monitoring data, operational log data, events operational data, and service request operational data;profiling the operational data using one or more normalizing functions to construct a key value set, wherein the one or more normalizing functions includes at least one or more of, template mining, variable extraction, correlation pattern identification, multi-variant mining, or relationship mining and are performed using at least one or more of, one or more machine learning models, deep learning algorithms, or natural language processing techniques, wherein the key value set is in a common format and comprised of a plurality of entities and relationships between the plurality of entities including a distribution of appearances, an assigned label, and a field name for each of the plurality of entities;extracting a plurality of patterns from the operational data;building one or more dictionaries based on at least the key value set and the plurality of patterns extracted from the operational data; andcompressing the operational data based on the plurality of patterns extracted by matching the plurality of patterns extracted with variables stored in the one or more dictionaries and assigning a key value from the key value set to the operational data corresponding to the variables stored in the one or more dictionaries.” The limitations of “receiving operational data. […] in real time,” “profiling the operational data…” “extracting […] patterns from the operational data,” “ building one or more dictionaries…” and “compressing the operational data…” as drafted cover mental activities which can be performed in the mind or with the aid of pen and paper. Taken individually, or as a whole, these limitations describe acts which are equivalent to human mental work or methods of organizing human activity. The human mind is capable of reading a continuous feed of log data, identifying important or repetitive elements therein, and applying techniques that broadly reduce the amount of writing required to make and keep a summary record. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be performed mentally, and no additional features in the claims would preclude them from being performed as such, since the steps performed by a generic machine learning model may be embodied by a human actor. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 4, the claim depends from claim 1, and thus recites the limitations of claim 1, “wherein the one or more dictionaries are stored in a knowledge corpus and utilized in compressing the operational data.” Taken as a whole with claim 1, these limitations may be embodied by an individual creating a summary of an activity log using a template and retaining that template for later use. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 7, the claim depends from claim 1, and thus recites the limitations of claim 1, “further comprising: receiving a retrieval inquiry from a user; andretransforming the operational data from a compressed format to an original format.” The limitation of “receiving a retrieval inquiry from a user,” and “retransforming the operational data from a compressed format to an original format,” as drafted cover mental activities which can be performed in the mind or with the aid of pen and paper. Taken individually, or as a whole with claim 1, these limitations describe acts which are equivalent to human mental work of explanation. Referring to the example given for claim 1, these limitations may be embodied by an individual responding to a request to illustrate their summary and reversing their summarization process. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claims 8, 11 and 14, system claims 8, 11 and 14 and method claims 1, 4 and 7 are related as a method and system of using the same, with each system element’s function corresponding to the method step. Accordingly, claims 8, 11 and 14 are similarly rejected under the same rationale as applied to claims 1, 4 and 7. Regarding claims 15, 18 and 21, computer program product claims 15, 18 and 21 and method claims 1, 4 and 7 are related as method and computer-readable medium for performing the same, with each computer-readable medium element’s function corresponding to the method step. Accordingly, claims 15, 18 and 21 are similarly rejected under the same rationale as applied to claims 1, 4 and 7. Regarding claim 22, the claim depends from claim 1, and thus recites the limitations of claim 1, “further comprising: vectorizing, by the one or more machine learning models, message bodies of logs, events, tickets, and/or extracted features of the operational data; andclustering, using one or more clustering techniques, the message bodies of logs.” The limitations of “vectorizing […] message bodies of logs,” and “clustering […] the message bodies of logs” as drafted cover mental activities which are equivalent to human mental work of transcription and organization, which may be performed in the human mind, as argued in the example for claim 1. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 23, the claim depends from claim 1, and thus recites the limitations of claim 1, “further comprising: performing multiple-variable pattern mining using the one or more machine learning models.” Taken individually, or as a whole with claim 1, these limitations describe the human mental work of pattern recognition embodied by a generic computer. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 25, the claim depends from claim 1, and thus recites the limitations of claim 1, “wherein the plurality of patterns are extracted from the operational data utilizing one or more pattern mining techniques to extract a plurality of frequent sequences from a set of sequences based on a support setting, wherein the support setting is a probability variable.” Taken individually, or as a whole with claim 1, these limitations describe the human mental work of pattern recognition embodied by a generic computer. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 26, the claim depends from claim 7, and thus recites the limitations of claims 1 and 7, “further comprising: receiving a retrieval inquiry from a user; andretransforming the operational data from a compressed format to an original format.” The limitation of “receiving a retrieval inquiry from a user,” and “retransforming the operational data from a compressed format to an original format,” as drafted cover mental activities which can be performed in the mind or with the aid of pen and paper. Taken individually, or as a whole with claim 1, these limitations describe acts which are equivalent to human mental work of explanation. Referring to the example given for claim 1, these limitations may be embodied by an individual responding to a request to illustrate their summary and reversing their summarization process. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 27, the claim depends from claim 1, and thus recites the limitations of claim 1, “wherein the monitoring data includes time-series numeric data, wherein the operational log data, the events operational data, and the service request operational data all include time series based semi- structural data, and wherein the operational data is pre-processed into the common format of the key value set and stored in a knowledge corpus.” Taken individually, or as a whole with claim 1, these limitations describe the human mental work of pattern recognition and record keeping embodied by a generic computer. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 28, the claim depends from claim 22, and thus recites the limitations of claims 1 and 22, “further comprising: extracting, using a support setting, a plurality of frequent sequence words, wherein the support setting utilizes at least Frequent Pattern Tree (FP-Tree) and Frequent Pattern growth (FP- growth), wherein the FP-Tree is a data structure of the FP-growth algorithms for mining the frequent sequence words of the operational data based on association rules of the support setting.” Taken individually, or as a whole with the preceding claims, these limitations describe the human mental work of pattern recognition embodied by a generic computer. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 29, the claim depends from claim 1, and thus recites the limitations of claim 1, “wherein the relationship mining is performed using the natural language processing techniques, wherein the natural language processing techniques include at least Named-entity Recognition (NER) and Named Relation Recognition (NRR) in extracting the plurality of entities comprising the key value set.” Taken individually, or as a whole with claim 1, these limitations describe the human mental work of pattern recognition embodied by a generic computer. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 30, the claim depends from claim 29, and thus recites the limitations of claims 1 and 29, “wherein the relationships between the plurality of entities include interactions, interdependencies, and properties, and wherein sequence patterns or variables in which the relationships are extracted include at least log transaction sequences from the operational log data, event sequences from the events operational data, and ticket sequences from the service request operational data.” Taken individually, or as a whole with the preceding claims, these limitations describe the human mental work of pattern recognition embodied by a generic computer. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Regarding claim 31, the claim depends from claim 1, and thus recites the limitations of claim 1, “further comprises: generating a plurality of graphs from the operational data, wherein the plurality of graphs include at least services interaction graphs, network topology graphs, and event graphs; andextracting a plurality of graph patterns from the plurality of graphs by identifying frequent patterns according to variables, edges, and vertices.” Taken individually, or as a whole with claim 1, these limitations describe the human mental work of pattern recognition embodied by a generic computer using pictorial representations. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 4, 8, 11, 15, 18, 22, 25, 27 and 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over "Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression" by Liu et al. (hereinafter, "Liu") in view of U.S. Patent 12,101,232 to Li et al. (hereinafter, "Li") . 07-21-02-aia The applied reference Li has a common inventor and assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02. Regarding claims 1, 8 and 15, Liu teaches a method for data compression comprising: extracting a plurality of patterns from the operational data (section III. A. Overview, "A hierarchical division method is then applied to the sample logs to generate multiple clusters, from which templates can be extracted automatically ."); building one or more dictionaries based on at least the key value set and the plurality of patterns extracted from the operational data (section IV. B. Approach Level 2, "After that, the message content could be represented as its template and parameters, and we assign auto-incremental EventID initialized to 0 for unique templates, which forms a template mapping dictionary (EventID is the key and the corresponding template is the value) .") and compressing the operational data based on the plurality of patterns extracted by matching the plurality of patterns extracted with variables stored in the one or more dictionaries and assigning a key value from the key value set to the operational data corresponding to the variables stored in the one or more dictionaries (section III. D. Matching, "After collecting all templates from clusters, we use the templates to match each unsampled log message as described in Fig. 2. By matching, each log message is assigned a template thus the hidden structure is extracted . We use the hidden structure to facilitate log compression ."). While Liu teaches in section III. A. Overview, " The input of ISE is a log file consisting of raw log messages , and the output is extracted templates and structured logs," and section I. Introduction, "System logs typically comprise a series of log messages, each recording a specific event or state during the execution of both user applications and components of a large system ," and "Therefore, template extraction from software logs is the most widely-applicable approach and thus logzip proposes an iterative clustering algorithm to extract templates from logs automatically," Liu does not explicitly teach “receiving operational data directly from one or more applications in real time, wherein the operational data is comprised of time-stamped process event logs produced by an execution of one or more processes by the one or more applications including monitoring data, operational log data, events operational data, and service request operational data,” or “profiling the operational data using one or more normalizing functions to construct a key value set, wherein the one or more normalizing functions includes at least one or more of, template mining, variable extraction, correlation pattern identification, multi-variant mining, or relationship mining and are performed using at least one or more of, one or more machine learning models, deep learning algorithms, or natural language processing techniques, wherein the key value set is in a common format and comprised of a plurality of entities and relationships between the plurality of entities including a distribution of appearances, an assigned label, and a field name for each of the plurality of entities,” and thus, Li is introduced. Li teaches receiving operational data directly from one or more applications in real time, wherein the operational data is comprised of time-stamped process event logs produced by an execution of one or more processes by the one or more applications including monitoring data, operational log data, events operational data, and service request operational data (column 8, line 61, "In step 302, raw logs of real-time operation data a [ sic ] received . Step 302 is similar to step 202 of the dynamic graphing process 300. In at least one embodiment, the raw logs of real-time operation data from the distributed workload may be JSON files. These JSON files may be transmitted through the communications network 116 and may be received by the server 112. JSON files may be used for transmitting data between a web application and a computer server, e.g., server 112. In other embodiments, other real-time operation data such as XML files, YAML files, TOML files, or CSON files may be received. The raw logs of the real-time operation data may be received, e.g., at the server 112, from other components in a distributed workload of a hybrid cloud system ."); and profiling the operational data using one or more normalizing functions to construct a key value set, wherein the one or more normalizing functions includes at least one or more of, template mining, variable extraction, correlation pattern identification, multi-variant mining, or relationship mining and are performed using at least one or more of, one or more machine learning models, deep learning algorithms, or natural language processing techniques, wherein the key value set is in a common format and comprised of a plurality of entities and relationships between the plurality of entities including a distribution of appearances, an assigned label, and a field name for each of the plurality of entities (column 9, line 8, "In step 304, the logs are parsed into key-value format . FIG. 8 shows an example of raw data that has been pre-processed to obtain a key-value format 800. This raw data which may include raw logs was received from real-time operations of a distributed workload in a hybrid cloud. In this key-value format, a key is in a first column is a key. In a second column to the right of the first column is a value for the key. JSON objects are usually surrounded by curly braces { }. The keys for JSON objects may be strings. Values may include a string, a number, an object, an array, a boolean or a null," column 9, line 26, "In step 306, text patterns are extracted from each field of the key-value format . The dynamic graphing program 110a, 110b may process the text from each field as strings or as sequences of encoded characters," and column 10, line 20, "In step 312, each field is data profiled and statistical features of each profiling are selected . With the data profiling, the data from the key-value pairs is examined and statistics may be collected concerning the data . Informative summaries about that data may be generated. Data profiling may utilize methods of descriptive statistics such as minimum, maximum, mean, mode, percentile, standard deviation, frequency , variation, aggregates such as count and sum, and additional metadata information obtained during data profiling such as data type, length, discrete values, uniqueness , occurrence of null values, typical string patterns, and abstract type recognition to attempt to recognize meaningful information from the key-value pairs. In step 314 ops entities are determined by comparing the statistical features to pre-trained classifiers. The dynamic graphing program 110a, 110b may incorporate a machine learning model with classifiers to help analyze the results of the data profiling . The machine learning model may use the statistical features to suggest new terms or phrases that are identified as referring to a new type of an ops entity."). Liu and Li are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu with the teachings of Li for the purpose of improving system efficiency. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Regarding claims 4, 11 and 18, Li further teaches one or more dictionaries are stored in a knowledge corpus and utilized in compressing the operational data (column 11, line 1, "In step 320 of the ops entity/object extraction process 300 shown in FIG. 3, the dictionary that was accessed in step 310 and that is stored in a computer memory accessible to the dynamic graphing program 110, 110b is updated with any new ops entity that is discovered via steps 312 and 214."). Liu and Li are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu with the teachings of Li for the purpose of improving compression quality. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Regarding claim 22, Liu teaches vectorizing, by the one or more machine learning models, message bodies of logs, events, tickets, and/or extracted features of the operational data (section III. C. Clustering, "To achieve this, we first tokenize each log message to a list of tokens by using system defined (or as user input) delimiters (e.g., comma and space). Then, we count the frequency of each token in the sampled logs .") ; andclustering, using one or more clustering techniques, the message bodies of logs (section III. C. Clustering, " These sampled log lines are then grouped into clusters . ISE extracts a template from each cluster by hierarchical divisive clustering in a top-down manner, where we start with a single cluster that consists of all sampled log lines."). Regarding claim 25, Liu further teaches the plurality of patterns are extracted from the operational data utilizing one or more pattern mining techniques to extract a plurality of frequent sequences from a set of sequences based on a support setting, wherein the support setting is a probability variable (section III. C. Clustering, "These sampled log lines are then grouped into clusters. ISE extracts a template from each cluster by hierarchical divisive clustering in a top-down manner… Therefore, it is reasonable to group logs that share the same frequent tokens into the same cluster … Moreover, top-2, top-3,..top-N frequent tokens can be applied in the same way to further divide the clusters, where N is a tunable parameter that is normally set to 3 ."). Regarding claim 27, Liu does not explicitly teach a method “wherein the monitoring data includes time-series numeric data, wherein the operational log data, the events operational data, and the service request operational data all include time series based semi-structural data, and wherein the operational data is pre-processed into the common format of the key value set and stored in a knowledge corpus,” however, Li teaches the monitoring data includes time-series numeric data, wherein the operational log data, the events operational data, and the service request operational data all include time series based semi-structural data (column 10, line 40, "Steps 308, 310, and 312 may alternatively output context entities for those entries that do not match an entity entry from the saved dictionary or that are not recognized as an entity via data profiling. These entries may help provide context for the ops entities. Such context entities may include timestamps , indexes, messages, elapsed times , types, size requests, time requests , resource classes, etc."), and wherein the operational data is pre-processed into the common format of the key value set and stored in a knowledge corpus (column 11, line 66, "For workflow-based grouping, as part of step 404 the dynamic graphing program 110a, 110b may order the ops objects according to timestamps listed for the completion or for the entry of the log corresponding to the particular ops object . Groups may naturally be created where time intervals between adjacently listed operations entities , e.g., steps, are much longer than execution times of each step inside one task. Thus, comparing the times, e.g., the execution times, between each ops object may naturally indicate breaks to be interpreted as breaks between groups ."). Liu and Li are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu with the teachings of Li for the purpose of improving compression quality. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Regarding claim 29, Liu does not explicitly teach a method “wherein the relationship mining is performed using the natural language processing techniques, wherein the natural language processing techniques include at least Named-entity Recognition (NER) and Named Relation Recognition (NRR) in extracting the plurality of entities comprising the key value set,” however, Li teaches the relationship mining is performed using the natural language processing techniques, wherein the natural language processing techniques include at least Named-entity Recognition (NER) and Named Relation Recognition (NRR) in extracting the plurality of entities comprising the key value set (column 9, line 35, "In step 306, a pattern of values is compared with known patterns that are kept in a dictionary of patterns that is stored in a memory associated with the dynamic graphing program 110a, 110b, e.g., that may be stored in database 114. This dictionary of patterns may include known ops entities for the distributed workload such as requests, transactions, services, users, tenants, threads, pods, containers, workspaces, clients, hosts, conversations, etc. By keyword searching through the text, values, or patterns from the key-value pain [ sic ], the dynamic graphing program 110a, 110b may recognize entries of the key-value pairs as referring to ops entities ."). Liu and Li are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu with the teachings of Li for the purpose of improving compression quality. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Regarding claim 30, Liu does not explicitly teach a method “wherein the relationships between the plurality of entities include interactions, interdependencies, and properties, and wherein sequence patterns or variables in which the relationships are extracted include at least log transaction sequences from the operational log data, event sequences from the events operational data, and ticket sequences from the service request operational data,” however, Li teaches the relationships between the plurality of entities include interactions, interdependencies, and properties, and wherein sequence patterns or variables in which the relationships are extracted include at least log transaction sequences from the operational log data, event sequences from the events operational data, and ticket sequences from the service request operational data (column 9, line 35, "In step 306, a pattern of values is compared with known patterns that are kept in a dictionary of patterns that is stored in a memory associated with the dynamic graphing program 110a, 110b, e.g., that may be stored in database 114. This dictionary of patterns may include known ops entities for the distributed workload such as requests, transactions, services , users, tenants, threads, pods, containers, workspaces, clients, hosts, conversations, etc.," and column 11, line 37, "For identifier-based grouping, as part of step 404 the dynamic graphing program 110a, 110b may review each ops entity that was identified in the ops entity/object extraction process 300. For identifier-based grouping, the text of the ops entities may be searched by a string searching module that is part of the server 112 or that is otherwise associated with the dynamic graphing program 110a, 110b. This searching may help identify any ops entities with specific identifiers. Such specific identifiers may include a transaction code , a card ID, a thread ID, etc. The identifier may originate from a system component. An ops object may have a specific identifier, as an ops object is, due to its specific instance, distinguishable from other ops entities of the same entity type. Thus, the ops entity instance itself may in some embodiments act as a specific ID. Knowing the specific ID helps the dynamic graphing program 110a, 110b recognize multiple entries for an ops object throughout the raw data and use these various entries to discover the dependencies of that particular ops object ."). Liu and Li are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu with the teachings of Li for the purpose of improving compression quality. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Regarding claim 31, Liu does not explicitly teach a method “wherein the extracting the plurality of patterns from the operational data, further comprises: generating a plurality of graphs from the operational data, wherein the plurality of graphs include at least services interaction graphs, network topology graphs, and event graphs; and extracting a plurality of graph patterns from the plurality of graphs by identifying frequent patterns according to variables, edges, and vertices,” however, Li teaches generating a plurality of graphs from the operational data, wherein the plurality of graphs include at least services interaction graphs, network topology graphs, and event graphs (column 6, line 16, "In step 214 of the dynamic graphing process 200, nodes and edges are added to a temporal operations graph . A graphics program, e.g., a three-dimensional or two-dimensional graphics program, may be used to apply the nodes and edges determined in steps 208, 210, and 212 into a graph together. A node for each operations object and an edge for each operations object dependency may be entered into the graph ."); and extracting a plurality of graph patterns from the plurality of graphs by identifying frequent patterns according to variables, edges, and vertices (column 6, line 32, "In step 216 of the dynamic graphing process 200 shown in FIG. 2, the operation moves to sub-graph abstraction mode ," column 7, line 9, "If a small zoom size is selected in step 218, then in step 220 accessible paths finding is performed ," and column 7, line 17, "If a large zoom size is selected in step 218, then in step 222 a betweenness evaluation is performed , e.g., by the dynamic graphing program 110a, 110b."). Liu and Li are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu with the teachings of Li for the purpose of improving compression quality. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. 07-22-aia AIA Claim s 7, 14, 21 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Liu and Li as applied to claim s 1, 8 and 15 above, and further in view of U.S. Patent Application Publication 2021/0034598 to Arye et al. (hereinafter, "Arye") . Regarding claims 7, 14 and 21, the combination of Liu and Li does not explicitly teach “receiving a retrieval inquiry from a user; and retransforming the operational data from a compressed format to an original format,” and thus, Arye is introduced. Arye teaches receiving a retrieval inquiry from a user; and retransforming the operational data from a compressed format to an original format (paragraph [0057], "The database engine enables users to query data stored in compressed form (i.e., in the second representation). To support such queries, the database engine determines which row or rows in the second representation match a query, identifies which columns are being requested, uncompresses those columns specified in the query, and returns this uncompressed data ."). Liu, Li and Arye are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu and Li with the teachings of Arye for the purpose of improving system usability. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Regarding claim 26, Liu further teaches the method on claim 7 wherein the operational data is retransformed from the compressed format to the original format using the key value and a template identification of the one or more dictionaries (section IV. B. Approach, " As the reverse process of log compression , decompression should be able to recover the original dataset without losing any information.") . 07-22-aia AIA Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Liu and Li as applied to claim 1 above, and further in view of WIPO Publication WO 2022/093239 to Gu (hereinafter, "Gu") . Regarding claim 23, the combination of Liu and Li does not teach “The method of claim 7, further comprising: performing multiple-variable pattern mining using the one or more machine learning models,” and thus, Gu is introduced. Gu teaches performing multiple-variable pattern mining using the one or more machine learning models (page 5, line 11, "In an embodiment, the innovation may first perform metric event pattern extraction. In this embodiment, the innovation first provides automatic unsupervised multi-variant statistical classification methods to extract principle event patterns from large amounts of raw metric data streams for a system under analysis."). Liu, Li and Gu are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu and Li with the teachings of Gu for the purpose of improving compression quality. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results . 07-22-aia AIA Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Liu and Li as applied to claim s 1 and 22 above, and further in view of "FP-tree and SVM for Malicious Web Campaign Detection" by Kruczkowski et al. (hereinafter, "Kruczkowski") . Regarding claim 28, the combination of Liu and Li does not teach the method of claim 22 “further comprising: extracting, using a support setting, a plurality of frequent sequence words, wherein the support setting utilizes at least Frequent Pattern Tree (FP-Tree) and Frequent Pattern growth (FP- growth), wherein the FP-Tree is a data structure of the FP-growth algorithms for mining the frequent sequence words of the operational data based on association rules of the support setting,” and thus, Kruczkowski is introduced. Kruczkowski teaches extracting, using a support setting, a plurality of frequent sequence words, wherein the support setting utilizes at least Frequent Pattern Tree (FP-Tree) and Frequent Pattern growth (FP- growth), wherein the FP-Tree is a data structure of the FP-growth algorithms for mining the frequent sequence words of the operational data based on association rules of the support setting (section 4 FP-SVM system, page 5, "Once the final set of tokens LE T is built, the FP-growth algorithm is employed to discover frequent tokens and the FP-tree structure Total FP storing quantitative information about frequent tokens from LE T is constructed. In this tree each node (besides root) represents an extracted token that is shared by all subtrees consisting of itself and all the nodes beneath it. Each path in the tree shows a set of tokens that co-occur in URLs. Thus two URLs that contain several identical frequent tokens and differ in several infrequent tokens share a common path. The root is the node that has no superior and separates all disjoint sub-trees. The Total FP tree structure is analysed. Simple decision rules are used for data processing. These rules define the characteristics of each URL that is suspected to belong to any campaign. The final FP-tree structure Campaign FP formed by URLs with these characteristics is created. Next, all URLs from the dataset S URL containing the tokens from the Campaign FP tree are classified to the positive class denoted by ”+1”, and form the set S + URL of URLs forming malicious campaigns. Other URLs from S URL are classified to negative class denoted by ”-1”, and form the dataset S − URL consisting of URLs that are unrelated to any campaign ."). Liu, Li and Kruczkowski are considered analogous because they are each concerned with operational data processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Liu and Li with the teachings of Kruczkowski for the purpose of improving compression quality. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : U.S. Patent 10,599,668 to McLean. U.S. Patent Application Publication 2016/0092497 to Oberhofer et al. U.S. Patent Application Publication 2022/0012146 to Basu and Wang. U.S. Patent Application Publication 2022/0236904 to Miller et al. U.S. Patent Application Publication 2023/0017165 to Walters et al. China Invention Application CN 114968953 to Lin and Lao. “Memory Energy Minimization by Data Compression: Algorithms, Architectures and Implementations” by Benini et al. “Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks” by Sadler and Martonosi. “A Guided FP-growth algorithm for multitude-targeted mining of bog data” by Shabtay et al. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN T SMITH whose telephone number is (571)272-6643. The examiner can normally be reached Monday - Friday 8:00am - 5:00pm. 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, PIERRE-LOUIS DESIR can be reached at (571) 272-7799. 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. /SEAN THOMAS SMITH/Examiner, Art Unit 2659 /PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659 Application/Control Number: 17/934,583 Page 2 Art Unit: 2659 Application/Control Number: 17/934,583 Page 3 Art Unit: 2659 Application/Control Number: 17/934,583 Page 4 Art Unit: 2659 Application/Control Number: 17/934,583 Page 5 Art Unit: 2659 Application/Control Number: 17/934,583 Page 6 Art Unit: 2659 Application/Control Number: 17/934,583 Page 7 Art Unit: 2659 Application/Control Number: 17/934,583 Page 8 Art Unit: 2659 Application/Control Number: 17/934,583 Page 9 Art Unit: 2659 Application/Control Number: 17/934,583 Page 10 Art Unit: 2659 Application/Control Number: 17/934,583 Page 11 Art Unit: 2659 Application/Control Number: 17/934,583 Page 12 Art Unit: 2659 Application/Control Number: 17/934,583 Page 13 Art Unit: 2659 Application/Control Number: 17/934,583 Page 14 Art Unit: 2659 Application/Control Number: 17/934,583 Page 15 Art Unit: 2659 Application/Control Number: 17/934,583 Page 16 Art Unit: 2659 Application/Control Number: 17/934,583 Page 17 Art Unit: 2659 Application/Control Number: 17/934,583 Page 18 Art Unit: 2659 Application/Control Number: 17/934,583 Page 19 Art Unit: 2659 Application/Control Number: 17/934,583 Page 20 Art Unit: 2659 Application/Control Number: 17/934,583 Page 21 Art Unit: 2659 Application/Control Number: 17/934,583 Page 22 Art Unit: 2659 Application/Control Number: 17/934,583 Page 23 Art Unit: 2659
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