Notice of Pre-AIA or AIA Status
1. 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
2. This Office Action is in response to the filing with the office dated 09/24/2024.
Claims 1, 9 and 17 are independent claims, Claims 1-20 are presented in this office action.
Priority
3. Applicant’s claim for the benefit of a prior-filed Indian Application No. IN202441058694 filed on 08/02/2024 is acknowledged by the examiner.
Response to amendment/arguments
4. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 101 as the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, have been fully considered. However, Examiner respectfully disagrees with the applicant’s argument. See response to arguments section. The rejection has been maintained.
5. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 102 (a)(i) and 103(a) have been fully considered but are moot in view of the new grounds of rejection, thus necessitated the new ground of rejection as presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Response to 101 rejection
6. Applicant’s arguments on page 9 states “Applicant respectfully submits that such operations, particularly with regard to the detailed requirements in the steps are above, are not something that could be performed by a human mentally or with a pen and paper. Applicant is not merely reciting e.g. the categorization of data, but instead a specific set of structures and operations via those structures that produce a specific output achievable through the use of a computer system according to various embodiments. Thus, Applicant submits claim 1 (and the other independent claims, for similar reasons) are not abstract under Step 2A, part 1.
Examiner respectfully disagrees as the amended limitations “"a first output of the classifier module includes a set of crisp entities associated with the crisp entity category and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category" and "feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities." are all processes, that under broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. These limitations are essentially steps of generating and manipulating data at a high level of generality, which can be performed by a person using a computer as a tool. These limitations, at the high level of generality as drafted, would encompass a user to determine the entities in the unstructured data that breaks down the text into tokens to generate tagged data such as log data, text data and categorize them based on predefined categories and those categories are processed by different models. The hazy (unknown) category is labelled using the model to train the unlabeled entities based on user review/ annotation, feeding back the annotations/ review/ tags/ label by the user to the model to associate them with the crisp (known entities) in just using the model to train the entities, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Applicant further submits that under Step 2A, part 2, the amended claims are also integrated into a practical application, and thus are also patent eligible under that portion of the Section 101 framework.
Examiner respectfully disagrees and additional components in the form of “computer”, “system”, “memory”, :processor”, “computer-readable storage medium” are recited at a high level of generality as generic computer components. These additional elements amount to nothing more than mere instructions to apply the recited abstract idea on a computer, under MPEP 2106.05(f). Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106; See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Pursuant to Step 1, Claims 17-20 recite a computer-readable storage medium, which are directed to a manufacture. Claims 9-16 recite system which are directed to the statutory category of a machine.
Regarding Claims 1, 9 and 17 Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Under the 2019 PEG, claims are deemed to be directed to an abstract idea if they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Here, claims 1, 9 and 17 are directed to an abstract idea categorized under mental processes. Courts consider a mental process if it “can be performed in the human mind, or by a human using a pen and paper.” MPEP 2016(a)(2)(III). Courts also consider a mental process as one that can be performed in the human mind and is merely using a computer as a tool to perform the concept. MPEP 2016(a)(2)(III)(C)(3). Claims 1, 9 and 17 recites a mental process because the recited steps recite the actions of performing, receiving unstructured data and manipulating the data and providing structured sentence based on unstructured data. See MPEP 2106(a)(2)(III). For example, claims 1, 9 and 17 recite limitations of “determining and tagging entity categories…”, “determining clusters…”, “converting….”are all processes, that under broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. These limitations are essentially steps of generating and manipulating data at a high level of generality, which can be performed by a person using a computer as a tool. These limitations, at the high level of generality as drafted, would encompass a user to determine the entities in the unstructured data that breaks down the text into tokens to generate tagged data such as log data, text data and categorize them based on the reference data. Abstracting the entities to a higher level entity and determine the cluster based on predefined number of clusters for the tagged data and converting the clusters to a structured sentence which is mentally performable as an evaluation or judgement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the recited abstract idea is integrated into a practical application. In this case, as explained above, claims 1, 17, 18 and 19 merely recite a mental process. These limitations of “receiving….”, providing…”, “feeding the output to the classifier….” and additional components in the form of “computer”, “system”, “memory”, :processor”, “computer-readable storage medium” are recited at a high level of generality as generic computer components. These additional elements amount to nothing more than mere instructions to apply the recited abstract idea on a computer, under MPEP 2106.05(f). The additional elements “searching….” amount to mere data gathering which is insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and data gathering of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recitation of generic computing components is still mere instructions to apply the exception under MPEP 2106.05(f) and does not provide significantly more. The “receiving….”, providing…” elements that was identified as insignificant extra-solution activity as mere data gathering and outputting when re-evaluated still does not provide significantly more, since this generic data gathering steps. Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract.
Regarding claims 2, 10 and 18 recite “clusters are determined using an unsupervised clustering model” which depends on the same abstract idea as claim 1. This limitations, include additional element “using a model” is an insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and data gathering of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding claims 3, 11 and 19 recite “unstructured data is log data….” which depends on the same abstract idea as claim 1. This limitations, include additional element “using a model” is an insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and data gathering of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding claims 4, 12 and 20 recite “pre-defined categories associated with the remote system are associated with a domain environment of the remote system that comprises device names in the remote system” which depends on the same abstract idea as claim 1. This limitations, include additional element “using a model” is an insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and data gathering of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding claims 5 and 13 recite “wherein the classifier module is executed concurrently…” which depends on the same abstract idea as claim 1. This limitations, include additional element “using a model” is an insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and data gathering of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding claims 6, 7, 8, 14, 15 and 16 recite “iteratively re-clustering the clusters …” which depends on the same abstract idea as claim 1. This limitations, include additional element “using a model” is an insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and data gathering of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim Rejections - 35 U.S.C. § 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.
8. Claims 1- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yoon; Jae Young (US 20180102938 A1) in view of Jang; Dongwook (US 20250200428 A1) a further view of Zhang; Yuxuan (US 20250342406 A1).
Regarding independent claim 1, Yoon; Jae Young (US 20180102938 A1) teaches, a computer-implemented method comprising: receiving, at a computer system, unstructured data generated by a remote system (Paragraph [0062] discloses receiving unstructured data (Examiner interprets log data as unstructured data generated by different devices). Also see [0066], [0067]);
determining and tagging, by the computer system, entity categories in the unstructured data to generate tagged data, wherein the entity categories comprise a crisp entity category that [[are]] is associated with a plurality of pre-defined categories associated with the remote system (Paragraph [0062] log” data and/or log messages. A log message can include a set of log data that is configured to be written to a log (e.g., in a time-ordered and/or real-time manner). Log data may include multiple components that each correspond to a field. Log data may include one or more field tags that identify a field and/or one or more field values that include a value for a particular field. A log message may include (for example) a record from an event log, a transaction log, or a message log. Also see Paragraph [0099], [0122]);
converting, by the computer system, the entity categories to a set of key-value pairs, wherein individual ones of the set of key-value pairs comprise a respective key corresponding to a type of activity or event associated with the unstructured data and a respective value indicative of a characteristic of the type of activity or event; providing, by the computer system, the key-value pairs to a classifier module that is configured to tokenize the tagged data (Fig. 7, Paragraph [0089] discloses providing key-value pairs to tokenize the data such as “Power: ON”, such that “ON” is a value for a power field. Also see Paragraph [0137]);
wherein based on the providing, a first output of the classifier module includes a set of crisp entities associated with the crisp entity category; converting the clusters to a structured sentence based on the unstructured data; and providing the structured sentence to a user interface (Fig. 7, Paragraph [0137] For example, FIG. 7B shows an example presentation of an updated interface presented in response to user input corresponding to a selection of the “out” variable of the first representative message in FIG. 7A. FIG. 7B indicates that, for this particular variable component, two values were represented in the cluster: “out” or “in”. The count column indicates that 16237 messages in the cluster included the “out” value, while only 384 included the “in” value).
Yoon et al fails to explicitly teach, wherein the entity categories comprise a crisp entity category that is associated with a plurality of pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories; a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data.
Jang; Dongwook (US 20250200428 A1) teaches, determining and tagging entity categories in the unstructured data to generate tagged data (Paragraph [0033] For example, if the input data element is in textual format, the transformation may be performed based on tokenization that breaks down the text into tokens), wherein the entity categories comprise a crisp entity category that are associated with pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories (Fig. 3,12 Paragraph [0039] The present disclosure determines the representative samples from a large imperfectly labeled dataset that may further be used to support inference and/or subsequent processing, such as assigning a new input data set to a cluster and/or processing it accordingly. The system may access data samples and the respective reference labels from a data set. The data samples may be preprocessed to generate embedded vectors or encoded representations. For each label, clustering is performed to group at least some of the embedded vectors together into clusters based on the associated inherent patterns. The clusters are refined to select the related samples from the clustered patterns. If a cluster is found not belonging to a given label from the set of reference labels, the cluster is dropped (Based upon specification Paragraph [0011] known entities ("crisp" entities) and unknown entities ("hazy" entities). Therefore Examiner interprets labels that are from a set of reference labels are crisp entity and labels not belonging to the set of reference labels are hazy entities). Also see Paragraph [0004]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by providing wherein the entity categories comprise a crisp entity category that are associated with pre-defined categories associated with the remote system., as taught by Jang et al (Paragraphs [0039]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the prediction of the machine-learning model deploying representative embedded vectors can further be improved by utilizing a nil dataset to find a probability threshold for each label, where nil dataset refers to the data samples that do not belong to the given marked labels from the set of reference labels as taught by Jang et al (Paragraphs [0039]).
Yoon et al and Jang et al fails to explicitly teach, a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data.
Zhang; Yuxuan (US 20250342406 A1) teaches, wherein based on the providing, a first output of the classifier module includes a set of crisp entities associated with the crisp entity category and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category (Fig. 3 Paragraphs [0057], [0058] discloses, for all entity related information in the repository, entity uncertainty is determined (Examiner interprets that based on uncertainty determination/ assessment, crisp and hazy entities are determined). Also see [0082]);
feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data (Paragraphs [0035]- [0039] discloses, the output parameters from uncertainty categories are fed back to the machine learning model as input to train the machine learning model. The machine learning model can update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction and reference feedback information (e.g., user indication of accuracy, reference labels, or other information).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al and Jang et al by providing a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data, as taught by Zhang et al (Paragraphs [0035]-[0039], [0057], [0058]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, The system can process complex documents containing multiple entities and extract relevant information with high accuracy while maintaining full traceability and explainability of results. By leveraging advanced prompt engineering and document segmentation techniques, the system can identify and isolate content relevant to specific entities within multi-entity documents, eliminating confusion and conflicting information that plague conventional approaches as taught by Zhang et al (Paragraphs [0018], [0019]).
Regarding dependent claim 2, Yoon et al, Jang et al and Zhang et al teach, the method of claim 1.
Yoon et al further teaches, wherein the clusters are determined using an unsupervised clustering model Paragraph [0009] Each of one, more or all of the machine-generated data records being used to identify clusters and/or being assigned to clusters can include (in part or in its entirety) unstructured data, which does not have a pre-defined data model or schema (Examiner interprets an unsupervised clustering model as a type of machine learning algorithm that groups unlabeled data points into clusters based on similarity without any prior knowledge of cluster assignments. It aims to discover hidden patterns and structures within the data by identifying natural groupings)).
Jang et al also further teaches, wherein the clusters are determined using an unsupervised clustering model (Paragraph [0040] Clustering may be performed using one or more clustering techniques such as K-means, DBSCAN, hierarchical clustering, Gaussian mixture models etc. The quality of the clusters may be assessed in a refinement process).
Regarding dependent claim 3, Yoon et al, Jang et al and Zhang et al teach, the method of claim 1.
Yoon et al further teaches, wherein the unstructured data is log data (Paragraph [0105] ingest system 410 can receive one or more log messages from one or more data sources 420. The one or more log messages may include unstructured data. Also see Paragraph [0066]), and the remote system is a distributed information technology system that generates the unstructured data (Paragraph [0173] FIG. 9 depicts a simplified diagram of a distributed system 900 for implementing some embodiments. In the illustrated embodiment, distributed system 900 includes one or more client computing devices 902, 904, 906, and 908, which are configured to execute and operate a client application such as a web browser, proprietary client (e.g., Oracle Forms), or the like over one or more network(s) 910. Server 912 may be communicatively coupled with remote client computing devices 902, 904, 906, and 908 via network 910).
Jang et al also further teaches, wherein the unstructured data is log data (Paragraph [0043] As an illustrative example, if the input data samples are considered as text strings (e.g., log messages, webpages, articles)), and the remote system is a distributed information technology system that generates the unstructured data (Figs 9, 10 Paragraph [0067] discloses, distributed client systems that are located remotely).
Regarding dependent claim 4, Yoon et al, Jang et al and Zhang et al teach, the method of claim 1.
Yoon et al further teaches, wherein the pre-defined categories associated with the remote system are associated with a domain environment of the remote system that comprises device names in the remote system (Paragraph [0009] A machine-generated data record can include any collection of data, such as a log message, a device communication, or a digital file. Each of one, more or all of the machine-generated data records being used to identify clusters and/or being assigned to clusters can include (in part or in its entirety) unstructured data, which does not have a pre-defined data model or schema. Also see Paragraph [0062]).
Jang et al further teaches, wherein the pre-defined categories associated with the remote system are associated with a domain environment of the remote system (Paragraph [0043] discloses, pre-defined categories associated with a domain environment of the remote system).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by providing the pre-defined categories associated with the remote system are associated with a domain environment of the remote system, as taught by Jang et al (Paragraphs [0043]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, these techniques aim to find a balance between maximizing clustering separation and minimizing cluster size while considering domain-specific knowledge when available as taught by Jang et al (Paragraphs [0040]).
Regarding dependent claim 5, Yoon et al, Jang et al and Zhang et al teach, the method of claim 1.
Yoon et al further teaches, wherein the classifier module is executed concurrently on the tagged data with an unsupervised clustering model that generates the clusters (Paragraph [0124], [0125] discloses, classifying the tagged data using unsupervised model to generate clusters. Also see Paragraph [0009]).
Jang et al also further teaches, wherein the classifier module is executed concurrently on the tagged data with an unsupervised clustering model that generates the clusters (Paragraph [0051] discloses, classifying the tagged data using unsupervised model such as K-Means, DBSCAN, hierarchical clustering to generate clusters).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by providing wherein the classifier module is executed concurrently on the tagged data with an unsupervised clustering model that generates the clusters, as taught by Jang et al (Paragraphs [0051]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, these techniques aim to find a balance between maximizing clustering separation and minimizing cluster size while considering domain-specific knowledge when available as taught by Jang et al (Paragraphs [0040]).
Regarding dependent claim 6, Yoon et al, Jang et al and Zhang et al teach, the method of claim 1.
Jang et al further teaches, further comprising iteratively re-clustering the clusters in the tagged data to a minimum count of clusters (Paragraph [0118] discloses, iterative re-clustering of clusters to a predefined count of clusters. Also see Paragraph [0011], [0049], [0061], [0064]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by further comprising iteratively re-clustering the clusters in the tagged data to a minimum count of clusters, as taught by Jang et al (Paragraphs [0118).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, unsupervised learning models (e.g., K-Means clustering, hierarchical clustering, dimensionality reduction models), semi-supervised learning models (e.g., self-training, label propagation, and co-training), transfer learning models utilizing pre-trained models on large datasets and fine-tuned with the representative examples, ensemble models combining multiple base models to improve predictive performances taught by Jang et al (Paragraphs [0049]).
Regarding dependent claim 7, Yoon et al, Jang et al and Zhang et al teach, the method of claim 6.
Jang et al further teaches, wherein the re-clustering compares a number of entities in the cluster with a density threshold and the method further comprises: tagging a cluster with the number of entities that are in excess of the density threshold may correspond as an outlier entity (Paragraph [0118] discloses, iterative re-clustering of clusters to a predefined count of clusters using a clustering 120 technique such as k-means clustering, adaptive k-means clustering, DBSCAN, or agglomerative clustering etc. Also see Paragraph [0042], [0058]).
Regarding dependent claim 8, Yoon et al, Jang et al and Zhang et al teach, the method of claim 6.
Jang et al further teaches, wherein the re-clustering generates clusters based on a pre-determined cluster density value (Paragraph [0003] The number of clusters for each marked label may be either a predefined number selected by a user after observing the patterns in embedded vectors or it may be selected deploying one or more techniques that statistically determine the number of clusters. Subsequently, a set of clusters are generated for each marked label resulting in assigning the embedded vectors of each of at least some data samples in the subset to a cluster.
Regarding independent claim 9, Yoon; Jae Young (US 20180102938 A1) teaches, a system comprising: a memory; and a processor that are configured to execute machine readable instructions stored in the memory for causing the processor (Paragraph [0219]) to: receive unstructured data generated by a remote system (Paragraph [0062] discloses receiving unstructured data (Examiner interprets log data as unstructured data generated by different devices). Also see [0066], [0067]);
determine and tag entity categories in the unstructured data to generate tagged data (Paragraph [0062] Log data may include one or more field tags that identify a field and/or one or more field values that include a value for a particular field. Also see Paragraph [0099], [0122]);
convert the entity categories to a set of key-value pairs, wherein individual ones of the set of key-value pairs comprise a respective key corresponding to a type of activity or event associated with the unstructured data and a respective value indicative of a characteristic of the type of activity or event; provide the key-value pairs to a classifier module that is configured to tokenize the tagged data (Fig. 7, Paragraph [0089] discloses providing key-value pairs to tokenize the data such as “Power: ON”, such that “ON” is a value for a power field. Also see Paragraph [0137]);
wherein based on the providing, a first output of the classifier module(Fig. 7, Paragraph [0137] For example, FIG. 7B shows an example presentation of an updated interface presented in response to user input corresponding to a selection of the “out” variable of the first representative message in FIG. 7A. FIG. 7B indicates that, for this particular variable component, two values were represented in the cluster: “out” or “in”. The count column indicates that 16237 messages in the cluster included the “out” value, while only 384 included the “in” value).
Yoon et al fails to explicitly teach, , wherein the entity categories comprise a crisp entity category that is associated with a plurality of pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories; and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data.
Jang; Dongwook (US 20250200428 A1) teaches, determining and tagging entity categories in the unstructured data to generate tagged data (Paragraph [0033] For example, if the input data element is in textual format, the transformation may be performed based on tokenization that breaks down the text into tokens), wherein the entity categories comprise a crisp entity category that is associated with a plurality of pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories (Fig. 3,12 Paragraph [0039] The present disclosure determines the representative samples from a large imperfectly labeled dataset that may further be used to support inference and/or subsequent processing, such as assigning a new input data set to a cluster and/or processing it accordingly. The system may access data samples and the respective reference labels from a data set. The data samples may be preprocessed to generate embedded vectors or encoded representations. For each label, clustering is performed to group at least some of the embedded vectors together into clusters based on the associated inherent patterns. The clusters are refined to select the related samples from the clustered patterns. If a cluster is found not belonging to a given label from the set of reference labels, the cluster is dropped (Based upon specification Paragraph [0011] known entities ("crisp" entities) and unknown entities ("hazy" entities). Therefore Examiner interprets labels that are from a set of reference labels are crisp entity and labels not belonging to the set of reference labels are hazy entities). Also see Paragraph [0004]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by providing wherein the entity categories comprise a crisp entity category that are associated with pre-defined categories associated with the remote system., as taught by Jang et al (Paragraphs [0039]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the prediction of the machine-learning model deploying representative embedded vectors can further be improved by utilizing a nil dataset to find a probability threshold for each label, where nil dataset refers to the data samples that do not belong to the given marked labels from the set of reference labels as taught by Jang et al (Paragraphs [0039]).
Yoon et al and Jang et al fails to explicitly teach, and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data.
Zhang; Yuxuan (US 20250342406 A1) teaches, wherein based on the providing, a first output of the classifier module includes a set of crisp entities associated with the crisp entity category and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category (Fig. 3 Paragraphs [0057], [0058] discloses, for all entity related information in the repository, entity uncertainty is determined (Examiner interprets that based on uncertainty determination/ assessment, crisp and hazy entities are determined). Also see [0082]);
feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data (Paragraphs [0035]- [0039] discloses, the output parameters from uncertainty categories are fed back to the machine learning model as input to train the machine learning model. The machine learning model can update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction and reference feedback information (e.g., user indication of accuracy, reference labels, or other information).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al and Jang et al by providing a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data, as taught by Zhang et al (Paragraphs [0035]-[0039], [0057], [0058]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, The system can process complex documents containing multiple entities and extract relevant information with high accuracy while maintaining full traceability and explainability of results. By leveraging advanced prompt engineering and document segmentation techniques, the system can identify and isolate content relevant to specific entities within multi-entity documents, eliminating confusion and conflicting information that plague conventional approaches as taught by Zhang et al (Paragraphs [0018], [0019]).
Regarding dependent claim 10, Yoon et al, Jang et al and Zhang et al teach, the system of claim 9.
Yoon et al further teaches, wherein the clusters are determined using an unsupervised clustering model (Paragraph [0009] Each of one, more or all of the machine-generated data records being used to identify clusters and/or being assigned to clusters can include (in part or in its entirety) unstructured data, which does not have a pre-defined data model or schema (Examiner interprets an unsupervised clustering model as a type of machine learning algorithm that groups unlabeled data points into clusters based on similarity without any prior knowledge of cluster assignments. It aims to discover hidden patterns and structures within the data by identifying natural groupings)).
Jang et al also further teaches, wherein the clusters are determined using an unsupervised clustering model (Paragraph [0040] Clustering may be performed using one or more clustering techniques such as K-means, DBSCAN, hierarchical clustering, Gaussian mixture models etc. The quality of the clusters may be assessed in a refinement process).
Regarding dependent claim 11, Yoon et al, Jang et al and Zhang et al teach, the system of claim 9.
Yoon et al further teaches, wherein the unstructured data is log data (Paragraph [0105] ingest system 410 can receive one or more log messages from one or more data sources 420. The one or more log messages may include unstructured data. Also see Paragraph [0066]), and the remote system is a distributed information technology system that generates the unstructured data (Paragraph [0173] FIG. 9 depicts a simplified diagram of a distributed system 900 for implementing some embodiments. In the illustrated embodiment, distributed system 900 includes one or more client computing devices 902, 904, 906, and 908, which are configured to execute and operate a client application such as a web browser, proprietary client (e.g., Oracle Forms), or the like over one or more network(s) 910. Server 912 may be communicatively coupled with remote client computing devices 902, 904, 906, and 908 via network 910).
Jang et al also further teaches, wherein the unstructured data is log data (Paragraph [0043] As an illustrative example, if the input data samples are considered as text strings (e.g., log messages, webpages, articles)), and the remote system is a distributed information technology system that generates the unstructured data (Figs 9, 10 Paragraph [0067] discloses, distributed client systems that are located remotely).
Regarding dependent claim 12, Yoon et al, Jang et al and Zhang et al teach, the system of claim 9.
Yoon et al further teaches, wherein the pre-defined categories associated with the remote system are associated with a domain environment of the remote system that comprises device names in the remote system (Paragraph [0009] A machine-generated data record can include any collection of data, such as a log message, a device communication, or a digital file. Each of one, more or all of the machine-generated data records being used to identify clusters and/or being assigned to clusters can include (in part or in its entirety) unstructured data, which does not have a pre-defined data model or schema. Also see Paragraph [0062]).
Jang et al further teaches, wherein the pre-defined categories associated with the remote system are associated with a domain environment of the remote system (Paragraph [0043] discloses, pre-defined categories associated with a domain environment of the remote system).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by providing the pre-defined categories associated with the remote system are associated with a domain environment of the remote system, as taught by Jang et al (Paragraphs [0043]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, these techniques aim to find a balance between maximizing clustering separation and minimizing cluster size while considering domain-specific knowledge when available as taught by Jang et al (Paragraphs [0040]).
Regarding dependent claim 13, Yoon et al, Jang et al and Zhang et al teach, the system of claim 9.
Yoon et al further teaches, wherein the classifier module is executed concurrently on the tagged data with an unsupervised clustering model that generates the clusters (Paragraph [0124], [0125] discloses, classifying the tagged data using unsupervised model to generate clusters. Also see Paragraph [0009]).
Jang et al also further teaches, wherein the classifier module is executed concurrently on the tagged data with an unsupervised clustering model that generates the clusters (Paragraph [0051] discloses, classifying the tagged data using unsupervised model such as K-Means, DBSCAN, hierarchical clustering to generate clusters).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by providing wherein the classifier module is executed concurrently on the tagged data with an unsupervised clustering model that generates the clusters, as taught by Jang et al (Paragraphs [0051]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, these techniques aim to find a balance between maximizing clustering separation and minimizing cluster size while considering domain-specific knowledge when available as taught by Jang et al (Paragraphs [0040]).
Regarding dependent claim 14, Yoon et al, Jang et al and Zhang et al teach, the system of claim 9.
Jang et al further teaches, further comprising iteratively re-clustering the clusters in the tagged data to a minimum count of clusters (Paragraph [0118] discloses, iterative re-clustering of clusters to a predefined count of clusters. Also see Paragraph [0011, [0061], [0064]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by further comprising iteratively re-clustering the clusters in the tagged data to a minimum count of clusters, as taught by Jang et al (Paragraphs [0118).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, unsupervised learning models (e.g., K-Means clustering, hierarchical clustering, dimensionality reduction models), semi-supervised learning models (e.g., self-training, label propagation, and co-training), transfer learning models utilizing pre-trained models on large datasets and fine-tuned with the representative examples, ensemble models combining multiple base models to improve predictive performances taught by Jang et al (Paragraphs [0049]).
Regarding dependent claim 15, Yoon et al, Jang et al and Zhang et al teach, the system of claim 14.
Jang et al further teaches, wherein the re-clustering compares a number of entities in the cluster with a density threshold and the processor is further to: tag a cluster with the number of entities that are in excess of the density threshold may correspond as an outlier entity (Paragraph [0118] discloses, iterative re-clustering of clusters to a predefined count of clusters using a clustering 120 technique such as k-means clustering, adaptive k-means clustering, DBSCAN, or agglomerative clustering etc. Also see Paragraph [0042], [0058]).
Regarding dependent claim 16, Yoon et al, Jang et al and Zhang et al teach, the system of claim 14.
Jang et al further teaches, wherein the re-clustering generates clusters based on a pre-determined cluster density value (Paragraph [0042] For each label, one or more embeddings are selected within each cluster deploying method such as selecting embeddings that are closest to the center of the cluster in terms of cosine similarity or another distance metric. The other selection methods may include choosing the centroid vector of each cluster as representative embedded vector, randomly sampling a fixed number of embedded vectors from each cluster, using density-based criteria to select vectors focusing on areas with a high density of embedded vectors within the cluster. Also see Paragraph [0058]).
Regarding independent claim 17, Yoon; Jae Young (US 20180102938 A1) teaches, a non-transitory computer-readable storage medium storing a plurality of instructions executable by a processor, the plurality of instructions when executed by the processor (Paragraph [0219]) cause the processor to: receive unstructured data generated by a remote system (Paragraph [0062] discloses receiving unstructured data (Examiner interprets log data as unstructured data generated by different devices). Also see [0066], [0067]);
determine and tag entity categories in the unstructured data to generate tagged data (Paragraph [0062] Log data may include one or more field tags that identify a field and/or one or more field values that include a value for a particular field. Also see Paragraph [0099], [0122]);
convert the entity categories to a set of key-value pairs, wherein individual ones of the set of key-value pairs comprise a respective key corresponding to a type of activity or event associated with the unstructured data and a respective value indicative of a characteristic of the type of activity or event; provide the key-value pairs to a classifier module that is configured to tokenize the tagged data(Fig. 7, Paragraph [0089] discloses providing key-value pairs to tokenize the data such as “Power: ON”, such that “ON” is a value for a power field. Also see Paragraph [0137]);
wherein based on the providing, a first output of the classifier module includes a set of crisp entities associated with the crisp entity category; convert the clusters to a structured sentence based on the unstructured data; and provide the structured sentence to a user interface (Fig. 7, Paragraph [0137] For example, FIG. 7B shows an example presentation of an updated interface presented in response to user input corresponding to a selection of the “out” variable of the first representative message in FIG. 7A. FIG. 7B indicates that, for this particular variable component, two values were represented in the cluster: “out” or “in”. The count column indicates that 16237 messages in the cluster included the “out” value, while only 384 included the “in” value).
Yoon et al fails to explicitly teach, wherein the entity categories comprise a crisp entity category that is associated with a plurality of pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories; and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data.
Jang; Dongwook (US 20250200428 A1) teaches, determining and tagging entity categories in the unstructured data to generate tagged data (Paragraph [0033] For example, if the input data element is in textual format, the transformation may be performed based on tokenization that breaks down the text into tokens), wherein the entity categories comprise a crisp entity category that are associated with pre-defined categories associated with the remote system and a hazy entity category that excludes the pre-defined categories (Fig. 3,12 Paragraph [0039] The present disclosure determines the representative samples from a large imperfectly labeled dataset that may further be used to support inference and/or subsequent processing, such as assigning a new input data set to a cluster and/or processing it accordingly. The system may access data samples and the respective reference labels from a data set. The data samples may be preprocessed to generate embedded vectors or encoded representations. For each label, clustering is performed to group at least some of the embedded vectors together into clusters based on the associated inherent patterns. The clusters are refined to select the related samples from the clustered patterns. If a cluster is found not belonging to a given label from the set of reference labels, the cluster is dropped (Based upon specification Paragraph [0011] known entities ("crisp" entities) and unknown entities ("hazy" entities). Therefore Examiner interprets labels that are from a set of reference labels are crisp entity and labels not belonging to the set of reference labels are hazy entities). Also see Paragraph [0004]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al by providing wherein the entity categories comprise a crisp entity category that are associated with pre-defined categories associated with the remote system., as taught by Jang et al (Paragraphs [0039]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the prediction of the machine-learning model deploying representative embedded vectors can further be improved by utilizing a nil dataset to find a probability threshold for each label, where nil dataset refers to the data samples that do not belong to the given marked labels from the set of reference labels as taught by Jang et al (Paragraphs [0039]).
Yoon et al and Jang et al fails to explicitly teach, a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data.
Zhang; Yuxuan (US 20250342406 A1) teaches, wherein based on the providing, a first output of the classifier module includes a set of crisp entities associated with the crisp entity category and a second output of the classifier module includes a set of hazy entities associated with the hazy entity category (Fig. 3 Paragraphs [0057], [0058] discloses, for all entity related information in the repository, entity uncertainty is determined (Examiner interprets that based on uncertainty determination/ assessment, crisp and hazy entities are determined). Also see [0082]);
feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data (Paragraphs [0035]- [0039] discloses, the output parameters from uncertainty categories are fed back to the machine learning model as input to train the machine learning model. The machine learning model can update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction and reference feedback information (e.g., user indication of accuracy, reference labels, or other information).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Yoon et al and Jang et al by providing a second output of the classifier module includes a set of hazy entities associated with the hazy entity category; feeding back the second output of the classifier module including the set of hazy entities to an unstructured data analyzer module that is configured to output newly tagged data that includes one or more new tags for the set of hazy entities, wherein the one or more new tags corresponds to the set of crisp entities; determining, by the computer system, clusters in the tagged data and the newly tagged data, as taught by Zhang et al (Paragraphs [0035]-[0039], [0057], [0058]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, The system can process complex documents containing multiple entities and extract relevant information with high accuracy while maintaining full traceability and explainability of results. By leveraging advanced prompt engineering and document segmentation techniques, the system can identify and isolate content relevant to specific entities within multi-entity documents, eliminating confusion and conflicting information that plague conventional approaches as taught by Zhang et al (Paragraphs [0018], [0019]).
Regarding dependent claim 18, Yoon et al, Jang et al and Zhang et al teach, the non-transitory computer-readable storage medium of claim 17.
Yoon et al further teaches, wherein the clusters are determined using an unsupervised clustering model (Paragraph [0009] Each of one, more or all of the machine-generated data records being used to identify clusters and/or being assigned to clusters can include (in part or in its entirety) unstructured data, which does not have a pre-defined data model or schema (Examiner interprets an unsupervised clustering model as a type of machine learning algorithm that groups unlabeled data points into clusters based on similarity without any prior knowledge of cluster assignments. It aims to discover hidden patterns and structures within the data by identifying natural groupings)).
Jang et al also further teaches, wherein the clusters are determined using an unsupervised clustering model (Paragraph [0040] Clustering may be performed using one or more clustering techniques such as K-means, DBSCAN, hierarchical clustering, Gaussian mixture models etc. The quality of the clusters may be assessed in a refinement process).
Regarding dependent claim 19, Yoon et al, Jang et al and Zhang et al teach, the non-transitory computer-readable storage medium of claim 17.
Yoon et al further teaches, wherein the unstructured data is log data (Paragraph [0105] ingest system 410 can receive one or more log messages from one or more data sources 420. The one or more log messages may include unstructured data. Also see Paragraph [0066]), and the remote system is a distributed information technology system that generates the unstructured data (Paragraph [0173] FIG. 9 depicts a simplified diagram of a distributed system 900 for implementing some embodiments. In the illustrated embodiment, distributed system 900 includes one or more client computing devices 902, 904, 906, and 908, which are configured to execute and operate a client application such as a web browser, proprietary client (e.g., Oracle Forms), or the like over one or more network(s) 910. Server 912 may be communicatively coupled with remote client computing devices 902, 904, 906, and 908 via network 910).
Jang et al also further teaches, wherein the unstructured data is log data (Paragraph [0043] As an illustrative example, if the input data samples are considered as text strings (e.g., log messages, webpages, articles)), and the remote system is a distributed information technology system that generates the unstructured data (Figs 9, 10 Paragraph [0067] discloses, distributed client systems that are located remotely).
Regarding dependent claim 20, Yoon et al, Jang et al and Zhang et al teach, the non-transitory computer-readable storage medium of claim 17.
Yoon et al further teaches, wherein the pre-defined categories associated with the remote system are associated with a domain environment of the remote system that comprises device names in the remote system (Paragraph [0009] A machine-generated data record can include any collection of data, such as a log message, a device communication, or a digital file. Each of one, more or all of the machine-generated data records being used to identify clusters and/or being assigned to clusters can include (in part or in its entirety) unstructured data, which does not have a pre-defined data model or schema. Also see Paragraph [0062]).
Jang et al further teaches, wherein the pre-defined categories associated with the remote system are associated with a domain environment of the remote system (Paragraph [0043] discloses, pre-defined categories associated with a domain environment of the remote system).
Closest Prior Art
9. The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Saxena; Udit (US 20230315558 A1) teaches, Clustering structured log data by key-values includes receiving, via a user interface, a request to apply an operator to cluster log messages according to values for keys associated with the request. At least a portion of each log message comprises structured machine data including a set of key-value pairs. The method further includes receiving a log message and determining whether to include the log message in a cluster based at least in part on an evaluation of values in the structured machine data of the log message for the keys associated with the request. The cluster is included in a set of clusters. Each cluster in the set is associated with a different combination of values for the keys associated with the request. The method further includes providing, via the user interface, information associated with the cluster.
10. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968))).
Conclusion
Applicant’s amendments/Arguments necessitated new grounds of rejection as presented in this office action. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUMAN RAJAPUTRA whose telephone number is (571) 272-4669. The examiner can normally be reached between 8:00 AM - 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi (571) 272-4078 can be reached. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S. R./
Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163