Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The instant application having Application No. 17573498 has a total of 20 claims pending in the application.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Claim 12 is a machine type claim. Claim 18 is a manufacture type claim. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“detecting attributes and values in rules contained in a rules set, wherein the attributes comprise condition attributes and result attributes” A user mentally or with pencil and paper looks at rules from a rules set.
“determining definitions of the attributes detected in the rules from a data model that includes data objects with attributes that map to the attributes in the rules of the rules set” The user mentally or with pencil and paper identifies attributes in the rules using a data model associated with the rules and matches up the rules to the appropriate attributes.
“generating, as input data … an unlabeled dataset comprising multiple different unlabeled data entries having fields associated with the condition attributes detected in the rules and containing data values associated with the condition attributes, the generating of the unlabeled dataset comprising:” The user mentally or with pencil and paper creates the appropriate fields with values and attributes.
“determining data values for the fields of the unlabeled data entries based on the definitions of the condition attributes determined from the data model and a value appearing in a condition of the rules that refers to a condition attribute of the rules, wherein determining data values comprises selecting a data value that satisfies the condition of the rules” The user mentally or with pencil and paper looks at the rules and the data and determines appropriate values for them.
“populating the fields associated with the condition attributes with the data values such that execution of the rules set on the unlabeled data entries … identifies matching rules in the rules set” The user mentally or with pencil and paper fills in the appropriate fields with values and attributes taken from the rule set.
“forming a labeled dataset using the unlabeled data entries and logic contained in the rules set such that the labeled dataset comprises multiple different labeled data entries comprising fields and data values of the unlabeled data entries and labels, wherein the forming comprises” The user mentally or with pencil and paper creates and labels a dataset using the rule set.
“Executing the rules on the unlabeled data entries … to identify matching rules” The user mentally or with pencil and paper labels the unlabeled data using the established rules.
“adding values of result attributes of the matching rules as the labels to the unlabeled data entries to form the labeled data entries” The user mentally or with pencil and paper labels the unlabeled data using the established rules.
“forming a training dataset from at least a portion of the labeled dataset” The user mentally or with pencil and paper takes data they think might be useful for improving their model.
“training a … model by applying the training dataset to the … model” The user mentally or with pencil and paper works out the mathematics to make their model operate.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“computer” (mere instructions to apply the exception using a generic computer component);
“a rules engine configured to execute the rules set”, “a machine learning model”, “The rules engine” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The claimed machine learning model is a generic machine learning model with no details or limitations that make it beyond a generic, off the shelf machine learning model.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“computer” (mere instructions to apply the exception using a generic computer component)
“a rules engine configured to execute the rules set”, “a machine learning model”, “The rules engine” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The claimed machine learning model is a generic machine learning model with no details or limitations that make it beyond a generic, off the shelf machine learning model.
As per claims 2-7 contain additional mental steps similar to claim 1, and are rejected similarly to claim 1.
As per claims 8-11, these claims contain additional mental steps and generic machine learning steps similar to claim 1, and are rejected for similar reasons to claim 1.
As per claim 12,
2A Prong 1:
detecting attributes and values in rules contained in a rules set, wherein the attributes comprise condition attributes and result attributes” A user mentally or with pencil and paper looks at rules from a rules set.
“determining definitions of the attributes detected in the rules from a data model that includes data objects with attributes that map to the attributes in the rules of the rules set” The user mentally or with pencil and paper identifies attributes in the rules using a data model associated with the rules and matches up the rules to the appropriate attributes.
“generating, as input data … an unlabeled dataset comprising multiple different unlabeled data entries having fields associated with the condition attributes detected in the rules and containing data values associated with the condition attributes, the generating of the unlabeled dataset comprising:” The user mentally or with pencil and paper creates the appropriate fields with values and attributes.
“determining data values for the fields of the unlabeled data entries based on the definitions of the condition attributes determined from the data model and a value appearing in a condition of the rules that refers to a condition attribute of the rules, wherein determining data values comprises selecting a data value that satisfies the condition of the rules” The user mentally or with pencil and paper looks at the rules and the data and determines appropriate values for them.
“populating the fields associated with the condition attributes with the data values such that execution of the rules set on the unlabeled data entries … identifies matching rules in the rules set” The user mentally or with pencil and paper fills in the appropriate fields with values and attributes taken from the rule set.
“forming a labeled dataset using the unlabeled data entries and logic contained in the rules set wherein the labeled dataset comprises multiple different labeled data entries comprising fields and data values of the unlabeled data entries and labels, wherein the forming comprises selecting a unlabeled data entry from the unlabeled data entries, executing the rules set on the unlabeled data entry to obtain a result from a rule of the rules set that matches the unlabeled data entry, and using the result as a label for the unlabeled data entry, wherein the label is derived from the result attributes of the rule” The user mentally or with pencil and paper creates and labels a dataset by mentally or with pencil and paper executing rules and using the output as a label.
“forming a training dataset from at least a portion of the labeled dataset” The user mentally or with pencil and paper identifies the labeled dataset as a training dataset.
“training a machine learning model by applying the training dataset to the … model” The user mentally or with pencil and paper works out the mathematics to make their model operate.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“computing system”, “one or more processing units coupled to memory”, “one or more computer readable storage media” (mere instructions to apply the exception using a generic computer component);
“a machine learning model”, “the machine learning model”, “a rules engine configured to execute the rules set”, “the rules engine” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The claimed machine learning model is a generic machine learning model with no details or limitations that make it beyond a generic, off the shelf machine learning model.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“computing system”, “one or more processing units coupled to memory”, “one or more computer readable storage media” (mere instructions to apply the exception using a generic computer component)
“a machine learning model”, “the machine learning model”, “a rules engine configured to execute the rules set”, “the rules engine” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The claimed machine learning model is a generic machine learning model with no details or limitations that make it beyond a generic, off the shelf machine learning model.
As per claims 13-15 contain additional mental steps similar to claim 1, and are rejected similarly to claim 12.
As per claims 16-17, these claims contain additional mental steps and generic machine learning steps similar to claim 1, and are rejected for similar reasons to claim 12.
As per claim 18,
2A Prong 1:
“detecting attributes and values in rules contained in a rules set, wherein the attributes comprise condition attributes and result attributes” A user mentally or with pencil and paper looks at rules from a rules set.
“determining definitions of the attributes detected in the rules from a data model that includes data objects with attributes that map to the attributes in the rules of the rules set” The user mentally or with pencil and paper identifies attributes in the rules using a data model associated with the rules and matches up the rules to the appropriate attributes.
“generating, as input data … an initial dataset comprising multiple different initial data entries having fields associated with the condition attributes detected in the rules and containing data values associated with the condition attributes, the generating of the initial dataset comprising:” The user mentally or with pencil and paper creates the appropriate fields with values and attributes.
“determining data values for the fields of the initial data entries based on the definitions of the condition attributes determined from the data model and a value appearing in a condition of the rules that refers to a condition attribute of the rules, wherein determining data values comprises selecting a data value that satisfies the condition of the rules” The user mentally or with pencil and paper looks at the rules and the data and determines appropriate values for them.
“populating the fields associated with the condition attributes with the data values such that execution of the rules set on the initial data entries … identifies matching rules in the rules set” The user mentally or with pencil and paper fills in the appropriate fields with values and attributes taken from the rule set.
“forming a labeled dataset, using the initial data entries and logic contained in the rules set wherein the labeled dataset comprises multiple different labeled data entries comprising fields and data values of the initial data entries and labels, wherein the forming comprises selecting an initial data entry from the initial data entries, executing the rules set on the initial data entry to obtain a result from a rule of the rules set that matches the initial data entry, and using the result as a label for the initial data entry wherein the label is derived from the result attributes of the rule” The user mentally or with pencil and paper creates and labels a dataset by mentally or with pencil and paper executing rules and using the output as a label.
“forming a training dataset from at least a portion of the labeled dataset” The user mentally or with pencil and paper identifies the labeled dataset as a training dataset.
“training a … model by applying the training dataset to the … model” The user mentally or with pencil and paper works out the mathematics to make their model operate.
“Wherein populating the fields with data values comprises selecting values randomly” The user mentally or with pencil and paper chooses random values to fill the fields.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“non-transitory computer readable storage media” (mere instructions to apply the exception using a generic computer component);
“a machine learning model”, “the machine learning model”, “a rules engine configured to execute the rules set”, “the rules engine” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The claimed machine learning model is a generic machine learning model with no details or limitations that make it beyond a generic, off the shelf machine learning model.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“non-transitory computer readable storage media” (mere instructions to apply the exception using a generic computer component)
“a machine learning model”, “the machine learning model”, “a rules engine configured to execute the rules set”, “the rules engine” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: The claimed machine learning model is a generic machine learning model with no details or limitations that make it beyond a generic, off the shelf machine learning model.
As per claims 19-20 contain additional mental steps similar to claim 1, and are rejected similarly to claim 1.
Claim Rejections - 35 USC § 112
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) 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.
Claims 1-4, 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Taylor et al (“ClassBench: A Packet Classification Benchmark”) in view of Gu et al (“Mechanisms for Automatic Training Data Labeling for Machine Learning”).
As per claim 1, Taylor discloses, “a computer-implemented method comprising” (Pg.2078, particularly section VI; EN: this denotes monitoring load, power consumption, and other aspects of a computer system running programs, which inherently includes some form of computer/processor/memory to run the system).
“Detecting attributes” (Pg.2070, particularly C2, section B; EN; This denotes looking at filter sets (i.e. rules) and what attributes they had, such as protocols, port ranges, port pair class, etc). “and values” (Pg.2070, particularly C2, section B; EN: This denotes the values found in these things such as TCP IP, types of port ranges, etc). “in rules contained in a rule set” (Pg.2070, C1, Section III; EN: this denotes looking at premade filter sets to analyze them). “wherein the attributes comprise condition attributes” (Pg.2070, particularly section B; EN: This denotes protocols, port ranges, and port pair class, all of which are conditions of incoming data). “and result attributes” (Pg.2068, particularly C1, introduction section; EN: this denotes using the filter sets to apply security policies, application processing, and QoS guarantees, all of which are examples of results).
“determining definitions of the attributes detected in the rules contained in the rules set from a data model that includes data objects with attributes that map to the attributes in the rules of the rules sets” (Pg.2070, particularly C2, Section B; EN: this denotes the system identifying the different aspects of the rules).
“generating, …, an unlabeled dataset comprising multiple different unlabeled data entries having fields…” (Pg.2074, particularly section V; EN: this denotes creating new filter sets based on statistical values and distributions from a parameter file. This is “unlabeled” because the rules are created via statistics and are not made for a specific purpose). “associated with the condition attributes detected in the rules” (Pg.2073, particularly C1, section Iv; EN: this denotes making the parameter file based off of real filter sets). “and containing data values associated with the condition attributes” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters). “the generating of the unlabeled dataset comprising: Pg.2074, particularly section V; EN: this denotes creating new filter sets based on statistical values and distributions from a parameter file. This is “unlabeled” because the rules are created via statistics and are not made for a specific purpose).
“determining data values for the fields of the unlabeled data entries based on the definitions of the condition attributes determined from the data model and a value appearing in a condition of the rules that refers to a condition attribute of the rules” (Pg.2074, particularly Section V; EN: this denotes selecting values for the fields in the synthetic filters based on the real feature set, including things like port scope, port pair classes, port ranges, etc). “wherein determining data values comprises selecting a data value that satisfies the condition of the rules” (Pg.2074-2075, particularly section V and Figure 8; EN: this denotes filling in the various parts of the newly generated filters).
“populating the fields associated with the condition attributes with the data values… identifies matching rules in the rules set” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters based on the parameter set generated from the real filter set).
“Forming a … dataset using the unlabeled data entries and logic contained in the rules set, wherein the … dataset comprises multiple different … data entries comprising fields and data values of the unlabeled data entries and labels…wherein the forming comprises…” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters based on the parameter set generated from the real filter set).
“… identify matching rules” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters based on the parameter set generated from the real filter set).
However, Taylor fails to explicitly disclose, “… as input data for a rule engine configured to execute the rules set”, “… such that execution of the rules set on the unlabeled data entries with the rule engine …”, “forming a labeled dataset using the unlabeled data entries … wherein the labeled dataset comprises multiple different labeled entries comprising fields and data values of the unlabeled data entries and labels, wherein the forming comprises”, “executing the rules set on the unlabeled data entries with the rules engine to identify matching rules”, “adding values of result attributes of the matching rules as the labels to the unlabeled data entries to form the labeled data entries”, “forming a training dataset from at least a portion of the labeled dataset”, and “training a machine learning model by applying the training dataset to the machine learning model”
Gu discloses, “… as input data for a rule engine configured to execute the rules set” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
“… such that execution of the rules set on the unlabeled data entries with the rule engine …” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
“forming a labeled dataset using the unlabeled data entries … wherein the labeled dataset comprises multiple different labeled entries comprising fields and data values of the unlabeled data entries and labels, wherein the forming comprises” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, which when combined with the Taylor reference denotes the various fields and variables that are filled by the rules creation system).
“executing the rules set on the unlabeled data entries with the rules engine to identify matching rules” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, matching these various rules is how the training data is labeled).
“adding values of result attributes of the matching rules as the labels to the unlabeled data entries to form the labeled data entries” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, matching these various rules is how the training data is labeled).
“forming a training dataset from at least a portion of the labeled dataset”, and “training a machine learning model by applying the training dataset to the machine learning model” (abstract; EN: this denotes the use of the rules based system for training data for machine learning models).
Taylor and Gu are analogous art because both involve classification.
Before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Taylor and Gu in order to use rules to properly identify data for classification purposes.
The motivation for doing so would be because “Rule based systems are interpretable, require less “training data” than ML systems, and can encode information with a different perspective compared to ML algorithms” (Gu, Pg.12, Last paragraph) or in the case of Taylor, allow the system to use a rule based system to label the data for their rule creation system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Taylor and Gu in order to use rules to properly identify data for classification purposes.
As per claim 2, Taylor discloses, “wherein the definitions determined from the data model comprise value domains for the attributes detected in the rules” (Pg.2073, particularly C2, the bullet points; EN: this denote show each of the different types of data are determined and what kind of values they hold). “wherein determining the data values comprises determining permissible values for the fields of the unlabeled data entries based on the values detected in the rules and the value domains from the data model” (Pg.2075, particularly C1, second paragraph; EN: this denotes various information about what values are assigned to each domain, with appropriate ranges (i.e. permissible values) for each).
As per claim 3, Taylor discloses, “wherein generating the unlabeled dataset further comprises randomly assigning values to the fields of the unlabeled data entries from the permissible values” (Pg.2074, particularly C2, last paragraph; EN: this denotes using random variables to create the data).
As per claim 4, Taylor discloses, “Wherein generating the unlabeled dataset further comprises assigning values to the unlabeled data entries within the permissible values” (Pg.2075, particularly C1, second paragraph; EN: this denotes various information about what values are assigned to each domain, with appropriate ranges (i.e. permissible values) for each). “and from existing data with values for the attributes and permissible values” (Pg.2073, particularly C1, section Iv; EN: this denotes making the parameter file based off of real filter sets).
As per claim 5, Taylor discloses, “within the permissible values” (Pg.2075, particularly C1, second paragraph; EN: this denotes various information about what values are assigned to each domain, with appropriate ranges (i.e. permissible values) for each).
Gu discloses, “wherein executing the rules set on the unlabeled data entries comprises selecting an unlabeled data entry from the unlabeled data entries” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, which when combined with the Taylor reference denotes the various fields and variables that are filled by the rules creation system). “ and executing the rules set on the unlabeled data entry to obtain a result, and wherein forming the labeled dataset comprises using the result as a label for the unlabeled data entry” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
As per claim 6, Gu discloses, “Wherein executing the rules set on the unlabeled data entry to obtain a result comprises finding a rule in the rules set having a set of conditions that matches the unlabeled data entry and applying the found rule to the unlabeled data entry” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, which when combined with the Taylor reference denotes the various fields and variables that are filled by the rules creation system).
As per claim 7, Rogers discloses, “wherein using the result as a label for the unlabeled data entry comprises adding the result to the unlabeled data entry to form a labeled data entry” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
As per claim 11, Gu discloses, “further comprising making a prediction using the machine learning model” (pg.3, particularly the second to last paragraph; EN: this denotes using the machine learning model to make predictions with test data).
As per claim 12, Taylor discloses, “A computing system comprising” (Pg.2078, particularly section VI; EN: this denotes monitoring load, power consumption, and other aspects of a computer system running programs, which inherently includes some form of computer/processor/memory to run the system).
“one or more processing units coupled to memory” (Pg.2078, particularly section VI; EN: this denotes monitoring load, power consumption, and other aspects of a computer system running programs, which inherently includes some form of computer/processor/memory to run the system).
“one or more computer readable storage media storing instructions that when executed by the one or more processing units cause the computing system to perform operations comprising” (Pg.2078, particularly section VI; EN: this denotes monitoring load, power consumption, and other aspects of a computer system running programs, which inherently includes some form of computer/processor/memory to run the system).
“Detecting attributes” (Pg.2070, particularly C2, section B; EN; This denotes looking at filter sets (i.e. rules) and what attributes they had, such as protocols, port ranges, port pair class, etc). “and values” (Pg.2070, particularly C2, section B; EN: This denotes the values found in these things such as TCP IP, types of port ranges, etc). “in rules contained in a rule set” (Pg.2070, C1, Section III; EN: this denotes looking at premade filter sets to analyze them). “wherein the attributes comprise condition attributes” (Pg.2070, particularly section B; EN: This denotes protocols, port ranges, and port pair class, all of which are conditions of incoming data). “and result attributes” (Pg.2068, particularly C1, introduction section; EN: this denotes using the filter sets to apply security policies, application processing, and QoS guarantees, all of which are examples of results).
“determining definitions of the attributes detected in the rules contained in the rules set from a data model that includes data objects with attributes that map to the attributes in the rules of the rules sets” (Pg.2070, particularly C2, Section B; EN: this denotes the system identifying the different aspects of the rules).
“generating … an unlabeled dataset comprising multiple different unlabeled data entries having fields…” (Pg.2074, particularly section V; EN: this denotes creating new filter sets based on statistical values and distributions from a parameter file. This is “unlabeled” because the rules are created via statistics and are not made for a specific purpose). “associated with the condition attributes detected in the rules” (Pg.2073, particularly C1, section Iv; EN: this denotes making the parameter file based off of real filter sets). “and containing data values associated with the condition attributes” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters). “the generating of the unlabeled dataset comprising:” (Pg.2074, particularly section V; EN: this denotes creating new filter sets based on statistical values and distributions from a parameter file. This is “unlabeled” because the rules are created via statistics and are not made for a specific purpose).
“determining data values for the fields of the unlabeled data entries based on the definitions of the condition attributes determined from the data model and a value appearing in a condition of the rules that refers to a condition attribute of the rules” (Pg.2074, particularly Section V; EN: this denotes selecting values for the fields in the synthetic filters based on the real feature set, including things like port scope, port pair classes, port ranges, etc). “wherein determining data values comprises selecting a data value that satisfies the condition of the rules” (Pg.2074-2075, particularly section V and Figure 8; EN: this denotes filling in the various parts of the newly generated filters).
“populating the fields associated with the condition attributes with the data values… identifies matching rules in the rules set” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters based on the parameter set generated from the real filter set).
“forming a … dataset using the unlabeled data entries and logic contained in the rules set, wherein the … dataset comprises multiple different … data entries comprising fields and data values of the unlabeled data entries and labels” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters based on the parameter set generated from the real filter set). “
However, Taylor fails to explicitly disclose, “… as input data for a rule engine configured to execute the rules set…”, “… such that execution of the rules set on the unlabeled data entries with the rule engine…”, “forming a labeled dataset using the unlabeled data entries … wherein the labeled dataset comprises multiple different labeled entries comprising fields and data values of the unlabeled data entries and labels, wherein the forming comprises selecting an unlabeled data entry from the unlabeled data entry, executing the rules set on the unlabeled data entry to obtain a result from a rule of the rules set that matches the unlabeled data entry; using the result as a label for the unlabeled data entry, wherein the label is derived from the result attributes of the rule”, “forming a training dataset from at least a portion of the labeled dataset”, and “training a machine learning model by applying the training dataset to the machine learning model.”
Gu discloses, “… as input data for a rule engine configured to execute the rules set…” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
“… such that execution of the rules set on the unlabeled data entries with the rule engine…” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
“forming a labeled dataset using the unlabeled data entries … wherein the labeled dataset comprises multiple different labeled entries comprising fields and data values of the unlabeled data entries and labels” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, which when combined with the Taylor reference denotes the various fields and variables that are filled by the rules creation system). “wherein the forming comprises selecting an unlabeled data entry from the unlabeled data entry ” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, which when combined with the Taylor reference denotes the various fields and variables that are filled by the rules creation system). “executing the rules set on the unlabeled data entry to obtain a result from a rule of the rules set that matches the unlabeled data entry; using the result as a label for the unlabeled data entry, wherein the label is derived from the result attributes of the rule”,” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
“forming a training dataset from at least a portion of the labeled dataset”, and “training a machine learning model by applying the training dataset to the machine learning model” (abstract; EN: this denotes the use of the rules based system for training data for machine learning models).
Taylor and Gu are analogous art because both involve classification.
Before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Taylor and Gu in order to use rules to properly identify data for classification purposes.
The motivation for doing so would be because “Rule based systems are interpretable, require less “training data” than ML systems, and can encode information with a different perspective compared to ML algorithms” (Gu, Pg.12, Last paragraph) or in the case of Taylor, allow the system to use a rule based system to label the data for their rule creation system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Taylor and Gu in order to use rules to properly identify data for classification purposes.
As per claim 13, Taylor discloses, “wherein the definitions determined from the data model comprise value domains for the attributes detected in the rules” (Pg.2073, particularly C2, the bullet points; EN: this denote show each of the different types of data are determined and what kind of values they hold). “wherein determining the data values comprises determining permissible values for the fields of the unlabeled data entries based on the values detected in the rules and the value domains from the data model” (Pg.2075, particularly C1, second paragraph; EN: this denotes various information about what values are assigned to each domain, with appropriate ranges (i.e. permissible values) for each).
As per claim 14, Taylor discloses, “wherein generating the unlabeled dataset further comprises randomly assigning values to the fields of the unlabeled data entries from the permissible values” (Pg.2074, particularly C2, last paragraph; EN: this denotes using random variables to create the data).
As per claim 15, Taylor discloses, “Wherein generating the unlabeled dataset further comprises assigning values to the unlabeled data entries within the permissible values” (Pg.2075, particularly C1, second paragraph; EN: this denotes various information about what values are assigned to each domain, with appropriate ranges (i.e. permissible values) for each). “and from existing data with values for the attributes and permissible values” (Pg.2073, particularly C1, section Iv; EN: this denotes making the parameter file based off of real filter sets).
As per claim 17, Gu discloses, “wherein the operations further comprise making a prediction using the machine learning model” (pg.3, particularly the second to last paragraph; EN: this denotes using the machine learning model to make predictions with test data).
As per claim 18, Taylor discloses, “One or more non-transitory computer readable storage media storing computer executable instructions for causing a computer system to perform operations comprising” (Pg.2078, particularly section VI; EN: this denotes monitoring load, power consumption, and other aspects of a computer system running programs, which inherently includes some form of computer/processor/memory to run the system).
“Detecting attributes” (Pg.2070, particularly C2, section B; EN; This denotes looking at filter sets (i.e. rules) and what attributes they had, such as protocols, port ranges, port pair class, etc). “and values” (Pg.2070, particularly C2, section B; EN: This denotes the values found in these things such as TCP IP, types of port ranges, etc). “in rules contained in a rule set” (Pg.2070, C1, Section III; EN: this denotes looking at premade filter sets to analyze them). “wherein the attributes comprise condition attributes” (Pg.2070, particularly section B; EN: This denotes protocols, port ranges, and port pair class, all of which are conditions of incoming data). “and result attributes” (Pg.2068, particularly C1, introduction section; EN: this denotes using the filter sets to apply security policies, application processing, and QoS guarantees, all of which are examples of results).
“determining definitions of the attributes detected in the rules contained in the rules set from a data model that includes data objects with attributes that map to the attributes in the rules of the rules sets” (Pg.2070, particularly C2, Section B; EN: this denotes the system identifying the different aspects of the rules).
“generating … an initial dataset comprising multiple different initial data entries having fields…” (Pg.2074, particularly section V; EN: this denotes creating new filter sets based on statistical values and distributions from a parameter file. This is “unlabeled” because the rules are created via statistics and are not made for a specific purpose). “associated with the condition attributes detected in the rules” (Pg.2073, particularly C1, section Iv; EN: this denotes making the parameter file based off of real filter sets). “and containing data values associated with the condition attributes” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters). “the generating of the initial dataset comprising” (Pg.2074, particularly section V; EN: this denotes creating new filter sets based on statistical values and distributions from a parameter file. This is “unlabeled” because the rules are created via statistics and are not made for a specific purpose).
“determining data values for the fields of the initial data entries based on the definitions of the condition attributes determined from the data model and a value appearing in a condition of the rules that refers to a condition attribute of the rules” (Pg.2074, particularly Section V; EN: this denotes selecting values for the fields in the synthetic filters based on the real feature set, including things like port scope, port pair classes, port ranges, etc). “wherein determining data values comprises selecting a data value that satisfies the condition of the rules” (Pg.2074-2075, particularly section V and Figure 8; EN: this denotes filling in the various parts of the newly generated filters).
“populating the fields associated with the condition attributes with the data values… identifies matching rules in the rules set” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters based on the parameter set generated from the real filter set).
“forming a … dataset using the initial data entries and logic contained in the rules set, wherein the … dataset comprises multiple different … data entries comprising fields and data values of the initial data entries and labels” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters based on the parameter set generated from the real filter set).
“Wherein populating the fields with data values comprises selecting values randomly” (Pg.2074, particularly C2, last paragraph; EN: this denotes using random variables to create the data).
However, Taylor fails to explicitly disclose, “…as input data for a rule engine configured to execute the rules set…”, “such that execution of the rules set on the initial data entries with the rules engine…”
“forming a labeled dataset using the initial data entries … wherein the labeled dataset comprises multiple different labeled entries comprising fields and data values of the initial data entries and labels, wherein the forming comprises selecting an initial data entry from the initial data entries, executing the rules set on the initial data entry to obtain a result from a rule of the rules set that matches the initial data entry, and using the result as a label for the initial data entry, wherein the label is derived from the result attributes of the rule”, “forming a training dataset from at least a portion of the labeled dataset”, and “training a machine learning model by applying the training dataset to the machine learning model”
Gu discloses, “…as input data for a rule engine configured to execute the rules set…” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
“such that execution of the rules set on the initial data entries with the rules engine…” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
“forming a labeled dataset using the initial data entries … wherein the labeled dataset comprises multiple different labeled entries comprising fields and data values of the initial data entries and labels, wherein the forming comprises:” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, which when combined with the Taylor reference denotes the various fields and variables that are filled by the rules creation system).
“selecting an initial data entry from the initial data entries” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, which when combined with the Taylor reference denotes the various fields and variables that are filled by the rules creation system).
“executing the rules set on the initial data entry to obtain a result from a rule of the rules set that matches the initial data entry” (Pg.3, particularly the Knowledge base section; EN: this denotes the rules based being able to label 92 lexicons with 20 terms each and 104 separate patterns, matching these various rules is how the training data is labeled). “and using the result as a label for the initial data entry, wherein the label is derived from the result attributes of the rule” (Pg.2, the paragraph before the Literature Review section; EN: this denotes using Rules to label training data).
“forming a training dataset from at least a portion of the labeled dataset”, and “training a machine learning model by applying the training dataset to the machine learning model” (abstract; EN: this denotes the use of the rules based system for training data for machine learning models).
Taylor and Gu are analogous art because both involve classification.
Before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Taylor and Gu in order to use rules to properly identify data for classification purposes.
The motivation for doing so would be because “Rule based systems are interpretable, require less “training data” than ML systems, and can encode information with a different perspective compared to ML algorithms” (Gu, Pg.12, Last paragraph) or in the case of Taylor, allow the system to use a rule based system to label the data for their rule creation system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Taylor and Gu in order to use rules to properly identify data for classification purposes.
As per claim 19, Taylor discloses, “wherein the definitions determined from the data model comprise value domains for the attributes detected in the rules” (Pg.2073, particularly C2, the bullet points; EN: this denote show each of the different types of data are determined and what kind of values they hold)
“determining permissible values for the fields based on the values detected in the rules and the value domains specified in the data model” (Pg.2075, particularly C1, second paragraph; EN: this denotes various information about what values are assigned to each domain, with appropriate ranges (i.e. permissible values) for each).
“randomly assigning values to the fields of the initial data entries from the permissible values” (Pg.2074, particularly C2, last paragraph; EN: this denotes using random variables to create the data).
As per claim 20, Taylor discloses, “wherein the definitions determined from the data model comprise value domains for the attributed detected in the rules, and wherein generating the initial dataset comprise” (Pg.2073, particularly C2, the bullet points; EN: this denote show each of the different types of data are determined and what kind of values they hold)
“Determining permissible values for the fields based on the values detected in the rules and the value domains from the data models” (Pg.2075, particularly C1, second paragraph; EN: this denotes various information about what values are assigned to each domain, with appropriate ranges (i.e. permissible values) for each).
“assigning values to the fields of the initial data entries within the permissible values” (Pg.2075, particularly C1, second paragraph; EN: this denotes various information about what values are assigned to each domain, with appropriate ranges (i.e. permissible values) for each). “and from existing data obtained from use of a rule-based system including the rules set” (Pg.2073, particularly C1, section Iv; EN: this denotes making the parameter file based off of real filter sets).
Claim Rejections - 35 USC § 103
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Taylor et al (“ClassBench: A Packet Classification Benchmark”) in view of Gu et al (“Mechanisms for Automatic Training Data Labeling for Machine Learning”) and further in view of Bhatia et al (US 20200272741 A1).
As per claim 8, Taylor discloses, “receiving a new rules set and a new data model associated with the new rules set” (Pg.2070, particularly C1, section III; EN: this denotes working with multiple different rule sets in different formats (i.e. different data models)).
“forming a new … dataset from the new rules set and the new data model” (Pg.2074-2075, particularly section V; EN: this denotes filling in the various parts of the newly generated filters based on the parameter set generated from the real filter set).
Gu discloses, “and … training the machine learning model with the new labeled dataset” (abstract; EN: this denotes the use of the rules based system for training data for machine learning models).
However, Taylor and Gu fail to explicitly disclose, “forming a new labeled dataset from the new rules set and the new data model” and “retraining.”
Bhatia discloses, “forming a new labeled dataset from the new rules set and the new data model” (Pg.7, particularly paragraph 0060-0062; Figure 3; EN: this denotes parsing in rules from unstructured text, and placing the different pieces in labeled categories as seen in figure 3 such as rule name, tests, enabled, building block, response, etc).
However, Taylor and Bhatia fail to explicitly disclose “retraining.” However, the Examiner takes official notice that it would be obvious to one of ordinary skill at the time of filing to retrain a machine learning model based upon newly available data, as this allows the system to be kept up to date on new developments related to incoming data as needed.
As the Applicant failed to argue or respond to the official notice in the response filed 6/13/2025, the aspects of the official notice are applicant admitted prior art as per MPEP 2144.03(C).
Taylor and Bhatia are analogous art because both involve network rule creation.
Before the effective filing date it would have been obvious to one skilled in the art of network rule creation to combine the work of Taylor and Bhatia in order to properly label and import rules in order to use them to train a machine learning algorithm.
The motivation for doing so would be to “use[] natural language processing (NLP techniques … to identify and eliminate duplicate rules, combine similar rules together into ‘super rules’ align STEM rules with frameworks and/or standard rules from standard rules repositories, decompose the rules and their conditions into principal components for use in automatically generating new STEM rules, and train a machine learning model, such as a Recurrent Neural Network (RNN) to generate automated rules based on specific threat intelligence and learning of rule components that correspond to threat characteristics” (Bhatia, Pg.7, paragraph 0059) or in the case of Taylor, allow the system to fully parse and understand the pieces of the generated rules in order to improve the system via machine learning for rule generation.
Therefore before the effective filing date it would have been obvious to one skilled in the art of network rule creation to combine the work of Taylor and Bhatia in order to properly label and import rules in order to use them to train a machine learning algorithm.
Claim Rejections - 35 USC § 103
Claims 9-10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Taylor et al (“ClassBench: A Packet Classification Benchmark”) in view of Gu et al (“Mechanisms for Automatic Training Data Labeling for Machine Learning”) further in view of Korjani et al (US 20160179751 A1).
As per claim 9, Taylor modified by Gu fails to explicitly disclose, “validating the machine learning model using at least a portion of the labeled dataset.”
Korjani discloses, “validating the machine learning model using at least a portion of the labeled dataset” (Pg.3, particularly paragraph 0025; EN: this denotes breaking up the data used to train the system into training data, validation data, and testing data, and using that to help train the system).
Korjani and Taylor modified by Gu are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Korjani and Taylor modified by Gu in order to include validation and test data.
The motivation for doing so would be to use the validating data set to “estimate generalization error” (Korjani, Pg.4, paragraph 0032) or in the case of Taylor modified by Gu, allow the training to include a validation set to help estimate generalization errors for the machine learning process.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Korjani and Taylor modified by Gu in order to include validation and test data.
As per claim 10, Taylor modified by Gu fails to explicitly disclose, “further comprising testing the machine learning model using at least a portion of the labeled dataset”
Korjani discloses, “further comprising testing the machine learning model using at least a portion of the labeled dataset” (Pg.3, particularly paragraph 0025; EN: this denotes breaking up the data used to train the system into training data, validation data, and testing data, and using that to help train the system).
Korjani and Taylor modified by Gu are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Korjani and Taylor modified by Gu in order to include validation and test data.
The motivation for doing so would be to use the validating data set to “evaluat[e] the non-linear variable structure regression model with the testing data subset” (Korjani, Pg.4, paragraph 0030) or in the case of Taylor modified by Gu, allow the training to include a test set to help test the model being trained.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Korjani and Taylor modified by Gu in order to include validation and test data.
As per claim 16, Taylor modified by Gu fails to explicitly disclose, “wherein the operations further comprise validating or testing the machine learning model using at least a portion of the labeled dataset.”
Korjani discloses, “wherein the operations further comprise validating or testing the machine learning model using at least a portion of the labeled dataset” (Pg.3, particularly paragraph 0025; EN: this denotes breaking up the data used to train the system into training data, validation data, and testing data, and using that to help train the system).
Korjani and Taylor modified by Gu are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Korjani and Taylor modified by Gu in order to include validation and test data.
The motivation for doing so would be to use the validating data set to “estimate generalization error” (Korjani, Pg.4, paragraph 0032) or in the case of Taylor modified by Gu, allow the training to include a validation set to help estimate generalization errors for the machine learning process.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Korjani and Taylor modified by Gu in order to include validation and test data.
Response to Arguments
In pg.9, the Applicant argues in regards to the rejection under U.S.C. 101,
Thus, amended claim 1 is not merely directed to observing rules, mentally filling in fields, or generally training a generic machine learning model. Instead, amended claim 1 recites a particular computer-implemented pipeline in which generated unlabeled data entries are specifically generated as input data for a rule engine, the rules set is executed on those generated data entries to identify matching rules, values of result attributes of the matching rules are added as labels to form labeled data entries, and a training dataset formed from the labeled dataset is
In response, the Examiner maintains the rejection as shown above. The use of a rules engine to perform a series of steps to manipulate or fill in data is not significantly more than the abstract idea. If all the steps performed by the “rules engine” can be performed mentally or with pencil and paper, merely applying a generic rules engine is no different than using a processor or memory to implement the abstract idea. Since the rules engine is claimed generically with no additional details or limitations beyond a generic rules engine, the rejection is maintained as shown above.
In pg.9-10, the Applicant argues in regards to the rejection under U.S.C. 101,
This specific arrangement addresses a technical problem in developing machine learning models. As described in the Application, machine learning models require large datasets, but many systems do not generate adequately diverse data for machine learning. For example, available data may not include enough examples of rare events, data structures can change after training, gathering enough new data for retraining can take significant time, and training on historical data can perpetuate bias. The claimed arrangement provides a specific computer- implemented solution to these problems by generating labeled training data from rule logic and data-model-defined attributes, rather than requiring sufficient field data to already exist. In particular, the unlabeled data entries are generated SO that execution of the rules set on those entries with the rule engine identifies matching rules, and values of result attributes of the matching rules are then used as labels. This provides a concrete mechanism for producing training data that reflects the behavior of the rule-based logic, including rule-triggering examples, for use in training the machine learning model.
In response, the Examiner maintains the rejection as shown above. Producing training data is not a technology. Training data can be anything from words on a piece of paper, pictures of apples, to motion captured on video. Merely labeling/filling in data fields via generic machine learning models or rules engines is, once again, not significantly more than the abstract idea, and merely calling the data “Training data” does not change it from the mental process of looking at and filling in fields mentally or with pencil and paper. Therefore the rejection is maintained as shown above.
In pg.11, the Applicant argues in regards to the rejection under U.S.C. 101,
Applicant further notes that the claims are analogous to claims found eligible where computer-implemented rules were used in a specific manner to improve a computer- implemented process. Applicant does not rely on McRO merely because the present claims involve "rules." Rather, Applicant notes the principle that a claim reciting a specific use of rules to achieve a technological result in a computer-implemented process is not rendered abstract simply because rules or information are involved. Here, the claims recite a specific use of rule logic to generate and label training data for a machine learning model, addressing the technological problem of training and retraining machine learning models when sufficient representative data may be unavailable.
In response, the Examiner maintains the rejection as shown above. McRo dealt with very specific technology of automating lip synchronization in computer animations. The instant claims here merely deal with generic rules to label generic data that could be used to potentially train generic machine learning models. This amounts to no more than looking at data and using rules to mentally label that data. There is no technological problem that is being solved that is analogous to facial animations of computer graphics, and therefore the rejection is maintained as shown above.
Applicant's arguments with respect to claims 1-20 have been considered but are either moot in view of the new ground(s) of rejection, conclusory, or repetitions of the above arguments and rejected for similar reasons given above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection 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).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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