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 Action is responsive to the Amendments, and Remarks filed on 4/15/2026. Claims 1, 3-11, 13-17, 19-20, and 22-23 are pending claims. Claims 1, 11, and 17 are written in independent form. Claims 2, 12, 18, and 21 have been previously cancelled by Applicant.
Priority
Applicant’s claim for benefit of prior-filed provisional application 63/568,851 dated 3/22/2024 under 35. U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Claim Interpretation
Claims 4-7, 10, 14, and 20 recite the phrase “the LLM is fine-tuned for…” which is being understood as the intent to use the LLM, but is not actively performing any step/limitation of using the LLM. Examiner suggests to amend the claim limitations to recite all of the steps in a positive manner.
Claims 4, 14, and 20 recite the phrase “the LLM is fine-tuned for a cloud security application” which is being interpreted to have a scope of “the LLM is fine-tuned”. However, for the purpose of compact prosecution, the limitation is being addressed herein as if all of the steps are recited in a positive manner.
Claim 5 recites the phrase “the LLM is fine-tuned for performing automated entity extraction for multi-domain security applications” which is being interpretated to have a scope of “the LLM is fine-tuned”. However, for the purpose of compact prosecution, the limitation is being addressed herein as if all of the steps are recited in a positive manner.
Claims 6 and 10 recite the phrase “the LLM is fine-tuned for performing a cross-domain search…” which is being interpreted to have a scope of “the LLM is fine-tuned”. However, for the purpose of compact prosecution, the limitation is being addressed herein as if all of the steps are recited in a positive manner.
Claim 7 recites the phrase “the LLM is fine-tuned for performing a natural language (NL) query…” which is being interpreted to have a scope of “the LLM is fine-tuned”. However, for the purpose of compact prosecution, the limitation is being addressed herein as if all of the steps are recited in a positive manner.
Claims 6, 10 recites the phrase “to generate” which is being understand as the intent to generate, but is not actively performing any generating step/limitation. Examiner suggests to amend the claim limitation to recite all of the steps in a positive manner.
Claims 6 and 10 recite the phrase “…a cross-domain search to generate a search result…” which is being interpreted as not further narrowing the scope because the cross-domain search is not being actively performed. However, for the purpose of compact prosecution, the limitation is being addressed herein as if all of the steps are recited in a positive manner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-11, 13-17, 19-20, and 22-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1, 11, 17, and 22, the limitations “for a first set of samples using single attribute values having same asset attributes, generate, using the LLM, a plurality of queries having a plurality of attribute values for the same asset attribute…wherein the same asset attribute includes one or more of the following: asset name, asset type, asset class, cloud type, cloud account, finding name, finding type, and/or finding severity” and “wherein the plurality of queries include a first query and a second query, wherein the first query includes the asset class and the first finding type, wherein the second query includes the asset class and the second finding type” render the claims indefinite because an “asset class” is listed as a “same asset attribute” and the claims require “a plurality of attribute values for the same asset attribute” but the plurality of queries only necessarily include a single attribute value for “asset class” by reciting the first query and second query (the only queries necessarily included in the plurality of queries) both include “the asset class” that the natural language representation was categorized into. It is noted that because the natural language representation was categorized into a specific “asset class”, “the asset class” is understood as being the asset class value.
an “asset class” cannot be a “same asset attribute” of the generated “plurality of queries” when the first query and the second query of the generated “plurality of queries having a plurality of attribute values for the same asset attribute” only include the same “the asset class” value and no other query is necessarily included in the “plurality of queries” that contains an asset class value that is different from “the asset class” included in the first and second queries.
For purposes of compact prosecution, the claims are understood as reciting “for a first set of samples using single attribute values having same asset attributes, generate, using the LLM, a plurality of queries having a plurality of attribute values for the same asset attribute…wherein the same asset attribute includes one or more of the following: asset name, asset type,
Dependent Claims 3-10, 13-16, 19-20, and 23 inherit the deficiencies of their parent claims and are therefore being rejected based upon the same reason(s) stated for their parent claims.
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, 3-11, 13-17, 19-20, and 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claimed invention is directed to one or more abstract ideas without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below.
As per Claims 1, 11, and 17,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed system (claims 1, 3-10, and 22-23), method (claims 11 and 13-16), and computer program product (claims 17 and 19-20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The independent claims 1, 11, and 17 recite the following limitations directed to an abstract idea:
Automatically generate a seed dataset for a domain specific language (DSL);
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by making a judgement and/or opinion to generate (or think of) a seed dataset based on the observation and evaluation.
Wherein the automatically generating of the seed dataset comprises to:
Generate a natural language representation of the RQL to obtain the seed dataset;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating a resource query language, and based on the observation and evaluation, making a judgement and/or opinion of a natural language representation of the RQL.
Wherein the natural language representation is categorized into an asset class,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating the natural language representation, and based on the observation and evaluation, making a judgement and/or opinion of an asset class to categorize the natural language representation into.
Wherein the asset class is associated with a plurality of finding types, wherein the plurality of finding types includes a first finding type and a second finding type
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating the asset class and a plurality of finding types, and based on the observation and evaluation, making a judgement and/or opinion to associate the asset class with the plurality of finding types.
Expand, using a Large language Model (LLM), the seed dataset for the DSL; and
The limitation recites a mathematical concept of executing a mathematical function in the form of an LLM that takes as input the seed dataset and outputs an expanded seed data set.
Comprising to perform one or more of the following:
A) for a first set of samples using single attribute values having same asset attributes, generate, using the LLM, a plurality of queries having a plurality of attribute values for the same asset attribute to obtain the expanded dataset for the DSL; or
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating a first set of samples using single attribute value having the same asset attribute and an LLM, and based on the observation and evaluation, making a judgement and/or opinion of a plurality of queries having a plurality of attribute values for the same asset attribute to obtain the expanded dataset for the DSL.
B) for a second set of samples using single attribute values having the same asset attribute and have different filtering criteria, generate, using the LLM, a plurality of queries that mix the natural language of the RQL to obtain the expanded dataset for the DSL,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating an LLM, the natural language representation of the RQL, a second set of samples using single asset values having same asset attributes and have different filtering criteria, and based on the observation and evaluation, making a judgement and/or opinion of queries that mix the natural langue of the RQL to obtain the expanded dataset for the DSL.
Validate the expanded dataset for the DSL,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by observing and evaluating the expanded dataset for the DSL and making a judgement and/or opinion, based on the observation and evaluation, of the validity of the expanded dataset.
wherein the expanded dataset for the DSL is input to the LLM for fine tune training of the LLM;
The limitation recites a mathematical concept of executing a mathematical function in the form of fine tune training an LLM that takes as input the expanded dataset.
STEP 2A Prong Two:The claims recite using “a processor”, “a memory”, and “a non-transitory computer readable medium”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The claims recite the following additional elements:
Wherein the DSL includes a resource query language (RQL);
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the domain specific language as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the plurality of finding types includes a first finding type and a second finding type;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the plurality of finding types as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the same asset attribute includes one or more of the following: asset name, asset type, asset class, cloud type, cloud account, finding name, finding type, and/or finding severity,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the asset attribute as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the plurality of queries include a first query and a second query,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the plurality of queries as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the first query includes the asset class and the first finding type,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the first query as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the second query includes the asset class and the second finding type;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the second query as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the filtering criteria includes one or more of the following: a vulnerability having a Common Vulnerability Scoring System (CVSS) score within an allowed range, a vulnerability having an allowed severity, and/or a vulnerability having an allowed vulnerability identifier,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the filtering criteria as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the plurality of queries include a third query and a fourth query,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the plurality of queries as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the third query includes the asset class and the first finding type,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the third query as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the fourth query includes the asset class and the second finding type;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the fourth query as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
output search results for a query based on the validated expanded dataset;
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to “Wherein the DSL includes a resource query language (RQL).” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the plurality of finding types includes a first finding type and a second finding type;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the same asset attribute includes one or more of the following: asset name, asset type, asset class, cloud type, cloud account, finding name, finding type, and/or finding severity;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the plurality of queries include a first query and a second query,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the first query includes the asset class and the first finding type,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the second query includes the asset class and the second finding type;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the filtering criteria includes one or more of the following: a vulnerability having a Common Vulnerability Scoring System (CVSS) score within an allowed range, a vulnerability having an allowed severity, and/or a vulnerability having an allowed vulnerability identifier;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the plurality of queries include a third query and a fourth query,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the third query includes the asset class and the first finding type,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “Wherein the fourth query includes the asset class and the second finding type;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “output search results for a query based on the validated expanded dataset;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(i).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As per Dependent Claims 3-10, 13-16, 19-20, and 22-23,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed system (claims 1, 3-10, and 22-23), method (claims 11 and 13-16), and computer program product (claims 17 and 19-20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The dependent claims 3-10, 13-16, 19-20, and 22-23 recite the following limitations directed to an abstract idea:
The limitation of Dependent Claims 4, 14, and 20 include the step(s) of:
Wherein the LLM is fine-tuned.
The limitation recites a mathematical concept of executing a mathematical function in the form of fine tune training an LLM that takes as input the seed dataset.
The limitation of Dependent Claim 5 includes the step(s) of:
Wherein the LLM is fine-tuned for performing automated entity extraction for multi-domain security applications.
The limitation recites a mathematical concept of executing a mathematical function in the form of fine tune training an LLM that takes as input the seed dataset.
Analyzing the limitation as if the intended use language was recited in a positive step, the limitation recites a mathematical concept of executing a mathematical function in the form of the LLM that performs automated entity extraction.
The limitation of Dependent Claim 6 includes the step(s) of:
Wherein the LLM is fine-tuned for performing a cross-domain search to generate a search result using a plurality of data source domains that includes using a planner, executor, and aggregator to collect distinct results from each of the plurality of data source domains.
The limitation recites a mathematical concept of executing a mathematical function in the form of fine tune training an LLM that takes as input the seed dataset.
Analyzing the limitation as if the intended use language was recited in a positive step, the limitation recites a mathematical concept of executing a mathematical function in the form of the LLM that performs a cross-domain search and outputs search results.
The limitation of Dependent Claim 7 includes the step(s) of:
Wherein the LLM is fine-tuned for performing a natural language (NL) query for a plurality of data source domains for a cloud security application,
The limitation recites a mathematical concept of executing a mathematical function in the form of fine tune training an LLM that takes as input the seed dataset.
Analyzing the limitation as if the intended use language was recited in a positive step, the limitation recites a mathematical concept of executing a mathematical function in the form of the LLM that performs a natural language query for a plurality of data source domains.
The limitation of Dependent Claims 8 and 15 include the step(s) of:
Generate an RQL query in response to a natural language query using the fine-tuned LLM.
The limitation recites a mathematical concept of executing a mathematical function in the form of an LLM that takes as input a natural language query and outputs an RQL query.
The limitation of Dependent Claims 9 and 16 include the step(s) of:
automatically generate a configuration policy in RQL from a natural language (NL) input.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating a natural language input, and making a judgement and/or opinion of a configuration policy in RQL based on the observation and evaluation.
The limitation of Dependent Claim 10 includes the step(s) of:
Wherein the LLM is fine-tuned for performing a cross-domain search to generate a search result using a plurality of data source domains to collect distinct results from each of the plurality of data source domains, and
Analyzing the limitation as if the intended use language was recited in a positive step, the limitation recites a mathematical concept of executing a mathematical function in the form of the LLM that performs a cross-domain search and outputs search results.
automatically generate an output in response to a natural language query, wherein one or more of the plurality of data source domains are searched using queries in RQL.
The limitation recites a mathematical concept of executing a mathematical function in the form of the LLM that takes as input a natural language query and outputs a response based on performing a search of one or more data source domains using queries in RQL (based on the input natural language query).
The limitation of Dependent Claim 21 includes the step(s) of:
wherein the expanding of the seed dataset comprises to generate a plurality of variations of the natural language representation to obtain the expanded dataset for the DSL;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating the natural language representation and based on the observation and evaluation, making a judgement and/or opinion of variations of the natural language representation to obtain the expanded dataset for the DSL.
The limitation of Dependent Claim 22 includes the step(s) of:
wherein the expanding of the seed dataset comprises to, for a first set of samples using single attribute values having same asset attribute, generate, using the LLM, queries having a plurality of attribute values for the same asset attribute to obtain the expanded dataset for the DSL,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating a first set of samples using single asset values having same asset attributes, and based on the observation and evaluation, making a judgement and/or opinion of queries having a plurality of asset values for the same asset attributes to obtain the expanded dataset for the DSL.
The limitation of Dependent Claim 23 includes the step(s) of:
wherein the expanding of the seed dataset comprises to, for a second set of samples using single attribute values having the same asset attribute and have different filtering criteria, generate, using the LLM, queries that mix the natural language of the RQL to obtain the expanded dataset for the DSL,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by observing and evaluating an LLM, the natural language representation of the RQL, a second set of samples using single asset values having same asset attributes and have different filtering criteria, and based on the observation and evaluation, making a judgement and/or opinion of queries that mix the natural langue of the RQL to obtain the expanded dataset for the DSL.
STEP 2A Prong Two:The claim(s) recite the following additional elements:
The limitation of Dependent Claims 3, 13, and 19 include the step(s) of:
Wherein the RQL is generated for RQL for multi-domain security applications.
The limitation recites an insignificant extra-solution activity as selecting a particular type of resource data being used to represent the resource query language as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claims 4, 14, and 20 include the step(s) of:
Wherein the LLM is fine-tuned for a cloud security application.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data/application being performed by the LLM as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 7 includes the step(s) of:
Wherein the DSL is a resource query language (RQL),
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the domain specific language as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the LLM is fine-tuned for performing a natural language (NL) query for a plurality of data source domains for a cloud security application, and
The limitation recites an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ through executing a query/search as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the plurality of data source domains includes a configuration data set, an Identity and Asset Management (IAM) data set, and a vulnerability data set.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being represented in the data source domains as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claims 9 and 16 include the step(s) of:
Wherein the DSL is a resource query language (RQL),
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the domain specific language as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 22 includes the step(s) of:
wherein the same asset attribute includes one or more of the following: asset name, asset type, asset class, cloud type, cloud account, finding name, finding type, and/or finding severity.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the asset attribute as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the plurality of queries include a first query and a second query,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the plurality of queries as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the first query includes the asset class and the first finding type,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the first query as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the second query includes the asset class and the second finding type;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the second query as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 23 includes the step(s) of:
Wherein the filtering criteria includes one or more of the following: a vulnerability having a Common Vulnerability Scoring System (CVSS) score within an allowed range, a vulnerability having an allowed severity, and/or a vulnerability having an allowed vulnerability identifier;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the filtering criteria as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the plurality of queries include a third query and a fourth query,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the plurality of queries as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the third query includes the asset class and the first finding type,
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the third query as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Wherein the fourth query includes the asset class and the second finding type;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the fourth query as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to Claims 3, 13, and 19 reciting “Wherein the RQL is generated for RQL for multi-domain security applications.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claims 4, 14, and 20 reciting “Wherein the LLM is fine-tuned for a cloud security application.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 7 reciting “Wherein the DSL is a resource query language (RQL).” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 7 reciting “Wherein the plurality of data source domains includes a configuration data set, an Identity and Asset Management (IAM) data set, and a vulnerability data set.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claims 9 and 16 reciting “Wherein the DSL is a resource query language (RQL).” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 22 reciting “Wherein the same asset attribute includes one or more of the following: asset name, asset type, asset class, cloud type, cloud account, finding name, finding type, and/or finding severity;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 22 reciting “Wherein the plurality of queries include a first query and a second query,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 22 reciting “Wherein the first query includes the asset class and the first finding type,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 22 reciting “Wherein the second query includes the asset class and the second finding type;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 23 reciting “Wherein the filtering criteria includes one or more of the following: a vulnerability having a Common Vulnerability Scoring System (CVSS) score within an allowed range, a vulnerability having an allowed severity, and/or a vulnerability having an allowed vulnerability identifier;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 23 reciting “Wherein the plurality of queries include a third query and a fourth query,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 23 reciting “Wherein the third query includes the asset class and the first finding type,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 23 reciting “Wherein the fourth query includes the asset class and the second finding type;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-11, 13-17, 19-20, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Malkiel et al. (U.S. Pre-Grant Publication No. 2021/0182935, hereinafter referred to as Malkiel), and further in view of Das et al. (U.S. Patent No. 11,017,764, hereinafter referred to as Das) and Kim et al. (U.S. Pre-Grant Publication NO. 2021/0303555, hereinafter referred to as Kim).
Regarding Claim 1:
Malkiel teaches a system, comprising:
A processor configured to:
Automatically generate a seed dataset for a domain specific language (DSL),
Malkiel teaches “the seed item 108 is any item within the selected domain which has been selected or otherwise indicated by a user from a plurality of items within the selected domain” (Para. [0033]). thereby teaching a seed dataset for a domain specific language being generated from the selected content.
Expand, using a Large Language Model (LLM), the seed dataset for the DSL to obtain an expanded dataset for the DSL,
Malkiel teaches “the trained language model infers item similarities between a seed item and a plurality of candidate items via the trained language model at 1406. The trained language model outputs a set of recommended items from a plurality of candidate items in a catalog maximizing item similarities between the seed item and the candidate items at 1408.” (Para. [0109]).
Validate the expanded dataset for the DSL, wherein the expanded dataset for the DSL is input to the LLM for fine tune training of the LLM;
Malkiel teaches “the trained language model 106 generates a set of one or more recommended items 110 from a plurality of candidate items” and “The plurality of candidate items are described in the domain-specific corpus 104, which is used to train or fine-tune a specialist language model, such as, but not limited to, the trained language model 106” (Para. [0034]) thereby teaching that the expanded dataset of recommended items was input to the LLM for fine tune training.
Malkiel further teaches validating the expanded dataset by teaching “the domain-related corpus in other examples is split into two separate sets, a trained data set and a validation data set” (Para. [0044]) and “after every epoch, the model performance is evaluated on the hold-out validation data set” where “at the end of the language model training, the best model is selected according to the accuracy of the reconstructing masked words, reported on the validation set” (Para. [0051]).
Output search results for a query based on the validated expanded dataset; and
Malkiel teaches output search results (top K recommended items) based on an expanded dataset used to previously fine tune the LLM (the trained language model 202) by teaching “In order to produce similarity for a given seed item S, a calculation component 210 calculates a cosine similarity 212 between the embedding vector of the seed item “S” and the embedding vectors of all the other items in the catalog. A scoring component 216 optionally converts the cosine similarity 212 to a set of similarity scores 218 for each candidate item. A recommendation component 214 sorts the candidate items in a descending order according to this cosine score for each item in the set of similarity scores 218. The recommendation component 214 retrieves the top K items 220 from the plurality of candidate items 208 as recommendations to form a set of recommended items 222, where “K” represents a user-configurable or user-selected number of items. The set of recommended items 222 is a set of one or more recommended items which is the same or similar to the seed item with regard to one or more attributes or properties, such as, but not limited to, the recommended item 224.” (Para. [0040])
A memory coupled to the processor and configured to provide the processor with instructions.
Malkiel teaches “Some aspects and examples disclosed herein are directed to a system, method and/or computer executable instructions for generating cold-start recommendations based on title and description relationships comprising: a processor; and a computer-readable medium storing instructions that are operative upon execution by the processor” (Para. [0114]).
Malkiel explicitly teaches all of the elements of the claimed invention as recited above except:
Wherein the DSL includes a resource query language (RQL).
Wherein the automatically generating of the seed dataset comprises to: Generate a natural language representation of the RQL to obtain the seed dataset,
Wherein the natural language representation is categorized into an asset class,
Wherein the asset class is associated with a plurality of finding types,
Wherein the plurality of finding types includes a first finding type and a second finding type;
Wherein the expanding comprising to perform one or more of the following:
A) for a first set of samples using single attribute values having same asset attributes, generate, using the LLM, queries having a plurality of attribute values for the same asset attribute to obtain the expanded dataset for the DSL,
wherein the same asset attribute includes one or more of the following: asset name, asset type, asset class, cloud type, cloud account, finding name, finding type, and/or finding severity,
wherein the plurality of queries include a first query and a second query, wherein the first query includes the asset class and the first finding type, wherein the second query includes the asset class and the second finding type; and/or
B) for a second set of samples using single attribute values having the same asset attribute and have different filtering criteria, generate, using the LLM, queries that mix the natural language of the RQL to obtain the expanded dataset for the DSL,
wherein the filtering criteria includes one or more of the following: a vulnerability having a Common Vulnerability Scoring System (CVSS) score within an allowed range, a vulnerability having an allowed severity and/or a vulnerability having an allowed vulnerability identifier,
wherein the plurality of queries include a third query and a fourth query, wherein the third query includes the asset class and the first finding type, wherein the fourth query includes the asset class and the second finding type;
However, in the related field of endeavor of requesting domain specific information, Das teaches:
Wherein the DSL includes a resource query language (RQL).
Das teaches “receiving the NL request” at a request processing engine 2020 for translating “the NL request 2015 to the domain-specific language (DSL) request 2045” (Col. 74 Lines 14-33 & Fig. 21) thereby teaching a query/request in a language for a specific resource/domain.
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Das and Malkiel at the time that the claimed invention was effectively filed, to have combined the conversion of natural langue request to a domain specific language request, as taught by Das, with the systems and methods for recommending domain specific items, as taught by Malkiel.
One would have been motivated to make such combination because Das teaches allowing for a user to enter a natural language request and converting the natural language request to a domain specific language request (Col. 74 Lines 14-33) and it would have been obvious to a person having ordinary skill in the art that enabling users to request domain specific content through natural language input would expand the search options for users when combined with Malkiel.
Das and Malkiel explicitly teach all of the elements of the claimed invention as recited above except:
Wherein the automatically generating of the seed dataset comprises to: Generate a natural language representation of the RQL to obtain the seed dataset,
Wherein the natural language representation is categorized into an asset class,
Wherein the asset class is associated with a plurality of finding types,
Wherein the plurality of finding types includes a first finding type and a second finding type;
Wherein the expanding comprising to perform one or more of the following:
A) for a first set of samples using single attribute values having same asset attributes, generate, using the LLM, queries having a plurality of attribute values for the same asset attribute to obtain the expanded dataset for the DSL,
wherein the same asset attribute includes one or more of the following: asset name, asset type, asset class, cloud type, cloud account, finding name, finding type, and/or finding severity,
wherein the plurality of queries include a first query and a second query, wherein the first query includes the asset class and the first finding type, wherein the second query includes the asset class and the second finding type; and/or
B) for a second set of samples using single attribute values having the same asset attribute and have different filtering criteria, generate, using the LLM, queries that mix the natural language of the RQL to obtain the expanded dataset for the DSL,
wherein the filtering criteria includes one or more of the following: a vulnerability having a Common Vulnerability Scoring System (CVSS) score within an allowed range, a vulnerability having an allowed severity and/or a vulnerability having an allowed vulnerability identifier,
wherein the plurality of queries include a third query and a fourth query, wherein the third query includes the asset class and the first finding type, wherein the fourth query includes the asset class and the second finding type;
However, in the related field of endeavor of enhancing training data, Kim teaches:
Wherein the automatically generating of the seed dataset comprises to: generate a natural language representation of the RQL to obtain the seed dataset,
Kim teaches “facilitating increased levels of scaling training data for, in turn, more sophisticated and robust machine-learning models” (Para. [0024]) where “improved NL2QL machine-learning models from training on enhanced training data comprising NL2QL pairs generated via logical-form dialogue construction” (Para.[0093]) and “for an applicable predicate of the subsequent dialogue sequence, the NL2QL pair generation system can generate both a natural language query and a query-language representation by using the NL2QL template” (Para. [0022])).
Wherein the natural language representation is categorized into an asset class,
Kim teaches, given the broadest reasonable interpretation of “asset class”, categorizing into an asset class by teaching “For example, and as illustrated in FIG. 2, a first entity (‘Employee’) may correspond to two properties, ‘name’ and ‘hire year.’ Likewise, for instance, a second entity (‘Office’) may correspond to a third property, ‘floor.’” And “the entity relationship between the first entity and the second entity comprises ‘has_office,’ where an entity relationship can include 1:N mappings between entities” (Para. [0047]).
Malkiel also teaches categorizing into an asset class, given the broadest reasonable interpretation of “asset class”, by teaching: “Likewise, a domain for wines in another example includes a list of wines. Each wine item in the catalog include a name of a wine and a description of the wine. The description of each item in this non-limiting example may include the color (red or white), the producer or vineyard from which the wine originated, year of bottling, or other descriptive information.” (Para.[0025])
Wherein the asset class is associated with a plurality of finding types,
Kim teaches “an entity, such as ‘Employee,’ may correspond to a table in a relational database (e.g., ‘EmployeeTbl’ as shown in the domain database 206) or a node in a graph-based database. Further, an entity can possess one or more properties. In some embodiments, a property, such as ‘hire year,’ may correspond to a column or row in a relational database (e.g., ‘hireyear’ as shown in the domain database 206) or a node in a graph-based database. Additionally, in some implementations, an entity relationship like ‘has_office’ in a relational database corresponds to either a column/row (1:1 or 1:N mappings) or another table (N:N mappings).” (Para. [0048]). Therefore, Kim teaches asset attributes being associated in a one-to-one, one-to-many, and many-to-many manner and multiple finding types such as there being multiple floors.
Malkiel also teaches associating asset attributes including at least asset name, asset type, asset class, finding name, and finding type, given their broadest reasonable interpretations, by teaching: “Likewise, a domain for wines in another example includes a list of wines. Each wine item in the catalog include a name of a wine and a description of the wine. The description of each item in this non-limiting example may include the color (red or white), the producer or vineyard from which the wine originated, year of bottling, or other descriptive information.” (Para.[0025])
Wherein the plurality of finding types includes a first finding type and a second finding type;
Kim teaches “an entity, such as ‘Employee,’ may correspond to a table in a relational database (e.g., ‘EmployeeTbl’ as shown in the domain database 206) or a node in a graph-based database. Further, an entity can possess one or more properties. In some embodiments, a property, such as ‘hire year,’ may correspond to a column or row in a relational database (e.g., ‘hireyear’ as shown in the domain database 206) or a node in a graph-based database. Additionally, in some implementations, an entity relationship like ‘has_office’ in a relational database corresponds to either a column/row (1:1 or 1:N mappings) or another table (N:N mappings).” (Para. [0048]). Therefore, Kim teaches asset attributes being associated in a one-to-one, one-to-many, and many-to-many manner and multiple finding types such as there being multiple floors containing multiple offices for different employees.
Wherein the expanding comprising to perform one or more of the following:
A) for a first set of samples using single attribute values having same asset attributes, generate, using the LLM, a plurality of queries having a plurality of attribute values for the same asset attribute to obtain the expanded dataset for the DSL,
It is noted that Para. [0260] of the present Specification states that “It should be noted that one sample includes two parts: (1) the RQL query generated, and (2) the corresponding natural language (NL) representation for that query.”.
Malkiel teaches “The language model 600 iterates over each candidate item comparing each candidate item to the seed item 640 to generate a set of lists 642.” (Para. [0076]) and “the language model 700 iterates over the data samples of the training set, including textual data 702.” (Para. [0079]) and “The set of recommended items 222 is a set of one or more recommended items which is the same or similar to the seed item with regard to one or more attributes or properties,” (Para. [0040]).Kim teaches “the NL2QL pair generation system exploits inputs comprising a database, lexicon, and an ontology to rapidly generate a large-scale dataset of paired natural language queries and query-language representations” (Para. [0018]) and “the NL2QL pair generation system can generate both a natural language query and a query-language representation by using the NL2QL template” (Para. [0022]).
Therefore, Malkiel in combination with Kim teach for samples using attribute values and asset attributes (data samples of the training set including textual data - Malkiel), generating (using the LLM taught by Malkiel) queries having a plurality of attribute values for the same asset attribute to obtain the expanded dataset for the DSL (generating NL2QL pairs – Kim)
Wherein the same asset attribute includes one or more of the following: asset name, asset type, asset class, cloud type, cloud account, finding name, finding type, and/or finding severity,
Kim teaches asset attributes as at least asset name, asset class, finding name, and finding type, given their broadest reasonable interpretations, by teaching: “For example, and as illustrated in FIG. 2, a first entity (‘Employee’) may correspond to two properties, ‘name’ and ‘hire year.’ Likewise, for instance, a second entity (‘Office’) may correspond to a third property, ‘floor.’” (Para. [0047]).
Malkiel also teaches asset attributes as at least asset name, asset type, asset class, finding name, and finding type, given their broadest reasonable interpretations, by teaching: “Likewise, a domain for wines in another example includes a list of wines. Each wine item in the catalog include a name of a wine and a description of the wine. The description of each item in this non-limiting example may include the color (red or white), the producer or vineyard from which the wine originated, year of bottling, or other descriptive information.” (Para.[0025])
wherein the plurality of queries include a first query and a second query, wherein the first query includes the asset class and the first finding type, wherein the second query includes the asset class and the second finding type; and/or
Malkiel teaches “The language model 600 iterates over each candidate item comparing each candidate item to the seed item 640 to generate a set of lists 642.” (Para. [0076]) and “the language model 700 iterates over the data samples of the training set, including textual data 702.” (Para. [0079]) where “The set of recommended items 222 is a set of one or more recommended items which is the same or similar to the seed item with regard to one or more attributes or properties,” (Para. [0040]). Malkiel further teaches “The language model iterates over the vectors for each item to generates the similarity scores for each item. The scores are compared to identify or maximize the similarity between the seed item and the candidate items. The candidate items having scores indicating the closes similarities with the seed item are chosen for recommendation to the user.” (Para. [0109]).
Kim teaches “an entity, such as ‘Employee,’ may correspond to a table in a relational database (e.g., ‘EmployeeTbl’ as shown in the domain database 206) or a node in a graph-based database. Further, an entity can possess one or more properties. In some embodiments, a property, such as ‘hire year,’ may correspond to a column or row in a relational database (e.g., ‘hireyear’ as shown in the domain database 206) or a node in a graph-based database. Additionally, in some implementations, an entity relationship like ‘has_office’ in a relational database corresponds to either a column/row (1:1 or 1:N mappings) or another table (N:N mappings).” (Para. [0048]). Therefore, Kim teaches asset attributes such as asset class and finding type being associated in a one-to-one, one-to-many, and many-to-many manner and multiple finding types such as there being multiple floors containing multiple offices for different employees.
Malkiel also teaches categorizing into an asset class, given the broadest reasonable interpretation of “asset class”, by teaching: “Likewise, a domain for wines in another example includes a list of wines. Each wine item in the catalog include a name of a wine and a description of the wine. The description of each item in this non-limiting example may include the color (red or white), the producer or vineyard from which the wine originated, year of bottling, or other descriptive information.” (Para.[0025])
Therefore, Malkiel in combination with Kim teach generating (using the LLM taught by Malkiel) queries having a plurality of attribute values for the same asset attribute, some of the attribute values being the same and some of the attribute values being different to the seed/input (similarity between the seed item and the candidate items taught by Malkiel) where the attributes being at least asset class and finding type of the candidate items can be similar across candidate items, and thus some attributes are the same and some are different (attributes including at least name, office, name, hire year, whether the employee has an office, the office floor, and the office floor with a 1:1, 1:N, and N:N relationship, as taught by Kim).
B) for a second set of samples using single attribute values having the same asset attribute and have different filtering criteria, generate, using the LLM, a plurality of queries that mix the natural language of the RQL to obtain the expanded dataset for the DSL,
It is noted that Para. [0260] of the present Specification states that “It should be noted that one sample includes two parts: (1) the RQL query generated, and (2) the corresponding natural language (NL) representation for that query.”
Malkiel teaches “The language model 600 iterates over each candidate item comparing each candidate item to the seed item 640 to generate a set of lists 642.” (Para. [0076]) and “the language model 700 iterates over the data samples of the training set, including textual data 702.” (Para. [0079]) and “The set of recommended items 222 is a set of one or more recommended items which is the same or similar to the seed item with regard to one or more attributes or properties,” (Para. [0040]).Kim teaches “the NL2QL pair generation system exploits inputs comprising a database, lexicon, and an ontology to rapidly generate a large-scale dataset of paired natural language queries and query-language representations” (Para. [0018]) and “the NL2QL pair generation system can generate both a natural language query and a query-language representation by using the NL2QL template” (Para. [0022]).
Therefore, Malkiel in combination with Kim teach for samples using asset values and asset attributes (data samples of the training set including textual data - Malkiel), generating (using the LLM taught by Malkiel) queries having a plurality of assets or values for the same asset attributes to obtain the expanded dataset for the DSL (generating NL2QL pairs – Kim)
Wherein the filtering criteria includes one or more of the following: a vulnerability having a Common Vulnerability Scoring System (CVSS) score within an allowed range, a vulnerability having an allowed severity and/or a vulnerability having an allowed vulnerability identifier,
Das teaches “Examples of data models can include electronic mail, authentication, databases, intrusion detection, malware, application state, alerts, compute inventory, network sessions, network traffic, performance, audits, updates, vulnerabilities, etc. Data models and their objects can be designed by knowledge managers in an organization, and they can enable downstream users to quickly focus on a specific set of data.” (Col. 42 Lines 39-50).
wherein the plurality of queries include a third query and a fourth query, wherein the third query includes the asset class and the first finding type, wherein the fourth query includes the asset class and the second finding type;
Malkiel teaches “The language model 600 iterates over each candidate item comparing each candidate item to the seed item 640 to generate a set of lists 642.” (Para. [0076]) and “the language model 700 iterates over the data samples of the training set, including textual data 702.” (Para. [0079]) where “The set of recommended items 222 is a set of one or more recommended items which is the same or similar to the seed item with regard to one or more attributes or properties,” (Para. [0040]). Malkiel further teaches “The language model iterates over the vectors for each item to generates the similarity scores for each item. The scores are compared to identify or maximize the similarity between the seed item and the candidate items. The candidate items having scores indicating the closes similarities with the seed item are chosen for recommendation to the user.” (Para. [0109]).
Kim teaches “an entity, such as ‘Employee,’ may correspond to a table in a relational database (e.g., ‘EmployeeTbl’ as shown in the domain database 206) or a node in a graph-based database. Further, an entity can possess one or more properties. In some embodiments, a property, such as ‘hire year,’ may correspond to a column or row in a relational database (e.g., ‘hireyear’ as shown in the domain database 206) or a node in a graph-based database. Additionally, in some implementations, an entity relationship like ‘has_office’ in a relational database corresponds to either a column/row (1:1 or 1:N mappings) or another table (N:N mappings).” (Para. [0048]). Therefore, Kim teaches asset attributes such as asset class and finding type being associated in a one-to-one, one-to-many, and many-to-many manner and multiple finding types such as there being multiple floors containing multiple offices for different employees.
Malkiel also teaches categorizing into an asset class, given the broadest reasonable interpretation of “asset class”, by teaching: “Likewise, a domain for wines in another example includes a list of wines. Each wine item in the catalog include a name of a wine and a description of the wine. The description of each item in this non-limiting example may include the color (red or white), the producer or vineyard from which the wine originated, year of bottling, or other descriptive information.” (Para.[0025])
Therefore, Malkiel in combination with Kim teach generating (using the LLM taught by Malkiel) queries having a plurality of attribute values for the same asset attribute, some of the attribute values being the same and some of the attribute values being different to the seed/input (similarity between the seed item and the candidate items taught by Malkiel) where the attributes being at least asset class and finding type of the candidate items can be similar across candidate items, and thus some attributes are the same and some are different (attributes including at least name, office, name, hire year, whether the employee has an office, the office floor, and the office floor with a 1:1, 1:N, and N:N relationship, as taught by Kim).
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Kim, Das, and Malkiel at the time that the claimed invention was effectively filed, to have combined the improved NL2QL machine-learning models due to being trained on enhanced training data comprising NL2QL pairs generated via logical-form dialogue construction, as taught by Kim, with the conversion of natural langue request to a domain specific language request, as taught by Das, and the systems and methods for recommending domain specific items, as taught by Malkiel.
One would have been motivated to make such combination because Kim teaches “the third-party crowdsourcing system 374 can complement and add variety to NL2QL pairs, thereby further improving the NL2QL pair database 378” (Para. [0087]) and it would have been obvious to a person having ordinary skill in the art that allowing a third-party to complement and add variety to the NL2QL pairs would improve the diversity of the data.
Regarding Claim 3:
Kim, Das, and Malkiel further teach:
Wherein the RQL is generated for RQL for multi-domain security applications.
Das teaches “the search head combines the partial results and/or events received from the indexers to produce a final result for the query. In some examples, the results of the query are indicative of performance or security of the IT environment” (215)
Regarding Claim 4:
Kim, Das, and Malkiel further teach:
Wherein the LLM is fine-tuned for a cloud security application.
Malkiel teaches “The language model 102 in some examples is an untrained general language model or a pre-trained language model that is trained or fine-tuned using a domain-specific corpus 104 to create a more domain-specific, specialized trained language model 106.” (Para. [0033]).
Regarding Claim 5:
Kim, Das, and Malkiel further teach:
Wherein the LLM is fine-tuned for performing automated entity extraction for multi-domain security applications.
Malkiel teaches “The language model 102 in some examples is an untrained general language model or a pre-trained language model that is trained or fine-tuned using a domain-specific corpus 104 to create a more domain-specific, specialized trained language model 106.” (Para. [0033]).
Regarding Claim 6:
Kim, Das, and Malkiel further teach:
Wherein the LLM is fine-tuned for performing a cross-domain search to generate a search result using a plurality of data source domains that includes using a planner, executor, and aggregator to collect distinct results from each of the plurality of data source domains.
Malkiel teaches “The language model 102 in some examples is an untrained general language model or a pre-trained language model that is trained or fine-tuned using a domain-specific corpus 104 to create a more domain-specific, specialized trained language model 106.” (Para. [0033]).
Regarding Claim 7:
Kim, Das, and Malkiel further teach:
Wherein the DSL is a resource query language (RQL),
Das teaches “receiving the NL request” at a request processing engine 2020 for translating “the NL request 2015 to the domain-specific language (DSL) request 2045” (Col. 74 Lines 14-33 & Fig. 21).
Wherein the LLM is fine-tuned for performing a natural language (NL) query for a plurality of data source domains for a cloud security application, and
Malkiel teaches “The language model 102 in some examples is an untrained general language model or a pre-trained language model that is trained or fine-tuned using a domain-specific corpus 104 to create a more domain-specific, specialized trained language model 106.” (Para. [0033]).
Wherein the plurality of data source domains includes a configuration data set, an Identity and Asset Management (IAM) data set, and a vulnerability data set.
Das teaches “The enterprise security application can process many types of security-related information. In general, this security-related information can include any information that can be used to identify security threats. For example, the security-related information can include network-related information, such as IP addresses, domain names, asset identifiers, network traffic volume, uniform resource locator strings, and source addresses” and “Security-related information can also include malware infection data and system configuration information, as well as access control information, such as login/logout information and access failure notifications. The security-related information can originate from various sources within a data center, such as hosts, virtual machines, storage devices and sensors. The security-related information can also originate from various sources in a network, such as routers, switches, email servers, proxy servers, gateways, firewalls and intrusion-detection systems” (Col. 55 Lines 12-49).
Regarding Claim 8:
Kim, Das, and Malkiel further teach the processor is further configured to:
Generate an RQL query in response to a natural language query using the fine-tuned LLM.
Das teaches “receiving the NL request” at a request processing engine 2020 for translating “the NL request 2015 to the domain-specific language (DSL) request 2045” (Col. 74 Lines 14-33 & Fig. 21).
Regarding Claim 9:
Kim, Das, and Malkiel further teach:
Wherein the DSL is a resource query language (RQL), and
Das teaches “receiving the NL request” at a request processing engine 2020 for translating “the NL request 2015 to the domain-specific language (DSL) request 2045” (Col. 74 Lines 14-33 & Fig. 21).
Wherein the processor is further configured to automatically generate a configuration policy in RQL from a natural language (NL) input.
Das teaches “receiving the NL request” at a request processing engine 2020 for translating “the NL request 2015 to the domain-specific language (DSL) request 2045” (Col. 74 Lines 14-33 & Fig. 21).
Das further teaches “events are field-searchable using one or more configuration files associated with the events. Each configuration file includes one or more field names, where each field name is associated with a corresponding extraction rule and a set of events to which that extraction rule applies” (Col. 7 Line 52 – Col. 8 Line 15).
Regarding Claim 10:
Kim, Das, and Malkiel further teach:
Wherein the LLM is fine-tuned for performing a cross-domain search to generate a search result using a plurality of data source domains to collect distinct results from each of the plurality of data source domains, and
It is noted that the limitation recites intended use language of “…for performing…”, “…to generate…”, and “…to collect…” and is therefore not being given patentable weight. The scope of the limitation is understood as reciting “wherein the LLM is fine-tuned”.
Malkiel teaches “The language model 102 in some examples is an untrained general language model or a pre-trained language model that is trained or fine-tuned using a domain-specific corpus 104 to create a more domain-specific, specialized trained language model 106.” (Para. [0033]).
Wherein the processor is further configured to automatically generate an output in response to a natural language query, wherein one or more of the plurality of data source domains are searched using queries in RQL.
Das teaches “receiving the NL request” at a request processing engine 2020 for translating “the NL request 2015 to the domain-specific language (DSL) request 2045” and “execute the DSL request 2045” (Col. 74 Lines 14-33 & Fig. 21). Das further teaches “the NL application 1940 applies the DSL request 2045 to the associated domain-specific data source 1920” (Col. 75 Lines 18-25).
Regarding Claim 11:
All of the limitations herein are similar to some or all of the limitations of Claim 1.
Regarding Claim 13:
All of the limitations herein are similar to some or all of the limitations of Claim 3.
Regarding Claim 14:
All of the limitations herein are similar to some or all of the limitations of Claim 4.
Regarding Claim 15:
All of the limitations herein are similar to some or all of the limitations of Claim 8.
Regarding Claim 16:
All of the limitations herein are similar to some or all of the limitations of Claim 9.
Regarding Claim 17:
Some of the limitations herein are similar to some or all of the limitations of Claim 1.
Kim, Das, and Malkiel further teach:
A non-transitory computer readable medium comprising computer instructions.
Malkiel teaches “a system, method and/or computer executable instructions for generating cold-start recommendations based on title and description relationships comprising: a processor; and a computer-readable medium storing instructions that are operative upon execution by the processor” (Para. [0114]).
Regarding Claim 19:
All of the limitations herein are similar to some or all of the limitations of Claim 3.
Regarding Claim 20:
All of the limitations herein are similar to some or all of the limitations of Claim 4.
Regarding Claim 22:
All of the limitations herein are similar to some or all of the limitations of Claim 1.
Regarding Claim 23:
All of the limitations herein are similar to some or all of the limitations of Claim 1.
Response to Amendment
Applicant’s Amendments, filed on 4/25/2026, are acknowledged and addressed herein.
Response to Arguments
On pages 9-11 of the Remarks filed on 4/25/2026, Applicant states that “the claimed invention addresses the technical issue” related to “paragraphs [0020] – [0026] of the specification” and that “the claims are directed to provide technical improvements, as recited in paragraph [0057], where ‘users can utilize RQL to find answers to fundamental questions that help them understand what is happening on their network. For example, users can find answers to the following types of questions: (1) does our enterprise have S3 buckets with encryption disabled; (2) does our enterprise have databases that are directly accessible from the Internet; (3) who uses a root account to manage day-to-day administrative activities on my network; (4) which cloud resources are missing critical patches that make them exploitable; etc.’”Applicant’s argument is not convincing to overcome the 101 rejection because the claims very broadly recite outputting search results (any search results) “for a query based on the validated expanded dataset” which is understood as an insignificant extra solution activity as retrieval of data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
On pages 12-13 of the Remarks filed on 4/25/2026, Applicant states that “as discussed during the interview, the applied references fail to teach or render obvious” the amended limitations “as recited in claim 1 and similarly recited in claims 11 and 17”.Upon further review, Applicant’s amendments and remarks were not found to overcome the previously cited prior art. The newly amended limitations are addressed in full in the rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Peraud et al. (U.S. Pre-Grant Publication No. 2022/0230089) teaches a classifier may be trained with less than all datasets manually annotated with labels. A small subset of verbatims may be manually labeled with topic labels as seeds. Data augmentations can be used to acquire seed verbatim sets for known topics and to assign temporary pseudo labels to the rest of the verbatims based on their vector space proximity to the labeled seed verbatims. The training may involve classification epochs during which embeddings are updated with the assumption that the pseudo labels are ground-truth labels. The training may also involve labeling epochs during which the updated embeddings are used to update the vectors corresponding to the verbatims, and pseudo labels are updated based on updated vector coordinates in the vector space. As the training process progresses through the epochs, the embeddings will converge.The reference further teaches “This base natural language model 116 may then be fine-tuned using relevant domain language to develop a domain natural language model 118. For example, if the relevant domain is gaming, then the base natural language model 116 may be fine-tuned on a corpus of millions of examples of gaming domain language spanning a variety of gaming products and a variety of entry points for gaming user feedback” (Para. -0075])The reference further teaches “This fine-tuned domain natural language model 118 may then be further trained and scored with the seed verbatims 112 that have been manually labeled by human SMEs 110.” (Para. [0076]).
Akbacak et al. (U.S. Pre-Grant Publication No. 2015/0332670) teaches training language models using in-domain-like data collected automatically from one or more data sources. The data sources (such as text data or user-interactional data) are mined for specific types of data, including data related to style, content, and probability of relevance, which are then used for language model training. In one embodiment, a language model is trained from features extracted from a knowledge graph modified into a probabilistic graph, where entity popularities are represented and the popularity information is obtained from data sources related to the knowledge. Embodiments of language models trained from this data are particularly suitable for domain-specific conversational understanding tasks where natural language is used, such as user interaction with a game console or a personal assistant application on personal device.
Poirier et al. (U.S. Pre-Grant Publication No. 2024/0202464) teaches managing a plurality of agents to generate a response to a query using a multimodal model. An example method uses the plurality of agents to iteratively determine subsequent outputs of the multimodal model satisfies the query. It can generate a respective context associated with a respective output of the multimodal model. And determine, by the multimodal model based on the respective context, whether the respective subsequent output satisfies the query.
Wang et al. (U.S. Pre-Grant Publication No. 2025/0036616) teaches managing report requests are described. A device associated with an identity management platform may receive a message from a user of an organization via a client device. The message may include a natural language and may indicate a request for information associated with the organization. In response to the request, the device may generate a first query based on translating the message into an intermediary language using a machine learning model. The intermediary language may be associated with the identity management platform. The device may transmit a report to the user via the client device. The report may be based on the first query and include the information associated with the organization.The reference further teaches “the query component 530 may be configured to support training the machine learning model to translate the natural language into the intermediary language, where translating the message into the first query is based on the training.” (Para. [0083]).
Gottlob et al. (U.S. Pre-Grant Publication No. 2025/0045256) teaches (i) accessing and/or modifying a database to be automatically curated, (ii) optionally accessing additional data or information sources for further useful data or information, (iii) using one or more pre-trained large language models (LLMs) accessed via API or other connections, issuing prompts and retrieving prompt-answers and executing database curation requests that specify database curation tasks to be performed on at least one sub-structure of the database, the tasks comprising (a) a database enrichment task to compute new data records to be inserted into the database sub-structure, (b) a database verification task to verify, using the one or more LLMs, data contained in the sub-structure, and identify incorrect data, (c) a database update, and (d) a null-value or a missing value replacement task. The requested tasks are automatically performed via a computation comprising an adaptively generated prompt sequence.
Foreign Reference CN117726897A teaches a large language model can be used, for example, a large language model can be used, for example, a large language model can be used, for example, a large language model can be used, for example, a large language model can be used, for example, a large language model can ChatGPT, Wen Hsin and so on, expanding the attribute information. For example, the target attribute value corresponding to each type of attribute information can be determined specifically; expanding the text template based on the target attribute value to obtain at least one image description information.
Bernelas et al. (U.S. Pre-Grant Publication No. 2016/0132304) teaches parsing a domain-specific language (DSL) statement. The method includes one or more processors accessing a DSL statement that includes contracted phrases. The method further includes one or more processors identifying one or more contracted phrases in the DSL statement utilizing an annotated domain vocabulary for a DSL associated with the DSL statement and grammar rules for the DSL. The method further includes one or more processors determining expanded phrases corresponding to the identified one or more contracted phrases based on the annotated domain vocabulary and the grammar rules. The method further includes one or more processors creating an expanded abstract syntax tree (AST) that is representative of the DSL statement with the determined expanded phrases replacing the identified one or more contracted phrases.
O’Toole et al. (U.S. Pre-Grant Publication No. 2021/0216928) teaches performing natural language processing to categorize threats to assets and perform correlated risk analyses for threats. The instructions cause the one or more processors generate risk scores for assets using threat data and one or more asset risk profiles reflecting characteristics of the assets, the risk scores weighted based on impact weights of threats. Furthermore, the instructions cause one or more processors to generate a plurality of graphic user interfaces including one or more of a map providing visual representations of the assets, locations of the assets, asset risk scores, and threats to assets, a forecast of asset risk scores, an asset summary, an asset risk profile editor, and a consolidated view of rank ordered assets and threats.
Redlich et al. (U.S. Pre-Grant Publication No. 2010/0010968) teaches a method and system identifies and classifies and monetizes intangible asset (I.A.) unstructured digital data in a computer system by ownership characterizations and intellectual property (I.P.) classifications. Enterprise and non-enterprise data collections are searched based upon an expanded project specification (of descriptive functions, characteristics and objectives). Primary search terms from the project are expanded by content, contextual and taxonomic analysis to generate a plurality of search string parameters employed to search the data collections. The search engine returns data and documents, ownership characteristics are ascertained by comparison of the returns and I.P. classifications are made based upon the returns. This process also handles unstructured enterprise specific data not otherwise located in the data collections or unstructured data from other I.A. programs such as H.R. performance modules, supply chain modules or CRM modules. Unstructured data matching an organized data set from project and the search returns is integrated into the organized data set.
Pourmohammad (U.S. Pre-Grant Publication No. 2023/0419807) teaches building a new class of applications that improve operational and energy efficiency of building operations that take into account human activities. Such applications, however, rarely experience widespread adoption due to the lack of a common descriptive schema that would enable porting these applications and systems to different buildings. Our demo presents Brick [4], a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. Using a web application, we will demonstrate real buildings that have been mapped to the Brick schema, and show application queries that extracts relevant metadata from these buildings. The attendees would be able to create example buildings and write their own queries.
Rusnak et al. (U.S. Pre-Grant Publication No. 2023/0029420) teaches receiving a prefix for a geographic query and ranking received candidate results for the prefix of the geographic query. The systems, devices, and methods may provide a query expansion based on a highest ranked candidate result. The systems, devices, and methods may determine a likelihood of the user selecting the query expansion, and if the likelihood exceeds a threshold, provide the query expansion.The reference also teaches “another example factor that the machine learning models 22 use includes determining what type of geo-entity is the top suggestion in the list of ranked candidate results 18 (e.g., whether the top entity is a place, address, business, etc.). As such, the machine learning models 22 may derive the words or phrases to include in the query expansion 16 from the list of ranked candidate results 18 based on the highest ranked candidate result 26 in combination with a variety of factors.” (Para. [0060]).
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.
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/ROBERT F MAY/Examiner, Art Unit 2154 6/6/2026
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
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