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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/28/2025 has been entered.
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 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites (emphasis added):
A method, comprising:
processing a natural language query
using an artificial intelligence based framework of an external service that provides network and security operations to a private network;
generating an output query comprising a syntax, structure, and nomenclature of a proprietary query language that unifies network and security operations components of the external service by translating the natural language query into the output query
generating a network or security operations response to the natural language query by processing the output query using one or more network and security operations components of the external service.
Examiner finds that the emphasized portions of claim 1 recite an abstract idea—namely, mental processes. See MPEP 2106.04(a)(2)(III).
When read as a whole, the recited limitations are directed to using mental steps to observe, evaluate, and make judgments about data. See id (“Accordingly, the ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions”).
For example, the claim as a whole is directed to observing and evaluating a natural language query and mentally translating the query into a proprietary query language.
Turning to each limitation individually, the element “processing a natural language query” merely requires observation and evaluation of the query.
The element “generating an output query comprising a syntax, structure, and nomenclature of a proprietary query language that unifies network and security operations components of the external service by translating the natural language query into the output query” merely requires observation and evaluation of the natural language query and a judgment as to how to translate that natural language query such that it ”compris[es] a syntax, structure, and nomenclature of a proprietary query language that unifies network and security operations components of the external service.”
Turning to the additional elements and whether they integrate the exception and whether they provide an inventive concept, the elements “using an artificial intelligence based framework of an external service that provides network and security operations to a private network” and “using the artificial intelligence based framework of the external service” does nothing but provide instructions to implement the abstract idea (i.e. mental process) on a computer. See MPEP 2106.05(f). Thus this element fails to integrate and fails to recite an inventive concept.
This element also generally links the abstract idea to a technological environment (i.e. an “artificial intelligence based framework of an external service that provides network and security operations to a private network”) and thus fails to integrate and fails to recite an inventive concept.
The element “generating a network or security operations response to the natural language query by processing the output query using one or more network and security operations components of the external service” recites mere data gathering. This is insignificant extra solution activity that does not integrate the exception.
This element also recites a well-understood, routine, and conventional computer function and thus does not recite an inventive concept. See MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: storing and retrieving information in memory. .”)(emphasis added).
Claims 19 and 20 are rejected for the same reasons given above for claim 1. Also, the following language: “A system, comprising: a processor configured to. . .and a memory coupled to the processor and configured to provide the processor with instructions” and “A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for” recite mere instructions to apply the exception and thus do not integrate the exception nor provide an inventive concept.
Examiner would also like to bring to Applicant’s attention the recent Federal Circuit decision Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025) (emphasis added):
Machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology. Today, we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.
Claim 2 recites “2. The method of claim 1, wherein the generated output query specified in the proprietary query language is used to query network or security data.” This element recites insignificant extra solution activity in the form of mere data gathering and thus does not integrate the exception. See MPEP 2106.05(g). This element recites storing and retrieving information in memory and thus is a well-understood, routine, and conventional computer function that fails to provide an inventive concept. See MPE 2106.05(d). This element also amounts to nothing more than “generally linking the use of a judicial exception to a particular technological environment or field of use.” MPEP 2106.05(h). As such, it does not integrate the exception nor provide an inventive concept. See MPEP 2106.05(h).
Claim 3 recites “3. The method of claim 1, wherein the generated output query specified in the proprietary query language syntax is used to filter network or security data.” This element recites insignificant extra solution activity in the form of mere data gathering and thus does not integrate the exception. See MPEP 2106.05(g). This element recites storing and retrieving information in memory and thus is a well-understood, routine, and conventional computer function that fails to provide an inventive concept. See MPE 2106.05(d).
This element also amounts to nothing more than “generally linking the use of a judicial exception to a particular technological environment or field of use.” MPEP 2106.05(h). As such, it does not integrate the exception nor provide an inventive concept. See MPEP 2106.05(h).
Claim 4 recites “4. The method of claim 1, wherein processing the natural language query comprises analyzing the natural language query using one or more language models comprising the artificial intelligence based framework.” This element amounts to nothing more than “generally linking the use of a judicial exception to a particular technological environment or field of use.” MPEP 2106.05(h). As such, it does not integrate the exception nor provide an inventive concept. See MPEP 2106.05(h). This element also recites mere instructions to apply the exception for the reasons given above for claim 1. That is, the claim recites an outcome with no details as to how this outcome is achieved, uses a computer to perform an existing process, and has broad applicability across many fields of endeavor. See MPEP 2106.05(f). As such, it does not integrate the exception nor provide an inventive concept.
Claim 5 recites “5. The method of claim 1, wherein processing the natural language query comprises extracting relevant information from the natural language query.” This element merely requires observation and evaluation of the natural query and judgment/opinion as to what relevant information to extract. The element “using one or more language models comprising the artificial intelligence based framework” has been discussed in depth above. See claim 1 rejection above. It does not integrate the exception nor provide an inventive concept because it recites mere instructions apply the exception and/or generally links the use of the abstract idea to a particular technological environment or use. See MPEP 2106.05(f) and (h).
Claim 6 recites “6. The method of claim 1, wherein the natural language query is translated into the proprietary query language syntax” requires mere observation and evaluation of the query a judgment as to how to translate the query into the prescribed proprietary query language syntax.
The element “using one or more language models comprising the artificial intelligence based framework” places the abstract idea into the technological environment of language models and artificial intelligence. As such, it fails to integrate the exception and fails to provide an inventive concept. Additionally “using one or more language models comprising the artificial intelligence based framework” provides no details as to how the translation occurs, uses a computer/AI/language models to perform an existing process, and has broad applicability across many fields of endeavor. As such, it recites mere instructions to apply the exception and thus does not integrate the exception nor provide an inventive concept. See MPEP 2106.05(f).
Claim 7 recites “7. The method of claim 1, wherein custom network and security operations algorithms of the external service are defined using the proprietary query language.” This element places the abstract idea into the technological environment of database management systems. As such, it fails to integrate the exception and fails to provide an inventive concept. See MPEP 2106.05(h).
Claim 8 recites “8. (Currently Amended) The method of claim 1, wherein the artificial intelligence based framework is trained to identify or discover enhancements to the proprietary query language”
This element generally links the abstract to a technological environment (machine learning) and thus does not integrate the exception and does not recite an inventive concept. This element also recites mere instructions to apply an exception because the claim recites an outcome or solution (training a framework to identify/discover enhancements) without reciting the details of how this accomplished.
Claim 9 recites “identifying patterns and trends in the using one or more language models comprising the artificial intelligence based framework based on processing received natural language queries including the natural language query. ” This element merely requires observation and evaluation of the query language and an judgment/opinion as to how to identify the “patterns and trends.” The elements “using one or more language models comprising the artificial intelligence based framework based on processing received natural language queries including the natural language query” are subject to the analysis give in the rejection of claim 1 above. They are mere instructions to apply the exception and field of use limitations that do not integrate the exception and do not provide an inventive concept. They merely recite AI and language models as computer tools to perform an existing processes, fail to provide details as to how to accomplish the claimed “enhancements” , and are applicable across many fields of endeavor. See MPEP 2106.05(f).
Claim 10 recites “10. The method of claim 1, wherein the artificial intelligence based framework provides an option for receiving user feedback with respect to the generated output query.” This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a technological environment. It also recites mere instructions to apply an exception because it uses a computer as a tool to perform an existing process (i.e. receiving user feedback). See MPEP 2106.05 (f) and (h).
Claim 11 recites “11. The method of claim 1, wherein the artificial intelligence based framework comprises one or more language models.” This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a technological (i.e. AI environment.). See MPEP 2106.05(h). See also rejection of claim 1 above.
Claim 12 recite “12. The method of claim 1, wherein the artificial intelligence based framework comprises one or more natural language processing (NLP) models.” This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a technological environment (i.e. a NPL environment). See MPEP 2106.05(h) and rejection of claim 1 above.
Claim 13 recites “13. The method of claim 1, wherein the artificial intelligence based framework comprises one or more large language models (LLMs)..” This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a technological environment (i.e. a LLM environment). See MPEP 2106.05(h) and rejection of claim 1 above.
Claim 14 recites “14. The method of claim 1, wherein the artificial intelligence based framework is at least in part trained to learn one or more query languages including the proprietary query language” This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a technological environment (i.e. a machine learning environment). See MPEP 2106.05(h) and rejection of claim 1 above.
Claim 15 recites “15. The method of claim 1, wherein the artificial intelligence based framework is at least in part trained to learn domain-specific information associated with one or more private networks including the private network.” This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a technological environment (i.e. a machine learning environment and networking environment). See MPEP 2106.05(h) and rejection of claim 1 above.
Claim 16 recites “16. The method of claim 1, wherein the external service facilitates managing and optimizing the private network.” This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a technological environment (networking). See MPEP 2106.05(h) and rejection of claim 1 above. This claim also recites mere instructions to apply the exception because it fails to provide details as how “the external service facilitates managing and optimizing the private network.” See MPEP 2106.05(f).
Claim 17 recites “17. The method of claim 1, wherein the external service facilitates defending the private network from security threats and attacks.” This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a field of use (i.e. network security). See MPEP 2106.05(h). It also fails to recite how the service facilitates “defend[] the private network from security threats and attacks.” As such, it also recites mere instructions to apply the exception.
Claim 18 recites “18. The method of claim 1, wherein the external service comprises a route filtering and manipulation service.”
This element fails to integrate the exception and fails to provide an inventive concept. It generally links the abstract idea to a technological environment (i.e. a security platform and a networking platform). See MPEP 2106.05(h). It also fails to recite how the routine filtering and “manipulation” happens and thus also recites mere instructions to apply the exception.
The additional elements in the claims above “‘[a]dd nothing … that is not already present when the steps are considered separately’”. MPEP 2106.05 (I)(B)(quoting Alice).
As such, for the reasons above, claims 1-20 recite an abstract idea without significantly more.
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) 1-6, 11-12, and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi, Checking Network Security Policy Violations via Natural Language Questions, 2021 in view of Pradeep Natural Language To NoSQL Query Conversion using Deep Learning, 2019.
With respect to claim 1, Shi, Checking Network Security Policy Violations via Natural Language Questions, 2021, teaches “ A method, comprising: processing a natural language query” in the abstract and title in the abstract (“The system also takes advantage of recent advances in Natural Language Processing (NLP) to translate natural language policy questions into the corresponding network queries”);
“using an artificial intelligence based framework of an external service” on p. 2 left column first full paragraph (“Furthermore, it leverages emerging interactive conversation systems – driven by recent advances in natural language processing (NLP) and artificial intelligence (AI) – to read and respond to natural language questions from network operators or network policy writers”); Examiner finds NPCE is an external service by definition—see also p. 4 left column section C first two paragraphs and p. 4 section IV first two paragraphs (Examiner finds this illustrates NPCE is an external service testing, for example, university website network policies);
“that provides network and security operations” in the abstract and title;
“to a private network” on p. 1 left column section 1 (“An organization’s computer network, whether it be a small company network, a medium-size campus/enterprise network, or a large-scale ISP network, are governed by organization specific policies that define acceptable and unacceptable uses of the network”); (Examiner finds at least “a small company network, a medium-size campus/enterprise network” are private networks by definition);
“generating an output query comprising a syntax, structure, and nomenclature of a . . . query language” on p. 4 left column section C:
NPCE leverages the ELK stack [10] for this task. At the heart of ELK is Elasticsearch, a search and analytic engine that provides excellent performance and scalability and is built on Apache Lucene [12]. The syntax of the Elasticsearch query DSL (Domain Specific Language) is human-readable, which makes it easy to generate the queries from the entities returned by the NLP module. Moreover, it supports several “beats” that can efficiently collect data about packets and flows, ranging from Packetbeat [13] that collect NetFlow-style data (or we can use Netflow directly if the device supports it) as well as filebeats that can read and parse TCPdump files. Collected data is efficiently imported into the Elasticsearch database where it can be queried and analyzed (possibly passing through Logstash if filtering is required).
(Elasticsearch query DSL by definition comprises a g a syntax, structure, and nomenclature of a . . . query language);
“that unifies network and security operations components of the external service by translating the natural language query into the output query” in the abstract (“The system also takes advantage of recent advances in Natural Language Processing (NLP) to translate natural language policy questions into the corresponding network queries”); p. 5 section A1) ((unified network and security operations components are unified in that different websites on different network are handled by same NPCE system);
“generating a response to the natural language query by processing the output query using one or more network and security operations components of the external service” in the abstract and p. 4 section C paragraphs 1-3; (NPCE (external service) processes NL query by processing output query (Elasticsearch query DSL) using its network and security operations (e.g. Netflow, Tcpdump, ELK, Snort, Suricata, and Zeek)).
It appears Shi fails to explicitly teach a proprietary query language.
However, Pradeep teaches a proprietary query language in the abstract (MongoDB suggests the proprietary NoSQL query language MQL); p. 3 left column paragraphs 1-2; p. 4 tables I, III (training for NL to NoSQL MongoDB query language).
Pradeep and Shi are analogous art because they are from the same field of endeavor as the claimed invention.
It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the query language in Shi to include a proprietary query language as taught in Pradeep.
The motivation would have been to allow a user to quickly and easily query big information sets. See Pradeep abstract.
Claim 19 and 20 are rejected for the same reasons given above for claim 1.
With respect to claim 2, Shi teaches “2. (Currently Amended) The method of claim 1, wherein the generated output query specified in the prescribed proprietary query language syntax is used to query network or security data” in Figs. 4, 5, 7, 9, 11, (the “question” in each of these figures are translated into Elasticsearch query DSL; see p. 4 left column section C:
the questions are queries for network or security data). It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the query language in Shi to include a proprietary query language as taught in Pradeep.
The motivation would have been to allow a user to quickly and easily query big information sets. See Pradeep abstract.
With respect to claim 3, Shi teaches “3. (Currently Amended) The method of claim 1, wherein the generated output query specified in the prescribed proprietary query language syntax is used to filter network or security data” on abstract (network or security data); p. 4 section C second para. (“The challenge is to collect flow and packet-level data at scale. Fortunately, emerging big data collection and analysis systems offer services capable of collecting, filtering, compacting, storing, and later searching or analyzing flow and packet-level data at scale.”). It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the query language in Shi to include a proprietary query language as taught in Pradeep.
The motivation would have been to allow a user to quickly and easily query big information sets. See Pradeep abstract.
With respect to claim 4, Shi teaches “4. (Original) The method of claim 1, wherein processing the natural language query comprises analyzing the natural language query using one or more language models comprising the artificial intelligence based framework” on p. 8 left column
With respect to claim 5, Shi teaches “5. (Original) The method of claim 1, wherein processing the natural language query comprises extracting relevant information from the natural language query using one or more language models comprising the artificial intelligence based framework” on p. 8 left column second full paragraph (Examiner finds that the suggestion that NPCE is “better” is not a teaching away of using a ML model to translate NL queries to a structured query format—See MPEP 2143.01:
The court stated that "the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…." Id. In affirming the Board’s obviousness rejection, the court held that the prior art as a whole suggested the desirability of the combination of shoe sole limitations claimed, thus providing a motivation to combine, which need not be supported by a finding that the prior art suggested that the combination claimed by the applicant was the preferred, or most desirable combination over the other alternatives. Id. See also In re Urbanski, 809 F.3d 1237, 1244, 117 USPQ2d 1499, 1504 (Fed. Cir. 2016).
(emphasis added)).
With respect to claim 6, Shi teaches “6. (Currently Amended) The method of claim 1, wherein the natural language query is translated into the proprietary query language syntax using one or more language models comprising the artificial intelligence based framework. on p. 8 left column second full paragraph (Examiner finds that the suggestion that NPCE is “better” is not a teaching away of using a ML model to translate NL queries to a structured query format—See MPEP 2143.01:
The court stated that "the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…." Id. In affirming the Board’s obviousness rejection, the court held that the prior art as a whole suggested the desirability of the combination of shoe sole limitations claimed, thus providing a motivation to combine, which need not be supported by a finding that the prior art suggested that the combination claimed by the applicant was the preferred, or most desirable combination over the other alternatives. Id. See also In re Urbanski, 809 F.3d 1237, 1244, 117 USPQ2d 1499, 1504 (Fed. Cir. 2016).
(emphasis added)).
With respect to claim 11, Shi teaches “11. (Original) The method of claim 1, wherein the artificial intelligence based framework comprises one or more language models.” in the abstract; p. 2 left column 3rd full paragraph:
The system utilizes Google DialogFlow [6] to implement a conversational user interface where users can ask their questions and receive answers using natural language.
(Examiner finds Google DialogFlow suggests the use of a language model).
With respect to claim 12, Shi teaches “12. (Original) The method of claim 1, wherein the artificial intelligence based framework comprises one or more natural language processing (NLP) models” in the abstract; p. 2 left column 3rd full paragraph:
The system utilizes Google DialogFlow [6] to implement a conversational user interface where users can ask their questions and receive answers using natural language.
(Examiner finds Google DialogFlow teaches the use of a NL processing model).
With respect to claim 14, Pradeep teaches “14. (Currently Amended) The method of claim 1, wherein the artificial intelligence based framework is at least in part trained to learn one or more query languages, including the proprietary query language” in the abstract (MongoDB suggests the proprietary NoSQL query language MQL); p. 3 left column paragraphs 1-2; p. 4 tables I, III (MongoDB queries used as training set in order to learn NL to MongoDB NoSQL query language). It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the query language in Shi to include a propriety query language as taught in Pradeep.
The motivation would have been to allow a user to quickly and easily query and work with big information sets. See Pradeep abstract.
With respect to claim 15, Shi teaches “15. (Previously Presented) The method of claim 1, wherein the artificial intelligence based framework is at least in part trained to learn domain-specific information associated with one or more private networks, including the private network” on p. 4 section IV paras. 1-2 (entities are extracted by training Google Dialogflow; Examiner finds entities are domain-specific information associated with one or more private networks); p. 8 left column last paragraph:
We argue that NPCE fits the context of checking network policy violations better since it contains domain specific knowledge in the networking area that cannot be processed by other general purpose translation schemes
p. 4 section IV
As shown in Figure 2, we set up the topology in the Global
Environment for Network Innovations (GENI) platform [17],
where people can conduct networking research at scale. We
include an OpenVSwitch (OVS) in the topology to use its
Netflow module to capture flow information of network traffic.
The other components of the prototype implementation are
the same as have been discussed in the previous section. In
particular, we took advantage of Google Dialogflow to train
and extract the entities.
With respect to claim 16, Shi teaches “16. (Currently Amended) The method of claim 1, wherein the external service facilitates managing and optimizing the private network” in the abstract and on p. 1 right column 2nd full paragraph (Examiner finds writing accurate network policies facilitates managing and optimizing the private network”; NPCE assists in this process).
With respect to claim 17. Shi teaches “(Previously Presented) The method of claim 1, wherein the external service facilitates defending the private network from security threats and attacks” in the abstract and on p. 1 right column 2nd full paragraph (Examiner finds writing accurate network policies facilitates defending the private network from security threats and attacks; NPCE assists in this process);
With respect to claim 18, Shi teaches “18. (Previously Presented) The method of claim 1, wherein the external service comprises a route filtering and manipulation service” p. 7 left column, paragraphs 1-2 under “5)”; (IP source routing is a type of route filtering; IP source routing involves manipulating the route packets take).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Pradeep as applied to claim 1 above and further in view of Murali, A Domain-Specific Query Language to Investigate Industrial Network Security Data, Aug 2020.
With respect to claim 7, Shi teaches “custom network and security operations algorithms of the external service” See abstract and Figs. 5-12 (custom network and security operations include specific questions about what happened on the network). It appears Shi fails to explicitly teach “7. (Currently Amended) . . . are defined using the proprietary query language.”
However, Murali teaches “wherein custom network and security operations algorithms of the external service are defined using the proprietary query language” in the abstract; and p. 21 section 4.3.1 entire paragraph; p. 22-24 section 4.3.3 (all the use cases in this section teach custom network and security operations algorithms);
Murali and Shi et al. are analogous art because they are from the same field of endeavor as the claimed invention.
It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the custom network and security operations algorithms of the external service in Shi to include defining them using the proprietary query language as taught by Murali.
The motivation would have been to allow a security analyst to use flexible ad-hoc queries without having to know the underlying syntax of a native backend query language. See Murali p. 21 section 4.2.2 first paragraph.
It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the query language in Shi to include a proprietary query language as taught in Pradeep.
The motivation would have been to allow a user to quickly and easily query big information sets. See Pradeep abstract.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Pradeep as applied to claim 1 above and further in view of Mate, Improving security in NoSQL document databases through model-driven modernization, 2021.
With respect to claim 8, it appears Shi et al. fails to explicitly teach “8. (Currently Amended) The method of claim 1, wherein the artificial intelligence based framework is trained to identify or discover enhancements to the propriety query language.”
However, Maté teaches “wherein the artificial intelligence based framework is trained to identify or discover enhancements to the propriety query language” in the abstract, p. 2216-7 section 3.2 paragraphs 1-2; p. 2224 first paragraph including the 5 bullet points (privilege modifications to the database suggest that the query language needs to be modified to reflect this modification; Examiner finds this is an enhancement).
Mate and Shi are analogous art because they are from the same field of endeavor as the claimed invention.
It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the AI framework in Shi et al. to include “trained to identify or discover enhancements to the propriety query language.”
The motivation would have been the following:
(1) it takes into account the context of the system thanks to the introduction of domain ontologies, (2) it helps to avoid missing critical access control issues since the analysis is performed automatically, (3) it reduces the effort and costs of the modernization process thanks to the automated steps in the process, (4) it can be used with different NoSQL document-based technologies in a successful way by adjusting the metamodel, and (5) it is lined up with known standards, hence allowing the application of guidelines and best practices.
Mate abstract.
It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the query language in Shi to include a proprietary query language as taught in Pradeep.
The motivation would have been to allow a user to quickly and easily query big information sets. See Pradeep abstract.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Pradeep as applied to claim 1 above and further in view of Halabi US 11526512 B1.
With respect to claim 9, Shi teaches a language model and an AI based framework. See rejections of claim 1 and claim 4 above. it appears Shi et al. fails to explicitly teach “9. (Previously Presented) The method of claim 1, further comprising identifying patterns and trends using one or more language models comprising the artificial intelligence based framework based on processing received natural language queries including the natural language query. “
However, Halabi US 11526512 B1 teaches “identifying patterns and trends using one or more language models comprising the artificial intelligence based framework based on processing received natural language queries including the natural language query” in the abstract and col. 8:34-53.
Halabi and Shi et al. are analogous art because they are from the same field of endeavor as the claimed invention.
It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the AI framework and language model in Shi et al. to include “identifying patterns and trends using one or more language models comprising the artificial intelligence based framework based on processing received natural language queries including the natural language query” as taught by Halabi.
The motivation would have been to mitigate errors in queries and thereby increasing the speed and accuracy of user’s queries. See Halabi abstract and col. 2:3-55,
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Pradeep as applied to claim 1 above and further in view of Wang US 20230041181.
With respect to claim 10, Shi teaches an AI based framework. See above. It appears Shi et al. fails to explicitly teach “10. (Original) The method of claim 1, wherein the artificial intelligence based framework provides an option for receiving user feedback with respect to the generated output query.”
However, Wang US 20230041181 A1 teaches “wherein the artificial intelligence based framework provides an option for receiving user feedback with respect to the generated output query” in para. 20; and para. 50.
Wang and Shi et al. are analogous art because they are from the same field of endeavor as the claimed invention.
It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the “the artificial intelligence based framework” in Shi et al. to include “wherein the artificial intelligence based framework provides an option for receiving user feedback with respect to the generated output query” as taught by Wang.
The motivation would have been to improve the accuracy of user queries. See Wang para. 50.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi in view of Pradeep as applied to claim 1 above and further in view of Aghaei, SecureBERT: A Domain-Specific Language Model for Cybersecurity, 2022.
With respect to claim 13, Shi teaches language models in the abstract; p. 2 left column 3rd full paragraph:
The system utilizes Google DialogFlow [6] to implement a conversational user interface where users can ask their questions and receive answers using natural language.
(Examiner finds Google DialogFlow teaches using NLP models).
It appears Shi fails to explicitly teach “13. (Original) The method of claim 1, wherein the artificial intelligence based framework comprises one or more large language models (LLMs).”
However, Aghaei teaches LLMs in the abstract and p. 2 section 1 last 6 lines and p. 3 section 1 top of page (Examiner finds BERT and SecureBERT are both LLMs).
Aghaei and Shi are analogous art because they are from the same field of endeavor as the claimed invention.
It would have been obvious to modify the model in language model in Shi to include an LLM as taught by Aghaei.
The motivation would have been speed. See Aghaei, abstract (“This paper proposes SecureBERT, a cybersecurity language model capable of capturing text connotations in cybersecurity text (e.g., CTI) and therefore successful in automation for many critical cybersecurity tasks that would otherwise rely on human expertise and time-consuming manual efforts. successful in automation for many critical cybersecurity tasks that would otherwise rely on human expertise and time-consuming manual efforts”).
Conclusion
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/ALBERT M PHILLIPS, III/ Primary Examiner, Art Unit 2159