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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6-8, 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over O’Connor (20160352772) in view of Pantage et al (20250158953).
As per claim 1, O’Connor (20160352772) teaches a system, comprising:
a processor configured to: receive a user request for DNS, DHCP, and IPAM (DDI) related information (as, user request as a DNS query – see para 0022, 0023);
process the user request using a Large-Language Model (LLM) to generate a result, comprising to: (as using natural language processing to generate a relevant result to the user -- para 0074; the natural language processing includes the use of machine learned models – para 0116);
determine, using the LLM, user intent to obtain a user intent result, comprising to:
retrieve one or more relevant technical documentations relating to the user request using an embedding model (as using an embedded vector corpus -- para 0020, and using the vectors to perform semantic analysis via NLP and machine learning to determine proper content – para 0021, 0022; to include logged information – para 0023); and
present the one or more relevant technical documentation as context for question answering for the LLM to obtain the user intent result (as accessing a corpus of content vectors – para 0020, with the corpus containing domain content information – para 0020, which is used to compare the input to stored DNS/target domains/URL’s – para 0018);
generate, based on the results, a user interface (UI) output using the LLM and a vector embedding space (as, using natural language processing and the machine learned models, above, to generate vectors for an input target domain name and comparing to a database of vectorized dns’2 – see para 0064),
wherein the output includes generative UI content that was generated using the LLM the one or more relevant technical documentation for DDI related information that was selected based on a proximity to the user request in the vector embedding space (as, based on a similarity score between a vector of the target and the vector in a corpus – para 0064; displaying the content to the user in a ranking of corresponding sites – end of para 0064);
and a memory coupled to the processor and configured to provide the processor with instructions (as, memory/storage interfacing with a processor to execute the above steps – (para 0123).
As per claim 1, O’Connor (20160352772 ) teaches using/accessing previous DNS requests – para 0040; and using historical subscriber preferences for previous DNS – para 0045; but does not explicitly teach querying a table of the DNS/answer relationships; Pantage et al (20250158953) teaches the storage of query/answer relationships with answers to DNS queries against the database (para 0023). Therefore, it would have been obvious to one of ordinary skill in the art of DNS databases to modify the structure of O’Connor (20160352772 ) with query/answer pair of DNS information, as taught by Pantage et al (20250158953), because it would advantageously facilitate DNS implementation saving number of processors and memory resources (see Pantage et al (20250158953), para 0024).
As per claim 2, the combination of O’Connor (20160352772 ) in view of Pantage et al (20250158953) teaches the system recited in claim 1, wherein the user request includes a user query for DDI related information associated with the user's enterprise network (as, O’Connor (20160352772 ), part of the user query DNS request, access log data which includes differing clients/domains, as part of the enterprise network – para 0023; see also paragraphs 0050, 0051).
As per claim 3, the combination of O’Connor (20160352772 ) in view of Pantage et al (20250158953) teaches the system recited in claim 1, wherein the DDI related information includes customer information including network configuration information, network event data, and/or telemetry and log data (as, O’Connor (20160352772 ), DDI related info includes recursive DNS clusters and nameservers – para 0037).
As per claim 4, the combination of O’Connor (20160352772 ) in view of Pantage et al (20250158953) teaches the system recited in claim 1, wherein a conversation history is stored for a plurality of user requests (as, O’Connor (20160352772 ), using/accessing previous DNS requests – para 0040; and using historical subscriber preferences for previous DNS – para 0045).
As per claim 6, the combination of O’Connor (20160352772 ) in view of Pantage et al (20250158953) teaches the system recited in claim 1, wherein the UI output includes one or more of the following interactive elements: clickable responses, clickable follow-up questions, tables, graphs, and widgets (noting that the claim scope is in alternative format “one or more”, O’Connor (20160352772 ) teaches displaying a landing page in the format of a web browser – which could include graphs/table/widgets, etc – see end of para 0049).
As per claim 7, the combination of O’Connor (20160352772 ) in view of Pantage et al (20250158953) teaches the system recited in claim 1, wherein the UI output includes a UI widget for user input, and wherein the processor is further configured to: perform an action based on the user input in the UI widget (as, O’Connor (20160352772 ), displaying to the user, the end result which could be in the form of a landing page, as part of a web browser, email app, or stand alone app – end of para 0049; see also para 125 showing display capabilities to the user).
As per claim 8, the combination of O’Connor (20160352772 ) in view of Pantage et al (20250158953) teaches the system recited in claim 1, wherein the processor is further configured to: process a conversation history (as, O’Connor (20160352772 ), using/accessing previous DNS requests – para 0040; and using historical subscriber preferences for previous DNS – para 0045) and generate a query or a message that incorporates a relevant context using the LLM (as using natural language processing to generate a relevant result to the user -- para 0074; the natural language processing includes the use of machine learned models – para 0116).
As per claim 10, the combination of O’Connor (20160352772 ) in view of Pantage et al (20250158953) teaches the system recited in claim 1, wherein the processor is further configured to: perform retrieval augmented generation for using a vector database (as, O’Connor (20160352772 ), accessing a corpus of content vectors – para 0020) for DDI related technical documentation (the aforementioned corpus contains domain content information – para 0020, which is used to compare the input to stored DNS/target domains/URL’s – para 0018).
Claims 11-17 are method claims whose steps are performed by system claims 1-10 above and as such, claims 11-17 are similar in scope and content to claims 1-10 above; therefore, claims 11-17 are rejected under similar rationale as presented against claims 1-10 above.
Claims 18-20 are non-transitory computer readable medium claims embodying a computer program product, whose executable steps are performed by system claims 1-10 above and as such, claims 18-20 are similar in scope and content to claim elements found in claims 1-10 above; therefore, claims 18-20 are rejected under similar rationale as presented against claims 1-10 above.
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Examiner notes the introduction of the Pantage et al (20250158953) reference teaching DNS databases in a query/answer format to establish the DNS.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Please see related art listed on the PTO-892 form.
Furthermore, the following references were found that map/match certain features found in applicants specification/claims:
Yadav et al (20160359887) teaches DNS servers (para 0061) using machine learning techniques to analyze DNS exchanges (para 0072), using an interface for user input as well as presentation modules to the user (para 0068).
Arora (20210176181) teaches converting domain names to a vector space embeddings, generating an organized training dataset to process DNS queries, using a deep neural network. (para 0005, 0023, 0037-0038).
Lilingood (20220239693) teaches the use of machine learning models to process DNS queries compared to DNS entries (abstract, para 0003, 0025).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 09/23/2026
