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
Claims 1-20 are pending. Claims 1 and 12 are independent.
Claims 2-11 depend from Claim 1.
Claims 13-20 depend from Claim 12.
This Application was published as U.S. 2025/0028745.
Response to Amendment
Examiner thanks Applicant for response filed on 21 May 2026 which has been correspondingly accepted and considered in this office action. Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed 21 May 2026 have been fully considered but they are not persuasive. Each argument of Applicant’s arguments will be addressed in turn.
With regards to priority:
Applicant has not provided any arguments to the determination of priority. Thus, priority for this application will be based on provisional application 63/652384 with priority date of 28 May 2024 as discussed in 5 May 2026 Non-Final office action.
With regards to objection to drawings:
Applicant has amended the specification and provided a replacement Fig 4. Thus, the objection to drawings has been withdrawn and replacement to Fig 4 has been accepted.
With regards to 35 USC § 112:
Applicant has amended claim 12 and provided arguments filed 21 May 2026 which have been fully considered and are persuasive. Thus, 35 USC § 112 rejection has been withdrawn.
With regards to 35 USC § 101:
Applicant has amended independent claims 1 and 12, and has provided arguments filed 21 May 2026 which have been fully considered and are persuasive. Thus, 35 USC § 101 rejection has been withdrawn.
With regards to 35 USC § 103:
Applicant's arguments filed 21 May 2026 have been fully considered but they 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.
Claim Rejections - 35 USC § 112
Claim 4 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 4 refers to the prompt is received from a user via a user interface. The specification refers to the following:
user query may be transformed into a prompt before feeding to the AI module
712 along with the merged context as inputs (Par [0085])
the user 140 may submit, to the chat module 800 via the user interface, queries (e.g., questions in a natural language format, etc.) related to one or more specific domains ( e.g., one or more specific websites, one or more subject matter, etc.). (Par [0091])
Based on the applicant’s specification, the query is received from the user, and then the system transforms the query into a prompt which is also consistent with applicant claims 1 and 4 submitted 19 Jul 2024. The specification is silent about receiving the prompt from the user via a user interface, and based on the specification and claims, the query and the prompt are not the same thing. Furthermore, applicant provisional applications does not provide support.
Provisional application 63/6523684 dated 28 May 2024 and Provisional application 63/514438 dated 19 Jul 2023 states the following:
user query may be transformed into a prompt before feeding to the AI module 712 (63/523684 Par [00084]; 63/514438 Par [00083])
For the purpose of examination, the claim will be interpreted as if the claim states “wherein the prompt is received, and wherein …” which is consistent with claim 1 that does not specify where the prompt is received from.
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.
Claims 1-4, 9-10, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Jensen et al. (US2025/0265178 hereinafter Jensen) in view of Bhatt et al. (US12010184 hereinafter Bhatt) and Howard (US2021/0232632 hereinafter Howard)
With regards to claim 1, Jensen teaches:
A system, comprising: a non-transitory memory; [Jensen Fig 2 teaches online system (140) comprising a data store (240) that uses a computer-readable media (Par [0085]) which is non-transitory memory] and one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: [Jensen Par [0089]]
receiving a prompt associated with a web domain; [Jensen Fig1B teaches language model system (150) receiving a prompt from interface system 160 (Par [0039]) that is part of online system (140) which is associated with a web domain]
simulating a rendering of a plurality of webpages associated with the web domain; [Jensen Fig 2 teaches integration test module (225) generates a prompt to the Large Language Model (LLM) tool that “may simulate navigation of the website by taking a sequence of actions, in which each action may interact with the application” (Par [0078])]
obtaining a plurality of contents based on the simulating; [Jensen Fig 2 teaches integration test module (225) performs “identification and location of elements within a web page by matching a pattern against the structure and attributes of the HTML of the webpage” (Par [0075]) where elements in a webpage are a plurality of contents based on the simulating]
With regards to claim 1, Jensen fails to teach:
generating a ranking of the plurality of contents based on the prompt,
wherein one or more contents from the plurality of contents comprise information usable to generate a response to the prompt, and wherein the ranking indicates a relevancy of each content in the plurality of contents to the prompt; selecting, from the plurality of contents, one or more contents based on the ranking;
subsequent to obtaining the response generated by the AI model, providing the response to the device.
With regards to claim 1, Bhatt teaches:
generating a ranking of the plurality of contents based on the prompt, [Bhatt teaches AI engine “may perform a search for a plurality of funnel website templates based at least on the prompt. The AI engine may rank the plurality of funnel website templates returned in the search from the highest rank to the lowest rank.” (Col 4 lines 55-59) where ranking of funnel website templates is a plurality of contents based on the prompt]
wherein one or more contents from the plurality of contents comprise information usable to generate a response to the prompt, and wherein the ranking indicates a relevancy of each content in the plurality of contents to the prompt; selecting, from the plurality of contents, the one or more contents based on the ranking; [Bhatt teaches “AI engine may select a funnel website template having the highest rank, retrieve business information of the one subscriber (i.e., the requestor), and fill the selected funnel website template with the business information to generate the funnel website” (Col 5 lines 1-6) where the website selected is information usable to generate a response to the prompt]
subsequent to obtaining the response generated by the AI model, providing the response to the device. [Bhatt Fig 1 teaches providing funnel website application (100) response to the user interface (120).
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine using an online system and LLM for simulating a rendering of webpages as taught by Jensen with the generation of a funnel website template as taught by Bhatt. The motivation to combine the teachings of Jensen with Bhatt is because Bhatt teaches using artificial intelligence (AI) by using “AI-based website tools can automatically generate code, create content, and provide design recommendations based on user input” (Col 3 lines 6-8) which increases the capabilities of the invention of Jensen to provide better rendering of webpages]
With regards to claim 1, Jensen in view of Bhatt fails to teach:
enriching the prompt for an artificial intelligence (AI) model by integrating the one or more contents into the prompt, wherein the enriched prompt instructs the AI model to use the information from the one or more contents in generating a response to the prompt;
providing the enriched prompt to the AI model; and
With regards to claim 1, Howard teaches:
enriching the prompt for an artificial intelligence (AI) model by integrating the one or more contents into the prompt, wherein the enriched prompt instructs the AI model to use the information from the one or more contents in generating a response to the prompt; [Howard Fig 1 teaches subject matter prompt (112) that uses context analysis (121) to generate an enriched prompt that integrates the contents into the query by using “subject matter context in which the target identities act” (Par [0034]) where the enriched prompt instructs the virtual experience service (120) to use the information to “construct a virtual experience container 150 that presents a multi-faceted and flexibly-dimensional virtual experience of the selected target identities in a subject matter context, matched to the capabilities of the user experience device 10” (Par [0020])]
providing the enriched prompt to the AI model; and [Howard Fig 4A-4B teaches subject matter enriched prompt is provided to step 402, that is implemented by neural network (Par [0164-165]) where a neural network is an AI model.
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the online system and LLM for simulating a rendering of webpages as taught by Jensen and Bhatt with the multi-modal virtual experience system that uses prompts and contexts as taught by Howard. The motivation to combine the teachings of Jensen and Bhatt with Howard is because Howard teaches “multi-faceted and flexibly-dimensional virtual experience of the selected target identities in a subject matter context, matched to the capabilities of the user experience device 100” (Par [0020]) which increases the capabilities of the invention of Jensen and Bhatt to provide better user experiences for the user]
With regards to claim 2, Jensen in view of Bhatt and Howard teaches:
All the limitations of claim 1
wherein the web domain is associated with a network address, and wherein the plurality of contents is obtained based on the network address. [Bhatt teaches “funnel website typically has several landing pages, each with text, graphics, and at least some pages in which the user can input information useful for marketing purpose” (Col 3 lines 49-51) where a website has an associated network address.]
With regards to claim 3, Jensen in view of Bhatt and Howard teaches:
All the limitations of claim 1
wherein the simulating the rendering of the plurality of webpages comprises interacting with the plurality of webpages. [Jensen teaches interacting with the webpages by using the large language model (LLM) tool to “simulate navigation of the website by taking a sequence of actions, in which each action may interact with the application, such as invoking an API call, interaction with a UI of the application presented on the GUI of the computer system, and the like” (Par [0078])]
With regards to claim 4, Jensen in view of Bhatt and Howard teaches:
All the limitations of claim 1
wherein the prompt is received from a user via a user interface displayed on the device, and wherein the obtaining the plurality of contents comprises retrieving the plurality of contents from a plurality of servers different from the user device. [Jensen Fig1B teaches language model system (150) receiving a prompt from interface system 160 (Par [0039]) that is part of online system (140) which is associated with a first domain and using the online system to access servers that hosts the webpages can be include servers from different parts of the world which is different from the user device (see Cai et al. (US2017/0207989 Par [0024])]
With regards to claim 9, Jensen in view of Bhatt and Howard teaches:
All the limitations of claim 1
wherein the enriched prompt is provided to a plurality of AI models. [Howard teaches a plurality of models such as “RNNs, Long Short Term Memory models (LSTMs), Generational Adversarial Networks (GANs), and Convolutional Nets (CNNs), or other types of neural network model or combination thereof.” (Par [0168])]
With regards to claim 10, Jensen in view of Bhatt and Howard teaches:
All the limitations of claim 9
wherein the operations further comprise: obtaining a first output from a first AI model from the plurality of AI models; obtaining a second output from a second AI model from the plurality of AI models; generating a response for the prompt based on the first output and the second output. [Howard teaches a first output from “A neural network, such as a recurrent neural network (RNN), can be trained to associate existing content patterns with various subject matter prompt ” (Par [0164]) and “Each subject matter prompt may have its own trained neural network, or more than one subject matter prompt may share a trained neural network” (Par [0168]) where each AI model creates an output to generate the response]
With regards to claim 12, Jensen teaches:
A method comprising: receiving, by a computer system, a query associated with a web domain; [Jensen Fig1B teaches language model system (150) receiving a query (Par [0029]) that is part of online system (140) which is associated with a web domain]
generating, by the computer system, a prompt for an artificial intelligence (AI) model based on a query receiving a prompt associated with a web domain; [Jensen Fig1B teaches language model system (150) generating a prompt from interface system 160 (Par [0039]) that is part of online system (140) which is associated with a first domain]
simulating, by the computer system, a rendering of a plurality of webpages associated with the web domain; [Jensen Fig 2 teaches integration test module (225) generates a prompt to the Large Language Model (LLM) tool that “may simulate navigation of the website by taking a sequence of actions, in which each action may interact with the application” (Par [0078])]
obtaining, by the computer system, a plurality of contents based on the simulating the rendering of the plurality of webpages; [Jensen Fig 2 teaches integration test module (225) performs “identification and location of elements within a web page by matching a pattern against the structure and attributes of the HTML of the webpage” (Par [0075]) where elements in a webpage are a plurality of contents based on the simulating]
With regards to claim 12, Jensen fails to teach:
generating, by the computer system, a ranking of the plurality of contents,
wherein the ranking indicates a relevancy of each content in the plurality of contents to the query; selecting, from the plurality of contents, one or more contents based on the ranking;
subsequent to obtaining the response generated by the AI model, providing the response to the device.
With regards to claim 12, Bhatt teaches:
generating, by the computer system, a ranking of the plurality of contents, [Bhatt teaches AI engine “may perform a search for a plurality of funnel website templates based at least on the prompt. The AI engine may rank the plurality of funnel website templates returned in the search from the highest rank to the lowest rank.” (Col 4 lines 55-59) where ranking of funnel website templates is a plurality of contents based on the prompt]
wherein the ranking indicates a relevancy of each content in the plurality of contents to the query; selecting, from the plurality of contents, one or more contents based on the ranking; [Bhatt teaches “AI engine may select a funnel website template having the highest rank, retrieve business information of the one subscriber (i.e., the requestor), and fill the selected funnel website template with the business information to generate the funnel website” (Col 5 lines 1-6) where the website selected is information usable to generate a response to the prompt]
subsequent to obtaining the response generated by the AI model, providing the response to the device. [Bhatt Fig 1 teaches providing funnel website application (100) response to the user interface (120).
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine using an online system and LLM for simulating a rendering of webpages as taught by Jensen with the generation of a funnel website template as taught by Bhatt. The motivation to combine the teachings of Jensen with Bhatt is because Bhatt teaches using artificial intelligence (AI) by using “AI-based website tools can automatically generate code, create content, and provide design recommendations based on user input” (Col 3 lines 6-8) which increases the capabilities of the invention of Jensen to provide better rendering of webpages]
With regards to claim 12, Jensen in view of Bhatt fails to teach:
enriching, by the computer system, the prompt for the AI model by integrating the one or more contents into the prompt, wherein the enriched prompt instructs the AI model to use the information from the one or more contents in generating a response to the prompt;
providing the enriched prompt to the AI model; and
With regards to claim 12, Howard teaches:
enriching, by the computer system, the prompt for the AI model by integrating the one or more contents into the prompt, wherein the enriched prompt instructs the AI model to use the information from the one or more contents in generating a response to the prompt; [Howard Fig 1 teaches subject matter prompt (112) that uses context analysis (121) to generate an enriched prompt that integrates the contents into the query by using “subject matter context in which the target identities act” (Par [0034]) where the enriched prompt instructs the virtual experience service (120) to use the information to “construct a virtual experience container 150 that presents a multi-faceted and flexibly-dimensional virtual experience of the selected target identities in a subject matter context, matched to the capabilities of the user experience device 10” (Par [0020])]
providing the enriched prompt to the AI model; and [Howard Fig 4A-4B teaches subject matter enriched prompt is provided to step 402, that is implemented by neural network (Par [0164-165]) where a neural network is an AI model.
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the online system and LLM for simulating a rendering of webpages as taught by Jensen and Bhatt with the multi-modal virtual experience system that uses prompts and contexts as taught by Howard. The motivation to combine the teachings of Jensen and Bhatt with Howard is because Howard teaches “multi-faceted and flexibly-dimensional virtual experience of the selected target identities in a subject matter context, matched to the capabilities of the user experience device 100” (Par [0020]) which increases the capabilities of the invention of Jensen and Bhatt to provide better user experiences for the user]
Claim 13 is a method claim with limitations corresponding to the limitations of method Claim 2 and is rejected under similar rationale.
Claim 14 is a method claim with limitations corresponding to the limitations of method Claim 3 and is rejected under similar rationale.
With regards to claim 15, Jensen in view of Bhatt and Howard teaches:
All the limitations of claim 12
wherein the query is received from a user of the device. [Jensen Fig1B teaches language model system (150) receiving a query (Par [0029]) from a customer client device (100) through the online system (Par [0027-28]
Claims 5-7 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Jensen et al. (US2025/0265178), Bhatt et al. (US12010184), and Howard (US2021/0232632) in further view of Marey (US2022/0012296 hereinafter Marey)
With regards to claim 5, Jensen in view of Bhatt and Howard teaches:
All the limitations of claim 1
wherein the operations further comprise: generating a semantic-based ranking of the plurality of contents, the semantic-based ranking indicating a relevancy of each content in the plurality of contents to the prompt based on a semantic-based algorithm; and [Howard teaches various semantic analysis methods of ranking content such as “calculating a weighted average of the score or rank assigned by each semantic analysis method to a concept.” (Par [0111])]
With regards to claim 5, Jensen in view of Bhatt and Howard fails to teach:
generating a term-based ranking of the plurality of contents, the term-based ranking indicating a relevancy of each content in the plurality of contents to the prompt based on the term-based algorithm, wherein the ranking is generated based on the semantic-based ranking and the term-based ranking.
With regards to claim 5, Marey teaches:
generating a term-based ranking of the plurality of contents, the term-based ranking indicating a relevancy of each content in the plurality of contents to the prompt based on the term-based algorithm, wherein the ranking is generated based on the semantic-based ranking and the term-based ranking. [Marey teaches using vector semantics to “facilitate the recommendation of social media posts …[using] For example, models driven by statistical methods such as term frequency-inverse document frequency (TF-IDF)” (Par [0039]) where the social media posts are “ranked by frequency-weighted term overlap” (Par [0042]).
While Marey does not state using semantic-based rankings, it would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the semantic rankings as taught by Jensen in view of Bhatt and Howard with the vector semantics and term-based rankings as taught by Marey. The motivation to combine the teachings of Jensen in view of Bhatt and Howard and Marey is because Howard teaches that “Different semantic analysis methods may yield a different selection of key concepts in the search results, as some methods may be more effective with certain kinds of textual material than others. Hence, more than one kind of semantic analysis method may be used to determine key concepts” (Par [0111]) which increases the capabilities of Jensen in view of Bhatt and Howard in view of Marey provide better user experiences for the user device]
With regards to claim 6, Jensen in view of Bhatt, Howard, and Marey teaches:
All the limitations of claim 5
wherein the operations further comprise: generating first embeddings for the query;
generating second embeddings for each content in the plurality of contents; and comparing the first embeddings against the second embeddings, wherein the generating the semantic-based ranking is based on the comparing. [Marey Fig 4 teaches database (414) “finding semantically similar posts (e.g., based on word and/or sentence embeddings)” (Par [0033]) which is generating embeddings for the social media post and the semantically similar post.
While Marey does not state using semantic-based rankings, it would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the semantic rankings as taught by Jensen in view of Bhatt and Howard with the vector semantics and term-based rankings as taught by Marey. The motivation to combine the teachings of Jensen in view of Bhatt and Howard and Marey is because Howard teaches that “Different semantic analysis methods may yield a different selection of key concepts in the search results, as some methods may be more effective with certain kinds of textual material than others. Hence, more than one kind of semantic analysis method may be used to determine key concepts” (Par [0111]) which increases the capabilities of Jensen in view of Bhatt and Howard in view of Marey provide better user experiences for the user device]
With regards to claim 7, Jensen in view of Bhatt, Howard, and Marey teaches:
All the limitations of claim 5
wherein the operations further comprise: generating first word attributes based on the query; generating second word attributes based on each content in the plurality of contents; and comparing the first word attributes against the second word attributes, wherein the generating the term-based ranking is based on the comparing. [Marey Fig 4 teaches using term frequency-inverse document frequency (TF-IDF) as a way for database (414) to compare ”content for similar posts to be recommended (e.g., retrieving historical posts with similar statistical features to posts 402, 404)” (Par [0039]) which is generating word attributes for the social media post and similar posts where “ database 414 candidate social media posts (e.g., ranked by frequency-weighted term overlap), and a post may be recommended based on a closest match (e.g., based on sentence structure and/or overlap of content).” (Par [0042])]
Claim 16 is a method claim with limitations corresponding to the limitations of method Claim 5 and is rejected under similar rationale.
Claim 17 is a method claim with limitations corresponding to the limitations of method Claim 6 and is rejected under similar rationale.
Claim 18 is a method claim with limitations corresponding to the limitations of method Claim 7 and is rejected under similar rationale.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jensen et al. (US2025/0265178), Bhatt et al. (US12010184), Howard (US2021/0232632) and Marey (US2022/0012296) in further view of Prasad Tanniru et al. (US2021/0286832 hereinafter Prasad Tanniru)
With regards to claim 8, Jensen in view of Bhatt, Howard, and Marey teaches:
All the limitations of claim 5
With regards to claim 8, Jensen in view of Bhatt, Howard, and Marey fails to teach:
wherein the operations further comprise: determining a first weight associated with the semantic-based ranking and a second weight associated with the term-based ranking, wherein the ranking is generated further based on the first weight and the second weight.
With regards to claim 8, Prasad Tanniru teaches:
wherein the operations further comprise: determining a first weight associated with the semantic-based ranking and a second weight associated with the term-based ranking, wherein the ranking is generated further based on the first weight and the second weight. [Prasad Tanniru teaches “TF-IDF score may include a weight used in information retrieval and text mining, and used by search engines in scoring and ranking a document's relevance given a query” (Par [0039])
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the social media comment recommendation system as taught using TF-IDF as taught by Jensen in view of Bhatt, Howard, and Marey with the TF-IDF system using weights as taught by Prasad Tanniru. The motivation to combine the teachings of Jensen in view of Bhatt, Howard, and Marey with Prasad Tanniru is because Prasad Tanniru teaches “importance increases proportionally to a quantity of times a word appears in the document but may be offset by a frequency of the word in the corpus. In some implementations, the knowledge platform may scale up a second document score caused by rare words and/or may scale down a second document score caused by frequently appearing words” (Par [0039]) which increases the capabilities of the invention of Jensen in view of Bhatt, Howard, and Marey to provide better recommendations for the user and increase user experiences for the user device]
Claim 19 is a method claim with limitations corresponding to the limitations of method Claim 8 and is rejected under similar rationale.
Claims 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Marey (US2022/0012296) in view of Howard (US2021/0232632) in further view of Horesh et al. (US11972333 hereinafter Horesh)
With regards to claim 11, Jensen in view of Bhatt and Howard teaches:
All the limitations of claim 10
With regards to claim 11, Jensen in view of Bhatt and Howard fails to teach:
wherein the operations further comprise: identifying one or more portions that are common in the first output and the second output, wherein the response is generated based on the one or more portions.
With regards to claim 11, Horesh teaches:
wherein the operations further comprise: identifying one or more portions that are common in the first output and the second output, wherein the response is generated based on the one or more portions. [Horesh Fig 2 teaches classification model (230) compares the outputs from the first AI model (210) with the outputs of the second AI model (220), and outputs the response based on the common portions. (Col 13 lines 32-47)
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the online system and LLM for simulating a rendering of webpages as taught by Jensen in view of Bhatt and Howard with the generative AI model as taught by Horesh. The motivation to combine the teachings of Jensen in view of Bhatt and Howard with Horesh is because Horesh teaches two generative AI modes to “ensure that the outputs generated and provided by a generative AI model are relevant … [and] second generative AI model is trained using fresher data than the managed generative AI model so that outputs from the second generative AI model may be more relevant for, e.g., evolving topics.” (Col 56-66) which increases the capabilities of the invention of Jensen in view of Bhatt and Howard to provide better recommendations for the user and increase user experiences for the user device]
Claim 20 is a method claim with limitations corresponding to the limitations of method Claim 10 and is rejected under similar rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph J Yamamoto whose telephone number is (571)272-4020. The examiner can normally be reached M-F 1000-1800 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JOSEPH J. YAMAMOTO
Examiner
Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
Prosecution Insights