DETAILED ACTION
This Office Action is in response to Applicant's Communication received on 05/20/2026 for the above identified application.
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 .
Response to Amendment
The amendment filed on 05/20/2026 has been entered.
Claims 1, 8, and 15 are amended. Claims 2, 5, 6, 9, 12, 13, 16, and 19 are canceled. Claims 21-29 are new. Claims 1, 3-4, 7-8, 10-11, 14-15, 17-18, and 21-29 are pending in the application.
Claim Objections
Claims 21, 24, and 27 are objected to because of the following informalities: in these claims, “the prompt quality module” has no antecedent basis. Appropriate correction is required.
Claim 15 are objected to because of the following informalities: in this claim, “the one or more processors” has no antecedent basis. Appropriate correction is required.
Claims 17-18 are objected to because of the following informalities: in these claims, “the computer-readable storage media” has no antecedent basis. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21, 24, and 27 are 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 21 recites (and similar in claims 24 and 27) “the prompt is modified based on the evaluation of the quality of the prompt”. Nowhere does the specification describe modifying a prompt in response to, or as a function of, a prompt quality evaluation. The specification [0055] describes: “the prompt quality scoring module provides a readability score for each prompt and can selectively inhibit sending of the prompt to a GAI model in response (e.g., if the readability score is below a threshold score)”; [0058] describes: “the prompt quality indicator module analyzes prompt quality for readability (e.g., by a respective GAI model)”. At best, the specification suggests that based on quality evaluation, inhibiting transmission of the prompt. Therefore, the language - the prompt is modified based on the evaluation of the quality of the prompt -party application - constitutes new matter. (See also 37 C.F.R. 1.121(f), MPEP 608.04, 706.03(o)). For the purposes of examination, the Examiner will interpret the limitation “the prompt is modified based on the evaluation of the quality of the prompt” as: inhibiting transmission the prompt based on the evaluation of the quality of the prompt.
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, 3-4, 7-8, 10-11, 14-15, 17-18, and 21-29 are rejected under 35 U.S.C. 103 as being unpatentable over Austin et al. (US 2024/0420012 A1 hereinafter Austin) in view of Siebel et al. (US 2024/0202225 A1 hereinafter Siebel), further in view of Sankaranarayanan et al. (US 2023/0281281 A1 hereinafter Sankaranarayanan).
Regarding Claim 1, Austin teaches a computer-implemented method for remote integration of generative artificial intelligence (GAI) systems to enterprise systems ([0027] orchestration platform facilitate usage of generative AI systems in a safe and democratized manner; the orchestration platform can safely “teach” LLM(s) at scale about a given enterprise such that enterprise users can safely ask questions and receive safe output responses), the method comprising:
receiving, by a GAI integration platform, a request from an application executed by an enterprise system of an enterprise, the application being executed remotely from the GAI integration platform ([0027] orchestration platform facilitate usage of generative AI systems in a safe and democratized manner; the orchestration platform can safely “teach” LLM(s) at scale about a given enterprise such that enterprise users can safely ask questions and receive safe output responses; [0033] FIG. 1 illustrates environment 100 for generative AI orchestration; [0034] the enterprise system 104 (i.e., an enterprise system of an enterprise)correspond to an enterprise and include one or more computing devices; [0036] the orchestration platform 102 configured as an intelligent system; the orchestration platform 102 may be privately hosted - fig. 1 shows the enterprise system remote from the generative orchestration platform; [0072] FIG. 2G illustrates a method performed by the orchestration platform 102 (i.e., GAI integration platform); [0073] the orchestration platform 102 perform one or more operations that include obtaining a user query, user query is submitted by an authenticated user or bot (i.e., request from an application executed by an enterprise system));
processing, through a control tier of the GAI integration platform, at least a portion of the request through a set of modules to generate a prompt that is responsive to the request, the set of modules comprising one or more of a prompt template module, a prompt quality module, and a personally identifiable information (PII) detection module ([0074] based on the obtaining, evaluating a context of the user query; the orchestration platform 102 can, similar to that described above with respect to FIG. 2B, perform one or more operations, that include, based on the obtaining, evaluating a context of the user query; [0075] causing the user query to be routed to one or more of the processing pipelines in accordance with the context; [0076] based on the one or more of the processing pipelines to which the user query is routed, generating one or more curated LLM prompts for the user query; [0077] combining the one or more curated LLM prompts and the one or more extracts with the user query, resulting in a modified query (i.e., generating prompt that is responsive to the request); [0042] FIG. 2B is a flow diagram illustrating query orchestration by the generative AI orchestration platform 102; [0043] referring to FIG. 2B, query orchestration begin with an authenticated user/bot submitting a question; examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question; the context evaluation functionality 102 e employ AI-based logic that is capable of understanding user questions and determining the path or pipeline for routing the question; [0046] the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question - thus, the generative AI orchestration platform processes the user query through context evaluation functionality 102e, user access & authorization management functionality 102a, prompt generation functionality 102g, etc. to generate prompt (i.e., generative AI orchestration platform/ GAI integration platform processes user query through control tier including one or more of a prompt template module, a prompt quality module, and a personally identifiable information (PII) detection module)),
wherein one or more of the request and the prompt is processed by the GAI integration platform to mitigate presence of one or more of PII and profanity before transmitting the prompt to a GAI system ([0017] sensitive personal information (e.g., name, age, phone number, social security number, etc.); [0043] referring to FIG. 2B, query orchestration begin with an authenticated user/bot submitting a question; examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question; the context evaluation functionality 102 e employ AI-based logic that is capable of understanding user questions and determining the path or pipeline for routing the question; [0046] the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question; LLM prompt may be directed to a particular generative AI LLM (i.e., GAI system); [0051] when a question is asked, the context evaluation functionality 102 e (“determines context”) of the orchestration platform 102 evaluate the question and determine if the question contains sensitive information; if it does, that portion can be redacted at the outset or the question can be rejected with notification - thus, the orchestration platform/ GAI integration platform mitigate presence of one or more of PII and profanity before transmitting the prompt to the GAI system);
transmitting, by the GAI integration platform, the prompt to the GAI system of a plurality of GAI systems ([0077] combining the one or more curated LLM prompts and the one or more extracts with the user query, resulting in a modified query; [0078] performing response generation by submitting the modified query to the one or more generative AI LLMs (i.e., GAI system of a plurality of GAI systems) that correspond to the one or more of the processing pipelines to which the user query has been routed, so as to derive a response to the user query);
receiving, by the GAI integration platform, a response from the GAI system, the response comprising content generated by the GAI system in response to the prompt ([0078] performing response generation by submitting the modified query to the one or more generative AI LLMs (i.e., GAI system of a plurality of GAI systems) that correspond to the one or more of the processing pipelines to which the user query has been routed, so as to derive a response to the user query; similar to that described above with respect to FIG. 2B, perform one or more operations that include performing response generation; [0046] as shown in fig. 2B, an LLM prompt directed to a particular generative AI LLM 106, and constitute “additional instructions” on how the question should be asked to the generative AI LLM 106 and/or how the generative AI LLM 106 should answer the question; [0048] a response generation functionality 102 n obtain the combined prompt and transmit it to the relevant generative AI LLM(s) 106 for generating answers; [0049] the generated answer may then be checked against copyright, plagiarism, and/or company ethical/bias policies and subsequently provide the answer to the user. See fig. 2B - it shows the response generated by the response generation 102n/ LLM (i.e., GAI system) received by the generative AI orchestration platform (i.e., GAI integration platform));
transmitting, by the GAI integration platform, the response to the application ([0049] the generated answer may then be checked against copyright, plagiarism, and/or company ethical/bias policies and subsequently provide the answer to the user - 102 w, 102 x, 102 y, and 102 z of FIG. 2C. See figs. 2B and 2C - it shows the response generated by the response generation 102n/ LLM received by the generative AI orchestration platform (i.e., GAI integration platform) and the generative AI orchestration platform outputting the response to the authenticated enterprise user/ bot (i.e., transmitting response to the application)); and
logging interaction data representative of requests from and responses to the application ([0043] referring to FIG. 2B, query orchestration begin with an authenticated user/bot submitting a question; examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question; [0049] the generated answer may then be checked against copyright, plagiarism, and/or company ethical/bias policies and subsequently provide the answer to the user; [0050] as shown in FIG. 2B, the user may provide feedback regarding the answer, which the system can utilize, in a reinforcement learning with human feedback (RLHF) process to improve future response accuracy - thus, logging interaction data representative of requests and answers).
However, Austin fails to expressly teach wherein the processing to generate the prompt includes populating a prompt template at least partially based on data provided in a payload of the request.
In the same field of endeavor, Siebel teaches wherein the processing to generate the prompt includes populating a prompt template at least partially based on data provided in a payload of the request ([0138] input such as, request, query, can be input in various natural forms for easy human interaction; [0110] the comprehension module 510 generate a prompt template for processing an initial input, a prompt template for processing iterative inputs, and another prompt template for the output result phase; prompt templates can be modified to generate prompts - thus, the comprehensive module generates and modifies prompt templates for processing input requests and the input requests in various natural forms is processed to formulate the model prompt (i.e., the request payload populates the template)).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the processing to generate the prompt includes populating a prompt template at least partially based on data provided in a payload of the request, as taught by Siebel into Austin. Doing so would be desirable because it would allow for efficiently processing a wide variety of inputs received from disparate data sources and return results in a common data form (Siebel [0022]).
However, Austin and Siebel fail to expressly teach wherein logging interaction data representative of requests from and responses to the application and providing one or more dashboards that graphically depict at least a portion of the interaction data.
In the same field of endeavor, Sankaranarayanan teaches wherein logging interaction data representative of requests from and responses to the application and providing one or more dashboards that graphically depict at least a portion of the interaction data ([0043]-[0045] all inference requests/responses for ML model are received/monitored; the entire content of the inference request at 302 can be logged using a logging/monitoring system; the request and response are joined into a single entry in the logging system; logs may be ingested in a logging system and generates monitoring metrics, generates telemetry data, system event data, etc., which are ingested in a monitoring system, implemented by a “Prometheus” based system; the captured data can be plotted in form of dashboards using tools such as “Grafana” dashboard monitoring).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein logging interaction data representative of requests from and responses to the application and providing one or more dashboards that graphically depict at least a portion of the interaction data, as taught by Sankaranarayanan into Austin and Siebel. Doing so would be desirable because it would allow for the user requests across multiple tenancies and regions to be evaluated and monitored through a logging and telemetry system (Sankaranarayanan [0076]).
As to dependent Claim 3, Austin, Siebel, and Sankaranarayanan teach all the limitations of Claim 1. Austin further teaches wherein processing, through a control tier of the GAI integration platform, at least a portion of the request through a set of modules to generate a prompt that is responsive to the request ([0043] referring to FIG. 2B, query orchestration begin with an authenticated user/bot submitting a question; examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question; the context evaluation functionality 102 e employ AI-based logic that is capable of understanding user questions and determining the path or pipeline for routing the question; [0046] the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question- thus, the generative AI orchestration platform processes the user query through context evaluation functionality 102e, user access & authorization management functionality 102a, prompt generation functionality 102g, etc. to generate prompt (i.e., generative AI orchestration platform/ GAI integration platform processes user query through control tier including a set of modules) comprises determining context data representative of one or more of the enterprise, and an enterprise operation and providing the prompt as a few-shot prompt that includes at least a portion of the context data ([0043] the context evaluation functionality 102 e employ AI-based logic that is capable of understanding user questions and determining the path or pipeline for routing the question; [0046] the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question; customization of a prompt may be a function of previous questions (e.g., maintaining context for multi-turn questions) (i.e., determining context) and may involve phrasing the question in the context of the relevant company user persona (i.e., few-shot prompt including at least a portion of the context data)).
As to dependent Claim 4, Austin, Siebel, and Sankaranarayanan teach all the limitations of Claim 1. Austin further teaches wherein processing, through a control tier of the GAI integration platform, at least a portion of the request through a set of modules to generate a prompt that is responsive to the request ([0043] referring to FIG. 2B, query orchestration begin with an authenticated user/bot submitting a question; examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question; the context evaluation functionality 102 e employ AI-based logic that is capable of understanding user questions and determining the path or pipeline for routing the question; [0046] the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question- thus, the generative AI orchestration platform processes the user query through context evaluation functionality 102e, user access & authorization management functionality 102a, prompt generation functionality 102g, etc. to generate prompt (i.e., generative AI orchestration platform/ GAI integration platform processes user query through control tier including a set of modules) comprises determining context data from at least one external source based on data provided in a payload of the request ([0043] query orchestration may begin with an authenticated user/bot submitting a question, where context evaluation functionality 102 e (“determines context”)examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question (e.g., if it is an HR question, the context evaluation functionality 102 e may select an HR policy path for query traversal); [0046] the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question; customization of a prompt may be a function of previous questions (e.g., maintaining context for multi-turn questions) (i.e., determining context) and may involve phrasing the question in the context of the relevant company user persona (i.e., context data from external source/ company information based on the payload of the request/ data provided in the question submitted by authenticated user/bot)), and providing the prompt as a few-shot prompt that includes at least a portion of the context data ([0046] the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question; customization of a prompt may be a function of previous questions (e.g., maintaining context for multi-turn questions) (i.e., determining context) and may involve phrasing the question in the context of the relevant company user persona (i.e., few-shot prompt including at least a portion of the context data)).
As to dependent Claim 7, Austin, Siebel, and Sankaranarayanan teach all the limitations of Claim 1. Austin further teaches wherein a GAI model of the GAI system is fine-tuned based on enterprise data provided by the enterprise ([0027] orchestration platform facilitate usage of generative AI systems in a safe and democratized manner; the orchestration platform can safely “teach” LLM(s) at scale about a given enterprise such that enterprise users can safely ask questions and receive safe output responses; [0050] the user may provide feedback regarding the answer, which the system can utilize, in a reinforcement learning with human feedback (RLHF) process to improve future response accuracy; RLHF feedback may be captured by telemetry and curated as RLHF information for influencing a model's responses - thus, the model is fine-tuned based on the enterprise users' feedback (i.e., enterprise data provided by the enterprise)).
As to dependent Claim 21, Austin, Siebel, and Sankaranarayanan teach all the limitations of Claim 1. Austin further teaches wherein the prompt quality module evaluates a quality of the prompt prior to transmitting the prompt to the GAI system, and wherein the prompt is modified based on the evaluation of the quality of the prompt ([0043] referring to FIG. 2B, query orchestration begin with an authenticated user/bot submitting a question; examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question; the context evaluation functionality 102 e employ AI-based logic that is capable of understanding user questions and determining the path or pipeline for routing the question; [0046] the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question; LLM prompt may be directed to a particular generative AI LLM; [0051] when a question is asked, the context evaluation functionality 102 e (“determines context”) of the orchestration platform 102 evaluate the question and determine if the question contains sensitive information; if it does, that portion can be redacted at the outset or the question can be rejected with notification).
As to dependent Claim 22, Austin, Siebel, and Sankaranarayanan teach all the limitations of Claim 1. Austin further teaches wherein the GAI integration platform selects the GAI system from the plurality of GAI systems based on one or more of the request and the prompt, and transmits the prompt to the selected GAI system ([0043] query orchestration facilitate answering of a user question using the trained generative AI LLM(s); determines context, examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question, the context evaluation functionality select an HR policy path for query traversal; [0078] performing response generation by submitting the modified query to the one or more generative AI LLMs that correspond to the one or more of the processing pipelines (i.e., GAI system from the plurality of GAI systems) to which the user query has been routed, so as to derive a response to the user query).
As to dependent Claim 23, Austin, Siebel, and Sankaranarayanan teach all the limitations of Claim 1. Austin further teaches wherein the request is received by the GAI integration platform via an application programming interface (API) of the GAI integration platform, the API enabling the application to remotely access the GAI integration platform ([0081] this cloud networking architecture is an open architecture that leverages application programming interfaces (APIs); [0032] orchestration platform that solves these problems for enterprise users while providing an interoperable approach to leveraging publicly-available LLMs to generate (e.g., conversational) responses; [0073] the orchestration platform 102 perform one or more operations that include obtaining a user query, user query is submitted by an authenticated user or bot. See fig. 1).
Claims 8, 10, 11, 14, and 24-26 are system claims corresponding to the method claims 1, 3, 4, 7, and 21-23 respectively and therefore, rejected for the same reasons. Austin further teaches wherein a system, comprising: one or more processors ([0028] a device, comprising a processing system including a processor, and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations); and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations ([0028] a device, comprising a processing system including a processor, and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations).
Claims 15, 17, 18, and 27-29 are medium claims corresponding to the method claims 1, 3, 4, and 21-23 respectively and therefore, rejected for the same reasons. Austin further teaches wherein a non-transitory computer-readable storage medium coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations ([0030] a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations).
Response to Arguments
35 U.S.C. §101: Applicant’s amendments and arguments with respect to 101 rejections have been fully considered and are persuasive. The 101 rejections are withdrawn.
35 U.S.C. §103: In the remarks, applicant argues that: (a) Siebel does not teach or suggest “populating a prompt template at least partially based on data provided in a payload of the request" as required by the amended claims. Siebel's prompt templates are generated by a "comprehension module" as part of Siebel's own internal multi-phase processing framework for understanding and classifying inputs. They are not templates that are populated based on data provided in a payload of an enterprise request as required by the amended claim. The claimed limitation requires a specific mechanism: a prompt template module within a control tier of a GAI integration platform that functions as an "intermediary between enterprise systems and GAI systems", and that populates a template "at least partially based on data provided in a payload of the request." (b) None of the cited references teach or suggest logging interaction data representative of requests from and responses to the application; and providing one or more dashboards that graphically depict at least a portion of the interaction data.
As to point (a), Examiner respectfully disagrees with applicant’s arguments. The cited references do teach the features recited in amended independent claims. Austin teaches that based on the obtaining, evaluating a context of the user query; the orchestration platform 102 can perform one or more operations, that include, based on the obtaining, evaluating a context of the user query; causing the user query to be routed to one or more of the processing pipelines in accordance with the context; based on the one or more of the processing pipelines to which the user query is routed, generating one or more curated LLM prompts for the user query; combining the one or more curated LLM prompts and the one or more extracts with the user query, resulting in a modified query; performing response generation by submitting the modified query to the one or more generative AI LLMs that correspond to the one or more of the processing pipelines to which the user query has been routed, so as to derive a response to the user query; query orchestration begin with an authenticated user/bot submitting a question; examines the question and determines the type of (e.g., private) company documents that may be relevant for generating answers to the question; the context evaluation functionality 102 e employ AI-based logic that is capable of understanding user questions and determining the path or pipeline for routing the question; the user access & authorization management functionality 102 a check the authorization level of the authenticated user/bot; a prompt generation functionality 102 g tailor or customize one or more LLM prompts for the question (see [0042] –[0046], [0074]- [0078]). Thus, Austin teaches that the generative AI orchestration platform processes the user query through context evaluation functionality, user access & authorization management functionality, prompt generation functionality, etc. to generate prompt (i.e., generative AI orchestration platform/ GAI integration platform processes user query through control tier to generate and transmit prompt). Siebel teaches that input such as, request, query, can be input in various natural forms for easy human interaction; the comprehension module 510 generates a prompt template for processing an initial input, a prompt template for processing iterative inputs, and another prompt template for the output result phase; prompt templates can be modified to generate prompts. (see [0110], [0138]). Thus, Siebel teaches that the interface module receives an input (e.g., request, query) and processes it to formulate the model input, and the comprehension module uses and modifies prompt templates to generate the resulting prompts. Examiner notes that Siebel is relied upon to teach the feature of populating a prompt template at least partially based on data provided in a payload of the request. Under the broadest reasonable interpretation, populating a prompt template at least partially based on data provided in a payload of the request requires only that data from the received request be used to fill or complete a prompt template – it does not require specific intermediatory control-tier architecture that Applicant emphasizes. The feature of processing, through a control tier of the GAI integration platform, at least a portion of the request through a set of modules to generate a prompt that is responsive to the request is taught by the primary reference, Austin. Examiner respectfully reminds Applicant that that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Thus, Examiner reasserts that the combination of the cited arts clearly and sufficiently teaches all the limitations recited in the amended independent claims. See the 103 rejections above for details.
As to point (b), Applicant's arguments with respect to the 103 rejections have been considered, but are moot in view of new ground of rejection made under 35 U.S.C. § 103.
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 extension fee 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 REJI KARTHOLY whose telephone number is (571)272-3432. The examiner can normally be reached on Monday - Thursday 7:30 am - 3:30 pm.
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/REJI KARTHOLY/Primary Examiner, Art Unit 2143
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