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
Summary
This action is in reply to Applicant’s Amendments and Remarks filed on 8/4/2026.
Claims 1-4, 6-12, 14-19 and 21-23 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 5/18/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed on 8/4/2026 have been fully considered but they are not persuasive. Regarding arguments on page 12-18 of the Remarks for 101 rejections, the applicants contend that claim 1, 9 and 16 as amended cannot perform in the human mind and the amended claim can integrate into a practical application. However, the Examiner respectfully disagrees. The claims 1, 9 and 16 recite a method/system for generating a request payload having a common request body specification based on an utterance (i.e., receiving data step), converting the common request body specification into a custom request body specification having a data input format associated with a GenAl model provider selected from the plurality of GenAl model providers (i.e., receiving data and a data process); communicating, by the GenAI interface, the request payload with the custom request body specification to the GenAl model provider for processing by a GenAl model (i.e., receiving/sending data steps); receiving, at the GenAI interface from the GenAI model provider, a response payload associated with: (i) an error, (ii) processing the request payload, or (iii) both in response to the GenAI model processing the request payload with the custom request body specification (i.e., receiving data and a data process/evaluate); generating a custom response body specification associated with the response payload, wherein the custom response body specification has a data output format associated with the GenAl model provide (i.e., a data analysis/evaluate); converting the custom response body specification into a common response body specification; determining whether that the response payload includes an error or invalid data based on an evaluation of the common response body specification (i.e., a data analysis/evaluate); determining whether that the response payload includes the error or invalid data, executing one or more actions for handling the error or invalid data when the response payload includes the error or invalid data (i.e., a data analysis/evaluate); determining that refinement data associated with the GenAl model and the response payload is associated with historical refinement data (i.e., a data analysis/evaluate); modifying the refinement data by adjusting or removing at least one historical refinement item of the historical refinement data (i.e., a data analysis); generating a modified request payload based on the refinement data (i.e., a data generation step); and communicating the modified request payload to the GenAI model provider for processing by the GenAI model to generate a modified response payload (i.e., a data analysis/evaluate and generation), and as such the actions (processing steps) correspond to the mental processes category of abstract ideas.
Applicant's arguments with respect to amended claims and originally presented claims have been fully considered but they are moot in view of the new grounds of rejection.
During patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification." Phillips v. AWH Corp., 415 F.3d 1303, at 1316 (Fed. Cir. 2005). See also In re Hyatt, 211 F.3d 1367, 1372, 54 USPQ2d 1664, 1667 (Fed. Cir. 2000).
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 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses three sets of claims: a method in claims 1-9 (i.e., a process), a system in claims 9-15 (i.e., a manufacture) and one or more non-transitory computer-readable media in claims 16-20 (i.e., a machine). All claims are directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significant more. The instant invention is broadly directed to “generating a custom response with a plurality of (generative artificial intelligence) GenAI models based on a request specification”. Claim 1 recites the following (with emphasis added):
Claim 1: A computer-implemented method comprising:
generating a request payload having a common request body specification based on an utterance, wherein the common request body specification is a standardized data input format used by a generative artificial intelligence (GenAI) interface for interacting with a plurality of GenAI model providers;
converting the common request body specification into a custom request body specification having a data input format associated with a GenAI model provider selected from the plurality of GenAI model providers;
communicating, by the GenAI interface, the request payload with the custom request body specification to the GenAI provider for processing by a GenAI model;
receiving, at the GenAI interface from the GenAI model provider,
a response payload associated with: (i) an error, (ii) processing the request payload, or (iii) both in response to the GenAI model processing the request payload with the custom request body specification;
generating a custom response body specification associated with the response payload, wherein the custom response body specification has a data output format associated with the GenAI model provider;
converting the custom response body specification into a common response body specification, wherein the common response body specification is a standardized data output format used by the GenAI interface for interacting with the plurality of GenAI model providers;
determining whether the response payload includes an error or invalid data based on an evaluation of the common response body specification; and
responsive to determining whether the response payload includes an error or invalid data, (i) executing one or more actions for handling the error or invalid data when the response payload includes the error or invalid data, wherein executing the one or more actions comprises:
determining that refinement data associated with the GenAl model and the response payload is associated with historical refinement data;
modifying the refinement data by adjusting or removing at least one historical refinement item of the historical refinement data;
generating a modified request payload based on the refinement data; and
communicating the modified request payload to the GenAI model provider for processing by the GenAI model to generate a modified response payload.
Claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2-8, and substantially also encompassed by claims 9-15 and 16-20.
Claims 1, 9, and 16 recite the steps to generate a custom response with a plurality of (generative artificial intelligence} GenAI models based on a request specification. These limitations, when given their broadest reasonable interpretation, are directed to certain performing of organizing human activity and mental processes, which is abstract idea.
Prong Two
This judicial exception is not integrated into a practical application because mere instruction to implement on computers (i.e. storage medium or processors in claim 9) or a computer model (a plurality of GenAI models here in claim 1), or merely using computers as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment for field of use is not considered integration into a practical application. Claim 1 recites using GenAI models prompt to generate custom response. Using input data to a trained machine-learning or GenAI model is a generic feature of modeling process, which does not represent a technological improvement. The using of the computer and GenAI models does not add improvement to the functioning of a computer or to any other technology field, which failed to enable the abstract idea to integrate into a practical application. The claims are drafted in a result-oriented fashion, without the requisite specificity needed to provide a nonabstract technological solution. The computing system and GenAI model process are directed to the components of a system amount to merely field of use type limitations and/or extra solution activity to implement the abstract idea as presented.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a processor, storage medium and GenAI models (in claim 1 and 9). These additional elements are merely conventional computer and computer model. Any potentially technical aspects of the claims are well-known generic computer components performing conventional functions (e.g., a processor performing a mental process). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine and conventional in the field. Thus, the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claims 1-8 are not patent eligible.
Claims 16-20 and claim 9-15 recite similar limitations of claims 1-8, thus are abstract idea and not patent eligible.
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) 1-2, 6-7, 9-10, 14-17 and 22-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al (US 12424209 B1) in view of “KARTAL: Web Application Vulnerability Hunting Using Large Language Models” (Sakaoglu) and Orn et al (US 20240394751 A1).
Regarding claim 1, NIKUMB discloses a computer-implemented method [e.g. FIG. 8-10; natural language modeling implemented by a computer or processors] comprising:
generating a request payload having a common request body specification [e.g. FIG. 1 and 5; input data 127 may include input text (or tokenized) data when the user input is a typed natural language user input] based on an utterance [e.g. column 6 lines 22-28; input utterance], wherein the common request body specification is a standardized data input format [e.g. ASR data that includes an ASR N-best list including multiple ASR hypotheses and corresponding confidence scores representing what the user may have said] used by a generative artificial intelligence (GenAI) interface [e.g. FIG. 1; 135; process the ASR data using one or more language models];
converting the common request body specification into a custom request body specification [e.g. FIG. 1; generating plan prompt by 140] having a data input format [e.g. column 10 lines 33-67; a template format] associated with a GenAI model provider selected from the plurality of GenAI model providers [e.g. FIG. 1, 135];
communicating, by the GenAI interface, the request payload with the custom request body specification to the GenAI provider for processing by a GenAI model [e.g. FIG 1-2; prompt data input to the language model];
receiving, at the GenAI interface from the GenAI model provider, a response payload [e.g. FIG. 1 and 4; 470; output to perform the action responsive to the user input] associated with: (i) an error, (ii) processing the request payload [e.g. FIG. 1 and 4; column 22 lines 1-27], or (iii) both in response to the GenAI model processing the request payload with the custom request body specification;
generating a custom response body specification associated with the response payload [e.g. FIG. 1-2 and 4; sending an instruction to the personalized context component 210 to generate clarifying information for the user input or generating output data 490 for output to the user], wherein the custom response body specification has a data output format [e.g. 490 to TTS; text to speech] associated with the GenAI model provider;
converting the custom response body specification into a common response body specification [e.g. TTS; text to speech format], wherein the common response body specification is a standardized data output format [e.g. speech data] used by the GenAI interface for interacting with the plurality of GenAI model providers;
Guo differs to the present invention in that Guo fails to explicitly disclose determining whether the response payload includes an error or invalid data based on an evaluation of the common response body specification and detail action for handling the error or invalid data when the response payload includes the error or invalid data.
However, Sakaoglu teaches the well-known concept of a language model [e.g. page 35; section 3.2; GPT3-based LLM] generating a response payload [e.g. generating a response] associated with: (i) an error [e.g. negative samples or abnormal examples], (ii) processing the request payload [e.g. response to the user’s request], or (iii) both; determining whether the response payload includes an error or invalid data based on an evaluation of the common response body specification [e.g. page 35; section 3.2; evaluating the quality of the results]; and responsive to determining whether the response payload includes an error or invalid data [e.g. negative samples], (i) executing one or more actions for handling the error or invalid data when the response payload includes the error or invalid data [e.g. page 35; section 3.2; removing the negative label description and samples or filtering abnormal examples], or (ii) executing one or more other actions for processing the response payload when the response payload does not include the error or invalid data.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35].
Moreover, Orn teaches the well-known concept of the one or more actions for handling the error or invalid data when the response payload [e.g. FIG. 4-5; [0050]; select which historical examples to provide as (positive) training examples to the large language model 420] comprises determining that refinement data [e.g. historical examples for fine-tuning the model 420] associated with the GenAl model and the response payload is associated with historical refinement data [e.g. historical examples]; modifying the refinement data by adjusting or removing at least one historical refinement item of the historical refinement data [e.g. select a number of relatively highest-performing releases]; generating a modified request payload based on the refinement data; and communicating the modified request payload to the GenAI model provider for processing by the GenAI model to generate a modified response payload [e.g. input/output training examples in the model training data 470 may then be used by a language model training 480 process to fine-tune the large language model 420].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu and the well-known concept of determining training data for fine-tuning the AI model technique taught by Orn as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35] and effective content of the press release by fine-tuning a large language model [See Orn: [0048-0050]].
Regarding claim 2, Gui, Sakaoglu and Orn further disclose by generating by another artificial intelligence model [e.g. Gui; FIG. 1-2 and 4; models], information concerning presence or absence of the error or invalid data within the common response body specification [e.g. Sakaoglu: page 35; section 3.2; evaluating the quality of the results], wherein the generating is performed by: (i) retrieving information from one or more authoritative external sources to confirm accuracy of the response payload [e.g. Sakaoglu: page 35; section 3.2-3.2.1; negative samples], (ii) assessing whether the response maintains contextual integrity, ensuring that the response payload is appropriate and relevant within a given context, (iii) identifying errors or inconsistencies in the response payload based on a list of errors or inconsistencies [e.g. Sakaoglu: page 35; section 3.2], (iv) generating a reasoning chain or step-by-step explanation and validating whether the response payload follows the reasoning or explanation, or any combination thereof.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu and the well-known concept of determining training data for fine-tuning the AI model technique taught by Orn as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35] and effective content of the press release by fine-tuning a large language model [See Orn: [0048-0050]].
Regarding claim 6, Gui, Sakaoglu and Orn further disclose the modified request payload includes an instruction that informs the GenAI model provider, the GenAI model, or both of the error or invalid data and requests that the GenAI model provider, the GenAI model, or both correct the error or invalid data [e.g. Sakaoglu: page 35; section 3.2; removing the negative label description and samples or filtering abnormal examples; Orn: FIG. 4-5].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu and the well-known concept of determining training data for fine-tuning the AI model technique taught by Orn as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35] and effective content of the press release by fine-tuning a large language model [See Orn: [0048-0050]].
Regarding claim 7, Gui, Sakaoglu and Orn further disclose the modified response payload includes and instruction that informs the GenAI model provider, the GenAI model, or both of the error or invalid data and requests that the GenAI model provider, the GenAI model [e.g. Sakaoglu: page 35; section 3.2; removing the negative label description and samples or filtering abnormal examples; true positive generation rate doubled], or both correct the error or invalid data; and
communicating the modified response payload to the GenAI model provider for processing by the GenAI model [e.g. Gui: FIG. 4; column 22 lines 1-27; Sakaoglu: page 35; section 3.2-3.2.1; Orn: FIG. 4-5].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu and the well-known concept of determining training data for fine-tuning the AI model technique taught by Orn as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35] and effective content of the press release by fine-tuning a large language model [See Orn: [0048-0050]].
Regarding claim 9-10 and 14-15, this is an apparatus that includes same limitation as in claim 1-2 and 6-7 above respective, the rejection of which are incorporated herein.
Regarding claim 16-17, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 1-2 above respective, the rejection of which are incorporated herein.
Regarding claim 22, Gui, Sakaoglu and Orn further disclose appending a corrective prompt to the modified request payload [e.g. Gui: FIG. 1-3; the prompt generation components discussed herein may be configured to append one or more portions of the data; Orn: FIG. 3-5; he prompt and ways of combining the input information about a press release vary in different embodiments and may be designed by an operator of the release generation system].
Regarding claim 23, Gui, Sakaoglu and Orn further disclose selecting an alternate GenAI model provider from the plurality of GenAI model providers [e.g. Gui: FIG. 1; Orn: FIG. 4-6; provider of general language models]; and converting the common request body specification into a custom request body specification associated with the alternate GenAI model provider [e.g. Gui: FIG. 1; 135; Orn: FIG. 4-6].
Claim(s) 3-4, 11-12 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al (US 12424209 B1) in view of “KARTAL: Web Application Vulnerability Hunting Using Large Language Models” (Sakaoglu), Orn et al (US 20240394751 A1) and Wang et al (US 20240289629 A1)
Regarding claim 3, Gui, Sakaoglu and Orn further disclose values for entities with data in the response payload; evaluating [e.g. Sakaoglu: page 35; section 3.2;evaluating the quality of the results if there are negative samples], comparing whether there are any missing matches the data in the response payload; and responsive to determining there are missing matches, determining whether the missing matches are indicative of the error or invalid data [e.g. Sakaoglu: page 35-37; section 3.2 and 3.2.1] but Gui and Sakaoglu fail to disclose the detail of the comparison.
However, Wang teaches the well-known concept of comparing values for entities with data in the response payload [e.g. FIG. 1-2; receiving response to a specific request; comparing entity and detecting entity matching; ]; evaluating, based on the comparing, whether there are any missing matches between the values for the entities and the data in the response payload [e.g. confidence value of match between entities]; and responsive to determining there are missing matches [e.g. FIG. 3 match or no-match].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu. the well-known concept of determining training data for fine-tuning the AI model technique taught by Orn and the well-known determining entities matching technique taught by Wang as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35], effective content of the press release by fine-tuning a large language model [See Orn: [0048-0050]] and improved ability to use natural language processing in various industries [See Wang: [0031 and 0055-0056]].
Regarding claim 4, Gui, Sakaoglu, Orn and Wang further disclose generate a modified request payload [e.g. Sakaoglu: page 35; section 3-2;evaluating the quality of the results if there are negative samples; removing the negative samples], wherein the modified request payload includes an instruction that informs the GenAI model provider, the GenAI model [e.g. Sakaoglu: page 35; section 3.2; removing the negative label description and samples or filtering abnormal examples; true positive generation rate doubled, or both of the missing matches and requests that the GenAI model provider, the GenAI model, or both to revise the response payload to include the values for the entities associated with missing matches [e.g. Gui: FIG. 4; column 22 lines 1-27; Sakaoglu: page 35; section 3.2-3.2.1; Orn: FIG. 4-5].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu. the well-known concept of determining training data for fine-tuning the AI model technique taught by Orn and the well-known determining entities matching technique taught by Wang as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35], effective content of the press release by fine-tuning a large language model [See Orn: [0048-0050]] and improved ability to use natural language processing in various industries [See Wang: [0031 and 0055-0056]].
Regarding claim 11-12, this is an apparatus that includes same limitation as in claim 3-4 above respective, the rejection of which are incorporated herein.
Regarding claim 18-19, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 3-4 above respective, the rejection of which are incorporated herein.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al (US 12424209 B1) in view of “KARTAL: Web Application Vulnerability Hunting Using Large Language Models” (Sakaoglu), Orn et al (US 20240394751 A1) and NIKUMB et al (US 20210397418 A1).
Regarding claim 8, Gui, Sakaoglu and Orn further disclose generating the request payload having the common request body specification, comprises representing the utterance as one or more messages [e.g. Guo FIG. 1-2], but Gui and Sakaoglu fail to explicitly disclose the detail of the message.
However, NIKUMB teaches the well-known concept of generating the request payload having the common request body specification as one or more messages based on JavaScript object syntax (JSON) [e.g. NIKUMB: FIG. 1L and 1O; flow definitions in JSON format] ; and generating the custom response body specification associated with the response payload, comprises representing the response payload as one or more candidate messages or a set of candidate messages based on JSON [e.g. NIKUMB: FIG.1O-1R and FIG. 3].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu. the well-known concept of determining training data for fine-tuning the AI model technique taught by Orn and the well-known identifying user input data technique taught by NIKUMB as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35], effective content of the press release by fine-tuning a large language model [See Orn: [0048-0050]] and more accurate output [See NIKUMB; [0058]].
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al (US 12424209 B1) in view of “KARTAL: Web Application Vulnerability Hunting Using Large Language Models” (Sakaoglu), Orn et al (US 20240394751 A1) and Chakhvadze et al (US 20250045023 A1).
Regarding claim 21, Gui, Sakaoglu and Orn further disclose determining the refinement data is associated with historical refinement data [e.g. Guo FIG. 1-2; Orn: FIG. 4-5], but Gui, Sakaoglu and Orn fail to disclose the details of modifying the refinement data.
However, Chakhvadze teaches the well-known concept of modifying the data is performed responsive to determining that a token count associated with the data exceeds a maximum token limit of the model [e.g. FIG. 1 and 6; determines that the collection of data units corresponds to a number of tokens that exceeds an upper limit of tokens (e.g., token budget) that a large language model can process; updating the internal state parameter based on the output].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the computer implemented method system disclosed by Guo to exploit the well-known determining negative examples within the prompt templates technique taught by Sakaoglu. the well-known concept of determining training data for fine-tuning the AI model technique taught by Orn and the well-known determining token number for the data unit technique taught by Chakhvadze as above, in order to provide reduced target vulnerabilities in the LLM [See Sakaoglu; page 35], effective content of the press release by fine-tuning a large language model [See Orn: [0048-0050]] and managing the token number budget supported by the language model [See Chakhvadze; [0082]].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cummings et al (US 20220027792 A1).
Davis et al (US 20240370241 A1).
THIS ACTION IS MADE FINAL. 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 ZHUBING REN whose telephone number is (571)272-2788. The examiner can normally be reached Monday-Friday 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, 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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/ZHUBING REN/Primary Examiner, Art Unit 2658
Prosecution Insights