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 . The present office action is responsive to communication received 1/29/2026.
Claims 11 and 18 have been canceled
Claims 21 and 22 are new.
Claims 1, 16, and 19
Claims 1-9, 11-17, and 19-22 are pending.
Response to Arguments
Applicant’s arguments with respect to claims 1, 16, and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The examiner introduces reference Parmar to read on the claim limitation “wherein the plurality of filters includes a plurality of input filters to filter an input to the second AI model and a plurality of output filters to filter an output of the second AI model”. Parmar [0018] discloses the an LLM module having an input and output filter.
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 1-6, 8, 10-12, and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over Salem et al. (US 20250175497) in view of Gardner et al. (US 20240296279) in further view of Parmar et al. (US 20240289628).
Regarding claim 1,
Salem teaches a method, comprising:
generating, via a first artificial intelligence (AI) model, a plurality of prompt variations based on an indication of a vulnerability;
[the LGM 230 generates a new set of variant prompt injection attacks (Salem et al., paragraph 72, LGM 230 being the first model)]
determining that a second AI model is vulnerable to the vulnerability based on at least one prompt variation in the plurality of prompt variations;
[The attack defense system 206 also includes the target model manager 214, which provides variant prompt injection attacks 224 to the targeted LGM 240 and obtains targeted LGM outputs 226. (Salem et al., paragraph 55)]
[the prompt variation evaluator 216, which determines the success and/or the effectiveness of the variant prompt injection attacks 224 at attacking the targeted LGM 240 based on the targeted LGM outputs 226 (Salem et al., paragraph 56, LGM 240 being the second model)]
generating a plurality of filter variations based on a plurality of filters and the at least one prompt variation;
[the defense robust model 710 obtains the variant prompt injection attacks 430, which may include prompt variation effectiveness scores 632. Using the variant prompt injection attacks 430 the defense robust model 710 generates and/or provides robustness measures 712 to the targeted LGM 240. The robustness measures 712 provide improved security for the targeted LGM 240 through various methods. (Salem et al., paragraph 147, the filters being interpreted to be the robustness measures)]
Salem fails to explicitly disclose testing the plurality of filter variations and the at least one prompt variation on the second AI model and generating, by a processing device and based on the testing, a report indicative of an effectiveness of the plurality of filter variations in mitigating the vulnerability with respect to the second AI model.
However in an analogous art Gardner discloses testing the plurality of filter variations and the at least one prompt variation on the second AI model;
[Based on a determination that the dialogue context contains at least one adverse input, the orchestrator 420 may determine effectiveness of adverse input mitigation on the at least one adverse input. Such determination may be made either by analyzing the outputs or result(s) of 455 after performing the adverse input mitigation steps as described in detail above or by generating a prompt to an AI Model (e.g., the second AI Model 450) that includes the result(s) 455, the dialogue context, and the similar example pairs 440. (Gardner et al., paragraph 0101, testing effectiveness of mitigation techniques)]
and generating, by a processing device and based on the testing, a report indicative of an effectiveness of the plurality of filter variations in mitigating the vulnerability with respect to the second AI model.
[The prompt may cause the AI Model to perform such analysis and/or determination to output results (e.g., result(s) 455) to indicate whether or not (and/or to what degree) the adverse input mitigation is effective. One or more messages may then be generated, either by the orchestrator 420 or by the AI Model (e.g., the second AI Model 450 based on prompts generated by the orchestrator 420). The one or more messages may indicate presence (or absence) of one or more adverse inputs and/or may indicate effectiveness of adverse input mitigation on any adverse input. (Gardner et al., paragraph 101, message contains output results regarding mitigations effectivity)]
Salem and Gardner are considered to be analogous to the claimed invention because they are in the same field of model vulnerability mitigation. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem to incorporate the teachings of Gardner et al. to include testing the plurality of filter variations and the at least one prompt variation on the second AI model and generating, by a processing device and based on the testing, a report indicative of an effectiveness of the plurality of filter variations in mitigating the vulnerability with respect to the second AI model, in order to understand to what level whether or not (and/or to what degree) the adverse input mitigation is effective. (Gardner et al., paragraph 101)]
Salem in view of Gardner fails to explicitly disclose wherein the plurality of filters includes a plurality of input filters to filter an input to the second Al model and a plurality of output filters to filter an output of the second Al model.
However in an analogous art Parmar discloses wherein the plurality of filters includes a plurality of input filters to filter an input to the second Al model and a plurality of output filters to filter an output of the second Al model.
[The LLM is deployed in a LLM module (102) further comprising an input filter (1021) and at least an output filter (1022). (Parmar et al., paragraph 18)]
Salem, Gardner, and Parmar are considered to be analogous to the claimed invention because they are in the same field of model vulnerability mitigation. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem and Gardner to incorporate the teachings of Parmar et al. to include wherein the plurality of filters includes a plurality of input filters to filter an input to the second Al model and a plurality of output filters to filter an output of the second Al model, in order to prevent the generation of harmful content. (Parmar et al., paragraph 10)]
Regarding claim 16,
Salem discloses a system, comprising: memory; and a processing device, cause the processing device operatively couple to the memory, to:
[a computer-readable medium can include instructions that, when executed by a processing system having a processor, cause a computing device to perform the acts in FIG. 8. In some implementations, a system (e.g., a processing system comprising a processor) can perform the acts in FIG. 8. For example, the system includes a processing system and a computer memory including instructions that, when executed by the processing system, cause the system to perform various actions or steps. (Salem et al., paragraph 153)]
The claim recites substantially the same content as claim 1 and is rejected with the rationales set forth for claim 1.
Regarding claim 19,
Salem discloses a non-transitory computer readable medium, having instructions stored thereon which, when executed by a processing device, cause the processing device to:
[the techniques may be realized at least in part by a non-transitory processor-readable storage medium, including instructions that, when executed by at least one processor, perform one or more of the methods described herein (including computer-implemented methods). (Salem et al., paragraph 178)]
The claim recites substantially the same content as claim 1 and is rejected with the rationales set forth for claim 1.
Regarding claim 2,
Salem in view of Gardner in further view of Parmar discloses the method of claim 1, wherein the first AI model comprises a first large language model (LLM) and the second AI model comprises a second LLM.
[the computing environment 200 includes a cloud computing system 202 associated with the attack defense system 206, an LGM 230 (large generative model), and a targeted LGM 240 (Salem et al., paragraph 50]
[The application system 000 also includes the targeted LGM 240. The targeted LGM 240 represents an LGM, such as an LLM, that may be attacked using prompt injection attacks. Accordingly, the attack defense system 206 aims to discover, generate, and/or determine a wide range of prompt injection attacks (e.g., variant prompt injection attacks) that may be used against the targeted LGM 240, including prompt injection attacks deployed against different types of LGMs. (Salem et al., paragraph 58)]
Regarding claims 3, 17, and 20,
Salem in view of Gardner in further view of Parmar discloses the method of claim 1, the system of claim 16, and the non-transitory computer readable medium of claim 19,
wherein the vulnerability comprises at least one of a prompt injection, a prompt leakage, a toxicity, a personally identifiable information (PII) leakage, a hallucination, a sponge attack, or a denial-of-service (DoS) attack.
[the attack defense system 206 generates a set of variant attacks of a prompt injection attack that are successful against the targeted LGM. (Salem et al., paragraph 53)]
Regarding claim 4,
Salem in view of Gardner in further view of Parmar discloses The method of claim 1, further comprising:
testing each prompt variation in the plurality of prompt variations on the second AI model,
[The attack defense system 206 also includes the target model manager 214, which provides variant prompt injection attacks 224 to the targeted LGM 240 and obtains targeted LGM outputs 226. (Salem et al., paragraph 55)]
wherein the determining that the second AI model is vulnerable to the vulnerability is based on the at least one prompt variation in the plurality of prompt variations being tested on the second AI model.
[the prompt variation evaluator 216, which determines the success and/or the effectiveness of the variant prompt injection attacks 224 at attacking the targeted LGM 240 based on the targeted LGM outputs 226 (Salem et al., paragraph 56)]
Regarding claim 5,
Salem in view of Gardner in further view of Parmar discloses The method of claim 4,
wherein the testing each prompt variation in the plurality of prompt variations on the second AI model comprises: providing, as an input to the second AI model, each prompt variation;
[The attack defense system 206 also includes the target model manager 214, which provides variant prompt injection attacks 224 to the targeted LGM 240 and obtains targeted LGM outputs 226. (Salem et al., paragraph 55)]
and obtaining, as an output from the second AI model and based on the input, a prompt response for each prompt variation, wherein the determining that the second AI model is vulnerable to the vulnerability is based on at least one of the input or the output.
[the prompt variation evaluator 216, which determines the success and/or the effectiveness of the variant prompt injection attacks 224 at attacking the targeted LGM 240 based on the targeted LGM outputs 226 (Salem et al., paragraph 56)]
Regarding claim 6,
Salem in view of Gardner in further view of Parmar discloses the method of claim 1,
wherein the testing the plurality of filter variations and the at least one prompt variation on the second AI model comprises:
applying at least one filter variation in the plurality of filter variations to the second AI model; providing, as an input to the second AI model, the at least one prompt variation;
[the defense robust model 710 obtains the variant prompt injection attacks 430, which may include prompt variation effectiveness scores 632. Using the variant prompt injection attacks 430 the defense robust model 710 generates and/or provides robustness measures 712 to the targeted LGM 240. The robustness measures 712 provide improved security for the targeted LGM 240 through various methods. (Salem et al., paragraph 147)]
obtaining, as an output from the second AI model and based on the input, a prompt response for the at least one prompt variation;
[the prompt variation evaluator 216, which determines the success and/or the effectiveness of the variant prompt injection attacks 224 at attacking the targeted LGM 240 based on the targeted LGM outputs 226 (Salem et al., paragraph 56)]
and determining whether the second AI model with the at least one filter variation applied thereto prevents or mitigates the vulnerability based at least one of the input or the output.
[The prompt may cause the AI Model to perform such analysis and/or determination to output results (e.g., result(s) 455) to indicate whether or not (and/or to what degree) the adverse input mitigation is effective. One or more messages may then be generated, either by the orchestrator 420 or by the AI Model (e.g., the second AI Model 450 based on prompts generated by the orchestrator 420). The one or more messages may indicate presence (or absence) of one or more adverse inputs and/or may indicate effectiveness of adverse input mitigation on any adverse input. (Gardner et al., paragraph 101)]
Salem, Gardner, and Parmar are considered to be analogous to the claimed invention because they are in the same field of model vulnerability mitigation. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem and Parmar to incorporate the teachings of Gardner et al. to include determining whether the second AI model with the at least one filter variation applied thereto prevents or mitigates the vulnerability based at least one of the input or the output, in order to understand to what level whether or not (and/or to what degree) the adverse input mitigation is effective. (Gardner et al., paragraph 101)]
Regarding claim 8,
Salem in view of Gardner in further view of Parmar discloses the method of claim 6,
wherein a filter variation in the plurality of filter variations prevents or mitigates the vulnerability, and wherein the report indicates that the filter variation prevents or mitigates the vulnerability.
[The prompt may cause the AI Model to perform such analysis and/or determination to output results (e.g., result(s) 455) to indicate whether or not (and/or to what degree) the adverse input mitigation is effective. One or more messages may then be generated, either by the orchestrator 420 or by the AI Model (e.g., the second AI Model 450 based on prompts generated by the orchestrator 420). The one or more messages may indicate presence (or absence) of one or more adverse inputs and/or may indicate effectiveness of adverse input mitigation on any adverse input. (Gardner et al., paragraph 101)]
Salem, Gardner, and Parmar are considered to be analogous to the claimed invention because they are in the same field of model vulnerability mitigation. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem and Parmar to incorporate the teachings of Gardner et al. to include wherein a filter variation in the plurality of filter variations prevents or mitigates the vulnerability, and wherein the report indicates that the filter variation prevents or mitigates the vulnerability, in order to understand to what level whether or not (and/or to what degree) the adverse input mitigation is effective. (Gardner et al., paragraph 101)]
Regarding claims 10 and 18,
Salem in view of Gardner in further view of Parmar discloses the method of claim 1 and the system of claim 16,
wherein the plurality of filters includes a plurality of input filters configured for an input to the second AI model and a plurality of output filters configured for an output of the second AI model.
[the defense robust model 710 obtains the variant prompt injection attacks 430, which may include prompt variation effectiveness scores 632. Using the variant prompt injection attacks 430 the defense robust model 710 generates and/or provides robustness measures 712 to the targeted LGM 240. The robustness measures 712 provide improved security for the targeted LGM 240 through various methods. (Salem et al., paragraph 147)]
Regarding claim 11, 21, and 22,
Salem in view of Gardner in further view of Parmar discloses The method of claim 1, the system of claim 16, and the non-transitory computer readable medium of claim 19, further comprising:
outputting the report.
[The prompt may cause the AI Model to perform such analysis and/or determination to output results (e.g., result(s) 455) to indicate whether or not (and/or to what degree) the adverse input mitigation is effective. One or more messages may then be generated, either by the orchestrator 420 or by the AI Model (e.g., the second AI Model 450 based on prompts generated by the orchestrator 420). The one or more messages may indicate presence (or absence) of one or more adverse inputs and/or may indicate effectiveness of adverse input mitigation on any adverse input. (Gardner et al., paragraph 101)]
Salem, Gardner, and Parmar are considered to be analogous to the claimed invention because they are in the same field of model vulnerability mitigation. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem and Parmar to incorporate the teachings of Gardner et al. to include outputting the report, in order to understand to what level whether or not (and/or to what degree) the adverse input mitigation is effective. (Gardner et al., paragraph 101)]
Regarding claim 12,
Salem in view of Gardner in further view of Parmar discloses the method of claim 11,
wherein the outputting the report comprises at least one of: transmitting the report over a network; storing the report in computer-readable storage; or transmitting the report for display.
[One or more messages may then be generated, either by the orchestrator 420 or by the AI Model (e.g., the second AI Model 450 based on prompts generated by the orchestrator 420). The one or more messages may indicate presence (or absence) of one or more adverse inputs and/or may indicate effectiveness of adverse input mitigation on any adverse input. (Gardner et al., paragraph 101, messages are provided to some entity)]
Salem, Gardner, and Parmar are considered to be analogous to the claimed invention because they are in the same field of model vulnerability mitigation. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem and Parmar to incorporate the teachings of Gardner et al. to include wherein the outputting the report comprises at least one of: transmitting the report over a network; storing the report in computer-readable storage; or transmitting the report for display, in order to understand to what level whether or not (and/or to what degree) the adverse input mitigation is effective. (Gardner et al., paragraph 101)]
Regarding claim 15,
Salem in view of Gardner in further view of Parmar discloses The method of claim 1,
wherein the second AI model comprises a plurality of AI models trained to generate language,
[one or more evaluation models to assess the effectiveness of these variant prompt injection attacks on the targeted LGM. (Salem et al., paragraph 14)]
[Large generative models have applications in natural language (Salem et al., paragraph 28)]
wherein determining that the second AI model is vulnerable to the vulnerability comprises determining that at least one AI model in the plurality of AI models is vulnerable to the vulnerability based on the at least one prompt variation in the plurality of prompt variations,
[the prompt variation evaluator 216, which determines the success and/or the effectiveness of the variant prompt injection attacks 224 at attacking the targeted LGM 240 based on the targeted LGM outputs 226 (Salem et al., paragraph 56)]
wherein testing the plurality of filter variations on the second AI model comprises testing the plurality of filter variations on the at least one AI model,
[Based on a determination that the dialogue context contains at least one adverse input, the orchestrator 420 may determine effectiveness of adverse input mitigation on the at least one adverse input. Such determination may be made either by analyzing the outputs or result(s) of 455 after performing the adverse input mitigation steps as described in detail above or by generating a prompt to an AI Model (e.g., the second AI Model 450) that includes the result(s) 455, the dialogue context, and the similar example pairs 440. (Gardner et al., paragraph 101)]
and wherein the report is indicative of the effectiveness of the plurality of filter variations in mitigating the vulnerability with respect to the at least one AI model.
[The prompt may cause the AI Model to perform such analysis and/or determination to output results (e.g., result(s) 455) to indicate whether or not (and/or to what degree) the adverse input mitigation is effective. One or more messages may then be generated, either by the orchestrator 420 or by the AI Model (e.g., the second AI Model 450 based on prompts generated by the orchestrator 420). The one or more messages may indicate presence (or absence) of one or more adverse inputs and/or may indicate effectiveness of adverse input mitigation on any adverse input. (Gardner et al., paragraph 101)]
Claims 7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Salem et al. (US 20250175497) in view of Gardner et al. (US 20240296279) in further view of Parmar et al. (US 20240289628 ) and in further view of Ohayon et al. (US 20240054233). Ohayon is cited and indicated in IDS dated 6/28/2024.
Regarding claim 7,
Salem in view of Gardner in further view of Parmar discloses the method of claim 6, but fails to explicitly disclose wherein a filter variation in the plurality of filter variations fails to prevent or mitigate the vulnerability, and wherein the report indicates that the filter variation fails to prevent or mitigate the vulnerability.
However in an analogous art Ohayon discloses wherein a filter variation in the plurality of filter variations fails to prevent or mitigate the vulnerability, and wherein the report indicates that the filter variation fails to prevent or mitigate the vulnerability.
[For example, a database or a dataset may be created, with characteristics of various types of ML/DL/AI engines, with characteristics of their particular dataset and model, with characteristics of known attacks that were detected towards them, with the mitigation/protection operations that are relevant for each type of engine and dataset and attack, and for the results (success/failure; or a success score as a percentage value) for a particular mitigation/protection operation with result to a particular attack towards a particular engine or dataset or or engine-and-dataset combination. (Ohayon et al., paragraph 115, results reports report on whether or not mitigation or protection (filter) fails to prevent/ mitigate vulnerability)]
Salem, Gardner, Parmar, and Ohayon are considered to be analogous to the claimed invention because they are in the same field of model vulnerability mitigation. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem, Gardner, and Parmar to incorporate the teachings of Ohayon et al. to include wherein a filter variation in the plurality of filter variations fails to prevent or mitigate the vulnerability, and wherein the report indicates that the filter variation fails to prevent or mitigate the vulnerability, in order to deduce which attack vectors may be relevant towards a particular ML/DL/Ai engine and/or which mitigation techniques may be useful or effective towards such attacks. (Ohayon et al., paragraph 115)]
Regarding claim 9,
Salem in view of Gardner in further view of Parmar discloses the method of claim 8, but fails to explicitly disclose further comprising: adding the filter variation to the plurality of filters based on the filter variation preventing or mitigating the vulnerability.
However in an analogous art Ohayon discloses further comprising: adding the filter variation to the plurality of filters based on the filter variation preventing or mitigating the vulnerability.
[the Offline Protection Unit 122 may automatically analyze the structure and/or operations of the ML/DL/AI Engine 101; may automatically detect that the ML/DL/AI Engine 101 lacks a particular filtering mechanism (e.g., a rate-limiting component or filter, that limits the number or the frequency of incoming queries from a particular sender); and may add or introduce such component or filter. (Ohayon et al., paragraph 27, adding filter)]
Salem, Gardner, Parmar, and Ohayon are considered to be analogous to the claimed invention because they are in the same field of model vulnerability mitigation. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem, Gardner, and Parmar to incorporate the teachings of Ohayon et al. to include adding the filter variation to the plurality of filters based on the filter variation preventing or mitigating the vulnerability, in order to deduce which attack vectors may be relevant towards a particular ML/DL/Ai engine and/or which mitigation techniques may be useful or effective towards such attacks. (Ohayon et al., paragraph 115)]
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Salem et al. (US 20250175497) in view of Gardner et al. (US 20240296279) in further view of Parmar et al. (US 20240289628) and in further view of Chen et al. (US 20250045621).
Regarding claim 13,
Salem in view of Gardner in further view of Parmar discloses the method of claim 1, but fails to explicitly disclose wherein the generating the plurality of filter variations based on the plurality of filters and the at least one prompt variation comprises generating the plurality of filter variations via a third AI model.
However in an analogous art Chen discloses wherein the generating the plurality of filter variations based on the plurality of filters and the at least one prompt variation comprises generating the plurality of filter variations via a third AI model.
[the third machine learning model may act as a plurality of filters, and the plurality of filters may share parameters with regard to the machine learning models. (Chen et al., paragraph 68)]
Salem, Gardner, Parmar, and Chen are considered to be analogous to the claimed invention because they are in the same field of vulnerability detection. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem, Gardner, and Parmar to incorporate the teachings of Chen et al. to include adding the filter variation to the plurality of filters based on the filter variation preventing or mitigating the vulnerability, in order to provide a more accurate solution for fraud detection and prevention. (Chen et al., paragraph 48)]
Regarding claim 14,
Salem in view of Gardner in further view of Parmar and in further view of Chen discloses the method of claim 13,
wherein the first AI model and the third AI model are a same AI model.
[the first machine learning model, the second machine learning model, and the third machine learning model may be the same machine learning model. (Chen et al., paragraph 68)]
Salem, Gardner, Parmar, and Chen are considered to be analogous to the claimed invention because they are in the same field of vulnerability detection. Therefore, it would have been obvious to one of ordinary skill in the art before the instant application effective filing date of the claimed invention to have modified the teachings of Salem, Gardner, and Parmar to incorporate the teachings of Chen et al. to include wherein the first AI model and the third AI model are a same AI model, in order to provide a more accurate solution for fraud detection and prevention. (Chen et al., paragraph 48)]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dasgupta et al. (US 20210406364) discloses A Dual-Filtering (DF) system to provide a robust Machine Learning (ML) platform against adversarial attacks, which employs different filtering mechanisms to thwart adversarial attacks. The DF framework utilizes two filters based on positive (input filter) and negative (output filter) verification strategies that can communicate with each other for higher robustness.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. 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 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 DANIEL ELAHIAN whose telephone number is (703) 756-1284. The examiner can normally be reached on Monday – Friday from 7:30am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Thiaw can be reached at telephone number 571-270-1138. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR for authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/D.E./DANIEL ELAHIAN, Examiner, Art Unit 2407
/David J Pearson/Primary Examiner, Art Unit 2407
Prosecution Insights