Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, US 2025/0200356 A1, in view of Madisetti, US 2024/0370475 A1.
Regarding Claim 1, Zhang teaches:
A computerized method comprising: (a) obtaining a first dataset of pre-labeled textual items, wherein each of the pre-labeled textual items is already associated with a pre-label (paragraph 66: “the training examples also include a label that represents an expected output of the machine learning model”);
(b) feeding each of said pre-labeled textual items into a Large Language Model (LLM), and prompting the LLM to generate a textual reasoning that supports the pre-label of each said pre-labeled textual item (paragraph 71: “The instruction prompt provides an instruction for the machine-learned language model to generate evaluation labels for the outputs of a classification model”; And, Abstract: “The instruction prompt may include an instruction to generate an evaluation label of a training sample of a classification model and a textual format”);
(d) obtaining a second dataset of not-yet-labeled textual items (paragraph 82: “an evaluation request prompt may include simple instructions such as “please assign the evaluation label based on my instructions in the instruction prompt” followed by the data.”);
(e) feeding each of said not-yet-labeled textual items into the LLM, and commanding the LLM to utilize said textual instruction prompt and to generate a textual label for each of said not-yet-labeled textual items (paragraph 80: “the online system provides 320 a batch of evaluation request prompts to the machine-learned language model. By providing the machine-learned language model in a batch, the machine-learned language model can automatically mass produce labeled data such as training samples for a classification model for supervised learning”);
(f) collecting textual items, that were labeled by the LLM in step (e), into a third dataset of LLM-labeled textual items (paragraph 80: “the online system provides 320 a batch of evaluation request prompts to the machine-learned language model. By providing the machine-learned language model in a batch, the machine-learned language model can automatically mass produce labeled data such as training samples for a classification model for supervised learning”);
(g) automatically training a Machine Language (ML) classification model of textual items, on said third dataset of LLM-labeled textual items (paragraph 80: “the online system provides 320 a batch of evaluation request prompts to the machine-learned language model. By providing the machine-learned language model in a batch, the machine-learned language model can automatically mass produce labeled data such as training samples for a classification model for supervised learning”);
(h) deploying said ML classification model, that was automatically trained in step (g) on said third dataset of LLM-labeled textual items, in an ML-based classification platform for classification of textual items (paragraphs 71-72: “the online system provides 310 an instruction prompt to a machine-learned language model. The machine-learned language model can be any language model provided by the model serving system 150. The instruction prompt provides an instruction for the machine-learned language model to generate evaluation labels for the outputs of a classification model. The classification model is another machine-learned model that may have been trained or untrained. The online system collects outputs generated by the classification model and instructs the machine-learned language model to evaluate the outputs based on both the inputs and the outputs of the classification model. The evaluation results may take the form of evaluation labels that can serve different purposes. For example, the evaluation labels may serve as the training labels for the classification model to be trained or further trained in supervised training”; And, “A classification model discussed in this disclosure may be any type of suitable machine learning model, such as a binary classifier, a multi-label classifier, a regression model, a score generation model, a Bayesian model, a probabilistic model, etc. For example, the classification model may be an item query engine or a search query engine that identifies an output based on a user query”).
Although Zhang may have taught the following, Madisetti more directly shows:
(c) collating a plurality of textual reasonings generated in step (b), and generating therefrom a textual instruction prompt (Claim 20: “generating a context-aware prompt from the text of the selected label”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Madisetti with that of Zhang for collating a plurality of textual reasonings and generating therefrom a textual instruction prompt.
The ordinary artisan would have been motivated to modify Zhang in the manner set forth above for the purposes of creating new context-aware prompts [Madisetti: paragraph 64].
Regarding Claim 2, Zhang further teaches:
The computerized method of claim 1, wherein the LLM is configured to provide textual reasoning that supports binary classification of the pre-labeled textual items; wherein the LLM is configured to perform binary classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items; wherein step (g) comprises training said ML classification model to perform binary classification of not-yet-labeled textual items, based on the third dataset of LLM-labeled textual items (paragraph 159: “the training techniques for a machine learning model may be supervised or semi-supervised. In supervised learning, the machine learning models may be trained with a set of training samples that are labeled. The labels may be generated by the process 300 or the process 500. For example, for a machine learning model trained to select items, the training samples may include query-item pairs, attributes, images, descriptions, and other metadata. The labels for each training sample may be binary”).
Regarding Claim 3, Zhang further teaches:
The computerized method of claim 1, wherein the LLM is configured to provide textual reasoning that supports multi-class classification of the pre-labeled textual items; wherein the LLM is configured to perform multi-class classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items; wherein step (g) comprises training said ML classification model to perform multi-class classification of not-yet-labeled textual items, based on the third dataset of LLM-labeled textual items (paragraph 159: “the training techniques for a machine learning model may be supervised or semi-supervised. In supervised learning, the machine learning models may be trained with a set of training samples that are labeled. The labels may be generated by the process 300 or the process 500. For example, for a machine learning model trained to select items, the training samples may include query-item pairs, attributes, images, descriptions, and other metadata. The labels for each training sample may be binary or multi-class”).
Regarding Claim 4, Zhang further teaches:
The computerized method of claim 1, wherein step (h) comprises: deploying said ML classification model, that was automatically trained in step (g) on said third dataset of LLM-labeled textual items, in an online real-time ML-based classification platform for online and real-time classification of newly-created and newly-incoming not-yet-labeled textual items (paragraph 3: “an online system may provide an instruction prompt to a machine-learned language model. The instruction prompt may include an instruction to generate an evaluation label of a training sample of a classification model”).
Regarding Claim 5, Zhang further teaches:
The computerized method of claim 1, wherein step (h) comprises: deploying said ML classification model, that was automatically trained in step (g) on said third dataset of LLM-labeled textual items, in an offline or back-end ML-based classification platform for offline classification of not-yet-labeled textual items (paragraph 147: “The online concierge system 140 provides 630 an instruction prompt 400 to a machine-learned language model to request the machine-learned language model to determine a result category of an item. The instruction prompt 400 may be provided as part of the backend determination”).
Regarding Claim 15, Zhang further teaches:
The computerized method of claim 1, further comprising: upon generation of the textual instruction prompt in step (c), and prior to feeding of the textual instruction prompt into the LLM in step (e), displaying to a user an LLM-suggested version of the textual instruction prompt, obtaining user feedback to the LLM-suggested version of the textual instruction prompt, performing modifications to the LLM-suggested version of the textual instruction prompt based on said user feedback, and utilizing in step (e) a modified version of the textual instruction prompt (paragraph 73: “Prompt engineering techniques may be used to refine the instruction prompt to improve the performance of the machine-learned language model. The components in the instruction prompt may include instructions, examples, chain-of-thoughts, formatting, data types, attribute descriptions, context, control codes or tokens, prefix tuning, rules, constraints, evaluation, validation, and other suitable specifications for the analysis process and outputs of the machine-learned language model. In some embodiments, the instruction prompt includes a textual format related to how data is arranged to allow the machine-learned language model to understand how the data should be analyzed”. Prompt engineering representative of user modification of a given/displayed prompt based on their feedback).
Regarding Claim 16, Zhang further teaches:
The computerized method of claim 1, further comprising: obtaining Accuracy Feedback about accuracy or inaccuracy of ML-based classifications of textual items that were performed by the ML-based classification platform in step (h); based on said Accuracy Feedback, re-training said ML classification model (paragraph 80: “By providing the machine-learned language model in a batch, the machine-learned language model can automatically mass produce labeled data such as training samples for a classification model for supervised learning. This reduces the manual work that needs to be performed to generate labels for data. This also increases the accuracy of the conventional method or other machine labeled method because the machine-learned language model is required to provide an explanation of the assignment. In various embodiments, the batch of evaluation request prompts may take different forms”; And, paragraph 85: “the online system uses the training samples to train or re-train the classification model using a supervised learning technique”; And paragraph 169: “the training samples described above may be refined and continue to re-train the model, which is the model's ability to perform the inference tasks. In some embodiments, this training and re-training processes may repeat, which results in a computer system that continues to improve its functionality through the use-retraining cycle. For example, after the model is trained, multiple rounds of re-training may be performed”).
Regarding Claim 17, Zhang further teaches:
The computerized method of claim 16, wherein the re-training of said ML classification model comprises: fine-tuning the LLM based on said Accuracy Feedback; commanding the LLM to re-label textual items in the third dataset that contains LLM-labeled textual items; re-training said ML classification model on an updated version of the third dataset that contains LLM-labeled textual items (paragraph 80: “By providing the machine-learned language model in a batch, the machine-learned language model can automatically mass produce labeled data such as training samples for a classification model for supervised learning. This reduces the manual work that needs to be performed to generate labels for data. This also increases the accuracy of the conventional method or other machine labeled method because the machine-learned language model is required to provide an explanation of the assignment. In various embodiments, the batch of evaluation request prompts may take different forms”; And, paragraph 85: “the online system uses the training samples to train or re-train the classification model using a supervised learning technique”; And paragraph 169: “the training samples described above may be refined and continue to re-train the model, which is the model's ability to perform the inference tasks. In some embodiments, this training and re-training processes may repeat, which results in a computer system that continues to improve its functionality through the use-retraining cycle. For example, after the model is trained, multiple rounds of re-training may be performed”; And paragraph 74: “the chain-of-thoughts instruction includes an explanation of how an input should be classified to an evaluation label. The chain-of-thoughts instruction may include an explanation for each of the evaluation labels”. The chain-of-thoughts instruction can lead to re-labeling).
Regarding Claim 18, Zhang further teaches:
The computerized method of claim 16, wherein the re-training of said ML classification model comprises: providing said Accuracy Feedback to the LLM as additional context; commanding the LLM to re-label textual items in the third dataset that contains LLM-labeled textual items; re-training said ML classification model on an updated version of the third dataset that contains LLM-labeled textual items (paragraph 80: “By providing the machine-learned language model in a batch, the machine-learned language model can automatically mass produce labeled data such as training samples for a classification model for supervised learning. This reduces the manual work that needs to be performed to generate labels for data. This also increases the accuracy of the conventional method or other machine labeled method because the machine-learned language model is required to provide an explanation of the assignment. In various embodiments, the batch of evaluation request prompts may take different forms”; And, paragraph 85: “the online system uses the training samples to train or re-train the classification model using a supervised learning technique”; And paragraph 169: “the training samples described above may be refined and continue to re-train the model, which is the model's ability to perform the inference tasks. In some embodiments, this training and re-training processes may repeat, which results in a computer system that continues to improve its functionality through the use-retraining cycle. For example, after the model is trained, multiple rounds of re-training may be performed”; And paragraph 74: “the chain-of-thoughts instruction includes an explanation of how an input should be classified to an evaluation label. The chain-of-thoughts instruction may include an explanation for each of the evaluation labels”. The chain-of-thoughts instruction can lead to re-labeling).
Claims 6-12 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, US 2025/0200356 A1, in view of Madisetti, US 2024/0370475 A1, and further in view of Lee, US 2024/0056458 A1.
Regarding Claim 6, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, and particularly with Zhang teaching binary classification as pointed out above, neither Zhang nor Madisetti may have taught all of the following, however, Lee shows:
The computerized method of claim 1, wherein the first dataset comprises textual items that are pre-labeled as spam or non-spam; wherein the LLM is configured to provide textual reasoning that supports binary classification of the pre-labeled textual items as spam or non-spam; wherein the LLM is configured to perform binary classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items, as either spam or non-spam; wherein step (g) comprises training said ML classification model to perform binary classification of not-yet-labeled textual items as either spam or non-spam, based on the third dataset of LLM-labeled textual items (Abstract: “a malicious content detector to identify new malicious text content, such as phishing messages, malicious documents, and/or malicious web content. A computing device is used to generate input data which contains an instruction, examples of content, and content to be analyzed. The examples include malicious and benign content samples, designed to recognize similar malicious content. The computing device feeds this input into a generative language model, which produces text labels that indicate the maliciousness of the content to be analyzed”; And paragraph 133: “classifying text messages as spam (i.e., malicious) or not spam (i.e., benign) using the generative natural language model of the malicious content detector”. Examiner’s note: see also Dalla, US 2025/0184361 A1, for example paragraph 29). (Emphasis added).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Lee with that of Zhang and Madisetti for classification of textual items as either spam or non-spam.
The ordinary artisan would have been motivated to modify Zhang and Madisetti in the manner set forth above for the purposes of developing a malicious content detector to identify new malicious text content [Lee: Abstract].
Regarding Claim 7, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, and particularly with Zhang teaching binary classification as pointed out above, neither Zhang nor Madisetti may have taught all of the following, however, Lee further shows:
The computerized method of claim 1, wherein the first dataset comprises textual items that are pre-labeled as phishing or non-phishing; wherein the LLM is configured to provide textual reasoning that supports binary classification of the pre-labeled textual items as phishing or non-phishing; wherein the LLM is configured to perform binary classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items, as either phishing or non-phishing; wherein step (g) comprises training said ML classification model to perform binary classification of not-yet-labeled textual items as either phishing or non-phishing, based on the third dataset of LLM-labeled textual items (Abstract: “a malicious content detector to identify new malicious text content, such as phishing messages, malicious documents, and/or malicious web content. A computing device is used to generate input data which contains an instruction, examples of content, and content to be analyzed. The examples include malicious and benign content samples, designed to recognize similar malicious content. The computing device feeds this input into a generative language model, which produces text labels that indicate the maliciousness of the content to be analyzed”; And paragraph 114: “the threat classification may indicate an evaluation of whether one or more of the text messages in the analysis object are spam or phishing”). (Emphasis added).
Regarding Claim 8, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, and particularly with Zhang teaching binary classification as pointed out above, neither Zhang nor Madisetti may have taught all of the following, however, Lee further shows:
The computerized method of claim 1, wherein the first dataset comprises textual items that are pre-labeled as legitimate or fraud-related; wherein the LLM is configured to provide textual reasoning that supports binary classification of the pre-labeled textual items as legitimate or fraud-related; wherein the LLM is configured to perform binary classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items, as either legitimate or fraud-related; wherein step (g) comprises training said ML classification model to perform binary classification of not-yet-labeled textual items as either legitimate or fraud-related, based on the third dataset of LLM-labeled textual items (Abstract: “a malicious content detector to identify new malicious text content, such as phishing messages, malicious documents, and/or malicious web content. A computing device is used to generate input data which contains an instruction, examples of content, and content to be analyzed. The examples include malicious and benign content samples, designed to recognize similar malicious content. The computing device feeds this input into a generative language model, which produces text labels that indicate the maliciousness of the content to be analyzed”; And paragraph 6: “a malicious content detector may be implemented as a generative natural language classifier and selected text content samples. For example, in an implementation, a small number of text content samples may be classified as malicious content or benign content”). (Emphasis added).
Regarding Claim 9, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, and particularly with Zhang teaching binary classification as pointed out above, neither Zhang nor Madisetti may have taught all of the following, however, Lee further shows:
The computerized method of claim 1, wherein the first dataset comprises textual items that are pre-labeled as urgent or non-urgent; wherein the LLM is configured to provide textual reasoning that supports binary classification of the pre-labeled textual items as urgent or non-urgent; wherein the LLM is configured to perform binary classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items, as either urgent or non-urgent; wherein step (g) comprises training said ML classification model to perform binary classification of not-yet-labeled textual items as either urgent or non-urgent, based on the third dataset of LLM-labeled textual items (paragraph 109: “The malicious content detector 510 evaluates target content, for example, text content. The malicious content detector may be implemented with a generative natural language model 514 and samples 518. In some implementations, a relatively small number of samples 518 are used. The samples 518 and the target content 512 to be evaluated are presented to the generative natural language model 514 as a prediction problem, where the prediction problem is to predict the classification of the target content 512 based on the samples”. The target content classification can be textual items to be classified as either urgent or non-urgent). (Emphasis added).
Regarding Claim 10, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, and particularly with Zhang teaching binary classification as pointed out above, neither Zhang nor Madisetti may have taught all of the following, however, Lee further shows:
The computerized method of claim 1, wherein the first dataset comprises textual items that are pre-labeled as containing Personally Identifiable Information (PII) or not containing PII; wherein the LLM is configured to provide textual reasoning that supports binary classification of the pre-labeled textual items as containing PII or not containing PII; wherein the LLM is configured to perform binary classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items, as either containing PII or not containing PII; wherein step (g) comprises training said ML classification model to perform binary classification of not-yet-labeled textual items as either containing PII or not containing PII, based on the third dataset of LLM-labeled textual items (Abstract: “methods and systems enable rapid development of security protection”; And paragraph 72: “By checking outgoing files, the security management facility 122 may be able discover threats that were not detected on one of the compute instances 10-26, or policy violation, such transmittal of information that should not be communicated”. Examiner’s note: see also Dalla, US 2025/0184361 A1, for example paragraph 29). (Emphasis added).
Regarding Claim 11, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, and particularly with Zhang teaching binary classification as pointed out above, neither Zhang nor Madisetti may have taught all of the following, however, Lee further shows:
The computerized method of claim 1, wherein the first dataset comprises textual items that are pre-labeled as either: (i) related to Department A in an organization, or (ii) related to Department B in the organization; wherein the LLM is configured to provide textual reasoning that supports binary classification of the pre-labeled textual items as either related to Department A or related to Department B; wherein the LLM is configured to perform binary classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items, as either related to Department A or related to Department B; wherein step (g) comprises training said ML classification model to perform binary classification of not-yet-labeled textual items, as either related to Department A or related to Department B, based on the third dataset of LLM-labeled textual items (paragraph 109: “The malicious content detector 510 evaluates target content, for example, text content. The malicious content detector may be implemented with a generative natural language model 514 and samples 518. In some implementations, a relatively small number of samples 518 are used. The samples 518 and the target content 512 to be evaluated are presented to the generative natural language model 514 as a prediction problem, where the prediction problem is to predict the classification of the target content 512 based on the samples”. The target content classification can be textual items to be classified as related to Department A or related to Department B). (Emphasis added).
Regarding Claim 12, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, and particularly with Zhang teaching multi-class classification as pointed out above, neither Zhang nor Madisetti may have taught all of the following, however, Lee further shows:
The computerized method of claim 1, wherein the first dataset comprises textual items that are pre-labeled as either: (i) related to Department A in an organization, or (ii) related to Department B in the organization, (iii) related to Department C in the organization; wherein the LLM is configured to provide textual reasoning that supports multi-class classification of the pre-labeled textual items as related to Department A or related to Department B or related to Department C; wherein the LLM is configured to perform multi-class classification, utilizing said textual instruction prompt, of said not-yet-labeled textual items, as related to Department A or related to Department B or related to Department C; wherein step (g) comprises training said ML classification model to perform multi-class classification of not-yet-labeled textual items, as related to Department A or related to Department B or related to Department C, based on the third dataset of LLM-labeled textual items (paragraph 109: “The malicious content detector 510 evaluates target content, for example, text content. The malicious content detector may be implemented with a generative natural language model 514 and samples 518. In some implementations, a relatively small number of samples 518 are used. The samples 518 and the target content 512 to be evaluated are presented to the generative natural language model 514 as a prediction problem, where the prediction problem is to predict the classification of the target content 512 based on the samples”. The target content classification can be textual items to be classified as related to Department A or related to Department B or related to Department C). (Emphasis added).
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, US 2025/0200356 A1, in view of Madisetti, US 2024/0370475 A1, and further in view of Singh, US 2024/0045411 A1.
Regarding Claim 13, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, neither Zhang nor Madisetti may have taught all of the following, however, Singh shows:
The computerized method of claim 1, further comprising: upon generation of the textual instruction prompt in step (c), and prior to feeding of the textual instruction prompt into the LLM in step (e), performing one or more modifications to said textual instruction prompt by a Prompt Engineering expert to improve accuracy or efficiency of the textual instruction prompt (paragraph 29: “Prompt engineering may be performed manually by a user or administrator, or automatically within system 100 and/or large language model”). (Emphasis added).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Singh with that of Zhang and Madisetti for performing one or more modifications to said textual instruction prompt by a Prompt Engineering expert.
The ordinary artisan would have been motivated to modify Zhang and Madisetti in the manner set forth above for the purposes of refining prompts within a scope or within the capabilities of a large language model [Singh: paragraph 29].
Regarding Claim 14, with Zhang and Madisetti teaching those limitations of the claim as previously pointed out, neither Zhang nor Madisetti may have taught all of the following, however, Singh further shows:
The computerized method of claim 13, wherein performing the one or more modifications to said textual instruction prompt comprises: automatically performing said one or more modifications by an Artificial Intelligence (AI) unit that was pre-trained to specifically excel in automated Prompt Engineering (paragraph 29: “Prompt engineering may be performed manually by a user or administrator, or automatically within system 100 and/or large language model). (Emphasis added).
Claims 19-20 are similar to Claim 1 and are rejected under the same rationale as stated above for that claim.
Examiner's Note:
The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or
disclosed by the examiner and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 for the relevant prior art where for example Lee, US 2024/0056458 A1, teaches feeding input into a generative language model, which produces text labels that indicate the maliciousness of the content to be analyzed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at 5712703169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVE MISIR/Primary Examiner, Art Unit 2127
