Detailed Action
Claims 1-20 are currently pending.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “lightweight language model” in claim 4 is a relative term which renders the claim indefinite. The term “lightweight” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Specification paragraph [0018] mentions ‘lightweight’ but in generic fashion. How big should the model be to require the computational resources of a generalist LLM or external API access?
For purpose of the examination, the examiner interprets the limitation to mean: The lightweight language model is a single language understanding model or a single classifier.
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 1-10, 12-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1: Claim 1 recites a system comprising: a memory and a processor. Therefore, it is directed to the statutory category of an apparatus.
2A Prong 1:
scoring a received input string against a plurality of target classes to derive two or more scores, wherein each target class is associated with an independent score, and the scoring is performed without applying a softmax function to the independent scores; (mental process of evaluation – evaluating the received input string based on scores which can be done in one’s mind)
using the scores to generate a rule-based determination that indicates whether to pass or filter the input string; (mental process of evaluation – evaluating the received input string and determining whether to pass or modify the input string can be performed in one’s mind)
upon a determination to filter the input string, performing filtering processing on the input string; (mental process of evaluation – See the spec para [0005], the filtering process include: modifying the input string, which can be done with the aid of pen and paper)
2A Prong 2:
A system comprising: a memory comprising instructions; and a processor communicatively coupled to the memory and configured to execute the instructions, the instructions comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
otherwise, performing output processing on the input string. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – performing the abstract idea of scoring and evaluation using the system)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity, disclosed in combination of generic computer functions are implemented to perform the disclosed abstract idea above.
2B:
A system comprising: a memory comprising instructions; and a processor communicatively coupled to the memory and configured to execute the instructions, the instructions comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
otherwise, performing output processing on the input string. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – performing the abstract idea of scoring and evaluation using the system)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding claim 2,
Step 1: Apparatus, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the input string is scored using a zero-shot classifier (ZSC). (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein the input string is scored using a zero-shot classifier (ZSC). (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 3,
Step 1: Apparatus, as above.
2A Prong 1: Incorporates the rejection of claim 2.
2A Prong 2: wherein the ZSC is configured in a multi-label mode, the multi-label mode allowing the input string to be classified into more than one target class contemporaneously. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – applying ZSC, which is a ML model, to perform the abstract idea)
2B: wherein the ZSC is configured in a multi-label mode, the multi-label mode allowing the input string to be classified into more than one target class contemporaneously. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – applying ZSC, which is a ML model, to perform the abstract idea)
Regarding claim 4,
Step 1: Apparatus, as above.
2A Prong 1: Incorporates the rejection of claim 2.
2A Prong 2: wherein the ZSC is a lightweight language model that operates without requiring a generalist large language model or access to external application programming interfaces. (a field of use and technological environment MPEP 2106.05(h))
2B: wherein the ZSC is a lightweight language model that operates without requiring a generalist large language model or access to external application programming interfaces. (a field of use and technological environment MPEP 2106.05(h))
Regarding claim 5,
Step 1: Apparatus, as above.
2A Prong 1: Incorporates the rejection of claim 2.
2A Prong 2: wherein the ZSC is based on a language model trained on tasks involving evaluation of similarity or entailment between strings. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – applying a generic machine learning model to perform the scoring)
2B: wherein the ZSC is based on a language model trained on tasks involving evaluation of similarity or entailment between strings. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – applying a generic machine learning model to perform the scoring)
Regarding claim 6,
Step 1: Apparatus, as above.
2A Prong 1: The system of claim 1, wherein the rule-based determination is generated using a decision tree. (mental process of evaluation – mere selection of a final output based on multiple criteria, which can be done with the aid of pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 7,
Step 1: Apparatus, as above.
2A Prong 1: The system of claim 1, wherein the rule-based determination to filter the input string includes applying a set of threshold scores for particular target classes that are deemed unacceptable. (mental process of evaluation – evaluating the input string by applying threshold scores, which can be done in one’s mind)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 8,
Step 1: Apparatus, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the system is used to moderate content in an enterprise environment for interaction with internal policies and documentation. (a field of use and technological environment MPEP 2106.05(h) – limiting the usage of the system)
2B: wherein the system is used to moderate content in an enterprise environment for interaction with internal policies and documentation. (a field of use and technological environment MPEP 2106.05(h) – limiting the usage of the system)
Regarding claim 9,
Step 1: Apparatus, as above.
2A Prong 1: The system of claim 8, wherein scoring the input string includes using prompt engineering with various labels and example sentences to verify correct capture of aspects of the sentences that are useful for the content moderation. (mental process of evaluation – selecting the correct capture from the sentences, which can be done with the aid of pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 10,
Step 1: Apparatus, as above.
2A Prong 1: Incorporates the rejection of claim 9.
2A Prong 2: wherein the instructions further comprise extending moderation capabilities to multi-modal inputs that represent a combination of modalities, if a multi-modal ZSC exists for the combination of modalities. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f). The limitation merely recites processing the multimodal input data using a ZSC)
2B: wherein the instructions further comprise extending moderation capabilities to multi-modal inputs that represent a combination of modalities, if a multi-modal ZSC exists for the combination of modalities. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f). The limitation merely recites processing the multimodal input data using a ZSC)
Regarding claim 12,
Step 1: Apparatus, as above.
2A Prong 1: Incorporates the rejection of claim 11.
2A Prong 2: wherein a conversational agent that received the input string is configured to perform the further processing. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: wherein a conversational agent that received the input string is configured to perform the further processing. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 13,
Step 1: Apparatus, as above.
2A Prong 1: The system of claim 1, wherein the output processing includes: performing one or more of the following: providing a predetermined response to a user; (mental process of judgment – determining an appropriate response) or modifying the input string. (mental process of evaluation)
2A Prong 2: allowing the input string to proceed for further processing; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: allowing the input string to proceed for further processing; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 14,
Step 1: Apparatus, as above.
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the input string is received from a user or a conversational agent. (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
2B: wherein the input string is received from a user or a conversational agent. (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Therefore, it is re-evaluated as well understood, routine and conventional activity MPEP 2106.05(d)(II)(iv) of gathering statistics)
Regarding claim 15,
Step 1: Apparatus, as above.
2A Prong 1: The system of claim 1, wherein the independent scores range between 0 and 1. (mental process of evaluation – scoring, which can be done with the aid of pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 16,
Step 1: Claim 16 recites a method. Therefore, it is directed to the statutory category of Processes.
2A Prong 1: Claim 16 is a method claim which recites the same feature as the apparatus claim 1, and is rejected for at least the same reasons.
2A Prong 2: The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity, disclosed in combination of generic computer functions are implemented to perform the disclosed abstract idea above.
2B: The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 17 is a method claim which recites the same feature as the apparatus claim 2, and is rejected for at least the same reasons.
Claim 18 is a method claim which recites the same feature as the apparatus claim 6, and is rejected for at least the same reasons.
Regarding claim 20,
Step 1: Claim 20 recites a non-transitory processor-readable storage medium having stored thereon program code of one or more software programs. Therefore, it is directed to the statutory category of an apparatus.
2A Prong 1: Claim 20 is an apparatus claim which recites the same feature as the apparatus claim 1, and is rejected for at least the same reasons.
2A Prong 2: A non-transitory processor-readable storage medium having stored thereon program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity, disclosed in combination of generic computer functions are implemented to perform the disclosed abstract idea above.
2B: A non-transitory processor-readable storage medium having stored thereon program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 7, 11-16, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mars et al. (US 20190130244, hereinafter ‘Mars’).
Regarding claim 1, Mars teaches:
A system comprising: a memory comprising instructions; and a processor communicatively coupled to the memory and configured to execute the instructions, the instructions comprising: ([Mars, 0025] The competency classification engine 120 may be implemented by the one or more computing servers, computer processors, and the like of the AI platform)
scoring a received input string against a plurality of target classes to derive two or more scores, wherein each target class is associated with an independent score, and the scoring is performed without applying a softmax function to the independent scores; ([Mars, 0045-0048] The competency classification deep machine learning algorithm analyze the user input data including words and phrases (i.e., input string). S220 may function to calculate and output a competency classification for each of the multiple areas of competency (i.e., two or more scores that target classes associated with) of an artificially intelligent virtual assistant and may generate a classification label for each of Income, Balance, and Spending. [0048] further discloses deriving the two or more scores (respective probability of intent match values). [0043] The model may include a single deep machine learning model or any classification model, and Mars does not specifically disclose that the Softmax function is essential to implement the system)
using the scores to generate a rule-based determination that indicates whether to pass or filter the input string; ([Mars, 0048], [0052] and [0053] collectively disclose that the classification model configured to output and pass only the competency classification label having a highest probability of intent match. S220 may function to apply a predetermined competency threshold to each of the outputs of the specific-competency machine learning algorithms to filter the results, accordingly. Depending on a setting of the predetermined competency threshold (i.e., rule-based determination), one or more of the competency classification labels may pass to a subsequent process (e.g., S230 or the like) )
upon a determination to filter the input string, performing filtering processing on the input string; and ([Mars, 0048], [0052] and [0053] collectively disclose that the classification model configured to output and pass only the competency classification label having a highest probability of intent match. S220 may function to apply a predetermined competency threshold to each of the outputs of the specific-competency machine learning algorithms to filter the results, accordingly. Depending on a setting of the predetermined competency threshold (i.e., rule-based determination), one or more of the competency classification labels may pass to a subsequent process (e.g., S230 or the like))
otherwise, performing output processing on the input string. ([Mars, 0048], [0052] and [0053] indicate that the competency classification labels may pass to a subsequent process)
Regarding claim 7, Mars teaches:
The system of claim 1, wherein the rule-based determination to filter the input string includes applying a set of threshold scores for particular target classes that are deemed unacceptable. ([Mars, 0062] discloses filtering those slot classification labels with high confidence or probability values in S230, after the first filtering process performed in S220. This indicates that the slot labels with lower confidence value will be filtered as well. The slot classification threshold may be some predetermined value)
Regarding claim 11, Mars teaches:
The system of claim 1, wherein the filtering processing includes: blocking the input string from further processing; and ([Mars, 0048], [0052] and [0053] collectively disclose that the classification model configured to output and pass only the competency classification label having a highest probability of intent match. S220 may function to apply a predetermined competency threshold to each of the outputs of the specific-competency machine learning algorithms to filter the results, accordingly. Depending on a setting of the predetermined competency threshold (i.e., rule-based determination), one or more of the competency classification labels may pass to a subsequent process (e.g., S230 or the like) )
performing one or more of the following: providing a predetermined response to a user; notifying a moderator or security team about the input string; or modifying the input string. ([Mars, 0069] S240 performs 1. collecting slot data and associated slot classification labels of user input data generated in S220, and generating slot values by converting or mapping the slot data for a given slot of user input data (i.e., modifying the input string) and the one or more slot classification labels assigned to the slot to a machine and program-comprehensible object or operation. Since the limitations are connected by ‘or’, the examiner is not required to provide mappings for the other limitations)
Regarding claim 12, Mars teaches:
The system of claim 11, wherein a conversational agent that received the input string is configured to perform the further processing. ([Mars, 0035-0036] discloses receiving user input such as textual data, query or commands using an artificially intelligent virtual assistant. [Mars, 0062] discloses that the step S230 involves an artificially intelligent assistant (i.e., the conversational agent))
Regarding claim 13, Mars teaches:
The system of claim 1, wherein the output processing includes: allowing the input string to proceed for further processing; and ([Mars, 0048], [0052] and [0053] collectively disclose that the classification model configured to output and pass only the competency classification label having a highest probability of intent match. S220 may function to apply a predetermined competency threshold to each of the outputs of the specific-competency machine learning algorithms to filter the results, accordingly. Depending on a setting of the predetermined competency threshold (i.e., rule-based determination), one or more of the competency classification labels may pass to a subsequent process (e.g., S230 or the like) )
performing one or more of the following: providing a predetermined response to a user; or modifying the input string. ([Mars, 0069] S240 performs 1. collecting slot data and associated slot classification labels of user input data generated in S220, and generating slot values by converting or mapping the slot data for a given slot of user input data (i.e., modifying the input string) and the one or more slot classification labels assigned to the slot to a machine and program-comprehensible object or operation. Since the limitations are connected by ‘or’, the examiner is not required to provide mappings for the other limitations)
Regarding claim 14, Mars teaches:
The system of claim 1, wherein the input string is received from a user or a conversational agent. ([Mars, 0035-0036] discloses receiving user input such as textual data, query or commands using an artificially intelligent virtual assistant)
Regarding claim 15, Mars teaches:
The system of claim 1, wherein the independent scores range between 0 and 1. ([Mars, 0054] discloses that the probability score range may be 0-100%, Low to High … The 0%-100% is equivalent to 0.0 – 1.0)
Claim 16 is a method claim which recites the same feature as the apparatus claim 1, and is rejected for at least the same reasons.
Claim 19 is a method claim which recites the same feature as the apparatus claim 11, and is rejected for at least the same reasons.
Regarding claim 20, Mars teaches:
A non-transitory processor-readable storage medium having stored thereon program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps: ([Mars, 0025] The competency classification engine 120 may be implemented by the one or more computing servers, computer processors, and the like of the AI platform)
Claim 20 is an apparatus claim which recites the same feature as the apparatus claim 1, and is rejected for at least the same reasons.
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 2-6, 8-10 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mars in view of Kusano et al. (US 20250148316, hereinafter ‘Kusano’).
Regarding claim 2, Mars teaches:
The system of claim 1.
However, Mars does not specifically disclose:
wherein the input string is scored using a zero-shot classifier (ZSC).
Kusano teaches:
wherein the input string is scored using a zero-shot classifier (ZSC). ([0046-0048] discloses that the label inferring section infers label to be assigned the target data by calculating goodness-of-fit score between the input of the text and a label set C, goodness-of-fit score of the respective labels (independent score for each target class). The classification is performed using a zero-shot text classifier, and without any softmax function, as Kusano does not specifically disclose any softmax or normalization process)
Before the effective filing date of the invention to a person of ordinary skill in the art, it would
have been obvious, having the teachings of Mars and Kusano to use the Zero-Shot Classifier of Kusano to implement the classification machine learning model of the present invention. The suggestion and/or motivation for doing so is to improve the efficiency of the machine learning model training method by allowing the model to be flexibly applied to various types of data and topics without any manually-labeled training data [Kusano, 0048-0049].
Regarding claim 3, Mars in view of Kusano teaches:
The system of claim 2, wherein the ZSC is configured in a multi-label mode, the multi-label mode allowing the input string to be classified into more than one target class contemporaneously. ([Kusano, 0046-0048] discloses that the label inferring section infers label to be assigned the target data by calculating goodness-of-fit score between the input of the text and a label set C (multi-label mode), goodness-of-fit score of the respective labels (independent score for each target class). The classification is performed using a Zero-Shot text Classifier (ZSC), and without any softmax function, as Kusano does not specifically disclose any softmax or normalization process)
Regarding claim 4, Mars in view of Kusano teaches:
The system of claim 2, wherein the ZSC is a lightweight language model that operates without requiring a generalist large language model or access to external application programming interfaces. ([Kusano, 0049-0050] discloses that the zero-shot text classifier may be a language understanding model (single language model). Although [0052] discloses that the model is not limited to the example and may use an LLM, the paragraph does not require the language model to be an LLM)
Regarding claim 5, Mars in view of Kusano teaches:
The system of claim 2, wherein the ZSC is based on a language model trained on tasks involving evaluation of similarity or entailment between strings. ([Kusano, 0116] discloses that the evaluating a link between elements in the target data D and each of the labels of the label set includes evaluating relatedness or similarity between character strings. [0046-0048] discloses that the label inferring section uses ZSC)
Regarding 6, Mars in view of Kusano teaches:
The system of claim 1, wherein the rule-based determination is generated using a decision tree. ([Kusano, 0137-0139] and [Fig. 11] collectively disclose that the language model assigns labels (category) to the input data by finding the cosine similarities for each of the categories from the dominant categories to the further subcategory. This process is equivalent to the decision tree which classifies data from dominant categories to the further subcategories)
Regarding claim 8, Mars in view of Kusano teaches:
The system of claim 1, wherein the system is used to moderate content in an enterprise environment for interaction with internal policies and documentation. ([Kusano, 0058-0060] discloses that the target data includes the company name and the number of people, which indicates that the data converting method of Kusano is used to moderate content in an enterprise environment for interaction with documentation)
Regarding claim 9, Mars teaches:
The system of claim 8, wherein scoring the input string includes using prompt engineering with various labels and example sentences to verify correct capture of aspects of the sentences that are useful for the content moderation. ([Mars, 0061-0062] collectively disclose that the classification machine learning algorithm may function to estimate classifications for each user input data by scoring it based on probability or confidence value. The confidence value is an indication of the correctness of the capture of aspects (labels) of the input data, which includes commands and queries (i.e., sentences) [0055])
Regarding claim 10, Mars in view of Kusano teaches:
The system of claim 9, wherein the instructions further comprise extending moderation capabilities to multi-modal inputs that represent a combination of modalities, if a multi-modal ZSC exists for the combination of modalities. ([Kusano, 0046-0048] discloses that the label inferring section infers label to be assigned the target data by calculating goodness-of-fit score between the input of the text and a label set C (multi-label mode), goodness-of-fit score of the respective labels (independent score for each target class). The classification is performed using a Zero-Shot text Classifier (ZSC) )
Claim 17 is a method claim which recites the same feature as the apparatus claim 2, and is rejected for at least the same reasons.
Claim 18 is a method claim which recites the same feature as the apparatus claim 6, and is rejected for at least the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang et al., “A novel feature-based model for zero-shot object detection with simulated attributes”, 2022, (This prior art is pertinent as it discloses performing a classification task without Softmax layers)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUN KWON whose telephone number is (571)272-2072. The examiner can normally be reached Monday – Friday 8:00AM – 5:00PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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