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 .
DETAIL ACTION
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on ***. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
CLAIM INTERPRETATION
3. The following is a quotation of 35 U.S.C. 112(f): (FP 7.30.03)
(f) ELEMENT IN CLAIM FOR A COMBINATION.—An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
4. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
5. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an embedding module for generating call transcript embeddings; a clustering module for clustering; a domination analysis module for identifying one or more emerging topics in calls to the call center; a driving analysis module for identifying terminology driving each of the one or more emerging topics; and a report generation module for generating a report in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. (FP 7.30.06)
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 1-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses five sets of claims: a system in claims 1-11 (i.e., a manufacture), a system in claims 12-13 (i.e., a manufacture), a system in claims 14-15 (i.e., a manufacture), a non-transitory computer-readable medium in claims 16-17 (i.e., a manufacture), and a method in claims 18-27 (i.e., a process). All claims are directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significant more. The instant invention is broadly directed to “identifying emerging topics in calls and generating a report for the emerging topics”. Claim 1 recites the following (with emphasis added):
Claim 1: A system for identifying emerging topics in calls to a call center, the system comprising: a call transcript database for storing textual call transcripts of calls to the call center; and an emerging topic identification computer system in communication with the call transcript database for identifying emerging topics in the calls to the call center based on the textual call transcripts, wherein the emerging topic identification computer system comprises:
an embedding module for generating call transcript embeddings for the calls from the textual call transcripts, wherein each call transcript embedding a vector indicative of a contextual significance of one or more words in the call transcript;
a clustering module for clustering, using a clustering algorithm, calls to the call centers into multiple clusters based on the call transcript embeddings;
a domination analysis module for identifying one or more emerging topics in calls to the call center based on the multiple clusters;
a driving analysis module for identifying terminology driving each of the one or more emerging topics; and
a report generation module for generating a report for the emerging topics.
The bold portions of claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2-11, and substantially also encompassed by claims 12-13, 14-15, 16-17 and 18-27.
Claims 1, 12, 14, 16 and 18 recite the steps to identify emerging topics in calls and generate a report for the emerging topics including audio data processing. These limitations, when given their broadest reasonable interpretation, are directed to certain performing of organizing human activity and mental processes, which is abstract idea.
Prong Two
This judicial exception is not integrated into a practical application because mere instruction to implement on computers (i.e. storage medium or computer in claim 1, 12, 14 and 16) or a computer implemented processing modules (embedding module or/and clustering modules here in claim 1), or merely using computers as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment for field of use is not considered integration into a practical application. Claim 1 recites using computer implemented processing modules to identify emerging topics in calls and generate a report for the emerging topics. Using computer implemented processing modules to perform audio data processing is a generic feature of data process, which does not represent a technological improvement. The using of the computer and audio data process does not add improvement to the functioning of a computer or to any other technology field, which failed to enable the abstract idea to integrate into a practical application. The claims are drafted in a result-oriented fashion, without the requisite specificity needed to provide a nonabstract technological solution. The computing system and audio process are directed to the components of a system amount to merely field of use type limitations and/or extra solution activity to implement the abstract idea as presented.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a processor, storage medium, database and audio data processing modules (in claim 1). These additional elements are merely conventional computer and computer model. Any potentially technical aspects of the claims are well-known generic computer components performing conventional functions (e.g., a processor performing a mental process). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine and conventional in the field. Thus, the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claims 1-11 are not patent eligible.
Claims 12-13, 14-15, 16-17 and 18-27 recite similar limitations of claims 1-11, thus are abstract idea and not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6, 12, 14, 16, 18-21 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Churgin et al (US 20240371367 A1) in view of Zhiboedova et al (US 20250328570 A1).
Regarding claim 1, Churgin discloses a system for identifying emerging topics [e.g. identifying call topics] in calls to a call center [e.g. FIG. 1; system 100 for summarization of customer service calls in a specialized field; e.g. a technology supports/an enterprise call center], the system comprising:
a call transcript database [e.g. FIG. 1 and 7; 180 database; [0038 and 0111]] for storing textual call transcripts of calls; call transcript data in text] to the call center; and
an emerging topic identification computer system [e.g. FIG. 1 and 7; computer system for identifying and labeling the intent of each utterance and one or more topics for each utterance and/or the call as a whole] in communication with the call transcript database [e.g. 780] for identifying emerging topics [e.g. FIG.1; identifying call topics] in the calls to the call center based on the textual call transcripts [e.g. call transcripts in text], wherein the emerging topic identification computer system comprises:
an embedding module for generating call transcript embeddings for the calls from the textual call transcripts [e.g. FIG. 1-2; 212; generating embeddings in call transcript data object], wherein each call transcript embedding a vector [e.g. 214; feature vectors to support intent classification, entity recognition, and/or topic classification] indicative of a contextual [e.g. tagging of the text transcript] significance of one or more words in the call transcript [e.g. FIG. 1-2 and 6-7; 212-214; [0047]; identifying key terms within the utterances; key words];
a clustering module for clustering [e.g. FIG. 2; classifier or a clustering model], using a clustering algorithm [e.g. [0028 and 0047]], calls to the call centers into multiple clusters [e.g. [0048]; a set of intent labels, such as greeting, customer question, CSR answer, next step, etc.] based on the call transcript embeddings;
a domination analysis module [e.g. 226] for identifying one or more emerging topics in calls to the call center based on the multiple clusters [identifying the most important aspects of the call content];
a driving analysis module for identifying terminology driving each of the one or more emerging topics [e.g. FIG. 1-3 [0037]; one or more classifier models may be trained for domain-specific key terms, such as provider names, plan/claim identifiers, pharmaceutical names, medical terminology, etc.] and
Although Churgin discloses a summary generation module for generating a summary for the emerging topics [e.g. FIG. 1-3; summarization; [0036 and 0039]; generate a natural language call summary], it is noted that Churgin differs to the present invention in that Churgin fails to explicitly disclose a report generation.
However, Zhiboedova (US 20250328570 A1) teaches the well-known concept of a system for identifying emerging topics [e.g. FIG. 10-11; system for identifying call tropics of telephone calls] comprising a report generation module for generating a report for the emerging topics [e.g. FIG. 1-2; and 9-10; modules to perform some or all of the functions of the systems; reporting such business insights and/or actionable items can be performed in real time].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]].
Regarding claim 2, Churgin and Zhiboedova further disclose a call recordings database for storing digital call recordings of the calls to the call center [e.g. Churgin: FIG. 1 3 and 7; call records; Zhiboedova: FIG. 3 and 9-11; audio recordings]; and a computer-implemented automatic speech recognition system for generating the textual call transcripts from the digital call recordings, wherein the textual call transcripts are stored in the call transcript database [e.g. Churgin: FIG. 1; speech recognition engine].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]].
Regarding claim 3, Churgin and Zhiboedova further disclose the embedding module is for generating call transcript embeddings for calls to the call center from a first time window and calls to the call center from a second time window, wherein the first and second time windows do not overlap [e.g. Churgin: FIG. 1 3 and 7; embedding generation; Zhiboedova: FIG. 1 and 3-5; vectorizing and embedding model; time windows 0-1:60 and 1:60-3:21] and , and the second time window is more recent than the first time window [e.g. Zhiboedova: FIG. 1 and 3-5]; the clustering module is for clustering the calls to the call centers into the multiple clusters for both the first and second time windows; and the domination analysis module is for identifying one or more emerging topics in calls to the call center by identifying clusters in the multiple clusters that are dominated by calls from the second time window [e.g. Churgin: FIG. 1 3 and 7; embedding generation; ; classifier or a clustering model; Zhiboedova: FIG. 1 and 6-8; extracting clusters].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]].
Regarding claim 4, Churgin and Zhiboedova further disclose the domination analysis module uses a statistical-based proportions test to identify the clusters in the multiple clusters that are dominated by calls from the second time window [Churgin: FIG. 1-2 and 6; classifier or a clustering model; variance calculation may be determined for repeated occurrences of similar terms and across more than two correlated data sources to provide weighted variances based on the number of sources; Zhiboedova: FIG. 1, 3-5 and 7; extracting clusters; clustering (i.e., identifying or extracting clusters using a clustering algorithm), running statistics or other analytics].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]].
Regarding claim 6, Churgin and Zhiboedova further disclose the clustering algorithm comprises a density-based clustering algorithm [e.g. Zhiboedova: FIG. 9-11; generating the clusters is performed according to hierarchical density-based spatial clustering].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]].
Regarding claim 12, this is a system that includes same limitation as in claim 1 above, the rejection of which are incorporated herein.
Regarding claim 14, this is a system that includes same limitation as in claim 1 above, the rejection of which are incorporated herein
Regarding claim 16, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 1 above, the rejection of which are incorporated herein.
Regarding claim 18-21 and 23, this is a method that includes same limitation as in claim 1-4 and 6 above, the rejection of which are incorporated herein.
Claim(s) 5 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Churgin et al (US 20240371367 A1) in view of Zhiboedova et al (US 20250328570 A1) and PERRI et al (US 20200294528 A1).
Regarding claim 5, Churgin and Zhiboedova further disclose the proportions test to identify the clusters [e.g. Churgin: FIG. 2; clustering or classifiers] in the multiple clusters that are dominated by calls from the second time window [Churgin: FIG. 2; Zhiboedova: FIG. 3-5], but Churgin and Zhiboedova fail to explicitly disclose the detail of identifying cluster.
However, PERRI teaches the well-known concept of a one-sided binomial test to identify the clusters in the multiple clusters [e.g. FIG. 1 and 3-5; A binary classifier implemented using binomial logistic regression with sigmoid function supports two values: frustrated and non-frustrated].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova and identifying cluster technique taught by PERRI as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]] and a method for automatically detecting frustration in an interaction [See PERRI; abstract and [0002]].
Regarding claim 22, this is a method that includes same limitation as in claim 5 above, the rejection of which are incorporated herein.
Claim(s) 7-9, 13, 15, 17 and 24-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Churgin et al (US 20240371367 A1) in view of Zhiboedova et al (US 20250328570 A1) and FAULKNER et al (US 20220383867 A1).
Regarding claim 7, Churgin and Zhiboedova further disclose the clustering algorithms, but Churgin and Zhiboedova fail to explicitly disclose the detail of the clustering algorithms.
However, FAULKNER teaches the well-known concept of the clustering algorithm comprises a centroid-based clustering algorithm [e.g. FIG. 2, 6 and 9-10; [0113]; cluster centroid].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova and identifying cluster technique taught by FAULKNER as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]] and fine-grained call reasons from customer service call transcripts [See FAULKNER; [abstract and [0002]].
Regarding claim 8, Churgin, Zhiboedova and FAULKNER further disclose the embedding module generates the call transcript embeddings from the textual call transcripts using term frequency-inverse document frequency [e.g. FAULKNER: FIG. 1-2 and 6; [0097]; outperforming term frequency/inverse document frequency (TF-IDF)-based and latent Dirichlet allocation (LDA)-based baselines for encoding/clustering].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova and identifying cluster technique taught by FAULKNER as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]] and fine-grained call reasons from customer service call transcripts [See FAULKNER; [abstract and [0002]].
Regarding claim 9, Churgin, Zhiboedova and FAULKNER further disclose the embedding module further uses dimensional reduction to reduce dimensions of the call transcript embeddings [e.g. Churgin: FIG. 2; 212-216; generate corresponding numerical value vectors in a lower-dimensional space].
Regarding claim 13 and 15, this is a system that includes same limitation as in claim 9 above, the rejection of which are incorporated herein.
Regarding claim 17, this is a non-transitory computer-readable storage medium that includes same limitation as in claim 9 above, the rejection of which are incorporated herein.
Regarding claim 24-25, this is a method that includes same limitation as in claim 8-9 above, the rejection of which are incorporated herein.
Claim(s) 10-11 and 26-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Churgin et al (US 20240371367 A1) in view of Zhiboedova et al (US 20250328570 A1), PERRI et al (US 20200294528 A1) and FAULKNER et al (US 20220383867 A1).
Regarding claim 10, Churgin and Zhiboedova further disclose the clustering algorithm [e.g. Churgin: FIG. 2; clustering or classifiers], the clustering algorithm comprises a density-based clustering algorithm[e.g. Zhiboedova: FIG. 9-11; generating the clusters is performed according to hierarchical density-based spatial clustering] and dimension reduction [e.g. Churgin: FIG. 2; 212-216; generate corresponding numerical value vectors in a lower-dimensional space]; but Churgin and Zhiboedova fail to explicitly disclose the detail of clustering algorithm;.
However, FAULKNER teaches the well-known concept of the embedding module generates the call transcript embeddings from the textual call transcripts using term frequency-inverse document frequency [e.g. FAULKNER: FIG. 1-2 and 6; [0097]; outperforming term frequency/inverse document frequency (TF-IDF)-based and latent Dirichlet allocation (LDA)-based baselines for encoding/clustering].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova and identifying cluster technique taught by PERRI and FAULKNER as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]], a method for automatically detecting frustration in an interaction [See PERRI; abstract and [0002]] and fine-grained call reasons from customer service call transcripts [See FAULKNER; [abstract and [0002]].
Regarding claim 11, Churgin, Zhiboedova, PERRI and PERRI further disclose a summary of each emerging topic [e.g. Churgin: FIG. 1-2 and 6]; and for at least one emerging topic in the report e.g. Churgin: FIG. 1-2 and 6; Zhiboedova: FIG. 1-2 and 10-11], a link to an audio file that [e.g. Churgin: FIG. 1-2 and 6; the call summary may be displayed and/or identified by a link in the CSR interface for each related call], when played, provides an audible example of a call to the call center pertaining to the at least one emerging topic [e.g. Zhiboedova: FIG. 3-5; playback the audio recording].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the processing of call data system disclosed by Churgin to exploit the well-known identifying call transcript of a call center data technique taught by Zhiboedova and identifying cluster technique taught by PERRI and FAULKNER as above, in order to provide improved accuracy of large language models to summarize each support call transcript [See Zhiboedova; [0003]], a method for automatically detecting frustration in an interaction [See PERRI; abstract and [0002]] and fine-grained call reasons from customer service call transcripts [See FAULKNER; [abstract and [0002]].
Regarding claim 26-27, this is a method that includes same limitation as in claim 10-11 above, the rejection of which are incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pereira Penha et al (US 20220239775 A1).
MANGALAM et al (US 20250217603 A1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHUBING REN whose telephone number is (571)272-2788. The examiner can normally be reached Monday-Friday 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZHUBING REN/ Primary Examiner, Art Unit 2658
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