DETAILED ACTION
Status of Claims
This communication is a non-final action on the merits in response to the amendments and arguments filed on December 23, 2025. Claims 1, 9, 16, and 20 were amended. Claims 2, 7-8, and 18-19 were canceled. Claims 21-25 were added. Claims 1, 3-6, 9-17, and 20-25 are currently pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 23, 2025 has been entered.
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.
Claim 23 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 23 recites the limitation "the vector representation." There is insufficient antecedent basis for this limitation in the claim.
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, 3-6, 9-17, and 20-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1, 3-6, 9-15, and 21-23 are directed to a process. Claims 16-17 and 24-25 are directed to a machine. Claim 20 is directed to an article of manufacture. As such, each claim is directed to a statutory category of invention.
Step 2A Prong 1
The examiner has identified independent Claim 16 as the claim that represents the claimed invention for analysis and is similar to independent Claims 1 and 20.
Independent Claim 16 recites the following abstract ideas: “stored internal representations of a plurality of automated processes comprising a plurality of tasks; with substitute approvers observed as assigned as substitute approvers for original approvers of the automated processes and predict one or more substitute approvers for an original approver; during execution of a given automated process instance specifying an original approver for a task in the given automated process instance, receiving an out-of-office message of the original approver; extracting features from text of the out-of-office message, wherein extracting features comprises the text of the out-of-office message, finding attributes for named entities, and building a knowledge graph of the named entities and attributes; sending the extracted features and an identifier of the original approver ; based on the extracted features and the identifier of the original approver, predicting a substitute approver for the original approver and outputting an identifier of the predicted substitute approver; receivingthe identifier of the predicted substitute approver; sending a message to the substitute approver seeking approval of the task in the automated process instance; and continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the substitute approver.”
The limitations, as drafted, are a process that, under its broadest reasonable interpretation, relates to managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions (i.e., stored internal representations of a plurality of automated processes comprising a plurality of tasks; with substitute approvers observed as assigned as substitute approvers for original approvers of the automated processes and predict one or more substitute approvers for an original approver; during execution of a given automated process instance specifying an original approver for a task in the given automated process instance, receiving an out-of-office message of the original approver; extracting features from text of the out-of-office message, wherein extracting features comprises the text of the out-of-office message, finding attributes for named entities, and building a knowledge graph of the named entities and attributes; sending the extracted features and an identifier of the original approver; based on the extracted features and the identifier of the original approver, predicting a substitute approver for the original approver and outputting an identifier of the predicted substitute approver; receiving the identifier of the predicted substitute approver; sending a message to the substitute approver seeking approval of the task in the automated process instance; and continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the substitute approver), but for the recitation of generic computer components (i.e., A computing system comprising at least one hardware processor, at least one memory coupled to the at least one hardware processor, a machine learning model trained and configured to predict data, one or more non-transitory computer-readable media having stored therein computer-executable instructions, an automatic electronic message, and applying named entity recognition (NER)). If a claim limitation, under its broadest reasonable interpretation, relates to managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). In particular, the claim recites the additional elements of a computing system comprising at least one hardware processor, at least one memory coupled to the at least one hardware processor, a machine learning model trained and configured to predict data, one or more non-transitory computer-readable media having stored therein computer-executable instructions, an automatic electronic message, and applying named entity recognition (NER). The computer hardware is recited at a high level of generality (i.e., generic trained machine learning model predicting and outputting data, generic computers receiving, processing, and transmitting data, and applying NER to data in a generic manner) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application, since they do not involve improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)), they do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and they do not apply or use the abstract idea in some other meaningful way beyond generally linking its use to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claim is directed to an abstract idea without a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of using computer hardware (a computing system comprising at least one hardware processor, at least one memory coupled to the at least one hardware processor, a machine learning model trained and configured to predict data, one or more non-transitory computer-readable media having stored therein computer-executable instructions, an automatic electronic message, and applying named entity recognition (NER)) amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claim is not patent-eligible.
Dependent claim 17 recites “a user interface,” which is recited as a generic interface. Dependent claim 22 recites “retraining the machine learning model,” which is recited in a generic manner. The additional elements are generic technology used to implement the abstract idea, and they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Dependent claims 3-6, 9-15, 21, and 23-25 do not include any additional elements beyond those identified above. They further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. As such, they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Therefore, dependent claims 3-6, 9-15, 17, and 21-25 are directed to an abstract idea, and do not include additional elements that integrate the abstract idea into a practical application, or that are sufficient to amount to significantly more than the abstract idea. Thus, the aforementioned claims are not patent-eligible.
Allowable Subject Matter
Claims 1, 3-6, 9-17, and 20-25 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101 set forth in this Office action.
Bresloff teaches receiving an out-of-office message of an original approver for a task in an automated process instance, extracting features from the message, sending features to a machine learning model trained to predict a substitute approver for the original approver, predicting a substitute approver, receiving an identifier of the substitute approver, and sending a message to the substitute approver seeking approval of the task. Meunier teaches applying NER to text of the out-of-office message, finding attributes for named entities, and building a knowledge graph of the named entities and attributes. However, the combination of references does not teach continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the substitute approver.
The closest NPL, “Create out-of-office approvals for expenses and fund requests,” teaches a system for selecting pre-defined business rules for colleagues when away from the office, such as assigning an approval to another colleague while away. However, it does not teach extracting features from an out-of-office message, sending features to a machine learning model trained to predict a substitute approver, or a machine learning model predicting the substitute approver.
Response to Arguments
Applicant’s Argument Regarding 35 USC 101 Rejection of Claims 1-20:
Step 2A, Prong 1:
Using independent claim 16 as a claim that represents the claimed invention for analysis, the Action alleges that claim 16 falls within the "certain methods of organizing human activity" grouping of abstract ideas. Action, pp. 2-4. Applicant respectfully disagrees because the claim recites a technical solution to the technical problem of electronic out-of-office message processing to find a substitute approver for an automated process.
Just as Example 47 is not directed simply to the abstract idea of taking actions to improve network safety, claim 16 is not directed simply to "determining a substitute approver." Instead, there are particular details, and the claim is squarely within the technical domain of electronic out-of-office message processing. Such a domain is described in "Recommendations for Automatic Responses to Electronic Mail," which is disclosed in an IDS herein.
The 2019 PEG limits the grouping of "certain methods of organizing human activity" to specific sub-categories: fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people.
The claims of the present application are directed to technical improvements in how a process automation system executes an automated process instance by receiving an electronic out-of-office message relating to an original approver, extracting features from the electronic out-of-office message, and using a machine learning model to predict a substitute approver for the original approver. The claims do not recite any fundamental economic practice, legal agreement, sales interaction, or interpersonal coordination. The characterization of the claimed steps as relating to "managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions" (Action, pp. 3-4) misrepresents the technical nature of the invention, which involves automated processes for analyzing electronic inputs—processes that fall outside the enumerated sub-groupings of organizing human activity.
Step 2A, Prong 2:
Even assuming purely for the sake of argument that the claims are deemed to recite a judicial exception, the claims clearly integrate such alleged exception into a practical application. Specifically, the claims are directed to a technical solution in the context of electronic out-of- office message processing for improving the performance and efficiency during execution of automated process instances.
The claimed solutions achieve these improvements through the use of a machine learning model to predict a substitute approver for an original approver based on an electronic out-of-office message of the original approver. The claimed solutions are thus relevant to the technical field of automated electronic out-of-office message handling, in which technical solutions are demanded. The technical nature of this field may be evidenced, for example, with reference to publications of The Internet Society, such as the document entitled "Recommendations for Automatic Responses to Electronic Mail" referenced above.
The claimed solutions can enable more efficient and versatile execution of automated process instances because determinations of a substitute approver are determined via features appearing in the electronic out-of-of office message, even if the relevant information is not actually included in the reply, unlike traditional approaches. As emphasized in [0003]-[0004] of the published application:
[0003] Although such a system generally works well, work can come to a halt in the face of an absent approver. One solution to address the absent approver is to specify rules about who can substitute for the approver when the original approver is absent. However, such an approach is fraught with problems.
[0004] First, such rules can be complicated to specify. Second, organizations change rapidly over time, so such rules need to be updated on a regular basis. Thus, the original, primary approvers must maintain such rules, which can become a tedious task requiring continual manual effort. Therefore, the rules are often not maintained, and no approver can be found.
This benefit is further discussed in [0031]-[0033] of the published application:
[0031] Intelligent substitution as described herein reduces the extra effort required by the primary approver to maintain substitution rules during absence while on emergency leave or on vacation, whether planned or unplanned. Intelligent substitution can drastically reduce planned development and maintenance of a rule-based system and the task providers' efforts to enable such an approach.
[0032] Instead, the primary approver, in case of an unplanned or planned vacation, simply maintains an automatic out-of-office reply in their mail clients (e.g., Microsoft Outlook or the like). A substitute's contact details in human readable text can be extracted from the out-of-office response. After training, substitutes can be found even if relevant information is not included in the out-of-office reply.
[0033] The described technologies thus offer considerable improvements over conventional automated process techniques such as having users maintain substitution rules.
These disclosures demonstrate that the claimed invention addresses a technical problem—the relative complexity and inflexibility of rules-based analyses—and provides a technical solution by receiving an identifier of a substitute approver from the machine learning model. The claims recite concrete steps, including extracting features from an electronic out-of- office message, sending the extracted features and other relevant information to a machine learning model, predicting a substitute approver with the machine learning model, and receiving an identifier of the substitute approver from the machine learning model, thereby tying the invention to a specific implementation in the field electronic out-of-office processing in the context of process automation. The subject matter is therefore far from claiming an abstract idea.
In the December 4, 2025 telephonic interview with Applicant's representatives, the Examiner stated that Example 47 of the 2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG") is relevant to the determination that the pending claims are not eligible under § 101. In particular, the Examiner stated that claim 2 of Example 47, deemed to be ineligible in the 2019 PEG, is analogous to independent claim 16 (which was discussed as a representative claim in the interview).
In describing the reasons why claim 2 of Example 47 is ineligible, the 2019 PEG characterizes claim 2 as lacking details regarding how the recited functions are performed. For example, the 2019 PEG states, "The claim does not provide any details about how the trained ANN operates or how the detection is made, and the plain meaning of 'detecting' encompasses mental observations or evaluations, e.g., a computer programmer's mental identification of an anomaly in a data set."
By the above amendments, each of independent claims 1, 16, and 20 presently is amended to recite specific technical details associated with the recited steps, in contrast with claim 2 of Example 47. For example, each of amended claim 1 and amended claim 16 recites "wherein extracting features comprises applying named entity recognition (NER) to the text of the electronic out-of-office message, finding attributes for named entities, and building a knowledge graph of the named entities and attributes," while amended claim 20 recites "wherein generating the representation comprises applying named entity recognition (NER) to the text of the electronic out-of-office message, finding attributes for named entities, and building a knowledge graph of the named entities and attributes."
Moreover, while not necessary for the patentability of the independent claims, new dependent claims 22-23 recite further examples of technical details, including "continuously re-training the machine learning model during execution of the computer-implemented method" (claim 22) and "retraining using the vector representation" (claim 23).
Such features not only represent specific technical details relating to the implementation of the recited steps (in contrast with claim 2 of Example 47), but more specifically represent steps that would not be possible to perform with the human mind alone. Such features thus represent technical and non-abstract improvements to the field of process automation.
Additionally, by the above amendments, each of independent claims 1, 16, and 20 is amended to recite additional features that further integrate the recited subject matter into a practical application. For example, each of amended claims 1 and 16 recites "sending a message to the substitute approver seeking approval of the task in the automated process instance; and continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the substitute approver." Similarly, amended claim 20 recites "for the step in the automated process instance, redirecting an original request for approval to the email address of the substitute approver; and continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the substitute approver."
Applicant submits that such features bring the claims into closer alignment with claim 3 of Example 47, which is deemed eligible in the 2019 PEG. For example, in describing the eligibility of claim 3, the 2019 PEG states (with emphasis added): “The claimed steps of (e), automatically dropping the one or more malicious network packets, and (f), blocking future traffic from the source address, provide specific computer solutions that use the output from the ANN to provide security solutions to the detected anomalies. As indicated in paragraph six of the background, the system may "automatically" perform dropping of malicious network packets and blocking future traffic without the need for any action by a network administrator. Instead, the ANN may make decisions about whether a network packet is potentially malicious and take action to drop malicious network packets and block future traffic.”
Amended claims 1, 16, and 20 similarly provide specific computer solutions that improve the performance and efficiency of process automation systems by automatically performing a step (e.g., identifying a substitute approver and sending a message or request to the substitute approver) that otherwise would be performed manually and/or based on inflexible organizational rules.
Applicant additionally submits that Example 42 of the 2019 PEG further demonstrates the eligibility of the independent claims.
The 2019 PEG states that claim 1 of Example 42 is eligible. In particular, the 2019 PEG states that, while the claim as a whole recites a method of organizing human activity, the claim is integrated into a practical application. Specifically, the 2019 PEG states: “The claim recites a combination of additional elements including storing information, providing remote access over a network, converting updated information that was input by a user in a non-standardized form to a standardized format, automatically generating a message whenever updated information is stored, and transmitting the message to all of the users. The claim as a whole integrates the method of organizing human activity into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. Thus, the claim is eligible because it is not directed to the recited judicial exception (abstract idea).”
Similar to claim 1 of Example 42, the independent claims recite a specific technical improvement into a practical application by reciting "sending a message to the substitute approver seeking approval of the task in the automated process instance; and continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the substitute approver" (claims 1 and 16) and "redirecting an original request for approval to the email address of the substitute approver; and continuing execution of the given automated process instance responsive to receiving approval or rejection of the task from the substitute approver" (claim 20). As described throughout the specification (e.g., in paragraphs [0003]-[0004] and [0031]-[0033] as excerpted above), such integration into a practical application represents a specific improvement over prior art systems by allowing for automated processes to proceed without interruption and with a dynamic flexibility that is not provided by conventional methods.
Because the claims improve the functioning of a computer-based system (specifically, a process automation system), they reflect a practical application of any purported judicial exception. Thus, even if the claims somehow do not pass Prong 1 of the Step 2A inquiry, it satisfies Prong 2 of Step 2A test.
Step 2B:
The specific combination of features recited in the claims represents a non-conventional approach to directing an approval request in the field of process automation systems. As discussed in more detail below, none of the cited references, alone or in combination, teaches or suggests each and every limitation recited in the claims. This supports the conclusion that the claims, either individually or in combination, "are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present." Thus, even if the claims somehow do not pass the Step 2A inquiry, they satisfy the Step 2B test because the claims as a whole amount to an "inventive concept."
New claim 25 goes further by reciting particular relationships in the knowledge graph, taking the subject matter still further from being an abstract idea.
In sum, because the pending claims pass both the Steps 2A and 2B of the eligibility analysis outlined in 2019 PEG, they are directed to eligible subject matter and the outstanding rejections under 35 U.S.C. § 101 should be withdrawn.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive.
Step 2A, Prong 1:
Regarding "Recommendations for Automatic Responses to Electronic Mail," disclosed in the IDS, this memo is on the formatting of an automated response to different types of email messages, and does not discuss automated selection of substitute approvers. Nevertheless, this memo also does not provide a technical improvement. Regarding the claim itself, the claim is directed to the abstract idea, and the additional elements are recited as mere tools to implement the abstract idea.
The steps of receiving an out-of-office message relating to an original approver, extracting features from the message, and predicting a substitute approver for the original approver are all part of the abstract idea, specifically the managing personal behavior or interactions between people CMO subgrouping. The message being an electronic message is generally linking it to a technological environment. The machine learning model is recited at a high level of generality, used as a tool to implement the abstract idea. Regarding the automated processes for analyzing electronic inputs, this is mere automation of manual processes on a generic computer, and further, it is not sufficient to show an improvement in computer functionality.
Step 2A, Prong 2:
Regarding “improving the performance and efficiency during execution of automated process instances,” the claim recites only the idea of a solution or outcome, and fails to recite details of how a solution to a problem is accomplished. The use of a machine learning model to predict a substitute approver for an original approver based on an electronic out-of-office message of the original approver is using the machine learning model in its ordinary capacity, as a tool to implement the abstract idea of predicting a substitute approver for an original approver based on an out-of-office message. The claim does not provide any improvement to the functioning of machine learning models or computers or any other technology.
The efficiency and versatile execution of automated process instances in the claim is not a result of a technical improvement; it is the result of using the technology, such as the machine learning model and the computer, in its ordinary capacity, and this does not integrate the abstract idea into a practical application.
Paragraphs [0004] and [0031]-[0033] of the specification recite improvements to the abstract idea itself, rather than a technical improvement. They recite extracting data from a message, training a machine learning model, and using the machine learning model to output data. As previously stated, the technology is used in its ordinary capacity, and no technical improvement is provided to the machine learning model or the computer itself.
As previously stated, the steps of extracting features from an out-of-office message, predicting a substitute approver, and receiving an identifier of the substitute approver are all part of the abstract idea, specifically the managing personal behavior or interactions between people CMO subgrouping. The message being an electronic message is generally linking it to a technological environment. The machine learning model is recited at a high level of generality, for receiving, determining, and outputting information, used as a tool to implement the abstract idea.
Regarding Claim 2 of Example 47 and the claim amendments: The steps of finding attributes for named entities and building a knowledge graph of the named entities and attributes are all part of the abstract idea. The application of NER to text of the message is used as a tool to implement the abstract idea, and does not provide a technical improvement. Further, in comparing the claims to Claim 2 of Example 47, similar to the PEG stating “the plain meaning of 'detecting' encompasses mental observations or evaluations, e.g., a computer programmer's mental identification of an anomaly in a data set,” though the amended claims recite NER as well as utilizing a machine learning model, they recite steps that are directed to managing personal behavior or interactions between people. Further, though the claims were not rejected under Mental Processes, the claims do recite steps that can be performed by the human mind, or with pen and paper, such as the extracting features from an out-of-office message, finding attributes, building a knowledge graph, predicting a substitute approver, and sending a message to the substitute approver.
Regarding claims 22-23, the retraining of the machine learning model using data is recited at a high level of generality, and does not provide a technical improvement to machine learning technology.
The steps of sending a message to the substitute approver seeking approval of the task and continuing execution of the given process instance responsive to receiving approval or rejection of the task from the substitute approver are part of the abstract idea. The process instance being automated is mere automation of a manual process on a generic computer, and does not provide an improvement in computer functionality.
The amended claims are not analogous to Claim 3 of Example 47, as unlike Claim 3, they do not provide a technical improvement. The eligibility of Claim 3 was not simply due to automatically performing any step, or mere automation of a manual process; rather, the specification provided a technical explanation of a technical improvement, which was reflected in the claims, and the claim as a whole integrated the abstract idea into a practical application.
The amended claims are not analogous to Claim 1 of Example 42. As previously stated, the claims are directed to the abstract idea, and the additional elements are used as mere tools to implement the abstract idea. The claimed invention does not pertain to an improvement in the functioning of the computer itself or any other technology or technical field. Thus, the additional elements do not integrate the abstract idea into a practical application.
Step 2B:
Regarding prior art and cited references, even if a claim overcomes a prior art rejection, this is not indicative that it is eligible under 101. 35 U.S.C. 101 requires that the claimed invention is directed to patent eligible subject matter, which requires that the claimed invention be to one of the four statutory categories, and that the claimed invention must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception. However, a 35 U.S.C. 103 is a rejection based on the teaching of the claimed invention in prior art, where 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.
Regarding claim 25, the knowledge graph itself, and the relationships that constitute the knowledge graph, are all part of the abstract idea itself.
For at least all the reasons above, the additional elements are not sufficient to amount to significantly more than the abstract idea.
Applicant’s Argument Regarding 35 USC 102 and 103 Rejections of Claims 1-20: The cited references fail to disclose or suggest the features of the amended claims.
Examiner’s Response: Applicant’s arguments have been fully considered and are persuasive. The rejection has been withdrawn.
Conclusion
The prior art made of record and not relied upon, considered pertinent to applicant’s disclosure or directed to the state of art, is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARMA EL-CHANTI whose telephone number is (571)272-3404. The examiner can normally be reached T-Sa 10am-6pm ET.
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/KARMA A EL-CHANTI/Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629