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 .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The December 5, 2026, Memorandum, "Advance notice of change to the MPEP in light of Ex Parte Desjardins" stated, in part, ...because "[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can," the Federal Circuit held that the eligibility determinations should turn on whether "the claims are directed to an improvement to computer functionality versus being directed to an abstract idea." Id. at 1336. (Desjardins, page 8). In particular, Enfish recognized that "[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes." 822 F.3d at 1339. Moreover, because "[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can," the Federal Circuit held that the eligibility determination should turn on whether "the claims are directed to an improvement to computer functionality versus being directed to an abstract idea." Id. at 1336.,” Examiner respectfully disagrees. The present claims do not provide an analogous improvement to the machine learning model (e.g. trained predictive engagement model). Examiner respectfully asserts that the claims are unlike the Des Jardins decision because the claims are directed to an abstract idea versus being directed to an improvement to computer functionality. The present claims do not provide an analogous technical solution to that of Des Jardins because the claims do not “address challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” The machine learning model of the present claims is merely a tool to perform the abstract process. An improvement to predicting engagement or generating a schedule would be an improvement to the abstract limitations for consideration under Step 2A, Prong 1 and not to the trained predictive engagement model itself. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “Here, based on outcome from the April 30 interview, claim 1 has been amended to positively recite specific improvement to the functioning of the machine learning model itself. Claim 1 as amended recites, in part, "the predictive engagement model has been trained using associated historical engagement data and fine-tuning multiple layers of parameters in the multi- layered neural network, the multiple layers of parameters characterize correlation between categories of the historical engagement data and values of the categories of the predictive engagement data." Specification, at para. [0077].,” Examiner respectfully disagrees with Applicant’s assertion. Examiner respectfully asserts that the “fine-tuning” limitation is not sufficient to prove integration into a practical application. The present claims do not recite any particular improvements. Rather, the neural network is tool used to analyze particular data, such as historical engagement data. The limitations related to fine-tuning provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, the present claims are rejected under 35 USC 101.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 08/04/2026 has been fully considered.
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 1-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without significantly more.
Step 1: Claims 1-9 are directed to a method and claims 10-15 and 16-20 are directed to a system. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 10, and 16 recite limitations related to generating scheduling data for an agent, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals. The limitations of “generating, based on the activity data, historical engagement data, wherein the historical engagement data includes analytics data associated with the agent engaging in a task, and wherein the historical engagement data comprise historical reliability data and historical contact handling data; generating, based on the historical engagement data, a historical engagement score associated with the agent, wherein the historical engagement score comprises an aggregate of weighted values associated with a plurality of categories of the historical engagement data, and the plurality of categories include historical reliability and historical contact handling; generating, based on the historical engagement data, predictive engagement data using a predictive engagement model, wherein the predictive engagement data indicates future engagement of the agent in future tasks, the predictive engagement model predicts the predictive engagement data based on the historical engagement data, and the predictive engagement data comprise predictive reliability data and predictive contact handling data; generating, based on the predictive engagement data, a predictive engagement score, wherein the predictive engagement score comprises a predictive reliability score and a predictive contact handling score; generating, based on the predictive engagement data, schedule data associated with the agent, wherein the schedule data includes attribute data associated with one or more tasks, wherein the schedule data is operable to generate a schedule of tasks for the agent,” as drafted, is a process that, under its broadest reasonable interpretation, covers an abstract idea but for the recitation of generic computer components. That is, other than the preamble of the claims, nothing in the claim elements preclude the steps from being interpreted as an abstract idea. The present claims recite generating schedule data for an agent, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals. Therefore, the independent claims recite an abstract idea.
Dependent claims 3, 5, 7-9, 12, 14, and 19 further narrow the abstract idea identified under Step 2A, Prong 1.
Dependent claims 2, 4, 6, 11, 13, 15, 17, 18, and 20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Claims 1, 10, and 16 do not integrate the judicial exception into a practical application. Claim 1 is a “computer implemented method,” as recited within the preamble of the claim. Claim 10 is a system and claim 16 is a device comprising a memory and processor configured to execute the method. Additionally, claims 1, 10, and 16 recite the additional elements of “retrieving activity data associated with an agent from an activity database,” “transmitting the schedule data to a remote client device associated with the agent, wherein transmitting the schedule data causes the remote client device to notify the agent, via an application on the remote client device, of an assigned task,” and “receiving, from the work tracking application initiated on the remote client device, a set of activity data.” Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Claims 1, 10, and 16 further recite the additional element further recite the additional element of “further causes the remote client device to initiate a work tracking application associated with the assigned task upon initiation of the assigned task.” This limitation does not integrate the judicial exception into a practical application because it adds insignificant extra-solution activity to the judicial exception. This limitation recites mere data gathering, which is extra-solution activity. See MPEP 2106.05(g).
Claims 1, 10, and 16 further recite the additional element further recite the additional elements of “generating, based on the historical engagement data, predictive engagement data using a predictive engagement model comprising a neural network,” “the predictive engagement model comprises a multi-layered neural network,” “wherein the predictive engagement model has been trained using associated historical engagement data and fine-tuning multiple layers of parameters in the multi-layered neural network, the multiple layers of parameters characterize correlation between categories of the historical engagement data and values of the categories of the predictive engagement data,” and “wherein the predictive engagement model is updated by executing a training process on the set of activity data.” These additional elements, as drafted, under consideration of the broadest reasonable interpretation, are nothing more than generally linking the use of the judicial exception to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, such as machine learning, which does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Furthermore, the limitations related to fine-tuning provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 3, 5, 7-9, 12, 14, and 19 further narrow the abstract idea and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claims 2, 11, and 17 introduce the additional element of “storing the set of activity data associated with the agent in the activity database, wherein the activity database includes data associated with customer calls, and wherein the activity database is indexed for retrieving activity data with one or more parameters including an agent identity, a date, a call duration, and the task assigned to the agent.” Dependent claims 4, 13, and 18 introduce the additional element of “displaying predictive engagement data associated with the agent performing the one or more tasks,” “receiving historical engagement data associated with the predictive engagement data as the ground truth data,” and “and storing the trained predictive engagement model.” Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Additionally, dependent claims 4, 13, and 18 introduce the additional element of “training the predictive engagement model using a combination of the associated historical engagement data and the predictive engagement data as training data.” Dependent claims 6, 15, and 20 introduce the additional element of “wherein the neural network, once trained, outputs the predictive engagement data associated with the agent based on the historical engagement data associated with the agent.” These additional elements, as drafted, under consideration of the broadest reasonable interpretation, are nothing more than generally linking the use of the judicial exception to the field of machine learning. The present claims do not provide any improvements to the field of machine learning and merely provide a general link to the field. Therefore, these additional elements do not integrate the judicial exception into a practical application because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims are not sufficient to prove integration into a practical application.
Step 2B: Claims 1, 10, and 16 do not comprise anything significantly more than the judicial exception. Claim 1 is a computer implemented method, claim 10 is a system comprising a processor and memory configured to perform the claimed invention, and claim 16 is a device comprising a processor and memory configured to perform the claimed invention. Additionally, claims 1, 10, and 16 recite the additional elements of “retrieving activity data associated with an agent from an activity database,” “transmitting the schedule data to a remote client device associated with the agent, wherein transmitting the schedule data causes the remote client device to notify the agent, via an application on the remote client device, of an assigned task,” and “receiving, from the work tracking application initiated on the remote client device, a set of activity data.” Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception.
Additionally, with respect to the Berkheimer court case, below can be found evidence provided by the Examiner that provides, based on 2B analysis, how the additional elements of the independent claims are viewed as well-understood, routine, and conventional activity for consistency with the Federal Circuit’s decision in Berkheimer and MPEP 2106.5(d). Claims 1, 10, and 16 further recite the additional element further recite the additional element of “further causes the remote client device to initiate a work tracking application associated with the assigned task upon initiation of the assigned task.” This limitation recites mere data gathering, which is extra-solution activity. Section 2106.05(d)(II) of the MPEP states that “mere data gathering,” and specifically “consulting and updating an activity log,” is nothing more than insignificant extra-solution activity. Furthermore, as can be seen in at least [0026, 0056-0057, 0080-0081] of the instant specification, the specification merely recites the use of an application to record agent activity. The Applicant has not proven the application of this type of computer application is anything other than what is well-understood, routine, and conventional because the Applicant is relying on a person having ordinary skill in the art at the time of effective filing to recognize how a software application would be utilized to track the word of the agent upon initiation of the assigned task. In particular, the cited portions of the instant specification merely recite the type of data gathered while tracking of agent work without details regarding the implementation and functionality of the application itself, and thus this limitation is determined to be nothing more than well-understood, routine, and conventional. This limitation is nothing more than mere data gathering, which is not anything significantly more than the judicial exception.
Claims 1, 10, and 16 further recite the additional element further recite the additional elements of “generating, based on the historical engagement data, predictive engagement data using a predictive engagement model comprising a neural network,” “the predictive engagement model comprises a multi-layered neural network,” “wherein the predictive engagement model has been trained using associated historical engagement data and fine-tuning multiple layers of parameters in the multi-layered neural network, the multiple layers of parameters characterize correlation between categories of the historical engagement data and values of the categories of the predictive engagement data,” and “wherein the predictive engagement model is updated by executing a training process on the set of activity data.” These additional elements, as drafted, under consideration of the broadest reasonable interpretation, are nothing more than generally linking the use of the judicial exception to the field of machine learning. The present claims do not provide any improvements to the field of machine learning and merely provide a general link to the field. Therefore, these additional elements are not anything significantly more than the judicial exception because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). Furthermore, the limitations related to fine-tuning provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception n. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 3, 5, 7-9, 12, 14, and 19 further narrow the abstract idea and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 2, 11, and 17 introduce the additional element of “storing the set of activity data associated with the agent in the activity database, wherein the activity database includes data associated with customer calls, and wherein the activity database is indexed for retrieving activity data with one or more parameters including an agent identity, a date, a call duration, and the task assigned to the agent.” Dependent claims 4, 13, and 18 introduce the additional element of “displaying predictive engagement data associated with the agent performing the one or more tasks,” “receiving historical engagement data associated with the predictive engagement data as the ground truth data,” and “and storing the trained predictive engagement model.” Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Additionally, dependent claims 4, 13, and 18 introduce the additional element of “training the predictive engagement model using a combination of the associated historical engagement data and the predictive engagement data as training data.” Dependent claims 6, 15, and 20 introduce the additional element of “and wherein the neural network, once trained, outputs the predictive engagement data associated with the agent based on the historical engagement data associated with the agent.” These additional elements, as drafted, under consideration of the broadest reasonable interpretation, are nothing more than generally linking the use of the judicial exception to the field of machine learning. The present claims do not provide any improvements to the field of machine learning and merely provide a general link to the field. Therefore, these additional elements are not anything significantly more than the judicial exception because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims do not comprise anything significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected under 35 USC 101.
Allowable Subject Matter
Claims 1-20 are allowable over the available field of prior art. The independent claims, as drafted, are rendered neither obvious nor anticipated by the available field of prior art. The claims overcome the prior art such that none of the cited prior art references can be applied to form the basis of a 35 USC 102 rejection nor can they be applied to form the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 USC 101, as set forth above. The closest prior art of the record discloses:
Munoz et al. (US 20220360669 A1) discloses retrieving activity data associated with an agent from an activity database; generating, based on the activity data, historical engagement data, wherein the historical engagement data includes analytics data associated with the agent engaging in a task, and wherein the historical engagement data comprise historical reliability data and historical contact handling data; generating, based on the historical engagement data, predictive engagement data using a predictive engagement model comprising a neural network; wherein the predictive engagement data indicates future engagement of the agent in future tasks, the predictive engagement model predicts the predictive engagement data based on the historical engagement data, and the predictive engagement data comprise predictive reliability data and predictive contact handling data; generating, based on the predictive engagement data, a predictive engagement score, wherein the predictive engagement score comprises a predictive reliability score and a predictive contact handling score; and receiving, a set of activity data, wherein the predictive engagement model is updated by executing a training process on the set of activity data. However, Munoz fails to explicitly disclose generating, based on the historical engagement data, a historical engagement score associated with the agent, wherein the historical engagement score comprises an aggregate of weighted values associated with a plurality of categories of the historical engagement data, and the plurality of categories include historical reliability and historical contact handling; the predictive engagement model comprises a multi-layered neural network; wherein the predictive engagement model is trained using associated historical engagement data and fine-tuning multiple layers of parameters in the multi-layered neural network, the multiple layers of parameters characterize correlation between categories of the historical engagement data and values of the categories of the predictive engagement data; generating, based on the predictive engagement data, schedule data associated with the agent, wherein the schedule data includes attribute data associated with one or more tasks, wherein the schedule data is operable to generate a schedule of tasks for the agent; transmitting the schedule data to a remote client device associated with the agent, wherein transmitting the schedule data causes the remote client device to notify the agent, via an application on the remote client device, of an assigned task and further causes the remote client device to initiate a work tracking application associated with the assigned task upon initiation of the assigned task; and receiving, from the work tracking application initiated on the remote client device, a set of activity data.
Hughes et al. (US 20080027783 A1) discloses generating, based on the historical engagement data, a historical engagement score associated with the agent, wherein the historical engagement score comprises an aggregate of weighted values associated with a plurality of categories of the historical engagement data, and the plurality of categories include historical reliability and historical contact handling; generating, based on the predictive engagement data, schedule data associated with the agent, wherein the schedule data includes attribute data associated with one or more tasks, wherein the schedule data is operable to generate a schedule of tasks for the agent. However, Hughes fails to explicitly disclose the predictive engagement model comprises a multi-layered neural network; wherein the predictive engagement model has been trained using associated historical engagement data and fine-tuning multiple layers of parameters in the multi-layered neural network, the multiple layers of parameters characterize correlation between categories of the historical engagement data and values of the categories of the predictive engagement data; transmitting the schedule data to a remote client device associated with the agent, wherein transmitting the schedule data causes the remote client device to notify the agent, via an application on the remote client device, of an assigned task and further causes the remote client device to initiate a work tracking application associated with the assigned task upon initiation of the assigned task; and receiving, from the work tracking application initiated on the remote client device, a set of activity data.
Hunter et al. (US 20220092521 A1) discloses wherein the predictive engagement model is trained using associated historical engagement data; transmitting the schedule data to a remote client device associated with the agent, wherein transmitting the schedule data causes the remote client device to notify the agent, via an application on the remote client device, of an assigned task. However, Hunter fails to explicitly disclose the predictive engagement model comprises a multi-layered neural network; and fine-tuning multiple layers of parameters in the multi-layered neural network, the multiple layers of parameters characterize correlation between categories of the historical engagement data and values of the categories of the predictive engagement data; wherein transmitting the schedule data further causes the remote client device to initiate a work tracking application associated with the assigned task upon initiation of the assigned task; and receiving, from the work tracking application initiated on the remote client device, a set of activity data.
Balasubramanian et al. (US 20210073713 A1) discloses wherein transmitting the schedule data further causes the client device to initiate a work tracking application associated with the assigned task upon initiation of the assigned task; and receiving, from the work tracking application initiated on the client device, a set of activity data. However, Balasubramanian fails to explicitly disclose the predictive engagement model comprises a multi-layered neural network; and fine-tuning multiple layers of parameters in the multi-layered neural network, the multiple layers of parameters characterize correlation between categories of the historical engagement data and values of the categories of the predictive engagement data; transmitting the schedule data to a remote client device associated with the agent, wherein transmitting the schedule data causes the remote client device to notify the agent, via an application on the remote client device, of an assigned task and wherein transmitting the schedule data further causes the remote client device to initiate a work tracking application associated with the assigned task upon initiation of the assigned task; and receiving, from the work tracking application initiated on the remote client device, a set of activity data. .
Examiner notes that the present claims are not in condition for allowance as they remain rejected under 35 USC 101, as set forth in the current office action. Therefore, the claims are not in condition for allowance at this time.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Holman et al. (US 12019410 B1) discloses the system can begin recording the actions the user performs when performing a new command until a “done” action is performed, as well as training a neural network with a plurality of layers, which is related to the claim limitations of at least “fine-tuning multiple layers of parameters in the multi-layered neural network” and “initiate a work tracking application”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sara G Brown whose telephone number is (469)295-9145. The examiner can normally be reached M-F 8:00 am- 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
