DETAILED ACTION
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 Amendment
Applicant’s response filed 09/02/2026 has been considered and entered. Accordingly, claims 21-40 are pending in this application. Claims 1-20 are cancelled; claims 21-23, 25, 26, 30-32, and 35-40 are currently amended; claims 24, 27-29, 33, and 34 are as previously presented.
Response to Arguments
Applicant’s arguments, see page 10, with respect to the double patenting rejections of claims 21-40 have been fully considered. Although a terminal disclaimer was discussed to be filed in order to overcome the double patenting rejections, it was not able to be filed in a timely manner. Accordingly, the double patenting rejections are maintained, updated in view of Applicant’s amendments, as set forth below.
Applicant’s arguments, see pages 11-13, with respect to the rejections of claims 21-40 under 35 USC §101 have been fully considered and are persuasive. The rejections of claims 21-40 under 35 USC §101 have been withdrawn.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,299,744. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of US Pat No. 12,299,744 render obvious the claims of the instant application as set forth in the table below.
Instant Application
US Pat No. 12,299,744
21. (Currently Amended) A non-transitory computer readable medium having computer- executable instructions embodied thereon, wherein, when executed by a processor, the computer-executable instructions cause the processor to:
receive, from a first electronic device of an initial user and by the processor, an initial first set of information associated with the initial user;
receive, at the processor, an initial second set of information associated with financial advisors as a function of the initial first set of information;
identify, using the processor and a matching algorithm, an initial plurality of financial advisors as a function of the initial first set of information and the initial second set of information;
using the processor to cause the first electronic device of the initial user to display at least a portion of the initial plurality of financial advisors;
receive, from the first electronic device of the initial user and by the processor, a selection from the initial user for a selected financial advisor of the initial plurality of financial advisors;
receive, at the processor, a third set of information associated with the selected financial advisor;
monitor, using the processor, relationship information generated between the initial user and the selected financial advisor:
train, at the processor, the matching algorithm to modify the initial second set of information as a function of the third set of information and the relationship information generated between the initial user and the selected financial advisor, thereby producing a revised second set of information;
receive, from a second electronic device of an additional user and by the processor, an additional first set of information associated with the additional user;
after training the matching algorithm, identify, using the processor and the matching algorithm, an additional plurality of financial advisors as a function of the additional first set of information and the revised second set of information, wherein a baseline plurality of financial advisors identified using the matching algorithm, after training, as a function of the additional first set of information and the initial second set of information is unequal to the additional plurality of financial advisors; and
using the processor to cause the second electronic device of the additional user to display at least a portion of the additional plurality of financial advisors.
1. A non-transitory computer readable medium having computer-executable instructions embodied thereon, wherein, when executed by a processor, the computer-executable instructions cause the processor to:
communicatively couple to a plurality of client devices that are accessible by an initial user to enable the user to communicate with the processor;
provide a graphical user interface to each of the plurality of client devices, wherein each of the plurality of client devices is configured to display the graphical user interface;
receive an initial request from one of the plurality of client devices, the initial request being a request for a financial advisor;
receive a first initial set of information associated with the initial user of the client device making the initial request;
receive an initial second set of information associated with financial advisors as a function of the first initial set of information;
identify, using a machine learning matching algorithm, a plurality of initial financial advisors as a function of the initial first and initial second sets of information, wherein the machine learning matching algorithm determines a location of the initial user of the client device making the request based at least in part on a global positioning system signal, and wherein the machine learning matching algorithm is comprised in a management engine;
generate notifications based at least in part on a proximity of the client device making the request to a collocated client device;
display the identified initial plurality of financial advisors on the graphical user interface;
receive a selection for a financial advisor among the displayed initial plurality of financial advisors from the initial user;
receive a third set of information associated with the selected financial advisor, wherein the third set of information is generated from a scoring algorithm developed using performance attributes based on the relationship between the initial user, financial advisor, and historical financial advisor relationship information;
train the machine learning matching algorithm to modify the initial second set of financial advisor information as a function of the third set of information, thereby producing a revised second set of financial advisor information;
receive an additional request from an additional client device that is accessible by an additional user for an additional financial advisor;
receive a first additional set of information associated with the additional user of the client device making the additional request;
identify, using the machine learning matching algorithm, an additional plurality of additional financial advisors as a function of the additional first and revised second sets of information; and
display the identified additional plurality of financial advisors on an additional graphical user interface provided to the additional client device; and
wherein a plurality of baseline financial advisors identified using the machine learning matching algorithm as a function of the additional first and the initial second sets of information is unequal to the plurality of additional financial advisors.
22. (Currently Amended) The non-transitory computer readable medium of claim 21, wherein the computer-executable instructions further cause the processor to:
communicatively couple to the first electronic device of the initial user; and
receive a request from the first electronic device of the initial user, wherein the request is for a financial advisor.
1. A non-transitory computer readable medium having computer-executable instructions embodied thereon, wherein, when executed by a processor, the computer-executable instructions cause the processor to:
communicatively couple to a plurality of client devices that are accessible by an initial user to enable the user to communicate with the processor;
provide a graphical user interface to each of the plurality of client devices, wherein each of the plurality of client devices is configured to display the graphical user interface;
receive an initial request from one of the plurality of client devices, the initial request being a request for a financial advisor;
…
display the identified initial plurality of financial advisors on the graphical user interface;
23. (Currently Amended) The non-transitory computer readable medium of claim 22, wherein the third set of information is independent of the request from the first electronic device of the initial user and the initial first set of information associated with the initial user.
1. A non-transitory computer readable medium…
receive an initial request from one of the plurality of client devices, the initial request being a request for a financial advisor;
…
receive a third set of information associated with the selected financial advisor, wherein the third set of information is generated from a scoring algorithm
24. (Previously Presented) The non-transitory computer readable medium of claim 22, wherein the third set of information is generated from performance attributes based on at least a relationship between the initial user and the selected financial advisor.
1. A non-transitory computer readable medium…
receive a third set of information associated with the selected financial advisor, wherein the third set of information is generated from a scoring algorithm developed using performance attributes based on the relationship between the initial user, financial advisor, and historical financial advisor relationship information;
25. (Currently Amended) The non-transitory computer readable medium of claim 21, wherein the initial first set of information comprises at least one of assets, a financial need of the initial user, a life stage of the initial user, demographics of the initial user, a lifestyle of the initial user, hobbies of the initial user, a financial goal of the initial user, social media information of the initial user, a home ownership status of the initial user, a school attended by the initial user, or a location of the initial user.
2. The non-transitory computer readable medium of claim 1, wherein the first set of information comprises at least one of assets, financial need, life stage, demographics, lifestyle, hobbies, financial goal, social media information, house ownership, school attended, or location.
26. (Currently Amended) The non-transitory computer readable medium of claim 21, wherein the initial second set of information comprises at least one of a financial expertise of a respective financial advisor, a professional designation of the respective financial advisor, a tenure status of the respective financial advisor, a number of clients of the respective financial advisor, a school attended by the respective financial advisor, a probable maximum loss by the respective financial advisor, a business location of the respective financial advisor, or a performance rating of the respective financial advisor.
3. The non-transitory computer readable medium of claim 1, wherein the second set of information comprises at least one of financial advisor expertise, professional designation, financial advisor tenure, number of clients, education, school attended, probability of maximum loss, location, or performance.
27. (Previously Presented) The non-transitory computer readable medium of claim 21, wherein the third set of information comprises at least one of client satisfaction, client attrition or assets under care.
4. The non-transitory computer readable medium of claim 1, wherein the third set of information comprises at least one of client satisfaction, client attrition or assets under care.
28. (Previously Presented) The non-transitory computer readable medium of claim 21, wherein a portion of the initial first set of information is received using at least one of cookies or identification keys.
29. (Previously Presented) The non-transitory computer readable medium of claim 21, wherein the initial plurality of financial advisors correlates to a best fit match determined by the matching algorithm.
6. The non-transitory computer readable medium of claim 1, wherein the identified plurality of financial advisors correlates to a best fit match determined by the machine learning matching algorithm.
30. (Currently Amended) The non-transitory computer readable medium of claim 21, wherein the computer-executable instructions further cause the processor to transmit for display, on the first electronic device of the initial user, a business location associated with the selected financial advisor.
7. The non-transitory computer readable medium of claim 1, wherein the computer-executable instructions further cause the processor to display a business location associated with the selected financial advisor.
31. (Currently Amended) The non-transitory computer readable medium of claim 21, wherein the computer-executable instructions further cause the processor to transmit for display, on the first electronic device of the initial user, available meeting times associated with a calendar of the selected financial advisor.
8. The non-transitory computer readable medium of claim 1, wherein the computer-executable instructions further cause the processor to display available meeting times associated with a calendar of the selected financial advisor.
32. (Currently Amended) The non-transitory computer readable medium of claim 31, wherein the computer-executable instructions further cause the processor to facilitate the initial user to schedule an appointment or make a phone call, on the first electronic device of the initial user.
9. The non-transitory computer readable medium of claim 1, wherein the computer-executable instructions further cause the processor to guide the user to schedule an appointment or make a phone call on the client device.
33. (Previously Presented) The non-transitory computer readable medium of claim 21, wherein the computer-executable instructions further cause the processor to provide monitoring of performance and statistics of the matching algorithm.
10. The non-transitory computer readable medium of claim 1, wherein the computer-executable instructions further cause the processor to provide monitoring of the machine learning matching algorithm performance and statistics.
34. (Previously Presented) The non-transitory computer readable medium of claim 21, wherein the third set of information is generated from a scoring algorithm developed using performance attributes based on a relationship between the initial user, the selected financial advisor, and historical financial advisor relationship information.
1. A non-transitory computer readable medium…
receive a third set of information associated with the selected financial advisor, wherein the third set of information is generated from a scoring algorithm developed using performance attributes based on the relationship between the initial user, financial advisor, and historical financial advisor relationship information;
35. (Currently Amended) The non-transitory computer readable medium of claim 21, wherein:
the matching algorithm determines a location of the initial user using at least a global position system of the first electronic device of the initial user, wherein the matching algorithm is comprised in a management engine; and
the computer-executable instructions further cause the processor to generate notifications based at least on a location of a collocated electronic device collocated with the first electronic device of the initial user.
1. A non-transitory computer readable medium…
identify, using a machine learning matching algorithm, a plurality of initial financial advisors as a function of the initial first and initial second sets of information, wherein the machine learning matching algorithm determines a location of the initial user of the client device making the request based at least in part on a global positioning system signal, and wherein the machine learning matching algorithm is comprised in a management engine;
generate notifications based at least in part on a proximity of the client device making the request to a collocated client device;
36. (Currently Amended) The non-transitory computer readable medium of claim 22, wherein the computer-executable instructions further cause the processor to:
generate notifications based at least on a proximity of the electronic device of the initial user to a collocated electronic device.
1. A non-transitory computer readable medium …
generate notifications based at least in part on a proximity of the client device making the request to a collocated client device;
display the identified initial plurality of financial advisors on the graphical user interface;
37. (Currently Amended) The non-transitory computer readable medium of claim 21, wherein the relationship information generated between the initial user and the selected financial advisor comprises at least one of a number of appointments scheduled by the initial user for the selected financial advisor, a number of accounts opened by the initial user with the selected financial advisor, a number of assets of the initial user under care by the selected financial advisor, a client satisfaction score given by the initial user for the selected financial advisor, or a termination of a relationship between the initial user and the selected financial advisor.
17. The system of claim 11, wherein the third set information comprises at least one of client satisfaction, account opened, appointments set up, assets under care, or attrition.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to recognize that the method performed by the medium of claim 1 could readily be modified to implement the method steps performed system of claims 11 and 17, and to include any in the method of claim 17 to implement the same functionality without undue experimentation and a reasonable expectation of success. Said artisan would have been motivated to do so in order to implement the method steps more on a system.
38. (Currently Amended) A system comprising a server configured to:
receive, from a first electronic device of an initial user and by a processor, an initial first set of information associated with the initial user;
receive, at the processor, an initial second set of information associated with financial advisors as a function of the initial first set of information;
identify, using the processor and a matching algorithm, an initial plurality of financial advisors as a function of the initial first set of information and the initial second set of information;
using the processor to cause the first electronic device of the initial user to display at least a portion of the initial plurality of financial advisors;
receive, from the first electronic device of the initial user and by the processor, a selection from the initial user for a selected financial advisor of the initial plurality of financial advisors ;
receive, at the processor, a third set of information associated with the selected financial advisor;
monitor, using the processor, relationship information generated between the initial user and the selected financial advisor;
train, at the processor, the matching algorithm to modify the initial second set of information as a function of the third set of information and the relationship information generated between the initial user and the selected financial advisor, thereby producing a revised second set of information;
receive, from a second electronic device of an additional user and by the processor, an additional first set of information associated with the additional user;
after training the matching algorithm, identify, using the processor and the matching algorithm, an additional plurality of financial advisors as a function of the additional first set of information and the revised second set of information, wherein a baseline plurality of financial advisors identified using the matching algorithm, after training, as a function of the additional first set of information and the initial second set of information is unequal to the additional plurality of financial advisors; and
using the processor to cause the second electronic device of the additional user to display at least a portion of the additional plurality of financial advisors.
11. A system comprising:
a server comprising:
a memory for storing computer readable code; and
a processor operatively coupled to the memory, the processor configured to:
communicatively couple to a plurality of client devices that are accessible by an initial user to enable the initial user to communicate with the server;
provide a graphical user interface to each of the plurality of client devices, wherein each of the plurality of client devices is configured to display the graphical user interface;
receive an initial request from one of the plurality of client devices, the initial request being a request by a user for a financial advisor;
receive a first initial set of information associated with the initial user of the client device making the initial request;
receive an initial second set of information associated with financial advisors as a function of the first initial set of information;
identify, using a machine learning matching algorithm, a plurality of initial financial advisors as a function of the initial first and initial second sets of information, wherein the machine learning matching algorithm determines a location of the initial user of the client device making the request based at least in part on a global positioning system signal, and wherein the machine learning matching algorithm is comprised in a management engine;
generate notifications based at least in part on a proximity of the client device making the request to a collocated client device;
display the identified initial plurality of financial advisors on the graphical user interface;
receive a selection for a financial advisor among the displayed initial plurality of financial advisors from the initial user;
in response to receiving a selection for a financial advisor:
display for selection a business location associated with the selected financial advisor;
display available meeting times associated with a calendar of the selected financial advisor;
guide the at least one user to schedule an appointment or make a phone call on the client device;
receive a third set of information associated with the selected financial advisor, wherein the third set of information is generated from a scoring algorithm developed using performance attributes based on the relationship between the initial user, financial advisor, and historical financial advisor relationship information;
train the machine learning matching algorithm to modify the initial second set of financial advisor information based on the third set of information, thereby producing a revised second set of financial advisor information;
receive an additional request from an additional client device that is accessible by an additional user for an additional financial advisor;
receive a first additional set of information associated with the additional user of the additional client device making the additional request;
identify, using the machine learning matching algorithm, an additional plurality of additional financial advisors as a function of the additional first and revised second sets of information; and
display the identified additional plurality of financial advisors on an additional graphical user interface provided to the additional client device; and
wherein a plurality of baseline financial advisors identified using the machine learning matching algorithm as a function of the additional first and the initial second sets of information is unequal to the plurality of additional financial advisors.
39. (Currently Amended) A method, comprising:
receiving, from a first electronic device of an initial user and by a processor, an initial first set of information associated with the initial user;
receiving, at the processor, an initial second set of information associated with financial advisors as a function of the initial first set of information;
identifying, using the processor and a matching algorithm, an initial plurality of financial advisors as a function of the initial first set of information and the initial second set of information;
using the processor to cause the first electronic device of the initial user to display at least a portion of the initial plurality of financial advisors;
receiving, from the first electronic device of the initial user and by the processor, a selection from the initial user for a selected financial advisor of the initial plurality of financial advisors;
receiving, at the processor, a third set of information associated with the selected financial advisor;
monitoring, using the processor, relationship information generated between the initial user and the selected financial advisor;
training, at the processor, the matching algorithm to modify the initial second set of information as a function of the third set of information and the relationship information generated between the initial user and the selected financial advisor, thereby producing a revised second set of information;
receiving, from a second electronic device of an additional user and by the processor, an additional first set of information associated with the additional user;
after training the matching algorithm, identifying, using the processor and the matching algorithm, an additional plurality of financial advisors as a function of the additional first set of information and the revised second set of information, wherein a baseline plurality of financial advisors identified using the matching algorithm, after training, as a function of the additional first set of information and the initial second set of information is unequal to the additional plurality of financial advisors; and
using the processor to cause the second electronic device of the additional user to display at least a portion of the additional plurality of financial advisors.
20. A method comprising:
providing a graphical user interface to each of a plurality of client devices that are accessible by an initial user, wherein each of the plurality of client devices is configured to display the graphical user interface;
receiving, at a processor, an initial request from one of the plurality of client devices, the initial request being a request for a financial advisor;
receiving, at the processor, a first initial set of information associated with the initial user of the client device making the initial request;
receiving, at the processor, an initial second set of information associated with financial advisors as a function of the first initial set of information;
identifying, at the processor, using a machine learning matching algorithm, a plurality of initial financial advisors as a function of the initial first and initial second sets of information, wherein the machine learning matching algorithm determines a location of the initial user of the client device making the request based at least in part on a global positioning system signal, and wherein the machine learning matching algorithm is comprised in a management engine;
generating, at the processor, notifications based at least in part on a proximity of the client device making the request to a collocated client device;
displaying the identified initial plurality of financial advisors on the graphical user interface;
receiving, at the processor, a selection for a financial advisor among the displayed initial plurality of financial advisors from the initial user;
receiving, at the processor, a third set of information associated with the selected financial advisor, wherein the third set of information is generated from a scoring algorithm developed using performance attributes based on the relationship between the initial user, financial advisor, and historical financial advisor relationship information;
training the machine learning matching algorithm to modify the initial second set of financial advisor information as a function of the third set of information, thereby producing a revised second set of financial advisor information;
receiving an additional request from an additional client device that is accessible by an additional user for an additional financial advisor;
receiving a first additional set of information associated with the additional user of the client device making the additional request;
identifying, using the machine learning matching algorithm, an additional plurality of additional financial advisors as a function of the additional first and revised second sets of information; and
displaying the identified additional plurality of financial advisors on an additional graphical user interface provided to the additional client device; and
wherein a plurality of baseline financial advisors identified using the machine learning matching algorithm as a function of the additional first and the initial second sets of information is unequal to the plurality of additional financial advisors.
40. (Currently Amended) The method of claim 39, wherein:
the matching algorithm comprises a machine learning matching algorithm;
the initial first set of information comprises at least one of assets, a financial need of the initial user, a life stage of the initial user, demographics of the initial user, a lifestyle of the initial user, hobbies of the initial user, a financial goal of the initial user, social media information of the initial user, a home ownership status of the initial user, a school attended by the initial user, or a location of the initial user;
the initial second set of information comprises at least one of a financial expertise of a respective financial advisor, a professional designation of the respective financial advisor, a tenure status of the respective financial advisor, a number of clients of the respective financial advisor, a school attended by the respective financial advisor, a probable maximum loss by the respective financial advisor, a business location of the respective financial advisor, or a performance rating of the respective financial advisor; and
the third set of information is generated from performance attributes based on at least a relationship between the initial user and the selected financial advisor.
20. A method comprising:
...
identifying, at the processor, using a machine learning matching algorithm, a plurality of initial financial advisors as a function of the initial first and initial second sets of information, wherein the machine learning matching algorithm determines a location of the initial user of the client device making the request based at least in part on a global positioning system signal, and wherein the machine learning matching algorithm is comprised in a management engine;
…
2. The non-transitory computer readable medium of claim 1, wherein the first set of information comprises at least one of assets, financial need, life stage, demographics, lifestyle, hobbies, financial goal, social media information, house ownership, school attended, or location.
3. The non-transitory computer readable medium of claim 1, wherein the second set of information comprises at least one of financial advisor expertise, professional designation, financial advisor tenure, number of clients, education, school attended, probability of maximum loss, location, or performance.
20. A method …receiving, at the processor, a third set of information associated with the selected financial advisor, wherein the third set of information is generated from a scoring algorithm developed using performance attributes based on the relationship between the initial user, financial advisor, and historical financial advisor relationship information;
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to recognize that the method of claim 20 could readily be modified to implement the method steps performed instructions on the non-transitory medium of claims 21-37 and to include any in the method of claim 20 to implement the same functionality without undue experimentation and a reasonable expectation of success. Said artisan would have been motivated to do so in order to implement the method steps more generically by other means.
Conclusion
THIS ACTION IS MADE FINAL. 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 JAMES E RICHARDSON whose telephone number is (571)270-1917. The examiner can normally be reached Mon-Fri 9:00-5:30.
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, Sherief Badawi can be reached at (571) 272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James E Richardson/Primary Examiner, Art Unit 2169
