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 .
This Office Action is in response to the amendment filed June 24, 2025. Claims 1 and 9-11 have been amended. Claims 1-20 are pending.
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 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 17/750,787. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are encompassed and/or made obvious by the claims of copending application 17/750,787
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims of 17/870,626
Claims of 17/750,787
A method for directing textual conversation based on user workflow, the method comprising: obtaining, by a web server, information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session; determining, based on the user workflow, an activity target for the user; presenting, by the web server, a user interface capable of accepting freeform text input from the user; receiving, by the web server, a character string via the user interface; generating a vector encoding of the character string; calculating, based on the vector encoding, a user sentiment score for the character string; dynamically generating, based on the user sentiment score, a response to the character string using a first machine learning model, wherein the response to the character string contains information routing the user to an updated path to the activity target; transmitting, by the web server, the generated response to the user device; and causing the user device to operate the user interface to provide a visual representation of the generated response in real time.
A method for directing textual conversation based on user workflow, the method comprising: obtaining, by a web server, information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session; determining, based on the user workflow, an activity target for the user; presenting, by the web server, a user interface capable of accepting freeform text input from the user; receiving, by the web server, a character string via the user interface; generating a vector encoding of the character string; calculating, based on the vector encoding, a user sentiment score for the character string; dynamically generating, based on the user sentiment score, a response to the character string in real-time, wherein the response to the character string contains information routing the user to an updated path to the activity target; transmitting, by the web server, the generated response to the user device; and causing the user device to operate the user interface to provide a visual representation of the generated response in real time.
10. The method of claim 1, wherein the generating a response to the character string comprises: applying a machine learning model to the user sentiment score and available self-solve actions to generate the response to the character string.
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 11-20 are provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 11-20 of copending Application No. 17/750,787. This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
Claims of 17/870,626
Claims of 17/750,787
11. A system comprising: a memory configured to store (a) one or more vector encodings of textual data and (b) information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session; and at least one processor configured to: determine, based on the user workflow, an activity target for the user; transmit, to a user device, a user interface capable of accepting freeform text input from the user; receive one or more character strings via the user interface; generate a vector encoding of the one or more character strings; calculate, based on the vector encoding, a user sentiment score for the character string; dynamically generate, based on the user sentiment score, a response to the character string in real time, wherein the response to the character string contains information routing the user to an updated path to the activity target; and transmit, via the user interface, the generated response to the user device; and cause the user device to operate the user interface to provide a visual representation of the generated response in real-time.
11. A system comprising: a memory configured to store (a) one or more vector encodings of textual data and (b) information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session; and at least one processor configured to: determine, based on the user workflow, an activity target for the user; transmit, to a user device, a user interface capable of accepting freeform text input from the user; receive one or more character strings via the user interface; generate a vector encoding of the one or more character strings; calculate, based on the vector encoding, a user sentiment score for the character string; dynamically generate, based on the user sentiment score, a response to the character string in real time, wherein the response to the character string contains information routing the user to an updated path to the activity target; and transmit, via the user interface, the generated response to the user device; and cause the user device to operate the use interface to provide a visual representation of the generated response in real-time.
12. The system of claim 11, wherein the at least one processor is further configured to: obtain information regarding a continued user workflow of the user; determine, from the continued user workflow, whether the user completed the activity target; modify the user sentiment score based on whether the user completed the activity target; and take one or more responsive actions based on the modified user sentiment score.
12. The system of claim 11, wherein the at least one processor is further configured to: obtain information regarding a continued user workflow of the user; determine, from the continued user workflow, whether the user completed the activity target; modify the user sentiment score based on whether the user completed the activity target; and take one or more responsive actions based on the modified user sentiment score
13. The system of claim 11, where the user interface is a chat application permitting real-time exchange of text between a user device and a remote system, wherein the character string is provided by the user as freeform text entry into the chat application and the generated response to the character string is provided to the user in the chat application in reply to the freeform text entry into the chat application.
13. The system of claim 11, where the user interface is a chat application permitting real-time exchange of text between the user device and a remote system, wherein the character string is provided by the user as freeform text entry into the chat application and the generated response to the character string is provided to the user in the chat application in reply to the freeform text entry into the chat application.
14. The system of claim 11, wherein the generating of the response to the character string is further based on at least one of: user location data and user language data stored, in a memory, in association with information identifying the user.
14. The system of claim 11, wherein the generating of the response to the character string is further based on at least one of: user location data and user language data stored, in a memory, in association with information identifying the user.
15. The system of claim 11, wherein the calculating of the user sentiment score for the character string comprises: applying one or more natural language processing (NLP) models to perform a sentiment analysis of the character string.
15. The system of claim 11, wherein the calculating of the user sentiment score for the character string comprises: applying one or more natural language processing (NLP) models to perform a sentiment analysis of the character string.
16. The system of claim 11, wherein the updated path to the activity target comprises at least one of: a self-solve workflow, an agent-controlled workflow, and a cancellation workflow.
16. The system of claim 11, wherein the updated path to the activity target comprises at least one of: a self-solve workflow, an agent-controlled workflow, and a cancellation workflow.
17. The system of claim 12, wherein the one or more responsive actions comprises: (a) displaying, via the user interface, one or more instructions regarding a self-solve action, and (b) generating a support ticket and transmitting the support ticket, via a network, to a support agent.
17. The system of claim 12, wherein the one or more responsive actions comprises: (a) displaying, via the user interface, one or more instructions regarding a self-solve action, and (b) generating a support ticket and transmitting the support ticket, via a network, to a support agent.
18. The system of claim 17, wherein the support agent is a human actor.
18. The system of claim 17, wherein the support agent is a human actor.
19. The system of claim 11, wherein the at least one processor is further configured to apply a machine learning model to the character string to generate the vector encoding.
19. The system of claim 11, wherein the at least one processor is further configured to apply a machine learning model to the character string to generate the vector encoding.
20. The system of claim 11, wherein the at least one processor is further configured to apply a machine learning model to the user sentiment score and available self-solve actions to generate the response to the character string.
20. The system of claim 11, wherein the at least one processor is further configured to apply a machine learning model to the user sentiment score and available self-solve actions to generate the response to the character string.
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 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites a method for directing textual conversation based on user workflow, the method comprising: obtaining, by a web server, information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session; determining, based on the user workflow, an activity target for the user; presenting, by the web server, a user interface capable of accepting freeform text input from the user; receiving, by the web server, a character string via the user interface; generating a vector encoding of the character string; calculating, based on the vector encoding, a user sentiment score for the character string; dynamically generating, based on the user sentiment score, a response to the character string using a first machine learning model, wherein the response to the character string contains information routing the user to an updated path to the activity target; and transmitting, by the web server, the generated response to the user device; and causing the user device to operate the user interface to provide a visual representation of the generated response in real time. The step for “obtaining…” is a data gathering step that can be achieved by the a person accessing and reviewing user interactions; the step for “determining…” can be achieved by the person reviewing the interactions and using mental processing, identifying a pertinent activity; the step for “presenting…” can be achieved by the person, using pen and paper, a list of questions/survey to the user; the step for “receiving…” is a data gathering step that can be achieved by the person retrieving the paper with the user’s input written on it; the step for “generating…” can be achieved by, using pen and paper and language processing techniques, organizing text in vector format; the step for “calculating..” can be achieved by the user analyzing the vector and determining a sentiment score for the text; the step for “dynamically generating….” Can be achieved by the person determining the appropriate response to the user’s input using known natural language processing rules/algorithms fundamentals via mental processing or pen and paper; the step for “transmitting…and causing…” can be achieved by the person, using pen and paper, presenting the response.
Claim 11 recites A system comprising: a memory configured to store (a) one or more vector encodings of textual data and (b) information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session; and at least one processor configured to: determine, based on the user workflow, an activity target for the user; transmit, to a user device, a user interface capable of accepting freeform text input from the user; receive one or more character strings via the user interface; generate a vector encoding of the one or more character strings; calculate, based on the vector encoding, a user sentiment score for the character string; dynamically generate, based on the user sentiment score, a response to the character string in real-time, wherein the response to the character string contains information routing the user to an updated path to the activity target; transmit, via the user interface, the generated response to the user device; and cause the user device to operate the user interface to provide a visual representation of the generated response in real-time. The feature to “store…” is a data gathering and organizing step that can be achieved by the a person accessing and reviewing user interactions and formatted textual data; the step for “determining…” can be achieved by the person reviewing the interactions and using mental processing, identifying a pertinent activity; the step to “transmit…” can be achieved by the person, using pen and paper, giving a list of questions/survey to the user; the step to “receive…” is a data gathering step that can be achieved by the person retrieving the paper with the user’s input written on it; the step to “generate…” can be achieved by, using pen and paper and language processing techniques, organizing text in vector format; the step to “calculate..” can be achieved by the user analyzing the vector and determining a sentiment score for the text; the step to “dynamically generate….” Can be achieved by the person determining the appropriate response to the user’s input; the step to “transmit…and cause” can be achieved by the person, using pen and paper, presenting the response.
The recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer, web server, apparatus, computer program product, and generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recited generic computer components, web server, device and various modules amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claims are not patent eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the computer components, web server, computing device and modules to perform the various steps 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. The claims are not patent eligible.
Dependent claims 2-9 and 12-20 do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of the dependent claims are directed to steps of organizing or manipulating functions and commands for the input text, applying natural language processing models and techniques to process input text, determine sentiment scores and generating responses, determining and identifying activities for user, determining when and how a human user will participate, and using pen and paper, displaying outputs.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lam (US Patent Application Publication No. 2019/0114321) in view of Wu (EP 3 822 814 A2).
Lam teaches an auto tele-interview solutions with improved generation and control of conversations. Regarding claim 1, Lam teaches a method for directing textual conversation based on user workflow, the method comprising: obtaining, by a web server, information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; determining, based on the user workflow, an activity target for the user [next question -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; presenting, by the web server, a user interface capable of accepting freeform text input from the user [0019; 0075]; receiving, by the web server, a character string via the user interface [user responses -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; calculating a user sentiment score for the character string [disposition score – p0008; 0076]; dynamically [para 0019 – dynamic interface objects; generating, based on the user sentiment score, a response to the character string using a machine learning model [neural network system -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164], wherein the response to the character string contains information routing the user to an updated path to the activity target [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0111 -- display a request to confirm the changes and then ask additional questions as required depending on the changes; 0126-0155; 0163-0164]; transmitting, by the web server the generated response to the user device [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; and causing the user device to operate the user interface to provide a visual representation of the generated response in real-time [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0111 -- display a request to confirm the changes and then ask additional questions as required depending on the changes; 0126-0155; 0163-0164]. Lam fails to teach generating a vector encoding of the character string. In a similar field of endeavor, Wu teaches human machine interaction using a conversation control system to provide responses to user inputs, and implements vector encoding of text data [para 0088] for ensuring that the reply is in line with the current human-machine interaction and the conversation logic is clear. Therefore, one having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the vector encoding processing suggested by Wu, in the conversation system of Lam, for the purpose of ensuring that the reply is in line with the current human-machine interaction and the conversation logic is clear, as taught by Wu, and therefore provide an improved system and interaction to the user.
Regarding claim 2, the combination of Lam and Wu teaches obtaining, by the web server, information regarding a continued user workflow [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; determining, from the continued user workflow, whether the user completed the activity target [para 0096; 0119]; modifying the user sentiment score based on whether the user completed the activity target [disposition score – p0008; 0076]; and taking one or more responsive actions based on the modified user sentiment score [next question -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 3, the combination of Lam and Wu teaches where the user interface is a chat application permitting real-time exchange of text between a user device and a remote system, wherein the character string is provided by the user as freeform text entry into the chat application and the generated response to the character string is provided to the user in the chat application in reply to the freeform text entry into the chat application [para 0019; 0067].
Regarding claim 4, the combination of Lam and Wu teaches of the response to the character string is further based on at least one of user location data and user language data stored, in a memory, in association with information identifying the user [user profile - para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 5, the combination of Lam and Wu teaches the calculating of the user sentiment score for the character string comprises: applying one or more natural language processing (NLP) models to perform a sentiment analysis of the character string [disposition score – p0008; 0076].
Regarding claim 6, the combination of Lam and Wu teaches updated path to the activity target comprises at least one of: a self-solve workflow, an agent-controlled workflow, and a cancellation workflow [chat with live agent – para 0019; 0067 -para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 7, the combination of Lam and Wu teaches: (a) displaying, via the user interface, one or more instructions regarding a self-solve action, and (b) generating a support ticket and transmitting the support ticket, via a network, to a support agent [chat with live agent – para 0019; 0067; 0075 -para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 8, the combination of Lam and Wu teaches the support agent is a human actor [chat with live agent – para 0019; 0067 -para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 9, the combination of Lam and Wu teaches generating a vector encoding of the character string comprises: applying a machine learning model to the character string to generate the vector encoding [Wu’s neural network system -- para 0086-0092].
Regarding claim 10, the combination of Lam and Wu teaches generating a response to the character string comprises: applying a machine learning model to the user sentiment score and available self-solve actions to generate the response to the character string [neural network system -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Claims 11-20 are rejected under similar rationale as claims 1-10.
Response to Arguments
Applicant's arguments filed June 24, 2025 have been fully considered but they are not persuasive.
Applicant argues the amendment to the claims to recite "dynamically generating, based on the user sentiment score, a response to the character string using a first machine learning model, wherein the response to the character string contains information routing the user to an updated path to the activity target," "transmit, by the web server, the generated response to the user device," and "cause the user device to operate the user interface to provide a visual representation of the generated response in real-time" integrate the alleged mental process into a practical application and are sufficient to amount to significantly more than the judicial exception. The Examiner respectfully disagrees, as indicated in the rejection above, the amended recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer, web server, apparatus, computer program product, and generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recited generic computer components, web server, device and various modules amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claims are not patent eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the computer components, web server, computing device and modules to perform the various steps 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. The claims are not patent eligible. The rejections under 35 USC 101 are maintained.
Applicant argues the Lam reference fails to teach or suggest at least one of elements "dynamically generating, based on the user sentiment score, a response to the character string using a first machine learning model, wherein the response to the character string contains information routing the user to an updated path to the activity target," "wherein the response to the character string contains information routing the user to an updated path to the activity target." The Examiner respectfully disagrees. As indicated in the rejection above, Lam teaches providing for [para 0019] generating dynamic interface objects and [0111] displaying a request to confirm changes and then asking additional questions as required depending on the changes, which is a form of dynamically generating responses and generating responses based on an updated path to an activity target.
Conclusion
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 ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00.
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659