DETAILED ACTION
This action is in response to the amendment filed on 06/25/2026.
Response to Amendment
Applicant’s amendment filed on 06/25/2026 has been entered. Claims 1, 2, 13, 14 and 20 have been amended. No claims have been canceled. No claims have been added. Claims 1 – 20 are still pending in this application, with claims 1,13 and 20 being independent.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 3, 13, 14 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tsun et al. (US 2024/0296293) (“Tsun”).
As to claim 1, Tsun teaches a method of evaluating context-specific content generated by a generative artificial intelligence model (Abstract), comprising: obtaining user data (user input and attribute data which comprises data contained within a user profile including historical activity data) that is specific to a user of a software application (application system software, Fig.2,230, [0033] [0037] [0068 – 0070] [0072] [0073]) (Fig.1, 102, 104, Fig.19, 1905; [0033] [0035] [0037] [0099] [0118] [0119]), the user data indicative of a contextual situation of the user (user input and attribute data including job title, industry, skills, experience, certifications, publications, honors, education and historical activity data which can be used to determine a contextual situation. Contextual situations include a messaging intent and/or a connection relationship between the user and a message recipient., [0037] [0041- 0043] [0083] [0084]); providing an initial prompt (The initial prompt includes a prompt for generating messaging content related to “seeking work”.) to the generative artificial intelligence model (deep learning model, Fig.1, 108; [0055 -0057]) based on the user data ( Fig.1, 106, 160, 162, 164, 166, Fig.19, 1910 and 1915; [0039] [0041 - 0050] [0052] [0079 – 0084] [0086] [0087] [0120 - 0122]), the initial prompt instructing the generative artificial intelligence model to automatically generate initial content that is specific to the contextual situation of the user (The prompt for generating messaging content related to “seeking work” comprises instructions, inputs, plans of action which instruct a generative artificial intelligence model to generate a message. This message is specific to the contextual situation/intent of seeking work. This message is specific to the contextual situation/connection relationship between user and message recipient with regards to tone., [0039] [0041 - 0050] [0052] [0079 – 0084] [0086] [0087] [0120 - 0122]) and example prompt data comprising one or more examples of different contextual situations (The prompt comprises instructions indicating that a deep learning model should use examples to generate content. The examples include examples with based on a selected intent and/or tone., [0039] [0052] [0082 - 0086] [0097] [0102] [0121] [0122]); obtaining the initial content from the generative artificial intelligence model (Fig.1, 114, Fig.14, 1000, 1010, Fig.19, 1925; [0034] [0053] [0123]); generating feedback data on the initial content according to one or more quality metrics (Fig.1, 168, Fig.14, 1405 and Fig.16,1610; [0059] [0061] [0105 – 0107] [0124]) and performing one or more actions based on the feedback data, wherein the one or more actions comprise modifying the example prompt data based on the feedback data (“As explained with reference to FIG. 3, in response to receiving negatively labeled feedback (e.g., feedback 1405 with the thumbs down), content generation system 100 generates an updated prompt through extracting updated attribute data, mapping an updated set of user attributes, generating an updated set of instructions, and/or generating an example.”, [0059] [0061 – 0063] [0083] [0086] [0107]).
As to claims 2 and 14, Tsun further teaches, wherein the one or more actions further comprise: modifying the initial prompt provided to the generative artificial intelligence model based, at least in part, on the feedback data to generate a modified prompt (updated prompt) ([0059] [0061 – 0063] [0066] [0083] [0086] [0105 - 0107]); providing the modified prompt to the generative artificial intelligence model ([0061] [0062] [0066] [0083] [0122]); and obtaining updated content from the generative artificial intelligence model ([0066] [0083] [0123] [0124]), the updated content being improved compared to the initial content according to the one or more quality metrics (User feedback is positive for the updated suggested message., [0105] [0106] [0124]).
As to claim 3, Tsun further teaches wherein modifying the initial prompt includes: adding information to the initial prompt based on the feedback data (A different example, e.g. tone example, is added to the initial prompt., [0059] [0061] [0062] [0086] [0107]); or removing information from the initial prompt based on the feedback data.
As to claim 13, Tsun teaches a system for evaluating context-specific content generated by a generative artificial intelligence model (Abstract), comprising: a memory (Fig.20, 2004; [0125 – 0127]) including computer executable instructions ([0133] [0134]); and a processor (Fig.20, 2002); configured to execute the computer executable instructions and cause the system ([0128]) to: obtain user data (user input and attribute data which comprises data contained within a user profile including historical activity data) that is specific to a user of a software application (application system software, Fig.2,230, [0033] [0037] [0068 – 0070] [0072] [0073]) (Fig.1, 102, 104, Fig.19, 1905; [0033] [0035] [0037] [0099] [0118] [0119]), the user data indicative of a contextual situation of the user (user input and attribute data including job title, industry, skills, experience, certifications, publications, honors, education and historical activity data which can be used to determine a contextual situation. Contextual situations include a messaging intent and/or a connection relationship between the user and a message recipient., [0037] [0041- 0043] [0083] [0084]); provide an initial prompt (The initial prompt includes a prompt for generating messaging content related to “seeking work”.) to the generative artificial intelligence model (deep learning model, Fig.1, 108; [0055 -0057]) based on the user data ( Fig.1, 106, 160, 162, 164, 166, Fig.19, 1910 and 1915; [0039] [0041 - 0050] [0052] [0079 – 0084] [0086] [0087] [0120 - 0122]), the initial prompt instructing the generative artificial intelligence model to automatically generate initial content that is specific to the contextual situation of the user (The prompt for generating messaging content related to “seeking work” comprises instructions, inputs, plans of action which instruct a generative artificial intelligence model to generate a message. This message is specific to the contextual situation/intent of seeking work. This message is specific to the contextual situation/connection relationship between user and message recipient with regards to tone., [0039] [0041 - 0050] [0052] [0079 – 0084] [0086] [0087] [0120 - 0122]) and example prompt data comprising one or more examples of different contextual situations (The prompt comprises instructions indicating that a deep learning model should use examples to generate content. The examples include examples with based on a selected intent and/or tone., [0039] [0052] [0082 - 0086] [0097] [0102] [0121] [0122]); obtaining the initial content from the generative artificial intelligence model (Fig.1, 114, Fig.14, 1000, 1010, Fig.19, 1925; [0034] [0053] [0123]); generate feedback data on the initial content according to one or more quality metrics (Fig.1, 168, Fig.14, 1405 and Fig.16,1610; [0059] [0061] [0105 – 0107] [0124]) and perform one or more actions based on the feedback data, wherein the one or more actions comprise modifying the example prompt data based on the feedback data (“As explained with reference to FIG. 3, in response to receiving negatively labeled feedback (e.g., feedback 1405 with the thumbs down), content generation system 100 generates an updated prompt through extracting updated attribute data, mapping an updated set of user attributes, generating an updated set of instructions, and/or generating an example.”, [0059] [0061 – 0063] [0083] [0086] [0107]).
As to claim 20, Tsun teaches a non-transitory computer-readable medium (Abstract; Fig.20, 2004; [0125 – 0127]) comprising instructions to be executed in a computer system to evaluate context-specific content generated by a generative artificial intelligence model (Abstract; [0133] [0134]), wherein the instructions when executed in the computer system cause the computer system to: obtain user data (user input and attribute data which comprises data contained within a user profile including historical activity data) that is specific to a user of a software application (application system software, Fig.2,230, [0033] [0037] [0068 – 0070] [0072] [0073]) (Fig.1, 102, 104, Fig.19, 1905; [0033] [0035] [0037] [0099] [0118] [0119]), the user data indicative of a contextual situation of the user (user input and attribute data including job title, industry, skills, experience, certifications, publications, honors, education and historical activity data which can be used to determine a contextual situation. Contextual situations include a messaging intent and/or a connection relationship between the user and a message recipient., [0037] [0041- 0043] [0083] [0084]); provide an initial prompt (The initial prompt includes a prompt for generating messaging content related to “seeking work”.) to the generative artificial intelligence model (deep learning model, Fig.1, 108; [0055 -0057]) based on the user data ( Fig.1, 106, 160, 162, 164, 166, Fig.19, 1910 and 1915; [0039] [0041 - 0050] [0052] [0079 – 0084] [0086] [0087] [0120 - 0122]), the initial prompt instructing the generative artificial intelligence model to automatically generate initial content that is specific to the contextual situation of the user (The prompt for generating messaging content related to “seeking work” comprises instructions, inputs, plans of action which instruct a generative artificial intelligence model to generate a message. This message is specific to the contextual situation/intent of seeking work. This message is specific to the contextual situation/connection relationship between user and message recipient with regards to tone., [0039] [0041 - 0050] [0052] [0079 – 0084] [0086] [0087] [0120 - 0122]) and example prompt data comprising one or more examples of different contextual situations (The prompt comprises instructions indicating that a deep learning model should use examples to generate content. The examples include examples with based on a selected intent and/or tone., [0039] [0052] [0082 - 0086] [0097] [0102] [0121] [0122]); obtaining the initial content from the generative artificial intelligence model (Fig.1, 114, Fig.14, 1000, 1010, Fig.19, 1925; [0034] [0053] [0123]); generate feedback data on the initial content according to one or more quality metrics (Fig.1, 168, Fig.14, 1405 and Fig.16,1610; [0059] [0061] [0105 – 0107] [0124]) and perform one or more actions based on the feedback data, wherein the one or more actions comprise modifying the example prompt data based on the feedback data (“As explained with reference to FIG. 3, in response to receiving negatively labeled feedback (e.g., feedback 1405 with the thumbs down), content generation system 100 generates an updated prompt through extracting updated attribute data, mapping an updated set of user attributes, generating an updated set of instructions, and/or generating an example.”, [0059] [0061 – 0063] [0083] [0086] [0107]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4, 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsun et al. (US 2024/0296293) (“Tsun”) and further in view of Leslie et al. (US 2025/0061116) (“Leslie”) and further in view of Pineda et al. (US 2025/0335455) (“Pineda”).
For claims 4 and 15, Tsun further discloses the following, wherein generating the feedback data comprises: providing a user interface displaying at least a portion of the initial prompt (A prompt includes instructions with mapped attribute data, e.g. “In another example, the instructions are “Create a message to [JobPoster] for [JobApplicant] applying to [JobPosition] based on [Experience] and [Education].” In such examples, the bracketed phrases are used as placeholders for user attributes of attribute data 104.” The mapped attribute data, e.g. experience/skills, is displayed, “expertise in developing accessible productivity tools”., Fig.14, 1110; [0039] [0045] [0079] [0098] [0102] [0103] [0105] [0118 – 0122]), the user interface further displaying the initial content generated by the generative artificial intelligence model (The initial content comprises the complete suggested message., Fig.14, 1010; [0105] [0123] [0124]); and receiving feedback data comprising user input with respect to the initial content from an expert (administrator, [0077]) via one or more user interface elements of the user interface (Fig.1, 110, 116, 168, Fig.14, 1405 and Fig.16,1610; [0059] [0061] [0077] [0078] [0105 – 0107] [0124])
Yet, Tsun fails to teach the following: generating a file comprising at least a portion of the user data and the initial content generated by the generative artificial intelligence model; and updating the file with the feedback data.
However, Leslie discloses a system and method for generating natural language responses to user queries (Abstract), comprising the following: a database is generated comprising at least a portion of user data (user information) and content (model’s response) generated by a generative artificial intelligence model ([0043 – 0047] [0125 - 0129] [0132 – 0135] [0150]); and the database is updated with feedback (user ratings) ([0114] [0151 - 0152]).
Additionally, Pineda discloses a system and method for the purpose of managing data using artificial intelligence models (Abstract), wherein data is stored in either a file or a database ([0045]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve Tsun’s invention in the same way that Leslie’s invention has been improved to achieve the following, predictable results for the purpose of effectively assessing the performance of a generative model to improve response generation (Leslie, [0150- 0152]): further generating a database comprising at least a portion of the user data and the initial content generated by the generative artificial intelligence model; and further updating the database with the feedback data.
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Tsun and Leslie in the same way that Pineda’s invention has been improved to achieve the following, predictable results for the purpose of effectively assessing the performance of a generative model to improve response generation (Leslie, [0150- 0152]): the user data, initial content and feedback data are further stored in either a file or database.
For claim 5, Leslie and Pineda further disclose that the file comprises a comma separated value (CSV) file (Leslie, [0150 – 0152])(Pineda, [0160]).
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsun et al. (US 2024/0296293) (“Tsun”) in view of ØHRN et al.(US 2024/0403568) (“ØHRN”).
For claims 6 and 16, Tsun further discloses, wherein the initial content comprises a plurality of responses (one or more outputs) generated by the generative artificial intelligence mode (Fig.14; [0052] [0053] [0104] [0105] [0118 – 0123]) in response to input included in the initial prompt ([0118 – 0123]).
Yet, Tsun fails to teach that the responses are answers, and the input is a question.
However, ØHRN discloses a system and method for automatically generating content for a user (Abstract), comprising the following: a generative artificial intelligence model (Fig.1, 130; [0022]) generates answers (responses) in response to a question (query)( “Draft an email from me to Jennifer Smith about going over this month’s budget” ) included in a prompt (Fig.4A, 412 and 414; [0022 – 0024] [0047]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify Tsun’s teachings with ØHRN’s teachings for the purpose of using an artificial intelligence model to generate content with tones, semantics and syntaxes that are applicable for a desired domain (Tsun, [0030] [0031]), wherein: the instruction in the initial prompt (Tsun, “Create a message to [JobPoster] for [JobApplicant] applying to [JobPosition] based on [Experience] and [Education].”, [0039]) further comprises a question (query) (Tsun, Create a message FirstName1 LastName …). Furthermore, the initial content comprises a plurality of answers generated by the generative artificial intelligence model in response to the question included in the initial prompt.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsun et al. (US 2024/0296293) (“Tsun”) in view of Bodegas Martinez et al. (US 2019/0303610) (“Bodegas”) and further in view of Sharma et al. (US 2022/0129369) (“Sharma”).
For claim 7, Tsun fails to teach, wherein obtaining the user data comprises: obtaining data for the user based, at least in part, on a unique identifier for the user, the data comprising personal information about the user; removing or anonymizing the personal information to generate anonymized data; and generating a test account based on the anonymized data.
However, Bodegas discloses a system and method for on-demand de-identification of data in a computer storage system (Abstract), comprising the following: obtaining data for a user based, at least on part on a unique identifier for the user ([0054] and claim 1), the data comprising personal information about the user (name and phone number, Fig.2A, 115); and removing or anonymizing the personal information to generate anonymized data (Fig.2C, Retention Table; [0054] and claim 1).
Additionally, Sharma discloses a system and method for generating test accounts(Abstract), wherein the test accounts are generated based on anonymized data ([0022 -0025]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve Tsun’s invention in the same way that Bodegas’s invention has been improved to achieve the following, predictable results for the purpose of cost-effectively storing user data in accordance with data privacy laws and requirements (Bodegas, [0002 – 0005]): further obtaining data for the user based, at least in part, on a unique identifier for the user, the data comprising personal information about the user; and removing or anonymizing the personal information to generate anonymized data.
Additionally, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Tsun and Bodegas in the same way that Sharma’s invention has been improved to achieve the following, predictable results for the purpose of improving the accuracy and reliability of the system by further testing the system using user data which satisfies privacy requirements (Sharma, [0001 – 0004]): further generating a test account based on the anonymized data.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable Tsun et al. (US 2024/0296293) (“Tsun”) in view of Bodegas Martinez et al. (US 2019/0303610) (“Bodegas”), and further in view of Sharma et al. (US 2022/0129369) (“Sharma”) and further in view of Ganesan et al. (US 10,698,794) (“Ganesan”).
For claim 8, the combination of Tsun, Bodegas and Sharma fails to teach the following: providing an initial prompt to the generative artificial intelligence model comprises: obtaining credentials associated with the test account; accessing the test account via the credentials to establish a session associated with the test account; in response to establishing the session, obtaining the anonymized data; and generating the initial prompt based, at least in part, on the anonymized data.
However, Ganesan discloses a system and method for servicing application requests (Abstract), comprising the following: obtaining credentials associated with a test account (column 3 lines 32 – 50; column 8 lines 15 – 26; column 12 lines 7 -22); accessing the test account via the credentials to establish a session associated with the test account (column 8 lines 35 – column 9 line 5; column 12 lines 36 - 42); and in response to establish the session, obtaining test data and executing the software application with the test data (column 5 lines 10 -25; column 9 lines 17 – 40; column 12 lines 43 – column 13 line 15).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Tsun, Bodegas and Sharma in the same way that Ganesan’s invention has been improved to achieve the following, predictable results for the purpose of improving the accuracy and reliability of the system by anonymously testing the system (Sharma, [0001 – 0004]) (Ganesan, column 3 lines 40 – 50), wherein providing an initial prompt to the generative artificial intelligence model further comprises: obtaining credentials associated with the test account; accessing the test account via the credentials to establish a session associated with the test account; in response to establishing the session, obtaining test data, e.g. anonymized data; and executing the software application by generating the initial prompt based, at least in part, on the test data, e.g. anonymized data.
Claim(s) 9, 10, 11, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsun et al. (US 2024/0296293) (“Tsun”) in view of Peng et al. (US 2024/0362418) (“Peng”).
For claim 9, Tsun fails to teach, wherein generating the feedback data comprises: providing the initial content and at least a portion of the initial prompt to an additional generative artificial intelligence model trained to determine quality of the initial content.
However, Peng discloses a system and method for interacting with a language model (Abstract), comprising the following: providing initial content and at least a portion of an initial prompt (knowledge information retrieved from a KAC, [0035 – 0037]) to an additional generative artificial intelligence model trained to determine quality of the initial content (A utility system comprises a scoring component that computes a usefulness score for a response generated by a language model. The scoring component uses a machine-trained model to compute the usefulness measure, [0042] [0043] [0091 – 0099]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve Tsun’s invention in the same way that Peng’s invention has been improved to achieve the following, predictable results for the purpose of increasing user satisfaction by providing content which fulfills a user’s expectation, wherein generating the feedback data further comprise automatically generating feedback data (Tsun, Fig.1, 168, Fig.14, 1405 and Fig.16,1610; [0059] [0061] [0105 – 0107] [0124]) by: providing the initial content and at least a portion of the initial prompt to an additional generative artificial intelligence model trained to determine quality of the initial content.
For claims 10 and 17, Tsun fails to teach, wherein generating the feedback data comprises: providing the initial content to an additional generative artificial intelligence model configured to evaluate the initial content; determining whether additional evaluation of the initial content is needed based, at least in part, on a confidence score output by the additional generative artificial intelligence model and associated with the initial content.
However, Peng discloses a system and method for interacting with a language model (Abstract), comprising the following: providing the initial content to an additional generative artificial intelligence model configured to evaluate the initial content (A utility system comprises a scoring component that computes a usefulness score for a response generated by a language model. The scoring component uses a machine-trained model to compute the usefulness measure, [0042] [0043] [0091 – 0099]); and determining whether additional evaluation of the initial content is needed based, at least in part, on a confidence score output by the additional generative artificial intelligence model and associated with the initial content (In an alternative manner of operation, the RAS 104 uses the user interface component 118 to inform the user whenever a response generated by the language model 106 is deemed deficient based on analysis performed by the utility system 138. A response is deemed deficient if a usefulness measure does not satisfy a threshold value, [0043] [0044] [0091 – 0094]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve Tsun’s invention in the same way that Peng’s invention has been improved to achieve the following, predictable results for the purpose of increasing user satisfaction by providing content which fulfills a user’s expectation, wherein generating the feedback data further comprise automatically generating feedback data (Tsun, Fig.1, 168, Fig.14, 1405 and Fig.16,1610; [0059] [0061] [0105 – 0107] [0124]) by: providing the initial content to an additional generative artificial intelligence model configured to evaluate the initial content; determining whether additional evaluation of the initial content is needed based, at least in part, on a confidence score output by the additional generative artificial intelligence model and associated with the initial content.
For claims 11 and 18, Peng further discloses, wherein determining whether additional evaluation of the initial content is needed comprises: determining whether the confidence score output by the additional generative artificial intelligence model exceeds a threshold confidence score (Peng, In an alternative manner of operation, the RAS 104 uses the user interface component 118 to inform the user whenever a response generated by the language model 106 is deemed deficient based on analysis performed by the utility system 138. A response is deemed deficient if a usefulness measure does not satisfy a threshold value, [0043] [0044] [0091 – 0094]); and in response to determining the confidence score does not exceed the threshold confidence score, providing the initial content for additional evaluation (Peng, In an alternative manner of operation, the RAS 104 uses the user interface component 118 to inform the user whenever a response generated by the language model 106 is deemed deficient based on analysis performed by the utility system 138. A response is deemed deficient if a usefulness measure does not satisfy a threshold value, [0043] [0044] [0091 – 0094]).
Claim(s) 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsun et al. (US 2024/0296293) (“Tsun”) in view of Peng et al. (US 2024/0362418) (“Peng”) and further in view of Pineda et al. (US 2025/0335455) (“Pineda”).
For claims 12 and 19, the combination of Tsun and Peng further discloses, wherein providing the initial content for additional evaluation comprises generating a user interface displaying the initial content (Peng, [0043] [0045]), the user interface comprising one or more user interface elements configured to receive input from one or more user’s (Peng, [0032] [0033 [0043] [0045]), the input indicative of the one or more user’s evaluation of the initial content (Peng, user, [0032] [0033 [0043] [0045]).
Yet, the combination of Tsun and Peng fails to teach that the users are experts.
However, Pineda discloses a system and method for the purpose of managing data using artificial intelligence models (Abstract), wherein data is stored in either a file or a database ([0045]). Furthermore, a user of system can be either a novice or expert ([0160]).
Therefore, it would have been obvious to one or ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Tsun and Peng in the same way that Pineda’s invention has been improved to achieve the following, predictable results of the users being either novices or experts for the purpose of increasing user satisfaction by enabling a variety of users to generate content using artificial intelligence model (Tsun, [0030] [0031]).
Response to Arguments
Applicant’s arguments, filed on 06/25/2026, with respect to claim(s) 1 - 20 have been considered but are moot in view of the new ground(s) of rejection.
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.
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/SONIA L GAY/Primary Examiner, Art Unit 2657
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