DETAILED ACTION
This is responsive to the amendment filed 20 February 2026.
Claims 1, 4-11 and 14-20 are currently pending and considered below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 20 February 2026 have been fully considered but they are not persuasive.
Regarding the 35 USC 101 rejections, Applicant argues:
Embodiments of the invention transform source code into machine-generated natural language functional summaries that can be searched based on code stories. Thus, there is a distinction between a story and a summary.
This improves code search and codebase management technologies. This improves the ability to recall and use code across heterogeneous version control systems (which each have at least one code repository, provides a functional-level search, which is distinct from vector-based search. This practical application of code retrieval and code duplication reduction is not an abstract idea, but is a technological improvement.
The Examiner respectfully disagrees. The claimed judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – a “non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations” (claim 11) which are recited at a high-level of generality (i.e., as generic processors performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using a generic computer components.
The claims also recite the additional elements “providing the code in the one or more codebase repositories whose natural language summarizations match the one or more extracted natural language outputs to the user”. The claims do not impose any limits on how the code is provided, i.e., the claims fail to recite details of how a solution to a problem is accomplished. These limitations therefore represent extra-solution activity because they are mere nominal or tangential addition to the claims. Accordingly, the additional 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 therefore directed to an abstract idea.
Applicant further argues:
Claim 1 cannot be performed mentally at least because code in code repositories is physically split into functional modules that are then summarized. New data (the summaries and stores) are generated in embodiments of the invention. The code is indexed across multiple code repositories.
Even if isolated steps may resemble mental acts, for argument only, the combination as a whole includes automated code parsing, transformer-based summarization, and indexed retrieval across distributed code repositories-operations not practically performable in the human mind.
Notwithstanding the traversal, claim 1 is amended to require that the code is split into functional modules and that summaries are generated for each of the modules. Further, claim 1 compares functional structures (stories to summaries) , which is distinct from directly comparing a user query to existing code. More specifically, summaries are searched using natural language outputs of the user stores.
However, a human may perform a search of natural language summaries however those summaries were generated. Further, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Applicant also argues:
A natural language search of summaries in identifying code is not only distinct from the cited references, but is a a distinct method that is substantially more than the abstract idea at least because of the need to generate functional descriptions of the stories, split the code into functional modules, generate summaries of the functional modules, and perform the search. In addition the summaries are generated from functional modules of code from multiple heterogeneous version control systems. Claim 1 natural language enables semantic comparisons rather than vector representations.
However, a person may access one or more natural language user stories that have been generated by a user and that describe at least functionality for code located in one or more codebase repositories stored in multiple version control systems (e.g. a human may examine user generated natural language documents that describe code functionality); extract one or more natural language outputs from the one or more natural language user stories that are related to the functionality (e.g. a human may pull out words, terms, phrases, sentences, paragraphs etc. from the documents); perform a search of natural language summarizations that have been generated for the code located in the one or more code repositories, wherein the natural language summarizations are generated by splitting the code into function based modules, wherein each of the natural language summarizations are associated with a function based modules (e.g. a human may search natural language summaries generated for the code); and in response to finding one or more natural language summarizations that match the one or more extracted natural language outputs … (e.g. a human may determine a match between one of the on the summaries and the terms, phrases, sentences, paragraphs etc.).
Applicant finally argues:
Claim 1 is directed to patent-eligible subject matter based on the holdings of Enfish and McRo. For example, Enfish identifies benefits such as faster searching, increased flexibility, and improves the way computers store and retrieve data. Claim 1 improves the way code is stored. searched, and retrieved. By using stories and summaries, code can be searched from a functional perspective, which is distinct from a vector comparison. Further, claim 1 also improves code development by improving the ability to identify or find similar code.
Like McRo, claim 1 also provides a specific path that improves the technology of reducing development work duplication. New searchable data structures that are derived from source code are generated and improve work development technology. These transformations also weigh in favor of patent-eligible subject matter.
Thus, claim 1 is both directed to a practical application and sets forth substantially more than the abstract idea. For at least these reasons, Applicant respectfully requests that the rejection under § 101 be withdrawn.
However, the additional elements are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications (see Applicant’s specification [0076] and [0083]). Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Therefore, unlike McRo and Enfish they neither improve the way computers work nor improve a particular technology.
Regarding the 35 USC 102 rejections, Applicant argues:
In Bahrami, a user may perform a natural language search by submitting a query. The natural language search or query of the use, however, is not a story of corresponding code. In claim 1, the stories may be user generated, but they describe the functionality of existing code. The stories, further are not queries in the same way as Bahrami.
In Bahrami, the initial search is not based on a functional description of any existing code. Thus, Bahrami is attempting to find source code that is responsive to a user's natural language query. Claim 1, in contrast, is performed in the context of identifying or reducing duplicative work. Even if there are some similarities, the method of claim 1 as a whole is distinct from Bahrami.
For example, claim 1 recites "accessing one or more natural language user stories that have been generated by a user and that describe at least functionality for code located in one or more codebase repositories". The natural language search query 140 of Bahrani, in contrast, is generated by the user and does not represent the functionality of existing code in the "one or more codebase repositories".
Stated differently, the search of claim 1 is driven by existing user stories - not arbitrary user queries. Further, the search in claim 1 is not on the code direction, but on the summarizations. Thus, the search is a natural language search, which is distinct from a vector based search.
The Examiner respectfully disagrees. Bahrami explicitly discloses accessing one or more natural language user stories that have been generated by a user and that describe at least functionality for code (e.g. plotting a line) located in one or more codebase repositories (“The natural language search query 140 may be any suitable search query related to performing tasks that utilize source code to implement. For example, the natural language search query 140 may include the phrase “plot a line.” In this example embodiment, the natural language search query 140 may represent the user 130 searching for source code that allows the user to plot a line”, [0021], see also [0034]). Therefore Bahrami’s queries read on the claimed stories.
Applicant further argues:
Further, Bahrami's teachings do not solve the work duplication problem. Assume, for example, that the codebase of Bahrami includes duplicative code. The search of Bahrami will simply return the code that is closest to the query. Claim 1, in contrast, helps identify duplicative code and can prevent duplicative work.
In claim 1, natural language stories are accessed and natural language outputs are extracted. These natural language outputs thus represent the functionality of existing code. The search in claim 1 is then performed on natural language summarizations of the code.
In contrast, Bahrami is comparing a vector of a user's query to vectors of code. Claim 1, on the other hand, is comparing natural language outputs to code summaries.
The code identified in claim 1 may be similar to the code associated with the user stories. The code identified in Bahrami, in contrast, is responsive to a user query - not existing code or functionality.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., solve the work duplication problem) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Also as put forth above, Bahrami’s queries read on the claimed stories.
Applicant also argues:
Bahrami does not split the training code into modules. Even if paragraphs 26 and 44 indicate that features are extracted and that the features may include code snippets, there is no disclosure of generating a summary for each of these features. Rather, the features are mapped to vectors and compared to vectors of the query.
Thus, Bahrami does not disclose stories that are generated by a user and that describe functionality for code. Bahrami does not extract natural language output from these stories that is used to search of natural language summarizations of the functional modules of the code. Bahrami simply maps a user query to the code using vectors. Bahrami searches code vectors directly - not natural language summaries of the code.
However, Bahrami explicitly discloses wherein the natural language summarizations are generated by splitting the code into function based modules, wherein each of the natural language summarizations are associated with a function based modules (“The extracted features of the training code may include code snippets that perform distinct functions. For example, training code that performs the task of “sending an SMS when a user receives an email with the title of X” may include a snippet of code that perform the distinct function of sending an SMS, a code snippet that performs the distinct function of verifying a user has received an email, and a code snippet that performs the distinct function of verifying the email has a title of X”, [0026], see also “code summaries may be predicted and generated based on the extracted features”, [0044]).
Therefore all of Applicant’s arguments have been addressed and they are unpersuasive.
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, 4-11 and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Further, the judicial exception is not integrated into a practical application.
In claims 1 and 11, the limitations accessing one or more natural language user stories that have been generated by a user and that describe at least functionality for code located in one or more codebase repositories stored in multiple version control systems; extracting one or more natural language outputs from the one or more natural language user stories that are related to the functionality; performing a search of natural language summarizations that have been generated for the code located in the one or more code repositories, wherein the natural language summarizations are generated by splitting the code into function based modules, wherein each of the natural language summarizations are associated with a function based modules; and in response to finding one or more natural language summarizations that match the one or more extracted natural language outputs,
That is, other than reciting a “non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations” (claim 11) nothing in the claims precludes the steps from practically being performed in the mind. For example, a person may access one or more natural language user stories that have been generated by a user and that describe at least functionality for code located in one or more codebase repositories stored in multiple version control systems (e.g. a human may examine user generated natural language documents that describe code functionality); extract one or more natural language outputs from the one or more natural language user stories that are related to the functionality (e.g. a human may pull out words, terms, phrases, sentences, paragraphs etc. from the documents); perform a search of natural language summarizations that have been generated for the code located in the one or more code repositories, wherein the natural language summarizations are generated by splitting the code into function based modules, wherein each of the natural language summarizations are associated with a function based modules (e.g. a human may search natural language summaries generated for the code); and in response to finding one or more natural language summarizations that match the one or more extracted natural language outputs … (e.g. a human may determine a match between one of the on the summaries and the terms, phrases, sentences, paragraphs etc.).
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. In particular, the claims recite the additional elements – a “non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations” (claim 11) which are recited at a high-level of generality (i.e., as generic processors performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using a generic computer components.
The claims also recite the additional elements “providing the code in the one or more codebase repositories whose natural language summarizations match the one or more extracted natural language outputs to the user”. The claims do not impose any limits on how the code is provided, i.e., the claims fail to recite details of how a solution to a problem is accomplished. These limitations therefore represent extra-solution activity because they are mere nominal or tangential addition to the claims. Accordingly, the additional 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 therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As stated above, the claims recite the additional limitations of a “non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations” (claim 11). However, these are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications (see Applicant’s specification [0076] and [0083]). Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
The claims also recite “providing the code in the one or more codebase repositories whose natural language summarizations match the one or more extracted natural language outputs to the user”. The claims do not impose any limits on how the code is provided, i.e., the claims fail to recite details of how a solution to a problem is accomplished. These limitations represent the extra-solution activity of outputting data which is well-understood, routine and conventional activity. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
The dependent claims recite:
wherein providing the code in the one or more codebase repositories to the user comprises providing a link to the code in the one or more codebase repositories;
wherein providing the code in the one or more codebase repositories to the user comprises providing actual code located in one or more codebase repositories to the user;
providing additional information to the user when providing the code located in one or more codebase repositories to the user, the additional information including one or more of information specifying how the user is to be given access to the code and contact information for an author of the code;
wherein natural language outputs include keywords related to the functionality, an intent of the code located in one or more codebase repositories, and entities and objects in the code located in one or more codebase repositories;
wherein the one or more natural language outputs are extracted from the one or more user stories using one or more machine learning natural language processing models;
wherein the one or more natural language summarizations are generated using one or more machine learning transformer models;
in response to not finding one or more natural language summarizations that match the one or more extracted natural language outputs: providing notification to the user; or taking no action.
The additional recited limitations further narrow the steps of the independent claims without however providing “a practical application of” or "significantly more than" the underlying “Mental Processes” abstract idea. Therefore, the dependent claims are also not patent eligible.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1, 5, 7-8, 11, 15 and 17-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bahrami et al. (US 2022/0138240).
Claim 1:
Bahrami discloses a method, comprising:
accessing one or more natural language user stories that have been generated by a user and that describe at least functionality for code located in one or more codebase repositories (“The natural language search query 140 may be any suitable search query related to performing tasks that utilize source code to implement. For example, the natural language search query 140 may include the phrase “plot a line.” In this example embodiment, the natural language search query 140 may represent the user 130 searching for source code that allows the user to plot a line”, [0021], see also [0034]);
extracting one or more natural language outputs from the one or more natural language user stories that are related to the functionality (“a natural language search query of “plot a line” may be tokenized into the three individual words “plot,” “a,” and “line.””, [0029], see also “the natural language search query 140 may include the phrase “send SMS to my phone when I receive an email with the title of X.” In this example embodiment, the natural language search query 140 may include a first natural language search section relating to “send SMS” and a second natural language search section relating to “when I receive an email with the title of X.””, [0021]);
performing a search of natural language summarizations (code summaries) that have been generated for the code located in the one or more code repositories (“the natural language search vector may be compared to the natural language code vectors. The comparison between the natural language search vector and the natural language code vectors may determine a degree of similarity between the natural language search vector and the natural language code vectors”, [0030], see also “the natural language search query may be mapped to a natural language search vector”, [0029] and “natural language code vectors may be mapped based on the predicted and generated code summaries”, [0045]), wherein the natural language summarizations are generated by splitting the code into function based modules, wherein each of the natural language summarizations are associated with a function based modules (“The extracted features of the training code may include code snippets that perform distinct functions. For example, training code that performs the task of “sending an SMS when a user receives an email with the title of X” may include a snippet of code that perform the distinct function of sending an SMS, a code snippet that performs the distinct function of verifying a user has received an email, and a code snippet that performs the distinct function of verifying the email has a title of X”, [0026], see also “code summaries may be predicted and generated based on the extracted features”, [0044]); and
in response to finding one or more natural language summarizations that match the one or more extracted natural language outputs, providing the code in the one or more codebase repositories whose natural language summarizations match the one or more extracted natural language outputs to the user (“suggested source code responsive to the natural language search query may be returned based on the comparison between the natural language search vector and the natural language code vectors”, [0031], see also [0054]-[0055]).
Claim 5:
Bahrami discloses the method of claim 1, wherein providing the code in the one or more codebase repositories to the user comprises providing actual code located in one or more codebase repositories to the user ([0031]).
Claim 7:
Bahrami discloses the method of claim 1, wherein natural language outputs include keywords (send SMS) related to the functionality, an intent of the code (send SMS to my phone when I receive an email with the title of X) located in one or more codebase repositories, and entities (send, receive) and objects (SMS, email) in the code located in one or more codebase repositories (“the natural language search query 140 may include the phrase “send SMS to my phone when I receive an email with the title of X.” In this example embodiment, the natural language search query 140 may include a first natural language search section relating to “send SMS” and a second natural language search section relating to “when I receive an email with the title of X.””, [0021]).
Claim 8:
Bahrami discloses the method of claim 1, wherein the one or more natural language outputs are extracted from the one or more user stories using one or more machine learning natural language processing models ([0046]).
Claims 11, 15 and 17-18:
Bahrami discloses a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors ([0058], see also [0060]) to perform operations comprising the steps of process claims 1, 5 and 7-8 as shown above.
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.
Claims 4, 10, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bahrami et al. (US 2022/0138240) in view of Rush et al. (US 2010/0106705).
Claim 4:
Bahrami discloses the method of claim 1, but does not explicitly disclose wherein providing the code in the one or more codebase repositories to the user comprises providing a link to the code in the one or more codebase repositories.
In an analogous code searching system similarly providing code in one or more repositories to a user, Rush discloses wherein providing the code in the one or more codebase repositories to the user comprises providing a link to the code in the one or more codebase repositories (“Search notification icon 270 indicates that a potential match has been found for code currently being developed in window 110 … Clicking on icon 270 leads to a display of software code which were found by the search”, [0089]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of providing a link to Bahrami’s code in order to avoid crowding the user’s screen with the full code unless the user specifically chooses by clicking the link (see Rush, [0089]).
Claim 10:
Bahrami discloses the method of claim 1, but does not explicitly disclose: in response to not finding one or more natural language summarizations that match the one or more extracted natural language outputs: providing notification to the user; or taking no action.
In an analogous code searching system similarly providing code in one or more repositories to a user, Rush discloses in response to not finding one or more code that match a search request: providing notification to the user; or taking no action (“The search query is issued, and at module 530, the search request is received and executed. This may involve various search algorithms and database queries to find matches of varying quality. At module 535, the number of matches received is calculated and passed back to a client issuing the search query. At module 540, a determination is made as to how many results were found. If no results were found, the search is ignored at module 545 (presumably returning to module 510 to await detection of another change)”, [0095]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of: in response to not finding one or more natural language summarizations that match Bahrami’s one or more extracted natural language outputs: providing notification to the user; or taking no action in order to avoid presenting to the user results that are not useful (see Rush, [0095]).
Claims 14 and 20:
Bahrami in view of Rush discloses a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors (Bahrami, [0058], see also [0060]) to perform operations comprising the steps of process claims 4 and 10 as shown above.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bahrami et al. (US 2022/0138240) in view of Ramachandra (US 2021/0357209).
Claim 6:
Bahrami discloses the method of claim 1, but does not explicitly disclose providing additional information to the user when providing the code located in one or more codebase repositories to the user, the additional information including one or more of information specifying how the user is to be given access to the code and contact information for an author of the code.
In an analogous code searching system similarly providing code in one or more repositories to a user, Ramachandra discloses providing additional information to the user when providing the code located in one or more codebase repositories to the user, the additional information including one or more of information specifying how the user is to be given access to the code (“A lock icon 520 may also be depicted to show whether the source code is editable and/or accessible by the user. If not, the user may need to request permission from the authorized individual, project leader, or administrator”, [0060]) and contact information for an author of the code (“Each developer who has worked on the particular source code file can be identified”, [0063], see also “the user may contact a developer or team leader for the source code to receive enhanced access rights like illustrated in FIG. 6. Direct links for direct contact with code maintainers or developers can be used”, [0078] and “Another benefit is easy identification by additional metadata such as labels, source code summaries, contact information for developers and team leaders, etc.”, [0079]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of providing additional information to the user when providing Bahrami’s code to the user, the additional information including one or more of information specifying how the user is to be given access to the code and contact information for an author of the code in order to give code access only to authorized users and to allow easy communication between users and code developers (see Ramachandra, [0063] and [0078]).
Claim 16:
Bahrami in view of Ramachandra discloses a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors (Bahrami, [0058], see also [0060]) to perform operations comprising the steps of process claim 6 as shown above.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bahrami et al. (US 2022/0138240) in view of Chandel et al. (US 2025/0068665).
Claim 9:
Bahrami discloses the method of claim 1, wherein the one or more natural language summarizations are generated using one or more machine learning models ([0044]).
Bahrami does not explicitly disclose that the machine learning models are transformer models.
In an analogous code searching system similarly providing code in one or more repositories to a user, Chandel uses transformer models as machine learning models (“A large language model (LLM) is a type of machine learning model trained on a massively-large training dataset of text and/or source code resulting in the model containing billions of parameters. The large language model is used to perform various tasks such as natural language processing, text generation, machine translation, and source code generation. The large language model is based on deep learning neural networks such as a neural transformer model with attention”, [0001]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of using transformer models as Bahrami’s machine learning models because transformers have the advantage of having no recurrent units, therefore requiring less training time than earlier deep learning models.
Claim 19:
Bahrami in view of Chandel discloses a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors (Bahrami, [0058], see also [0060]) to perform operations comprising the steps of process claim 9 as shown above.
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 SAMUEL G NEWAY whose telephone number is (571)270-1058. The examiner can normally be reached Monday-Friday 9:00am-5:00pm EST.
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/SAMUEL G NEWAY/Primary Examiner, Art Unit 2657