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 .
DETAILED ACTION
This action is responsive to communications regarding the applicant’s amendments and arguments, filed on 5/19/2026.
Claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
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 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.
Response to Arguments and Amendments
Applicant's arguments filed on 5/19/2026 have been fully considered but they are not persuasive for the following reasons:
Applicant’s main argument is that Manu and Imbruce does not teach the amended claimed features “wherein the one or more top-ranked metadata is synchronized into the database to update the usage history associated with the one or more top-ranked metadata”.
Examiner respectfully disagrees with the above argument.
In response to Applicant’s above argument, it is noted that Manu teaches metadata is synchronized into the database to update the usage history (Fig. 11, par. 0138-0139, storing new metadata in local source, i.e. “[0138] In block 1108, a metadata update communication is received at the search engine from a remote source of metadata. The metadata update communication may be received in response to a request for metadata from the search engine, which may have been sent by the search engine in response to any suitable trigger. Exemplary triggers for the search engine include a lapse of a predetermined amount of time or receiving a search relating to a particular piece of metadata… [0139] In block 1110, the local source of metadata is updated with metadata included in the metadata update communication. Updating the local source of metadata may include replacing previously-stored metadata and/or adding new metadata.”).
Further, Imbruce teaches generating a collection of ranked metadata…selecting one or more top ranked metadata (Fig. 8C, 8D, 16B, par. 0055, 0100-0101, 0125-126, mixed-media modules comprise top ranked metadata based on usage history is provided to user in response to receiving query 810, 1610, i.e. “[0100] ... The user is identified by the system with a corresponding personalized profile retrieved from a profile database that includes any number of user defined settings. For example, the user profile may include user input data regarding their name, hobbies, likes, dislikes, web portal preferences, contact information, friends, purchases, application information, language, posts, tweets, and the like. Other user profile information may include social network connections such as the people a user follows on Twitter or on other social media networks. The user profile may also include information collected while the user is logged into the web portal, but not transparent to the user, such as information related to their usage on web sites, user search history and so forth. Together, this user profile includes data that are actively collected directly from the user, and passively collected based on user activity… [0125] The metadata and the characteristics of the multimedia module may be based on predetermined criteria, such as popularity and/or those determined by an advertiser. The databases, unstructured data and metadata may also be ranked based on a user profile, user search history, user preferences, user customization, social network connections, and advertiser. For example, user search history may indicate that a user frequently visits only a handful of websites, for which metadata parsed from those sites is ranked higher than metadata from other websites. Similarly, the databases may be selected based on a user search history.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Manu with the teaching of Imbruce because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Imbruce would allow Manu to “…providing interactive search results, and search integration… allow for search results of improved nature, such as results that are interactive, expanded, deeper and/or richer as a function of mixed-media components, as well as improved value to all participants and improved user experience…” (Imbruce, par. 0003-0013).
For the above reasons, Examiner believed that rejection of the last Office action was proper and within their broadest reasonable interpretation in light of the specification. See MPEP 2111 [R-1] Interpretation of Claims-Broadest Reasonable Interpretation.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 8-9 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20110225133 to Manu et al. (hereinafter “Manu”), and further in view of U.S. Patent Application Publication No. 20170060860 to Imbruce et al. (hereinafter “Imbruce”.)
As to claim 1, Manu teaches a computer-implemented method performed by one or more computing devices, comprising (computer implemented method in a system comprising processor and non-transitory computer readable device, par. 0006-0010, 0147-0157):
receiving a search query from a user (Fig. 1, par. 0032, input describing search 102, i.e. “[0032] The process 100 of FIG. 1 begins in block 102, in which an entity requests that a search be performed by providing input to a search engine describing a search the entity desires to be performed. The nature of the entity and the nature of the input is not essential. In embodiments, an entity may be any requestor of a search, including users such as human users and software agents, and the input may be any suitable input to a search engine, including text (e.g., keywords) or binary data (e.g., an image file). For ease of description, in examples below an entity may be described as a user or human user and the input may be described as text keywords, though embodiments are not limited in this respect.”);
generating an updated search query by at least providing the search query into a natural language processing (NLP) engine, wherein the NLP engine refines textual information of the search query to generate the updated search query (Fig. 1, par. 0076-0078, using natural language processing to identify topic, context and keywords, to be used to identify an artifact, i.e. “[0076] In some embodiments, the input regarding the search to be performed may be analyzed using natural language processing techniques. Natural language processing techniques are known in the art, and as such will not be discussed in detail herein. In some such embodiments, the natural language processing techniques may be used to identify a topic of a query to be used to identify an artifact, while in other such embodiments the natural language processing techniques may be used to identify an artifact”);
retrieving a collection of metadata from a database by at least querying a search engine with the updated search query, wherein the search engine searches metadata information that meets one or more criteria identified by the updated search query (Fig. 1, par. 0041, 0076-0078, 0089, query metadata, i.e. “[0041] In block 106, the source of metadata may be queried for metadata relating to the artifact. This query may be done in any suitable manner, including according to any of the exemplary techniques described in greater detail below. In one exemplary technique, the artifact may be identified in a query sent to the source of metadata. For example, at least a portion of the input may be included in a query sent to the source of metadata. In block 108, metadata is received from the source of metadata in response to the query of block 106.”);
selecting one or more “[0105] Once the search engine has retrieved the metadata, the search engine may perform the search using the metadata. As discussed above, the metadata may be used in any part of the search. Performing a search may include configuring a search engine to perform a search, searching a set of content, processing results of the searching, presenting results to a consumer of search results, or any other acts related to searching.”), wherein “[0138] In block 1108, a metadata update communication is received at the search engine from a remote source of metadata. The metadata update communication may be received in response to a request for metadata from the search engine, which may have been sent by the search engine in response to any suitable trigger. Exemplary triggers for the search engine include a lapse of a predetermined amount of time or receiving a search relating to a particular piece of metadata… [0139] In block 1110, the local source of metadata is updated with metadata included in the metadata update communication. Updating the local source of metadata may include replacing previously-stored metadata and/or adding new metadata.”).
Manu does not explicitly teach generating a collection of ranked metadata by at least providing, a usage history of a table or an attribute access by the user that is associated with the collection of metadata over a period of time into a ranking engine, wherein the ranking engine ranks the collection of metadata based on the usage history; and selecting one or more top ranked metadata as claimed.
Imbruce teaches generating a collection of ranked metadata by at least providing, associated with the collection of metadata, a usage history of a table or an attribute access by the user over a period of time into a ranking engine, wherein the ranking engine ranks the collection of metadata based on the usage history and; selecting one or more top ranked metadata (Fig. 8C, 8D, 16B, par. 0055, 0100-0101, 0125-126, mixed-media modules comprise top ranked metadata based on usage history is provided to user in response to receiving query 810, 1610, i.e. “[0100] ... The user is identified by the system with a corresponding personalized profile retrieved from a profile database that includes any number of user defined settings. For example, the user profile may include user input data regarding their name, hobbies, likes, dislikes, web portal preferences, contact information, friends, purchases, application information, language, posts, tweets, and the like. Other user profile information may include social network connections such as the people a user follows on Twitter or on other social media networks. The user profile may also include information collected while the user is logged into the web portal, but not transparent to the user, such as information related to their usage on web sites, user search history and so forth. Together, this user profile includes data that are actively collected directly from the user, and passively collected based on user activity… [0125] The metadata and the characteristics of the multimedia module may be based on predetermined criteria, such as popularity and/or those determined by an advertiser. The databases, unstructured data and metadata may also be ranked based on a user profile, user search history, user preferences, user customization, social network connections, and advertiser. For example, user search history may indicate that a user frequently visits only a handful of websites, for which metadata parsed from those sites is ranked higher than metadata from other websites. Similarly, the databases may be selected based on a user search history.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Manu with the teaching of Imbruce because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Imbruce would allow Manu to “…providing interactive search results, and search integration… allow for search results of improved nature, such as results that are interactive, expanded, deeper and/or richer as a function of mixed-media components, as well as improved value to all participants and improved user experience…” (Imbruce, par. 0003-0013).
As to claim 2, the rejection of claim 1 is hereby incorporated by reference, the combination of Manu and Imbruce teaches the method according to claim 1, further comprising storing new metadata into the database, wherein the new metadata is searchable by providing a new search query (Fig. 11, par. 0138-0139, storing new metadata in local source, i.e. “[0138] In block 1108, a metadata update communication is received at the search engine from a remote source of metadata. The metadata update communication may be received in response to a request for metadata from the search engine, which may have been sent by the search engine in response to any suitable trigger. Exemplary triggers for the search engine include a lapse of a predetermined amount of time or receiving a search relating to a particular piece of metadata… [0139] In block 1110, the local source of metadata is updated with metadata included in the metadata update communication. Updating the local source of metadata may include replacing previously-stored metadata and/or adding new metadata.”).
Regarding claim 8, 9, is essentially the same as claim 1, 2, respectively, except that it sets forth the claimed invention as a system rather than a method and rejected for the same reasons as applied hereinabove.
Regarding claim 15, 16, is essentially the same as claim 1, 2, respectively, except that it sets forth the claimed invention as a non-transitory computer-readable storage device rather than a method and rejected for the same reasons as applied hereinabove.
Claim(s) 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20110225133 to Manu et al. (hereinafter “Manu”), U.S. Patent Application Publication No. 20170060860 to Imbruce et al. (hereinafter “Imbruce”), and further in view of U.S. Patent Application Publication No. 20220067102 to Sen et al. (hereinafter “Sen”)
As to claim 3, the rejection of claim 1 is hereby incorporated by reference, the combination of Manu and Imbruce teaches the method according to claim 1. The combination of Manu and Imbruce does not teach wherein the NLP engine comprises performing at least one of a special character removal, a stop words removal, an acronym expansion, a segmentation, or a lemmatization as claimed.
Sen teaches wherein the NLP engine comprises performing at least one of a special character removal, a stop words removal, an acronym expansion, a segmentation, or a lemmatization (Fig. 2, par. 0027, NLP engine performs special character removal, stop words removal, i.e. “[0027]… A natural language processing model may preprocess a spoken query or text query using various methods (described further below in FIG. 4) including, but not limited to, tokenization, stemming, lemmatization, part-of-speech tagging, removing accented characters, expanding contractions, removing special characters, removing stop words, named entity recognition, shallow parsing, and sentence chunking. In some embodiments, natural language processing can assign semantic meaning to words using various methods, for example, word embedding (Word2Vec, GloVe, bag-of-words . . . etc.) and abstract meaning representation” .)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of the combination of Manu and Imbruce with the teaching of Sen because they are in the same field of endeavor in processing natural query language. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Sen would allow the combination of Manu and Imbruce to “…transforming a query into a structured proposal and generate a reasoning-based meaning representation of the structured proposal… preprocess the query placing it in condition to allow a meaning representation to be assigned to the query…” (Sen, par. 0001-0003, 0028).
Regarding claim 10, is essentially the same as claim 3, except that it sets forth the claimed invention as a system rather than a method and rejected for the same reasons as applied hereinabove.
Regarding claim 17, is essentially the same as claim 3, except that it sets forth the claimed invention as a non-transitory computer-readable storage device rather than a method and rejected for the same reasons as applied hereinabove.
Claim(s) 4-6, 11-13, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20110225133 to Manu et al. (hereinafter “Manu”), U.S. Patent Application Publication No. 20170060860 to Imbruce et al. (hereinafter “Imbruce”), and further in view of U.S. Patent Application Publication No. 20210081436 to Kapoor et al. (hereinafter “Kapoor”)
As to claim 4, the rejection of claim 1 is hereby incorporated by reference, the combination of Manu and Imbruce teaches the method according to claim 1. The combination of Manu and Imbruce does not explicitly teach wherein the search engine comprises identifying a type of the updated search query and generating one or more search sequences associated with the updated search query as claimed.
Kapoor teaches wherein the search engine comprises identifying a type of the updated search query and generating one or more search sequences associated with the updated search query (par. 0063-0067, 0076-0079, classify a search query a keyword query or a natural language query and generating search sequences associated with search query such as selectable elements 714, i.e. “[0063] At 620, the computing system classifies the search query is a keyword query or a natural language query. In some embodiments, the computing system evaluates the search query to determine whether to implement the query in a first mode or in a second mode and implements the query in the determined mode… In some embodiments, classification 722 includes a database query generated by server system 110 based on a classification of user search query 122. Query remediation interface 760 also displays selectable elements 714, in the illustrated embodiment, which may include one or more input elements, pop-up elements (such as a drop-down menu or an interface component), etc. A user selection may include, for example, user input to various input fields, a user mouse-over event, and a user selecting a user interface element, or any combination thereof. … For example, selectable elements may include the following SQL query: [0078] SELECT*FROM meetings WHERE First_Name=“Christian” AND Last_Name=“Posse” In this example, server system 110 has generated an SQL query that specifies to select one or more records from a “meetings” table that have a first name field with the value “Christian” and a last name field with the value “Posse.” Further in this example, the user may select one or more portions of the database query to alter the database query (to generate updated database query 734).”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of the combination of Manu and Imbruce with the teaching of Kapoor because they are in the same field of endeavor in processing natural query language. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Kapoor would allow the combination of Manu and Imbruce to “…improve the efficiency of search queries…” while avoid the situation when “…search efficiency may not be optimal for many users in some cases, resulting in repetitive searches in order to find relevant results…” (Kapoor, par. 0003, 0021).
As to claim 5, the rejection of claim 4 is hereby incorporated by reference, the combination of Manu, Imbruce and Kapoor teaches the method according to claim 4, wherein the identifying comprises categorizing a context or the type of the updated search query to direct the search engine, based on the context or the type, to generate the one or more search sequences (Kapoor, par. 0063-0067, 0076-0079, classify a search query a keyword query or a natural language query and generating search sequences associated with search query such as selectable elements 714, i.e. “[0063] At 620, the computing system classifies the search query is a keyword query or a natural language query. In some embodiments, the computing system evaluates the search query to determine whether to implement the query in a first mode or in a second mode and implements the query in the determined mode… In some embodiments, classification 722 includes a database query generated by server system 110 based on a classification of user search query 122. Query remediation interface 760 also displays selectable elements 714, in the illustrated embodiment, which may include one or more input elements, pop-up elements (such as a drop-down menu or an interface component), etc. A user selection may include, for example, user input to various input fields, a user mouse-over event, and a user selecting a user interface element, or any combination thereof. … For example, selectable elements may include the following SQL query: [0078] SELECT*FROM meetings WHERE First_Name=“Christian” AND Last_Name=“Posse” In this example, server system 110 has generated an SQL query that specifies to select one or more records from a “meetings” table that have a first name field with the value “Christian” and a last name field with the value “Posse.” Further in this example, the user may select one or more portions of the database query to alter the database query (to generate updated database query 734).”).
As to claim 6, the rejection of claim 4 is hereby incorporated by reference, the combination of Manu, Imbruce and Kapoor teaches the method according to claim 4, wherein the type of the updated search query comprises a numerical input search query, a specified search query, and a full text search query (Kapoor, par. 0041, 0054, 0063-0067, classify a search query a keyword query or a natural language query. Examiner interprets numerical input search query as keyword query; and a specified search query, and a full text search query as natural language query, i.e. “[0041] As shown, classification engine 142 receives search query 122 and outputs the query to either keyword query handler 370 or natural language query handler 380, thus “classifying” the search. It is contemplated that classification engine 142 may classify a query into one of any number of possible search types in other embodiments. Search query 122 can include not only the search terms themselves, but also metadata about the query, including user identification, time, place, etc. The information in search query 122 is provided to tagging module 362 and context module 366... [0054] Context module 366 in the illustrated embodiment receives the search query 122 with query metadata 402. Note that in some cases, module 366 may receive metadata 402 without the terms of search query 122. As shown, context module 366 includes user information 412, user preferences 414, and a user history 416. User information 412 may include: user account information (e.g., account number, username, password, user ID, age of the account, etc.), an organization of the user, user access-level (e.g., is the user authorized to access certain information in the database), an occupation description, etc… [0063] At 620, the computing system classifies the search query is a keyword query or a natural language query. In some embodiments, the computing system evaluates the search query to determine whether to implement the query in a first mode or in a second mode and implements the query in the determined mode…”.
Regarding claim 11, 12, 13, is essentially the same as claim 4, 5, 6, respectively, except that it sets forth the claimed invention as a system rather than a method and rejected for the same reasons as applied hereinabove.
Regarding claim 18, 19, is essentially the same as claim 4, 5, respectively, except that it sets forth the claimed invention as a non-transitory computer-readable storage device rather than a method and rejected for the same reasons as applied hereinabove.
Claim(s) 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20110225133 to Manu et al. (hereinafter “Manu”), U.S. Patent Application Publication No. 20170060860 to Imbruce et al. (hereinafter “Imbruce”), and further in view of U.S. Patent Application Publication No. 20100042610 to Lakhani et al. (hereinafter “Lakhani”)
As to claim 7, the rejection of claim 1 is hereby incorporated by reference, the combination of Manu and Imbruce teaches the method according to claim 1. The combination of Manu and Imbruce does not explicitly teach wherein the generating the collection of ranked metadata comprises: computing one or more scores associated with the collection of metadata, wherein a score is computed from the usage history of at least the table or the attribute access by the user within a period of time; ranking the element of the collection of metadata based on a ranked version of the one or more scores; and clustering the one or more scores into one or more priority categories as claimed.
Lakhani teaches wherein the generating the collection of ranked metadata comprises: computing one or more scores associated with the collection of metadata (par. 0041-0042, computing metadata score such as metadata popularity score per category, i.e. “[0041] In further embodiments, the popularity of a metadata value is not necessarily absolute across documents. It is often the case that the value is conditioned on a secondary value on the document. By way of example, the popularity of Microsoft as an employer is different depending on the category of the job. Microsoft may be popular for Engineering jobs, but may not be so popular for Human Resources jobs. For instance, if the metadata popularity score is a numerical value, the employer popularity for Microsoft if the job category of a document is engineering is 100, whereas employer popularity for Microsoft if the job category of document is human resources is 20.[0042] As a result, an optional processing step is included in some embodiments to calculate the popularity of the key metadata attribute based on the occurrence of secondary metadata values. This is referred to herein as the conditional metadata popularity. This step can include any number of secondary values to consider when conditioning the key metadata popularity. In embodiments, the secondary value is determined manually based on analysis of the document domain. By way of example, the metadata popularity may be determined for a document having key metadata=X, given the occurrence of secondary metadata=Y, to reflect the probability that a user is interested in X given the occurrence of secondary value Y. This is represented as P(X|Y). Based on Bayes theorem, P(X|Y) is proportional to P(X)*P(Y|X)=Normalized Frequency of X in Queries*Percentage of documents with X that have Y”), wherein a score is computed from the usage history of at least the table or the attribute access by the user within a period of time (par. 0005, 0015, 0019-0020, 0022, 0031-0033, 0039-0042, usage history such as frequency of metadata in query logs of users, i.e. ” [0005] Embodiments of the present invention relate to ordering search results for search queries based on popularity of metadata from documents. Generally, key metadata is identified from a document and the popularity of the metadata is determined. Metadata popularity may be identified using a variety of sources, but in some embodiment, the metadata popularity for a document is determined by comparing extracted metadata from the document to query logs to identify the frequency with which the extracted metadata appears in the query logs. In such embodiments, the frequency of metadata in query logs is used as an indicator of the popularity of that metadata to users. In some embodiments, metadata popularity for documents is used to order search results for user search queries. Accordingly, documents containing popular metadata will be ranked higher than documents having less popular metadata.”); ranking the element of the collection of metadata based on a ranked version of the one or more scores (par. 0039, ranking metadata, i.e. “[0039] ... The frequency of metadata amongst the search queries in the query logs is used to generated a metadata popularity value. Accordingly, the metadata popularity value for a given metadata may be a value that represents the frequency of that metadata in the search queries or may be ranking based on comparison with other metadata in the same domain.”); and clustering the one or more scores into one or more priority categories (par. 0032, 0041-0045, computing metadata score such as metadata popularity score per category, or a priority category such as “employment domain”, i.e. “[0032] In some embodiments, all or a substantial portion of the search queries from the query logs 206 are analyzed to determine the popularity of metadata. In other embodiments, the user search queries are classified and the system uses only those search queries that correspond with a classification matching the document classification for a document from which the metadata was extracted. For instance, if popularity is being determined for metadata from a source document classified within the employment domain, the system may identify search queries intended for the employment domain and use only those search queries to identify popularity for the metadata. ..[0041] In further embodiments, the popularity of a metadata value is not necessarily absolute across documents. It is often the case that the value is conditioned on a secondary value on the document. By way of example, the popularity of Microsoft as an employer is different depending on the category of the job. Microsoft may be popular for Engineering jobs, but may not be so popular for Human Resources jobs. For instance, if the metadata popularity score is a numerical value, the employer popularity for Microsoft if the job category of a document is engineering is 100, whereas employer popularity for Microsoft if the job category of document is human resources is 20).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of the combination of Manu and Imbruce with the teaching of Lakhani because they are in the same field of endeavor in processing natural query language. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Lakhani would allow the combination of Manu and Imbruce to improve “…users' expectations that the results at the top of the result list are the most relevant to their search, such that the users do not need to sift through the search result list to find the desired information or document…” (Lakhani, par. 0001-0003).
Regarding claim 14, is essentially the same as claim 7, except that it sets forth the claimed invention as a system rather than a method and rejected for the same reasons as applied hereinabove.
Regarding claim 20, is essentially the same as claim 7, except that it sets forth the claimed invention as a non-transitory computer-readable storage device rather than a method and rejected for the same reasons as applied hereinabove.
Conclusion
Applicants’ 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANHTAI V TRAN whose telephone number is (571)270-5129. The examiner can normally be reached on Monday through Thursday from 8:00 AM to 4:00 PM.
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/ANHTAI V TRAN/Primary Examiner, Art Unit 2168
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