DETAILED ACTION
This communication is in response to the Applicant Arguments/Remarks filed 7/9/2025. Claims 1, 6, 7-8, 13-15, 20 and 22-27 are pending in the application.
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 7/9/2025 have been fully considered. Regarding the arguments in relating to the amended limitation “displaying a recommendation window below the search bar including a first group of the two or more existing groups and a second group of the two or more existing groups, wherein the recommendation window displays the first group before the second group based on the first group having a first label with a highest text similarity to the search phrase, wherein the recommendation window displays a first ranked list of web pages associated with the first group and a second ranked list of web pages associated with the second group” and “within the first ranked list of web pages, wherein the plurality of weighted scores include at least a first weighted scored based on a relevance to the first group; and updating the recommendation window such that the first ranked list of web pages associated with the first group includes the first web page”, please see the new combination of references with newly cited columns and lines.
Regarding the Applicant’s Representative on pages 12-15 that Ghoshal does not relate to "existing groups". Accordingly, Ghoshal cannot be said to teach "a recommendation window below the search bar" that "displays the first group before the second group based on the first group having a first label with a highest text similarity to the search phrase." Further, Ghoshal also cannot be said to teach "wherein the recommendation window displays a first ranked list of web pages associated with the first group and a second ranked list of web pages associated with the second group", please see the newly combination of references for the amended/underlined limitations above.
It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. "The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain." In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claims above for the convenience of the Applicants. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claims, typically other passages and figures will apply as well.
Specification, para. 27: a determination can be made whether the search phrase (e.g., one or more words used to execute a search) is substantially similar to any existing groups; para. 36 discloses: one or more word embedding techniques (e.g., word2vec, tf-idf, etc.) can be used to generate numerical data for each word, and a similarity function (e.g., based on cosine similarity, Euclidean distance, Jaccard distance, etc.) can be used to compare the generated numerical data (e.g., vectors).
Ghoshal teaches at para. 179: a user may input one or more search terms into a search engine query interface, the recommendation system may recommend one or more relevant content items (e.g., web pages, documents, images, etc.) that most closely match the input search terms relating to the user; para. 203-204: if the user input corresponded to a search query input by a user via a webpage displayed by a browser executed by the user device, the information regarding the recommendations may also be output to the user via that webpage or additional webpages displayed by the browser.
Ray teaches at fig. 4: your search history by category displayed under the search bar; fig. 5-7: detect a query being submitted by a user at a first UI display, assign one or more categories to the query via a categorization process, generate a mapping between the identified categories and the query; para. 23: generally, the categories from which the query categorizer targets the selected category are generated from a user's previous queries. These previous queries may be included within a dynamically updatable search history associated with the user. The search history, as will be discussed more fully below, is stored in data store(s) such that the queries are accessible on demand from other components within the system architecture. During access, the queries may be located by linking them to a user ID and inspecting the data store(s) with the user ID of the user; para. 42: the search engine that interfaces with the web index to retrieve the search results that are responsive to the query, the editing component, the query categorizer, the rendering component, the user-history component that interfaces with data store(s), and, potentially, a taxonomy of predefined/existing categories; para. 50: matching categories to queries that are previously submitted by the user; para. 55-57: the query categorizer is also capable of accepting manual (explicit) input from the user to select a category and establish a relationship between the selected category and a particular query; para. 61: the parameter governing relevance may instruct the rendering component to rank categories that are mapped to newer queries higher than those categories that are mapped to older queries, where the high-ranking categories may be surfaced on the UI display first and in a more prominent position. Thus, the combination of references does teach the argued limitations.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made
Claim(s) 1, 6, 8, 13, 15, 20-27 are rejected under 35 U.S.C. 103 as being unpatentable over Ghoshal (US 20200125575) in view of Ray et al. (US 20130006914) and further in view of Chaman (US 20060047643) and Patil et al. (US 20170017861).
Specification, para. 27: a determination can be made whether the search phrase (e.g., one or more words used to execute a search) is substantially similar to any existing groups; para. 36 discloses: one or more word embedding techniques (e.g., word2vec, tf-idf, etc.) can be used to generate numerical data for each word, and a similarity function (e.g., based on cosine similarity, Euclidean distance, Jaccard distance, etc.) can be used to compare the generated numerical data (e.g., vectors); para. 53: search sessions of users. Figs. 1-2 disclose browser search history management application 160, browser search history manager 205, smart history data store 250 etc.
As per claims 1, 8, 15, Ghoshal (US 20200125575) teaches
a method comprising: detecting a search initiated by a user within an internet browsing application using a search phrase, wherein the search phrase is input into a search bar (para. 51: requests may be
received through a browser client – see fig. 3: browser client, item 303, catalog services, item 326; para. 55, 179: a user may input one or more search terms into a search engine query interface, the recommendation system
may recommend one or more relevant content items (e.g., web pages, documents, images, etc.) that most closely match the input search terms; para. 181, 200: the user input can be a document (e.g., a webpage) accessed by a user. (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. As yet another example, the user content could be search terms/phase input by a user (e.g., a browser-based search engine) for performing a search);
determining a text similarity between the search phrase and a label of each of two or more existing groups, each of the two or more existing groups including two or more web pages and designated by each respective label (para. 41: similar components and/or features may have the same reference label; para. 93, 96: the concept vectors for all the words in the text document can be combined to form a weighted concept vector for the text document. The content recommendation engine then may measure the similarity between each word concept vector and the text concept vector. All words above a certain threshold then may be selected as the "keywords" for the document; para. 179: a user may input one or more search terms into a search engine query interface, the recommendation system may recommend one or more relevant content items (e.g., web pages, documents, images, etc.) that most closely match the input search terms relating to the user; para. 203-204: if the user input corresponded to a search query input by a user via a webpage displayed by a browser executed by the user device, the information regarding the recommendations may also be output to the user via that webpage or additional webpages displayed by the browser; para. 212),
displaying a recommendation window below the search bar including a first group of the two or more existing groups and a second group of the two or more existing groups, wherein the recommendation window displays the first group before the second group based on the first group having a first label with a highest text similarity to the search phrase, wherein the recommendation window displays a first ranked list of web pages associated with the first group and a second ranked list of web pages associated with the second group (fig. 38: find/search bar with similar results/recommendations display below the search bar; fig. 42: a ranked list of content items is sent to the client display by the recommendation selector; para. 179: recommend specific content (content items) in response to input from a client or user, the process of evaluating and ranking the content items relative to each other plays an important role in the overall recommendation. For example, a user may input one or more search terms into a search engine query interface, the recommendation system may recommend one or more relevant content items (e.g.,
web pages, documents, images, etc.) that most closely match the input search terms; para. 240: recommend specific content (content items) in response to input from a client or user, the process of evaluating and ranking the content items relative to each other plays an important role in the overall recommendation. For example, a user may input one or more search terms into a search engine query interface, the recommendation system may recommend one or more relevant content items (e.g., web pages, documents, images, etc.) that most closely match the input search terms);
obtaining, after the search within the internet browsing application using the search phrase is executed, a first web page returned from the search browsed by the user; and adding the first web page to the first group (para. 56-58: analyze text and/or visual input, extract keywords or topics from the input, classify and tag the input content, and store the classified/tagged content in one or more content repositories… ( d) converting the original content ( e.g., input text and/or images) into vectors within a multi-dimensional vector space, ( e) comparing such vectors to a plurality of other content vectors, each of which represents additional content in a content repository, in order to find and identify various potentially-relevant additional content related to the original content input authored by the user/author, and finally (f) retrieve and present the identified additional content to the author via the smart digital content recommendation tool; para. 66, 243: if the user input corresponded to a search query input by a user via a webpage displayed by a browser executed by the user device, the information regarding the recommendations may also be output to the user via a webpage showing results of the search or additional webpages displayed by the browser; fig. 43: group the matching content items into groups/buckets based on the tag count scores calculated for the content items.)
within the first ranked list of web pages (para. 57, 179: a user may input one or more search terms into a search engine query interface, the recommendation system may recommend one or more relevant content items (e.g., web pages, documents, images, etc.) that most closely match the input search terms; para. 215: the resultant values allow content items to be ranked within a particular group or bucket);
updating the recommendation window such that the first ranked list of web pages associated with the first group includes the first web page (para. 210: continuously (or periodically) analyze the new text input received from the user (e.g., content input by the user and to re-initiate the process of FIG. 43 in response to text updates, so that the content item recommendations may be continuously or periodically updated in real time; para. 215: the resultant values allow content items to be ranked within a particular group or bucket; para. 243: if the user input corresponded to a search query input by a user via a webpage displayed by a browser executed by the user device, the information regarding the recommendations may also be output to the user via a webpage showing results of the search or additional web pages displayed by the browser).
Ghoshal does not explicitly teach web pages historically browsed by the user, wherein respective ones of the two or more existing groups are selectable graphical user interface (GUI) elements displayed within the internet browsing application.
Ray et al. teaches
web pages historically browsed by the user (fig. 2: user-history component, data store log; figs. 4-5: search history by category),
wherein respective ones of the two or more existing groups are selectable graphical user interface (GUI) elements displayed within the internet browsing application (fig. 4: your search history by category displayed under the search bar; fig. 5-7: detect a query being submitted by a user at a first UI display, assign one or more categories to the query via a categorization process, generate a mapping between the identified categories and the query; para. 23: generally, the categories from which the query categorizer targets the selected category are generated from a user's previous queries. These previous queries may be included within a dynamically updatable search history associated with the user. The search history, as will be discussed more fully below, is stored in data store(s) such that the queries are accessible on demand from other components within the system architecture. During access, the queries may be located by linking them to a user ID and inspecting the data store(s) with the user ID of the user; para. 42: the search engine that interfaces with the web index to retrieve the search results that are responsive to the query, the editing component, the query categorizer, the rendering component, the user-history component that interfaces with data store(s), and, potentially, a taxonomy of predefined/existing categories; para. 50: matching categories to queries that are previously submitted by the user; para. 55-57: the query categorizer is also capable of accepting manual (explicit) input from the user to select a category and establish a relationship between the selected category and a particular query; para. 61: the parameter governing relevance may instruct the rendering component to rank categories that are mapped to newer queries higher than those categories that are mapped to older queries, where the high-ranking categories may be surfaced on the UI display first and in a more prominent position);
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ghoshal and the display of matching existing search groups of Ray et al. in order to effectively allow users to better view, interact, and/or analyze the available search results.
Ghoshal and Ray do not teach ranking the first web page with respect to the first ranked list of web pages within the first group, wherein the ranking is based on a plurality of weighted scores associated with respective ones of the ranked web pages.
Chaman teaches
ranking the first web page with respect to the first ranked list of web pages within the first group, wherein the ranking is based on a plurality of weighted scores associated with respective ones of the ranked web pages (para. 15: pages are ranked based on the "F-Rank", which is a ranking algorithm that takes into account link analysis, importance, time-based usage, and relevance of the page. A weighted average of these various scoring components is computed, giving pages that have been recently accessed a higher weight. As time goes by the pages lose their score unless visited by the user or other users--this ensures that important pages that people see on the Web are kept fresh in the index. As the F-Rank of a page is computed multiple times every hour, the user gets the most relevant, important, popular and recent results matching their search query; para. 65: rank a list of relevant documents returned for the search based on the popularity and perceived usefulness of the document by other users or by the user's own browsing habits--such as number of visits made to that page, time spent on that page, recency of visit, etc.; para. 81)
within the first ranked list of web pages, wherein the plurality of weighted scores include at least a first weighted scored based on a relevance to the first group (para. 15: pages are ranked based on the "F-Rank", which is a ranking algorithm that takes into account link analysis, importance, time-based usage, and relevance of the page. A weighted average of these various scoring components is computed, giving pages that have been recently accessed a higher weight. As time goes by the pages lose their score unless visited by the user or other users-this ensures that important pages that people see on the Web are kept fresh in the index. As the F-Rank of a page is computed multiple times every hour, the user gets the most relevant, important, popular and recent results matching their search query; para. 21: an entity index which keeps track of all documents relevant to the user and a document tracker which collects information via access logs and data feeds including documents visited, client/user identifiers, date, time and links within the documents visited, to compute a score for relevance; para. 31: the data is ranked according to link analysis, importance, time-based usage and relevance of the page. This ranking is computed multiple times per hour; para. 84).
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ghoshal, Ray and ranking web page of Chaman in order to allow the user to search pages that are most frequently accessed and offer up-to-date, useful information.
Even if Ghoshal, Ray, Chaman do not explicitly teach displaying a recommendation window below the search bar step,
Patil teaches said step at para. 133-136: the user interface includes a portion that displays the content player. The content player presents the first content to the user of the user-computing device. The user interface comprises a search bar that enables the user to input the search query. Further, the user interface includes a portion that used to display recommendation to the user on the user-computing device. The one or more features include a feature F1/category 1 and feature F2/category 2. Under each of the one or more features displayed in the portion, the set of second content is displayed. As discussed, the content in the set of second content has same/similar features as that of the feature under which it is being displayed in the portion. For example, the content C1/web page is displayed under the feature F1 has the feature F1.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ghoshal, Ray, Chaman and the displaying a recommendation window below the search bar of Patil in order to display to the user useful recommendations based on each feature/group.
As per claims 6, 13, 20 Ghoshal teaches
detecting a second search initiated by the user within the internet browsing application using a second search phrase; recommending the selected existing group to the user based on a second text similarity between the second search phrase and the first label of the selected existing group (para. 58: interactions between client device and content recommendation engine may be Internet-based web browsing sessions, or client-server application sessions, during which users access may input original authored content via the client device, and receive content recommendations from content recommendation engine in the form of additional content that is retrieved from the content repository and linked or embedded into the content authoring user interface at the client device – See para. 82, 209: search phrases; para. 94: text classification, classes/categories).
Ray et al. also teaches at para. 23: generally, the categories from which the query categorizer targets the selected category are generated from a user's previous queries. These previous queries may be included within a dynamically updatable search history associated with the user. The search history, as will be discussed more fully below, is stored in data store(s) such that the queries are accessible on demand from other components within the system architecture. During access, the queries may be located by linking them to a user ID and inspecting the data store(s) with the user ID of the user; para. 50: matching categories to queries that are previously submitted by the user; para. 55-57: automatically categorizing the users search history, or recent portion thereof, provides substantial benefits in relocating a search result without the inherent inefficiencies of manually saving information at the search engine; figs. 2-4: display search history in categories
As per claim 22, Ghoshal teaches
detecting a second search initiated by the user within the internet browsing application using a second search phrase; determining a second text similarity between the second search phrase and the label of each of the two or more existing groups (fig. 11: search by tag: waitress; fig. 33: search by topics to identify potentially related topics on a topic matching process (FIG. 34). Finally, in step 3107 (FIG. 35), the articles identified as being potentially related to the newly-created article may be transmitted back (in whole, or just links) to be embedded within the user interface; para. 96: measure the similarity between each word concept vector and the text concept vector. All words above a certain threshold then may be selected as the "keywords" for the document; para. 122);
determining that text within the second search phrase does not match any labels of the two or more existing groups based on the second text similarity; generating, in response to determining that the text of the second search phrase does not match any labels of the two or more existing groups, a new group wherein any browsed web pages returned from the second search using the second search phrase are added to the new group, the new group designated by a new label, the new label having text associated with the text of the second search phrase (para. 93: fig. 33: search by topics to identify potentially related topics on a topic matching process (FIG. 34). Finally, in step 3107 (FIG. 35), the articles identified as being potentially related to the newly-created article may be transmitted back (in whole, or just links) to be embedded within the user interface; para. 210: continuously (or periodically) analyze the new text input received from the user (e.g., content input by the user in 4411 and or 4412) and to re-initiate the process of FIG. 43 in response to text updates, so that the content item recommendations may be continuously or periodically updated in real time; para. 66-67: each time a user within the operating organization of the system 400 imports or creates new content such as an image or article, a software component may retrieve the content back to the content management and classification system, for various processing and analysis described below (e.g., image processing, keyword extraction, topic analysis, etc.; para. 77, 194: when the retrieved content items do not include the tags information, and/or when the content recommendation system is configured to determine additional tags for the content items, then the content tagger may be used to update or generate new tags for the retrieved content items).
Ray teaches at para. 55-57: the search engine 225 may detect that the user 210 has input a search string at the search box 310 of a homepage 280 during a search session and, upon detection, automatically adds the search string to the search history of the log.
As per claim 23, Ghoshal teaches
wherein determining that the text within the second search phrase does not match any labels of the two or more existing groups based on the second text similarity includes: comparing the second text similarity to a text similarity threshold; and determining that the second text similarity does not satisfy the text similarity threshold (para. 77: extracting features and/or tags/labels from the content in real-time (or near real-time) during the user's authoring session, vectorizing the authored content (also in real-time or near real-time), and comparing the vector of the original authored content to one or more existing vector spaces in order to identify and retrieve related/associated content from one or more available content repositories; para. 95: discard insignificant associations between words and concepts, by removing those concepts whose weights for a given word are below a certain threshold).
As per claim 24, Ghoshal teaches
wherein determining the text similarity between the search phrase and the label of each of the two or more existing groups comprises: generating, using a word embedding technique, numerical data for each word of the search phrase and each word of the label of each of the two or more existing groups; comparing the numerical data between each word of the search phrase and each word of the label of each of the two or more existing groups using a similarity function; and applying a decision function to determine the text similarity based on the comparing (fig. 4: content retrieval/embedding; para. 96-100: measure the similarity between each word concept vector and the text concept vector. All words above a certain threshold then may be selected as the "keywords" for the document; para. 122, 179: a user may input one or more search terms into a search engine query interface, the recommendation system may recommend one or more relevant content items (e.g., web pages, documents, images, etc.) that most closely match the input search terms relating to the user; para. 195: applying a decision tree model).
As per claims 25-27, Ghoshal and Ray does not teach said claims.
Chaman teaches
wherein the plurality of weighted scores associated with respective ones of the ranked web pages further includes a second weighted score based on time spent browsing, and a third weighted score based on a number of times being opened (para. 15: pages are ranked based on the "F-Rank", which is a ranking algorithm that takes into account link analysis, importance, time-based usage, and relevance of the page. A weighted average of these various scoring components is computed, giving pages that have been recently accessed a higher weight. As time goes by the pages lose their score unless visited by the user or other users--this ensures that important pages that people see on the Web are kept fresh in the index. As the F-Rank of a page is computed multiple times every hour, the user gets the most relevant, important, popular and recent results matching their search query; para. 65: rank a list of relevant documents returned for the search based on the popularity and perceived usefulness of the document by other users or by the user's own browsing habits--such as number of visits made to that page, time spent on that page, recency of visit, etc.)
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ghoshal, Ray and ranking web page of Chaman in order to effectively provide users relevant query results for viewing and/or analyze the available search results.
Claim(s) 7, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ghoshal (US 20200125575) in view of Ray et al. (US 20130006914) and further in view of Chaman (US 20060047643), Patil et al. (US 20170017861) and Dolan (US 20160063993).
As per claims 7, 14, Ghoshal teaches
relabeling the first group based on text extracted from the first web page wherein the first label of the first group is replaced with a second label based on the relabeling (para. 71-72: a new content item being stored in the content repository, and/or to a modification to an item in the content repository; the content item retrieved may be parsed/analyzed/etc. in order to extract a set of item features or characteristics. The type of parsing, processing, feature extraction, and/or analysis performed may depend on the type of the content item; para. 210: continuously (or periodically) analyze the new text input received from the user (e.g., content input by the user in 4411 and or 4412) and to re-initiate the process of FIG. 43 in response to text updates, so that the content item recommendations may be continuously or periodically updated in real time).
Ray teaches at para. 66: the user is enabled to rename or delete individual specific-category links upon selecting the controls 440 and 450, respectively
Ghoshal, Ray, Chaman, Patel do not explicitly teach relabeling.
Dolan teaches at para. 96: the Facet Recommender uses Brown clustering to replace each word in sentiment-bearing segments with a cluster label from Brown clusters trained on existing review data. In the case of restaurant conversational topics, such many clusters tend to correspond to restaurant facets such as desserts, alcoholic drinks, locations, etc., thereby again helping to increases the effectiveness of the training data.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ghoshal, Ray, Chaman, Patel and Dolan in order to effectively update tags or labels of existing groups/clusters based on the input text content in order to provide the most relevant/desired search results to the users.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kenthapadi et al. (US 20200372304) teaches at para. 33: data that is inputted into machine learning models 114 may include, but is not limited to, profile updates, profile views, connections, endorsements, invitations, follows, posts, comments, likes, shares, searches, clicks, conversions, messages, interactions with groups, job applications, job views, job searches, interaction between job seekers and recruiters, address book interactions, responses to recommendations, purchases, and/or other implicit or explicit feedback from the users. In turn, machine learning models 114 may generate output that includes scores (e.g., connection strength scores, reputation scores, seniority scores, relevance scores, etc.), classifications (e.g., classifying users as job seekers or employed in certain roles), recommendations (e.g., content recommendations, job recommendations, skill recommendations, connection recommendations, etc.).
Ramer (US 20120215602) teaches at para. 246: ranking content/webpages can be based on a number of metrics; para. 127-128: content viewing history, a history of clicks and click-throughs; para. 369, 454: a mobile content rank may include any and all rankings of aspects of the mobile content. The rankings of aspects of the content may be combined in a variety of ways including adding the rankings to generate a mobile content rank total. Each aspect may be weighted such that all aspects may not contribute equally to the total rank.
Wang (US 20100005069) teaches at para. 106: when the "customize" menu item 1718 is activated by the user clicking thereon, the categorized browsing customization user interface element 1730 may be shown. The user may pick categorized browsing accesses to be used with categorized browsing.
Dai (US 20200265218) teaches at para. 83: any relabeled feature vectors moved to cluster groups will be added to a special high confidence cluster within the cluster group. Koch et al. (US 20200279191) teaches at para. 11-12: receive a historical dataset comprising customer activity information characterizing one or more historical browsing sessions generated by the first customer at the first customer device.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH BLACK whose telephone number is (571)272-4106. The examiner can normally be reached 9AM-5PM EST M-F.
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/LINH BLACK/Examiner, Art Unit 2163 10/13/2025
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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