DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Disclosure Statement filed 26 February 2024 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP §609 because there are no dates of publication provided for the non-patent literature. All non-patent literature must include at least a month and year of publication to be considered with an Information Disclosure Statement. See 37 CFR 1.98(b)(5) and MPEP 609.04(a) I. It cannot be determined if this non-patent literature is prior art because a date of publication cannot be easily obtained. Applicants are requested to resubmit this Information Disclosure with dates of publication for the non-patent literature so as to satisfy their duty of disclosure. This non-patent literature is now lined through to indicate that it cannot be considered without information of a publication date.
Applicants are advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing elements will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP §609.05(a).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 16 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 16 depends upon independent claim 15 but includes limitations that are already set forth by the independent claim so claim 16 fails to further limit independent claim 15. Specifically, claim 16 sets forth limitations of “the different categories include search logs each of which records searches conducted using different terms, content covering at least one topic consistent with what the term candidate represents, and engagement data with respect to the content”, which are already set forth by independent claim 15. Applicants have canceled these corresponding limitations from dependent claims 2 and 9, but have not provided the corresponding cancelations to dependent claim 16.
Applicants may amend the claim to place the claim in proper dependent form or present a sufficient showing that the dependent claim complies with the statutory requirements.
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 1 to 5, 8 to 12, and 15 to 18 are rejected under 35 U.S.C. 103 as being unpatentable over Paulik et al. (U.S. Patent Publication 2016/0078860) in view of Yong et al. (U.S. Patent Publication 2018/0091468).
Concerning independent claims 1, 8, and 15, Paulik et al. discloses a method, apparatus, and computer program product for discovering trending terms, comprising:
“obtaining a list of multiple term candidates and features, wherein the list defines a range of terms to be selected representing trending content” – candidate terms, e.g., words, phrases, etc., can be identified based on trending usage as candidate terms are found in speech traffic; notifications can be generated that identify the candidate terms, provide relevant usage statistics, and identify the context in which the terms are used (Abstract; ¶[0015]); candidate terms can be identified based on frequency of occurrence in an electronic data source; frequency of occurrence of these terms can be determined, and candidate terms can be selected based on the determined frequency; terms having a highest frequency of occurrence in an electronic data source can be selected as a candidate term (¶[0040]: Figure 3); a list of candidate terms can be compiled from external data sources that may be relevant to a speech recognizer (“obtaining a list of multiple term candidates for discovering trending terms”) (¶[0059]: Figure 5); here, usage statistics of frequency of occurrence and context are “features”;
“updating the list by, with respect to each of the multiple term candidates” – trending terms can be detected quickly to handle requests associated with new trending terms; statistics can be leveraged for updating a variety of speech driven services to better support speech requests that include these trending terms (¶[0016]); a frequency count can be generated for various recent time periods (e.g., instances in the last hour, instances in the last day, instances in the last week, etc.) (¶[0049]); identified candidate terms can be added to a vocabulary associated with an ASR system (¶[0056]); candidate term spotter 520 can provide a periodically updated list of candidate terms (e.g., trending terms) (“updating the list, with respect to each of the multiple term candidates”); a list of candidate terms can be compiled from external data sources that may be relevant to a speech recognizer (¶[0059]: Figure 5);
“obtaining information associated with the term candidate in different categories from different sources, wherein the information satisfies a recency requirement defined based on a time period” – candidate terms can be identified based on trending usage in a variety of electronic data sources, e.g., social network feeds, news sources, search queries, etc. (“different categories from different sources”) (Abstract; ¶[0015]); a variety of electronic data sources can be used to identify candidate terms including a social media feed of messages or posts on a social networking site, a news source including news websites, news feeds, and email messages, search histories including Internet searches, computer searches, and terms used in online store searches (“obtaining information associated with the term candidate in different categories from different sources”) (¶[0041]); a frequency count can be generated for various recent time periods, e.g., instances in the last hour, instances in the last day, instances in the last week, etc. (“wherein the information satisfies a recency requirement defined based on a time period”) (¶[0049]); speech traffic archiver 524 can store recent speech traffic from within a certain time period, e.g., within the last week, within the last day, etc., and can store a variety of additional information including user group identification, geographic location, etc. (¶[0060]: Figure 5); Compare claims 2, 9, and 16 which define ‘categories’ to include search logs and content;
“and the different categories include search logs each of which records searches conducted using different terms, content covering at least one topic consistent with what the term candidate represents[, and engagement data with respect to the content]” – candidate terms (e.g., words, phrases, etc.) can be identified based on trending usage in a variety of electronic data sources (e.g., social network feeds, news sources, search queries, etc.) (Abstract); candidate terms with increased popularity can be identified based on trending usage in a variety of electronic data sources (e.g., social network feeds, news sources, search queries, etc.) (¶[0015]); general assistance and the user experience can be improved by making a virtual assistant more knowledgeable about trending terms, topics and phrases (¶[0016]); a variety of electronic data sources can be used to identify candidate terms including search histories of Internet searches, computer searches, and terms used in online store searches (“search logs each of which records searches using different terms”) (¶[0041]); a service associated with a social network can provide terms or topics that may be appearing frequently in social media posts, messages, and the like (“content covering at least one topic consistent with what the term candidate represents”) (¶[0042]);
“linking the term candidate and the information associated therewith to generate a data group for the term candidate” – frequency of occurrence of these terms can be determined; a term having a highest frequency of occurrence in an electronic data source can be selected as a candidate term (¶[0040]: Figure 3); a notification of a candidate term can include a variety of contextual information associated with instances of a candidate term; statistics can be provided along with the candidate term (¶[0053]); identified terms and statistics can be used to personalize systems for different user groups having similar characteristics (¶[0057]); analyzer 526 can compile relevant statistics with regard to candidate terms including counts for various recent time periods, correlations of trending terms with a locality and user group (¶[0062]: Figure 5); here, frequency of occurrence, relevant statistics, contextual information, and a user group are “the information associated therewith to generate a data group” ‘linked’ to “the candidate term”; that is, a candidate term is associated (‘linked’) to various information to generate “a data group”;
“determining dynamic features related to the term candidates based on the data group for the term candidate” – a frequency count can be generated for various recent time periods (e.g., instances in the last hour, instances in the last day, instances in the last week, etc.) (¶[0049]); analyzer 526 can compile relevant statistics with regard to candidate terms discovered in speech traffic by spoken term detector 522; statistics can include frequency counts for various recent time periods (¶[0062]: Figure 5); here, usage statistics of frequency counts are “features” that are for recent time periods so these usage statistics of frequency counts are “dynamic features related to the term candidates”;
“replacing the features associated with the term candidates with the dynamic features” – candidate term spotter 520 can provide a periodically updated list of candidate terms (e.g., trending terms); a list of candidate terms can be compiled from external data sources that may be relevant to a speech recognizer (¶[0059]: Figure 5); statistics can include frequency counts for various recent time periods; information derived by analyzer 526 can be used in downstream processing for a variety of purposes, such as adding candidate terms to vocabularies and language models of speech recognizers and virtual assistants or training virtual assistants to respond to queries related to candidate terms (¶[0062]: Figure 5); here, if usage statistics (“features”) are determined periodically to update a list of candidate terms, then, these statistics are replaced with new statistics (“replacing the features associated with term candidates with dynamic features”); that is, some trending terms are added and some are removed if a list of candidate terms is updated;
“selecting, based on the updated list, trending terms from the multiple candidates [based on a trendiness score] of the multiple term candidates” – a candidate term can be identified based on a frequency of occurrence in an electronic data source (¶[0040]: Figure 3: Step 302); trending terms can be identified based on whether they have a relatively low language model probability compared to their trending use (¶[0042]); terms can be identified based on overlapping usage in multiple sources; relevant trending terms can be identified from social network feeds 410, news sources 412, and other data sources 414 (¶[0047]: Figure 4).
Concerning independent claims 1, 8, and 15, Paulik et al. discloses all of the limitations with the exception of obtaining information associated with trending term candidates of different categories including “engagement data with respect to the content” and “computing a trendiness score for the term candidate based on the dynamic features in accordance with a scoring model”. Arguably, Paulik et al. discloses “computing a trendiness score for the term candidate” because a term candidate is determined to be trending based on a frequency of occurrence in recent searches, so that a frequency of occurrence could be construed as “a trendiness score”.
Concerning independent claims 1, 8, and 15, Yong et al. teaches indicating trending topics on social networks. (Abstract) Trending topics may be topics that have increased popularity on an online social network within a threshold amount of time. If posts and user interaction with a particular topic exceed a threshold amount or frequency, social-networking system 160 may identify the particular topic as a trending topic. If posts and user interaction exceed a threshold number within a threshold period of time, social-networking system 160 may identify a trending topic. An increase in proportion may be set by social-networking system 160 as any suitable increase. A criteria for a topic to become a trending topic may be a combination of an absolute number of posts and user interaction and an increase in proportion. (¶[0055]: Figure 1) Social-networking system 160 may rank the trending topics. Social-networking system 160 may rank the references 511 according to one or more ranking algorithms that take into account various factors, including user click-through data, the number of people talking about the particular trending topic referenced by reference 511, and the recency of the trending topic. (¶[0065]: Figure 1) Social-networking system 160 may rank trending topics that appear in a trending module based on one or more engagement metrics. Engagement metrics may comprise user click-through data, likes, shares, or comments. Social-networking system 160 may consider metrics that are related to user engagement including click-through data, likes, shares, or comments (“the different categories include . . . engagement data with respect to the content”). (¶[0066]: Figure 1) Here, ranking trending topics by a criteria that takes into account factors of click-through data, number of people talking about a trending topic, and a recency of the trending topic is “a trendiness score . . . in accordance with a scoring model” (“computing a trendiness score for the term candidate based on the dynamic features in accordance with a scoring model”). Yong et al., then, teaches the limitations directed to determining candidate terms for trending content from “engagement data with respect to the content” and selecting trending terms by “computing a trendiness score for the term candidates based on the dynamic features in accordance with a scoring model”. An objective is to identify a trending topic on an online social network. (¶[0051]: Figure 1) It would have been obvious to one having ordinary skill in the art to determine trending terms based on engagement data and a scoring model as taught by Yong et al. to discover trending terms in Paulik et al. for a purpose of identifying trending topics on a social network.
Concerning claims 2 and 9, Yong et al. teaches that trending topics can be determined by interactions with advertisements (“wherein the content comprises . . . advertisements”) (¶[0023]: Figure 1); a user may like an article about a brand of shoes (¶[0028]: Figure 1); social-networking system 160 may compare n-grams to a list of topics that are currently trending along with any articles, posts, or content objects that are already associated with a trending topic (¶[0053]: Figure 1); information related to a news event may include an article about a news event which may be used as a basis for identifying posts that match the trending topic (“wherein the content comprises articles”) (¶[0059]: Figure 1); content may include advertisements (¶[0071]: Figure 1).
Concerning claims 3, 10, and 16, Yong et al. teaches that social-networking system 160 may rank trending topics that appear in a trending module based on one or more engagement metrics. Engagement metrics may comprise user click-through data (“click through rate (CTR)”), likes, shares, or comments. Social-networking system 160 may consider metrics that are related to user engagement including click-through data, likes, shares, or comments (“wherein the engagement data includes one or more of: click-through rate (CTR)”). (¶[0066]: Figure 1)
Concerning claims 4, 11, and 17, Paulik et al. discloses that candidate terms can be identified based on trending usage in a variety of electronic data sources, e.g., social network feeds, news sources, search queries, etc. (Abstract; ¶[0015]); a variety of electronic data sources can be used to identify candidate terms including a social media feed of messages or posts on a social networking site (“semi-private sources including at least one of a social media platform, a membership based interest group, and a chatroom”), a news source including news websites, news feeds, and email messages (“private sources including at least one of electronic mails and communication messages”), search histories including Internet searches, computer searches, and terms used in online store searches (“public online sources including at least one of a content portal, a search engine, and a website”) (¶[0041]).
Concerning claims 5, 12, and 18, Paulik et al. discloses that a trending notification is generated based on a variety of statistics associated with a candidate term (¶[0049]); analyzer 326 can compile relevant statistics with regard to candidate terms including frequency counts for various recent time period (“wherein the features related to the term candidates includes at least one of: . . . one or more statistics computed based on the information included in the data group of the term candidate”) (¶[0062}: Figure 5).
Claims 6, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Paulik et al. (U.S. Patent Publication 2016/0078860) in view of Yong et al. (U.S. Patent Publication 2018/0091468) as applied to claims 1, 8, and 15, above, and further in view of Pi et al. (U.S. Patent No. 12,299,703).
Concerning claims 6, 13, and 19, Paulik et al. discloses ‘metrics’ of “a first metric representing the number of searches conducted during the time period using a term candidate” and “a second metric representing the number of articles in the content relating to the term candidate that are available during the time period” because identification of trending terms can be based on overlapping term usage in multiple sources including search histories 414 and news sources 412. (¶[0047]: Figure 4) A count can be generated for various recent time periods, e.g., in the last hour, the last day, the last week, etc. (“during the time period”). (¶[0049]) That is, a frequency of occurrence in a specific time period of terms in search histories and news sources represent “a first metric” and “a second metric”. Additionally, Paulik et al. discloses determining a frequency of occurrence of terms in electronic data sources (“a third metric characterizing the number of occurrences of the term candidate in the articles”) (¶[0040]: Figure 2). Paulik et al. does not disclose “a fourth metric on the number of clicks on content items included in the content” or “a fifth metric indicative of engagement with each content item in the content related to the term candidate accessed during the time period.” However, Pi et al. teaches providing trending product recommendations for data metrics associated with various products that may be analyzed to identify trending products. Specifically, click-through-rate (CTR) and conversion-rate (CVR) may be analyzed and keyphrases extracted from information associated with these trending products. (Abstract) Shopper engagement is reflected in CTR and CVE. (Column 2, Lines 21 to 42) Pi et al., then, teaches “a fourth metric on the number of clicks on content items included in the content” by a click-through-rate (CTR) and “a fifth metric indicative of engagement with each content item in the content related to the term candidate accessed during the time period” because a conversion rate reflects shopper engagement. An objective is to enable users interested in viewing trending products within a certain category of products and online shopping platforms with an indication of trending products and reasons the products are trending. (Column 1, Lines 6 to 15) It would have been obvious to one having ordinary skill in the art to determine trending terms in Paulik et al. according to engagement data of click through rate and conversion rate as taught by Pi et al. for a purpose of presenting an indication of trending products to users of online shopping platforms.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Paulik et al. (U.S. Patent Publication 2016/0078860) in view of Yong et al. (U.S. Patent Publication 2018/0091468) as applied to claims 1, 8, and 15, above, and further in view of Hendrickson et al. (U.S. Patent Publication 2016/0359993).
Concerning claims 7, 14, and 20, Yong et al. teaches all of these limitations with the exception of “wherein data groups associated with the determined trending terms are labeled as positive data to generate labeled training data for training the scoring model via supervised learning.” Specifically, Yong et al. teaches that trending topics may be topics that have increased popularity on an online social network within a threshold amount of time. If posts and user interaction with a particular topic exceed a threshold amount or frequency, social-networking system 160 may identify the particular topic as a trending topic. If posts and user interaction exceed a threshold number within a threshold period of time, social-networking system 160 may identify a trending topic. An increase in proportion may be set by social-networking system 160 as any suitable increase. A criteria for a topic to become a trending topic may be a combination of an absolute number of posts, user interaction, and an increase in proportion (“the scoring model specifies a function of the features related to the term candidates”). (¶[0055]: Figure 1) Social-networking system 160 may rank trending topics that appear in a trending module based on one or more engagement metrics. (¶[0066]: Figure 1) Social-networking system 160 may rank the trending topics that appear in a trending module. The social-networking system 160 may rank references 511 according to one or more ranking algorithms that take into account various factors, including user click-through data, the number of people talking about the particular trending topic referenced by reference 511, and recency of the trending topic (“the step of selecting trending terms from the multiple term candidates comprises: ranking the multiple term candidates to generate a ranked list of term candidates according to the respective trendiness scores of the multiple term candidates, and determining the trending terms from the ranked list based on the ranks of the multiple term candidates”).
Concerning claims 7, 14, and 20, Hendrickson et al. teaches that trend module 224 determines a trending score 314 for a feature vector as a ‘function’ of particular countable parameters (“the scoring model specifies a function of the features related to the term candidate”) (¶[0125]: Figure 11); instances of social data share a countable parameter; for each of a number of bins, occurrences of one or more of the countable parameters are counted in each instance of social data assigned to that bin; based at least in part on the trend detection model and on the count for each bin, a measure of a trend associated with the countable parameters are counted; a measure of the trend is determined to satisfy a trend threshold and, responsive to determining that the measure of the trend satisfies the trend threshold, at least one indication of the detected trend is output (¶[0003]); content provider interface may send a content provider a set of top 10 trending hashtags or top 10 trending counts tracked by trend analysis module 116 (¶[0111]: Figure 11); using the trending score, trend analytics module 116 may determine whether a trending score satisfies a threshold; if the trending score satisfies the threshold, trend analytics module 116 notifies content provider system 124 (“the step of selecting trending terms from the multiple term candidates comprises: ranking the multiple term candidates to generate a ranked list of term candidates according to the respective trendiness scores of the multiple term candidates” and “determining the trending terms from the ranked list based on the ranks of the multiple term candidates”) (¶[0125]: Figure 11); broadly, ranking in a ranked list is provided by determining a score and thresholding by a score to produce a ‘list’ of top-10 trends; that is, higher scores corresponds to higher ranking in a list, and terms that satisfy a threshold are “determining the trending terms from the ranked list”; trend analysis module 116 has access to historical data that is labeled with truth (trend or no-trend) (“wherein data groups associated with the determined trending terms are labeled as positive data to generate labeled training data”) (¶[0056]: Figure 1); trend analytics module 116 may use supervised and/or reinforcement learning to train a model that generates a trending score (¶[0116]: Figure 11); learning module 222 trains trend model 224 based on a training set of metrics and corresponding trending scores (“to generate labeled training data for training the scoring model via supervised learning”) (¶[0123]: Figure 11). An objective is to quantify changes in social data to improve time-to-detection, precision, and recall of real trends. (¶[0027]) It would have been obvious to one having ordinary skill in the art to select trending terms from candidate terms in Paulik et al. to train a scoring model with labeled training data via supervised learning as taught by Hendrickson et al. for a purpose of quantifying changes in social data to improve time-to-detection, precision, and recall of trends.
Response to Arguments
Applicants’ arguments filed 02 March 2026 have been considered but are moot in view of new grounds of rejection as necessitated by amendment.
Applicants provide various claim amendments and present arguments traversing the prior rejection of the independent claims as being obvious under 35 U.S.C. §103 over Paulik et al. (U.S. Patent Publication 2016/0078860) in view of Hendrickson et al. (U.S. Patent Publication 2016/0359993). Specifically, Applicants incorporate some of the limitations of claims 2, 9, and 16 into the independent claims along with some additional amendments. Then Applicants add some features to claims 2 and 9 directed to content being articles and advertisements. Applicants argue that Paulik et al. and Hendrickson et al. fail to disclose or teach obtaining a list of term candidates and updating the list by dynamically updating the trendiness score of each term candidate, and then selecting trending terms based on the updated list.
Applicants’ amendments overcome the objections to the drawings, the title, and the Specification. Applicants’ Replacement Sheet for Figure 2A and the Substitute Specification are being approved.
Applicants’ Information Disclosure Statement filed on 26 February 2024 remains informal due to an absence of publication dates for non-patent literature. This informality continues to be noted in the event that Applicants may wish to provide a corrected Information Disclosure Statement to accompany a filing of a Request for Continued Examination.
Applicants’ amendments to independent claim 15 necessitates new grounds of rejection for claim 16 under 35 U.S.C. §112(d). Here, claim 16 now repeats some of the limitations of independent claim 15, as amended, so that claim 16 fails to further limit independent claim 15 under 35 U.S.C. §112(d).
New grounds of rejection are set forth as directed to independent claims 1, 8, and 15 as being obvious over Paulik et al. (U.S. Patent Publication 2016/0078860) in view of Yong et al. (U.S. Patent Publication 2018/0091468). Here, Yong et al. is cited to address the claim amendments of different categories including “engagement data with respect to the content”. The rejection of some of the dependent claims continues to rely upon Pi et al. (U.S. Patent No. 12,299,703) and Hendrickson et al. (U.S. Patent Publication 2016/0359993). These new grounds of rejection are necessitated by amendment.
However, Applicants’ argument is not persuasive that Paulik et al. fails to disclose obtaining a list of term candidates and updating the list dynamically of each term candidate, and then selecting trending terms based on the updated list. Specifically, Paulik et al. discloses candidate term spotter 520 can provide a periodically updated list of candidate terms (e.g., trending terms). (¶[0059]: Figure 5) Moreover, Paulik et al. discloses that trending terms are selected based on a frequency count and can be generated for various recent time periods (e.g., instances in the last hour, instances in the last day, instances in the last week, etc.). (¶[0049]) These frequency counts are being construed as “features”. Consequently, these frequency counts are “dynamic features” that change with time so that a list of trending terms is ‘updated’ based on these changing usage statistics. Similarly, Yong et al. teaches that topics may be identified as trending topics by a proportional increase in popularity within a threshold amount of time. (¶[0055]: Figure 1) Yong et al., then, similarly teaches “updating the list” of trending topics and “determining dynamic features related to the term candidate” because trending topics are determined to change with time.
Paulik et al. and Yong et al. provide a proper basis for obviousness in accordance with a rationale under KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007). Here, a combination can be premised upon: (A) Combining prior art elements according to known methods to yield predictable results. Paulik et al. does not provide engagement data with respect to content to determine trending term candidates, but engagement data is a known prior art element for determining trending terms as taught by Yong et al. It would be predictable to provide engagement data as an additional factor to determine trending terms in social networks. Even if usage statistics cannot be construed as computing a trendiness score in Paulik et al., Yong et al. teaches ranking trending topics according to a ranking algorithm (“computing a trendiness score for the term candidate . . . in accordance with a scoring model”) that takes into account various factors including click-through data and a recency of a trending topic. (¶[0065]) It would be predictable that trending terms of Paulik et al. could be scored according to various factors as taught by Yong et al.
New grounds of rejection are set forth in this Office Action. All of the new grounds of rejection are necessitated by amendment. Applicants’ arguments are not persuasive. Accordingly, this rejection is properly FINAL.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure.
Shukla et al. discloses related prior art.
Applicants’ amendment necessitated the new grounds of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP §706.07(a). Applicants are 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 MARTIN LERNER whose telephone number is (571) 272-7608. The examiner can normally be reached Monday-Thursday 8:30 AM-6:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARTIN LERNER/Primary Examiner
Art Unit 2658 April 10, 2026
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