DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 12/19/2025.
• Claims 1-2, 6-7, 10, 12, 14, 17, and 20-21 have been amended.
• Claims 13 and 15-16 have been canceled.
• Claims 1-12, 14, and 17-23 are currently pending and have been examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of Application No. CN 202010819808.8, filed on 08/14/2020 has been received.
The examiner acknowledges that the instant application is a national stage entry of PCT/CN2021/112612, filed 08/13/2021.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12, 14, and 17-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-11 are directed to a method, claims 12 and 17-23 are directed to a machine and claim 14 is directed to a manufacture. Therefore, claims 1-12, 14, and 17-23 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
acquiring label data of a user, wherein the label data is used to represent a feature of the user;
calculating, according to the label data, a similarity between the user and each user group of a plurality of user groups acquired in advance; wherein each user group is used to represent a class of users;
acquiring at least one target user group according to the similarities between the user and each user group, wherein a similarity between the at least one target user group and the user is greater than a similarity between another user group and the user;
acquiring at least one module corresponding to the at least one target user group;
displaying the at least one module on a recommendation corresponding to the user, wherein each module of the at least one module comprises information of at least one candidate target;
wherein the method further comprises:
performing module configuration according to clustering attributes of a plurality of candidate targets, to obtain a plurality of modules;
wherein the performing module configuration according to the clustering attributes of the plurality of candidate targets comprises: acquiring historical browsing data of users of each user group, clustering the historical browsing data to obtain the clustering attributes of candidate targets selected by the users of each user group, and performing the module configuration according to the clustering attributes wherein each module corresponds to one clustering attribute and each module comprises information of one or more candidate targets with a clustering attribute corresponding to the respective module.
The above limitations recite the concept of grouping users to provide recommendation information. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, the providing of recommendation information including a product pertains to marketing, advertising and sales activities. These limitations, under their broadest reasonable interpretation, further fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, acquiring and analyzing the information are mental processes such as observations and evaluations. The limitations of the claims recite collecting information, analyzing it, and displaying certain results of the collection and analysis. Claims 12 and 14 recite similar concepts as claim 1 and accordingly fall within the same grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the MPEP, claims 1, 12, and 14 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 12, and 14 recite additional elements, such as an electronic device; a page module; a recommendation page; page modules; an apparatus, comprising: a processor and a memory; wherein the memory stores computer executable instructions; and when the processor, when executing the computer executable instructions stored in the memory; and non-transitory storage medium on which a computer program is stored, wherein when the computer program is executed by a processor. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1, 12, and 14 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1, 12, and 14 merely recite a commonplace business method (i.e., grouping users to provide recommendation information) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 12, and 14 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 12, and 14 specifying that the abstract idea of grouping users to provide recommendation information is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 12 and 14 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 12, and 14 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 12, and 14 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1, 12, and 14, these claims recite additional elements, such as an electronic device; a page module; a recommendation page; page modules; an apparatus, comprising: a processor and a memory; wherein the memory stores computer executable instructions; and when the processor, when executing the computer executable instructions stored in the memory; and non-transitory storage medium on which a computer program is stored, wherein when the computer program is executed by a processor. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1, 12, and 14 are manual processes, e.g., receiving information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1, 12, and 14 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims 1, 12, and 14 specifying that the abstract idea of grouping users to provide recommendations is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 12, and 14 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 12, and 14 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-11 and 17-23, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. Dependent claims 2-11 and 17-23 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Additionally, the claims fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, such as observations, evaluations, judgments, and opinions. Dependent claims 2-8, 10-11, and 17-22 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 9 and 23 further identify the additional element of a Continuous Bag-of-Words (CBOW) model. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-11 and 17-23 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1, 12, and 14, dependent claims 2-11 and 17-23 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-12, 14, and 17-23 are ineligible.
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.
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
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-8, 12, 14, 17, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Worthen et al. (US 20130204711 A1), hereinafter Worthen, in view of newly cited Ma (US 20200311071 A1), hereinafter Ma.
In regards to claim 1, Worthen discloses a user feature-based page displaying method, applied in an electronic device, comprising (Worthen: [0007]; [0047]):
acquiring label data of a user, wherein the label data is used to represent a feature of the user (Worthen: [0057] – “information about a new user is received, a current user information analyzer 612 may analyze the new user's information in light of the user groups…when a new user is recognized as a mother”; [0055] – “user information may include user attributes, such as age, marital status, profession, income, geographical location, hobby, family structure, etc.…characteristics, features, interests, or preferences”);
calculating, according to the label data, a similarity between the user and each user group of a plurality of user groups acquired in advance (Worthen: [0071] – “identified campaign AP corresponds to one for which the characterization of the target group associated with the identified campaign AP is the most consistent with the information about the user. If it is determined that none of the target groups associated with each retrieved campaign AP is substantially similar to the information about the user”; [0058] and Fig. 7 – “the target groups and their characterizations are stored for future use in generating and selecting campaign APs and corresponding patterns in a manner that is appropriate with respect to the characterization of the target group”; [0053] – “based on various user attributes, such as but not limited to, age, marital status, profession, income, geographical location, hobby, family structure, etc., each user may be categorized into a particular user type for selecting and customizing an appropriate campaign AP”; the examiner notes that identifying a most similar/consistent target group is interpreted to be calculating because it is determining an amount of similarity/consistency);
wherein each user group is used to represent a class of users (Worthen: [0045] – “target group for the promotion (e.g., wild life lovers, male, and professionals)”; [0051] – “the intended target groups are audience in Michigan (cold climate) and Florida (warm climate), appropriate regional profiling information for both regions may be retrieved”; [0055-0056] – “a user categorization unit 606 may group user data into groups, each of which may represent a group of users who share certain characteristics, features, interests, or preferences… In other words, customized target groups/user types with group characteristics may be created and stored in the BT archive 124”);
acquiring at least one target user group according to the similarities between the user and each user group, wherein a similarity between the at least one target user group and the user is greater than a similarity between another user group and the user (Worthen: [0071] – “identified campaign AP corresponds to one for which the characterization of the target group associated with the identified campaign AP is the most consistent with the information about the user”; [0057] – “analyze the new user's information in light of the user groups and their recorded preferences archived in the BT archive 124. The behavior targeting module 120 then may select a group to which most likely the new user belongs…when a new user is recognized as a mother, advertisements related to a van may be recommended or displayed to the new user”);
acquiring at least one page module corresponding to the at least one target user group (Worthen: [0071] and Fig. 12 – “a campaign AP customized with respect to a target group to which the user belongs is identified. For example, a comparison may be made between the information about the user and the characterization of the target group associated with each retrieved campaign AP for the product. The identified campaign AP corresponds to one for which the characterization of the target group associated with the identified campaign AP is the most consistent with the information about the user…an actionable indicator associated with the identified campaign AP, such as a simplified URL, is transmitted to the user as a response to the request. The actionable indicator is to be used to activate the presentation of the identified campaign AP, for example, on a webpage, to the user”);
displaying the at least one page module on a recommendation page corresponding to the user, wherein each page module of the at least one page module comprises information of at least one candidate target (Worthen: [0071] and Fig. 12 – “a campaign AP customized with respect to a target group to which the user belongs is identified. For example, a comparison may be made between the information about the user and the characterization of the target group associated with each retrieved campaign AP for the product. The identified campaign AP corresponds to one for which the characterization of the target group associated with the identified campaign AP is the most consistent with the information about the user…an actionable indicator associated with the identified campaign AP, such as a simplified URL, is transmitted to the user as a response to the request. The actionable indicator is to be used to activate the presentation of the identified campaign AP, for example, on a webpage, to the user”; [0038] – “A set of campaign assembly packages (APs), which may include the digital multimedia content and their corresponding patterns, may be created based on data feed(s) from sources associated with certain products or services thereof, e.g., manufacturers of goods, providers of services associated with the goods such as dealers. Such data feed may provide information with respect to promotion of certain product or service”; [0072] and Fig. 13 – “A campaign AP 1310 and a corresponding pattern 1312 are selected by the campaign assembly package delivery module 118 and presented as a multimedia webpage 1314 based on the financing offer 1308. On the multimedia webpage 1314, digital content about the financing offer 1308, such as the video clip and picture of "0% APR financing offer," the logo of the manufacture (Ford), and the background image showing the promoted product (2012 Ford Mustang)”);
wherein the method further comprises: performing page module configuration according to clustering attributes of a plurality of candidate targets, to obtain a plurality of page modules (Worthen: [0051] – “the profile for Michigan may indicate that audience in that region care quite a bit about the snow tire feature of any car being sold while the profile for Florida audience may indicate that potential buyers there do not care about snow tires but do care about sun roofs in cars. Such regional profiling information may then be used to select appropriate video footages in the asset storage 122 about Ford Explorer and the footages related to snow tire feature may be used to construct the campaign AP for the Michigan audience but not for the campaign AP for the Florida audience. At the same time, the footages related to sun roofs may be used for both campaign APs. Based on the selected assets for each campaign AP, multiple campaign APs are generated for the promoted product at step 250. Each campaign AP is constructed to be directed to a particular target group”; [0048] – “The campaign APs and corresponding patterns constructed by the offline assert constructor 114 and the online assert constructor 116 may be stored in a storage for future use”; [0038] – “A set of campaign assembly packages (APs), which may include the digital multimedia content and their corresponding patterns, may be created based on data feed(s) from sources associated with certain products or services thereof, e.g., manufacturers of goods, providers of services associated with the goods such as dealers. Such data feed may provide information with respect to promotion of certain product or service…Based on such information and the data feed, multimedia advertisements can be customized for each target audience group”; see also [0045]; [0047]);
wherein the performing page module configuration according to the clustering attributes of the plurality of candidate targets comprises: acquiring historical browsing data of users of each user group, to obtain the clustering attributes of candidate targets selected by the users of each user group, and performing the page module configuration according to the clustering attributes wherein each page module corresponds to one clustering attribute and each page module comprises information of one or more candidate targets with a clustering attribute corresponding to the respective page module (Worthen: [0038] – “A set of campaign assembly packages (APs), which may include the digital multimedia content and their corresponding patterns, may be created based on data feed(s) from sources associated with certain products or services thereof, e.g., manufacturers of goods, providers of services associated with the goods such as dealers…campaign APs and patterns may be dynamically created based on information associated with dynamically targeted audience that is collected and made available. The information associated with target audience includes, e.g.,…past historical activities, etc. Based on such information and the data feed, multimedia advertisements can be customized for each target audience group, whether it is related to a particular local population”; [0056] – “various types of information related to user behavior may also be collected and analyzed by a user behavior analyzer 610. Such user behavior information may include past online clicks on certain products,…activities such as browsing certain web content…Such behavior information feed may be combined with user categorization information to link certain behavior, liking, or preferences with certain groups of users”; [0051] – “the profile for Michigan may indicate that audience in that region care quite a bit about the snow tire feature of any car being sold while the profile for Florida audience may indicate that potential buyers there do not care about snow tires but do care about sun roofs in cars. Such regional profiling information may then be used to select appropriate video footages in the asset storage 122 about Ford Explorer and the footages related to snow tire feature may be used to construct the campaign AP for the Michigan audience but not for the campaign AP for the Florida audience. At the same time, the footages related to sun roofs may be used for both campaign APs. Based on the selected assets for each campaign AP, multiple campaign APs are generated for the promoted product at step 250. Each campaign AP is constructed to be directed to a particular target group”; [0048] – “The campaign APs and corresponding patterns constructed by the offline assert constructor 114 and the online assert constructor 116 may be stored in a storage for future use”; see also [0045]; [0047]).
Worthen further discloses that user behavior such as browsing activity and clicks may be linked with specific user groups to determine information such as preferences, where preferences can include feature preferences (Worthen: [0056-0057). This strongly implies that browsing activity is clustered to make these determinations, yet Worthen does not explicitly disclose clustering the historical browsing data.
However, Ma teaches a similar shopping method (Ma: [0002]), including
clustering the historical browsing data (Ma: [0044] – “user feedback data (search log) may be used to cluster click logs of the past nine months in the feedback data and extract the commodities with sufficient click volumes and high click rates, such as the commodities having top 200 click volumes and top 50 click rates under the product term”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the clustering of browsing data of Ma in the method of Worthen because Worthen already discloses determining browsing data and Ma is merely demonstrating that it may be clustered. Additionally, it would have been obvious to have included clustering the historical browsing data as taught by Ma because clustering is well-known and the use of it in a shopping setting would have improved user experience (Ma: [0048]).
In regards to claim 2, Worthen/Ma discloses the method of claim 1. Worthen further discloses wherein the clustering attribute of the candidate target in each page module of the at least one page module is further displayed on the recommendation page (Worthen: [0038] – “Based on such information and the data feed, multimedia advertisements can be customized for each target audience group, whether it is related to a particular local population or even individuals. For example, if data feeds are from both a manufacturer of cars and from a dealership from a specific locale, the promotion information contained in both may be automatically combined to create a marketing advertisement for that particular locale with possibly, customizations specific to that locale as well. For instance, if a locale is in Northern part of the USA, the particular feature related to snow tire of a car being promoted may be highlighted or shown in length in the customized marketing advertisement for that locale”).
In regards to claim 5, Worthen/Ma discloses the method of claim 1. Worthen further discloses wherein a number of target user groups is determined according to the recommendation page (Worthen: [0071] – “Moving to step 1206, one or more campaign APs associated with the product are retrieved assuming such campaign APs have already been generated for the product. At step 1208, a campaign AP customized with respect to a target group to which the user belongs is identified. For example, a comparison may be made between the information about the user and the characterization of the target group associated with each retrieved campaign AP for the product. The identified campaign AP corresponds to one for which the characterization of the target group associated with the identified campaign AP is the most consistent with the information about the user. If it is determined that none of the target groups associated with each retrieved campaign AP is substantially similar to the information about the user, a new campaign AP for the product may be constructed online for the user”; [0059] – “Thus, campaign APs may be constructed for, e.g., each product provider (e.g., dealer, manufacturer), each user type (target group), each promotion, each product/service, and for any other relevant variable/dimension. This may result in is a large number of campaign APs, each of which corresponding to a product provider (e.g., dealer, manufacturer), a user type, a product, a product promotion, etc.”).
In regards to claim 6, Worthen/Ma discloses the method of claim 1. Worthen further discloses wherein the acquiring the label data of the user comprises: acquiring, upon reception of a page acquisition request carrying identity information of the user, historical browsing data of the user according to the identity information of the user; acquiring the label data of the user according to the historical browsing data (Worthen: [0038] – “A set of campaign assembly packages (APs), which may include the digital multimedia content and their corresponding patterns, may be created based on data feed(s) from sources associated with certain products or services thereof, e.g., manufacturers of goods, providers of services associated with the goods such as dealers…campaign APs and patterns may be dynamically created based on information associated with dynamically targeted audience that is collected and made available. The information associated with target audience includes, e.g.,…past historical activities, etc. Based on such information and the data feed, multimedia advertisements can be customized for each target audience group, whether it is related to a particular local population”; [0056] – “various types of information related to user behavior may also be collected and analyzed by a user behavior analyzer 610. Such user behavior information may include past online clicks on certain products,…activities such as browsing certain web content…Such behavior information feed may be combined with user categorization information to link certain behavior, liking, or preferences with certain groups of users”; [0057-0058] – “information about a new user is received, a current user information analyzer 612 may analyze the new user's information in light of the user groups…user behavior information such as online transactions, activities, discussions, etc. At step 704, the user-related data feed is analyzed to generate behavior targeting information, such as shared characteristics, features, interests, or preferences for a group of users”; [0059] – “when a request is made by a user, the campaign assembly package delivery module 118 may rapidly select the appropriate campaign AP from all the generated campaign APs based on, for example, the target group to which the user belongs as stored in the BT archive 124”; [0055] – “user information may include user attributes, such as age, marital status, profession, income, geographical location, hobby, family structure, etc.…characteristics, features, interests, or preferences”).
In regards to claim 7, Worthen/Ma discloses the method of claim 1. Worthen further discloses wherein the acquiring the label data of the user comprises: acquiring the historical browsing data of the user according to the identity information, of the user who subscribes to page recommendation, in page recommendation subscription information; acquiring the label data of the user according to the historical browsing data (Worthen: [0038] – “A set of campaign assembly packages (APs), which may include the digital multimedia content and their corresponding patterns, may be created based on data feed(s) from sources associated with certain products or services thereof, e.g., manufacturers of goods, providers of services associated with the goods such as dealers…campaign APs and patterns may be dynamically created based on information associated with dynamically targeted audience that is collected and made available. The information associated with target audience includes, e.g.,…past historical activities, etc. Based on such information and the data feed, multimedia advertisements can be customized for each target audience group, whether it is related to a particular local population”; [0056] – “various types of information related to user behavior may also be collected and analyzed by a user behavior analyzer 610. Such user behavior information may include past online clicks on certain products,…activities such as browsing certain web content…Such behavior information feed may be combined with user categorization information to link certain behavior, liking, or preferences with certain groups of users”; [0065] – “if the user is known, e.g., based on his personal profile of some purchase history of cars, to like sports cars, the online asset constructor 116 may dynamically compose a new campaign AP and its corresponding pattern, featuring a promotion on a sports car from a dealer residing close to where the new user lives”; [0057-0058] – “information about a new user is received, a current user information analyzer 612 may analyze the new user's information in light of the user groups…when a new user is recognized as a mother…Starting from step 702, user-related data feed is received. The user-related data feed includes, for example, user attributes such as sex, age, profession, etc., and user behavior information such as online transactions, activities, discussions, etc. At step 704, the user-related data feed is analyzed to generate behavior targeting information, such as shared characteristics, features, interests, or preferences for a group of users”; [0059] – “when a request is made by a user, the campaign assembly package delivery module 118 may rapidly select the appropriate campaign AP from all the generated campaign APs based on, for example, the target group to which the user belongs as stored in the BT archive 124”; [0055] – “user information may include user attributes, such as age, marital status, profession, income, geographical location, hobby, family structure, etc.…characteristics, features, interests, or preferences”; the examiner notes the user making a request is interpreted to be subscribing (i.e., assenting) to recommendation).
In regards to claim 8, Worthen/Ma discloses the method of claim 1. Worthen further discloses acquiring, according to channel information visited by the user, the recommendation page corresponding to the channel information, wherein at least one page module is displayable on the recommendation page (Worthen: [0041] – “user-related data feed comes from and is associated with its user profile, such as but not limited to, age, marital status, profession, income, geographical location, hobby, family structure, etc., and user behavior, such as past online clicks on certain products, online transactions involving the users, or activities such as browsing certain web content, forwarding certain advertisement or product information to others, or contributing to online discussions on certain topics”; [0074] – “A user input detection component 1416 on the webpage 1400 may detect the user input with respect to any components on the webpage 1400, for example, whether the user clicks on a button on the webpage 1400. The user input may cause the current position in the timeline 1412 to be changed. The display then may continue from the changed position in the timeline 1412. In some embodiments, the user input detection component 1416 may cause a new webpage to be loaded and displayed, new functionalities to be invoked, for example, a chat, or any other action possible from a webpage and compatible with embodiments of the present teaching.”).
In regards to claim 12, claim 12 is directed to an apparatus. Claim 12 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The method of Worthen/Ma teaches the limitations of claim 1 as noted above. Worthen further discloses a user feature-based page displaying apparatus, applied in an electronic device, comprising: a processor and a memory; wherein the memory stores computer executable instructions; and when the processor, when executing the computer executable instructions stored in the memory (Worthen: [0083]). Claim 12 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claim 14, claim 14 is directed to a medium. Claim 14 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The method of Worthen/Ma teaches the limitations of claim 1 as noted above. Worthen further discloses a non-transitory storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the user feature based page displaying method described in any one of claims1 to 11 is implements (Worthen: [0083]). Claim 14 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 17 and 20-22, all the limitations in apparatus claims 17 and 20-22 are closely parallel to the limitations of method claims 2 and 6-8 analyzed above and rejected on the same bases.
Claims 3-4 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Worthen, in view of Ma, in view of previously cited Yang (US 20200320646 A1), hereinafter Yang.
In regards to claim 3, Worthen/Ma teaches the method of claim 1. Worthen further discloses wherein the calculating, according to the label data, the similarity between the user and each user group of the plurality of user groups acquired in advance comprises: for each user group, calculating a similarity between the label data and feature information of the user group (Worthen: [0071] – “identified campaign AP corresponds to one for which the characterization of the target group associated with the identified campaign AP is the most consistent with the information about the user. If it is determined that none of the target groups associated with each retrieved campaign AP is substantially similar to the information about the user”; [0058] and Fig. 7 – “he target groups and their characterizations are stored for future use in generating and selecting campaign APs and corresponding patterns in a manner that is appropriate with respect to the characterization of the target group”; “based on various user attributes, such as but not limited to, age, marital status, profession, income, geographical location, hobby, family structure, etc., each user may be categorized into a particular user type for selecting and customizing an appropriate campaign AP”),
yet Worthen does not explicitly disclose calculating a cosine similarity by using a cosine similarity calculation mode, wherein the similarity between the user and the user group comprises the cosine similarity.
However, Yang teaches a similar user classification method (Yang: [abstract]), including
calculating a cosine similarity by using a cosine similarity calculation mode, wherein the similarity between the user and the user group comprises the cosine similarity (Yang: [0163] – “a target similarity calculation module 1330, configured to calculate an interest similarity between each pair of users in the sample set according to a cosine similarity formula”; [0010] – “predicting interest similarities between the target user and a user group according to the feature information by using an interest similarity prediction model, the interest similarity prediction model being implemented according to an interest similarity between each pair of users in a sample set of historical records of users”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the cosine similarity of Yang in the method of Worthen/Ma because Worthen/Ma already discloses determining similarity and Yang is merely demonstrating how a similarity may be determined. Additionally, it would have been obvious to have included calculating a cosine similarity by using a cosine similarity calculation mode, wherein the similarity between the user and the user group comprises the cosine similarity as taught by Yang because cosine similarity is well-known and the use of it in a recommendation setting would have provided more accurate predictions (Yang: [0082]).
In regards to claim 4, Worthen/Ma teaches the method of claim 1, yet Worthen does not explicitly disclose wherein the acquiring, according to the similarities between the user and each user group, the at least one target user group comprises: performing sorting on the similarities between the user and the plurality of user groups in a descending order or in an ascending order, and acquiring, starting from the largest similarity, at least one target similarity which is largest; acquiring, according to the at least one target similarity, at least one corresponding target user group.
However, Yang teaches a similar user classification method (Yang: [abstract]), including
wherein the acquiring, according to the similarities between the user and each user group, the at least one target user group comprises: performing sorting on the similarities between the user and the plurality of user groups in a descending order or in an ascending order, and acquiring, starting from the largest similarity, at least one target similarity which is largest; acquiring, according to the at least one target similarity, at least one corresponding target user group (Yang: [0141] – “After interest similarities between the target user and each of the users in the user group are obtained, the similarities are sorted in descending order, and K users having highest similarities with the target user are selected as recommended users”; [0157] – “The recommended user determining module 1230 further includes a sorting module 1231, configured to sort the interest similarities between the target user and each of users in the user group in descending order, and select several users that rank the highest as recommended users. The number of selected recommended users may be preset and may be adjusted based on requirement”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the target similarity analysis of Yang in the method of Worthen/Ma because Worthen/Ma already discloses determining similarity and Yang is merely demonstrating how a similarity may be determined. Additionally, it would have been obvious to have included wherein the acquiring, according to the similarities between the user and each user group, the at least one target user group comprises: performing sorting on the similarities between the user and the plurality of user groups in a descending order or in an ascending order, and acquiring, starting from the largest similarity, at least one target similarity which is largest; acquiring, according to the at least one target similarity, at least one corresponding target user group as taught by Yang because similarity analysis is well-known and the use of it in a recommendation setting would have provided more accurate predictions (Yang: [0082]).
In regards to claims 18-19, all the limitations in apparatus claims 18-19 are closely parallel to the limitations of method claims 3-4 analyzed above and rejected on the same bases.
Claims 9-11 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Worthen, in view of Ma, in view of previously cited Ouyang et al. (US 11270185 B1), hereinafter Ouyang.
In regards to claim 9, Worthen/Ma teaches the method of claim 1. Worthen further discloses acquiring, according to labeled user data, a label feature of each user, wherein the label feature comprises a plurality of labels corresponding to the user; clustering users according to the label feature of each user to obtain a plurality of user groups (Worthen: [0053] and Fig. 4 – “Behavior targeting and profiling information may include different user types based on user profiling as target groups for marketing. Each user type may be defined by one or more attributes related to the users. The user-related data includes various pieces of information regarding an end-user. These pieces of information may be used for generating campaign APs for different target groups and for selecting the appropriate campaign APs for delivering advertisements to dynamically targeted audience. For example, video marketing materials for a married customer may be quite different from the materials for a single customer, even for the same product. As shown in FIG. 4, based on various user attributes, such as but not limited to, age, marital status, profession, income, geographical location, hobby, family structure, etc., each user may be categorized into a particular user type for selecting and customizing an appropriate campaign AP and corresponding pattern.”),
yet Worthen does not explicitly disclose a label feature vector; and for each user group, calculating, according to the feature vector of each user in the user group, feature information corresponding to the user group by using a Continuous Bag-of- Words (CBOW) model.
However, Ouyang teaches a similar user classification method (Ouyang: Col. 15, Ln. 50-60), including
a label feature vector; and for each user group, calculating, according to the feature vector of each user in the user group, feature information corresponding to the user group by using a Continuous Bag-of- Words (CBOW) model (Ouyang: Col. 13, Ln. 25-31 – “predictive models such as the…continuous-bag-of-words (CBOW)…may be used”; Col. 15, Ln. 49-60 – “The multi-stage clustering process may the process the user information at 556 into a plurality of user vector representations with one or more word embedding modules. For example, these one or more word embedding modules may normalize the user information into normalized user information and vectorizes the normalized user information into a plurality of user vector representations where user vector representations in closer proximity of each other indicate more similar user information that corresponds the user vector representations”; Col. 16, Ln. 1-7 – “determine the distance values between the user vectors and classify users that are within certain close proximity into a user cluster in some embodiments. In some other embodiments, these one or more word embedding modules may determine similarity scores among the user vector representations”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the calculation and vectors of Ouyang in the method of Worthen/Ma because Worthen/Ma already discloses determining similarity and Ouyang is merely demonstrating how a things are calculated. Additionally, it would have been obvious to have included a label feature vector; and for each user group, calculating, according to the feature vector of each user in the user group, feature information corresponding to the user group by using a Continuous Bag-of- Words (CBOW) model as taught by Ouyang because vectors and calculations are well-known and the use of it in a recommendation setting would have improved accuracy (Ouyang: Col. 15, Ln. 9-10).
In regards to claim 10, Worthen/Ma/Ouyang teaches the method of claim 9. Worthen further discloses establishing and storing, according to the feature information of each user group, a mapping relationship between the page module and the user group (Worthen: [0053] and Fig. 4 – “Behavior targeting and profiling information may include different user types based on user profiling as target groups for marketing. Each user type may be defined by one or more attributes related to the users. The user-related data includes various pieces of information regarding an end-user. These pieces of information may be used for generating campaign APs for different target groups and for selecting the appropriate campaign APs for delivering advertisements to dynamically targeted audience. For example, video marketing materials for a married customer may be quite different from the materials for a single customer, even for the same product. As shown in FIG. 4, based on various user attributes, such as but not limited to, age, marital status, profession, income, geographical location”; [0038] – “if a locale is in Northern part of the USA, the particular feature related to snow tire of a car being promoted may be highlighted or shown in length in the customized marketing advertisement for that locale”; see also [0047]).
In regards to claim 11, Worthen/Ma/Ouyang teaches the method of claim 10. Worthen further discloses wherein the acquiring the at least one page module corresponding to the at least one target user group comprises: acquiring, according to the mapping relationship between the page module and the user group, the at least one page module corresponding to the at least one target user group (Worthen: [0053] and Fig. 4 – “Behavior targeting and profiling information may include different user types based on user profiling as target groups for marketing. Each user type may be defined by one or more attributes related to the users. The user-related data includes various pieces of information regarding an end-user. These pieces of information may be used for generating campaign APs for different target groups and for selecting the appropriate campaign APs for delivering advertisements to dynamically targeted audience. For example, video marketing materials for a married customer may be quite different from the materials for a single customer, even for the same product. As shown in FIG. 4, based on various user attributes, such as but not limited to, age, marital status, profession, income, geographical location”; [0038] – “if a locale is in Northern part of the USA, the particular feature related to snow tire of a car being promoted may be highlighted or shown in length in the customized marketing advertisement for that locale”; see also [0047]).
In regards to claim 23, all the limitations in apparatus claim 23 are closely parallel to the limitations of method claim 9 analyzed above and rejected on the same bases.
Response to Arguments
Applicant’s arguments, filed 12/19/2025, have been fully considered.
35 U.S.C. § 101
Applicant argues the claims are integrated into a practical application because the claims achieve technical effects of recommendations that are "more accurate and more fast…closer to the user preference" and "power consumption…can be saved," thereby "solv[ing]…technical problems" and "improv[ing] 'upon convention technology or technological processes'" (Remarks pages 10-11). The examiner disagrees. The MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” The MPEP further states that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art,” and that, “conversely, if the specification explicitly sets forth an improvement but in a conclusory manner…the examiner should not determine the claim improves technology” (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of grouping users to provide recommendation information. This is illustrated in specification paragraph [0041] which discusses that the invention might provide improvements to recommendations of products to users. With respect to Applicant’s argument that the claimed invention provides the benefit of recommendations that are more accurate and more fast, and closer to the user preference, the examiner notes that improving recommendations is not a technical improvement, but rather an improvement to the abstract idea. With respect to Applicant’s arguments regarding power consumption being saved, this statement is not supported with any other information as to how an improvement is being made to existing technology.
Although the claims include computer technology such as an electronic device; a page module; a recommendation page; page modules; an apparatus, comprising: a processor and a memory; wherein the memory stores computer executable instructions; and when the processor, when executing the computer executable instructions stored in the memory; and non-transitory storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, such elements are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of grouping users to provide recommendation information in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to technological improvements but are directed to improving the business method of grouping users to provide recommendation information. The claimed process, while arguably resulting in a more accurate process for providing recommendations, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the server and/or computer components that operate the system. Rather, the claimed process is utilizing data sets related to users and preferences while still employing the same server and/or computer components used in conventional systems to improve grouping users to provide recommendation information, e.g. a business method, and therefore is merely applying the abstract idea using generic computing components. As such, the claims are not integrated into practical application.
Applicant argues the claims are eligible because “[g]iven that claim 1 of Yang is not directed to a judicial exception without significantly more…claim 1 of this application likewise is not directed to a judicial exception without significantly more” (Remarks pages 11-12). The examiner disagrees. Initially, the examiner notes that cases are examiner on an individual basis and are to be interpreted based on the fact patterns within, as other fact patterns may have different eligibility outcomes. Furthermore, the eligibility outcome of Yang has no bearing on the eligibility of the case at issue, and the Yang case is not precedential case law. One consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. Evaluation of this consideration includes evaluating whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. Cases have found that additional elements are more than "apply it" or are not "mere instructions" when the claim recites a technological solution to a technological problem. As discussed above, Applicant's claims provide no technological solution to a technological problem. Rather, the claims at issue only merely recite the abstract idea of grouping users to provide recommendation information along with the requirement to perform it on a set of generic computer components. For example, Applicant's claims merely recite steps of grouping users to provide recommendation information with generic computer components being recited in a generic manner. The specificity of the claims is directed toward the abstract idea of grouping users to provide recommendation information and not toward any technology, and accordingly, is insufficient to integrate the abstract idea into a practical application. While additional elements are included within the claims, they are claimed in a generic manner and merely perform generic functions. Applicant’s disclosure does not articulate or suggest how these additional elements function, individually or in combination, in any manner other than using generic functionality. As such, the claims represent mere instructions to apply an abstract idea to a general purpose computer.
Applicant argues the claims are patent eligible because they “cannot be considered as commercial activities or organizational methods” (Remarks pages 12-13). The examiner disagrees. Initially, the examiner notes that the claims also fall within the “Mental Processes” grouping of abstract ideas. Furthermore, the MPEP enumerates groupings of abstract ideas, thereby synthesizing the holdings of various court decisions to facilitate examination. See MPEP 2106.04. Among the enumerated groupings is the Certain Methods of Organizing Human Activity grouping, which includes activity that falls within the enumerated sub-grouping of commercial or legal interactions, including advertising, marketing or sales activities or behaviors. With respect to the claims, the examiner notes an electronic device; a page module; a recommendation page; page modules; an apparatus, comprising: a processor and a memory; wherein the memory stores computer executable instructions; and when the processor, when executing the computer executable instructions stored in the memory; and non-transitory storage medium on which a computer program is stored, wherein when the computer program is executed by a processor have been analyzed as additional elements and accordingly are not analyzed under Step 2A, Prong 1. The claims further recite limitations such as acquiring a user group that is similar to a user and displaying information based on the user group. These amendments represent certain methods of organizing human activity. Paragraph [0003-0004] of the specification illustrates that the invention pertains to product recommendations. Accordingly, the recited limitations claim collection and analysis of data for the purposes of recommendations, such as product recommendations of products for purchase. These limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions (including marketing or sales activities or behaviors). Specifically, the limitations of claim 1 represent marketing and sales activities and behaviors because the limitations recite collection and analysis of data pertaining to users in order to provide recommendations, such as product recommendations. These are marketing and sales activities because they pertain to product purchases (Spec: [0003-0004]). Accordingly, these claims recite Certain Methods of Organizing Human Activity.
35 U.S.C. § 102/103
Applicant argues the claims are allowable because the cited art does not disclose “performing page module configuration according to clustering attributes” and “acquiring historical browsing data of users of each user group, clustering the historical browsing data to obtain the clustering attributes of candidate targets selected by the users of each user group, and performing the page module configuration according to the clustering attributes; wherein each page module corresponds to one clustering attribute and each page module comprises information of one or more candidate targets with a clustering attribute corresponding to the respective page module” (Remarks pages 13-17). The examiner disagrees. Initially, the examiner notes that the amendments have necessitated a new grounds of rejection and a new reference has been cited to teach clustering the historical browsing data. Furthermore, Worthen discloses performing page module configuration according to clustering attributes. Worthen discloses this in [0051], which discloses that the profile for Michigan may indicate that audience in that region care quite a bit about the snow tire feature of any car being sold while the profile for Florida audience may indicate that potential buyers there do not care about snow tires but do care about sun roofs in cars. This information may then be used to select campaign APs, where the footages related to a snow tire feature may be used to construct the campaign AP for the Michigan audience but not for the campaign AP for the Florida audience. At the same time, the footages related to sun roofs may be used for both campaign APs. The examiner notes that in this instance, features such as the sun roof and the snow tires are interpreted to be clustering attributes, and the campaign APs, which are interpreted to be page module configurations, are based on the clustering attributes. Further, Worthen discloses acquiring historical browsing data of users of each user group, to obtain the clustering attributes of candidate targets selected by the users of each user group, and performing the page module configuration according to the clustering attributes; wherein each page module corresponds to one clustering attribute and each page module comprises information of one or more candidate targets with a clustering attribute corresponding to the respective page module. Worthen discloses this at least in [0038], which discloses that campaign APs may be created based on information associated with dynamically targeted audience that is collected and made available, such as past historical activities. Worthen further discloses in [0056-0057] that information such as past online clicks on certain products and activities such as browsing certain web content may be used to determine preferences with certain groups of users. Preferences may then be used to target advertisements based on preferences of a group to which a user belongs. For instance, if the group “mothers’ prefer vans, safety features, and TVs in cars, a user who belongs to the group “mothers” might be targeted with van advertisements. The examiner notes that in this case, features such as vans, safety features and TVs are interpreted to be clustering attributes. Worthen further discloses in [0051] that is people in Michigan care about the snow tire feature of any car being sold while the people in Florida do not care about snow tires but do care about sun roofs in cars, this information may then be used to select appropriate video footages about Ford Explorer and the footages related to snow tire feature may be used to construct the campaign AP for the Michigan audience but not for the campaign AP for the Florida audience. At the same time, the footages related to sun roofs may be used for both campaign Aps. In this instance the sun roof and snow tires are clustering attributes and each campaign AP (interpreted to be a page module) corresponds to a product with the clustering attribute. Thus, the cited art teaches these limitations.
Applicant argues independent claims 12 and 14 and the dependent claims are allowable for the same reasons as claim 1 (Remarks pages 15-18). The examiner disagrees. As discussed in the 103 rejection and response to remarks above, claim 1 is not allowable and claims 12, 14, and the dependent claims are not allowable for the same, and additional, reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Previously cited NPL reference U, initially cited in the Office action dated 09/23/2025, teaches recommendation system algorithms. Collaborative filtering may be used. Users are analyzed to find people with similar interests and provide recommendations based on that information.
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 ANNA MAE MITROS whose telephone number is (571)272-3969. The examiner can normally be reached Monday-Friday from 9:30-6.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
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