DETAILED ACTION
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 .
This action is in response to the amendments filed on June 8, 2026. Claims 1, 3, 11, 13 and 18-19 are amended; and claims 1-20 are pending and examined below.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-6, 9-14, 16-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Halecky et al. (U.S. 2022/0188698) in view of Shirwadkar et al. (U.S. 2025/0005085).
With regard to claim 1, Halecky teaches a method comprising:
obtaining, by a computing device (Fig. 28; [0171] computing devices and computing networks by providing specific mechanisms of collecting network session events 118 from user devices (e.g., computers 232 and 1404 of FIGS. 2 and 14, and platform 2800 of FIG. 28)), usage information for an application ([0026] provide information objects such as electronic documents, webpages, forms, applications (e.g., web apps), data, services, web services, media, and/or content to different user/client devices; [0029] include webpages provided on (or served) by one or more web servers and/or application servers operated by different service provides, businesses, and/or individuals. For example, information objects 112 may come from different web sites operated by online retailers and wholesalers, online newspapers, universities, blogs, municipalities, social media sites, or any other entity that supplies content);
generating, by the computing device and using a machine learning model ([0002] Embodiments described herein generally relate to machine learning (ML) and artificial intelligence (AI); [0011] a machine learning (ML) classification system uses various ML techniques to determine interest in a particular web resource (e.g., websites, webpages, web apps, etc.); [0015] machine learning predictive model) and based on the usage information, at least one intent score (Fig. 5, user intent vector; [abstract] a weighted intent score based on the resource interest score and the topic cluster interest score; [0263] FIG. 26 shows an example of how an event processor 244 combines resource cluster interest score S.sub.RCI with topic cluster interest score S.sub.TCI to generate a first party weighted intent score according to various embodiments. In some examples, the weighted intent score may be alternatively referred to as a weighted intent score (S.sub.BI));
determining, by the computing device and based on the at least one intent score, one or more navigation settings for the application ([0013] web tracking technologies; [0035] device fingerprinting can be used to track users), wherein the one or more navigation settings indicate a particular page having a corresponding type that the application should open upon launching of the application ([0044] CCM 100 may provide a “yes” or “no” as to whether a particular advertisement should be shown to a particular user; [0045] By monitoring accesses to information objects 112, CCM 100 may identify current user interests even though those interests may not align with the content currently provided by service provider 118. Service provider 118 might reengage the cold contacts by providing content 114 more aligned with the most relevant topics identified in information objects 112; [0051] An event profiler 240 in CCM 100 forwards the URL identified in event 108A to a content analyzer 242. Content analyzer 242 generates a set of topics 236 associated with or suggested by white paper 112A…Each topic 236 may have an associated relevancy score indicating the relevancy of the topic in white paper 112A; [0194] the information object 1744 is a website and each node 1748 is a webpage belonging to the website. In another example, the information object 1744 is a webpage and each node 1748 is a data element that contains a data item, a content item, and/or one or more attributes (if any) (e.g., as indicated by an opening tag, closing tag, and any content therebetween)… The DOM is a data representation of the objects that comprise the structure and content of an information object 1744 (e.g., a webpage or web app, XML document, etc.)); and
causing, by the computing device and upon launching of the application, the application to open the particular page ([0051] An event profiler 240 in CCM 100 forwards the URL identified in event 108A to a content analyzer 242. Content analyzer 242 generates a set of topics 236 associated with or suggested by white paper 112A…Each topic 236 may have an associated relevancy score indicating the relevancy of the topic in white paper 112A; [0194] the information object 1744 is a website and each node 1748 is a webpage belonging to the website; [0195] a first home page 1748A on website 1744 may include sublinks to webpages 1748B-1748H. Webpage 1748G may include second level sublinks 1746 to webpages 1748H and 1748F. Webpage 1748D may include a second level sublink 1746 to webpage 1748I). However, Halecky does not specifically teach:
- intent score that reflects user interest in a corresponding type of page
Shirwadkar teaches a system and method for filtering user profiles based on sensitive topics [abstract]. Shirwadkar also teaches intent score ([0036] user profile will consist of higher affinity scores for finance-related topics) that reflects user interest in a corresponding type of page ([0001] A content provider can utilize the user profile to identify topics that are of interest to the user, such as where the user has an interest in running, sports cars, and swimming. The content provider may select content to provide to the user based upon the content corresponding to a topic of interest to the user; [0031] One such user profile consists of canonical representations of topics and affinity scores of the topics. In some embodiments, a user interest profile may be based on article reading activity of the user. The interests are ordered by interest scores such as where the user mainly engages with sports and celebrity content articles; [0036] When users engage with online content of a website (e.g., a homepage website for accessing news, email, videos, etc.), this activity is used to derive user interest information). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the system for generating a topic cluster interest score as taught by Halecky, with the user interest associated with an interest score as taught by Shirwadkar, to have achieved a system and method of user profiles to select and provide content to the users.
With regard to claim 2, the limitations are addressed above and Halecky teaches wherein the at least one intent score includes a game intent score that is indicative of a user seeking games ([0026] the service provider 118 may provide search engine services; social media/networking services; content (media) streaming services; e-commerce services; blockchain services; communication services; immersive gaming experiences) and an application intent score that is indicative of the user seeking applications ([0026] The user/client devices that utilize services provided by service provider 118 may be referred to as “subscribers.” Although FIG. 1 shows only a single service provider 118, the service provider 118 may represent multiple service providers 118, each of which may have their own subscribing users).
With regard to claim 4, the limitations are addressed above and Halecky teaches further comprising weighting, by the computing device, the at least one intent score based on one or more factors ([abstract] a weighted intent score based on the resource interest score and the topic cluster interest score; [0263] FIG. 26 shows an example of how an event processor 244 combines resource cluster interest score S.sub.RCI with topic cluster interest score S.sub.TCI to generate a first party weighted intent score according to various embodiments. In some examples, the weighted intent score may be alternatively referred to as a weighted intent score (S.sub.BI)).
With regard to claim 5, the limitations are addressed above and Halecky teaches further comprising:
outputting, by the computing device and for display via one or more display components, the particular page of the application ([ [0047] the search engine may display links or other references to information objects 112A and 112B on website1 and website2, respectively (note that website1 and website2 may also be respective information objects 112 or collections of information objects 112). The user may click on the link to website1, and website1 may download a webpage to a client app operated by computer 230 that includes a link to information object 112A; [0051] An event profiler 240 in CCM 100 forwards the URL identified in event 108A to a content analyzer 242. Content analyzer 242 generates a set of topics 236 associated with or suggested by white paper 112A…Each topic 236 may have an associated relevancy score indicating the relevancy of the topic in white paper 112A).
With regard to claim 6, the limitations are addressed above and Halecky teaches further comprising:
determining, by the computing device, whether the application has been accessed within an immediately preceding period of time ([0083] The aggregator may also create snapshots of intent data 106 for selected time periods; [0115] The timestamp (TS) may identify a date and/or time the user accessed information objects 112, and may be included in the TS field in any suitable timestamp format such as those defined by ISO 8601; [0126] Week time periods are just one example and CCM 100 may accumulate events 108 for any selectable time period; [0178] the CCM 100 identifies/determines a content dwell time. The dwell time may indicate how long the user actively views a page of content); and
responsive to determining that the application had been accessed within the immediately preceding period of time, determining, by the computing device, which page of the application was last accessed ([0178] tag 110 may stop a dwell time counter when the user changes page tabs or becomes inactive on a page. Tag 110 may start the dwell time counter again when the user starts scrolling with a mouse or starts tabbing…the CCM 100 may assign a larger value in operation 1522 when the user spends more time actively dwelling on a page and may assign a smaller value when the user spends less time actively dwelling on a page; [0246] entity 1912 that accessed any information object over a predetermined period of time (e.g., a day, week, month, hour, and/or any other time period)),
wherein causing the application to open the particular page further comprises causing the application to open the page of the application that was last accessed ([0178] The dwell time may indicate how long the user actively views a page of content. In one example, tag 110 may stop a dwell time counter when the user changes page tabs or becomes inactive on a page. Tag 110 may start the dwell time counter again when the user starts scrolling with a mouse or starts tabbing).
With regard to claim 9, the limitations are addressed above and Halecky teaches further comprising:
obtaining, by the computing device, an indication of which page of the application should be opened upon launch of the application from a computing system ([0177] the engagement metrics 1410 may indicate any user interaction with information objects 112 including tab selections that switch to different pages, page movements; [0178] the CCM 100 may determine how much of a page the user scrolled through or reviewed).
With regard to claim 10, the limitations are addressed above and Halecky teaches wherein generating the at least one intent score includes:
providing, by the computing device and to the machine learning model, the usage information as an input ([0011] determine interest in a particular web resource (e.g., websites, webpages, web apps, etc.) based on actions taken by users; [0046] A user may enter a search query 232 into a computer 230, for example, via a search engine; [0047] The user may click on the link to website1); and
obtaining, from the machine learning model ([0011] a machine learning (ML) classification system), an output that includes the at least one intent score generated by the machine learning model using the input (Fig. 4; [0076] Event data 454A is associated with a user downloading a white paper. Event profiler 240 identifies a car topic 402 and a fuel efficiency topic 402 in the white paper. Event profiler 240 may assign a 0.5 relevancy value to the car topic and assign a 0.6 relevancy value to the fuel efficiency topic 402.
[0077] Event processor 244 may assign a weight value 464 to event data 454A. Event processor 244 may assign larger a weight value 264 to more assertive events, such as downloading the white paper).
With regard to claim 11, the device claim corresponds to the method claim 1, respectively, and therefore is rejected with the same rationale.
With regard to claim 12, the device claim corresponds to the method claim 2, respectively, and therefore is rejected with the same rationale.
With regard to claim 13, the device claim corresponds to the method claim 3, respectively, and therefore is rejected with the same rationale.
With regard to claim 14, the device claim corresponds to the method claim 6, respectively, and therefore is rejected with the same rationale.
With regard to claim 16, the medium claim corresponds to the method claim 1, respectively, and therefore is rejected with the same rationale.
With regard to claim 17, the medium claim corresponds to the method claim 2, respectively, and therefore is rejected with the same rationale.
With regard to claim 19, the medium claim corresponds to the method claim 6, respectively, and therefore is rejected with the same rationale.
Claims 3, 7-8, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Halecky et al. (U.S. 2022/0188698) in view of Shirwadkar et al. (U.S. 2025/0005085) and further in view of Veettil (U.S. 2021/0295364).
With regard to claim 3, the limitations are addressed above and Halecky teaches wherein the machine learning model includes a first independent tower for determining the game intent score ([0026] the service provider 118 may provide search engine services; social media/networking services; content (media) streaming services; e-commerce services; blockchain services; communication services; immersive gaming experiences) and wherein generating the at least one intent score further comprises:
providing the usage information ([0026] provide information objects such as electronic documents, webpages, forms, applications (e.g., web apps), data, services, web services, media, and/or content to different user/client devices; [0029] include webpages provided on (or served) by one or more web servers and/or application servers operated by different service provides, businesses, and/or individuals. For example, information objects 112 may come from different web sites operated by online retailers and wholesalers, online newspapers, universities, blogs, municipalities, social media sites, or any other entity that supplies content) to the machine learning model ([0002] Embodiments described herein generally relate to machine learning (ML) and artificial intelligence (AI); [0011] a machine learning (ML) classification system uses various ML techniques to determine interest in a particular web resource (e.g., websites, webpages, web apps, etc.); [0015] machine learning predictive model); and
receiving, from the machine learning model, the game intent score from the first independent tower ([0026] the service provider 118 may provide search engine services; social media/networking services; content (media) streaming services; e-commerce services; blockchain services; communication services; immersive gaming experiences). However, Halecky does not specifically teach:
- wherein the machine learning model is a shared tower model,
- and a second independent tower for determining the application intent score,
- and the application intent score from the second independent tower
Veettil teaches a system for generating reward packages by estimating a first intent score for the user for purchase of a first secondary product [abstract]. Veettil teaches a machine learning model ([0040] the computer system applies statistical methods (e.g., regression analysis), artificial intelligence, and/or machine learning, etc.; [0049] machine learning techniques etc. to derive correlations between customer demographic data, total basket size, and individual products purchased) is a shared tower model ([0034] – [0035] characterize a first intent score for the user as “high” for purchase of a pair of wool socks; characterize a second intent score for the user as “low” for purchase of a pair of shorts…; [0087] estimate a second intent score for the user for purchase of a second secondary product, in the set of products), and a second independent tower for determining the application intent score ([0034] – [0035] characterize a first intent score for the user as “high” for purchase of a pair of wool socks; characterize a second intent score for the user as “low” for purchase of a pair of shorts…; [0087] estimate a second intent score for the user for purchase of a second secondary product, in the set of products…calculate a second minimum reward value predicted to increase the second predicted intent score toward a second target intent score for the second secondary product; [0088] calculating a second set of intent scores for purchase of secondary products, in a second set of secondary products offered by the merchant, by the user based on the primary product, the first secondary product, and purchase history of customers at the merchant in Block S160; and calculating a second set of expected revenues for the second set of products based on the second set of intent scores in Block S164). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the machine learning model as taught by Halecky and the user interest associated with an interest score as taught by Shirwadkar, with the shared tower model taught by Veettil, to have achieved a user website relating to machine learning and artificial intelligence with immersive gaming services.
With regard to claim 7, the limitations are addressed above and Halecky teaches wherein the at least one intent score (Fig. 5, user intent vector; [abstract] a weighted intent score based on the resource interest score and the topic cluster interest score; [0263] FIG. 26 shows an example of how an event processor 244 combines resource cluster interest score S.sub.RCI with topic cluster interest score S.sub.TCI to generate a first party weighted intent score according to various embodiments. In some examples, the weighted intent score may be alternatively referred to as a weighted intent score (S.sub.BI)) is indicative of user interest within a game category ([0026] immersive gaming experiences) or app category of the application ([0026] provide information objects such as electronic documents, webpages, forms, applications (e.g., web apps), data, services, web services, media, and/or content to different user/client devices; [0029] include webpages provided on (or served) by one or more web servers and/or application servers operated by different service provides, businesses, and/or individuals. For example, information objects 112 may come from different web sites operated by online retailers and wholesalers, online newspapers, universities, blogs, municipalities, social media sites, or any other entity that supplies content), and wherein the one or more navigation settings include a first one or more navigation settings ([0013] web tracking technologies; [0035] device fingerprinting can be used to track users), the method further comprising:
determining, by the computing device and based on the at least one intent score, one or more navigation settings ([0013] web tracking technologies; [0035] device fingerprinting can be used to track users), wherein the one or more navigation settings indicate a particular page that the application should open upon launching of the application ([0044] CCM 100 may provide a “yes” or “no” as to whether a particular advertisement should be shown to a particular user; [0045] By monitoring accesses to information objects 112, CCM 100 may identify current user interests even though those interests may not align with the content currently provided by service provider 118. Service provider 118 might reengage the cold contacts by providing content 114 more aligned with the most relevant topics identified in information objects 112; [0051] An event profiler 240 in CCM 100 forwards the URL identified in event 108A to a content analyzer 242. Content analyzer 242 generates a set of topics 236 associated with or suggested by white paper 112A…Each topic 236 may have an associated relevancy score indicating the relevancy of the topic in white paper 112A; [0194] the information object 1744 is a website and each node 1748 is a webpage belonging to the website. In another example, the information object 1744 is a webpage and each node 1748 is a data element that contains a data item, a content item, and/or one or more attributes (if any) (e.g., as indicated by an opening tag, closing tag, and any content therebetween)… The DOM is a data representation of the objects that comprise the structure and content of an information object 1744 (e.g., a webpage or web app, XML document, etc.)). However, Halecky does not specifically teach:
- includes at least one intent sub-score, wherein the at least one intent sub-score is a sub-score indicative of user interest
- based on the at least one intent sub-score, one or more second navigation settings, wherein the one or more second navigation settings indicate a particular subpage that the application should open
Veettil teaches a system for generating reward packages by estimating a first intent score for the user for purchase of a first secondary product [abstract]. Veettil also includes at least one intent sub-score ([0040] – [0041] the product intent model can output “intent scores” representing probabilities of purchase of [product.sub.2] through [product.sub.1000] by a user when [product. sub.1] is added to the user's basket and/or purchased by the user; [0069] For each product in the first set of products, the computer system can then: calculate a combination (e.g., product) of the intent score of the product and the price of the product and store this combination as an expected revenue for the product. In this example, the computer system can then select a subset of (e.g., three) secondary products associated with the highest expected revenues in the first set of products), wherein the at least one intent sub-score is a sub-score indicative of user interest ([0040] – [0041] the product intent model can output “intent scores” representing probabilities of purchase of [product.sub.2] through [product.sub.1000] by a user when [product. sub.1] is added to the user's basket and/or purchased by the user). Veettil additionally teaches based on the at least one intent sub-score, one or more second navigation settings, wherein the one or more second navigation settings indicate a particular subpage that the application should open ([0040] the product intent model can output “intent scores” representing probabilities of purchase of [product.sub.2] through [product.sub.1000] by a user when [product.sub.1] is added to the user's basket and/or purchased by the user). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the machine learning model as taught by Halecky and the user interest associated with an interest score as taught by Shirwadkar, with the intent sub-score taught by Veettil, to have achieved a user website relating to machine learning and artificial intelligence with immersive gaming services and an intent sub-score pertaining to the user’s intent.
With regard to claim 8, the limitations are addressed above and Halecky teaches wherein the at least one intent sub-score corresponds to a segment of users of a plurality of segments of users ([0060] Service provider 118 may have a contact list 120 of users (see e.g., FIG. 1). Service provider 118 may hash email addresses in contact list 120 and compare the hashed identifiers with the encrypted or hashed user IDs X, A, B, and C; [0195] the edges 1746 between the individual nodes 1748 may represent links or other like relationships between the different nodes 1748 (also referred to as “sublinks 1746” or “links 1746”). In this example, a first home page 1748A on website 1744 may include sublinks to webpages 1748B-1748H. Webpage 1748G may include second level sublinks 1746 to webpages 1748H and 1748F).
With regard to claim 15, the device claim corresponds to the method claim 7, respectively, and therefore is rejected with the same rationale.
With regard to claim 18, the medium claim corresponds to the method claim 3, respectively, and therefore is rejected with the same rationale.
With regard to claim 20, the medium claim corresponds to the method claim 7, respectively, and therefore is rejected with the same rationale.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The Shirwadkar reference was brought forth as it teaches an intent score ([0036] user profile will consist of higher affinity scores for finance-related topics) which identifies the interests of the user based on article reading activity of the user ([0001] A content provider can utilize the user profile to identify topics that are of interest to the user, such as where the user has an interest in running, sports cars, and swimming. The content provider may select content to provide to the user based upon the content corresponding to a topic of interest to the user; [0031] One such user profile consists of canonical representations of topics and affinity scores of the topics. In some embodiments, a user interest profile may be based on article reading activity of the user. The interests are ordered by interest scores such as where the user mainly engages with sports and celebrity content articles; [0036] When users engage with online content of a website (e.g., a homepage website for accessing news, email, videos, etc.), this activity is used to derive user interest information).
Conclusion
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 ANDREA C. LEGGETT whose telephone number is (571)270-7700. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kieu Vu can be reached at 571-272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREA C LEGGETT/Primary Examiner, Art Unit 2171
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