DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Response to Amendment
In light of Applicant's submission filed January 30, 2026, the Examiner has updated the 35 USC § 103 rejection.
CLAIM INTERPRETATION
The examiner is using the following interpretation for the identified claims language:
familiarity category – the applicant’s specification does not define “familiarity category”. However at best the applicant’s specification gives an example at page 34, that states, “Depending on whether the user is determined to be unfamiliar with the area (e.g., a "Tourist" category), familiar with the area (e.g., a "Familiar" category), or very familiar (e.g., a "Local" category), that user may 20 be more or less likely to select particular categories of contextual content. For instance, a "Tourist" category may be more interested in seeing content from a "Sights" category, and therefore, based on the location of the user, the user may be determined to be in the Tourist category, and therefore, different contextual content will be shown to this particular user based on location and/or recency of arrival. “includes evaluating an environmental factor biasing the consumer to either one of the online redemption or the in-store redemption of the targeted offer.” Thus, as stated the term is undefined and for the purposes of examination, the Examiner interprets the term “familiarity category” to be equivalent to using contextual and/or location information to determine a category and/or subcategory. As stated by Ramer at [1014], for example, if the user is frequently visiting sites that have many links to sports content and or sites, the user may be characterized into a sports profile. (also see [1354])
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived 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 pre-AIA 35 U.S.C. 103(a) 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-13, 39-43, and 45-51 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Ramer et al. (US 2009/0222329) in view of Kapicioglu et al. (US 2013/0325855) and in further view of Reitter et al. ( US 2007/0288431) and in further view of Hjelm (WO 2010/050863) in further view of Rachitsky et al. (US 2015/0017616)
Claims: 1 and 39 Ramer discloses a method for presenting information to a user in a computer system, the method comprising acts of: determining an identity of the user in the system; (see for example[0159] identity of the user) based on the familiarity category(see for example [0077] would be considered “interested” to be equivalent to the familiarity category), identifying a content category (see for example [0131/0190] the search query/keyword/calls is considered equivalent to a content category)comprising content associated with the familiarity category, wherein the content category is associated with a set of rules for determining the when the content is relevant to the user; (0123, 0128, parameter may also relate to a user history, a user transaction, a geographic location, geographic proximity, a user device, a time, and or other user characteristics. For example, parameters relating to a user may include age (27), sex (male), previous user transactions (purchase of a jazz recording), and geographic location (New York City)
Determining contextual content for the content category using the set of rules ([0447], content may include advertisements and may be stored locally on the mobile communication facility 102 (e.g., in the cache memory) and periodically updated according to the time of day and/or changes in location of the mobile communication facility) and
identifying a content category comprising content associated with the familiarity classification; ([1033], this category of user profile may be associated with the user of the mobile communication facility 102 from which the web browser activities were recorded, and sponsored content may be presented to the mobile communication facility 102 based at least in part on the category of the user profile. ) Ramer do not explicitly disclose determining, for the user, the location of a mobile device of the user, wherein the location is determined using a first machine learning model to identify one or more locations based upon one or more signals received from the mobile device, wherein the first machine learning model is trained using legged data comprising location data and prior check-ins. However Kapicioglu discloses determining, for the user, the location of a mobile device of the user, wherein the location is determined using a first machine learning model to identify one or more locations based upon one or more signals received from the mobile device, wherein the first machine learning model is trained using data comprising location data and prior check-ins([ 0066 - 0069, discloses using a training model that uses past check in data and ranks venues according to relevance and identifies venues to display to users. Also see [0123], discloses the check-in venues have been generated as the result of a contextual ranking, in terms of time-of-day. As a result of the contextual ranking and the time of day being 11:00 pm on a Saturday night, the venues are primarily made up of late-night activities, including entertainment clubs and eateries) Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to have included in Ramer to include determining for the user, the location of a mobile device of the user, wherein the location is determined using a first machine learning model to identify one or more locations based upon one or more signals received from the mobile device, wherein the first machine learning model is trained using data comprising location data and prior check-ins, in order to provide information that is currently relevant to the user while they are at a specific location. (abstract, Kapicioglu) Ramer and Kapicioglu does not explicitly disclose determining contextual content for the content category, wherein the contextual content is determined using a second machine learning model which receives as input a stitch file which associates impressions and user actions and generates output that is processed at runtime to determine the contextual information, wherein the second machine learning model is trained using a data set comprising prior user content selections and is updated using a feedback loop to determine which contextual content are of interest to the user; However Reitter discloses determining contextual content for the content category, wherein the contextual content is determined using algorithm which receives as input a stitch file which associates impressions and user actions and generates output that is processed at runtime to determine the contextual information,;(see for example [0138], that discloses using a batch file(e.g. inputting of a stitch file) into an algorithm (e.g. machine learning model) This batch job takes the detailed level tracking metrics for the feedback system and aggregates them into a useful form that can be used to complete the feedback loop. The batch job should aggregate data on a weekly basis. For the feedback data at the Editor Kit level, the primary purpose is to separate the data out at a keyword-URL level so when the aggregated data is folded back into the ranking algorithm that keywords on URLs that have higher clickthrough rates or higher onsite activity will have those keywords rank higher as time progresses. When ranking keywords for a given URL, if specific {URL|Page|Domain} level aggregated feedback data exists, then this should be used in the ranking algorithm to refine the final ranking of the keyword. Also see [0116, 0127], that discloses tracking metrics as impressions and clickthroughs) Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to have included in Ramer and Kapicioglu to include determining contextual content for the content category, wherein the contextual content is determined using algorithm which receives as input a stitch file which associates impressions and user actions and generates output that is processed at runtime to determine the contextual information, in order to minimize the vast number of possible content items for ranking and for tracking users.( Reitter, [0116, 0127) Ramer, Kapicioglu and Reitter do not explicitly disclose a second machine learning model; wherein the second machine learning model is trained using a data set comprising prior user content selections and is updated using a feedback loop to determine which contextual content are of interest to the user; However Hjelm discloses disclose a second machine learning model; wherein the second machine learning model is trained using a data set comprising prior user content selections and is updated using a feedback loop to determine which contextual content are of interest to the user; (see for example page 4 lines 10-25 include a machine learning system employing genetic algorithms and feedback loops. The feedback loop may be iterative and/or recursive. For example, the machine learning system may employ a feedback loop based on its input data, through the utilization of genetic algorithms, to derive a best fit for or match to a set of preconditions…)
It would have been obvious to one of ordinary skill in the art at the time the invention to have modified the method and system of Reitter to have included a second machine learning model; wherein the second machine learning model is trained using a data set comprising prior user content selections and is updated using a feedback loop to determine which contextual content are of interest to the user. The reference of Reitter discloses using an algorithm to determine contextual information. The reference of Hjelm discloses using a machine learning model to determine the best fit of information (e.g. business context) for the user. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the method/system of a machine learning model of the secondary reference(s) for the algorithm means of the reference of Reitter. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Ramer, Kapicioglu, Reitter, and Hjelm do not explicitly disclose periodically determining familiarity category of the user, wherein the familiarity category represents a familiarity of the user with a region associated with the location, of the mobile device and the familiarity category is determined using at least historical visit data of the user for the region; displaying the contextual content to the user in a display of the mobile device. However Rachitsky discloses periodically determining a familiarity classification for the user based upon the location of the mobile device, wherein the familiarity classification represents a familiarity of the user with a region associated with the location of the mobile device, and the familiarity category is determined using at least historical visit data of the user for the region; based on the familiarity category classification, ([0073] stores the location history of every user in the data storage area 45, and can be queried to retrieve the information. For example, the tracker module 150 can be queried to provide the last location of a user, and the last time a user has been to a specific venue/event, and the number of times a user has been to a specific venue/event. and [0078] determines the level of expertise in each of the types of expertise (e.g. venue/event, category, scale) by analyzing the number of points accumulated for that type of expertise. Once a user has reached a certain threshold (for example, 100 points) that user is determined to be an expert for the specific venue/event, category, scale, etc. (e.g. expert in Coffee Shops, expert in Sushi, expert in Downtown, expert in geographic cell "8effa93"). In one embodiment, there are a plurality of thresholds which determine a plurality of expert levels. For example, 100 points may represent level one, 200 points may represent level two, and so on. These thresholds are variable, and can be set manually or can be dynamically generated based on the user population size and distribution of points in the system.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to have included in Ramer to include periodically determining a familiarity classification for the user based upon the location of the mobile device, wherein the familiarity classification represents a familiarity of the user with a region associated with the location of the mobile device, and the familiarity category is determined using at least historical visit data of the user for the region; based on the familiarity category classification, in order to associate a user with a particular region. [0076], Rachitsky
Claims 2 and 40: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 1 and 39, further comprising an act of determining movement of the mobile device of the user. [0191], Ramer Claim 3 and 41: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 2 and 40, further comprising an act of determining whether there is a context change of the user responsive to movement of the mobile device of the user. [0191], Ramer Claim 4 and 42: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 1 and 39, further comprising an act of logging a historical display of contextual information to the user in the display of the mobile device. [01355], Ramer Claim 5 and 43: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 1 and 39, further comprising an act of logging a historical selection of contextual information by the user in the display of the mobile device. [01355], Ramer
Claim 6: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 1 and 39, further comprising an act of determining, by a plurality of modules each associated with a designated content type, contextual content for display to the user. [1464], Ramer Claim 7 and 45: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 6 and 44, further comprising an act of ranking the respective contextual content of the plurality of modules. [1100 and 1464], Ramer Claim 8 and 46: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 7 and 45, further comprising an act of displaying the highest ranked contextual content to the user in the display of the mobile device. [0911 and 0958], Ramer Claim 9 and 47: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 8 and 46, wherein the act of displaying the highest ranked contextual content to the user in the display of the mobile device includes displaying the contextual content in at least one of a stream of content within a location-based service application and a notification pushed to the mobile device. [0911], Ramer Claim 10 and 48: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 1 and 39, further comprising an act of determining the context change of the user based on an arrival of the user at a venue. [0329], Ramer Claim 11 and 49: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 10 and 48, further comprising an act of determining the arrival of the user at the venue by a model based on previous check-in data. [0100], Ramer Claim 12 and 50 Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 1 and 39, further comprising an act of determining a confidence score indicating that the user is likely located at the venue, and wherein the act of determining the context includes an act of determining that the user is located at the venue responsive to the confidence score. [1388] Ramer Claim 13 and 51: Ramer, Kapicioglu, Reitter, Hjelm, and Rachitsky discloses the method according to claim 11 and 49, further comprising an act of determining a time of delivery of contextual information coincident with a determined arrival time of the user at the venue.[0329], Ramer
Response to Arguments
Applicant's arguments filed January 30, 2026 have been fully considered but are moot due to the updated rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matsuura et al. (US 2006/0149459) - information providing system is disclosed to provide information to a traveling user, the information providing system including: a visit history record unit (306) which detects that the user visits a place which is away from a user's living area in order to generate a travel history, and records, as a visit history, the travel history together with a visit area which represents the visited place; a visit history database (303); a facility information database (304) which provides the user with the information regarding the visit area recorded in the visit history database (303); a facility information search unit (308); and the like. (but does not appear to provide content based on the aforementioned 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 DARNELL A POUNCIL whose telephone number is (571)270-3509. The examiner can normally be reached Monday - Friday 10:00 - 6:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.A.P/Examiner, Art Unit 3622 /ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
Prosecution Insights