DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/20/2025 has been entered.
Response to Amendment
In light of Applicant's submission filed 06/20/2025, 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)
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 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;(see for example [0077, the mobile communication facility 102 may use a locator facility 110 (e.g. GPS system) to locate itself in its present location, or locations of interest to the user, whether explicitly stated or determined by PIM data, location history, or previous searches) also see [0079, 0128,0131, 0162- 0164]) displaying the contextual content to the user in a display of the mobile device. ([0676] that location as determined by a location based service may be used as a parameter for aggregating search results into categories. Location may be provided by a GPS system or a cell phone triangulation service. Also at [1013] discloses contextual information relating to the location and/or content. Also at [1014] contextual information based on user interactions with content and/or location. Also at [1354 and 1357] discloses ads targeted by categorized location, the categorized location can be based on a plurality of categories such as home, work, etc. Especially at [1409] a navigation application may be associated with application contextual data including but not limited to geographic coordinates corresponding to a location of a mobile communication facility, a data inquiry made within the navigation application by a user (e.g., "where is the nearest gas station"). The aforementioned citations are substantially equivalent to the applicant’s added limitation. (also see [1429])) 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.
Claims 2 and 40: Ramer, Kapicioglu, Reitter, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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, and Hjelm 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 September 16, 2024 have been fully considered but they are not persuasive. The applicant argues in regards to the 103 rejection that the reference of Ramer does not disclose, “periodically determining a 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.” as recited in independent claim 1. The Examiner respectfully disagrees the applicant’s specification states at [0008] such as "familiarity with the area", social signals, the user's last check-in, as well as others. Classifying a user's familiarity with the area or locale may
provide a good contextual signal of what types of information should be delivered to the user ( e.g., upon arrival to a venue, city, neighborhood, region or other location). And at [113] 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. The reference of Ramer discloses an equivalent concept at [0150] GPS data the locator facility 110 may indicate that the cell phone user is in the vicinity of a sponsor's restaurant. In addition, the clock contained in the mobile communication facility 102 and/or the wireless communication facility may indicate that it is mid-evening. A predictive algorithm could merge this information and make the implicit query that the user is interested in restaurants in his immediate vicinity at which he could purchase dinner, and then push content (ads, phone numbers, menus, reviews) to his mobile communication facility 102 for immediate display. The location of the user being used to infer that the user is a potential customer of a restaurant.Also as stated previously, Ramer discloses 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. [01354], a categorized location such as home, work, etc being correlated with a geographic location.) Thus, as stated above the term is undefined and for the purposes of examination, the Examiner interprets the term “familiarity category” to be equivalent to using contextual information and/or location information to determine a category and/or subcategory. As stated by Ramer at [1014 and 1354], Thus using broadest reasonable interpretation the Examiner stance is that the cited sections of Ramer are substantially equivalent to the applicant’s limitation.
Furthermore see for example [0077, the mobile communication facility 102 may use a locator facility 110 (e.g. GPS system) to locate itself in its present location, or locations of interest to the user, whether explicitly stated or determined by PIM data, location history, or previous searches) which discloses historical visit data association with a mobile device. also see [0079], discloses device type and device version, device characteristics, usage patterns (including those based on location, time of day, or other variables), device and/or subscriber unique identifiers, [0128], a 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). The automatically generated search may return search results that are ranked, ordering, indexed, and or prioritized by their relevance to a user characteristic or plurality of user characteristics. In this example, the fact that the user is a young, male, located in New York City with a history of purchasing jazz recordings, may result in the prioritization of relevant content for delivery to the user's mobile communication facility 102, such as, retail establishments selling jazz recordings, retail establishments selling jazz recordings within New York City, retail establishments selling jazz recordings within walking distance of the user. Thus it is the Examiner’s position that reference of Ramer is equivalent to the applicant’s limitation.
Conclusion
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.
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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.
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/D.A.P/Examiner, Art Unit 3622
/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
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