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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/10/2025 has been entered.
Response to Arguments
Claims 1 – 20 are pending in this Office Action. After a further search and a thorough examination of the present application, claims 1 – 20 remain rejected.
Applicant's arguments filed with respect to claims 1 – 20 have been fully considered but they are moot in view of new rejection.
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 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.
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.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Erman et al. (‘Erman’ herein after) (US 2011/0307354 A1) further in view of Mehta et al. (‘Mehta’ herein after) (US 2011/0320307 A1) further in view of Sailesh Sathish (‘Sathish’ herein after) (US 2012/0143791 A1) further in view of Ralston et al. (‘Ralston’ herein after) (US 2014/0324965 A1).
With respect to claim 1, 8, 15,
Erman discloses a method for recommending software applications that are relevant to a user of a computing device, the method comprising, at a server computing device: receiving, from the computing device, a request for at least one software application recommendation (paragraphs 29 – 31 teach receiving user profile data from variety of sources, based on a variety of triggers for the purpose of recommending application to a user, Erman); obtaining or generating a user profile associated with the user, wherein the user profile includes a plurality of properties (figure 1 & 2A depicts a database of user profiles, paragraphs 36 and 37 use identified information from the user profile, Erman); accessing a plurality of SAPs, wherein each SAP of the plurality of SAPs is associated with a respective software application and includes at least respective properties that correspond to the plurality of properties included in the user profile (figure 1 and 2B depict the software application profiles, paragraph 68 teaches the application being managed and received from developers or other options, paragraph 40 teaches the matching the application profile to the user profile information, paragraph 69 teaches the profile of the application being generated based on profiling techniques, paragraphs 73 – 75 discuss the updating of profiles based on use over time, Erman); analyzing the plurality of properties of the user profile against corresponding respective properties of the plurality of SAPs to identify, among the respective software applications associated with the plurality of SAPs, at least one software application to recommend to the user (paragraph 40 teaches the matching the application profile to the user profile information, paragraph 56, paragraph 83 teaches matching of application profile information and user profile information for purposes of selecting recommendations of applications for users, Erman); causing the computing device to display information associated with the at least one software application (paragraph 40 teaches the listing of the recommendations based on matching the application profile to the user profile information, paragraph 83 teaches matching of application profile information and user profile information for purposes of selecting recommendations of applications for users, paragraph 84 teaches the recommended application information being sent to the user device for display, also see paragraphs 110 – 114, Erman).
Erman teaches profiling techniques for the application profiles and popularity of applications amongst users but does not explicitly state the interactions with users as claimed.
However, Mehta teaches the recommendation analyzing the user interaction with applications before recommending in paragraph 28 – 29 teaching the recommendation engine also may generate recommendations based at least in part on detected patterns relating to applications, e.g. installation, deletion, and/or usage, over a population of device users. Also see paragraphs 8, 32 and 35.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Erman to include the teachings of Mehta because both of the references are in the same field of study, recommendation of software. Furthermore, Mehta adds more criteria of recommendation of applications to the user in paragraph 8 and paragraph 9 states the advantage of providing current and personalized recommendations to device users, the users may save time and money, and can discover new applications that they may not have been aware of. Users are able to quickly discover potentially relevant applications, and are unlikely to discard such applications upon installation. Additionally, application providers are enabled to increase customer satisfaction and retention, as device users may purchase applications with increased confidence.
Erman combined with Mehta teaches user profiles based on properties but does not explicitly teach as claimed that the user profile is based at least in part on a profile of a software application the user has utilized.
However Sathish teaches the user profile is based at least in part on a profile of a software application the user has utilized in paragraph 50 stating that the recommendation module may search the user behavior model for contextual characteristics that are similar to current contextual characteristics [equivalent to user profile], and then recommend one or more applications based on the profiles of the previously used applications that have been used under similar circumstances or by a prediction in the user behavior model made by the data model builder. Also see paragraphs 46 – 54 where it teaches more about the applications user by the user and its role in recommendation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Erman and Mehta to include the teachings of Sathish because, Sathish has the criteria of recommendation of applications to the user based on context including usage of the applications, along with user models of interactions. Sathish also states embodiments of the invention may provide a way to build user behavior models based on user interactions with applications and related contextual characteristics, thereby improving relevant recommendations.
Erman combined with Mehta further in combination with Sathish teaches user profiles but does not teach explicitly as claimed wherein the user profile is one of a plurality of user profiles of a cluster profile, wherein the plurality of user profiles of the cluster profile share one or more characteristics, and wherein the method further comprises recommending at least one additional software application to the user based on the at least one additional software application being engaged by at least a threshold number of users associated with ones of the plurality of user profiles of the cluster profile.
However, Ralston teaches wherein the plurality of user profiles of the cluster profile share one or more characteristics, and wherein the method further comprises recommending at least one additional software application to the user based on the at least one additional software application being engaged by at least a threshold number of users associated with ones of the plurality of user profiles of the cluster profile in paragraphs 45 – 55 stating that a recommendation module can be configured to use the gathered application purchase data along with the application purchase clusters to identify the application purchase profile of the user. Furthermore, a recommendation module can select a media item to recommend to a user based on the user's determined application preference profile and the preference relationship identified for the application purchase cluster that represents the user's application preference profile. For example, upon determining the application preference profile of a user, recommendation module can use the preference relationships associated with the application preference cluster that represents the user's application preference profile to identify a media preference profile that the user likely has.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Erman, Mehta and Satish to include the teachings of Ralston because it incorporates the advantage of people with similar cluster profiles having similar behaviors, thus predicatable. Ralston teaches that by identifying preference relationships between software application preferences and media item preferences that indicate that a user with a specified software application preference is likely to also have a specified media item preference, a user's purchase history can be used to determine the user's likely media item preferences, and thus recommend a media item to the user, paragraphs 19 – 26, Ralston.
With respect to claim 2, 9, 16,
Erman as modified discloses the method of claim 1, wherein the plurality of SAPs omits SAPs associated with respective applications that were previously displayed as recommended software applications to the user within a threshold period of time (paragraph 150 – 151 teach removal of previously displayed software applications and replacing them, Erman).With respect to claim 3, 10, 17,
Erman as modified discloses the method of claim 1, wherein the server computing device implements machine-learning when analyzing the plurality of properties of the user profile against the corresponding respective properties of the plurality of SAPs (paragraph 18, 23 – 25, Mehta).With respect to claim 4, 11, 18,
Erman as modified discloses the method of claim 1, further comprising: receiving, from a software developer, a second request to register a new software application with the server computing device; over a threshold period of time, gathering information about one or more of: user interaction information associated with the new software application, derived information associated with the new software application, usage information associated with the new software application, or metadata information associated with the new software application (paragraph 28 teaches application profile may be derived from description of application, types of users to which the application is targeted based on characteristics of users, paragraph 30 also further discloses at a portion of the application profile information is provided by the devices of the users providing feedback on the applications); generating, for the new software application, an associated new SAP based on the information; and adding the new SAP to the plurality of SAPs (figure 2B, paragraph 68 – 69, and 74 – 75, 82 – 83 teach the gathering of the data connected with the application when new and periodically or by triggered methods, Erman).With respect to claim 5, 12, 19,
Erman as modified discloses the method of claim 4, wherein: the user interaction information identifies one or more of: downloads, in-app purchases, ratings/reviews, demographics, search queries, or clicks/impressions associated with the new software application; the derived information identifies one or more of: ranking positions, a trending factor, a stability factor, compatibilities, or accolades associated with the new software application; the usage information identifies one or more of: a usage factor or an installation retention factor associated with the new software application; and the metadata information identifies one or more of: curation information, metadata information, or tag information associated with the new software application (paragraph 40, 43, 47, 53, Erman and figure 3 paragraphs 32, 35, 50 – 55, Mehta).With respect to claim 6, 13, 20,
Erman as modified discloses the method of claim 1, further comprising: receiving, from a new computing device, a second request to register a new user with the server computing device; adding, to a plurality of user profiles, a new user profile associated with the new user (figure 6A, 6C, paragraph 158 – 159 and 181 – 187, teach the query related results displaying in order and a subset Erman and 49 – 50 and 56 teach the ranking based on various criteria, Mehta); over a threshold period of time, gathering information about one or more of: engagements by the new user with a plurality of software applications, or demographics associated with the new user and updating the new user profile based on the information (figure 2A, paragraph 58 – 59, and 62 – 33 teach the gathering of the data connected with the user when new and periodically or by triggered methods, Erman).With respect to claim 7, 14,
Erman as modified discloses the method of claim 6, wherein: the engagements by the new user identify one or more of: downloads, in-app purchases, ratings/reviews, search queries, or clicks /impressions; and the demographics associated with the new user include one or more of: a gender, or an age (paragraph 40, 43, 47, 53, Erman and figure 3 paragraphs 32, 35, 50 – 55, Mehta).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20140040231 A1 teaches collection of application data from the app, storing the data, on receiving a query, searching and transmitting information regarding apps or applications whose corresponding stored application is relevant to the query.
US 20180101576 A1 teaches content recommendation to a user using a threshold of time along with other criteria.
US 20140052542 A1 teaches recommendations of software/application based on user behavior and interaction/history.
US 20150242750 A1 teaches recommendations using a machine learning model.
US 9613118 B2 teaches recommendations based on various profiles and domains.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is 571-272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 12/12/2025
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166