DETAILED ACTION
The following is a FINAL office action upon examination of the application number 18/478459. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-13 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention. Claims 14-19 are directed to a system comprising at least one processor; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claim 20 is directed to a non-transitory computer-readable storage medium, which is a manufacture, and this a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to generate and provide application recommendations, which is described by claim limitations reciting:
receive, from at least one data store, user engagement data associated with at least one user-accessed application, wherein the user engagement data is associated with an entity identifier of a particular entity;
receive, from the at least one data store, a corpus of historical user engagement data associated with a plurality of additional entities, wherein respective entities of the plurality of additional entities are associated with the at least one user-accessed application and at least one of a plurality of candidate applications;
determine a subset of the plurality of additional entities associated with the particular entity by performing a similarity analysis between the user engagement data and the corpus of historical user engagement data;
generate, using a … model, respective recommendation scores for the plurality of candidate applications based on the user engagement data, wherein:
the recommendation score indicates a likelihood of the particular entity performing at least one application action respective to the corresponding candidate application;
generate a recommendation for the particular entity based on the respective recommendation scores, wherein the recommendation indicates at least one of the plurality of candidate applications; and
cause provision of the recommendation to the at least one user...
The identified limitations in the claims describing generating and providing application recommendations (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers commercial interactions and marketing activities. Dependent claims 3, 4, 7, 9, 10, 15, 16, 17, 18, and 19 recite limitations that further narrow the abstract idea; therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the at least one user-accessed application and computing device associated with the entity identifier in claim 1; the apparatus comprising at least one processor and at least one non-transitory memory comprising program code, at least one user-accessed application, and computing device associated with the entity identifier in claim 14; the at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, at least one user-accessed application and computing device associated with the entity identifier in claim 20, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a computer/processor.
Additional elements such as generate, using a machine learning model… and the machine learning model was previously trained using a subset of the corpus of historical user engagement data corresponding to the subset of the plurality of additional entities do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 2, 5, and 13 related to a machine learning model do not provide an improvement and only generally link the abstract idea to a technological environment (i.e., machine learning).
Additional elements related to provision of the recommendation to the at least one user-accessed application, wherein the at least one user-accessed application causes provision of the recommendation to a computing device associated with the entity identifier do not improve the computer or technology; these additional elements only add insignificant extra-solution activities (data transmission). Similarly, additional elements in claims 2 and 12 related to a GUI and data are received from a remote feature service add additional elements that do not yield an improvement and only add insignificant extra-solution activities (data display/data gathering).
Additional elements in claims 6, 8, and 11 related to action initiated … within the particular instance of the at least one user-accessed application, the at least one user- accessed application by at least one of the plurality of end-users, and the at least one application action is at least one of an application purchase, an application upgrade, an application downgrade, or an application removal are recited at a high level and only generally link the abstract idea to a technological environment. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as generate, using a machine learning model… and the machine learning model was previously trained using a subset of the corpus of historical user engagement data corresponding to the subset of the plurality of additional entities do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 2, 5, and 13 related to a machine learning model do not provide an improvement and only generally link the abstract idea to a technological environment (i.e., machine learning).
Additional elements related to provision of the recommendation to the at least one user-accessed application, wherein the at least one user-accessed application causes provision of the recommendation to a computing device associated with the entity identifier do not improve the computer or technology; these additional elements only add insignificant extra-solution activities (data transmission). Similarly, additional elements in claims 2 and 12 related to a GUI and data are received from a remote feature service add additional elements that do not yield an improvement and only add insignificant extra-solution activities (data display/data gathering). With respect to data transmission/gathering limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). With respect to data display limitations, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)). Additional elements in claims 6, 8, and 11 related to action initiated … within the particular instance of the at least one user-accessed application, the at least one user- accessed application by at least one of the plurality of end-users, and the at least one application action is at least one of an application purchase, an application upgrade, an application downgrade, or an application removal are recited at a high level and only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 103
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 1-4, 9-12, and 14-20 rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0153857 (Li); in view of US 2013/0085886 (Satish).
As per claim 1, Li teaches: a computer-implemented method for optimizing application utilization, comprising: receiving, from at least one data store, user engagement data associated with at least one user-accessed application…; receiving, from the at least one data store, a corpus of historical user engagement data associated with a plurality of additional entities, wherein respective entities of the plurality of additional entities are associated with the at least one user-accessed application and at least one of a plurality of candidate applications; ([0047] …and all historical records of user interaction in both a source domain and a target domain are incorporated into learning [0104] Interaction behavior data of the user is obtained in a plurality of scenarios such as a video app, a music app, and a browser app in FIG. 10, and then processed by the event collection module, to obtain an event log. The event log is stored in a big data platform module. For example, the event collection module processes the interaction behavior data of the user, including operations [0115] …The recommendation system may predict, based on a historical behavior log of the user, for example, a historical download record of the user… [0405] …an interaction history (for example, a user behavior log) of a user).
generating, using a machine learning model, respective recommendation scores for the plurality of candidate applications based on the user engagement data, wherein: the machine learning model was previously trained using a subset of the corpus of historical user engagement data corresponding to the subset of the plurality of additional entities; and ([0109] The recommendation model training method provided in the embodiments of this application may be specifically applied to a data processing method such as data training, machine learning, or deep learning, to perform symbolic and formal intelligent information modeling, extraction, preprocessing, training, and the like on training data (for example, a first training sample in this application), so as to finally obtain a trained recommendation model. In addition, in the recommendation method provided in the embodiments of this application, the foregoing trained recommendation model may be used, and input data (for example, information about a target user and information about a candidate recommended object in this application) is input into the trained recommendation model, to obtain output data (for example, a predicted probability that the target user performs an operation action on the candidate recommended object in this application). [0116] For example, when there are p display locations in the application market (where p is a positive integer), the recommendation system may select p candidate applications with highest predicted download probabilities for display, display an application with a higher predicted download probability in the p candidate applications at a higher-ranking location, and display an application with a lower predicted download probability in the p candidate applications at a lower-ranking location. [0282] The recommendation model may be used to predict whether the user performs an operation action on the recommended object when the recommended object is recommended to the user. A value output by the recommendation model is a prediction label. The prediction label is used to indicate whether the user performs an operation action on the recommended object. For example, the prediction label may be 0 or 1. In other words, 0 or 1 is used to indicate whether the user performs an operation action on the recommended object. The prediction label may alternatively be a probability value. In other words, the probability value is used to represent a probability that the user performs an operation action on the recommended object. In other words, an output of the recommendation model may be 0 or 1, or may be a probability value).
the recommendation score indicates a likelihood of the plurality of end-users of the particular domain performing at least one application action respective to the corresponding candidate application; ([0109] … obtain output data (for example, a predicted probability that the target user performs an operation action on the candidate recommended object in this application). [0116] For example, when there are p display locations in the application market (where p is a positive integer), the recommendation system may select p candidate applications with highest predicted download probabilities for display, display an application with a higher predicted download probability in the p candidate applications at a higher-ranking location, and display an application with a lower predicted download probability in the p candidate applications at a lower-ranking location).
generating a recommendation … based on the respective recommendation scores, wherein the recommendation indicates at least one of the plurality of candidate applications; and causing provision of the recommendation to the at least one user-accessed application, wherein the user-accessed application causes provision of the recommendation to a computing device … ([0115] …The recommendation system may predict, based on a historical behavior log of the user, for example, a historical download record of the user, and a feature of the application market, for example, environment feature information such as a time and a location, a probability that the user downloads each candidate application in the recommendation system. The recommendation system may display candidate applications…. [0116] For example, when there are p display locations in the application market (where p is a positive integer), the recommendation system may select p candidate applications with highest predicted download probabilities for display, display an application with a higher predicted download probability in the p candidate applications at a higher-ranking location, and display an application with a lower predicted download probability in the p candidate applications at a lower-ranking location [0132] In addition, the execution device 110 includes a calculation module 111, an I/O interface 112, a preprocessing module 113, and a preprocessing module 114. The calculation module 111 may include a target model/rule 101 [0136] …the trained recommendation model may be the target model/rule 101. [0137] The target model/rule 101 can be used to predict whether the user selects the recommended object or predict a probability that the user selects the recommended object. [0139] … execution device 110 is provided with the input/output (I/O) interface 112, configured to exchange data with an external device; I/O interface (user accessed application) causes provision of recommendation to user device. [0143] Finally, the I/O interface 112 provides a processing result for the user. For example, in a recommendation system, the target model/rule 101 may be used to predict whether the target user selects a candidate recommended object or a probability of selecting a candidate recommended object, and obtain a recommendation result based on whether the target user selects the candidate recommended object or the probability of selecting the candidate recommended object. The recommendation result is presented to the client device 140, so that the recommendation result is provided for the user; target mode/rule 101 provides recommendation to I/O interface 112, also see [Fig. 3] showing communication path).
Although not explicitly taught by Li, Satish teaches: an identifier of a particular domain, wherein the domain is associated with a plurality of end-users of a particular instance of the at least one user-accessed application; ([0009] … Another example user metric may measure the user's system profile, e.g. device model, device manufacturer, memory, available resources, etc. Still another example user metric may measure the geo-location of the user's device. The user profile may also measure the actual usage of the various applications installed on the user device [0054] …the user's device information may be associated with comparable devices based on geo-specific device location, device system resources, category of installed applications).
determining a subset of the plurality of additional entities associated with the particular domain by performing a similarity analysis between the user engagement data and the corpus of historical user engagement data; [0055] … The recommendation engine 310 then analyzes the device profiles of other comparable devices, and discovers that other users of the same or similar geo-specific location [0059] …The recommendation engine 310 (FIG. 3) compares the user profile 302 (FIG. 3) to similar user profiles to identify specific applications users download in each category (e.g. user choices), the system profile, geo-location, etc. In various embodiments recommended applications may be automatically customized for a given user's language and/or region).
generating a recommendation for the particular domain … wherein the user-accessed application causes provision of the recommendation to a computing device associated with the identifier of the particular domain ([0051] …a recommendation engine 310 and an application store 312. However, in other embodiments the recommendation engine 310 and/or the application store 312 may be located elsewhere, e.g. on different servers [0052] The user may access the application store 312 with the user device 304, e.g. with a web browser, purchase application, etc. When the user selects an application for purchase and/or download, or in response to the user requesting application information and/or a suggestion, the recommendation engine 310 may provide a recommendation [0053] … the recommendation engine 310 may suggest a number of suitable applications for the user device 304 when the user first connects to the application store 312. [0055] …recommendation engine 310 automatically makes application suggestions to the user, e.g. through an application store user interface 416 [0059] …The recommendation engine 310 (FIG. 3) compares the user profile 302 (FIG. 3) to similar user profiles to identify specific applications users download in each category (e.g. user choices), the system profile, geo-location, etc. In various embodiments recommended applications may be automatically customized for a given user's language and/or region [0062] Furthermore, fuzzy clustering allows the same user profile to be associated with distinct clusters at the same time. Thus, the application recommendation system 300 may recommend specific and relevant applications to the user device 304 on the basis of shared profile attributes…the analytics of the recommendation engine 310 recognize that "Phage" is the most popular game for all "Angry Bird" users generally or specifically on the basis of other profile attributes, system specifications, and geo-location. [0071] … the similar determined patterns are selected based on geolocation. For example, in FIG. 5 the recommendation engine compares the user profile to similar user profiles to identify specific applications users download in each category (e.g. user choices), the system profile on which they downloaded the applications, geo-location, etc.).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Li with the aforementioned teachings of Satish with the motivation of providing users with applications of interest (Satish [0008]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Satish to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for targeted application recommendations.
As per claim 2, Li teaches: the recommendation is provisioned to the computing device via rendering of a graphical user interface (GUI) on a display of the computing device; the GUI comprises a user input field configured to receive user feedback to the recommendation from at least one of the plurality of end-users; and the method further comprises: retraining the machine learning model using at least one user input received via the user input field ([0114] …The user browses the displayed item and may perform an operation action, such as a browsing action or downloading action. The operation action of the user may be stored in a user behavior log, and training data may be obtained by processing the user behavior log. The recommendation model may be trained based on the training data, or a model parameter of the recommendation model may be continuously updated, to improve a prediction effect of the recommendation model)
As per claim 3, Li teaches: the at least one application action is at least one of an application purchase, an application version change, or an application trial ([0015] …. Operation actions performed by the user on the recommended object may include a clicking action of the user, a downloading action of the user, a purchasing action of the user; download (trial) action and purchase action. [0114] …The user browses the displayed item and may perform an operation action, such as a browsing action or downloading action. The operation action of the user may be stored in a user behavior log, and training data may be obtained by processing the user behavior log).
As per claim 4, Li teaches: generating a ranking of the plurality of candidate applications based on the respective recommendation scores, wherein the recommendation comprises a subset of top-ranked entries from the ranking for which the corresponding recommendation score meets a predetermined threshold ( [0109] … obtain output data (for example, a predicted probability that the target user performs an operation action on the candidate recommended object in this application). [0116] For example, when there are p display locations in the application market (where p is a positive integer), the recommendation system may select p candidate applications with highest predicted download probabilities for display).
As per claim 9, Li teaches: the user engagement data comprises at least one application feature associated with the at least one user-accessed application or one or more historical user-accessed applications associated with the domain identifier ([0053] … one or more recommended objects are determined based on user behavior data in an application in the at least one application. [0115] … recommendation system may predict, based on a historical behavior log of the user, for example, a historical download record of the user).
As per claim 10, Li teaches: the user engagement data further comprises at least one temporal feature associated with the at least one application feature ([0053] … one or more recommended objects are determined based on user behavior data in an application in the at least one application. [0014] …a time point at which the user interacts with the recommended object [0485] …user behavior log in a most recent period of time before a moment at which the recommendation model is launched).
As per claim 11, Li teaches: the at least one application feature comprises at least one application action; and the at least one application action is at least one of an application purchase, an application upgrade, an application downgrade, or an application removal ([0114] …The user browses the displayed item and may perform an operation action, such as a browsing action or downloading action. The operation action of the user may be stored in a user behavior log, and training data may be obtained by processing the user behavior log. [0015] …. Operation actions performed by the user … include a clicking action of the user, a downloading action of the user, a purchasing action of the user).
As per claim 12, Li teaches: the user engagement data and the corpus of historical user engagement data are received from a remote feature service comprising the at least one data store ([0131] FIG. 3 is a schematic diagram of a system architecture according to an embodiment of this application. As shown in FIG. 3, the system architecture 100 includes an execution device 110,… a database 130, a client device 140, a data storage system 150, and a data collection device 160. [0133] The data collection device 160 is configured to collect training data. In a recommendation model training method in embodiments of this application, a recommendation model may be further trained based on the training data).
As per claim 14, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claim 15, Li teaches: the corresponding recommendation score for the at least one of the plurality of candidate applications ([0109] The recommendation model training method provided in the embodiments of this application may be specifically applied to a data processing method such as data training, machine learning, or deep learning, to perform symbolic and formal intelligent information modeling, extraction, preprocessing, training, and the like on training data (for example, a first training sample in this application), so as to finally obtain a trained recommendation model. In addition, in the recommendation method provided in the embodiments of this application, the foregoing trained recommendation model may be used, and input data (for example, information about a target user and information about a candidate recommended object in this application) is input into the trained recommendation model, to obtain output data (for example, a predicted probability that the target user performs an operation action on the candidate recommended object in this application). [0116] For example, when there are p display locations in the application market (where p is a positive integer), the recommendation system may select p candidate applications with highest predicted download probabilities for display, display an application with a higher predicted download probability in the p candidate applications at a higher-ranking location, and display an application with a lower predicted download probability in the p candidate applications at a lower-ranking location. [0282] The recommendation model may be used to predict whether the user performs an operation action on the recommended object when the recommended object is recommended to the user. A value output by the recommendation model is a prediction label. The prediction label is used to indicate whether the user performs an operation action on the recommended object. For example, the prediction label may be 0 or 1. In other words, 0 or 1 is used to indicate whether the user performs an operation action on the recommended object. The prediction label may alternatively be a probability value. In other words, the probability value is used to represent a probability that the user performs an operation action on the recommended object. In other words, an output of the recommendation model may be 0 or 1, or may be a probability value).
Although not explicitly taught by Li, Satish teaches: the recommendation further indicates the corresponding recommendation score for the at least one of the plurality of candidate applications ([0004] … may also list other information pertinent to an application, e.g. an application's popularity, user ratings, and user reviews).
One of ordinary skill in the art would have recognized that applying the teachings of Satish to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for display of score information.
As per claim 16, although not explicitly taught by Li, Satish teaches: perform the similarity analysis by segmenting the plurality of additional users based on at least one segmentation factor to determine the subset of the plurality of additional entities associated with the particular entity ([0054] …the user's device information may be associated with comparable devices based on geo-specific device location, device system resources, category of installed applications [0055] … The recommendation engine 310 then analyzes the device profiles of other comparable devices, and discovers that other users of the same or similar geo-specific location [0059] …The recommendation engine 310 (FIG. 3) compares the user profile 302 (FIG. 3) to similar user profiles to identify specific applications users download in each category (e.g. user choices), the system profile, geo-location, etc. In various embodiments recommended applications may be automatically customized for a given user's language and/or region [0061] … Embodiments of the present invention may group together the other user's profiles 313 into fuzzy clusters. Thus, the other user's profiles 313 may be clustered according to one or more of the metrics 520(1)-(N) (FIG. 5). For example, users with similar usage patterns may be grouped into the fuzzy cluster 624. The fuzzy cluster 624 may then be data mined and/or analyzed by the recommendation engine 310 to determine other characteristics of the fuzzy cluster 624. [0062] Furthermore, fuzzy clustering allows the same user profile to be associated with distinct clusters at the same time. Thus, the application recommendation system 300 may recommend specific and relevant applications to the user device 304 on the basis of shared profile attributes).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Li with the aforementioned teachings of Satish with the motivation of recommending specific and relevant applications based on shared profile attributes (Satish [0062]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Satish to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the segmentation of users to identify patterns.
As per claim 17, although not explicitly taught by Li, Satish teaches: the at least one segmentation factor comprises an application action record for the at least one user-accessed application ([0062] Furthermore, fuzzy clustering allows the same user profile to be associated with distinct clusters at the same time. Thus, the application recommendation system 300 may recommend specific and relevant applications to the user device 304 on the basis of shared profile attributes. For example, a user who downloads Application(1) 626, e.g. "Angry Birds," from the Games/Strategy category on an Android Motorola ATRIX 4G system in San Jose may be recommended Application(2) 628, e.g. "Phage," from the Game/Strategy category of the application store 312, because the analytics of the recommendation engine 310 recognize that "Phage" is the most popular game for all "Angry Bird" users generally or specifically on the basis of other profile attributes; users segmented based on usage of an application (Angry Bird)).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Li with the aforementioned teachings of Satish with the motivation of recommending specific and relevant applications based on shared profile attributes (Satish [0062]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Satish to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the segmentation of users to identify patterns.
As per claim 18, although not explicitly taught by Li, Satish teaches: the at least one segmentation factor comprises domain similarity between a domain associated with the particular entity and a respective domain associated with the plurality of additional entities ([0061] FIG. 6 depicts a block diagram of the application recommendation system 300 including a fuzzy cluster 624, according to an exemplary embodiment of the present invention. Embodiments of the present invention may group together the other user's profiles 313 into fuzzy clusters. Thus, the other user's profiles 313 may be clustered according to one or more of the metrics 520(1)-(N) (FIG. 5). For example, users with similar usage patterns may be grouped into the fuzzy cluster 624. The fuzzy cluster 624 may then be data mined and/or analyzed by the recommendation engine 310 to determine other characteristics of the fuzzy cluster 624. [0062] Furthermore, fuzzy clustering allows the same user profile to be associated with distinct clusters at the same time. Thus, the application recommendation system 300 may recommend specific and relevant applications to the user device 304 on the basis of shared profile attributes).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Li with the aforementioned teachings of Satish with the motivation of recommending specific and relevant applications based on shared profile attributes (Satish [0062]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Satish to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the segmentation of users to identify patterns.
As per claim 19, although not explicitly taught by Li, Satish teaches: the at least one segmentation factor comprises demographic similarity between demographic data associated with the particular entity and respective demographic data for the plurality of additional entities; the user engagement data comprises the demographic data associated with the particular entity; and the corpus of historical user engagement data comprises the respective demographic data for the plurality of additional entities ([0062] Furthermore, fuzzy clustering allows the same user profile to be associated with distinct clusters at the same time. Thus, the application recommendation system 300 may recommend specific and relevant applications to the user device 304 on the basis of shared profile attributes. [0055] … then analyzes the device profiles of other comparable devices, and discovers that other users of the same or similar geo-specific location [0061] …user's profiles 313 may be clustered according to one or more of the metrics [0067] … user metric may measure the geo-location of the user's device. [0070] … The recommendation engine compares the user profile to other users' profiles that have been gathered and stored by the recommendation engine).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Li with the aforementioned teachings of Satish with the motivation of recommending specific and relevant applications based on shared profile attributes (Satish [0062]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Satish to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the segmentation of users to identify patterns.
As per claim 20, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0153857 (Li); in view of US 2013/0085886 (Satish); in view of US 2022/0084073 (Murgai).
As per claim 5, Li teaches: … a trigger event for causing provision of the recommendation to the computing device…; … trigger events for recommending the at least one of the plurality of candidate applications; in response to receiving an indication of an occurrence of the trigger event, causing the at least one user-accessed application to initiate the provision of the recommendation to the computing device ([0094] …A recommendation system may be configured to determine a to-be-displayed application and a corresponding display location of the application. When the user enters the application market, a recommendation request is triggered).
Although not explicitly taught by Li, Satish teaches: a trigger event for causing provision of the recommendation to the computing device associated with the domain identifier, ([0055] … The recommendation engine 310 then analyzes the device profiles of other comparable devices, and discovers that other users of the same or similar geo-specific location [0059] …The recommendation engine 310 (FIG. 3) compares the user profile 302 (FIG. 3) to similar user profiles to identify specific applications users download in each category (e.g. user choices), the system profile, geo-location, etc. In various embodiments recommended applications may be automatically customized for a given user's language and/or region).
in response to receiving an indication of an occurrence of the trigger event, causing the at least one user-accessed application to initiate the provision of the recommendation to the computing device associated with the domain identifier ([0015] … the recommending is performed in response to receiving user selections of the user in an online application store. [0059] …The recommendation engine 310 (FIG. 3) compares the user profile 302 (FIG. 3) to similar user profiles to identify specific applications users download in each category (e.g. user choices), the system profile, geo-location, etc. In various embodiments recommended applications may be automatically customized for a given user's language and/or region).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Li with the aforementioned teachings of Satish with the motivation of providing users with applications of interest (Satish [0008]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Satish to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for targeted application recommendations.
Although not explicitly taught by Li, Murgai teaches: generating, using a second machine learning model, a trigger event for causing provision of the recommendation …wherein: the second machine learning model was previously trained using the user engagement data and the subset of the corpus to generate predictive output indicative of optimal trigger events for recommending ([0069] … For example, server 102 may determine the success, or lack thereof, of an advertisement may be keyed to the trigger event that caused the advertisement to be delivered to the consumer. [0070] In step 906, server 102 may be configured to execute instructions that update, or train, the analytical model used to create the recommendation matrix. This may be a form of supervised learning. [Clam 22] … determining the number of potential trigger events based on whether the one or more trigger events are successful; and determining the potential offers based on an analysis of a consumer using the machine learning algorithm).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Li with the aforementioned teachings of Murgai with the motivation of maximizing success of recommendations. Further, one of ordinary skill in the art would have recognized that applying the teachings of Murgai to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the analysis of trigger events.
As per claim 6, Li teaches: the trigger event comprises at least one action initiated by at least one of the plurality of end-users within the particular instance of the at least one user-accessed application ([0094] …A recommendation system may be configured to determine a to-be-displayed application and a corresponding display location of the application. When the user enters the application market, a recommendation request is triggered).
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0153857 (Li); in view of US 2013/0085886 (Satish); in view of US 2022/0084073 (Murgai); in view of US 10241772 (Ning).
As per claim 7, although not explicitly taught by Li, Ning teaches: the trigger event comprises a particular time interval (Col 20 ln 62-67 if the user does not interact with the particular application within a predefined amount of time (e.g., two weeks, two months, etc.) or the duration of interaction within a predefined amount of time is less than a threshold duration, application recommendation module 228 may select one or more alternative applications to substitute for the particular application and may output an indication of the selected alternative applications.)
One of ordinary skill in the art would have recognized that applying the teachings of Ning to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of a time interval as a trigger.
As per claim 8, although not explicitly taught by Li, Ning teaches: the trigger event comprises a predetermined utilization level of the at least one user- accessed application by at least one of the plurality of end-users (Col 20 ln 62-67 if the user does not interact with the particular application within a predefined amount of time (e.g., two weeks, two months, etc.) or the duration of interaction within a predefined amount of time is less than a threshold duration, application recommendation module 228 may select one or more alternative applications to substitute for the particular application and may output an indication of the selected alternative applications.)
One of ordinary skill in the art would have recognized that applying the teachings of Ning to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of application use as a trigger event.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0153857 (Li); in view of US 2013/0085886 (Satish); in view of US 2023/0297781 (Kurian).
As per claim 13, although not explicitly taught by Li, Kurian teaches: providing, to a model service, a model request, wherein the model request indicates at least one of the at least one user-accessed application, the domain identifier, or the subset of the plurality of additional entities; and receiving, from the model service, the machine learning model, wherein the model service retrieves the machine learning model from a plurality of stored machine learning models based on the model request ([0055] … the enterprise computing system 106 may be programmed to select a trained machine learning model that is unique to a domain with which a particular transcript is a part of In some embodiments, the selected trained machine learning model is unique to a domain associated with an enterprise and/or clients or potential clients thereof. [0068] … a machine learning model or type is selected based on the domain and domain data. The machine learning model or type may be selected from a list of available engines stored in a database or accessible over the network. In some embodiments, the second computer analyzes the domain and the domain data and references a lookup table of best machine learning model engines).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Li with the aforementioned teachings of Kurian with the motivation of using the best machine learning model for each domain (Kurian [0068]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Kurian to the system of Li would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of specific models for different domains.
Response to Arguments
Applicant's arguments filed 1/30/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 101, Applicant argues that the claims do not fall into any of the groupings of abstract ideas.
Examiner respectfully disagrees. Examiner maintains that the identified limitations in the claims describing generating and providing application recommendations (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers commercial interactions and marketing activities. The generation of recommendations to be provided to users pertains to commercial interactions and marketing activities. The Specification supports this finding as it describes user actions in response to the recommended application include purchasing ([0003] … wherein the user-accessed application causes provision of the recommendation to a computing device associated with the entity identifier. [0004] In some embodiments, the at least one application action is at least one of an application purchase [0044] … the user-accessed application 105 may include graphical user interfaces (GUIs) by which a user of a recommendation client device 103 may purchase and download a candidate application 106).
With respect to the rejection under 35 USC 101, Applicant argues that the claims are integrated into a practical application.
Examiner respectfully disagrees. In Ex Parte Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and the claims reflected the improvement identified in the specification. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. In contrast, the present claims recite a machine learning model previously trained but are not directed to an improvement to machine learning. Additionally, the use of machine learning to more rapidly respond to recommendation requests does not show an improvement. The court in FairWarning found that accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer did not show an improvement, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016).
With respect to the rejection under 35 USC 101, Applicant argues that the claims recite significantly more.
Examiner respectfully disagrees. Additional elements related to provision of the recommendation to the at least one user-accessed application, wherein the at least one user-accessed application causes provision of the recommendation to a computing device associated with the entity identifier do not improve the computer or technology; these additional elements only add insignificant extra-solution activities (data transmission). Similarly, additional elements in claims 2 and 12 related to a GUI and data are received from a remote feature service add additional elements that do not yield an improvement and only add insignificant extra-solution activities (data display/data gathering). With respect to data transmission/gathering limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). With respect to data display limitations, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)). Additional elements in claims 6, 8, and 11 related to action initiated … within the particular instance of the at least one user-accessed application, the at least one user- accessed application by at least one of the plurality of end-users, and the at least one application action is at least one of an application purchase, an application upgrade, an application downgrade, or an application removal are recited at a high level and only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
With respect to the rejection under 35 USC 103, Applicant argues that the art of record does not disclose the claimed features.
Examiner respectfully disagrees. With respect to ‘user-accessed application’, the claims do not recite any limitations that exclude a marketplace-type application; therefore, the marketplace applications that users access disclosed by the art of record is found to satisfy the claimed ‘user-accessed application’. Examiner further notes that the Specification describes the user-accessed application as one that provides recommendations of applications for purchase and download ([0044] … the user-accessed application 105 embodies a platform or portal accessible to the recommendation client device 103 and by which the recommendation client device 103 may perform application actions. For example, the user-accessed application 105 may include graphical user interfaces (GUIs) by which a user of a recommendation client device 103 may purchase and download a candidate application 106).
Li teaches ‘causing provision of the recommendation to the at least one user-accessed application, wherein the user-accessed application causes provision of the recommendation to a computing device’ ([0115] …The recommendation system may predict, based on a historical behavior log of the user, for example, a historical download record of the user, and a feature of the application market, for example, environment feature information such as a time and a location, a probability that the user downloads each candidate application in the recommendation system. The recommendation system may display candidate applications…. [0116] For example, when there are p display locations in the application market (where p is a positive integer), the recommendation system may select p candidate applications with highest predicted download probabilities for display, display an application with a higher predicted download probability in the p candidate applications at a higher-ranking location, and display an application with a lower predicted download probability in the p candidate applications at a lower-ranking location [0132] In addition, the execution device 110 includes a calculation module 111, an I/O interface 112, a preprocessing module 113, and a preprocessing module 114. The calculation module 111 may include a target model/rule 101 [0136] …the trained recommendation model may be the target model/rule 101. [0137] The target model/rule 101 can be used to predict whether the user selects the recommended object or predict a probability that the user selects the recommended object. [0139] … execution device 110 is provided with the input/output (I/O) interface 112, configured to exchange data with an external device; I/O interface (user accessed application) causes provision of recommendation to user device. [0143] Finally, the I/O interface 112 provides a processing result for the user. For example, in a recommendation system, the target model/rule 101 may be used to predict whether the target user selects a candidate recommended object or a probability of selecting a candidate recommended object, and obtain a recommendation result based on whether the target user selects the candidate recommended object or the probability of selecting the candidate recommended object. The recommendation result is presented to the client device 140, so that the recommendation result is provided for the user; target mode/rule 101 provides recommendation to I/O interface 112, also see [Fig. 3]).
Satish also discloses ‘causing provision of the recommendation to the at least one user-accessed application, wherein the user-accessed application causes provision of the recommendation to a computing device associated with the identifier of the particular domain’ ([0051] …a recommendation engine 310 and an application store 312. However, in other embodiments the recommendation engine 310 and/or the application store 312 may be located elsewhere, e.g. on different servers [0052] The user may access the application store 312 with the user device 304, e.g. with a web browser, purchase application, etc. When the user selects an application for purchase and/or download, or in response to the user requesting application information and/or a suggestion, the recommendation engine 310 may provide a recommendation [0053] … the recommendation engine 310 may suggest a number of suitable applications for the user device 304 when the user first connects to the application store 312. [0055] …recommendation engine 310 automatically makes application suggestions to the user, e.g. through an application store user interface 416; recommendations are relayed to user through the application store (user-accessed application)).
Examiner maintains that the combination of the art of record disclose the claimed limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11023905 (Choi) – discloses a system that makes recommendations of digital assets including applications.
THIS ACTION IS MADE FINAL. 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 ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625
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