CTFR 18/169,011 CTFR 100952 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendment The amendments filed 02/13/2026 which provides amendments to claims 1, 3-6, 12, 14-17 and 20 have been entered. Response to Arguments Applicant’s arguments with respect to 35 U.S.C § 101 filed 02/13/2026 (pages 1 of applicant’s arguments) have been fully considered but they are not persuasive. Applicant argues that the “claims 6,7, 17, and 18 which are related to the aggregation techniques, improve the functioning of the computer running the machine learning models and the machine learning models themselves” and citing paragraph [0013] of the specification to support the improvement. The examiner respectfully disagrees. Claim 6 is directed to identifying users and identifying the data of the users, both of which are mental processes. According to the MPEP 2106.04(d) the improvement cannot come from the judicial exception (abstract idea) alone. The additional elements recites extra-solution activity by storing, retrieving and moving data and training the machine learning model. Both of these do not show an improvement. Thus the 101 rejection is maintained. Applicant’s arguments with respect to 35 U.S.C § 102 filed 02/13/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 6-9 and 17-19 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. 101 Subject Matter Eligibility Analysis Step 1: Claims 6-9 and 17-19 are within the four statutory (a process, machine, manufacture or composition of matter.) Claims 6-9 describe a process and 17-19 describes a machine. With respect to claim 6: Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG. identifying the plurality of users that have viewed the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; (This is an abstract idea of a "Mental Process." The " identifying " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.) identifying a set of data for each of the plurality of users; and (This is an abstract idea of a "Mental Process." The " identifying " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.) Step 2A Prong 2: The judicial exception is not integrated into a practical application. Additional elements (some from claim 1): aggregating the set of data for each of the plurality of users. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). receiving, from a client device of a user, a digital component request comprising one or more contextual signals that describe an environment in which a selected digital component will be presented; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). providing the one or more contextual signals as input to a trained machine learning model that is trained to output, based on input contextual signals, predicted data about the user, wherein the trained machine learning model is trained using a set of aggregated data comprising, for each of a set of aggregation keys, aggregated data for a plurality of users having electronic resource views that match the aggregation key wherein each aggregation key comprises contextual data that describes a context in which an electronic resource is presented at client devices, the contextual data of each aggregation key comprising a resource locator that links to the electronic resource, and wherein the plurality of users for each aggregation key are identified based on the plurality of users viewing the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception and mere instructions to “apply” the exception using a generic computer component.) receiving, as an output of the trained machine learning model, the predicted data about the user; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). selecting one or more digital components based on the predicted data about the user; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). sending, to the client device, the one or more digital components for presentation at the client device. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional elements “aggregating…”, “receiving, from a client device of a user…”, “receiving, as an output of the trained machine learning model…”, “selecting…” and “sending…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)). The additional element “providing…” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)). When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept. Therefore, claim 6 is ineligible. With respect to claim 7: Step 2A Prong 1: claim 7, which incorporates the rejection of claim 6, does not recite an abstract idea. Step 2A Prong 2: The judicial exception is not integrated into a practical application. the set of data for each user comprises (i) one or more interests of the user or (ii) attributes of the user. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception). Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)). Therefore, claim 7 is ineligible. With respect to claim 8: Step 2A Prong 1: claim 8, which incorporates the rejection of claim 6, recites an additional abstract idea: generating the set of aggregated data comprises identifying the set of aggregation keys, including selecting, for inclusion in the set of aggregation keys, only aggregation keys for which the plurality of users satisfies a k-anonymity condition. (This is an abstract idea of a "Mental Process." The " identifying " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification of keys satisfying k-anonymity could be done manually by an individual.) Step 2A Prong 2: claim 8 does not recite any additional elements and thus cannot be integrated into a practical application. Step 2B: claim 8 does not recite an additional element. Therefore, claim 8 is ineligible. With respect to claim 9: Step 2A Prong 1: claim 9, which incorporates the rejection of claim 6, recites an additional abstract idea: applying differential privacy to the set of aggregated data by adjusting a count of users in the plurality of users for one or more aggregation keys. (This is an abstract idea of a "Mental Process." The " adjusting " step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The adjustment could be made manually by an individual.) Step 2A Prong 2: claim 9 does not recite any additional elements and thus cannot be integrated into a practical application. Step 2B: claim 9 does not recite an additional element. Therefore, claim 9 is ineligible. With respect to claim 17: The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 17. Therefore, claim 17 is ineligible. With respect to claim 18: The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible. With respect to claim 19: The claim recites similar limitations as corresponding to claim 8. Therefore, the same subject matter analysis that was utilized for claim 8, as described above, is equally applicable to claim 19. Therefore, claim 19 is ineligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-7, 12-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 2013/0254152 A1) in view of Ramage (US 10,824,958 B2) and Meredith (US 2016/0127499 A1) . Regarding claim 1, Zhang teaches: A computer-implemented method comprising: ([0049] “FIG. 3 presents a flow chart illustrating a method 300 for generating a recommendation based on a group-activity model in accordance with an embodiment.”) receiving, from a client device of a user, a digital component request comprising one or more contextual signals that describe an environment in which a selected digital component will be presented; ([0044] “During operation, population-modeling server 204 can receive user information 206 from a client device 202.1. User information 206 can include a group identifier as well as profile and behavior data for a user of client device 202.1. The behavior data, for example, can also include contextual information about the user of client device 202.1, such as raw contextual information or aggregated contextual information. The raw contextual information can include geolocation data, audio and/or video content, text, and any other information obtained from the user. The aggregated contextual information can include statistical information derived from the raw contextual information for a certain time window.”) providing the one or more contextual signals as input to a trained machine learning model that is trained to output, based on input contextual signals, predicted data about the user, ([0048] “In some embodiments, the user's client device can provide activity information about the user's behavior to a central system,” and [0049] “determines a group identifier for the local user (operation 304), and sends the information about the local user and the group identifier to a population-modeling server (operation 306).”) receiving, as an output of the trained machine learning model, the predicted data about the user; ([0045] “Population-modeling server 204 can use user information 206 to generate or update population knowledge 208, which can include information about a population group that the user is associated with. Population-modeling server 204 can also send population knowledge 208 to client device 202.1 so that client device can predict the user's behavior using population knowledge 208.”) selecting one or more digital components based on the predicted data about the user; and ([0039] “Client device 106 can use these user-activity models to predict the behavior of user 104 and to generate a recommendation (e.g., an advertisement or a coupon) that is targeted to the user's current activity.”) sending, to the client device, the one or more digital components for presentation at the client device. ([0043] “When population-modeling system 116 sends a group-activity model to client device 106, population modeling system 116 can also send one or more recommendations associated with the target activity to client device 106.”) Zhang does not teach wherein the trained machine learning model is trained using a set of aggregated data comprising, for each of a set of aggregation keys, aggregated data for a plurality of users having electronic resource views that match the aggregation key. However, Ramage does (Col 3 lines 11-14 “A global model refers to a model trained using population-level data from a collection of users operating one or more respective computing devices.”) Zhang and Ramage are considered analogous art to the claimed invention because they are in the same field of endeavor being recommendation models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage. One would want to do this to incorporate data from the overall population into the model. Neither Zhang nor Ramage teaches: wherein each aggregation key comprises contextual data that describes a context in which an electronic resource is presented at client devices, the contextual data of each aggregation key comprising a resource locator that links to the electronic resource, and wherein the plurality of users for each aggregation key are identified based on the plurality of users viewing the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; However Meredith does: wherein each aggregation key comprises contextual data that describes a context in which an electronic resource is presented at client devices, the contextual data of each aggregation key comprising a resource locator that links to the electronic resource, and wherein the plurality of users for each aggregation key are identified based on the plurality of users viewing the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; ([0020] “The method then aggregates the URL visitation data that is gathered from the various user endpoint devices. For example, for each URL visited by the user via the various endpoint devices, the method aggregates together a number of sessions, a number of clicks, and a size of content that is transferred. After the aggregation over the various endpoint devices, the aggregated URL visitation data for the user indicates: the URLs visited, the number of sessions, the number of clicks, and the size of content that is transferred. In sum, the aggregated URL visitation data appears as if the URL visitations occurred via a single user endpoint device.” And Zhang teaches the aggregation key being the group identifier) Zhang, Ramage and Meredith are considered analogous art to the claimed invention because they are in the same field of endeavor being aggregating data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage with the URL based aggregation of Meredith. One would motivated to do this as Zhang mentions the contextual data could be web page views (Zhang [0038]). Regarding claim 2, Zhang in view of Ramage and Meredith teaches claim 1 as outlined above. Zhang further teaches: the predicted data about the user comprises at least one of (i) one or more interests of the user or (ii) one or more attributes of the user. ([0034] “As another example, members of a social club or people that are mutual "friends" on an online social network may have similar activity interests, and may be likely to perform a certain activity when facing certain specific conditions. These group members may be interested in purchasing similar types of merchandise while shopping, or they may be interested in performing similar types of activities during the weekend.”). Regarding claim 3, Zhang in view of Ramage and Meredith teaches claim 1 as outlined above. Zhang further teaches: the one or more contextual signals comprise (i) at least a portion of an additional resource locator for an electronic resource being presented by the client device, (ii) a type of the client device, or (iii) a geographic location of the client device. ([0038] “The contextual information can include a geographic location, a motion trajectory, a time range, a logical name associated with a geographic location, Email/short messaging service (SMS) messages, audio recordings, shows or movies viewed by user 104, web page views, and any other information that is gathered about the behavior and activities of user 104.”) Regarding claim 4, Zhang in view of Ramage and Meredith teaches claim 1 as outlined above. Zhang further teaches: each aggregation key comprises at least one topic of interest. ([0065] “The group identifier can be any piece of information that can be used to match the user with other users, or to select user information for users that belong to a certain population group. For example, the group identifier can be an explicit group identifier that other users have indicated, or can be contextual information that is used to cluster different users together into a group (e.g., a current geographic location). As a further example, the group identifier can be information from the user's profile (e.g., personal interests, a home address, an occupation type, or a place of employment), or can be a social network attribute (e.g., an online screen name or a group name). The group identifier can also be a current or past activity, such as "hiking," "working," "grocery shopping," etc.”) Regarding claim 5, Zhang in view of Ramage and Meredith teaches claim 4 as outlined above. Zhang further teaches: the contextual data a type of device, or a geographic location. ([0038] “The contextual information can include a geographic location, a motion trajectory, a time range, a logical name associated with a geographic location , Email/short messaging service (SMS) messages, audio recordings, shows or movies viewed by user 104, web page views , and any other information that is gathered about the behavior and activities of user 104.”) Regarding claim 6, Zhang in view of Ramage and Meredith teaches claim 4 as outlined above. Zhang further teaches: identifying the plurality of users that have viewed the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; ([0035] “The client device can send the information about the user to the central system that combines information obtained from multiple users of a given group.”) identifying a set of data for each of the plurality of users; and ([0055] “During operation, the client device selects profile information from the user's profile (operation 402), and selects contextual information from a plurality of historical activities (operation 404).”) aggregating the set of data for each of the plurality of users. ([0055] “The client device then generates aggregated data from the contextual information (operation 406), and sends the profile information and aggregated data to the population-modeling server (operation 408).”) Regarding claim 7, Zhang in view of Ramage and Meredith teaches claim 6 as outlined above. Zhang further teaches: the set of data for each user comprises (i) one or more interests of the user or (ii) attributes of the user. ([0038] “The contextual information can include a geographic location, a motion trajectory, a time range, a logical name associated with a geographic location, Email/short messaging service (SMS) messages, audio recordings, shows or movies viewed by user 104, web page views, and any other information that is gathered about the behavior and activities of user 104.”). Regarding claim 12, Zhang teaches: A system comprising: one or more processors; and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operating comprising: ([0074] “FIG. 9 illustrates an exemplary computer system 902 that facilitates generating an activity-prediction model for a user population in accordance with an embodiment. Computer system 902 includes a processor 904, a memory 906, and a storage device 908. Memory 906 can include a volatile memory (e.g., RAM) that serves as a managed memory, and can be used to store one or more memory pools.”) receiving, from a client device of a user, a digital component request comprising one or more contextual signals that describe an environment in which a selected digital component will be presented; ([0044] “During operation, population-modeling server 204 can receive user information 206 from a client device 202.1. User information 206 can include a group identifier as well as profile and behavior data for a user of client device 202.1. The behavior data, for example, can also include contextual information about the user of client device 202.1, such as raw contextual information or aggregated contextual information. The raw contextual information can include geolocation data, audio and/or video content, text, and any other information obtained from the user. The aggregated contextual information can include statistical information derived from the raw contextual information for a certain time window.”) providing the one or more contextual signals as input to a trained machine learning model that is trained to output, based on input contextual signals, predicted data about the user, ([0048] “In some embodiments, the user's client device can provide activity information about the user's behavior to a central system,” and [0049] “determines a group identifier for the local user (operation 304), and sends the information about the local user and the group identifier to a population-modeling server (operation 306).”) receiving, as an output of the trained machine learning model, the predicted data about the user; ([0045] “Population-modeling server 204 can use user information 206 to generate or update population knowledge 208, which can include information about a population group that the user is associated with. Population-modeling server 204 can also send population knowledge 208 to client device 202.1 so that client device can predict the user's behavior using population knowledge 208.”) selecting one or more digital components based on the predicted data about the user; and ([0039] “Client device 106 can use these user-activity models to predict the behavior of user 104 and to generate a recommendation (e.g., an advertisement or a coupon) that is targeted to the user's current activity.”) sending, to the client device, the one or more digital components for presentation at the client device. ([0043] “When population-modeling system 116 sends a group-activity model to client device 106, population modeling system 116 can also send one or more recommendations associated with the target activity to client device 106.”) Zhang does not teach wherein the trained machine learning model is trained using a set of aggregated data comprising, for each of a set of aggregation keys, aggregated data for a plurality of users having electronic resource views that match the aggregation key. However, Ramage does (Col 3 lines 11-14 “A global model refers to a model trained using population-level data from a collection of users operating one or more respective computing devices.”) Zhang and Ramage are considered analogous art to the claimed invention because they are in the same field of endeavor being recommendation models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage. One would want to do this to incorporate data from the overall population into the model. Neither Zhang nor Ramage teaches: wherein each aggregation key comprises contextual data that describes a context in which an electronic resource is presented at client devices, the contextual data of each aggregation key comprising a resource locator that links to the electronic resource, and wherein the plurality of users for each aggregation key are identified based on the plurality of users viewing the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; However Meredith does: wherein each aggregation key comprises contextual data that describes a context in which an electronic resource is presented at client devices, the contextual data of each aggregation key comprising a resource locator that links to the electronic resource, and wherein the plurality of users for each aggregation key are identified based on the plurality of users viewing the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; ([0020] “The method then aggregates the URL visitation data that is gathered from the various user endpoint devices. For example, for each URL visited by the user via the various endpoint devices, the method aggregates together a number of sessions, a number of clicks, and a size of content that is transferred. After the aggregation over the various endpoint devices, the aggregated URL visitation data for the user indicates: the URLs visited, the number of sessions, the number of clicks, and the size of content that is transferred. In sum, the aggregated URL visitation data appears as if the URL visitations occurred via a single user endpoint device.” And Zhang teaches the aggregation key being the group identifier) Zhang, Ramage and Meredith are considered analogous art to the claimed invention because they are in the same field of endeavor being aggregating data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage with the URL based aggregation of Meredith. One would motivated to do this as Zhang mentions the contextual data could be web page views (Zhang [0038]). Regarding claim 13, Zhang in view of Ramage and Meredith teaches claim 12 as outlined above. Zhang further teaches: the predicted data about the user comprises at least one of (i) one or more interests of the user or (ii) one or more attributes of the user. ([0034] “As another example, members of a social club or people that are mutual "friends" on an online social network may have similar activity interests, and may be likely to perform a certain activity when facing certain specific conditions. These group members may be interested in purchasing similar types of merchandise while shopping, or they may be interested in performing similar types of activities during the weekend.”). Regarding claim 14, Zhang in view of Ramage and Meredith teaches claim 12 as outlined above. Zhang further teaches: the one or more contextual signals comprise (i) at least a portion of an additional resource locator for an electronic resource being presented by the client device, (ii) a type of the client device, or (iii) a geographic location of the client device. ([0038] “The contextual information can include a geographic location, a motion trajectory, a time range, a logical name associated with a geographic location, Email/short messaging service (SMS) messages, audio recordings, shows or movies viewed by user 104, web page views, and any other information that is gathered about the behavior and activities of user 104.”) Regarding claim 15, Zhang in view of Ramage and Meredith teaches claim 12 as outlined above. Zhang further teaches: each aggregation key comprises at least one topic of interest. ([0065] “The group identifier can be any piece of information that can be used to match the user with other users, or to select user information for users that belong to a certain population group. For example, the group identifier can be an explicit group identifier that other users have indicated, or can be contextual information that is used to cluster different users together into a group (e.g., a current geographic location). As a further example, the group identifier can be information from the user's profile (e.g., personal interests, a home address, an occupation type, or a place of employment), or can be a social network attribute (e.g., an online screen name or a group name). The group identifier can also be a current or past activity, such as "hiking," "working," "grocery shopping," etc.”) Regarding claim 16, Zhang in view of Ramage and Meredith teaches claim 15 as outlined above. Zhang further teaches: the contextual data a type of device, or a geographic location. ([0038] “The contextual information can include a geographic location, a motion trajectory, a time range, a logical name associated with a geographic location , Email/short messaging service (SMS) messages, audio recordings, shows or movies viewed by user 104, web page views , and any other information that is gathered about the behavior and activities of user 104.”) Regarding claim 17, Zhang in view of Ramage and Meredith teaches claim 16 as outlined above. Zhang further teaches: identifying the plurality of users that have viewed the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; ([0035] “The client device can send the information about the user to the central system that combines information obtained from multiple users of a given group.”) identifying a set of data for each of the plurality of users; and ([0055] “During operation, the client device selects profile information from the user's profile (operation 402), and selects contextual information from a plurality of historical activities (operation 404).”) aggregating the set of data for each of the plurality of users. ([0055] “The client device then generates aggregated data from the contextual information (operation 406), and sends the profile information and aggregated data to the population-modeling server (operation 408).”) Regarding claim 18, Zhang in view of Ramage and Meredith teaches claim 17 as outlined above. Zhang further teaches: the set of data for each user comprises (i) one or more interests of the user or (ii) attributes of the user. ([0038] “The contextual information can include a geographic location, a motion trajectory, a time range, a logical name associated with a geographic location, Email/short messaging service (SMS) messages, audio recordings, shows or movies viewed by user 104, web page views, and any other information that is gathered about the behavior and activities of user 104.”). Regarding claim 20, Zhang teaches: A non-transitory computer readable medium carrying instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: ([0078] “The data structures and code described in this detailed description are typically stored on a computer-readable storage medium, which may be any device or medium that can store code and/or data for use by a computer system.”). receiving, from a client device of a user, a digital component request comprising one or more contextual signals that describe an environment in which a selected digital component will be presented; ([0044] “During operation, population-modeling server 204 can receive user information 206 from a client device 202.1. User information 206 can include a group identifier as well as profile and behavior data for a user of client device 202.1. The behavior data, for example, can also include contextual information about the user of client device 202.1, such as raw contextual information or aggregated contextual information. The raw contextual information can include geolocation data, audio and/or video content, text, and any other information obtained from the user. The aggregated contextual information can include statistical information derived from the raw contextual information for a certain time window.”) providing the one or more contextual signals as input to a trained machine learning model that is trained to output, based on input contextual signals, predicted data about the user, ([0048] “In some embodiments, the user's client device can provide activity information about the user's behavior to a central system,” and [0049] “determines a group identifier for the local user (operation 304), and sends the information about the local user and the group identifier to a population-modeling server (operation 306).”) receiving, as an output of the trained machine learning model, the predicted data about the user; ([0045] “Population-modeling server 204 can use user information 206 to generate or update population knowledge 208, which can include information about a population group that the user is associated with. Population-modeling server 204 can also send population knowledge 208 to client device 202.1 so that client device can predict the user's behavior using population knowledge 208.”) selecting one or more digital components based on the predicted data about the user; and ([0039] “Client device 106 can use these user-activity models to predict the behavior of user 104 and to generate a recommendation (e.g., an advertisement or a coupon) that is targeted to the user's current activity.”) sending, to the client device, the one or more digital components for presentation at the client device. ([0043] “When population-modeling system 116 sends a group-activity model to client device 106, population modeling system 116 can also send one or more recommendations associated with the target activity to client device 106.”) Zhang does not teach wherein the trained machine learning model is trained using a set of aggregated data comprising, for each of a set of aggregation keys, aggregated data for a plurality of users having electronic resource views that match the aggregation key. However, Ramage does (Col 3 lines 11-14 “A global model refers to a model trained using population-level data from a collection of users operating one or more respective computing devices.”) Zhang and Ramage are considered analogous art to the claimed invention because they are in the same field of endeavor being recommendation models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage. One would want to do this to incorporate data from the overall population into the model. Neither Zhang nor Ramage teaches: wherein each aggregation key comprises contextual data that describes a context in which an electronic resource is presented at client devices, the contextual data of each aggregation key comprising a resource locator that links to the electronic resource, and wherein the plurality of users for each aggregation key are identified based on the plurality of users viewing the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; However Meredith does: wherein each aggregation key comprises contextual data that describes a context in which an electronic resource is presented at client devices, the contextual data of each aggregation key comprising a resource locator that links to the electronic resource, and wherein the plurality of users for each aggregation key are identified based on the plurality of users viewing the electronic resource of the aggregation key in a context that matches at least a portion of the contextual data of the aggregation key; ([0020] “The method then aggregates the URL visitation data that is gathered from the various user endpoint devices. For example, for each URL visited by the user via the various endpoint devices, the method aggregates together a number of sessions, a number of clicks, and a size of content that is transferred. After the aggregation over the various endpoint devices, the aggregated URL visitation data for the user indicates: the URLs visited, the number of sessions, the number of clicks, and the size of content that is transferred. In sum, the aggregated URL visitation data appears as if the URL visitations occurred via a single user endpoint device.” And Zhang teaches the aggregation key being the group identifier) Zhang, Ramage and Meredith are considered analogous art to the claimed invention because they are in the same field of endeavor being aggregating data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage with the URL based aggregation of Meredith. One would motivated to do this as Zhang mentions the contextual data could be web page views (Zhang [0038]) . 07-21-aia AIA Claim s 8-9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Ramage, Meredith and Holboke ( US 11,893,462 B2) . Regarding claim 8, Zhang in view of Ramage and Meredith teaches claim 6 as outlined above. Neither Zhang or Ramage teach: generating the set of aggregated data comprises identifying the set of aggregation keys, including selecting, for inclusion in the set of aggregation keys, only aggregation keys for which the plurality of users satisfies a k-anonymity condition . However, Holboke does: generating the set of aggregated data comprises identifying the set of aggregation keys, including selecting, for inclusion in the set of aggregation keys, only aggregation keys for which the plurality of users satisfies a k-anonymity condition . (Col 14 lines 58-63 “In some example embodiments, the training data is treated before it is used to train the model (e.g., via k-Anonymity or differential privacy) to obfuscate the data and prevent the unencrypted data from being derived from the model.”) Zhang, Ramage, Meredith and Holboke are considered analogous art to the claimed invention because they are in the same field of endeavor being recommendation/prediction models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage and the privacy preserving techniques of Holboke. One would want to do this to ensure privacy of user data. Regarding claim 9, Zhang in view of Ramage and Meredith teaches claim 6 as outlined above. Holboke further teaches: comprising applying differential privacy to the set of aggregated data by adjusting a count of users in the plurality of users for one or more aggregation keys. (Col 14 lines 58-63 “In some example embodiments, the training data is treated before it is used to train the model (e.g., via k-Anonymity or differential privacy) to obfuscate the data and prevent the unencrypted data from being derived from the model.”) Regarding claim 19, Zhang in view of Ramage and Meredith teaches claim 17 as outlined above. Neither Zhang or Ramage teach: generating the set of aggregated data comprises identifying the set of aggregation keys, including selecting, for inclusion in the set of aggregation keys, only aggregation keys for which the plurality of users satisfies a k-anonymity condition . However, Holboke does: generating the set of aggregated data comprises identifying the set of aggregation keys, including selecting, for inclusion in the set of aggregation keys, only aggregation keys for which the plurality of users satisfies a k-anonymity condition . (Col 14 lines 58-63 “In some example embodiments, the training data is treated before it is used to train the model (e.g., via k-Anonymity or differential privacy) to obfuscate the data and prevent the unencrypted data from being derived from the model.”) Zhang, Ramage, Meredith and Holboke are considered analogous art to the claimed invention because they are in the same field of endeavor being recommendation/prediction models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage and the privacy preserving techniques of Holboke. One would want to do this to ensure privacy of user data . 07-21-aia AIA Claim s 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Ramage, Meredith and Dave (US 10,938,979 B1) . Regarding claim 10, Zhang in view of Ramage and Meredith teaches claim 1 as outlined above. Neither Zhang or Ramage teach: comprising training the trained machine learning model using a transfer learning technique. However, Dave does: comprising training the trained machine learning model using a transfer learning technique. (Col 11 lines 19-26 “However, in order to provide users with personalized and dynamic content card recommendations, the model generation module 110 can adapt the base model 108 a - 108 n for the user to generate the user-specific content recommendation model 102 c that is then transferred to the mobile device 102 . To accomplish this goal, the model generation module 110 fine tunes the selected base model via transfer learning to generate the user-specific model.”) Zhang, Ramage, Meredith and Dave are considered analogous art to the claimed invention because they are in the same field of endeavor being recommendation/prediction models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data gathering and overall system structure of Zhang with the model of Ramage and the transfer learning of Dave. One would want to do this for faster training and so the models can work on smaller datasets. Regarding claim 11, Zhang in view of Ramage, Meredith and Dave teaches claim 10 as outlined above. Ramage further teaches: training the trained machine learning model comprises adding the set of aggregated data as labels or features of the trained machine learning model. (Col 4 lines 20-27 “During stage (A), the computing system 120 obtains a collection of population-level training data 122 , and inputs the population-level training data 122 to a model training module 140 . In general, the population-level training data 122 represents multiple observations collected by multiple client devices, where the observations are associated with one or more activities performed by each of a collection of users.”) Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL P GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DANIEL GRUSZKA/Examiner, Art Unit 2121 /Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121 Application/Control Number: 18/169,011 Page 2 Art Unit: 2121 Application/Control Number: 18/169,011 Page 3 Art Unit: 2121 Application/Control Number: 18/169,011 Page 4 Art Unit: 2121 Application/Control Number: 18/169,011 Page 5 Art Unit: 2121 Application/Control Number: 18/169,011 Page 6 Art Unit: 2121 Application/Control Number: 18/169,011 Page 7 Art Unit: 2121 Application/Control Number: 18/169,011 Page 8 Art Unit: 2121 Application/Control Number: 18/169,011 Page 9 Art Unit: 2121 Application/Control Number: 18/169,011 Page 10 Art Unit: 2121 Application/Control Number: 18/169,011 Page 11 Art Unit: 2121 Application/Control Number: 18/169,011 Page 12 Art Unit: 2121 Application/Control Number: 18/169,011 Page 13 Art Unit: 2121 Application/Control Number: 18/169,011 Page 14 Art Unit: 2121 Application/Control Number: 18/169,011 Page 15 Art Unit: 2121 Application/Control Number: 18/169,011 Page 16 Art Unit: 2121 Application/Control Number: 18/169,011 Page 17 Art Unit: 2121 Application/Control Number: 18/169,011 Page 18 Art Unit: 2121 Application/Control Number: 18/169,011 Page 19 Art Unit: 2121 Application/Control Number: 18/169,011 Page 20 Art Unit: 2121 Application/Control Number: 18/169,011 Page 21 Art Unit: 2121 Application/Control Number: 18/169,011 Page 22 Art Unit: 2121 Application/Control Number: 18/169,011 Page 23 Art Unit: 2121 Application/Control Number: 18/169,011 Page 24 Art Unit: 2121 Application/Control Number: 18/169,011 Page 25 Art Unit: 2121 Application/Control Number: 18/169,011 Page 26 Art Unit: 2121 Application/Control Number: 18/169,011 Page 27 Art Unit: 2121
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