DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
An Information Disclosure Statement (IDS) has not been submitted as of the mailing of the last Office Action dated 6 January 2026. Applicant is reminded of the continuing obligation under 37 CFR 1.56 to timely apprise the Office of any information which is material to patentability of the claims under consideration in this application.
Introductory Remarks
In response to communications filed on 6 April 2026, claims 1 and 12-18 are amended per Applicant's request. No claims were cancelled. No claims were withdrawn. No new claims were added. Therefore, claims 1-20 are presently pending in the application, of which claims 1, 13, and 18 are presented in independent form.
The previously raised 112(b), indefiniteness rejection of claim 17 is withdrawn in view of the amendments to the claims.
The previously raised 103 rejection of the pending claims is withdrawn in view of the amendments to the claims. A new ground(s) of rejection has been issued.
Response to Arguments
Applicant’s arguments filed 6 April 2026 with respect to the 112(b), indefiniteness rejection of claim 17 (see Remarks, p. 14) have been fully considered and are persuasive. The 112(b), indefiniteness rejection has been accordingly withdrawn in view of the amendments to the claim.
Applicant’s arguments filed 6 April 2026 with respect to the rejection of the claims under 35 U.S.C. 103 (see Remarks, p. 15-19) have been fully considered but are moot because the arguments do not apply to the new references (and thus new combination of references) being used to reject the amended claim limitations that Applicant’s arguments were directed to.
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, 7, 10-11, 13, 15-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Priyadarshan et al. (“Priyadarshan”) (US 2012/0041817 A1), in view of Barlik et al. (“Barlik”) (US 2020/0159955 A1), in further view of Eldering et al. (“Eldering”) (US 2002/0123928 A1, incorporating by reference DeWolf et al. (“IBR-DeWolf”) (App. No. 09/782,962, published as US 2002/0111172 A1) at [0010]).
Regarding claim 1: Priyardashan teaches A method, comprising:
generating, by a hardware processor, a plurality of group profiles comprising a first group profile associated with a first group corresponding to a first set of devices and a second group profile associated with a second group corresponding to a second set of devices (Priyardashan, [0017] and [0106-0107], where each user is grouped into one or more targeted segments based on user characteristics, where stored user characteristics include an associated device (see Priyardashan, [0008] and [0074]), i.e., each user being associated with one or more devices (Priyardashan, [0074], [0098] and [0100-0101]). See Priyardashan, [0085], where the segment database 114 stores defined segments and associations between the segments and users and/or invitational content that should be targeted to users associated with the segments (thus, these segments corresponding to the claimed “group profiles”). See Priyardashan, [0262-0263], where the disclosed system may include a computing device 3500 including a processing unit (CPU or processor) 3520 that is controlled by software modules for performing the various actions. See Priyardashan, [0086], where a targeted segment can be as simple as a single user characteristic identifier and a single user characteristic value, or more complex targeted segments can be defined consisting of one or more identifiers with one or more values associated with each identifier (i.e., “[each group] corresponding to a [first/second] set of devices”));
receiving a request for content associated with a first device of the first set of devices corresponding to the first group (Priyardashan, [0070], where the system receives a request for electronic content from one of user terminals 102);
determining, based upon a privacy classification associated with the first device, whether to use a group profile associated with the first device or a set of device information of the first device; responsive to the privacy classification corresponding to reception of a request not to use the set of device information of the first device, determining to use the first group profile, of the plurality of group profiles, instead of the set of device information of the first device based upon the request for content; and selecting a first content item for presentation via the first device based upon the first group profile (Priyardashan, [0094-0097], where when users “opt in” for participating in collection of personal information data during registration for delivery systems, the system may utilize gathered personal information data with respect to delivering (ad) content. See Priyardshan, [0097-0098] and [0102], where when users “opt out”, i.e., selectively block the use of, or access to, personal information data, content can be selected and delivered to users (when lacking all or a portion of such personal information data) by inferring preferences based on non-personal information data or a bare minimum amount of personal information. Interests in various products or user intent can be inferred based on analyzing user characteristic data and categorizing the user into a behavioral segment representative of that interest or intent (Priyardashan, [0226]), i.e., user characteristics are used to categorize a user into one or more targeted segments, and then invitational content is then delivered to the user based on their inclusion in the targeted segments (Priyardashan, [0106-0107]).
Thus, as seen, when users “opt in”, the system determines to use the user’s personal information data for delivering ad content. Otherwise, when a user “opts out” by selectively blocking access to personal information data, the system relies on inferred characteristics for grouping users into targeted segments (i.e., “determining to use the first group profile, of the plurality of group profiles, instead of the set of device information of the first device based upon the request for content”), and then delivering invitational content to be displayed/presented to the user based on their inclusion in the targeted segments (Priyardashan, [0071], [0078], and [0090]) (i.e., “selecting a first content item for presentation via the first device based upon the first group profile”)) … .
Priyardashan does not appear to explicitly teach [selecting the first content item for presentation], wherein at least one of: the method comprises determining that one or more first privacy regulations apply to the first device based upon first location information for the first device corresponding to the first device being covered by a first jurisdiction regulated by the one or more first privacy regulations, and determining that a first privacy requirement defined by the one or more first privacy regulations is indicative of one or more sets of information, wherein based upon the first device being associated with the one or more first privacy regulations defining the first privacy requirement indicative of the one or more sets of information, the selecting the first content item is performed without using the one or more sets of information indicated by the first privacy requirement while selecting a second content item for presentation via a second device, to which the first privacy requirement does not apply based upon second location information for the second device corresponding to the second device not being covered by the first jurisdiction regulated by the one or more first privacy regulations, is performed with the one or more sets of information indicated by the first privacy requirement; or generating the first group profile comprises determining that one or more second privacy regulations apply to the first device based upon the first location information for the first device corresponding to the first device being covered by the first jurisdiction regulated by the one or more second privacy regulations, determining that a second privacy requirement defined by the one or more second privacy regulations requires exclusion of at least some information and excluding the at least some information from the first group profile based upon the second privacy requirement that requires exclusion of the at least some information while generating the second group profile associated with the second set of devices, to which the second privacy requirement does not apply based upon location information for the second set of devices corresponding to the second set of devices not being covered by the first jurisdiction regulated by the one or more second privacy regulations, is performed based upon a different privacy requirement that does not require exclusion of the at least some information.
Barlik teaches [selecting the first content item for presentation], wherein at least one of: the method comprises determining that one or more first privacy regulations apply to the first device based upon first location information for the first device corresponding to the first device being covered by a first jurisdiction regulated by the one or more first privacy regulations, and determining that a first privacy requirement defined by the one or more first privacy regulations is indicative of one or more sets of information (Barlik, [0027], where the third party systems request online user settings (e.g., opt ins and opt outs) with respect to privacy rules and regulations, identifying an online user and a location of the online user to allow the clearinghouse 112 to determine whether the online user has opted into or out of respective rules and regulations for a jurisdiction associated with the online user’s location, and may delete some online user information for compliance based on the online user’s settings, as well as activating/deactivating some tracking, analysis, and content generation functions based on the online user’s settings. See, e.g., Barlik, [0028], where each of the data privacy law opt in or opt out indicators are associated with a particular data privacy law and/or specific permission associated with a particular data privacy law, e.g., a first indicator (FLAG 1 as depicted in [FIG. 1]) may be associated with the California Consumer Privacy Act (CCPA) permission regarding the tracking and use of data associated with the user device for monetization activity by a third party) … .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan and Barlik (hereinafter “Priyardashan as modified”) with the motivation of ensuring data privacy compliance even within different jurisdictions having one or more unique data privacy laws with distinct compliance requirements (Barlik, [0001] and [0011-0013]).
Priyardashan as modified does not appear to explicitly teach the selecting the first content item is performed without using the one or more sets of information indicated by the first privacy requirement while selecting a second content item for presentation via a second device, to which the first privacy requirement does not apply based upon second location information for the second device corresponding to the second device not being covered by the first jurisdiction regulated by the one or more first privacy regulations, is performed with the one or more sets of information indicated by the first privacy requirement; or generating the first group profile comprises determining that one or more second privacy regulations apply to the first device based upon the first location information for the first device corresponding to the first device being covered by the first jurisdiction regulated by the one or more second privacy regulations, determining that a second privacy requirement defined by the one or more second privacy regulations requires exclusion of at least some information and excluding the at least some information from the first group profile based upon the second privacy requirement that requires exclusion of the at least some information while generating the second group profile associated with the second set of devices, to which the second privacy requirement does not apply based upon location information for the second set of devices corresponding to the second set of devices not being covered by the first jurisdiction regulated by the one or more second privacy regulations, is performed based upon a different privacy requirement that does not require exclusion of the at least some information.
Eldering teaches the selecting the first content item is performed without using the one or more sets of information indicated by the first privacy requirement while selecting a second content item for presentation via a second device, to which the first privacy requirement does not apply based upon second location information for the second device corresponding to the second device not being covered by the first jurisdiction regulated by the one or more first privacy regulations, is performed with the one or more sets of information indicated by the first privacy requirement (Eldering, [0029-0030], where ad profiles are correlated with one or more subscriber profiles or one or more groups of subscribers, in which an operator is applied to the subscriber profiles to determine if a particular ad is applicable to the subscriber. See Eldering, [0154-0155], where the groups are formed by developing a restricted operator or set of operators to apply to the subscriber profiles, where the restricted operator allows the measurement of certain parameters (non-deterministic) to be made, but prohibits the measurement of other parameters (privacy invading determinations). In other words, the operators are used to group or cluster subscribers, and proper construction of the operators prevents inappropriate (privacy violating) measurements from being made.
See Barlik, [0027], where the third party systems request online user settings (e.g., opt ins and opt outs) with respect to privacy rules and regulations, identifying an online user and a location of the online user to allow the clearinghouse 112 to determine whether the online user has opted into or out of respective rules and regulations for a jurisdiction associated with the online user’s location (i.e., “the first location…corresponding to the first device being covered by the first jurisdiction regulated by the one or more second privacy regulations”)); or
generating the first group profile comprises determining that one or more second privacy regulations apply to the first device based upon the first location information for the first device corresponding to the first device being covered by the first jurisdiction regulated by the one or more second privacy regulations, determining that a second privacy requirement defined by the one or more second privacy regulations requires exclusion of at least some information and excluding the at least some information from the first group profile based upon the second privacy requirement that requires exclusion of the at least some information while generating the second group profile associated with the second set of devices, to which the second privacy requirement does not apply based upon location information for the second set of devices corresponding to the second set of devices not being covered by the first jurisdiction regulated by the one or more second privacy regulations, is performed based upon a different privacy requirement that does not require exclusion of the at least some information (Eldering, [0114] and [0156], where groups of subscribers may be based on geographic segmentation, e.g., grouping is based on geographic mechanisms for determining applicability of an ad. See Eldering, [0154-0155], where the groups are formed by developing a restricted operator or set of operators to apply to the subscriber profiles, where the restricted operator allows the measurement of certain parameters (non-deterministic) to be made, but prohibits the measurement of other parameters (privacy invading determinations). In other words, the operators are used to group or cluster subscribers, and proper construction of the operators prevents inappropriate (privacy violating) measurements from being made.
See Barlik, [0027], where the third party systems request online user settings (e.g., opt ins and opt outs) with respect to privacy rules and regulations, identifying an online user and a location of the online user to allow the clearinghouse 112 to determine whether the online user has opted into or out of respective rules and regulations for a jurisdiction associated with the online user’s location (i.e., “the first location…corresponding to the first device being covered by the first jurisdiction regulated by the one or more second privacy regulations”). See also Barlik, [0015], [0018], [0020], and [0028], where the user privacy settings may be in accordance with privacy laws and regulations by various jurisdictions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan as modified and Eldering (hereinafter “Priyardashan as modified”) with the motivation of increasing the effectiveness of ads by delivering ads that have been determined to be relevant to subscribers even when profiles are not comprehensive and also in order to protect users’ privacy (see, e.g., Eldering, [0019-0022] and [0089]).
Regarding claim 2: Priyardashan as modified teaches The method of claim 1, wherein:
the different privacy requirement used to generate the second group profile is based upon the second location information for the second device corresponding to the second device being covered by a second jurisdiction regulated by one or more different privacy regulations defining the different privacy requirement (Eldering, [0114] and [0156], where groups of subscribers may be based on geographic segmentation, e.g., grouping is based on geographic mechanisms for determining applicability of an ad. See Barlik, [0015], [0018], [0020], and [0027-0028], where the user privacy settings may be in accordance with location-based privacy laws and regulations by various jurisdictions).
Regarding claim 3: Priyardashan as modified teaches The method of claim 1, comprising:
prior to the receiving the request for content, generating a first user profile associated with the first device, wherein the first user profile comprises the first group profile and first identification information associated with the first device; storing the first user profile in a user profile database; and responsive to the receiving the request for content, analyzing the user profile database based upon second identification information comprised within the request for content to identify the first user profile, wherein the selecting the first content item based upon the first group profile is performed based upon a determination that the first user profile comprises the first group profile (Priyardashan, [0074] and [0099-0100], where the system includes a unique user identifier (UUID) database 116 used for managing sessions with various user terminal devices 102, in which one or more devices are assigned to a same entry in the UUID database 116 (i.e., implying that “a first user profile associated with the first device” was generated and stored in the database, as claimed). The system identifies a user in the UUID database 116 when the delivery system 106 receives a request for content, the request including some identifying information associated with the requesting user terminal or the associated user. This information can then be correlated to an entry in the UUID database 116 (implying that the first user profile associated with the first device was generated “prior to receiving the request for content” as claimed).
See Priyardashan, [0088-0090], where a segment assigner module applies a set of user characteristics associated with a user (including segments to which a user has been previously assigned) to assign the user to one or more targeted segments by obtaining a set of user characteristic values from the user profile database 120 and/or form the user’s activities during the current session, and assigning a user to the one or more defined targeted segments in the segment database 114. Based on the assigned segments, the user profile database 120 can be updated to reflect the segment assignments (i.e., “wherein the user profile comprises the first group profile”). The assigned segments can then be used to select targeted invitational content to be presented to a user (i.e., “wherein the selecting the first content item based upon the first group profile is performed based upon a determination that the first user profile comprises the first group profile”)).
Although Priyardashan does not appear to explicitly state that the devices are associated with the user profiles in the user profile database as claimed (but rather separates this as an entry in the UUID database), Priyardashan states using user profile information and/or the user’s activities during the current session, which is information from the UUID database, for selecting content for delivery; and that users must share some information about the device identification number so that the content delivery system knows which device to send the content back to (Priyardashan, [0008]). Therefore, one of ordinary skill in the art would have been suggested to have modified Priyardashan to have the device information from the UUID database incorporated in the user profile information as well, with the motivation of faster information retrieval, i.e., reduced lookup requests, due to denormalization of information (i.e., co-locating all the information in a single record as opposed to querying multiple databases for returning multiple records related to the same user).
Regarding claim 4: Priyardashan as modified teaches The method of claim 1, comprising:
prior to the receiving the request for content, generating a first user profile associated with the first device, wherein the first user profile comprises an indication of the first group profile and first identification information associated with the first device; storing the first user profile in a user profile database; and responsive to the receiving the request for content, analyzing the user profile database based upon second identification information comprised within the request for content to identify the first user profile, wherein the selecting the first content item based upon the first group profile is performed based upon a determination that the first user profile comprises the indication of the first group profile (Priyardashan, [0074] and [0099-0100], where the system includes a unique user identifier (UUID) database 116 used for managing sessions with various user terminal devices 102, in which one or more devices are assigned to a same entry in the UUID database 116 (i.e., implying that “a first user profile associated with the first device” was generated and stored in the database, as claimed). The system identifies a user in the UUID database 116 when the delivery system 106 receives a request for content, the request including some identifying information associated with the requesting user terminal or the associated user. This information can then be correlated to an entry in the UUID database 116 (implying that the first user profile associated with the first device was generated “prior to receiving the request for content” as claimed).
See Priyardashan, [0088-0090], where a segment assigner module applies a set of user characteristics associated with a user (including segments to which a user has been previously assigned) to assign the user to one or more targeted segments by obtaining a set of user characteristic values from the user profile database 120 and/or from the user’s activities during the current session, and assigning a user to the one or more defined targeted segments in the segment database 114. Based on the assigned segments, the user profile database 120 can be updated to reflect the segment assignments (i.e., “wherein the user profile comprises the first group profile”). The assigned segments can then be used to select targeted invitational content to be presented to a user (i.e., “wherein the selecting the first content item based upon the first group profile is performed based upon a determination that the first user profile comprises the first group profile”)).
Although Priyardashan does not appear to explicitly state that the devices are associated with the user profiles in the user profile database as claimed (but rather separates this as an entry in the UUID database), Priyardashan states using user profile information and/or the user’s activities during the current session, which is information from the UUID database, for selecting content for delivery; and that users must share some information about the device identification number so that the content delivery system knows which device to send the content back to (Priyardashan, [0008]). Therefore, one of ordinary skill in the art would have been suggested to have modified Priyardashan to have the device information from the UUID database incorporated in the user profile information as well, with the motivation of faster information retrieval, i.e., reduced lookup requests, due to denormalization of information (i.e., co-locating all the information in a single record as opposed to querying multiple databases for returning multiple records related to the same user).
Regarding claim 7: Priyardashan as modified teaches The method of claim 1, wherein:
the first group profile comprises first group information associated with the first set of devices corresponding to the first group; the first group profile is generated based upon a first plurality of sets of device information associated with the first set of devices corresponding to the first group the second group profile comprises second group information associated with the second set of devices corresponding to the second group; and the second group profile is generated based upon a second plurality of sets of device information associated with the second set of devices corresponding to the second group (Priyardashan, [0017], [0085], and [0106-0107], where each user is grouped into one or more targeted segments based on user characteristics, where stored user characteristics include an associated device (see Priyardashan, [0008] and [0074]), i.e., each user being associated with one or more devices (Priyardashan, [0074], [0098] and [0100-0101]). The segment database 114 stores defined segments and associations between the segments and users and/or invitational content that should be targeted to users associated with the segments, where a targeted segment can be defined based on one or more user characteristics. See Priyardashan, [0085], where the segment database 114 stores defined segments and associations between the segments and users and/or invitational content that should be targeted to users associated with the segments (thus, these segments corresponding to the claimed “group profiles”). See Priyardashan, [0086], where a targeted segment can be as simple as a single user characteristic identifier and a single user characteristic value, or more complex targeted segments can be defined consisting of one or more identifiers with one or more values associated with each identifier (i.e., “[each group] corresponding to a [first/second] set of devices”)).
Regarding claim 10: Priyardashan as modified teaches The method of claim 1, wherein:
the first location information is obtained from a first global positioning system (GPS) receiver of the first device; and the second location information is obtained from a second GPS receiver of the second device (IBR-Dewolf, [0054], where the wireless device 110 learns its location by utilizing the GPS chipset contained within it, where the GPS chipset receives the location coordinates for the wireless device 110 from the GPS network 160, and the GPS chipset knows the location of the device at all times, and the wireless device 110 may transmit the location data. The wireless device 110 then stores the location data. See Priyardashan, [0074], where the content delivery system includes communication with various user terminal devices 102).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan as modified and Eldering with the motivation of utilizing built-in components for determining location, which improves convenience for the developer, as well as providing precise location tracking.
Regarding claim 11: Priyardashan as modified teaches The method of claim 1, wherein:
the first location information is obtained from device information of the first device; and the second location information is obtained from second device information of the second device (IBR-Dewolf, [0041], where the location of the subscriber can be identified by the wireless system, e.g., determining the difference in time that a signal is received at three towers, or the difference in the angle that the signal is received at two towers connected to the network such as a telecommunications network (see, e.g., IBR-Dewolf, [0040]). See Priyardashan, [0074], where the content delivery system includes communication with various user terminal devices 102).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan as modified and Eldering with the motivation of utilizing built-in components for determining location, which improves convenience for the developer, as well as providing precise location tracking.
Regarding claim 13: Claim 13 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Priyardashan teaches A computing device comprising: a hardware processor; and a memory device comprising processor-executable instructions that when executed by the hardware processor cause performance of operations, the operations comprising [the claimed steps] (Priyardashan, [0262-0263], where the disclosed system may include a computing device 3500 including a processing unit (CPU or processor) 3520 that is controlled by software modules for performing the various actions; and storage devices 3560 including computer readable storage media providing nonvolatile storage of computer readable instructions, program modules, and other data for computing device 3500 to carry out the disclosed functions).
Regarding claim 15: Priyardashan as modified teaches The computing device of claim 13, wherein:
the privacy classification corresponds to reception of a request not to use the set of device information of the first device (Priyardashan, [0097], where users may selectively block the use of, or access to, personal information data, e.g., users may select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services).
Regarding claim 16: Priyardashan as modified teaches The computing device of claim 13, wherein:
the privacy classification corresponds to consent to use the set of device information for selection of content items not being received (Priyardashan, [0097], where users may selectively block the use of, or access to, personal information data, e.g., users may select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services).
Regarding claim 18: Claim 18 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Priyardashan teaches A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising [the claimed steps] (Priyardashan, [0262-0263], where the disclosed system may include a computing device 3500 including a processing unit (CPU or processor) 3520 that is controlled by software modules for performing the various actions; and storage devices 3560 including computer readable storage media providing nonvolatile storage of computer readable instructions, program modules, and other data for computing device 3500 to carry out the disclosed functions).
Regarding claim 19: Claim 19 recites substantially the same claim limitations as claim 2, and is rejected for the same reasons.
Claims 5-6, 8-9, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Priyadarshan et al. (“Priyadarshan”) (US 2012/0041817 A1), in view of Barlik et al. (“Barlik”) (US 2020/0159955 A1), in further view of Eldering et al. (“Eldering”) (US 2002/0123928 A1, incorporating by reference DeWolf et al. (“IBR-DeWolf”) (App. No. 09/782,962, published as US 2002/0111172 A1) at [0010]), in further view of Fordyce, III et al. (“Fordyce”) (US 2011/0302039 A1).
Regarding claim 5: Priyardashan as modified teaches The method of claim 1, but does not appear to explicitly teach comprising: responsive to determining that the first content item is presented via the first device, generating an identifier; transmitting the identifier to a server associated with the first content item; receiving conversion information, wherein the conversion information is indicative of a conversion event associated with the presentation of the first content item via the first device; and determining that the conversion event is associated with the presentation of the first content item via the first device based upon a determination that the conversion information comprises the identifier.
Fordyce teaches responsive to determining that the first content item is presented via the first device, generating an identifier (Fordyce, [0128], where a cookie identity is used as user data (125) to obtain user specific profile (131), where when the user (101) makes an online purchase from a web page that contains an advertisement that is tracked with the cookie identity, the cookie identity can be correlated to the online transaction and thus to the account of the user (101).
Although Fordyce does not appear to explicitly state that the cookie identity (i.e., “identity”) is generated in response to the advertisement being presented to the user, Fordyce discloses associating the (browser) cookie identity to the advertisement. Thus, although Fordyce does not appear to explicitly state when the cookie identity is generated at what point in the process, one of ordinary skill in the art would have found it obvious to modify Fordyce to (explicitly) generate the cookie identity after the advertisement is displayed with predictably equivalent operating characteristics, which is that an identifier is associated with the advertisement, and would have been motivated to do so such that the advertisement and identity are more uniquely associated);
transmitting the identifier to a server associated with the first content item (Fordyce, [0083-0087], where user data uses browser cookie information to identify the user (101), which is matched to account information or an account number to identify a user specific profile (131). A browser cookie can be used to map browser cookie information, e.g., cookie ID, to the account data that identifies the user (101) in the transaction handler (103). The cookie ID may be associated with a user account information (142) in a persistent way. See also Fordyce, [0187], where the retailer may provide the browser cookie associated with the user (101) to the operator of the transaction handler (103));
receiving conversion information, wherein the conversion information is indicative of a conversion event associated with the presentation of the first content item via the first device (Fordyce, [0104], where the correlator (117) receives information about the user specific advertisement data (119), monitors the transaction data (109), identifies transactions considered results of the advertisement corresponding to the user specific advertisement data (119), and generates the correlation result (123). See also, e.g., Fordyce, [0094], where the correlator (117) identifies transactions resulting from searches or online advertisements, where the correlator identifies a transaction performed by the user and sends the correlation result about the transaction to the user tracker, which allows the user tracker to combine the information about the transaction and online activities); and
determining that the conversion event is associated with the presentation of the first content item via the first device based upon a determination that the conversion information comprises the identifier (Fordyce, [0105], where an advertisement and corresponding transaction may occur in an online checkout process, where the advertisement may be presented to a particular consumer (Fordyce, [0095]) (i.e., the advertisement corresponding to the claimed “presentation of the first content item via the first device”). See Fordyce, [0096], where the profile generator uses the correlation result (123) to augment the transaction profiles (127) with data indicating the rate of conversion from searches or advertisements to purchase transactions.
See Fordyce, [0107], where user specific advertisement data is associated with the identity or characteristics of the user, such as global unique identifier (GUID), name or user name, user group, and/or user data. The correlator (117) can link or match the transactions with the advertisements based on the identity or characteristics of the user (101) associated with the user specific advertisement data (119). The portal (143) may receive a query identifying the user data (125) that tracks the user (101) and/or characteristics of the user specific advertisement data (119); and the correlator (117) identifies one or more transactions matching the user data (125) and/or the characteristics of the user specific advertisement data (119) to generate the correlation result (123)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan as modified and Fordyce (hereinafter “Priyardashan as modified”) with the motivation of presenting effective, timely, and relevant marketing information to the user, where based on transaction data, the user specific profile can improve audience targeting for online advertising where customers will get better advertisements and offers presented to them, and advertisers will achieve better return-on-investment for their advertisement campaigns (Fordyce, [0082]).
Regarding claim 6: Priyardashan as modified teaches The method of claim 5, comprising:
storing, in a transaction data structure, an indication of the identifier, wherein the determining that the conversion event is associated with the presentation of the first content item comprises analyzing the transaction data structure based upon the conversion information to determine that the identifier in the transaction data structure matches the identifier in the conversion information (Fordyce, [0107], where user specific advertisement data is associated with the identity or characteristics of the user, such as global unique identifier (GUID), name or user name, user group, and/or user data. The correlator (117) can link or match the transactions with the advertisements based on the identity or characteristics of the user (101) associated with the user specific advertisement data (119). The portal (143) may receive a query identifying the user data (125) that tracks the user (101) and/or characteristics of the user specific advertisement data (119); and the correlator (117) identifies one or more transactions matching the user data (125) and/or the characteristics of the user specific advertisement data (119) to generate the correlation result (123).
See Fordyce, [0037], where transaction data (109) is stored in a data warehouse (140) in the form of transaction records (see Fordyce, [0338] and [0361]) (i.e., “transaction data structure”)).
Regarding claim 8: Claim 8 recites substantially the same claim limitations as claim 5, and is rejected for the same reasons.
Regarding claim 9: Claim 9 recites substantially the same claim limitations as claim 6, and is rejected for the same reasons.
Regarding claim 20: Claim 20 recites substantially the same claim limitations as claim 5, and is rejected for the same reasons.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Priyadarshan et al. (“Priyadarshan”) (US 2012/0041817 A1), in view of Barlik et al. (“Barlik”) (US 2020/0159955 A1), in further view of Eldering et al. (“Eldering”) (US 2002/0123928 A1, incorporating by reference DeWolf et al. (“IBR-DeWolf”) (App. No. 09/782,962, published as US 2002/0111172 A1) at [0010]), in further view of Hirsch et al. (“Hirsch”) (US 2017/0171580 A1), in further view of Indyk (“Indyk”) (“Sketching via Hashing: from Heavy Hitters to Compressive Sensing to Sparse Fourier Transform”, published 2013).
Regarding claim 12: Priyardashan as modified teaches The method of claim 1, but does not appear to explicitly teach wherein the generating the plurality of group profiles comprises: performing, using at least one of one or more decomposition techniques or one or more reconstruction techniques, dimensional reduction of one or more sets of device information associated with devices of the first set of devices to generate a plurality of segments defining the plurality of group profiles including the first group profile, wherein the dimensional reduction transforms a plurality of device facts derived from the device information into a plurality of group facts associated with the first group profile, wherein the first group profile is represented as a plurality of dimensions associated with the plurality of group facts, wherein generating the plurality of group profiles comprises performing dimensional reduction of a plurality of sets of device information using a matrix sketching algorithm comprising at least one of a Frequent Directions algorithm, a heavy hitters algorithm, or a frequent items algorithm to generate segments defining the plurality of group profiles.
Hirsch teaches performing, using at least one of one or more decomposition techniques or one or more reconstruction techniques, dimensional reduction of one or more sets of device information associated with devices of the first set of devices to generate a plurality of segments defining the plurality of group profiles including the first group profile, wherein the dimensional reduction transforms a plurality of device facts derived from the device information into a plurality of group facts associated with the first group profile, wherein the first group profile is represented as a plurality of dimensions associated with the plurality of group facts … (Hirsch, [0134-0136] and [0190], where the system reduces high-dimensional spaces to a set of linear decision regions, where to facilitate dimension reduction and feature synthesis, each raw sparse vector of terms are segmented into syntactic blocks (groups of terms having similar forms or type), and/or into semantic blocks: groups of terms having similar function or meaning (i.e., “plurality of segments defining the plurality of … profiles”). Each of the raw text vector segments can be synthesized/encoded into a relatively small number of numeric scores, and are used for defining closeness in space, e.g., for clustering (see, e.g., Hirsch, [0176-0230]). See Hirsch, [0138-0139], where subscribers can be clustered using linear, nonlinear, and/or nonlinear manifold techniques, using feature selection (i.e., “the dimensional reduction transforms a plurality of [subscriber] facts derived from the [subscriber] information into a plurality of group facts”). See Hirsch, [0166], where a text feature vector can be a multidimensional vector of numeric features that represent a text element or group of text elements, e.g., a collection of attributes and/or groups of attributes such as soccer moms, millennials, etc. (i.e., “plurality of dimensions associated with the plurality of group facts”); see also, e.g., Hirsch, [0138], where the math model modules 1314 and 1324 can calculate all angles between multidimensional vectors between all entities or groups of entities (further indicating “plurality of dimensions associated with the plurality of group facts” as claimed). See Priyardashan in claim 1 above with respect to the “device” and “group profile”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan as modified and Hirsch (hereinafter “Priyardashan as modified”) with the motivation of complexity savings through dimensionality reduction (Hirsch, [0190]) and improve the speed of performance of the linear, nonlinear, and/or NLM clustering (Hirsch, [0136]).
Priyardashan as modified does not appear to explicitly teach wherein generating the plurality of group profiles comprises performing dimensional reduction of a plurality of sets of device information using a matrix sketching algorithm comprising at least one of a Frequent Directions algorithm, a heavy hitters algorithm, or a frequent items algorithm to generate segments defining the plurality of group profile.
Indyk teaches wherein generating the plurality of group profiles comprises performing dimensional reduction of a plurality of sets of device information using a matrix sketching algorithm comprising at least one of a Frequent Directions algorithm, a heavy hitters algorithm, or a frequent items algorithm to generate segments defining the plurality of group profile (Indyk, [Introduction], where sketching via hashing is a popular and useful method for processing large datasets in which a first hashing step (in which a hash function h is selected that maps the elements into an array) utilizes a linear mapping of the characteristic vector x of the set S to the vector c occurs, resulting in a sparse matrix A. Afterwards, for each element a in S, the algorithm increments the array/matrix (in other words, each time that the element occurs, it is incremented). At the end of the process, there is a count of all data elements a in S, where “frequent” elements are mapped to “heavy” buckets, thereby efficiently recovering the frequent elements. This described approach has been used to design improved algorithms for dimensionality reduction. See Piyardashan as modified above with respect to the data pertaining to “segments defining the plurality of group profiles”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Piyardashan as modified and Indyk with the motivation of allowing the approximation of counts of elements using very limited storage while making only a single pass over the data and simplicity of the hashing process (Indyk, [Introduction]) (thereby being efficient to perform), and enabling various tasks such as matrix-vector multiplication to be performed efficiently (Indyk, [Introduction]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Priyadarshan et al. (“Priyadarshan”) (US 2012/0041817 A1), in view of Barlik et al. (“Barlik”) (US 2020/0159955 A1), in further view of Eldering et al. (“Eldering”) (US 2002/0123928 A1, incorporating by reference DeWolf et al. (“IBR-DeWolf”) (App. No. 09/782,962, published as US 2002/0111172 A1) at [0010]), in further view of Hirsch et al. (“Hirsch”) (US 2017/0171580 A1).
Regarding claim 14: Priyardashan as modified teaches The computing device of claim 13, but does not appear to explicitly teach wherein the generating the plurality of group profiles comprises: performing, using at least one of one or more decomposition techniques or one or more reconstruction techniques, dimensional reduction of one or more sets of device information associated with devices of the first set of devices to generate a plurality of segments defining the plurality of group profiles including the first group profile, wherein the dimensional reduction transforms a plurality of device facts derived from the device information into a plurality of group facts associated with the first group profile, wherein the first group profile is represented as a plurality of dimensions associated with the plurality of group facts, wherein the first group profile comprises a vector representation having M dimensions corresponding to group facts derived from combinations of device facts of devices of the first set of devices, and wherein a dimension of the M dimensions indicates a proportion of devices of the first set of devices for which the corresponding group fact is true.
Hirsch teaches wherein the generating the plurality of group profiles comprises: performing, using at least one of one or more decomposition techniques or one or more reconstruction techniques, dimensional reduction of one or more sets of device information associated with devices of the first set of devices to generate a plurality of segments defining the plurality of group profiles including the first group profile, wherein the dimensional reduction transforms a plurality of device facts derived from the device information into a plurality of group facts associated with the first group profile, wherein the first group profile is represented as a plurality of dimensions associated with the plurality of group facts, wherein the first group profile comprises a vector representation having M dimensions corresponding to group facts derived from combinations of device facts of devices of the first set of devices … (Hirsch, [0134-0136] and [0190], where the system reduces high-dimensional spaces to a set of linear decision regions, where to facilitate dimension reduction and feature synthesis, each raw sparse vector of terms are segmented into syntactic blocks (groups of terms having similar forms or type), and/or into semantic blocks: groups of terms having similar function or meaning (i.e., “plurality of segments defining the plurality of … profiles”). Each of the raw text vector segments can be synthesized/encoded into a relatively small number of numeric scores, and are used for defining closeness in space, e.g., for clustering (see, e.g., Hirsch, [0176-0230]). See Hirsch, [0138-0139], where subscribers can be clustered using linear, nonlinear, and/or nonlinear manifold techniques, using feature selection (i.e., “the dimensional reduction transforms a plurality of [subscriber] facts derived from the [subscriber] information into a plurality of group facts”). See Hirsch, [0217-0223] and [0230], where a distance matrix from the input data contained within the formed clusters may be developed, such as using Singular Value Decomposition. See Hirsch, [0166], where a text feature vector can be a multidimensional vector of numeric features that represent a text element or group of text elements, e.g., a collection of attributes and/or groups of attributes such as soccer moms, millennials, etc. (i.e., “plurality of dimensions associated with the plurality of group facts”); see also, e.g., Hirsch, [0138], where the math model modules 1314 and 1324 can calculate all angles between multidimensional vectors between all entities or groups of entities (further indicating “plurality of dimensions associated with the plurality of group facts” as claimed). See Priyardashan in claim 1 above with respect to the “device” and “group profile”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan as modified and Hirsch (hereinafter “Priyardashan as modified”) with the motivation of complexity savings through dimensionality reduction (Hirsch, [0190]) and improve the speed of performance of the linear, nonlinear, and/or NLM clustering (Hirsch, [0136]).
Although Priyaradashan as modified does not appear to explicitly teach “wherein a dimension of the M dimensions indicates a proportion of devices of the first set of devices for which the corresponding group fact is true” , as claimed, Hirsch discloses measuring a keyword’s specificity, e.g., measuring whether the occurrence of that term is concentrated in a small percentage of the documents or found in many of the documents by computing the proportion of all documents that contain the term. Therefore, one of ordinary skill in the art would have been suggested by Priyardashan as modified to have applied the specificity concept (which pertains to a term relative to documents) by substituting the “document” for Priyardashan as modified’s “vector” with the motivation of being able to quickly ascertain which words (i.e., the claimed “facts”) are potentially important to a group (i.e., ascertaining use across multiple data points), instead of relying on potentially skewed data (i.e., when certain words may only be important to a relatively few/small number of devices in the group, and thus are not significant for other group members), thereby enabling better targeting of content to that particular group as a whole.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Priyadarshan et al. (“Priyadarshan”) (US 2012/0041817 A1), in view of Barlik et al. (“Barlik”) (US 2020/0159955 A1), in further view of Eldering et al. (“Eldering”) (US 2002/0123928 A1, incorporating by reference DeWolf et al. (“IBR-DeWolf”) (App. No. 09/782,962, published as US 2002/0111172 A1) at [0010]), in further view of Hirsch et al. (“Hirsch”) (US 2017/0171580 A1), in further view of Wood et al. (“Wood”) (US 2018/0018686 A1).
Regarding claim 17: Priyardashan as modified teaches The computing device of claim 13, but does not appear to explicitly teach wherein the generating the plurality of group profiles comprises: performing, using at least one of one or more decomposition techniques or one or more reconstruction techniques, dimensional reduction of one or more sets of device information associated with devices of the first set of devices to generate a plurality of segments defining the plurality of group profiles including the first group profile, wherein the dimensional reduction transforms a plurality of device facts derived from the device information into a plurality of group facts associated with the first group profile, wherein the first group profile is represented as a plurality of dimensions associated with the plurality of group facts, wherein the set of device information associated with the first device comprises a Boolean vector representation having N dimensions corresponding to N device facts associated with the first device, the device facts comprising at least one of demographic parameters associated with the first device or internet resources accessed by the first device, and wherein dimensional reduction of the Boolean vector representations associated with devices of the first set of devices generates the first group profile.
Hirsch teaches performing, using at least one of one or more decomposition techniques or one or more reconstruction techniques, dimensional reduction of one or more sets of device information associated with devices of the first set of devices to generate a plurality of segments defining the plurality of group profiles including the first group profile, wherein the dimensional reduction transforms a plurality of device facts derived from the device information into a plurality of group facts associated with the first group profile, wherein the first group profile is represented as a plurality of dimensions associated with the plurality of group facts …, the device facts comprising at least one of demographic parameters associated with the first device or internet resources accessed by the first device … (Hirsch, [0134-0136] and [0190], where the system reduces high-dimensional spaces to a set of linear decision regions, where to facilitate dimension reduction and feature synthesis, each raw sparse vector of terms are segmented into syntactic blocks (groups of terms having similar forms or type), and/or into semantic blocks: groups of terms having similar function or meaning (i.e., “plurality of segments defining the plurality of … profiles”). Each of the raw text vector segments can be synthesized/encoded into a relatively small number of numeric scores, and are used for defining closeness in space, e.g., for clustering (see, e.g., Hirsch, [0176-0230]). See Hirsch, [0138-0139], where subscribers can be clustered using linear, nonlinear, and/or nonlinear manifold techniques, using feature selection (i.e., “the dimensional reduction transforms a plurality of [subscriber] facts derived from the [subscriber] information into a plurality of group facts”). See Hirsch, [0217-0223] and [0230], where a distance matrix from the input data contained within the formed clusters may be developed, such as using Singular Value Decomposition. See Hirsch, [0166], where a text feature vector can be a multidimensional vector of numeric features that represent a text element or group of text elements, e.g., a collection of attributes and/or groups of attributes such as soccer moms, millennials, etc. (i.e., “plurality of dimensions associated with the plurality of group facts”); see also, e.g., Hirsch, [0138], where the math model modules 1314 and 1324 can calculate all angles between multidimensional vectors between all entities or groups of entities (further indicating “plurality of dimensions associated with the plurality of group facts” as claimed). See Priyardashan in claim 1 above with respect to the “device” and “group profile”. See Priyardashan, [0086], where a targeted segment is based on one or more user characteristics including one or more demographic identifiers for defining the corresponding targeted segments, as well as behaviors such as user behaviors with respect to content/content channels (Priyardashan, [0020], [0078], and [0100]) (i.e., “the device facts comprising at least one of demographic parameters associated with the first device or internet resources accessed by the first device”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan as modified and Hirsch (hereinafter “Priyardashan as modified”) with the motivation of complexity savings through dimensionality reduction (Hirsch, [0190]) and improve the speed of performance of the linear, nonlinear, and/or NLM clustering (Hirsch, [0136]).
Priyardashan as modified does not appear to explicitly teach wherein the set of device information associated with the first device comprises a Boolean vector representation having N dimensions corresponding to N device facts associated with the first device, and wherein dimensional reduction of the Boolean vector representations associated with devices of the first set of devices generates the first group profile.
Wood teaches wherein the set of device information associated with the first device comprises a Boolean vector representation having N dimensions corresponding to N device facts associated with the first device, and wherein dimensional reduction of the Boolean vector representations associated with devices of the first set of devices generates the first group profile (Wood, [0037-0040], where a vector may be determined for some or all members of the surveyed group, where the vector has a length equal to a number of measured values for each member. Each member may be represented by a binary vector with the length equal to the number of values being investigated. A value of “1” may be placed in the slot if both the value preference and value gap for that value is positive, and a “0” otherwise. See Hirsch above with respect to the “dimensional reduction” of the vector. See Priyardashan in claim 1 above with respect to the “devices of the first set of devices” being used to “generate[] the first group profile” as claimed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Priyardashan as modified and Wood with the motivation of greater efficiency in clustering by simplifying clustering mechanisms, e.g., faster to use a series of binary values for determining, in a rough approximation, whether or not a member should be clustered into a group or not, as opposed to a range of values.
Conclusion
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.
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/IRENE BAKER/Primary Examiner, Art Unit 2154
27 June 2026
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