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 .
This Office Action is responsive to: Amendment filed 02 Jan. 2026
Claims 1-20 are pending in this case. Claims 1, 8 and 15 are independent claims
Applicant’s Response
In Applicant’s Response dated 02 Jan. 2026, Applicant amended claims 1, 8 and 15; argued against all rejections previously set forth in the Office Action dated 02 Oct. 2025.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Arai et al. (Pat. No.: US 8,924,496 B; Date: Dec. 30, 20140) (hereinafter “Arai”) in view of Yates (Pub. No.: US 2021/03829525 A1; Filed: May 18, 2021).
Regarding independent claim 1, Arai disclose a computer-implemented method for causing an interactive computing environment hosted by an online platform to be modified, where the computer-implemented method causes one or more processing devices to perform operations comprising:
obtaining, by an impact identification system, aggregated interaction data associated with a plurality of users of the online platform, the aggregated interaction data comprising a total number of occurrences of a target action performed by the plurality of users with respect to the online platform (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52);
extrapolating, by the impact identification system, the aggregated interaction data by applying a mixed granularity model to generate extrapolated interaction data for each user in the plurality of users, the extrapolated interaction data comprising a series of actions leading to the target action for the user (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52);
identifying, by the impact identification system, an impact of each action in the series of actions for each user on leading to the user taking the target action based, at least in part, upon the extrapolating the series of actions associated with the user (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52); and
causing, by the impact identification system, user interfaces presented on the online platform to be modified based on at least the identified impacts (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52).
Arai does not expressly disclose wherein the series of action are different from the target action.
Yates disclose wherein the series of action are different from the target action (0031; 0039).
Therefore before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine Yates with Arai for the benefit of increasing awareness about products or services to online system users (0003).
Regarding dependent claim 2, Arai disclose the computer-implemented method of claim 1, wherein identifying the impact of each action in the series of actions for each user on leading to the target action 1s performed using an attribution model configured to accept the series of actions as input (col 4 line 26-57; col 5 lines 10-52).
Regarding dependent claim 3, Aria disclose the computer-implemented method of claim 1, wherein the total number of occurrences of the target action performed by the plurality of users in the aggregated interaction data is associated with a time period, and wherein extrapolating the aggregated interaction data further comprises distributing the total number of occurrences of the target action over the time period (col 3 lines 1532; col 5 lines 10-52).
Regarding dependent claim 4, Aria disclose the computer-implemented method of claim 3, wherein distributing the total number of occurrences of the target action over the time period further comprises:
dividing the time period into one or more time points (col 3 lines 1532; col 5 lines 10-52);
distributing the total number of occurrences across the one or more time points to generate a set of distributed occurrences (col 3 lines 1532; col 5 lines 10-52); and
providing the set of distributed occurrences to the mixed granularity model as input to extrapolate the aggregated interaction data (col 3 lines 1532; col 5 lines 10-52).
Regarding dependent claim 5, Aria disclose the computer-implemented method of claim 1, wherein extrapolating the aggregated interaction data by applying the mixed granularity model comprises:
determining, based on an identifier of a user of the plurality of users, that the user has performed at least one occurrence of the target action(col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52);
assigning a terminal action based on a set of distributed occurrences determined using the aggregated interaction data, wherein the terminal action is performed prior to an occurrence of the target action (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52); and
in response to assigning the terminal action, generating the series of actions of the extrapolated interaction data by assigning one or more additional actions based on the terminal action, wherein the one or more additional actions are performed by the user prior to the terminal action in the series of actions (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52).
Regarding dependent claim 6, Aria disclose the computer-implemented method of claim 1, wherein extrapolating the aggregated interaction data by applying the mixed granularity model comprises:
determining, based on an identifier of a user of the plurality of users, that the user has not performed the target action (col 6 lines 5-44); and
generating the series of actions corresponding to the user by assigning one or more actions performed by the plurality of users to the series of actions of the user based on a probability of the user performing at least one action of the one or more actions (col 6 lines 5-44).
Regarding dependent claim 7, Aria disclose the computer-implemented method of claim 1, wherein extrapolating the aggregated interaction data further comprises:
pre-processing the aggregated interaction data to generate a total number of actions performed by the plurality of users, wherein each action of the total number of actions is assigned to a respective user of the plurality of users by applying the mixed granularity model to generate the series of actions for each user (col 4 line 16-57; col 5 lines 20-52).
Regarding independent claim 8, Aria disclose a system comprising: a host system configured for:
hosting an online platform configured for presenting user interfaces to users, and
modifying the user interfaces presented to a user through the online platform based, at least in part, on impacts of individual actions on leading to a target action performed on the online platform(col 2 lines 11-22; col 5 lines 20-52); and
an online experience evaluation system comprising:
one or more processing devices configured for performing operations comprising:
applying a mixed granularity model on aggregated interaction data associated with a plurality of users of the online platform to generate extrapolated interaction data for each user in the plurality of users, the aggregated interaction data comprising a total number of occurrences of the target action performed by the plurality of users with respect to the online platform and the extrapolated interaction data comprising a series of actions leading to the user taking the target action (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52), and
identifying an impact of each action in the series of actions for each user on leading to the target action based, at least in part, upon the series of actions in the extrapolated interaction data (col 4 line 16-57; col 5 lines 20-52); and
a network interface device configured for transmitting, to the online platform, the identified impact of each action in the series of actions for each user on leading to the target action (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52).
Arai does not expressly disclose wherein the series of action are different from the target action.
Yates disclose wherein the series of action are different from the target action (0031; 0039).
Therefore before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine Yates with Arai for the benefit of increasing awareness about products or services to online system users (0003).
Regarding dependent claim 9, Aria disclose the system of claim 8, wherein identifying the impact of each action in the series of actions for each user on leading to the target action 1s performed using an attribution model configured to accept the series of actions as input (col 4 line 26-57; col 5 lines 10-52).
Regarding dependent claim 10, Aria disclose the system of claim 8, wherein the total number of occurrences of the target action performed by the plurality of users in the aggregated interaction data is associated with a time period, and wherein extrapolating the aggregated interaction data further comprises distributing the total number of occurrences of the target action over the time period (col 3 lines 1532; col 5 lines 10-52).
Regarding dependent claim 11, Aria disclose the system of claim 10, wherein distributing the total number of occurrences of the target action over the time period further comprises:
dividing the time period into one or more time points (col 3 lines 1532; col 5 lines 10-52);
distributing the total number of occurrences across the one or more time points to generate a set of distributed occurrences (col 3 lines 1532; col 5 lines 10-52); and
providing the set of distributed occurrences to the mixed granularity model as input to extrapolate the aggregated interaction data (col 3 lines 1532; col 5 lines 10-52).
Regarding dependent claim 12, Aria disclose the system of claim 8, wherein extrapolating the aggregated interaction data by applying the mixed granularity model comprises:
determining, based on an identifier of a user of the plurality of users, that the user has performed at least one occurrence of the target action(col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52);
assigning a terminal action based on a set of distributed occurrences determined using the aggregated interaction data, wherein the terminal action is performed prior to an occurrence of the target action(col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52); and
in response to assigning the terminal action, generating the series of actions of the extrapolated interaction data by assigning one or more additional actions based on the terminal action, wherein the one or more additional actions are performed by the user prior to the terminal action in the series of actions (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52).
Regarding dependent claim 13, Aria disclose the system of claim 8, wherein extrapolating the aggregated interaction data by applying the mixed granularity model comprises:
determining, based on an identifier of a user of the plurality of users, that the user has not performed the target action (col 6 lines 5-44); and
generating the series of actions corresponding to the user by assigning one or more actions performed by the plurality of users to the series of actions of the user based on a probability of the user performing at least one action of the one or more actions (col 6 lines 5-44).
Regarding dependent claim 14, Aria disclose the system of claim 8, wherein extrapolating the aggregated interaction data further comprises:
pre-processing the aggregated interaction data to generate a total number of actions performed by the plurality of users, wherein each action of the total number of actions is assigned to a respective user of the plurality of users by applying the mixed granularity model to generate the series of actions for each user (col 4 line 16-57; col 5 lines 20-52).
Regarding independent claim 15, Aria disclose a non-transitory computer-readable medium having program code that is stored thereon, the program code executable by one or more processing devices for performing operations comprising:
a step for applying a mixed granularity model on aggregated interaction data associated with a plurality of users of an online platform to generate extrapolated interaction data for each user in the plurality of users, the aggregated interaction data comprising a total number of occurrences of a target action performed by the plurality of users with respect to the online platform and the extrapolated interaction data comprising a series of actions leading to the target action for the user (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52);
a step for identifying impacts of individual actions in the series of actions on leading to the user taking the target action based, at least in part, upon the extrapolated interaction data (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52); and
causing the identified impacts to be accessible by the online platform, wherein the identified impacts are usable for changing user interfaces presented on the online platform (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52).
Arai does not expressly disclose wherein the series of action are different from the target action.
Yates disclose wherein the series of action are different from the target action (0031; 0039).
Therefore before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine Yates with Arai for the benefit of increasing awareness about products or services to online system users (0003).
Regarding dependent claim 16, Aria disclose the non-transitory computer-readable medium of claim 15, wherein identifying the impact of each action in the series of actions for each user on leading to the target action is performed using an attribution model configured to accept the series of actions as input (col 4 line 26-57; col 5 lines 10-52).
Regarding dependent claim 17, Aria disclose the non-transitory computer-readable medium of claim 15, wherein the total number of occurrences of the target action performed by the plurality of users in the aggregated interaction data 1s associated with a time period, and wherein extrapolating the aggregated interaction data further comprises distributing the total number of occurrences of the target action over the time period (col 3 lines 1532; col 5 lines 10-52).
Regarding dependent claim 18, Aria disclose the non-transitory computer-readable medium of claim 17, wherein distributing the total number of occurrences of the target action over the time period further comprises:
dividing the time period into one or more time points (col 3 lines 1532; col 5 lines 10-52);
distributing the total number of occurrences across the one or more time points to generate a set of distributed occurrences (col 3 lines 1532; col 5 lines 10-52); and
providing the set of distributed occurrences to the mixed granularity model as input to extrapolate the aggregated interaction data (col 3 lines 1532; col 5 lines 10-52).
Regarding dependent claim 19, Aria disclose the non-transitory computer-readable medium of claim 15, wherein extrapolating the aggregated interaction data by applying the mixed granularity model comprises:
determining, based on an identifier of a user of the plurality of users, that the user has performed at least one occurrence of the target action (col 6 lines 5-44);
assigning a terminal action based on a set of distributed occurrences determined using the aggregated interaction data, wherein the terminal action is performed prior to an occurrence of the target action (col 6 lines 5-44); and
in response to assigning the terminal action, generating the series of actions of the extrapolated interaction data by assigning one or more additional actions based on the terminal action, wherein the one or more additional actions are performed by the user prior to the terminal action in the series of actions (col 6 lines 5-44).
Regarding dependent claim 20, Aria disclose the non-transitory computer-readable medium of claim 15, wherein extrapolating the aggregated interaction data by applying the mixed granularity model comprises:
determining, based on an identifier of a user of the plurality of users, that the user has not performed the target action (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52); and
generating the series of actions corresponding to the user by assigning one or more actions performed by the plurality of users to the series of actions of the user based on a probability of the user performing at least one action of the one or more actions (col 2 lines 11-22; col 4 line 16-57; col 5 lines 20-52).
NOTE
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Response to Arguments
Applicant’s arguments 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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 JAMES J DEBROW whose telephone number is (571)272-5768. The examiner can normally be reached on 09:00 - 06:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Bashore can be reached on 571-272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center or Private PAIR to authorized users only. Should you have questions about access to Patent Center or the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/James J Debrow/
Primary Patent Examiner
Art Unit 2174
571-272-5768
Prosecution Insights