Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Baughman (Publication No. 2012/0216287 filed February 21, 2011); Baras et al. (Publication No. 2006/0242115 filed April 25, 2005, hereinafter Baras); and Praetorius et al. (“Dorfler Marking With Minimal Cardinality Is A Linear Complexity Problem,” Mathematics of Computation, Volume 89, November 326, November 2020, pages 2735-2752, hereinafter Praetorius).
Regarding Claims 1, 15, and 16, Baughman teaches receiving a request to determine a number of users that are included in a target group of users that received at least one transmission of a digital component ([0002] the user may simply be known via a screen name, the identity of the user could still be based on relationships. Even if the identity or features that could be used to infer the details about an individual are hidden, the cardinality (i.e., number) of features or links within a graph can be used to infer the identity of an individual. For example, if a user belongs to a given community the member will be connected to a certain member of records), wherein the request comprises a set expression specifying the target group of users ([0022] morphing of social network data into morphed social network data that includes four communities A, B, C, D. Each community has a set of members, e.g., community A has members c,d, f, g); the set expression is defined in terms of the collection of user sets ([0023] community A has been split into three communities 1, 2, and 3 (also as A1, A2, A3)); and each user set comprises one or more users satisfying a set-specific inclusion criterion ([0023] the ordering is changed namely community C1 was moved ahead of community B1 and B2, the result is a set of data with randomly split communities); and applying a cardinality model having a set of cardinality model parameters ([0024] a cardinality key is applied to the eight split communities, the cardinality key is an NxN matrix where N is the number of split communities. Each column in the matrix includes a value of “1” randomly or selectively placed at one of the eight vertical locations. The result is 1x8 mapping key associated with the cardinality key represented as [25211258]); determining the number of users included in the target group of users based on the cardinalities ([0027] each community is represented by a social graph. The graph includes a plurality of nodes that represent a member or feature(s) with edges being relationships); and cardinalities of subset unions of the collection of user sets , (Abstract, cardinality key causes subsets of split communities to be unioned together; [0022] Each community has a set of members…).
However, Baughman does not expressly teach generating an alternative representation of the set expression in terms of primitive sets of the collection of user sets.
Baras teaches generating an alternative representation of the set expression in terms of primitive sets of the collection of user sets (Abstract, classified into a series of input expressions followed by the steps. For each such series, the inference may be calculated based on input including a type for the input expression, an axis for the step, and a node test for the step. The cardinality of the input expression type is preserved for the calculation of the step type. Also, a set of one or more matching node types may be identified within the type repository. These matching node types are node types within the axis of the step that match the node test of the step. These matching node types are identified without calculating the full content type implied by the axis); a cardinality model having a set of cardinality model parameters to each primitive set included in the alternative representation of the set expression to generate a cardinality of each primitive set ([0030] the input expression has an associated input expression type with an associated input expression type cardinality; calculated based on the cardinality of the prime factorization of the input expression type); and determining the number the cardinalities of the primitive sets included in the alternative representation of the set expression ([0030] calculated based on preserved cardinality of the input expression and the retrieved type information for the matching nodes).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of Baughman’s cardinality method with Baras’s cardinality method because Baughman’s cardinality method includes morphed communities of cardinality using a cardinality key but does not include the set expression in terms of primitive sets. Baras teaches the set expression in terms of primitive sets in the cardinality method including an input expression having an associated input expression type with an associated input expression type cardinality. Incorporating the cardinality method of Baras with the cardinality method of Baughman would improve Baughman’s method to enable a calculation based on the cardinality of a prime factorization of the input expression type.
However, Baras does not expressly teach a linear combination of cardinalities of subset unions of the collection of user sets.
Praetorius teaches a linear combination of cardinalities of subset unions of the collection of sets (page 2736, see, paragraph 3, linear cost with respect to the number of elements #Te).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of Baras’s cardinality method with Praetorius’s cardinality method because Baughman’s cardinality method includes the cardinality of an input expression type but does not include a linear combination of cardinalities. Praetorius teaches the linear combination of cardinalities including a sorting strategy using a mark which satisfies the cardinality of the linear cost. Incorporating the cardinality method of Praetorius into the cardinality method of Baras would improve Baras’s method to enable a minimal cardinality of a linear cost to employ a sorting scheme using a binning process.
8. Regarding Claim 2 and 17, Praetorius teaches wherein the cardinality model is defined by a matrix, wherein the cardinality model parameters define entries of the matrix, wherein the entries of the matrix define weights of linear combinations used to generate cardinalities of primitive sets in terms of cardinalities of subset unions (page 2737, Algorithm 2, elements (i)-(iii)).
9. Regarding Claims 3 and 18, Praetorius teaches for each primitive set included in the alternative representation of the set expression (page 2738, The algorithm 4, output, remark the proposed algorithm), applying the cardinality model to the primitive set comprises (page 2738, The algorithm 4, output, remark the proposed algorithm): mapping the primitive set to: (i) a collection of subset unions (page 2738, The algorithm 4, output, remark the proposed algorithm), and (ii) for each subset union (page 2738, 2.4, Quasi-mini Dorfler marking with linear complexity by binning), a respective weight of the subset union in the linear combination (page 2738, 2.4, Quasi-mini Dorfler marking with linear complexity by binning); and generating the cardinality of the primitive set as a linear combination of the cardinalities of the subset unions weighted by the weights of the subset unions (page 2738, Algorithm 7, page 2739, Output, and Proposition 8).
10. Regarding Claims 4 and 19, Praetorius teaches the entries of the matrix comprise -1, 0, and +1 (page 2740, Algorithm 10, Output, Algorithm 11, Output).
11. Regarding Claims 5 and 20, Praetorius teaches the matrix defining the cardinality model is a sparse matrix (page 2740, Algorithm 10, Output, Algorithm 11, Output).
12. Regarding Claim 6, Praetorius teaches the set of cardinality model parameters are precomputed (page 2740, Algorithm 10, Output, Algorithm 11, Output).
13. Regarding Claim 7, Praetorius teaches the set of cardinality mode parameters are dynamically generated in response to receiving the request (page 2740, Algorithm 10, Output, Algorithm 11, Output).
14. Regarding Claim 8, Praetorius teaches a primitive set of the collection of user sets is defined by a set intersection that intersects, for each user set in the collection of user sets, either the user set or a complement of the user set (page 2740, Algorithm 10, Output, Algorithm 11, Output).
15. Regarding Claim 9, Praetorius teaches a subset union of the collection of user sets is defined by a set union of one or more user sets in the collection of user sets (page 2740, Algorithm 10, Output, Algorithm 11, Output).
16. Regarding Claim 10, Praetorius teaches generating the alternative representation of the set expression in terms of primitive sets of the collection of user sets comprises: replacing each user set in the set expression by a union of corresponding primitive sets (page 2740, Algorithm 10, Output, Algorithm 11, Output).
17. Regarding Claim 11, Praetorius teaches for each user set in the collection of user sets, the set- specific inclusion criterion specifies that users included in the user set received at least one transmission of the digital component by way of a respective publisher . (page 2740, Algorithm 10, Output, Algorithm 11, Output)
18. Regarding Claim 12, Praetorius teaches for each user set in the collection of user sets, the set- specific inclusion criterion specifies that users included in the user set received at least one transmission of the digital component in a respective window of time (page 2740, Algorithm 10, Output, Algorithm 11, Output).
19. Regarding Claim 13, Praetorius teaches the set expression is defined as a string comprising set identifiers and set operations (page 2740, Algorithm 10, Output, Algorithm 11, Output).
20. Regarding Claim 14, Praetorius teaches the set operations include one or more of: set union operations, set intersection operations, and set difference operations (page 2740, Algorithm 10, Output, Algorithm 11, Output).
Conclusion
21. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHERYL R LEWIS whose telephone number is (571)272-4113. The examiner can normally be reached Monday-Thursday, 8am-5pm, EST.
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, Sanjiv Shah can be reached at 571-272-4098. 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.
/CHERYL LEWIS/Primary Examiner, Art Unit 2166
April 3, 2026