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 Duncan (Publication No. 2017/0213127 filed January 24, 2017, priority to provisional application no. 62/286,427 filed January 24, 2016, hereinafter Duncan) and Dawn et al. (Patent No. 11,151,457 filed August 3, 2017, hereinafter Dawn).
Regarding Claims 1, 8, and 15, Duncan teaches determining, for a first data identifier and a second data identifier within a relationship cluster ([0033] These relatives (mostly distant cousins), are usually presented to the User in the form of a list of User-id's of other Users to whom the first User purportedly matches, a relationship confidence (e.g. ‘extremely high’, ‘very high’, high, medium, low) and a range estimate on the familial relationship distance; [0049] Such a collection forms a ‘Cluster’ wherein a group shares a common attribute, or set of attributes. Connecting an DNA-Match set to a VIP cluster, is described in the section on ‘Disembodied Cousin Triangulations’ in the invention description and who DNA match to one of each other) lived in a ‘connected’ community (a Cluster) ) comprising a plurality of data identifiers grouped according to data matches ([0176] genealogic family trees, with a key focus on discovery of Most Recent Common Ancestors between pairs or sets of Users (individuals) who have been predicted to be genetically related by some degree, or ‘genetic distance’, according to the lengths of the contiguous DNA segments shared between them. Assuming, as an example, that there are 2 million participating Users, and the average number of DNA matches per user is 3000, then there will be 6 billion DNA matches reported to the Users);
generating, based on the predicted data, a first candidate data-link tree and a second candidate data-link tree ([0009] candidates for most recent common ancestors (MRCA's) in the pedigrees between any pair of DNA matched users [0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic); [0039] As family trees in social genealogy sites are constantly updated by Users; [0079] If the two family trees of the DNA matching cousins do not have obviously similar ancestral lines, or are not filled out to the range of the DNA matches predicted distance, then they have the conundrum of trying to figure out which line (branch) the MRCA lies on), wherein the first candidate data-link tree comprises a first connection between a first node representing the first data identifier and a second node representing the second data identifier and wherein the second candidate data-link tree comprises a second connection between the first node and the second node ([0111] Another set contains nodes of virtual Ancestors and represent place-holders of the ‘most recent common ancestor’ (MRCA) between the two Users in the matched node. Another set of nodes represents attributes (records, traits, etc) that are shared between various Ancestors. Another set of nodes are derived from data-mining the prior mentioned nodes to form hierarchical clusters.);
calculating a first data-match value for the first candidate data-link tree and a second data- match value for the second candidate data-link tree ([0073] [0073] Example, Base Case: AncestryDNA™ currently bins DNA relative matches according to distance between the two members. The bins are parent,/child, 1.sup.st cousin, 2.sup.nd cousin . . . 3.sup.rd, 4.sup.th 4.sup.th -6.sup.th and 5.sup.th -8.sup.th cousins. Thus, if a match is calculated to be a 1.sup.st cousin, then the two DNA matched cousins only need to complete their respective trees, correctly, out to the 1.sup.st grandparents. [0612] The VFT Agents and VWT Agents contribute inputs and calculations to the VAR records.); and based on comparing the first data-match value and the second data-match value, generating a primary data-link tree including the first node and the second node arranged according to the first connection reflected by the first candidate data-link tree ([0790] The VFT VIA node sending the majority of packets (scaled by importance), is considered the leading candidate for the MRCA between the two User's who are rooted in the two VFTs. The algorithm assigns best VFT ancestors to Vdna Nodes, along with the confidence values calculated).
However, Duncan does not expressly teach a predicted data link between the first data identifier and the second data identifier.
Dawn teaches a predicted data link between the first data identifier and the second data identifier (Abstract, A first generation of predictor rules is determined using the records in the model generation set, and subsequent generations are constructed by iteratively identifying a first subset of predictor rules based on a precision measure of each predictor rule and identifying a second subset of predictor rules based on a recall measure of each predictor rule and generating the subsequent generation by OR combining the predictor rules of the first subset and by AND combining the predictor rules of the second subset.).
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 Duncan’s method with Dawn’s method because Duncan’s method includes most recent common ancestors in a family tree with massive individuals who have been predicted to be related according to shared DNA but does not include a predicted data link. Dawn’s method includes a first generation of predictor rules using the records in a model generation set, and subsequent generations are constructed by identifying a first subset of predictor rules based on a precision measure of each predictor rule and identifying a second subset of predictor rules. Incorporating the method of Dawn with the method of Duncan would improve Duncan’s method to enable multiple sets of predictor rules for multiple different target events to generate a collective result of several cycles of the genetic algorithm to form a compilation of predictor rules consisting of different entity types and attributes.
8. Regarding claim 2, Duncan teaches generating additional primary data-link trees by generating, over multiple iterations, candidate data-link trees reflecting different possible connections between data identifiers in the relationship cluster ([0009] candidates for most recent common ancestors (MRCA's) in the pedigrees between any pair of DNA matched users [0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
9. Regarding Claim 3, Duncan teaches merging the primary data-link tree and the additional primary data-link trees to form a universal data-link tree arranging the data identifiers in the relationship cluster according to data matches ([0112]).
10. Regarding Claim 4, Duncan teaches the first node is a placed node within the primary data-link tree and generating the first candidate data-link tree and the second candidate data link tree further comprises: generating, from the first connection between the placed node and the second data identifier, the first candidate data-link tree; and generating, from the second connection between the placed node and the second data identifier, the second candidate data-link tree.
([0033] These relatives (mostly distant cousins), are usually presented to the User in the form of a list of User-id's of other Users to whom the first User purportedly matches, a relationship confidence (e.g. ‘extremely high’, ‘very high’, high, medium, low) and a range estimate on the familial relationship distance; [0049] Such a collection forms a ‘Cluster’ wherein a group shares a common attribute, or set of attributes. Connecting an DNA-Match set to a VIP cluster, is described in the section on ‘Disembodied Cousin Triangulations’ in the invention description and who DNA match to one of each other)
11. Regarding Claim 5, Duncan teaches calculating the first data- match value and the second data-match value further comprises calculating, based on a likelihood distribution and a negative log likelihood calculation, the first data-match value for the first candidate data-link tree and the second data-match value for the second candidate data-link tree ([0033] These relatives (mostly distant cousins), are usually presented to the User in the form of a list of User-id's of other Users to whom the first User purportedly matches, a relationship confidence (e.g. ‘extremely high’, ‘very high’, high, medium, low) and a range estimate on the familial relationship distance; [0049] Such a collection forms a ‘Cluster’ wherein a group shares a common attribute, or set of attributes. Connecting an DNA-Match set to a VIP cluster, is described in the section on ‘Disembodied Cousin Triangulations’ in the invention description and who DNA match to one of each other. [0612] The VFT Agents and VWT Agents contribute inputs and calculations to the VAR records.).
12. Regarding Claim 6, Duncan teaches filtering among the primary data-link tree and the additional primary data-link trees by comparing data-match values of the primary data link tree and the additional primary data-link trees ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
13. Regarding Claim 7, Duncan teaches generating the primary data- link tree comprises generating more than one primary data-link tree by: generating a first primary data-link tree including the first node and the second node arranged according to the first connection reflected by the first candidate data-link tree; and generating a second primary data-link tree including the first node and the second node arranged according to the second connection reflected by the second candidate data-link tree ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
14. Regarding Claim 9, Duncan teaches to select the second data identifier from the relationship cluster according to at least one of shared centimorgans with the first data identifier, a number of segments shared with the first data identifier, or metadata associated with the second data identifier ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
15. Regarding Claim 10, Duncan teaches the at least one processor to associate more than one data-link type with the predicted data link ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
16. Regarding Claim 11, Duncan teaches generate the first candidate data-link tree according to a first data-link type; and generate the second candidate data-link tree according to a second data-link type ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
17. Regarding Claim 12, Duncan teaches the at least one processor to generate, within the first candidate data-link tree and the second candidate data-link tree, a ghost node for a node connecting the first data identifier and the second data identifier, wherein the ghost node represents an unknown data identifier ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
18. Regarding Claim 13, Duncan teaches at least one processor to utilize, according to the predicted data link, a first method to calculate a data-match value within a threshold and utilize a second method to calculate a data-value outside of the threshold ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
19. Regarding Claim 14, Duncan teaches at least one processor to utilize a machine learning model to determine the predicted data link ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
20. Regarding Claim 16, Duncan teaches to generate, for display with a graphical user interface of a client device, a confusion matrix, wherein the confusion matrix depicts prediction accuracy across a plurality of data-match levels by: comparing a predicted data link between a placed node and a proband in the primary data-link tree with an actual data link between the placed node and the proband; calculating an accuracy value for the predicted data link based on the actual data link; and graphing the predicted data link within the confusion matrix according to data-match level and depicting the accuracy value ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
21. Regarding Claim 17, Duncan teaches adjust, according to the confusion matrix, how to determine the predicted data link; and determine an adjusted predicted data link ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
22. Regarding Claim 18, Duncan teaches configured to evaluate the primary data-link tree for double relationships, underrepresentation of infrequently occurring data links, and endogamy ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
23. Regarding Claim 19, Duncan teaches to generate an alternate primary data-link tree by perturbing the first candidate data-link tree ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
24. Regarding Claim 20, Duncan teaches configured to perturb the first candidate data-link tree by adding or removing an edge between nodes at random ([0033] a link to the relatives' pedigree family tree—if one exists. Given an estimated DNA match to another participant, the User may be confident (to the suggested extent) that somewhere in their family tree pedigree, in the range according to the given relationship (genetic).
Conclusion
25. 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.
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/CHERYL LEWIS/Primary Examiner, Art Unit 2166 August 22, 2026
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