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 .
Response to Amendment
Applicant's arguments filed 2/19/26 have been fully considered but they are not persuasive.
Response to Arguments
In re pgs. 8-9 the applicant argues the term consanguinity was not addressed previously. In the applicant’s specification the only time the term appears is “[0061] The coefficient of relationship is a measure of the degree of consanguinity (or biological relationship) between two individuals. With a simplifying assumption of non- consanguineous common ancestors, it can be calculated”.
In response Anderson discloses consanguinity/biological relationships (“determining IBD segments includes analyzing phased genetic data obtained from DNA samples of individuals”, 0007; “The IBD affinity metric module 310 accesses estimated IBD segments from the user data store 145 in order to generate an IBD network (also referred to as an IBD graph)”, 0056, 0062) are based on probability, likelihood, confidence, affinity measures (Affinity measures are based on probability, frequency of co-occurrence, confidence, or strength of association, “the IBD affinity metric module 310 defines a mapping (also called an “affinity measure”) from the total length of the shared IBD segments between two individuals (e.g., i and j) to the weight of the edge linking nodes i and j in the network. In one or more embodiments, the affinity measure is a real number between 0 and 1. For example, if the total length of the shared IBD segment between nodes i and j is greater than 65 cM (e.g., third cousins), then the edge linking nodes i and j receives a value of 0.97 or greater”, 0063).
In re pg. 9 the applicant argues Applicant has found no notion in the cited sections of Anderson of a closest relative being identified based on a degree of consanguinity between the closest relative and the individual already in the formed pedigrees, much less determining pedigree membership based on pairs of individuals. For example, Anderson appears to be comparing an individual's DNA with that of a community rather than that of another individual.
In response, Anderson teaches the claimed invention (the applicant’s disclosure only uses the term once in the specification and says consanguinity is equivalent to a biological relationship; comparing pairs of individuals, “Phased genetic data from pairs of individuals in a sample are analyzed to estimate shared IBD chromosomal segments. The extent of IBD sharing between every pair can be mapped to an affinity metric”, 0007-8; “processes the DNA to identify shared IBD between pairs of individuals”, 0024; “The IBD estimation module 115 is responsible for identifying IBD segments (also referred to as IBD estimates) from phased genotype data (haplotypes) between pairs of individuals”, 0050; “between every pair of users”, 0052; “Probability(X≤x), where x is the IBD estimate between any pair of individuals”, 0065; “The IBD affinity metric module 310 accesses estimated IBD segments from the user data store 145 in order to generate an IBD network (also referred to as an IBD graph)”, 0056, 0062).
In re pgs. 9-10 applicant argues Anderson fails to disclose generating a pedigree graph using relationship data of the pedigree having a highest pedigree likelihood; and causing display of the pedigree graph on a display device, wherein the pedigree graph includes nodes representing individuals of the pedigree having the highest pedigree likelihood, and wherein each child node of the nodes is connected to a respective pair of parent nodes of the nodes.
In response, Anderson discloses generating a pedigree graph using relationship data of the pedigree having a highest pedigree likelihood (reads on IBD networks/graphs, “These affinity metrics computed for every pair of DNA samples are used to generate an IBD network, in which nodes in the network represent individuals, and weighted edges in the network represent the IBD-based affinity between individuals. Application of a network clustering algorithm allows for the identification of structure from the pattern of IBD. Each cluster within an IBD network may define a group of people that share common ancestral origins or a common history. To characterize this shared history underlying each IBD network cluster, identified clusters are annotated with historical data based on information available about the individuals in each cluster.”, 0008-9; 0052, 0054, “The IBD affinity metric module 310 accesses estimated IBD segments from the user data store 145 in order to generate an IBD network (also referred to as an IBD graph)”, 0056; 0061); and
causing display of the pedigree graph (“generate an IBD network” 0008) on a display device, wherein the pedigree graph includes nodes representing individuals of the pedigree having the highest pedigree likelihood (weighted nodes or affinity measure, 0063 based on probability, likelihood, confidence, affinity measures; Affinity measures are based on probability, frequency of co-occurrence, confidence, or strength of association, 0063; “each feature thus represents some “degree of connectedness” between an individual and a set of individuals”, 0097), and wherein each child node of the nodes is connected to a respective pair of parent nodes of the nodes (“the system 100 can determine and display graphical information showing any event-associated geographic extent of community members who share a significant proportion of ethnicity estimates, thereby indicating common ancestral origin (collapsed or un-collapsed). This information may be presented as part of a graphical user interface or report file provided to an individual.”, 0123; “generate an IBD network, in which nodes in the network represent individuals, and weighted edges in the network represent the IBD-based affinity between individuals.”, 0008; 0034; “The IBD network includes a number of nodes with one or more weighted edges connecting some of the nodes to each other. Each node corresponds to one of the individuals from the user data store 145. Each edge between one node and another node has a weight, a numerical value, based on the IBD estimate between the two nodes, as generated by IBD estimation module 115”, 0062; “the IBD affinity metric module 310 defines a mapping (also called an “affinity measure”) from the total length of the shared IBD segments between two individuals (e.g., i and j) to the weight of the edge linking nodes i and j in the network. In one or more embodiments, the affinity measure is a real number between 0 and 1. For example, if the total length of the shared IBD segment between nodes i and j is greater than 65 cM (e.g., third cousins), then the edge linking nodes i and j receives a value of 0.97 or greater.”, 0063).
Regarding the new limitation of display of the pedigree graph on a display device, displaying data is for human consumption and not the point of novelty in a utility patent. The underlying algorithm that generates the data to be displayed would be the point of novelty. Labeling data as displayed in formats, shapes, sizes, colors amounts to design choice. Patent office is going to grant one applicant a patent for the same exact invention because one applicant claims using red nodes and another claims green.
Anderson does not display random data that has no relation to an individual or family. Anderson’s graphs (IBDs, “The IBD affinity metric module 310 accesses estimated IBD segments from the user data store 145 in order to generate an IBD network (also referred to as an IBD graph)”, 0056, 0062) are based on probability, likelihood, confidence, affinity measures (Affinity measures are based on probability, frequency of co-occurrence, confidence, or strength of association, “the IBD affinity metric module 310 defines a mapping (also called an “affinity measure”) from the total length of the shared IBD segments between two individuals (e.g., i and j) to the weight of the edge linking nodes i and j in the network. In one or more embodiments, the affinity measure is a real number between 0 and 1. For example, if the total length of the shared IBD segment between nodes i and j is greater than 65 cM (e.g., third cousins), then the edge linking nodes i and j receives a value of 0.97 or greater”, 0063).
Anderson does disclose using colors (“Using this OR measure, the community annotation module 330 generates a graph (or plot) that visually depicts grid points in which the largest odds ratios are indicated visually by labels or distinguishable colors, for example.”, 0085).
Regarding the arguments pertaining to the USC 101,
it is well-settled that collecting and analyzing information by steps people go through in their minds or by mathematical algorithms, without more, are mental processes in the abstract-idea category. Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016); see SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) ("[S]electing certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis" is abstract); Intellectual Ventures I LLC v. Cap. One Fin. Corp., 850 F.3d 1332, 1341 (Fed. Cir. 2017) ("Organizing, displaying, and manipulating data of particular documents" is abstract.); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096-97 (Fed. Cir. 2016) (compiling and combining disparate data sources to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment to detect potential fraud does not differentiate a process from ordinary mental processes); In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022) ("These steps can be performed by a human, using 'observation, evaluation, judgment, [and] opinion,' because they involve making determinations and identifications, which are mental tasks humans routinely do").
The claims amount to data analysis/manipulation and using some form of AI as a tool. The transformation of data, or the mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic 'abstract idea,"' is not a transformation sufficient to integrate a judicial exception into a practical application. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360 (Fed. Cir. 1994)).
Although the claims may specify an improvement they are only improving the abstract idea not a computer.
"The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea." MPEP § 2106.04(a)(2).III. "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions." Id. For the purposes of this abstract idea, "[t]he courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation."
Regarding b. technical improvements
MPEP 2106.05(a) states, "the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement." The present disclosure provides the requisite detail.
MPEP 2106.05(a) also states, "After the examiner has consulted the specification and
determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology."
Applicant would need to be clear about any improvement specified in the disclosure and the improvement should be specified in the claim language as well; one should be able to point to the disclosure and say this part of the claim language is explicitly referring to the part of the disclosure that is discussing the improvement, unless of course, the improvement clearly specifically in the claims. The specification needs to include sufficient details such that one of ordinary skill in the art recognizes the claimed invention as providing an improvement. The claim needs to include the components or steps of the invention that provide the improvement described in the specification. The improvement can't be in the abstract idea itself, there has to be an additional element which integrates the abstract idea into a practical application; even a better way of performing mathematical concepts is still a mathematical concept. Applicant discloses various hardware and software of the claimed invention as being standard and conventional in the art and does not disclose transforming/converting a generic computing machine into a special purpose machine. In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). - See MPEP 2106.05(d)(1). Abstract concepts include: observation, evaluation, judgement, and opinion. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71 (2012) (quoting Gottschalk v. Benson, 409 U.S. 63, 67 (1972) ("'[M]ental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work"'). It is the examiner’s position that applicant’s claims do no more than merely invoking generic computer components merely as a tool in which the computer instructions apply the judicial exception. MPEP § 2106.05(f): Mere Instructions to Apply an Exception.
Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:
i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857;
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
iv. Recording, transmitting, and archiving digital images by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem presented by combining a camera and a cellular telephone, TLI Communications, 823 F.3d at 611-12, 118 USPQ2d at 1747;
v. Affixing a barcode to a mail object in order to more reliably identify the sender and speed up mail processing, without any limitations specifying the technical details of the barcode or how it is generated or processed, Secured Mail Solutions, LLC v. Universal Wilde, Inc., 873 F.3d 905, 910-11, 124 USPQ2d 1502, 1505-06 (Fed. Cir. 2017);
vi. Instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344-45, 127 USPQ2d 1553, 1559-60 (Fed. Cir. 2018);
vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because “an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality,” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). MPEP 2106.05(a)
All arguments have been addressed above or below in the body of the rejections.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 20-22, 24-26, 28, 120-121 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claims 1-9, 20-22, 24-28, 120-121 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 1:
2A Prong 1:
identifying, among the genetically related individuals not yet considered as the closest relative and not yet excluded from the formed pedigrees, a closest relative of an individual already in the formed pedigrees, wherein the closest relative is identified based on a degree of consanguinity between the closest relative and the individual already in the formed pedigrees (abstract idea of analyzing data using data to make a decision amounts to a mental process);
applying pairwise IBD data and pairwise age data of the closest relative and the individual already in the formed pedigrees to a probabilistic relationship model to obtain various likelihoods of various relationships between the closest relative and the individual already in the formed pedigrees (abstract idea of analyzing data and/or mathematical concepts. Mental process- a human-mind with pen and paper can generate/determine data or a mental process of modeling with assistance of pen and paper);
selecting one or more of the potential relationships between the closest relative and the individual already in the formed pedigrees that have relationship likelihoods meeting a relationship criterion, and adding each of the one or more of the potential relationships to the formed pedigrees already formed to grow the formed pedigrees into one or more growing pedigrees (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation; a human-mind with pen and paper can generate/determine data);
Selecting one or more of the growing pedigrees that have pedigree likelihoods meeting a pedigree criterion as the formed pedigrees (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to assist in making a decision; a human-mind with pen and paper can generate/determine data);
generating a pedigree graph using relationship data of the pedigree having a highest pedigree likelihood(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Computer system, processor, system memory, (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component);
causing display of the pedigree graph on a display device, wherein the pedigree graph includes nodes representing individuals of the pedigree having the highest pedigree likelihood, and wherein each child node of the nodes is connected to a respective pair of parent nodes of the nodes (insignificant extra solution and under 2B Berkheimer from MPEP 2106.05(d)(II)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Computer system, processor, system memory, (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component);
causing display of the pedigree graph on a display device, wherein the pedigree graph includes nodes representing individuals of the pedigree having the highest pedigree likelihood, and wherein each child node of the nodes is connected to a respective pair of parent nodes of the nodes (insignificant extra solution and under 2B Berkheimer from MPEP 2106.05(d)(II)).
Displaying on an interface: Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
2. (Original) The method of claim 1, wherein the pairwise IBD data comprise a length of IBD segments (further expand mental process user can analyze data and/or perform modeling).
3. (Original) The method of claim 2, wherein the lengths of IBD segments comprise a length of full IBD segments and/or a length of half IBD segments(further expand mental process user can analyze data and/or perform modeling).
4. (Currently Amended) The method of claim 1, wherein the pairwise IBD data comprise a number of IBD segments(further expand mental process user can analyze data and/or perform modeling).
5. (Original) The method of claim 4, wherein the number of IBD segments comprise a number of full IBD segments and/or a number of half IBD segments (further expand mental process user can analyze data and/or perform modeling).
6. (Currently Amended) The method of claim 1, wherein the probabilistic relationship model is a machine-learning model (further expand mental process user can analyze data and/or perform modeling; machine learning model -adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
7. (Currently Amended) The method of claim 1, wherein the probabilistic relationship model models the probability distribution of the pairwise IBD data for each relationship and/or the probability distribution of the pairwise age data for each relationship as a Gaussian distribution, a Poisson distribution, or an exponential distribution (additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
8. (Currently Amended) The method of claim 1, further comprising:
storing into a database or retrieving from a database relationship data of a pedigree having the highest pedigree likelihood among the growing pedigrees selected in (lg) (the receiving/storing steps are considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is not patent eligible).
9. (Original) The method of claim 8, further comprising:
a) generating a pedigree graph using the relationship data of the pedigree having the highest pedigree likelihood (mental process of modeling with assistance of pen and paper); and
b) displaying the pedigree graph on a display device (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component).
20. (Currently Amended) The method of claim 1, wherein pairwise IBD data between two individuals are used to determine how closely related the two individuals are (further expand mental process user can analyze data and/or perform modeling).
21. (Currently Amended) The method of claim 1, wherein the relationship criterion is a ratio of an instant relationship likelihood over a maximum relationship likelihood being larger than a value c (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
22. (Currently Amended) The method of claim 1, wherein the pedigree criterion is a ratio of an instant pedigree likelihood over a maximum pedigree likelihood being larger than a specific value d (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
24. (Currently Amended) The method of claim 1, further comprising:
b) identifying two pedigrees from among a plurality of pedigrees constructed using operations (1a)-(1 h), the two pedigrees being a genealogically closest pair of pedigrees among all pairs in the plurality pedigrees (further expand mental process user can analyze data and/or perform modeling).
25. (Original) The method of claim 24, wherein in operation (24c) a genealogical similarity between two pedigrees that are the genealogically closest pair of pedigrees is measured as a union over all IBD segments shared between an individual in a first pedigree in the genealogically closest pair of pedigrees and an individual in a second pedigree in the genealogically closest pair of pedigrees (further expand mental process user can analyze data and/or perform modeling).
26. (Currently Amended) The method of claim 24, further comprising:
c) combining the two pedigrees identified in operation (24c) into a combined pedigree (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
28. (Original) The method of claim 26, wherein in operation (26d) the two pedigrees are combined by:
a) identifying a first set of individuals in a first pedigree who share IBD with individuals in a second pedigree;
b) identifying a second set of individuals in the second pedigree who share IBD with individuals in the first pedigree;
c) identifying a common ancestor of the first set of individuals;
d) identifying a common ancestor of the second set of individuals;
e) inferring a degree of relatedness between the common ancestor of the first set and the common ancestor of the second set; and
f) connecting the two common ancestors by an inferred degree of relatedness between the common ancestors (Abstract ideas of analyzing data. Mental processes. A human-mind with pen and paper can generate/determine data).
120. (New) The method of claim 1, wherein each respective pair of the parent nodes is rendered in a different color (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can determine what data/color to use).
121. (New) The method of claim 1, wherein all of the parent nodes represent direct ancestors of an individual, and wherein the individual is represented by a focal node that is a child node of a pair of the parent nodes (insignificant extra solution and under 2B Berkheimer from MPEP 2106.05(d)(II)).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8, 20-22, 24-26, 28, 120 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Anderson (US 2019/0034587).
Anderson discloses:
1. (Original) A method, implemented using a computer system that includes one or more processors and system memory, for determining pedigree relationships among a plurality of genetically related individuals, the method comprising repeating until all individuals of the plurality of genetically related (DNA, 0023; genetic, 0028) individuals have been considered as a closest relative or excluded from formed pedigrees (“determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities. Ethnicity heterogeneity and age information may be tabulated and provided based on community membership information”, abstract; 0007-8), the method comprising:
identifying, among genetically related individuals not included in pedigrees (reads on e.g., communities/clusters, Figs. 4, 7-9, 0025) already formed, a closest relative of any individual already in a pedigree (potential relatives and/or families are determined and data pertaining to the two or more relatives or potential relatives is analyzed, models, compared, “the IBD-to-edge-weight mapping function was chosen based on a Beta cumulative density function (CDF) (i.e., Probability(X≤x), where x is the IBD estimate between any pair of individuals) with scale parameters α=1.1 and β=10 which defines the weights for all edges in the IBD network”, 0065; abstract; 0090-0091; “The IBD affinity metric module 310 accesses estimated IBD segments from the user data store 145 in order to generate an IBD network (also referred to as an IBD graph)”, 0056, 0062);
identifying, among the genetically related individuals not yet considered as the closest relative and not yet excluded from the formed pedigrees, a closest relative of an individual already in the formed pedigrees, wherein the closest relative is identified based on a degree of consanguinity (“determining IBD segments includes analyzing phased genetic data obtained from DNA samples of individuals”, 0007; “The IBD affinity metric module 310 accesses estimated IBD segments from the user data store 145 in order to generate an IBD network (also referred to as an IBD graph)”, 0056, 0062) are based on probability, likelihood, confidence, affinity measures (Affinity measures are based on probability, frequency of co-occurrence, confidence, or strength of association, “the IBD affinity metric module 310 defines a mapping (also called an “affinity measure”) from the total length of the shared IBD segments between two individuals (e.g., i and j) to the weight of the edge linking nodes i and j in the network. In one or more embodiments, the affinity measure is a real number between 0 and 1. For example, if the total length of the shared IBD segment between nodes i and j is greater than 65 cM (e.g., third cousins), then the edge linking nodes i and j receives a value of 0.97 or greater”, 0063) between the closest relative and the individual already in the formed pedigrees; (a first person entered on a GUI can be compared to one or many other people using e.g., age, birthdates, criteria, thresholds are used, “the IBD network is constructed using a filtered subset of users from the user data store 145 who meet at least one filtering criteria. The filtering of users for inclusion in the IBD network may be on the basis of age of the users, ethnicity estimates of the users, or both, or other factors. In the case of age filtering, filtering may include identifying users above a certain age (e.g., a birth date earlier than a certain date), below a certain age, or any other age-related criteria. For example, the user population for inclusion in the IBD network (generally) or in a particular reference panel (specifically) may be selected so as to result in an aggregate age (e.g., average age, median age) above a threshold age.”, 0058-0061; 0090; “the IBD-to-edge-weight mapping function was chosen based on a Beta cumulative density function (CDF) (i.e., Probability(X≤x), where x is the IBD estimate between any pair of individuals) with scale parameters α=1.1 and β=10 which defines the weights for all edges in the IBD network”, 0065; train model, 0022; model, 0090-0091);
applying pairwise IBD data (“Phased genetic data from pairs of individuals in a sample are analyzed to estimate shared IBD chromosomal segments. The extent of IBD sharing between every pair can be mapped to an affinity metric”, 0007-8, 0024, 0031, 0050, 0052) and pairwise age data of the closest relative and the individual already in the formed pedigrees to a probabilistic relationship model to obtain various likelihoods of various potential relationships between the closest relative and the individual already in the formed pedigrees(a first person entered on a GUI can be compared to one or many other people using e.g., age, birthdates, criteria, thresholds are used, “the IBD network is constructed using a filtered subset of users from the user data store 145 who meet at least one filtering criteria. The filtering of users for inclusion in the IBD network may be on the basis of age of the users, ethnicity estimates of the users, or both, or other factors. In the case of age filtering, filtering may include identifying users above a certain age (e.g., a birth date earlier than a certain date), below a certain age, or any other age-related criteria. For example, the user population for inclusion in the IBD network (generally) or in a particular reference panel (specifically) may be selected so as to result in an aggregate age (e.g., average age, median age) above a threshold age.”, 0058-0061; 0090; “the IBD-to-edge-weight mapping function was chosen based on a Beta cumulative density function (CDF) (i.e., Probability(X≤x), where x is the IBD estimate between any pair of individuals) with scale parameters α=1.1 and β=10 which defines the weights for all edges in the IBD network”, 0065; train model, 0022; model, 0090-0091);
selecting one or more potential relationships between the closest relative and the individual already in the pedigree that have relationship likelihoods meeting the relationship criterion, and adding each of the one or more potential relationships to each pedigree already formed to grow each pedigree into one or more growing pedigrees (potential relatives and/or families are determined and data pertaining to the two or more relatives or potential relatives is analyzed, models, compared, “the IBD-to-edge-weight mapping function was chosen based on a Beta cumulative density function (CDF) (i.e., Probability(X≤x), where x is the IBD estimate between any pair of individuals) with scale parameters α=1.1 and β=10 which defines the weights for all edges in the IBD network”, 0065; abstract);
selecting growing pedigrees that have pedigree likelihoods meeting a pedigree criterion as the pedigrees already formed (generate models to assign individuals to communities, 0090; potential relatives and/or families are determined and data pertaining to the two or more relatives or potential relatives is analyzed, models, compared, “the IBD-to-edge-weight mapping function was chosen based on a Beta cumulative density function (CDF) (i.e., Probability(X≤x), where x is the IBD estimate between any pair of individuals) with scale parameters α=1.1 and β=10 which defines the weights for all edges in the IBD network”, 0065; abstract);
generating a pedigree graph using relationship data of the pedigree having a highest pedigree likelihood (reads on IBD networks/graphs, “These affinity metrics computed for every pair of DNA samples are used to generate an IBD network, in which nodes in the network represent individuals, and weighted edges in the network represent the IBD-based affinity between individuals. Application of a network clustering algorithm allows for the identification of structure from the pattern of IBD. Each cluster within an IBD network may define a group of people that share common ancestral origins or a common history. To characterize this shared history underlying each IBD network cluster, identified clusters are annotated with historical data based on information available about the individuals in each cluster.”, 0008-9; 0052, 0054, “The IBD affinity metric module 310 accesses estimated IBD segments from the user data store 145 in order to generate an IBD network (also referred to as an IBD graph)”, 0056; 0061); and
causing display of the pedigree graph (“generate an IBD network” 0008) on a display device, wherein the pedigree graph includes nodes representing individuals of the pedigree having the highest pedigree likelihood (weighted nodes or affinity measure, 0063 based on probability, likelihood, confidence, affinity measures; Affinity measures are based on probability, frequency of co-occurrence, confidence, or strength of association, 0063; “each feature thus represents some “degree of connectedness” between an individual and a set of individuals”, 0097), and wherein each child node of the nodes is connected to a respective pair of parent nodes of the nodes (“the system 100 can determine and display graphical information showing any event-associated geographic extent of community members who share a significant proportion of ethnicity estimates, thereby indicating common ancestral origin (collapsed or un-collapsed). This information may be presented as part of a graphical user interface or report file provided to an individual.”, 0123; “generate an IBD network, in which nodes in the network represent individuals, and weighted edges in the network represent the IBD-based affinity between individuals.”, 0008; 0034; “The IBD network includes a number of nodes with one or more weighted edges connecting some of the nodes to each other. Each node corresponds to one of the individuals from the user data store 145. Each edge between one node and another node has a weight, a numerical value, based on the IBD estimate between the two nodes, as generated by IBD estimation module 115”, 0062; “the IBD affinity metric module 310 defines a mapping (also called an “affinity measure”) from the total length of the shared IBD segments between two individuals (e.g., i and j) to the weight of the edge linking nodes i and j in the network. In one or more embodiments, the affinity measure is a real number between 0 and 1. For example, if the total length of the shared IBD segment between nodes i and j is greater than 65 cM (e.g., third cousins), then the edge linking nodes i and j receives a value of 0.97 or greater.”, 0063).
2. (Original) The method of claim 1, wherein the pairwise IBD data comprise a length of IBD segments (reads on length of zero or more, communities have lengths based on probabilities, likelihoods, criteria and thresholds, 0090-0114) .
3. (Original) The method of claim 2, wherein the lengths of IBD segments comprise a length of “full” (reads on zero or more) IBD segments and/or a length of half IBD segments(reads on zero or more, segments read on part or all of communities 0090-0114 and/or clusters, Fig. 4).
4. (Currently Amended) The method of claim 1, wherein the pairwise IBD data comprise a number (reads on zero or more) of IBD segments (zero or more, segments read on part or all of communities 0090-0114 and/or clusters, Fig. 4).
5. (Original) The method of claim 4, wherein the number of IBD segments comprise a number of full IBD segments and/or a number of half IBD segments(reads on zero or more, segments read on part or all of communities 0090-0114 and/or clusters, Fig. 4).
6. (Currently Amended) The method of claim 1, wherein the probabilistic relationship model is a machine-learning model (models 0090-0114).
7. (Currently Amended) The method of claim 1, wherein the probabilistic relationship model models the probability distribution of the pairwise IBD data for each relationship and/or the probability distribution of the pairwise age data (00125-0139) for each relationship as a Gaussian distribution (Gaussian, 0138), a Poisson distribution, or an exponential distribution (“For each community in the IBD network store 155, the model training module 125 uses the selected features to train 540 the model for that community. Specifically, the model training module 125 uses the set of candidate features selected for that community to train a corresponding model using a supervised machine learning technique. Once training is completed, and the model is saved in the model store 150, the model is able to generate, for a single individual, a score, likelihood or probability for predicting assignment of that individual to the community. In one embodiment, the model outputs a probability (a real number between 0 and 1), in which a number close to 1 indicates that the individual is classified to the community with high confidence, and a number close to 0 indicates with high confidence that the individual is not a member of the community.”, 0105).
8. (Currently Amended) The method of claim 1, further comprising: storing into a database or retrieving from a database relationship data of a pedigree having the highest pedigree likelihood among the growing pedigrees selected in (lg) (database/memories are used to store community, cluster, relationship data, Fig. 4; probability or likelihood see e.g., “The community prediction module 130 then computes a score, probability or likelihood for each model. In one implementation, an individual is classified as belonging a given community if the probability computed by the trained model exceeds a threshold numerical value. The threshold for classifying individuals to communities may be the same or different for each model. The output of the community prediction module includes both the classification and score/probability/likelihood for each community”, 0114).
9. (Original) The method of claim 8, further comprising:
a) generating a pedigree graph (reads on e.g., communities/clusters, Figs. 4, 7-9) using the relationship data of the pedigree having the highest pedigree likelihood (“The graphical maps and plots described above are useful because in various implementations they may be presented to a user via a GUI”, 0086); and
b) displaying the pedigree graph on a display device ( e.g., communities/clusters, Figs. 4, 7-9; “The graphical maps and plots described above are useful because in various implementations they may be presented to a user via a GUI”, 0086).
20. (Currently Amended) The method of claim 1, wherein pairwise IBD data between two individuals are used to determine how closely related the two individuals are (comparing one to one or one to many, “the IBD-to-edge-weight mapping function was chosen based on a Beta cumulative density function (CDF) (i.e., Probability(X≤x), where x is the IBD estimate between any pair of individuals) with scale parameters α=1.1 and β=10 which defines the weights for all edges in the IBD network”, 0065; abstract).
21. (Currently Amended) The method of claim 1, wherein the relationship criterion is a ratio of an instant relationship likelihood over a maximum relationship likelihood being larger than a value c (“This process is applied to all communities identified in the IBD network, after removing communities that do not satisfy additional criteria for being “valid”.”, 0090; age calculation 0125-0127).
22. (Currently Amended) The method of claim 1, wherein the pedigree criterion is a ratio of an instant pedigree likelihood over a maximum pedigree likelihood being larger than a specific value d(“This process is applied to all communities identified in the IBD network, after removing communities that do not satisfy additional criteria for being “valid”.”, 0090; age calculation 0125-0127).
24. (Currently Amended) The method of claim 1, further comprising:
b) identifying two pedigrees from among a plurality of pedigrees constructed using operations (1a)-(1 h), the two pedigrees being a genealogically closest pair of pedigrees among all pairs in the plurality pedigrees (pedigrees/communities are dynamic and various factors are used over time, models are built, trained, retrained, pedigrees/communities are grown, 0090-0114).
25. (Original) The method of claim 24, wherein in operation (24c) a genealogical similarity between two pedigrees that are the genealogically closest pair of pedigrees is measured as a union over all IBD segments shared between an individual in a first pedigree in the genealogically closest pair of pedigrees and an individual in a second pedigree in the genealogically closest pair of pedigrees (probability or likelihood see e.g., “The community prediction module 130 then computes a score, probability or likelihood for each model. In one implementation, an individual is classified as belonging a given community if the probability computed by the trained model exceeds a threshold numerical value. The threshold for classifying individuals to communities may be the same or different for each model. The output of the community prediction module includes both the classification and score/probability/likelihood for each community”, 0114).
26. (Currently Amended) The method of claim 24, further comprising:
c) combining the two pedigrees identified in operation (24c) into a combined pedigree (a plurality of relationships are tested and those that are a high probability of being correct are added to the cluster/community, “This process is applied to all communities identified in the IBD network, after removing communities that do not satisfy additional criteria for being “valid”. In cases where the communities can be viewed as a hierarchy, in which each level of the hierarchy subdivides the communities from the previous level, this process can applied to all communities at all, or selected, levels of the hierarchy”, 0090; “generates one or more candidate features 530 that may be useful for predicting whether a given user is or is not a member of a given community based on the refined reference panels”, 0091; Figs. 4, 7-9).
28. (Original) The method of claim 26, wherein in operation (26d) the two pedigrees are combined by:
a) identifying a first set of individuals in a first pedigree who share IBD with individuals in a second pedigree(0105, 0065, 0090-0091; Figs. 4, 7-9);
b) identifying a second set of individuals in the second pedigree who share IBD with individuals in the first pedigree(0105, 0065, 0090-0091; Figs. 4, 7-9);
c) identifying a common ancestor of the first set of individuals (0105, 0065, 0090-0091; Figs. 4, 7-9);
d) identifying a common ancestor of the second set of individuals(0105, 0065, 0090-0091; Figs. 4, 7-9);
e) inferring a degree of relatedness between the common ancestor of the first set and the common ancestor of the second set (In one embodiment, the model outputs a probability (a real number between 0 and 1), in which a number close to 1 indicates that the individual is classified to the community with high confidence, and a number close to 0 indicates with high confidence that the individual is not a member of the community.”, 0105; 0065); and
f) connecting the two common ancestors by an inferred degree of relatedness between the common ancestors (a plurality of relationships are tested and those that are a high probability of being correct are added to the cluster/community, “This process is applied to all communities identified in the IBD network, after removing communities that do not satisfy additional criteria for being “valid”. In cases where the communities can be viewed as a hierarchy, in which each level of the hierarchy subdivides the communities from the previous level, this process can applied to all communities at all, or selected, levels of the hierarchy”, 0090; “generates one or more candidate features 530 that may be useful for predicting whether a given user is or is not a member of a given community based on the refined reference panels”, 0091; Figs. 4, 7-9).
121. (New) The method of claim 1, wherein all of the parent nodes represent direct ancestors of an individual, and wherein the individual is represented by a focal node that is a child node of a pair of the parent nodes (“Parent-child relationships between community members (or community-associated individuals) and their children are identified by accessing those relationships and from the user data”, 0126; “identity-by-descent (IBD). IBD analysis can be used to predict the familial relationship between any two people”, 0003, 0008;
IBD networks, 0008; “the system 100 can determine and display graphical information showing any event-associated geographic extent of community members who share a significant proportion of ethnicity estimates, thereby indicating common ancestral origin (collapsed or un-collapsed). This information may be presented as part of a graphical user interface or report file provided to an individual.”, 0123; “generate an IBD network, in which nodes in the network represent individuals, and weighted edges in the network represent the IBD-based affinity between individuals.”, 0008; 0034; “The IBD network includes a number of nodes with one or more weighted edges connecting some of the nodes to each other. Each node corresponds to one of the individuals from the user data store 145. Each edge between one node and another node has a weight, a numerical value, based on the IBD estimate between the two nodes, as generated by IBD estimation module 115”, 0062; “the IBD affinity metric module 310 defines a mapping (also called an “affinity measure”) from the total length of the shared IBD segments between two individuals (e.g., i and j) to the weight of the edge linking nodes i and j in the network. In one or more embodiments, the affinity measure is a real number between 0 and 1. For example, if the total length of the shared IBD segment between nodes i and j is greater than 65 cM (e.g., third cousins), then the edge linking nodes i and j receives a value of 0.97 or greater.”, 0063).
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.
Claim(s) 120 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anderson in view of Official Notice.
120. (New) The method of claim 1, wherein each respective pair of the parent nodes is rendered in a different color (“Using this OR measure, the community annotation module 330 generates a graph (or plot) that visually depicts grid points in which the largest odds ratios are indicated visually by labels or distinguishable colors, for example”, 0085).
Anderson fails to disclose how individual colors are used in the graphs/IBDs.
The examiner takes official notice that using multiple colors is well-known and/or a design choice.
It would have been obvious to combine the references before the effective filing date because using different colors on graphs is well-known and individual colors can represent different factors or make a graph easier to read for a human.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ostrovsky (US 2015/0112884) teaches (The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities.
Ethnicity heterogeneity and age information may be tabulated and provided based on community membership information”, abstract 0003-0008; 0030, 0050); Ostrovsky teaches using various numbers of individuals (“The development of these technologies were driven by the goal to perform genome-wide association studies (GWAS), where genetic variation information is collected from hundreds of thousands of individuals”, 0007).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID R VINCENT whose telephone number is (571)272-3080. The examiner can normally be reached ~Mon-Fri 12-8:30.
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/DAVID R VINCENT/Primary Examiner, Art Unit 2123
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