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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/22/2026 has been entered.
Status of the claims
Claims 1-5, 7-12 and 14-25 were pending, claims 1, 14-16, 24 and 25 have been amended, claims 2 and 3 have been canceled. Therefore, claims 1, 4, 5, 7-12 and 14-25 are currently pending for examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 5, 7, 8, 10-12, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Manrai et al. (US 20210225513, hereafter Manrai) in view of Kenedy et al. (US 11545269, hereafter Kenedy) and further in view of Walters et al. (US 20210397972, hereafter Walters)..
Regarding claim 1, Manrai disclose: A method for generating a merged dataset, the method comprising:
accessing data comprising a core dataset from the first data source and an additional dataset from the second data source different from the first data source (Manrai [0039; 0040; 0076] discloses: The data in the environmental and phenotypic relatedness matrix can represent different types of observational or obtained datasets organized in different databases as illustrated in FIG. 1. The data can be linked across datasets using a person's person-level identifier or group-level identifier. Person-level identifiers (such as name, patient identifier, social security number (SSN), etc.) can identify data related to a specific person. Group-level identifiers can identify group-level data for a person such as census tract-level data or subpopulation data);
identifying a plurality of common attributes between the core dataset and the additional dataset (Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics);
determining a plurality of similarity scores between an inquiring entity in the core dataset and a plurality of candidate entities in the additional dataset, including, for each candidate entity of the plurality of candidate entities (Manrai [0087; 0125] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics);
calculating a distance-based score for the candidate entity based at least in part on one or more of the plurality of identified common attributes (Manrai [0104; 0105] disclose: distance measure between each person in the cohort, such as a Hamming distance, correlation, or other distance measure, is to be defined to create a phenotypic and environmental relatedness matrix 251. The phenotypic and environmental relatedness matrix 251 is conceptually similar to a genetic relatedness matrix. Individuals who are genetic twins have a 0 distance between them, while individuals who are unrelated have a large genetic distance between them);
calculating a weight influence score for the candidate entity based at least in part on a weight assigned to the candidate entity (Manrai [0089] disclose: , we know that socioeconomic factors can influence the diet patterns. The causal effect between caloric intake and obesity will appear strong if we assume or hypothesize that similar diet patterns are shared between individuals that have shared environment (or have similar socioeconomic factors). The technology disclosed can thus reduce the impact of confounding factors when determining causal relationships by providing environmental and the phenotypic correlation matrix 351 as input to the machine learning model 610. The technology disclosed uses the correlation matrix 351 as a way of adjusting for similarities, between persons, which can act as confounders to influence the association between exposures and outcomes).
Manrai didn’t disclose, but Kennedy discloses: calculating a similarity score for the candidate entity based at least in part on the distance-based score and the weight influence score (Kenedy [Col.23, lines 35-40, lines 60 to col. 24, lines 20] discloses: similarity scores can be compounded to indicate the degree of similarity of two individuals separated by two or more degrees of separation; [Col. 62, lines 10-31] discloses: the attributes have the greatest influence or smallest influenced);
Manrai and Kenedy are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Kenedy in order to calculate the similarity between individuals. The suggestion to combine can be utilized to analyze relationships and to identify individual who might benefit from being connected.
Manrai as modified didn’t disclose, but Walters disclose: evaluating the plurality of common attributes to determine that the plurality of identified common attributes satisfies one or more predefined criteria related to an expected performance of a given attribute and/or an appropriate number of common attributes (Walters [0104; 0111;0136] discloses: a data similarity score (e.g., a score dependent on a number of matching or similar elements/(as number of common attributes )in the synthetic dataset and normalized reference dataset), or data quality score (e.g., a score dependent on at least one of a number of duplicate elements in each of the synthetic dataset and normalized reference dataset, a prevalence of the most common value in each of the synthetic dataset and normalized reference dataset, a maximum difference of rare values in each of the synthetic dataset and normalized reference dataset, the differences in schema between the synthetic dataset and normalized reference dataset, or the like). System 100 may be configured to calculate these scores using the synthetic dataset and a reference dataset) ;
selecting one or more matches for the inquiring entity in the core dataset from the plurality of candidate entities in the additional dataset based at least in part on the plurality of similarity scores (Walters [0136] discloses: the performance criteria may include a similarity metric (scores)(e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). For example, model optimizer 1303 may be configured to compare the covariances or univariate distributions of a synthetic dataset generated by the new synthetic data model and a reference data stream dataset. Likewise, model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset) and
following selecting the one or more matches, generating the merged dataset by adding attribute data of the one or more selected matches from the plurality of candidate entities in the first additional dataset to attribute data of the inquiring entity in the core dataset (Walters [0136] discloses: evaluate performance criteria of a newly created synthetic data model, the performance criteria may include a similarity metric (e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset, a prevalence of the most common value in synthetic dataset and reference data stream dataset; [0107] discloses: merge the reference dataset and synthetic dataset based on similarity metric may depend on a number of elements of a synthetic dataset that match elements of a reference dataset)
applying the merged dataset to one or more of: a data analytics operation, an artificial intelligence (AI) model training operation, a model diagnosis operation, and a model evaluation technique (Walters [0041; 0056; 0084] use a synthetic data model for financial service, healthcare records include cancel diagnosis, evaluate model characters and machine learning model) .
Manrai and Walters are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Walters in order to training the synthetic data generator to generate synthetic data corresponding to the additional class. The suggestion to combine to use of synthetic data generation may enable each party to maintain security of sensitive data.
Regarding claim 4, Manrai as modified disclose: The method of claim 1, wherein generating the merged dataset includes determining that one or more of the calculated similarity scores between the inquiring entity and the plurality of candidate entities in the additional dataset exceeds a predefined score threshold (Manrai [0082; 0169] disclose: The system can use a threshold (such as 0.6) between zero and one so that when the correlation value is above the threshold, the persons are predicted as digital twins. If the correlation value is less than threshold than persons are not considered as digital twins. Threshold can be set at a higher value than 0.6 to only predict persons that have matching values for most of the input data.).
Regarding claim 5, Manrai as modified disclose: The method of claim 1, comprising, prior to generating the merged dataset, evaluating the confidence and/or distribution of the one or more selected matches for the inquiring entity (Manrai [0036; 0082] disclose: The digital twins identifier can include logic to output a correlation value using trained machine learning model, indicating distance between the first person and the second person and compare the correlation value with a threshold to determine whether the second person is a digital twin of the first person. In one implementation, the correlation values can range between 0 and 1. If the correlation value is above a threshold, e.g., 0.6 then the second person can be predicted as a digital twin of the first person. The threshold can be set at a higher level or at a lower level than 0.6).
Regarding claim 7, Manrai as modified disclose: The method of claim 1, wherein the core dataset and additional dataset do not share an identical identifier for direct dataset merging (Kenedy [Col. 64, lines 58 to col. 65, lines 11] discloses: attribute analysis can be developed in which non-identical sets of genetic attributes comprising nucleotide sequence are compared to determine whether proteins encoded by those nucleotide sequences are functionally equivalent, and therefore whether genetic information contained in the sets of genetic attributes can be considered to be equivalent (i.e., a match, the same, and/or identical). A determination of equivalence between two or more non-identical yet essentially equivalent sets of genetic attributes can enable the compression of thousands of individual DNA nucleotide attributes into a single categorical genetic attribute assigned to represent those sets of genetic attributes, which is useful for methods such as attribute discovery, predisposition prediction and predisposition modification where a reduction in the amount of genomic data can enhance processing efficiency of the methods. Sets of genetic attributes can be determined to be equivalent based on whether they are able to satisfy one or more equivalence rules (i.e., requirements for equivalence) applied to their comparison; Walters [043]).
Regarding claim 8, Manrai as modified disclose: The method of claim 1, wherein the additional dataset is a subset of a superset, and wherein the weight assigned to the candidate entity in the subset is based on representativeness of a group of similar entities in the superset (Kenedy [Col. 32, lines 49-67] discloses: The two groups are analyzed for the occurrence of two attributes, A and X, which are candidates for causing predisposition to the disease. When frequencies of occurrence are computed individually for A and for X, the observed frequencies are identical (50%) for both groups. When the frequency of occurrence is computed for the combination of A with X for individuals of each group, the frequency of occurrence is dramatically higher in the positive group compared to the negative group (50% versus 0%). Therefore, while both A and X are significant contributors to predisposition in this theoretical example, their association with expression of the disease in individuals can only be detected by determining the frequency of co-occurrence of A with X in each individual; Walters [0168] discloses: subset of the dataset ).
Regarding claim 10, Manrai as modified disclose: The method of claim 1, wherein calculating the weight influence score includes normalization and bounding the weight assigned to the candidate entity with at least one cropping function (Kenedy [col. 23, lines 15-30 ] discloses: normalized score; [Col. 62, lines 10-31] discloses: the attributes have the greatest influence or smallest influenced); Walters [0096]).
Regarding claim 11, Manrai as modified disclose: The method of claim 1, wherein the weight influence score is based on a size of the additional dataset (Kenedy [col. 44, lines 10-33 ] discloses: when evaluating populations comprising millions of individuals and attribute profiles each comprising billions of attributes. To help accomplish this, a representative subset of query-attribute-positive attribute profiles can be selected from a larger set of query-attribute-positive attribute profiles. The representative subset of attribute profiles can be used to identify candidate attributes and attribute combinations associated with the query attribute much more efficiently than using the entire set of query-attribute-profiles, while still providing the potential to identify relevant co-associating attributes. While not absolutely required, selecting a representative subset of attribute profiles may be advantageous when the set of query-attribute-positive attribute profiles includes thousands or millions of attribute profiles. The selection of a subset of query-attribute-positive attribute profiles can be a random selection or another appropriate and/or statistically valid method of selection. The size of this subset can vary, but for example, can comprise as few as 10 or as many as 100 or more attribute profiles).
Regarding claim 12, Manrai as modified disclose: The method of claim 1, comprising validating the merged dataset using one or more of: internal validation and external validation (Manrai [0079; 0080] disclose: The system can use inputs from additional data sources such as genetic relatedness (e.g., sibling fraternal, or identical twin, or fraction of genetic relatedness) and determines digital twins between pairs of persons by calculating distance between vectors (or rows) representing persons in the environmental and phenotypic relationship matrix 251).
Regarding claim 14, Manrai as modified disclose: A non-transitory computer-readable storage medium storing one or more programs for generating a merged dataset, the programs for execution by one or more processors of an electronic device that when executed by the device, cause the device to (Manrai [0167]):
access data comprising a core dataset from the first data source and an additional dataset from the second data source different from the first data source (Manrai [0039; 0040; 0076] discloses: The data in the environmental and phenotypic relatedness matrix can represent different types of observational datasets organized in different databases as illustrated in FIG. 1. The data can be linked across datasets using a person's person-level identifier or group-level identifier. Person-level identifiers (such as name, patient identifier, social security number (SSN), etc.) can identify data related to a specific person. Group-level identifiers can identify group-level data for a person such as census tract-level data or subpopulation data);
identify a plurality of common attributes between the core dataset and the additional dataset Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
determine a plurality of similarity scores between an inquiring entity in the core dataset and a plurality of candidate entities in the additional dataset, including, for each candidate entity of the plurality of candidate entities (Manrai [0087; 0125] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics);
calculate a distance-based score for the candidate entity based at least in part on one or more of the plurality of identified common attributes (Manrai [0104; 0105] disclose: distance measure between each person in the cohort, such as a Hamming distance, correlation, or other distance measure, is to be defined to create a phenotypic and environmental relatedness matrix 251. The phenotypic and environmental relatedness matrix 251 is conceptually similar to a genetic relatedness matrix. Individuals who are genetic twins have a 0 distance between them, while individuals who are unrelated have a large genetic distance between them);
calculate a weight influence score for the candidate entity based at least in part on a weight assigned to the candidate entity (Manrai [0089] disclose: , we know that socioeconomic factors can influence the diet patterns. The causal effect between caloric intake and obesity will appear strong if we assume or hypothesize that similar diet patterns are shared between individuals that have shared environment (or have similar socioeconomic factors). The technology disclosed can thus reduce the impact of confounding factors when determining causal relationships by providing environmental and the phenotypic correlation matrix 351 as input to the machine learning model 610. The technology disclosed uses the correlation matrix 351 as a way of adjusting for similarities, between persons, which can act as confounders to influence the association between exposures and outcomes);
Manrai didn’t disclose, but Kennedy discloses: calculate a similarity score for the candidate entity based at least in part on the distance-based score and the weight influence score (Kenedy [Col.23, lines 35-40, lines 60 to col. 24, lines 20] discloses: similarity scores can be compounded to indicate the degree of similarity of two individuals separated by two or more degrees of separation; [Col. 62, lines 10-31] discloses: the attributes have the greatest influence or smallest influenced) ;
Manrai and Kenedy are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Kenedy in order to calculate the similarity between individuals. The suggestion to combine can be utilized to analyze relationships and to identify individual who might benefit from being connected.
Manrai as modified didn’t disclose, but Walters disclose: evaluating the plurality of common attributes to determine that the plurality of identified common attributes satisfies one or more predefined criteria related to an expected performance of a given attribute and/or an appropriate number of common attributes (Walters [0104; 0111;0136] discloses: a data similarity score (e.g., a score dependent on a number of matching or similar elements/(as number of common attributes )in the synthetic dataset and normalized reference dataset), or data quality score (e.g., a score dependent on at least one of a number of duplicate elements in each of the synthetic dataset and normalized reference dataset, a prevalence of the most common value in each of the synthetic dataset and normalized reference dataset, a maximum difference of rare values in each of the synthetic dataset and normalized reference dataset, the differences in schema between the synthetic dataset and normalized reference dataset, or the like). System 100 may be configured to calculate these scores using the synthetic dataset and a reference dataset) ;
selecting one or more matches for the inquiring entity in the core dataset from the plurality of candidate entities in the additional dataset based at least in part on the plurality of similarity scores (Walters [0136] discloses: the performance criteria may include a similarity metric (scores)(e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). For example, model optimizer 1303 may be configured to compare the covariances or univariate distributions of a synthetic dataset generated by the new synthetic data model and a reference data stream dataset. Likewise, model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset) and
following selecting the one or more matches, generating the merged dataset by adding attribute data of the one or more selected matches from the plurality of candidate entities in the first additional dataset to attribute data of the inquiring entity in the core dataset (Walters [0136] discloses: evaluate performance criteria of a newly created synthetic data model, the performance criteria may include a similarity metric (e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset, a prevalence of the most common value in synthetic dataset and reference data stream dataset; [0107] discloses: merge the reference dataset and synthetic dataset based on similarity metric may depend on a number of elements of a synthetic dataset that match elements of a reference dataset)
applying the merged dataset to one or more of: a data analytics operation, an artificial intelligence (AI) model training operation, a model diagnosis operation, and a model evaluation technique (Walters [0041; 0056; 0084] use a synthetic data model for financial service, healthcare records include cancel diagnosis, evaluate model characters and machine learning model) .
Manrai and Walters are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Walters in order to training the synthetic data generator to generate synthetic data corresponding to the additional class. The suggestion to combine to use of synthetic data generation may enable each party to maintain security of sensitive data.
Regarding claim 15, Manrai as modified disclose: A system for generating a merged dataset, comprising: one or more processors; memory; and one or more programs stored on the memory that when executed by the one or more processors cause the one or more processors to:
access data comprising a core dataset from the first data source and an additional dataset from the second data source different from the first data source (Manrai [0039; 0040; 0076] discloses: The data in the environmental and phenotypic relatedness matrix can represent different types of observational datasets organized in different databases as illustrated in FIG. 1. The data can be linked across datasets using a person's person-level identifier or group-level identifier. Person-level identifiers (such as name, patient identifier, social security number (SSN), etc.) can identify data related to a specific person. Group-level identifiers can identify group-level data for a person such as census tract-level data or subpopulation data);
identify a plurality of common attributes between the core dataset and the additional dataset Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
determine a plurality of similarity scores between an inquiring entity in the core dataset and a plurality of candidate entities in the additional dataset, including, for each candidate entity of the plurality of candidate entities (Manrai [0087; 0125] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics);
calculate a distance-based score for the candidate entity based at least in part on one or more of the plurality of identified common attributes (Manrai [0104; 0105] disclose: distance measure between each person in the cohort, such as a Hamming distance, correlation, or other distance measure, is to be defined to create a phenotypic and environmental relatedness matrix 251. The phenotypic and environmental relatedness matrix 251 is conceptually similar to a genetic relatedness matrix. Individuals who are genetic twins have a 0 distance between them, while individuals who are unrelated have a large genetic distance between them);
calculate a weight influence score for the candidate entity based at least in part on a weight assigned to the candidate entity (Manrai [0089] disclose: , we know that socioeconomic factors can influence the diet patterns. The causal effect between caloric intake and obesity will appear strong if we assume or hypothesize that similar diet patterns are shared between individuals that have shared environment (or have similar socioeconomic factors). The technology disclosed can thus reduce the impact of confounding factors when determining causal relationships by providing environmental and the phenotypic correlation matrix 351 as input to the machine learning model 610. The technology disclosed uses the correlation matrix 351 as a way of adjusting for similarities, between persons, which can act as confounders to influence the association between exposures and outcomes);
Manrai didn’t disclose, but Kennedy discloses: calculate a similarity score for the candidate entity based at least in part on the distance-based score and the weight influence score (Kenedy [Col.23, lines 35-40, lines 60 to col. 24, lines 20] discloses: similarity scores can be compounded to indicate the degree of similarity of two individuals separated by two or more degrees of separation; [Col. 62, lines 10-31] discloses: the attributes have the greatest influence or smallest influenced) ;
Manrai and Kenedy are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Kenedy in order to calculate the similarity between individuals. The suggestion to combine can be utilized to analyze relationships and to identify individual who might benefit from being connected.
Manrai as modified didn’t disclose, but Walters disclose: evaluating the plurality of common attributes to determine that the plurality of identified common attributes satisfies one or more predefined criteria related to an expected performance of a given attribute and/or an appropriate number of common attributes (Walters [0104; 0111; 0136] discloses: a data similarity score (e.g., a score dependent on a number of matching or similar elements/(as number of common attributes )in the synthetic dataset and normalized reference dataset), or data quality score (e.g., a score dependent on at least one of a number of duplicate elements in each of the synthetic dataset and normalized reference dataset, a prevalence of the most common value in each of the synthetic dataset and normalized reference dataset, a maximum difference of rare values in each of the synthetic dataset and normalized reference dataset, the differences in schema between the synthetic dataset and normalized reference dataset, or the like). System 100 may be configured to calculate these scores using the synthetic dataset and a reference dataset) ;
selecting one or more matches for the inquiring entity in the core dataset from the plurality of candidate entities in the additional dataset based at least in part on the plurality of similarity scores (Walters [0136] discloses: the performance criteria may include a similarity metric (scores)(e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). For example, model optimizer 1303 may be configured to compare the covariances or univariate distributions of a synthetic dataset generated by the new synthetic data model and a reference data stream dataset. Likewise, model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset) and
following selecting the one or more matches, generating the merged dataset by adding attribute data of the one or more selected matches from the plurality of candidate entities in the first additional dataset to attribute data of the inquiring entity in the core dataset (Walters [0136] discloses: evaluate performance criteria of a newly created synthetic data model, the performance criteria may include a similarity metric (e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset, a prevalence of the most common value in synthetic dataset and reference data stream dataset; [0107] discloses: merge the reference dataset and synthetic dataset based on similarity metric may depend on a number of elements of a synthetic dataset that match elements of a reference dataset)
applying the merged dataset to one or more of: a data analytics operation, an artificial intelligence (AI) model training operation, a model diagnosis operation, and a model evaluation technique (Walters [0041; 0056; 0084] use a synthetic data model for financial service, healthcare records include cancel diagnosis, evaluate model characters and machine learning model) .
Manrai and Walters are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Walters in order to training the synthetic data generator to generate synthetic data corresponding to the additional class. The suggestion to combine to use of synthetic data generation may enable each party to maintain security of sensitive data.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Manrai et al. (US 20210225513, hereafter Manrai) in view of Kenedy et al. (US 11545269, hereafter Kendy) in view of Walters et al. (US 20210397972, hereafter Walters) and further in view of Davar et al. (US 20160132811, hereafter Davar).
Regarding claim 9, Manrai as modified didn’t disclose, but Davar disclose: The method of claim 1, wherein calculating the distance-based score is based on a weighted Manhattan distance (Davar [0110] disclose: The distance may be computed as the Manhattan distance).
Manrai as modified and Davar are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Davar in order to calculate the similarity between organizations. The suggestion to combine for identifying links between companies that are similar.
Claims 16-25 are rejected under 35 U.S.C. 103 as being unpatentable over Manrai et al. (US 20210225513, hereafter Manrai) in view of Walters et al. (US 20210397972, hereafter Walters).
Regarding claim 16, Manrai as modified disclose: A method for generating a merged dataset, the method comprising:
accessing data comprising a core dataset from the first data source and a plurality of additional datasets (Manrai [0039; 0040; 0076] discloses: The data in the environmental and phenotypic relatedness matrix can represent different types of observational datasets organized in different databases as illustrated in FIG. 1. The data can be linked across datasets using a person's person-level identifier or group-level identifier. Person-level identifiers (such as name, patient identifier, social security number (SSN), etc.) can identify data related to a specific person. Group-level identifiers can identify group-level data for a person such as census tract-level data or subpopulation data);
selecting a first additional dataset of the plurality of additional datasets based on the ranking data, wherein the first additional dataset is from a second data source different from the first data source (Manrai [0037; 0109; 0110] discloses: determine a ranked list of causal relationships between a plurality of exposures and a plurality of outcomes. As described above, when we use data from observational datasets, confounding can cause problems when identifying causal relationships between exposures and outcomes. The technology disclosed includes logic to reduce the impact of confounding factors when determining the association between the exposures and outcomes. The causal relationship identifier 189 include the logic to provide the environmental and phenotypic correlation matrix as an additional input to the machine learning model as a “random effect” to control the environmental relatedness between individuals);
determining ranking data of the plurality of additional datasets (Manrai [0037; 0109; 0110] discloses: determine a ranked list of causal relationships between a plurality of exposures and a plurality of outcomes. As described above, when we use data from observational datasets, confounding can cause problems when identifying causal relationships between exposures and outcomes. The technology disclosed includes logic to reduce the impact of confounding factors when determining the association between the exposures and outcomes. The causal relationship identifier 189 include the logic to provide the environmental and phenotypic correlation matrix as an additional input to the machine learning model as a “random effect” to control the environmental relatedness between individuals);
identifying a plurality of common attributes between the core dataset and the first additional dataset (Manrai [0087] disclose: Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics);
Manrai as modified didn’t disclose, but Walters disclose: evaluating the plurality of common attributes to determine that the plurality of identified common attributes satisfies one or more predefined criteria related to an expected performance of a given attribute and/or an appropriate number of common attributes (Walters [0104; 0111; 0136] discloses: a data similarity score (e.g., a score dependent on a number of matching or similar elements/(as number of common attributes )in the synthetic dataset and normalized reference dataset), or data quality score (e.g., a score dependent on at least one of a number of duplicate elements in each of the synthetic dataset and normalized reference dataset, a prevalence of the most common value in each of the synthetic dataset and normalized reference dataset, a maximum difference of rare values in each of the synthetic dataset and normalized reference dataset, the differences in schema between the synthetic dataset and normalized reference dataset, or the like). System 100 may be configured to calculate these scores using the synthetic dataset and a reference dataset) ;
in accordance with determining that the plurality of identified common attributes satisfy one or more predefined criteria, for a given inquiring entity in the core dataset: selecting one or more matches for each inquiring entity in the core dataset from a plurality of candidate entities in the first additional dataset (Walters [0136] discloses: the performance criteria may include a similarity metric (scores)(e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). For example, model optimizer 1303 may be configured to compare the covariances or univariate distributions of a synthetic dataset generated by the new synthetic data model and a reference data stream dataset. Likewise, model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset) and
following selecting the one or more matches, generating the merged dataset by adding attribute data of the one or more selected matches from the plurality of candidate entities in the first additional dataset to attribute data of the inquiring entity in the core dataset (Walters [0136] discloses: evaluate performance criteria of a newly created synthetic data model, the performance criteria may include a similarity metric (e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset, a prevalence of the most common value in synthetic dataset and reference data stream dataset; [0107] discloses: merge the reference dataset and synthetic dataset based on similarity metric may depend on a number of elements of a synthetic dataset that match elements of a reference dataset)
applying the merged dataset to one or more of: a data analytics operation, an artificial intelligence (AI) model training operation, a model diagnosis operation, and a model evaluation technique (Walters [0041; 0056; 0084] use a synthetic data model for financial service, healthcare records include cancel diagnosis, evaluate model characters and machine learning model) .
Manrai and Walters are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Walters in order to training the synthetic data generator to generate synthetic data corresponding to the additional class. The suggestion to combine to use of synthetic data generation may enable each party to maintain security of sensitive data.
Regarding claim 17, Manrai as modified disclose: The method of claim 16, comprising, in accordance with determining that the plurality of common attributes between the core dataset and the first additional dataset do not satisfy the one or more predefined criteria, modifying the ranking data of the plurality of additional datasets (Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
Regarding claim 18, Manrai as modified disclose: The method of claim 17, comprising:
selecting a second additional dataset of the plurality of additional datasets based on the modified ranking data (Manrai [0111] disclose: If the rank of an X is robust to such perturbations of the analytic design, the more likely the finding is an association close to the true association between the exposures (X/Xs) and outcomes (Y/Ys). The pipeline can be configured to automatically iterate through combinations of a study design, e.g., analyzing multiple strata of a population, and further test how different estimates are in different strata, or how the risk estimates change); and
identifying a plurality of common attributes between the core dataset and the second additional dataset (Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
Regarding claim 19, Manrai as modified disclose: The method of claim 18, comprising, in accordance with determining that the second plurality of identified common attributes satisfies the one or more predefined criteria, selecting one or more matches for each inquiring entity in the core dataset from a plurality of candidate entities in the second additional dataset (Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
Regarding claim 20, Manrai as modified disclose: The method of claim 19, comprising generating the merged dataset by adding the one or more selected matches to the core dataset (Manrai [0101] disclose: input dataset to a digital twins pipeline can be an observational cohort dataset. FIG. 1 shows examples of digital twins pipeline datasets, which comprises insurance claims database with insurance claim data, health record database with digital health record data, personal (or application) database with health or lifestyle related digital data, or patient cohort dataset with any other patient medical data. The digital twins pipeline is configured to integrate (merge) these data, cross reference them, or join them with patient information. Patient information can comprise data of a person level, such as a person's identification, data of an area level, such as integrated data of addresses or geographical coordinates, and data of subpopulation level, such as integrated data by a range of value, e.g., a physiological measurement or age group).
Regarding claim 21, Manrai as modified disclose: The method of claim 16, wherein selecting the one or more matches for each inquiring entity in the core dataset comprises determining a plurality of similarity scores between the inquiring entity and each candidate entity of the plurality of candidate entities in the first additional dataset (Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
Regarding claim 22, Manrai as modified disclose: The method of claim 21, wherein determining a similarity score for the candidate entity of the plurality of candidate entities is based at least in part on a distance-based score calculated based at least in part on one or more of the plurality of identified common attributes between the core dataset and the first additional dataset (Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
Regarding claim 23, Manrai as modified disclose: The method of claim 21, wherein determining a similarity score for the candidate entity of the plurality of candidate entities is based at least in part on a weight influence score calculated based at least in part on a weight assigned to the candidate entity (Manrai [0089] disclose: , we know that socioeconomic factors can influence the diet patterns. The causal effect between caloric intake and obesity will appear strong if we assume or hypothesize that similar diet patterns are shared between individuals that have shared environment (or have similar socioeconomic factors). The technology disclosed can thus reduce the impact of confounding factors when determining causal relationships by providing environmental and the phenotypic correlation matrix 351 as input to the machine learning model 610. The technology disclosed uses the correlation matrix 351 as a way of adjusting for similarities, between persons, which can act as confounders to influence the association between exposures and outcomes).
Regarding claim 24, Manrai as modified disclose: A non-transitory computer-readable storage medium storing one or more programs for generating merged datasets, the programs for execution by one or more processors of an electronic device that when executed by the device, cause the device to:
access data comprising a core dataset and a plurality of additional datasets (Manrai [0039; 0040; 0076] discloses: The data in the environmental and phenotypic relatedness matrix can represent different types of observational datasets organized in different databases as illustrated in FIG. 1. The data can be linked across datasets using a person's person-level identifier or group-level identifier. Person-level identifiers (such as name, patient identifier, social security number (SSN), etc.) can identify data related to a specific person. Group-level identifiers can identify group-level data for a person such as census tract-level data or subpopulation data);
selecting a first additional dataset of the plurality of additional datasets based on the ranking data, wherein the first additional dataset is from a second data source different from the first data source (Manrai [0037; 0109; 0110] discloses: determine a ranked list of causal relationships between a plurality of exposures and a plurality of outcomes. As described above, when we use data from observational datasets, confounding can cause problems when identifying causal relationships between exposures and outcomes. The technology disclosed includes logic to reduce the impact of confounding factors when determining the association between the exposures and outcomes. The causal relationship identifier 189 include the logic to provide the environmental and phenotypic correlation matrix as an additional input to the machine learning model as a “random effect” to control the environmental relatedness between individuals);
determine ranking data of the plurality of additional datasets (Manrai [0037; 0109; 0110] discloses: determine a ranked list of causal relationships between a plurality of exposures and a plurality of outcomes. As described above, when we use data from observational datasets, confounding can cause problems when identifying causal relationships between exposures and outcomes. The technology disclosed includes logic to reduce the impact of confounding factors when determining the association between the exposures and outcomes. The causal relationship identifier 189 include the logic to provide the environmental and phenotypic correlation matrix as an additional input to the machine learning model as a “random effect” to control the environmental relatedness between individuals);
identify a plurality of common attributes between the core dataset and the first additional dataset Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
Manrai as modified didn’t disclose, but Walters disclose: evaluating the plurality of common attributes to determine that the plurality of identified common attributes satisfies one or more predefined criteria related to an expected performance of a given attribute and/or an appropriate number of common attributes (Walters [0104; 0111; 0136] discloses: a data similarity score (e.g., a score dependent on a number of matching or similar elements/(as number of common attributes )in the synthetic dataset and normalized reference dataset), or data quality score (e.g., a score dependent on at least one of a number of duplicate elements in each of the synthetic dataset and normalized reference dataset, a prevalence of the most common value in each of the synthetic dataset and normalized reference dataset, a maximum difference of rare values in each of the synthetic dataset and normalized reference dataset, the differences in schema between the synthetic dataset and normalized reference dataset, or the like). System 100 may be configured to calculate these scores using the synthetic dataset and a reference dataset) ;
in accordance with determining that the plurality of identified common attributes satisfy one or more predefined criteria, for a given inquiring entity in the core dataset: selecting one or more matches for each inquiring entity in the core dataset from a plurality of candidate entities in the first additional dataset (Walters [0136] discloses: the performance criteria may include a similarity metric (scores)(e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). For example, model optimizer 1303 may be configured to compare the covariances or univariate distributions of a synthetic dataset generated by the new synthetic data model and a reference data stream dataset. Likewise, model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset) and
following selecting the one or more matches, generating the merged dataset by adding attribute data of the one or more selected matches from the plurality of candidate entities in the first additional dataset to attribute data of the inquiring entity in the core dataset (Walters [0136] discloses: evaluate performance criteria of a newly created synthetic data model, the performance criteria may include a similarity metric (e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset, a prevalence of the most common value in synthetic dataset and reference data stream dataset; [0107] discloses: merge the reference dataset and synthetic dataset based on similarity metric may depend on a number of elements of a synthetic dataset that match elements of a reference dataset)
applying the merged dataset to one or more of: a data analytics operation, an artificial intelligence (AI) model training operation, a model diagnosis operation, and a model evaluation technique (Walters [0041; 0056; 0084] use a synthetic data model for financial service, healthcare records include cancel diagnosis, evaluate model characters and machine learning model) .
Manrai and Walters are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Walters in order to training the synthetic data generator to generate synthetic data corresponding to the additional class. The suggestion to combine to use of synthetic data generation may enable each party to maintain security of sensitive data.
Regarding claim 25, Manrai as modified disclose: A system for generating merged datasets, comprising: one or more processors; memory; and one or more programs stored on the memory that when executed by the one or more processors cause the one or more processors to (Manrai [0167]:
access data comprising a core dataset and a plurality of additional datasets (Manrai [0039; 0040; 0076] discloses: The data in the environmental and phenotypic relatedness matrix can represent different types of observational datasets organized in different databases as illustrated in FIG. 1. The data can be linked across datasets using a person's person-level identifier or group-level identifier. Person-level identifiers (such as name, patient identifier, social security number (SSN), etc.) can identify data related to a specific person. Group-level identifiers can identify group-level data for a person such as census tract-level data or subpopulation data);
selecting a first additional dataset of the plurality of additional datasets based on the ranking data, wherein the first additional dataset is from a second data source different from the first data source (Manrai [0037; 0109; 0110] discloses: determine a ranked list of causal relationships between a plurality of exposures and a plurality of outcomes. As described above, when we use data from observational datasets, confounding can cause problems when identifying causal relationships between exposures and outcomes. The technology disclosed includes logic to reduce the impact of confounding factors when determining the association between the exposures and outcomes. The causal relationship identifier 189 include the logic to provide the environmental and phenotypic correlation matrix as an additional input to the machine learning model as a “random effect” to control the environmental relatedness between individuals);
determine ranking data of the plurality of additional datasets (Manrai [0037; 0109; 0110] discloses: determine a ranked list of causal relationships between a plurality of exposures and a plurality of outcomes. As described above, when we use data from observational datasets, confounding can cause problems when identifying causal relationships between exposures and outcomes. The technology disclosed includes logic to reduce the impact of confounding factors when determining the association between the exposures and outcomes. The causal relationship identifier 189 include the logic to provide the environmental and phenotypic correlation matrix as an additional input to the machine learning model as a “random effect” to control the environmental relatedness between individuals);
identify a plurality of common attributes between the core dataset and the first additional dataset Manrai [0087] disclose: Propensity score tries to match individuals similar to determining digital twins but based on exposure or non-exposure. Suppose we want to understand or test association between smoking and lung cancer. Suppose X variable (or exposure) is smoking and Y variable (or outcome) is lung cancer. In the propensity score matching approach we find all persons in the dataset that smoke and all persons that do not smoke but the persons that smoke and that do not smoke are similar to each other on all other variables. For example, the persons in two groups (smoke vs do not smoke) may have same age, same sex, living in the same area, everything is the same except for smoking and not smoking. Propensity score indicates how similar the two persons are based on these characteristics).
Manrai as modified didn’t disclose, but Walters disclose: evaluating the plurality of common attributes to determine that the plurality of identified common attributes satisfies one or more predefined criteria related to an expected performance of a given attribute and/or an appropriate number of common attributes (Walters [0104; 0111;0136] discloses: a data similarity score (e.g., a score dependent on a number of matching or similar elements/(as number of common attributes )in the synthetic dataset and normalized reference dataset), or data quality score (e.g., a score dependent on at least one of a number of duplicate elements in each of the synthetic dataset and normalized reference dataset, a prevalence of the most common value in each of the synthetic dataset and normalized reference dataset, a maximum difference of rare values in each of the synthetic dataset and normalized reference dataset, the differences in schema between the synthetic dataset and normalized reference dataset, or the like). System 100 may be configured to calculate these scores using the synthetic dataset and a reference dataset) ;
in accordance with determining that the plurality of identified common attributes satisfy one or more predefined criteria, for a given inquiring entity in the core dataset: selecting one or more matches for each inquiring entity in the core dataset from a plurality of candidate entities in the first additional dataset (Walters [0136] discloses: the performance criteria may include a similarity metric (scores)(e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). For example, model optimizer 1303 may be configured to compare the covariances or univariate distributions of a synthetic dataset generated by the new synthetic data model and a reference data stream dataset. Likewise, model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset) and
following selecting the one or more matches, generating the merged dataset by adding attribute data of the one or more selected matches from the plurality of candidate entities in the first additional dataset to attribute data of the inquiring entity in the core dataset (Walters [0136] discloses: evaluate performance criteria of a newly created synthetic data model, the performance criteria may include a similarity metric (e.g., a statistical correlation score, data similarity score, or data quality score, as described herein). model optimizer 1303 may be configured to evaluate the number of matching or similar elements in the synthetic dataset and reference data stream dataset, a prevalence of the most common value in synthetic dataset and reference data stream dataset; [0107] discloses: merge the reference dataset and synthetic dataset based on similarity metric may depend on a number of elements of a synthetic dataset that match elements of a reference dataset);
applying the merged dataset to one or more of: a data analytics operation, an artificial intelligence (AI) model training operation, a model diagnosis operation, and a model evaluation technique (Walters [0041; 0056; 0084] use a synthetic data model for financial service, healthcare records include cancel diagnosis, evaluate model characters and machine learning model) .
Manrai and Walters are analogous art because they are in the same field of endeavor, calculating similarity/relevance scores. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Manrai to include the teaching of Walters in order to training the synthetic data generator to generate synthetic data corresponding to the additional class. The suggestion to combine to use of synthetic data generation may enable each party to maintain security of sensitive data.
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.
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/CINDY NGUYEN/Examiner, Art Unit 2156