Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the following limitations:
1. A computer-implemented method, comprising:
to segment the plurality of users into a first plurality of groups based in part on a first set of features extracted from the user data, the first set of features associated with relative research-skill levels of the respective plurality of users (user segmenting based on features in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
to segment the plurality of users into a second plurality of groups based in part on a second set of features extracted from the user data, the second set of features associated with relative engagement levels of the respective plurality of users (user segmenting based on features in high level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper);
to classify the plurality of users into a plurality of classes based in part on the relative research-skill levels and the relative engagement levels of the respective plurality of users (user classification based on features such as skill and engagement level is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper); and
The claim recites an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. The claim recites the following additional elements:
accessing user data associated with a plurality of users of a genealogy service, user data comprising data associated with user interactions with the genealogy service (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g));
using a first machine learning (ML) model, using a second ML model, using a third ML model (amounts to a generic computer component to perform a computer function as discussed in MPEP 2106.05(f)).
selecting and presenting content to the plurality of users based in part on their respective classifications (amounts to mere insignificant application, an insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
accessing user data associated with a plurality of users of a genealogy service, user data comprising data associated with user interactions with the genealogy service (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is well understood, routine and convention activity of receiving or gathering data as identified by the court in MPEP 2106.05(d));
accessing user data associated with a plurality of users of a genealogy service, user data comprising data associated with user interactions with the genealogy service (amounts to mere data gathering, an insignificant extra-solution activity as discussed in MPEP 2106.05(g), which is well understood, routine and convention activity of receiving or gathering data as identified by the court in MPEP 2106.05(d));
using a first machine learning (ML) model, using a second ML model, using a third ML model (amounts to a generic computer component to perform a computer function as discussed in MPEP 2106.05(f)).
The claim is not patent eligible.
Claim 2: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
2. The computer-implemented method of claim 1, wherein the third ML model further takes a third set of features associated with the plurality of users in addition to the relative research-skill levels and the relative engagement levels of the respective plurality of users (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 3: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
3. The computer-implemented method of claim 2, wherein the third set of features are extracted from survey data associated with the plurality of users (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 4: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
4. The computer-implemented method of claim 2, wherein the third set of features are labeled instances associated with the plurality of users, each of the plurality of users being labeled as one of the plurality of classes users (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 5: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
5. The computer-implemented method of claim 1, wherein the first set of features or second set of features are pre-processed to reduce dimensionality of features (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 6: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
6. The computer-implemented method of claim 1, wherein the first ML model or second ML model is trained using an unsupervised training method (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 7: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
7. The computer-implemented method of claim 6, wherein the unsupervised training method includes a k-means clustering method (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 8: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
8. The computer-implemented method of claim 1, wherein for each of the plurality of users, the first or second ML model computes a score for the user based on the first set of features or the second set of features, selects a set of cut-off scores, and segments the plurality of users into the first or second plurality of groups based on the set of cut-off scores (score computation, user segmenting based on scores is an observation, evaluation, judgment, opinion mental process which can reasonably be performed in one’s mind with the aid of pencil and paper).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 9: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
9. The computer-implemented method of claim 8, wherein the plurality of users includes a first subset of users who are current subscribers of the genealogy service, a second subset of users who are current free trial users of the genealogy service, and a third subset of users who are churners, and the first or second ML model selects different sets of cut-off scores for the first, second, or third subsets of users (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
The claim recites no additional element:
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 10: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
10. The computer-implemented method of claim 8, wherein the plurality of users includes a first subset of current subscribers within a first tenure band, and a second subset of current subscribers within a second tenure band, and the first or second ML model selects different sets of cut-off scores for the first or second subsets of users (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 11: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
11. The computer-implemented method of claim 1, wherein the third ML model is trained using a supervised training method (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 12: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
12. The computer-implemented method of claim 1, wherein the plurality of classifications includes a core user classification and a casual or curious user classification (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claim 13: Step 1: the claim is directed to statuary category.
Step 2A Prong 1: The claim recites the abstract idea of parent claim.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
The claim recites additional element(s):
13. The computer-implemented method of claim 1, wherein the first set of features or the second set of features include (1) a first subset of features associated with activities of the plurality of users during a first time frame, and (2) a second subset of features associated with activities of the plurality of users during a second time frame, and the first subset of features and the second subset of features are assigned different weights (amounts to generally linking the abstract ideas to the technological environment or field of use as discussed in in MPEP 2106.05(h)).
Step 2B: As shown above, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The judicial exceptions are not integrated into a practical application.
The claim is not patent eligible.
Claims 14-19 are non-transitory computer readable storage medium claims having similar limitation as claims 1-6 and are rejected under the same rationale. The additional elements in claim 14 is A non-transitory computer readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to: (amounts to performing generic function of execution of stored instructions (MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract into practical application and are not sufficient to amount to significant more than the abstract idea. Therefore, the claims are an abstract idea.
Claim 20 is system claim having similar limitation as claim 1 and is rejected under the same rationale. The additional elements in claim 20 is A computing system, comprising: a processor; and memory configured to store code comprising instructions, wherein the instructions, when executed by a processor, cause the processor to: (amounts to performing generic function of execution of stored instructions (MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract into practical application and are not sufficient to amount to significant more than the abstract idea. Therefore, the claims are an abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-5, 8-10, 12-15, 17-18, 20 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Zhang et al (“Deep Ensemble Learning for Early-Stage Chum Management in Subscription-Based Business” Oct 30, 2021)
1. A computer-implemented method, comprising: accessing user data associated with a plurality of users of a genealogy service, user data comprising data associated with user interactions with the genealogy service1 (See section 4.1 “Ancestry offers subscription-based service for customers to conduct genealogy research using rich online family history resources.”)
using a first machine learning (ML) model to segment the plurality of users into a first plurality of groups based in part on a first set of features extracted from the user data, the first set of features associated with relative research-skill levels of the respective plurality of users (See section 4.2 feature generation and subsection “Genealogy Research Customers rely on a variety of resources provided at Ancestry to conduct genealogy research. They could build family trees by adding family members as tree nodes, perform a search within a large collection of records/public tree nodes and attach relevant search results to ancestors in their trees, get recommendations (called “hints”) by the company about potentially relevant public tree nodes or records, and then decide whether to accept, reject, or pend these recommendations. See Fig. 1 layer 1 on XGBoost model);
using a second ML model to segment the plurality of users into a second plurality of groups based in part on a second set of features extracted from the user data, the second set of features associated with relative engagement levels of the respective plurality of users (See section 4.1 feature generation and subsection Other Ancestry Products Customers’ prior relationships with other Ancestry products may affect their decisions about purchasing the genealogy subscription. For example, separate from the genealogy subscription, Ancestry also offers DNA kits as scientific and conclusive evidence to help customers trace their lineage. Customers who are familiar with Ancestry DNA product may have higher affinity for our genealogy service. Therefore, we construct features to reflect if the customer has already purchased or activated any DNA kit and their engagement with our DNA product. See Fig. 1 layer 1 on RNN-DNN model).
using a third ML model to classify the plurality of users into a plurality of classes based in part on the relative research-skill levels and the relative engagement levels of the respective plurality of users (See Fig 1 layer 1 on survival analysis model); and
selecting and presenting content to the plurality of users based in part on their respective classifications (See Fig. 1 layer 2 and final prediction. See also section 4.4 “Rank customers based on the predicted churn propensity.2.Select a subset of customers at the top of the propensity ranking and offer them retention incentives. 3.Evaluate the effects of retention incentives on churn and profit via A/B tests”).
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2. The computer-implemented method of claim 1, wherein the third ML model further takes a third set of features associated with the plurality of users in addition to the relative research-skill levels and the relative engagement levels of the respective plurality of users (see section 4.2 Features Generation To develop churn prediction models, we extracted 96 features that capture diverse customer activities and preferences during the free trial period. The data collected includes both time-dependent and static variables. We categorize the features into the following three groups.)
4. The computer-implemented method of claim 2, wherein the third set of features are labeled instances associated with the plurality of users, each of the plurality of users being labeled as one of the plurality of classes (See section 4.3 Data “…Classification models and survival analysis models define target labels differently. Binary labels are defined for classification models. Customers are labeled as churners or non-churners depending on whether they purchase a subscription and stay as a subscriber beyond day 21. “).
5. The computer-implemented method of claim 1, wherein the first set of features or second set of features are pre-processed to reduce dimensionality of features (See section 4.2 “After the three groups of features are collected, we encode and preprocess features before feeding them into machine learning models. For example, one-hot encoding is applied to all categorical features. We drop the first encoded column to avoid collinearity. Imputation is applied to handle missing values in numerical and categorical features.” Examiner Note: one hot encoding is dimension reduction technique).
8. The computer-implemented method of claim 1, wherein for each of the plurality of users, the first or second ML model computes a score for the user based on the first set of features or the second set of features, selects a set of cut-off scores, and segments the plurality of users into the first or second plurality of groups based on the set of cut-off scores (See Fig. 1 layer 2 and final prediction. See also section 4.4 “4.4 Evaluation Metrics Overall model performance is evaluated using AUC, which is a standard metric that can evaluate the performance of predictive models using raw, unbinarized output scores. AUC is calculated on all customers, but retention campaigns typically only target customers with higher propensity scores who are predicted to be more likely to churn. In order to develop churn management strategies using estimated propensity scores, there are usually three steps involved [12, 14]:1. Rank customers based on the predicted churn propensity.2. Select a subset of customers at the top of the propensity ranking and offer them retention incentives. 3.Evaluate the effects of retention incentives on churn and profit via A/B tests.)
9. The computer-implemented method of claim 8, wherein the plurality of users includes a first subset of users who are current subscribers of the genealogy service, a second subset of users who are current free trial users of the genealogy service, and a third subset of users who are churners, and the first or second ML model selects different sets of cut-off scores for the first, second, or third subsets of users (See Fig. 1 layer 2 and final prediction. See also section 4.4 “Rank customers based on the predicted churn propensity.2.Select a subset of customers at the top of the propensity ranking and offer them retention incentives. 3.Evaluate the effects of retention incentives on churn and profit via A/B tests. See section 4.3 Data “Classification models and survival analysis models define target labels differently. Binary labels are defined for classification models. Customers are labeled as churners or non-churners depending on whether they purchase a subscription and stay as a subscriber beyond day 21.).
10. The computer-implemented method of claim 8, wherein the plurality of users includes a first subset of current subscribers within a first tenure band, and a second subset of current subscribers within a second tenure band, and the first or second ML model selects different sets of cut-off scores for the first or second subsets of users (See section 4.3 Data “Classification models and survival analysis models define target labels differently. Binary labels are defined for classification models. Customers are labeled as churners or non-churners depending on whether they purchase a subscription and stay as a subscriber beyond day 21. Classification models are then trained to predict whether new customers will churn or not in the prediction window. In survival analysis, the target variable is no longer binary. In the training phase, we examine the cancellation behaviors of customers within the first 21 days since their initial signup date. Customers are then labeled by their survival time, i.e., the number of days between the signup date and the churn date if they churn during that time period. If they do not churn, their survival time is marked as censored and will be handled by survival analysis models. In the testing phase, we apply the trained models to predict how likely customers will churn in the next 2 weeks given that they have survived the first 7 days. Examiner Note: the first 7 days is the first tenure band and 21 days is the second tenure band).
12. The computer-implemented method of claim 1, wherein the plurality of classifications includes a core user classification and a casual or curious user classification (See section 4.3 Data “Classification models and survival analysis models define target labels differently. Binary labels are defined for classification models. Customers are labeled as churners or non-churners depending on whether they purchase a subscription and stay as a subscriber beyond day 21. Examiner Note: those that stay is considered core user and those that didn’t is considered casual/curious customer).
13. The computer-implemented method of claim 1, wherein the first set of features or the second set of features include (1) a first subset of features associated with activities of the plurality of users during a first time frame, and (2) a second subset of features associated with activities of the plurality of users during a second time frame, and the first subset of features and the second subset of features are assigned different weights (see section 3.1 Standard Meta Stacking Model “The standard Meta Stacking framework consists of two layers, as illustrated in Fig. 1 [11, 21]. Layer 1 is composed of individually trained base learners, whose predictions are then used as features in Layer 2, a meta-classifier, to generate the final prediction. Separate training sets are required to train models in each layer. The first dataset is used to train and validate individual base learners. Next, the trained models are applied on the second training set to generate churn propensity scores; these intermediate churn scores are used to train a meta-level logistic regression classifier. The advantage of using a meta-classifier instead of simply averaging Layer 1 predictions is that the logistic regression classier automatically determines the optimal weights for combining predictions from different Layer 1 models.” See also section 4.2 “Genealogy Research Customers rely on a variety of resources provided at Ancestry to conduct genealogy research. They could build family trees by adding family members as tree nodes, perform a search within a large collection of records/public tree nodes and attach relevant search results to ancestors in their trees, get recommendations (called “hints”) by the company about potentially relevant public tree nodes or records, and then decide whether to accept, reject, or pend these recommendations. They can also upload their own content, such as photos and stories of their ancestors, to their trees. They could exchange information with others via messages. We therefore extract features to illustrate these activities including page visits, tree building, searches, hints, uploads of content, and collaboration between customers. We engineer time-dependent features, such as number of record search and hints received on day 1, day 2, and up to day 7, to capture dynamic product engagements.” Examiner Note: various days indicated various time frames).
Claims 14-19 are non-transitory computer readable storage medium claims having similar limitation as claims 1-6 and are rejected under the same rationale. The additional elements in claim 14 is A non-transitory computer readable medium configured to store code
14. A non-transitory computer readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to: (See Fig. 5 on computing system. Examiner note: A non-transitory computer readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to is inherent in computing system).
Claim 20 is system claim having similar limitation as claim 1 and is rejected under the same rationale. The additional elements in claim 20 is A computing system, comprising: a processor; and memory configured to store code comprising instructions, wherein the instructions, when executed by a processor, cause the processor to: (See Fig. 5 on computing system. Examiner note: A computing system, comprising: a processor; and memory configured to store code comprising instructions, wherein the instructions, when executed by a processor, cause the processor to is inherent in computing system).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (“Deep Ensemble Learning for Early-Stage Chum Management in Subscription-Based Business” Oct 30, 2021) in view of Kain (US 10621164 B1)
3. Zhang fails to disclose survey data. However, Kain disclose using survey data for genealogy (thereby in same filed of endeavor) and further disclose using survey data(C1L25-35 ” (2) The value of big data has become generally accepted, creating an industry drive to aggregate different types of data in order to use machine learning and other tools to drive discovery. At scale even weak data can be valuable in unlocking links between our health and our genomic information and our daily habits, such as diet, exercise, drinking, smoking, hours of sleep, etc. Therefore, it is valuable to collect an individual's data from health institutions and through direct surveys and interviews. Additionally, it is now possible to collect implicit data through grocery store loyal customer tracking of purchase records, credit card records, online search habits, etc.” C14L60-C15L20-30 “(43) Data member-specific account data: information relating to a members residence, contact info, tax filing number, ownership stake, birth date, etc.; member-specific contributed data: personal, health, medical, environment, historic, and omic data that is specific to a person contributing the information; data: depending upon the context, the general term data may apply to account data, contributed data, data based upon one or both of these, or to processed and/or aggregated data; data derived from contributed data: metadata, summarized data, or data emanating from a logical or mathematical analysis of the member data; medical data: electronic medical and health records, results of tests either analytical or subjective, medical diaries, prescriptions etc.; health data: data relating to the health, wellbeing, and quality of life including sensor data, biometric data, diet tracking, survey answers related to health, quality of life, family status, emotional state or condition, health diaries; personal data: data relating to an individual's behaviors, habits, and daily activities such as geographic locations visited, purchasing or spending activities, web browsing, friends, social media posts, employee record, academic records, etc. (in general, this may include any or all data relating to an individual, including genomic, health medical, etc.); familial data: family history including health and medical history, lineage, and genealogy; environmental data: “)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the data source of Zhang to incorporate survey of Kain.
Given the advantage of survey data (Kain’s C1L25-35 “The value of big data has become generally accepted, creating an industry drive to aggregate different types of data in order to use machine learning and other tools to drive discovery. At scale even weak data can be valuable in unlocking links between our health and our genomic information and our daily habits, such as diet, exercise, drinking, smoking, hours of sleep, etc. Therefore, it is valuable to collect an individual's data from health institutions and through direct surveys and interviews. Additionally, it is now possible to collect implicit data through grocery store loyal customer tracking of purchase records, credit card records, online search habits, etc), one having ordinary skill in the art would have been motivated to make this obvious modification with predictable result of The computer-implemented method of claim 2, wherein the third set of features are extracted from survey data associated with the plurality of users.
Claim 16 is non-transitory computer readable medium claim having similar limitation as of claim 3 and is rejected under the same rationale.
Claim(s) 6-7, 11, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (“Deep Ensemble Learning for Early-Stage Chum Management in Subscription-Based Business” Oct 30, 2021) in view of Gardner et al (US 20210081876 A1)
Claims 6 and 11. Zhang fails to disclose unsupervised/supervised training method. However, Gardner disclose machine learning for genealogy (thereby in same filed of endeavor)( [0098] FIGS. 24A and 24B illustrate how genealogy is recorded as a hierarchical parent list on each process, with each process lineage sharing a single process identifier, and each version of the process in the lineage having a unique version number within the scope of the lineage, in accordance with an embodiment of the present disclosure. [0371] In some embodiments, the machine learning is an unsupervised learning algorithm. One example of an unsupervised learning algorithm is cluster analysis.“ In some embodiments, the machine learning is supervised machine learning).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the training method of Zhang to incorporate unsupervised/supervised training method Gardner.
Given the fact that unsupervised/supervised training is one of the well known training method ([0371] In some embodiments, the machine learning is an unsupervised learning algorithm. One example of an unsupervised learning algorithm is cluster analysis. [0372] In some embodiments, the machine learning is supervised machine learning.), one having ordinary skill in the art would have been motivated to make this obvious modification with predictable result of wherein the first ML model or second ML model is trained using an unsupervised training method; wherein the third ML model is trained using a supervised training method.
7. Gardner disclose The computer-implemented method of claim 6, wherein the unsupervised training method includes a k-means clustering method ([0371] In some embodiments, the machine learning is an unsupervised learning algorithm. One example of an unsupervised learning algorithm is cluster analysis. [0394] More recently, Duda et al., Pattern Classification, 2.sup.nd edition, John Wiley & Sons, Inc. New York, has been published. Pages 537-563 describe clustering in detail. More information on clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, N.Y.; Everitt, 1993, Cluster analysis (3rd ed.), Wiley, New York, N.Y.; and Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, N.J., each of which is hereby incorporated by reference. Particular exemplary clustering techniques that can be used in the present disclosure include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering. In some embodiments, the clustering comprises unsupervised clustering where no preconceived notion of what clusters should form when the training set is clustered are imposed.).
Claim 19 is non-transitory computer readable medium claim having similar limitation as of claim 6 and is rejected under the same rationale.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SHRIVASTAVA et al (US 20210365965 A1) disclose using machine learning models for churn prediction of subscription service customers. See [0001], [0039], [0049]-[0050], Fig. 8.
Sotela et al (US 20150310336 A1) also disclose using machine learning models for churn prediction of subscription service customers. See abstract, [0003]-[0006]-[0024]-[0035].
Conclusion
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/LUT WONG/Primary Examiner, Art Unit 2127
1 [0031] In some embodiments, the computing server 130 also allows various users to create one or more genealogical profiles of the user. The genealogical profile may include a list of individuals (e.g., ancestors, relatives, friends, and other people of interest) who are added or selected by the user or suggested by the computing server 130 based on the genealogical records and/or genetic records.
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