DETAILED ACTION
Claims 34-53 are currently pending and have been examined.
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.
Claims 34-53 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 34-44 are directed to a method (i.e., a process); Claims 45-52 are directed to a system (i.e., a machine); and Claim 53 is directed to a CRM (i.e., a manufacture). Accordingly, claims 34-53 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 45 includes limitations that recite at least one abstract idea. Specifically, independent claim 45 recites:
45. A computing apparatus comprising:
one or more processors;
at least one memory device coupled with the one or more processors; and
a data communications interface operably associated with the one or more processors,
wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus to:
train a machine-learned model based on a patient data classification path process for each iteration of a plurality of iterations by:
obtaining patient data stored within a patient database, wherein the patient database is populated with a plurality of patient data elements associated with one or more patients;
evaluating the patient data elements to determine and identify classification results based on predetermined classification database tables;
determining a plurality of patient data classification path features based on the identified classification results; and
selecting one or more of the patient data classification path features for inclusion in a machine-learned patient data classification path process using a sequencing protocol that defines a minimal causal relationship that exists between a particular patient data classification path feature and identified patterns;
receive a phenotype classification request from a user device, wherein the phenotype classification request comprises a first set of patient data elements associated with a particular patient for a first time period;
determine, utilizing the machine-learned patient data classification path process, a plurality of path classification outcomes associated with the particular patient based on the patient data elements; and
determine, utilizing the machine-learned patient data classification path process, a unique phenotype classification associated with the particular patient for the first time period based on the plurality of path classification outcomes.
The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because obtaining patient data, evaluating the patient data to determine and identify classification results, determining and selecting classification features based on the results, receiving a phenotype classification request from a user, determining classification outcomes and determining a unique phenotype classification associated with a specific patient are associated with managing personal behavior or relationships or interactions between people. For example, but for the system, this claim encompasses a person facilitating data access, receiving data, and outputting data in the manner described in the identified abstract idea. The Examiner notes that “method of organizing human activity” includes a person’s interaction with a computer – see MPEP 2106.04(a)(2)(II)(C). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Accordingly, independent claim 45 and analogous independent claims 34 & 53 recite at least one abstract idea.
Furthermore, dependent claims 35-44, & 46-52 further narrow the abstract idea described in the independent claims. Claims 35-36, 40, 46-47, & 51 recite the patient data elements, Claims 37-39, 41-42, 48-50, & 52 recite the determining the unique phenotype classification, Claims 43-44 recites determining a classification path. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 45 and analogous independent claims 34 & 53, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
45. A computing apparatus comprising:
one or more processors;
at least one memory device coupled with the one or more processors; and
a data communications interface operably associated with the one or more processors,
wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus to:
train a machine-learned model based on a patient data classification path process for each iteration of a plurality of iterations by:
obtaining patient data stored within a patient database, wherein the patient database is populated with a plurality of patient data elements associated with one or more patients;
evaluating the patient data elements to determine and identify classification results based on predetermined classification database tables;
determining a plurality of patient data classification path features based on the identified classification results; and
selecting one or more of the patient data classification path features for inclusion in a machine-learned patient data classification path process using a sequencing protocol that defines a minimal causal relationship that exists between a particular patient data classification path feature and identified patterns;
receive a phenotype classification request from a user device, wherein the phenotype classification request comprises a first set of patient data elements associated with a particular patient for a first time period;
determine, utilizing the machine-learned patient data classification path process, a plurality of path classification outcomes associated with the particular patient based on the patient data elements; and
determine, utilizing the machine-learned patient data classification path process, a unique phenotype classification associated with the particular patient for the first time period based on the plurality of path classification outcomes.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the computing apparatus, processor, memory device, data communications interface, user device; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of training a machine-learned model, machine-learned patient data classification path process, the Examiner asserts that these limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, independent claim 45 and analogous independent claims 34 & 53 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recite at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 45 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, regarding the additional limitations of the computing apparatus, processor, memory device, data communications interface, user device; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation of training a machine-learned model, machine-learned patient data classification path process, the Examiner asserts that these limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Regarding the additional limitations of collecting various types of data (user interaction data, follow-up data) and outputting the workflow for display which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea, the Examiner has reevaluated such limitations and determined them to not be unconventional as they merely consist of receiving and transmitting data over a network. See MPEP 2106.05(d)(II).
Therefore, claims 34-53 are ineligible under 35 USC §101.
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.
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.
Claims 34-35, 37-46, & 48-53 are rejected under 35 U.S.C. 103 as being unpatentable over Short (US20160110524) in view of Athey (US20180330824).
As per claim 34, Short teaches a method comprising:
at an electronic device having a processor (para. 135: computer processor):
training a machine-learned model based on a patient data classification path process for each iteration of a plurality of iterations by (para. 70-72, 155: machine learning model trained using supervised process):
obtaining patient data stored within a patient database, wherein the patient database is populated with a plurality of patient data elements associated with one or more patients (para. 65, 178: patient data set obtained from database);
evaluating the patient data elements to determine and identify classification results based on predetermined classification database tables (abstract, 140-141: biological phenotype data obtained using classification algorithm that partitions data into various classes);
determining a plurality of patient data classification path features based on the identified classification results (para. 141, 188: correlations obtained between biological phenotype data and behavioral/emotional phenotype using classification process); and
selecting one or more of the patient data classification path features for inclusion in a machine-learned patient data classification path process using a sequencing protocol that defines a minimal causal relationship that exists between a particular patient data classification path feature and identified patterns (para. 143, 188: data mining algorithm includes sequential pattern mining where temporal patterns are detected; correlations between biological phenotype and behavioral/emotional phenotype determined);
receiving a phenotype classification request from a user device, wherein the phenotype classification request comprises a first set of patient data elements associated with a particular patient for a first time period (para. 22, 65: data collected actively by providing questions to user and obtaining answers regarding patient data);
determining, utilizing the machine-learned patient data classification path process, a plurality of path classification outcomes associated with the particular patient based on the patient data elements (para. 158, 188: machine learning algorithms including bayes classifier and others build probabilistic model of data from each class and SVM finds correlations between biological phenotype and behavioral/emotional phenotype; data results linked to user’s biological phenotype to yield patterns).
Short does not expressly teach determining, utilizing the machine-learned patient data classification path process, a unique phenotype classification associated with the particular patient for the first time period based on the plurality of path classification outcomes.
Athey, however, teaches to predicting phenotypes through collecting outcome phenotype data over time and using machine learning to predict pharmacological phenotypes that are classified into specific categories (para. 72, 196).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Athey with Short based on the motivation of accurately predicts pharmacological phenotypes including pharmacological response, disease risk, substance abuse or other pharmacological phenotypes based on panomic characteristics including genomics, epigenomics, chromatin state, proteomics, metabolomics, transcriptomics, etc., and sociological and environmental characteristics of a patient in near real-time (Athey - para. 6).
As per claim 35, Short and Athey teach the method of claim 34. Short teaches wherein the patient data elements associated with the particular patient comprises a first disease that includes an active time window (para. 196: patient data collected over various periods of time).
As per claim 37, Short and Athey teach the method of claim 34. Short does not expressly teach further comprising:
sending the unique use phenotype classification associated with the particular patient to the user device.
Athey, however, teaches to sending and displaying the phenotype identified for the patient to a provider’s device (para. 13, 83).
The motivations to combine the above mentioned references are discussed in the rejection of claim 34, and incorporated herein.
As per claim 38, Short and Athey teach the method of claim 34. Short does not expressly teach wherein determining the unique phenotype classification associated with the particular patient for the first time period is based on detecting a disease that is associated with the unique phenotype classification associated with the particular patient.
Athey, however, teaches to predicting phenotypes through collecting outcome phenotype data over time and using machine learning to predict pharmacological phenotypes that are classified into specific categories (para. 72, 196). Athey also teaches to associating disease risk with a specific phenotype (para. 108).
The motivations to combine the above mentioned references are discussed in the rejection of claim 34, and incorporated herein.
As per claim 39, Short and Athey teach the method of claim 34. Short teaches further comprising:
receiving a second path classification request from the user device, the second path classification request comprising a second set of patient data elements associated with the particular patient for a second time period (para. 22, 65, 184: data collected actively by providing questions to user and obtaining answers regarding patient data; new patient data can be continuously integrated into system to update various correlations).
Short does not expressly teach determining a second phenotype classification associated with the particular patient for the second time period.
Athey, however, teaches to predicting phenotypes through collecting outcome phenotype data over time and using machine learning to predict pharmacological phenotypes that are classified into specific categories (para. 72, 196).
The motivations to combine the above mentioned references are discussed in the rejection of claim 34, and incorporated herein.
As per claim 40, Short and Athey teach the method of claim 39. Short does not expressly teach wherein the first set of patient data elements comprises a first disease, the second set of patient data elements comprises a second disease that is different than the first disease, and the first disease and the second disease comprise interrelated attributes.
Athey, however, teaches to collecting patient data over various periods of time and where the data can include panomic data, sociomic data, physiomic data and environmental data and diseases the patient is suffering from (para. 58).
The motivations to combine the above mentioned references are discussed in the rejection of claim 34, and incorporated herein.
As per claim 41, Short and Athey teach the method of claim 40. Short does not expressly teach wherein determining the second phenotype classification associated with the particular patient for the second time period is based on analysis of a first active time window associated with the first disease and a second active time window associated with the second disease.
Athey, however, teaches to predicting phenotypes through collecting outcome phenotype data over time and using machine learning to predict pharmacological phenotypes that are classified into specific categories (para. 72, 196). Athey also teaches to determining the phenotype classifications based on various time periods (Fig. 6; para. 175).
The motivations to combine the above mentioned references are discussed in the rejection of claim 34, and incorporated herein.
As per claim 42, Short and Athey teach the method of claim 39. Short does not expressly teach wherein the unique phenotype classification is a first phenotype classification, and the second phenotype classification is different than the first phenotype classification.
Athey, however, teaches to predicting phenotypes through collecting outcome phenotype data over time and using machine learning to predict pharmacological phenotypes that are classified into specific categories (para. 72, 196). Athey further teaches to where the phenotype classifications can be different (para. 190, 207).
The motivations to combine the above mentioned references are discussed in the rejection of claim 34, and incorporated herein.
As per claim 43, Short and Athey teach the method of claim 34. Short does not expressly teach wherein the machine-learned patient data classification path process is based on determining a timeline of risk and detection of disease based on a patient's individual health status.
Athey, however, teaches to predicting phenotypes through collecting outcome phenotype data over time and using machine learning to predict pharmacological phenotypes that are classified into specific categories (para. 72, 196). Athey also teaches to determining the phenotype classifications based on various time periods and disease/condition data (Fig. 6; para. 175).
The motivations to combine the above mentioned references are discussed in the rejection of claim 34, and incorporated herein.
As per claim 44, Short and Athey teach the method of claim 34. Short teaches wherein the minimal causal relationship exists before that particular patient data classification path feature is included in the machine-learned patient data classification path process (para. 186: after correlations between biological phenotype and behavioral and/or emotional phenotype have been established, these correlations may then be used as “rules” to predict future behaviors or emotional states for an individual or group of individuals).
Claims 45-46, & 48-52 recite substantially similar limitations as those already addressed in claim 34-35 & 37-41, and, as such, is rejected for similar reasons as given above.
Claim 53 recite substantially similar limitations as those already addressed in claim 35, and, as such, is rejected for similar reasons as given above.
Claims 36 & 47 are rejected under 35 U.S.C. 103 as being unpatentable over Short (US20160110524) in view of Athey (US20180330824) as applied to claims 34 and 45, and in further view of Rubin (US20220028488).
As per claim 36, Short and Athey teach the method of claim 34, but do not expressly teach wherein the patient data elements associated with the particular patient comprises a type of disease and a date of contraction.
Rubin, however, teaches to collecting patient data including diagnostic data relating to coding for a disease and date/time of the diagnosis (para. 44).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Rubin with Short and Athey based on the motivation of identify and personalize treatments for patients based on their sub-classes of complex genetic and environmentally caused diseases. (Rubin - para. 40).
Claim 47 recite substantially similar limitations as those already addressed in claim 36, and, as such, is rejected for similar reasons as given above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wickson (US20180285526) teaches to cohort definition and selection system for a computer having a memory, a central processing unit and a display, the system including: a cohort definition module to configure the memory according to a phenotype vector.
Lefkofsky (US20200211716) teaches to a system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-7949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan Ng/ Primary Examiner, Art Unit 3619