DETAILED ACTION
The following is a first office action upon examination of application number 18/610822. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/20/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-10 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention. Claims 11-19 are directed to a system comprising one or more processors; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claim 20 is directed to a non-transitory computer-readable storage medium, which is a manufacture, and this a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to identify and add health indicators to member data, which is described by claim limitations reciting:
receiving … a data object associated with a user that includes a first parameter initially set to a first value;
determining … and based on a comparison of parameters of the data object with corresponding parameters of data objects associated with other users, that one or more of (1) a second value should override the first value or (2) a second parameter should be added into the data object;
generating … an augmented data object by modifying the data object to include the one or more of the second value or the second parameter based on the determining; and
storing or deleting … information about the user … based on the augmented data object.
The identified limitations in the claims describing identifying and adding health indicators to member data, (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices or, alternatively, the “Mental Processes” grouping of abstract ideas since the identified limitations can be performed by a human, mentally or with pen and paper. Dependent claims 2, 5, 6, 8, 9, 10, 12, 15, 17, 18, and 19 recite limitations that further narrow the abstract idea (i.e., identifying and adding health indicators to member data); therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the one or more processors and memory in claim 1, the memory and one or more processors communicatively coupled to the memory in claim 11, and the One or more non-transitory computer-readable storage media including instructions; one or more processors; and memory in claim 20, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer.
Additional elements such as receiving, by one or more processors… and storing or deleting, by the one or more processors, information about the user in memory… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these limitations only add insignificant extra-solution activities (data gathering/storage). Similarly, additional elements in claims 3, 4, 7, 13, 14, and 16, related to a plurality of machine-learning models, wherein each machine-learning model of the plurality of machine-learning models is trained add additional elements that do not yield an improvement to the computer or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Accordingly, these additional element 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 claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component (see Spec. 96-98). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as receiving, by one or more processors… and storing or deleting, by the one or more processors, information about the user in memory… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these limitations only add insignificant extra-solution activities (data gathering/storage). Additional elements in claims 3, 4, 7, 13, 14, and 16, related to a plurality of machine-learning models, wherein each machine-learning model of the plurality of machine-learning models is trained add additional elements that do not yield an improvement; these additional elements only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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) 1-5, 11-14, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2022/0059230 (Muse).
As per claim 1, Muse teaches: 1. A computer-implemented method comprising: receiving, by one or more processors, a data object associated with a user that includes a first parameter initially set to a first value; ([0033] … a machine learning model that is configured to process a health profile data object for a particular end user [0032] The term “health profile data object” may refer to a data object that represents the current health state of an end user. The term “current health state” may refer to an overall picture of the end user's current health. For example, an end user's health profile data object may identify medical conditions currently being experienced by the end user, as well as history of medical conditions experienced in the past. In addition, the end user's health profile data object may provide physiological data collected on the end user such as height, weight, blood type, heart rate, blood pressure, and the like. Further, the end user's health profile data object may provide a genetic profile for the end user and/or medical conditions the end user may be predisposed to develop, as well as family medical history. Furthermore, the end user's health profile data object may provide behavioral habits of the end user. Accordingly, in particular embodiments, the end user's health profile data object may be periodically updated with recent health information gathered on the end user so that the data object better reflects the end user's current health state).
determining, by the one or more processors and based on a comparison of parameters of the data object with corresponding parameters of data objects associated with other users, that one or more of (1) a second value should override the first value or (2) a second parameter should be added into the data object; generating, by the one or more processors, an augmented data object by modifying the data object to include the one or more of the second value or the second parameter based on the determining; and storing or deleting, by the one or more processors, information about the user in memory based on the augmented data object ([0033] … the medical conditions prediction machine learning model may include a trained machine learning model (e.g., trained using past historical end user data across one or many end users) that is configured to process the health profile data object associated with the particular end user to generate the medical conditions model for the end user. For instance, pooling of de-identified data in a central repository may be used to enhance the model's prediction. [0078] … medical conditions prediction machine learning model 230 may be trained using past historical end user data across many end users that includes health profile data objects 225 and known medical conditions for the end users. [0179] … user becomes diagnosed with type 2 diabetes and his or her health profile data object 225 is updated accordingly).
As per claim 2, Muse teaches: wherein the comparison of the parameters of the data object with the corresponding parameters of data objects associated with the other users is based on applying the parameters of the data object to a plurality of pre-defined rules, each pre-defined rule associated with a respective test condition ([0078] … medical conditions prediction machine learning model 230 may be trained using past historical end user data across many end users that includes health profile data objects 225 and known medical conditions for the end users. The term “clinical risk rule data object” may refer to a data object representing a rule used in identifying when an end user is trending towards a risk factor. In various embodiments, a clinical risk rule data object may be associated with a particular behavior that is known to contribute to the risk factor. For example, a clinical risk rule data object may be defined with respect to consuming sugary food items leading to obesity. Further, a clinical risk rule data object may identify a threshold for the behavior that when exceeded by an end user conducting the behavior indicates the end user is trending towards the associated risk factor. The term “compound risk rule data object” may refer to a data object representing a combination of two or more clinical risk rule data objects. [0062] A variety of risk factors may be identified that are considered important with respect to tracking in relation to end users. For instance, such risk factors may be identified and deemed important for tracking purposes because they are known to contribute significantly to medical conditions and/or healthcare costs. [0063] Accordingly, in various embodiments, instruments such as clinical prediction rules (CPRs) can be used in identifying various risk factors of importance for tracking purposes. Clinical prediction rules are used to estimate the probabilities of clinical conditions or future outcomes by considering a small number of highly valid indicators. In other words, such rules may be used to “rule in” or “rule out” a medical condition (issue) for a patient. Such rules are typically rigorously developed, validated, and evaluated before becoming commonly used in the medical field. [0064] Therefore, such rules can oftentimes be considered quite reliable in identifying what risks factors go into predicting a particular medical condition a patient may currently have (diagnostic CPRs) or may develop in the future (prognostic CPRs). Thus, in the example, a prognostic CPR for type 2 diabetes may identify obesity as a highly valid indicator for this medical condition).
As per claim 3, Muse teaches: wherein the determining further includes applying the data object to a plurality of machine-learning models, wherein each machine-learning model of the plurality of machine-learning models is trained to identify associations between parameters of the data object and a respective test condition ([0008] …the one or more risk factors are determined based on one or more medical conditions applicable to the end user and a health profile data object for the end user is processed using a medical conditions prediction machine learning model to identify the one or more medical conditions [0033] … a machine learning model that is configured to process a health profile data object for a particular end user to generate the medical conditions model for the end user. [0050] …utilizing disjoint machine learning models [0064] …a prognostic CPR for type 2 diabetes may identify obesity as a highly valid indicator for this medical condition. Accordingly, the risk factors identified through the evaluation of such rules can be used in training a medical conditions prediction machine learning modeling to identify medical conditions an end user may be likely to develop based on the end user's current health state. [0071] Accordingly, a clinical risk rule data object may be defined in various embodiments with respect to each of the identified behaviors and corresponding risk factor. For example, Clinical Risk Rule Data Object A 145 may be defined that averaging less than 7 hours of sleep a day over a month can lead to obesity. In addition, the recommended amount of daily exercise to prevent weight gain is 60 minutes. Therefore, Clinical Risk Rule Data Object B 150 may be defined that averaging less than 60 minutes of exercise each day over a month can lead to obesity. As previously noted, Clinical Risk Rule Data Object C 155 may be defined that consuming 2,700 grams of sugar over a three-month period can lead to obesity. Here, a compound risk rule data object for developing obesity could be defined by combining the three different risk rule data objects. [0179] … training of the models 230, 315, 335, 355 and enable the models 230, 315, 335, 355 in various embodiments to continuously update themselves)
As per claim 4, Muse teaches: wherein the comparison of parameters of the data object with corresponding parameters of data objects associated with other users is based on applying the parameters of the data object to a plurality of pre-defined rules, and wherein the determining further includes identifying the second value or the second parameter upon determining that an output of one or more rules of the plurality of pre-defined rules or one or more machine-learning models of the plurality of machine-learning models indicates that the second value or the second parameter is a target condition that should be added to the data object ([0071] Accordingly, a clinical risk rule data object may be defined in various embodiments with respect to each of the identified behaviors and corresponding risk factor. For example, Clinical Risk Rule Data Object A 145 may be defined that averaging less than 7 hours of sleep a day over a month can lead to obesity. In addition, the recommended amount of daily exercise to prevent weight gain is 60 minutes. Therefore, Clinical Risk Rule Data Object B 150 may be defined that averaging less than 60 minutes of exercise each day over a month can lead to obesity. As previously noted, Clinical Risk Rule Data Object C 155 may be defined that consuming 2,700 grams of sugar over a three-month period can lead to obesity. Here, a compound risk rule data object for developing obesity could be defined by combining the three different risk rule data objects. [0072] As further demonstrated in FIG. 1, a behavior can contribute to multiple risk factors and/or medical conditions. Here, Behavior C 120 related to consuming sugar also contributes to Medical Condition Z 140 of a cavity. Accordingly, a second clinical risk rule data object can be developed with respect to this medical condition. For example, Clinical Risk Rule Data Object D 160 may be defined that consuming 3,000 grams of sugar over a three-month period can lead to a cavity. Here, a behavior can result directly into a medical condition [0078] … the medical conditions prediction machine learning model 230 may be configured as a rule-based machine learning model that is configured to infer medical conditions the end user may be likely to develop based on the end user's health profile data object 225 [0179] … user becomes diagnosed with type 2 diabetes and his or her health profile data object 225 is updated accordingly).
As per claim 5, Muse teaches: wherein the target condition is an undiagnosed medical condition ([0008] …using a medical conditions prediction machine learning model to identify the one or more medical conditions [0064] …predicting a particular medical condition a patient may currently have).
As per claim 11, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claim 12, this claim recites limitations substantially similar to those addressed by the rejection of claim 2, above; therefore, the same rejection applies.
As per claim 13, this claim recites limitations substantially similar to those addressed by the rejection of claim 3, above; therefore, the same rejection applies.
As per claim 14, this claim recites limitations substantially similar to those addressed by the rejection of claim 4, above; therefore, the same rejection applies.
As per claim 20, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 6-10 and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0059230 (Muse); in view of US 2010/0332258 (Dahlke).
As per claim 6, Muse teaches: …the augmented data object… ([0033] … the medical conditions prediction machine learning model may include a trained machine learning model (e.g., trained using past historical end user data across one or many end users) that is configured to process the health profile data object associated with the particular end user to generate the medical conditions model for the end user. For instance, pooling of de-identified data in a central repository may be used to enhance the model's prediction. [0078] … medical conditions prediction machine learning model 230 may be trained using past historical end user data across many end users that includes health profile data objects 225 and known medical conditions for the end users. [0179] … user becomes diagnosed with type 2 diabetes and his or her health profile data object 225 is updated accordingly).
Although not explicitly taught by Muse, Dahlke teaches: generating, by the one or more processors, a retention score for the augmented data object by applying the augmented data object to a retention model ([Abstract] …retention includes creating patient trial scores based on ranking of patient traits or characteristics [0011] … data and traits are collected from patients and used to create patient profiles, and segmentation strategies are devised to rank patients according to factors that may prevent clinical trial retention. After assigning scores to patients, statistical clustering is used to rank clusters of patients from highest to lowest need of resources. By allocating the most resources to the neediest patients, retention of those patients to complete the trial is improved. [0056] Step 3.06 uses the member/patient profiles and the segmentation strategies to create unique and composite scores [0072] … creating individual and composite scores for each patient [0077] In FIG. 5, patient summed and average scores are added together to create a composite patient retention score).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Muse with the aforementioned teachings of Dahlke with the motivation of allocating resources to the neediest patients (Dahlke [0011]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Dahlke to the system of Muse would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for generation of a retention score.
As per claim 7, although not explicitly taught by Muse, Dahlke teaches: wherein the retention model includes a machine learning model trained to identify associations between one or more parameters of the augmented data object and one or more retention metrics ([0005] … a set of factors including age, marital status, race/ethnicity, socioeconomic status determined by proxy, and prior trial experience combined with self-reported characteristics or requirements. [0011] … rank patients according to factors that may prevent clinical trial retention. After assigning scores to patients, statistical clustering is used to rank clusters of patients from highest to lowest need of resources. By allocating the most resources to the neediest patients, retention of those patients to complete the trial is improved. [0042] …perform unsupervised learning. That is, the system will learn on its own, using the data (learning set) [0046] … patients are segmented into multiple groups and can be clustered based on a retention factor).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Muse with the aforementioned teachings of Dahlke with the motivation of allocating resources to the neediest patients (Dahlke [0011]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Dahlke to the system of Muse would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of retention factors applicable to patients.
As per claim 8, although not explicitly taught by Muse, Dahlke teaches: wherein applying the augmented data object to the retention model generates a set of retention factors for the augmented data object ([0010] … develop and update patients' retention factor [0011] … strategies are devised to rank patients according to factors that may prevent clinical trial retention. After assigning scores to patients, statistical clustering is used to rank clusters of patients from highest to lowest need of resources [0076] The diagram in FIG. 5 shows the process by which patients' composite scores are created by summing or averaging numerical factors associated with a set of characteristics).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Muse with the aforementioned teachings of Dahlke with the motivation of allocating resources to the neediest patients (Dahlke [0011]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Dahlke to the system of Muse would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the development of retention factors for a patient.
As per claim 9, although not explicitly taught by Muse, Dahlke teaches: initiating, by the one or more processors, based on the retention score for the augmented data object, performance of one or more actions in response to generating the retention score for the augmented data object. ([0009] To overcome these issues of costs, a method has been discovered to accelerate clinical trials through improved recruitment and patient retention wherein during recruitment of the patients for said clinical trials the patients are segmented based on a retention factor and the resources expended on retaining said patient is based on said retention factor. [0011] …After assigning scores to patients, statistical clustering is used to rank clusters of patients from highest to lowest need of resources. By allocating the most resources to the neediest patients, retention of those patients to complete the trial is improved).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Muse with the aforementioned teachings of Dahlke with the motivation of allocating resources to the neediest patients (Dahlke [0011]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Dahlke to the system of Muse would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the improvement of patient retention.
As per claim 10, although not explicitly taught by Muse, Dahlke teaches: wherein initiating the performance of the one or more actions includes generating an intervention for the user based on the retention score. ([0011] …After assigning scores to patients, statistical clustering is used to rank clusters of patients from highest to lowest need of resources. By allocating the most resources to the neediest patients, retention of those patients to complete the trial is improved. [0010] Patients with poor retention factors can be allocated extra resources in terms of telephone calls, visits, literature and educational materials in order to increase and support their retention in the clinical trial [0027] … determine the patient scores and what level of patient assistance may be required to assist with enrollment and to provide support for retention in a specific clinical trial. [0008] … retention of patients on clinical trials is accomplished using disease management techniques such as follow-up telephone calls).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Muse with the aforementioned teachings of Dahlke with the motivation of allocating resources to the neediest patients (Dahlke [0011]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Dahlke to the system of Muse would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for retention of patients by allocating additional resources.
As per claim 15, this claim recites limitations substantially similar to those addressed by the rejection of claim 6, above; therefore, the same rejection applies.
As per claim 16, this claim recites limitations substantially similar to those addressed by the rejection of claim 7, above; therefore, the same rejection applies.
As per claim 17, this claim recites limitations substantially similar to those addressed by the rejection of claim 8, above; therefore, the same rejection applies.
As per claim 18, this claim recites limitations substantially similar to those addressed by the rejection of claim 9, above; therefore, the same rejection applies.
As per claim 19, this claim recites limitations substantially similar to those addressed by the rejection of claim 10, above; therefore, the same rejection applies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN TORRICO-LOPEZ/Primary Examiner, Art Unit 3625