DETAILED ACTION
This action is in reference to the communication filed on 3 MAR 2026.
Applicant elects Group 1, consisting of claims 1-9. Applicant amends claims 1, 5, 8, and cancels claims 10-20.
Claims 1-9 are present and have been examined.
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 1-9 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-9 the independent claim(s) 1 recite(s) a system, which is a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-9, the independent claim(s) (claims 1) is/are directed, in part, to:
A system for deriving risk patterns corresponding to a set of medical conditions through analyzing historic treatment of a population of individuals, the system comprising:
a plurality of demographic data records comprising demographic information for each member of a plurality of members of a population, wherein each record of the plurality of demographic data records correlates to a corresponding portion of the plurality of treatment data records, and
a plurality of risk forecast models trained, using historic claims data corresponding to a large population of individuals, to predict a likelihood of each of a plurality of medical conditions based on treatment data analysis, each risk forecast model configured to identify a respective at least one risk pattern corresponding to a respective at least one medical condition of the plurality of medical conditions; and
accessing the plurality of treatment data records and the plurality of demographic data records,
segmenting the plurality of treatment data records into sets of segmented treatment data records according at least to a plurality of demographic groupings, for each respective risk forecast model of the plurality of risk forecast models, applying one or more sets of the segmented claims treatment data records to the respective risk forecast model to identify a portion of the plurality of members of the population represented in the one or more sets of the segmented claims data as a respective clinical population being likely to have or develop the respective at least one medical condition corresponding to the respective risk forecast model, analyzing information corresponding to each respective clinical population to determine one or more resource forecasts for treating the portion of the plurality of members over at least one predetermined future time period, wherein the one or more forecast resources resource forecasts comprise at least one of an estimated cost, an estimated staffing need, an estimated equipment need, or an estimated clinical resource need, and
prioritizing, based on the one or more resource forecasts, at least one of the plurality of medical conditions or the portion of the plurality of members.
These claim elements are considered to be abstract ideas because they are directed to a mental process – i.e. concepts performed in the human mind such as observation, evaluation, judgment and opinion. Collecting information, and using said information to make a prediction and/or a forecast based on the information, would involve concepts similar to those identified above as a mental process. The modeling limitations, i.e. the training and the application therein, are further examples of mathematical concepts – i.e. mathematical relationships, formulas, equations, and/or calculations. Accordingly, the claim recite(s) a(n) abstract idea(s).
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional element – “a non-transitory computer readable storage region comprising one or more computer readable storage devices.. configured to store…”, as well as “processing circuitry.” The “storage region,” (the computer readable medium being interpreted as a computer system) as well as the processing circuitry, are recited at a high-level of generality. Examiner finds this to categorically be the equivalent of adding the term “apply it” to the general realm of computing (see MPEP 2106.05(f)), and that these elements represent no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Examiner further notes no improvement is found to the functioning of the computer or any other technology or technological field (see MPEP 2106.05(a)). Examiner also notes that the “storage” of data is found to be analogous to adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g). Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as “a non-transitory computer readable storage region comprising one or more computer readable storage devices.. configured to store…”, as well as “processing circuitry.” When considered individually, the above identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0110] Aspects of the present disclosure may be implemented by software logic, including machine readable instructions or commands for execution via processing circuitry. The software logic may also be referred to, in some examples, as machine readable code, software code, or programming instructions. The software logic, in certain embodiments, may be coded in runtime-executable commands and/or compiled as a machine-executable program or file. The software logic may be programmed in and/or compiled into a variety of coding languages or formats.
[0111] Aspects of the present disclosure may be implemented by hardware logic (where hardware logic naturally also includes any necessary signal wiring, memory elements and such), with such hardware logic able to operate without active software involvement beyond initial system configuration and any subsequent system reconfigurations (e.g., for different object schema dimensions). The hardware logic may be synthesized on a reprogrammable computing chip such as a field programmable gate array (FPGA) or other reconfigurable logic device. In addition, the hardware logic may be hard coded onto a custom microchip, such as an application-specific integrated circuit (ASIC). In other embodiments, software, stored as instructions to a non-transitory computer-readable medium such as a memory device, on-chip integrated memory unit, or other non-transitory computer-readable storage, may be used to perform at least portions of the herein described functionality.
[0112] Various aspects of the embodiments disclosed herein are performed on one or more computing devices, such as a laptop computer, tablet computer, mobile phone or other handheld computing device, or one or more servers. Such computing devices include processing circuitry embodied in one or more processors or logic chips, such as a central processing unit (CPU), graphics processing unit (GPU), field programmable gate array (FPGA), application-specific integrated circuit (ASIC), or programmable logic device (PLD). Further, the processing circuitry may be implemented as multiple processors cooperatively working in concert (e.g., in parallel) to perform the instructions of the inventive processes described above.
[0113] The process data and instructions used to perform various methods and algorithms derived herein may be stored in non-transitory (i.e., non-volatile) computer-readable medium or memory. The claimed advancements are not limited by the form of the computer-readable media on which the instructions of the inventive processes are stored. For example, the instructions may be stored on CDs, DVDs, in FLASH memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk or any other information processing device with which the computing device communicates, such as a server or computer. The processing circuitry and stored instructions may enable the computing device to perform, in some examples, the method 200 of FIG. 2A and FIG. 2B, the method 230 of FIG. 2C, the process 700 of FIG. 8, and/or the process 800 of FIG. 8.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-9:
Dependent claims 2-9 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as description of the types of records used to make the predictions, the types of diseases/conditions considered and when the diagnosis occurred, further discussion about demographic and segmenting, and associated costs as tied to a given health condition. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
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) 1-8 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Cox et al (US 20170286622 A1), hereinafter Cox).
In reference to claim 1:
Cox teaches: A system for deriving risk patterns corresponding to a set of medical conditions through analyzing historic treatment of a population of individuals, the system comprising:
a non-transitory computer readable storage region comprising one or more computer readable storage devices (at least [fig 1 and related text], the non-transitory computer readable storage region being configured to store
a plurality of treatment data records collected over at least a threshold period of time (at least [fig 4 and related text] EMR Data source 420 is electronic patient records including treatment records),
a plurality of demographic data records comprising demographic information for each member of a plurality of members of a population, wherein each record of the plurality of demographic data records correlates to a corresponding portion of the plurality of treatment data records (at least [114-116, 0122] demographic information collected; “. The EMR sources 420 provide patient demographic and medical data, gathered from questionnaires, electronic medical records, and the like, to the medical data analysis engine 412 which analyzes the received data and extracts the necessary data for generating patient care plan from the demographic and medical data received. This information is then used as a basis for submitting a request to the patient care plan guidelines source 426 to retrieve patient care plan guidelines for the patient's specific demographics and medical data, e.g., the patient is a 40 year old female diagnosed with type 2 diabetes and thus, corresponding patient care plan guidelines for this combination of patient demographics and medical condition are retrieved from the patient care plan guidelines source 426.” see also fig 4, 5 and related text for discussion of demographic information collected), and
a plurality of risk forecast models trained, using historic claims data corresponding to a large population of individuals, to predict a likelihood of each of a plurality of medical conditions based on treatment data analysis, each risk forecast model configured to identify a respective at least one risk pattern corresponding to a respective at least one medical condition of the plurality of medical conditions (at least [004] trained algorithms, i.e. production of the models, at [0234-0236] “ The risk evaluation rules comprise criteria and corresponding score values that combine to evaluate various risk factors and generate a risk score indicative of a particular medical condition or event occurring. For example, using the type 2 diabetes patient diagnosis discussed above, separate risk evaluation rules, or sets of risk evaluation rules, may be established for determining a risk level for a risk of heart and blood vessel disease, nerve damage, kidney damage, eye damage, foot damage, hearing impairment, skin conditions, Alzheimer disease, general hospitalization, risk of amputation of a foot or other extremity, or the like. Each risk evaluation rule may specify a set of criteria to evaluate and one or more ways in which to generate a score for these criteria and the risk of the corresponding condition/event as a whole. For example, a set of medical criteria, lab test results, symptom information, patient vital statistics, and any other medical, diagnosis, or treatment information (hereafter referred to as “risk factors”), may be evaluated for a risk of foot damage for a type 2 diabetes patient, with score calculating functions, associated with or referencing each of the criteria, established as part of the risk evaluation rule for evaluating the risk of the corresponding medical condition/event with regard to each of these risk factors or combination of a plurality of these risk factors.”; at [0236, 242] a plurality of scores are calculated using different models); and
processing circuitry configured to perform operations comprising accessing the plurality of treatment data records and the plurality of demographic data records (at least [fig 4, 5, and related text] demographic & medical records are used to segment the patients, ,
segmenting the plurality of treatment data records into sets of segmented treatment data records according at least to a plurality of demographic groupings, for each respective risk forecast model of the plurality of risk forecast models, applying one or more sets of the segmented claims treatment data records to the respective risk forecast model to identify a portion of the plurality of members of the population represented in the one or more sets of the segmented claims data as a respective clinical population being likely to have or develop the respective at least one medical condition corresponding to the respective risk forecast model, analyzing information corresponding to each respective clinical population to determine one or more resource forecasts for treating the portion of the plurality of members over at least one predetermined future time period, wherein the one or more forecast resources resource forecasts comprise at least one of an estimated cost, an estimated staffing need, an estimated equipment need, or an estimated clinical resource need (at least [fig 4 and related text] “. In addition, the PCPCM system 410 maintains a personalized patient care plan database 416 that stores data corresponding to the personalized patient care plans generated for various patients and a patient cohort database 417 that stores cohort association information for various patients having similar characteristics, e.g., demographics and/or medical data. Entries in the personalized patient care plan database 416 may be associated with entries in the patient cohort database 417… In some illustrative embodiments, the resources 418 may be used to associate patients in a patient registry, which may comprise EMR and demographics courses 420, lifestyle information sources, and the like, with particular patient cohorts, where a patient cohort is a grouping of patients having the same or similar characteristics. In addition, the personalized care plan creation/update engine 414 may retrieve information from the patient cohort database 417 to classify the patient into a patient cohort. The patient cohort is a grouping of patients that have similar characteristics, e.g., similar demographics, similar medical diagnoses, etc. Patient cohorts may be generated using any known or later developed grouping mechanism. One example mechanism may be using a clustering algorithm that clusters patients based on key characteristics of the patient, e.g., age, gender, race, medical diagnosis, etc. As another example, rules in the resources database 418 may be defined for application to patient information in the EMR and demographics sources 420 and lifestyle information sources for identifying patients that have specified characteristics, e.g., patients that have diabetes and are in the age range of 18-45.” At [045] “ Thus, for example, a patient's demographic information and electronic medical records may indicate that the patient is a 40 year old female that has been diagnosed with diabetes. Various pre-established categories and sub-categories may be defined for different types of patients in an ontology based on the various demographic and medical history characteristics, e.g., a category for diabetes patients, a sub-category of patients in the age range of 40 to 50 years old, a sub-sub-category of female patients, and so on.” , and at [0243] “The risk scoring performed by the mechanisms of the illustrative embodiments may be fed back into the risk assessment system as a feedback input to adjust future risk scoring algorithms as well as segment patients into various risk categories. The segmentation of patients into various risk categories for various medical conditions/events may be used as a trigger to perform actions with regard to groups or patients as a whole. For example, a medication notification campaign for controlling blood pressure may be initiated with regard to a group of patients whose risk of a particular medical condition/event occurring that is highly tied to high blood pressure may be initiated. A communication campaign performed by a third party communication service vendor may be initiated to call patients in the identified group of patients to request that they schedule an appointment with their physician or obtain a particular lab test.” – i.e. the clinical resource of providing a communication campaign to a patient about a medication, see also [0193-194] for a similar example regarding communication with a patient as a clinical resource);
prioritizing, based on the one or more resource forecasts, at least one of the plurality of medical conditions or the portion of the plurality of members (at least [0236] “In one illustrative embodiment, a weighted aggregation function may be defined for each medical condition/event that may assign various weights to the various risk factors based on their degree of influence over whether the final medical condition/event is likely/unlikely to occur due to the particular risk factor. Thus, for one type of medical condition/event blood pressure risk scores may be more influential but for another type of medical condition/event blood pressure risk score may be less influential and instead a presence and/or frequency of the patient reporting dizziness may be more influential.” – i.e. using the different scores for a given patient the future issues may become more or less important)
In reference to claim 2:
Cox further teaches: wherein the plurality of medical conditions comprises at least one of one or more diseases or one or more chronic disorders (at least [034, 036, 037, 0122, 0123] diabetes is a chronic disorder/disease, and is used as an example throughout the reference).
In reference to claim 3:
Cox further teaches: wherein at least a portion of the plurality of demographic groupings each correspond to at least one risk forecast model of the plurality of risk forecast models (at least [045-046, 064, 0184-0186] examples of scoring algorithms as applied to a female with type 2 diabetes as compared to other females of similar age with a similar medical history).
In reference to claim 4:
Cox further teaches: wherein the plurality of treatment data records comprises a plurality of medical insurance claims records (at least [0173] “EMR information may comprise information from medical service providers about medical services and procedures performed, medical diagnosis information from medical personnel coded in electronic medical records, lab test results, medication information from electronic medical records, pharmacy computing systems, or the like, allergy information from medical records, immunization information, social history information as may be obtained from questionnaires presented by medical personnel (e.g., information about siblings, sexual history, notes about home life, abuse, etc.), reconciliation information from medical records (e.g., records of encounters with the patient after a medical service is provided), medical billing information, insurance claims information, “).
In reference to claim 5:
Cox further teaches: wherein the operations further comprise segmenting the plurality of treatment data records according to a plurality of medical condition groupings, wherein each medical condition grouping of the plurality of medical condition groupings each correspond corresponds to a respective preexisting medical condition of a plurality of preexisting medical conditions (at least [fig 17 and related text] “This leads to a tree-like structure such as shown in FIG. 17. As shown in FIG. 17, the tree-like structure 1700 comprises a pre-existing medical condition or diagnosis 1710 that is mapped to a plurality of risk categories 1720-1724, where each risk category 1720-1724 corresponds to a potential medical condition/event that may develop as a result of improper management or treatment of the pre-existing medical condition, particular medications that the patient is taking for treatment, activities performed/not performed by the patient, or other contributions to risk. “).
In reference to claim 6:
Cox further teaches: wherein the plurality of medical conditions comprises at least a portion of the plurality of preexisting medical conditions (at least [fig 17 and related text] “This leads to a tree-like structure such as shown in FIG. 17. As shown in FIG. 17, the tree-like structure 1700 comprises a pre-existing medical condition or diagnosis 1710 that is mapped to a plurality of risk categories 1720-1724, where each risk category 1720-1724 corresponds to a potential medical condition/event that may develop as a result of improper management or treatment of the pre-existing medical condition, particular medications that the patient is taking for treatment, activities performed/not performed by the patient, or other contributions to risk.“).
In reference to claim 7:
Cox further teaches: wherein the operations further comprise analyzing the plurality of treatment data records to identify, for one or more of the plurality of members, information identifying at least one of diagnosis or treatment of at least one medical condition of the plurality of medical conditions (at least [fig.. 17 and related text] “the tree-like structure 1700 comprises a pre-existing medical condition or diagnosis 1710 that is mapped to a plurality of risk categories 1720-1724, where each risk category 1720-1724 corresponds to a potential medical condition/event that may develop as a result of improper management or treatment of the pre-existing medical condition, particular medications that the patient is taking for treatment, activities performed/not performed by the patient, or other contributions to risk.”).
In reference to claim 8:
Cox further teaches: wherein the plurality of demographic data records comprise, for each member of a portion of the plurality of members, identification of one or more preexisting conditions of the plurality of preexisting medical conditions (at least [045-046] “Thus, for example, a patient's demographic information and electronic medical records may indicate that the patient is a 40 year old female that has been diagnosed with diabetes. Various pre-established categories and sub-categories may be defined for different types of patients in an ontology based on the various demographic and medical history characteristics, e.g., a category for diabetes patients, a sub-category of patients in the age range of 40 to 50 years old, a sub-sub-category of female patients, and so on… For example, a treatment guideline may specify that for female diabetes patients that are in the age range of 40 to 60 years old, the patient should follow a low sugar diet and have at least 30 minutes of stressful exercise per day. A database of such treatments and their guidelines may be provided that correlates various combinations of patient characteristics with a corresponding treatment. Thus, by categorizing the patient in accordance with their characteristic information as obtained from demographic and medical data for the patient, these categories may be used to evaluate the applicability of the various treatments by matching the categories with the patient characteristics of the treatments to identify the best treatment for the patient, i.e. the treatment having the most matches between the patient categories and the treatment's required patient characteristics.” See also [fig 17 and related text] for preexisting conditions).
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) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cox in view of Srinivas et al (US 20160378942 A1, hereinafter Srinivas).
In reference to claim 9:
Cox as cited teaches the limitations above, but does not specifically disclose a cost ranking. Srinivas however does teach: wherein prioritizing the at least one of the plurality of medical conditions or the plurality of members comprises identifying a top segment of the plurality of medical conditions according to greatest cost (at least [fig 9a 10 and related text] “At block 902, an illness condition A (903) may be predicted for the individual at an age B based on available healthcare data. At block 904, the direct cost to the insurer may be predicted using the methods and systems discussed herein, such as the models discussed above, as performed by the microprocessor 160. At block 905, the out-of-pocket expenses for illness condition A may be predicted. At block 906, availability of funds in the HIRA account for the individual based on past BMI history may be projected. Continuing over age, at blocks 907 through 911, other illness conditions may be predicted along with associated out-of-pocket costs to the individual, direct costs to the insurer, and projected availability of HIRA funds… Referring to FIG. 10, an iterative algorithm is described for a list of illness conditions. The algorithm may be stored in the memory 150 and executed by the microprocessor 160 of the predictive analytics system 190. For each illness condition in block 1002, personalized bio-markers and dependent and independent variables may be factored by illness condition. At block 1004, a probability may be established by bio-markers associated with the illness condition. At block 1005, a weight may be established by dependent variables associated with the illness condition. At block 1006, a weight may be established by independent variables associated with the illness condition. At block 1007, the likelihood of the illness condition in the individual may be determined based on the said probability and weights. At block 1008, the onset of the likely illness may be estimated. At block 1009, the duration of treatment for the illness condition may be estimated. At block 1010, the annual cost of treatment for the illness condition, including out-of-pocket expenses and insurer direct payments, using the two step regression model with healthy and unhealthy BMI discussed above, may be estimated. At block 1011, the iteration may continue for the next illness condition. Once the end of the list is reached, the algorithm may terminate at block 1012.”). Srinivas and Cox are analogous references as both disclose a means of modeling a patient’s predisposition to a given illness based on medical history and other factors such as demographics.
One of ordinary skill in the art would have of course found the inclusion of a cost consideration to be obvious, as healthcare related costs are often the most expensive and unpredictable costs to budget for, particularly when on a fixed income. Srinivas further teaches that “The other models imply an existing disease condition requiring a decision by the healthcare service provider to offer treatment at a level that cost effectiveness is achieved towards a most improved outcome. Further, the other models involve a treatment cycle where the subject has medical condition(s) and is seeking treatment(s), which may be provided as a preventive measure to avert a medical event in the future.” (see 0192) – i.e. mitigating costs spent on healthcare would allow for a most improved outcome.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200143946, to Lewis, discloses a risk scoring system for a plurality of diseases as applied to a patient group/demographic.
US 20210210207, to Bostic, discloses modeling and simulating future health care needs of a demographic group based on predisposition or pre existing conditions.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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, Mamon Obeid can be reached at 571-270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687