DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the present action filed on 12/29/2023.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/29/2023 was filed before the mailing date of the first action on the merits. The submission 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 USC § 101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claims 1, 10, and 19 are within the four statutory categories. Claims 1, 10, and 19 are directed toward a method, a system, and a non-transitory computer-readable medium (i.e., product), respectively. Dependent Claims 2-9, 11-18, and 20 are further directed to a method, a system, and a non-transitory computer-readable medium, respectively, and therefore the dependent claims also fall into one of the four statutory categories.
Step 2A Analysis – Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A computer-implemented method, the method comprising: receiving, by one or more processors, a first data object, the first data object including:
an entity data set containing a plurality of entities; a first data set including request data associated with the plurality of entities; an event data set; and a plurality of data sets associated with one or more performance metrics;
generating, by the one or more processors, based on at least one of the entity data set, the first data set, or the event data set, an entity data object for each of the plurality of entities;
applying, by the one or more processors, a machine-learning model to the entity data objects generated for the plurality of entities, the machine-learning model trained to identify a correlation between the entity data object for each of the plurality of entities and a probability of re-utilization of one or more resources;
determining, by the one or more processors, based on the application of the machine-learning model to the entity data objects, a prediction indicator for each entity of the plurality of entities;
generating, by the one or more processors, a re-utilization offset data object for each of the plurality of entities, the re-utilization offset data object based on the prediction indicator determined for the entity;
and causing, by the one or more processors, one or more of the re-utilization offset data objects generated for the plurality of entities to be displayed on a Graphical User Interface (GUI).
The series of limitations as shown in underline above, given the broadest reasonable interpretation, recite the abstract idea of certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions, and/or mental process that a neurologist should follow when testing a patient for nervous system malfunctions – in this case, receiving data, generating an entity data object, identifying a correlation between the data object and a probability of re-utilization of resources, determining a prediction indicator, generating re-utilization offset data, and displaying the re-utilization offset data objects), e.g., see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2-9, 11-18, and 20 include other limitations directed to the abstract idea. For example, Claims 2 and 11 recite for each entity, assigning an intervention flag based on the entity data object and the prediction indicator determined for the entity, Claims 3 and 12 recite the plurality of data sets associated with the one or more performance metrics includes a determinate data set, and assigning the intervention flag is further based on the determinate data set, Claims 4 and 13 recite the intervention flag includes instruction data which is associated with one or more management pathways for the respective entity, Claims 5 and 14 recite the prediction indicator is a numeric score indicative of a likelihood the entity re-utilizes a resource during a pre-determined time period, Claims 6 and 15 recite the event data set comprises an episode treatment groupers array, service categories array, and/or a data records array, Claims 7 and 16 recite the re-utilization offset data object is based on a reduction of total resource utilization associated with a management pathway, Claims 8, 17, and 20 recite the re-utilization offset data object is further based on a likelihood that an implementation of the management pathway avoids a utilization of one or more resources, Claim 9 recites the entity data object is generated for each member during a specific phase of an admission cycle. These limitations only server to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-9, 11-18, and 20 not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence dependent Claims 2-9, 11-18, and 20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 10, and 19.
Step 2A Analysis – Prong Two:
Claims 1, 10, and 19 are not integrated into a practical application because the additional elements (i.e., the non-underlined limitations above – in this case, the processors, machine learning model, and graphical user interface of Claim 1, the processors, machine learning mode, graphical user interface, and memory of Claim 10, and the processors, machine learning mode, graphical user interface, and non-transitory computer-readable medium of Claim 19) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, Applicant’s specification explains that the one or more processors are configured to perform such processes by having access to instructions…that, when executed by one or more processors, cause one or more processors to perform the processes. The instructions are stored in a memory of the computer system. A processor is a central processing unit (CPU), a graphics processing unit (GPU), or any suitable type of processing unit (see Applicant’s specification, ¶ 00136). Training the machine-learning model may include one or more machine-learning techniques, such as linear regression, logistical regression, random forest, gradient boosted machine (GBM), deep learning, and/or a deep neural network. Supervised and/or unsupervised training may be employed [0029]. [O]ne or more of the re-utilization offset data objects generated for the plurality of entities to be displayed on a Graphical User Interface (GUI) [0162]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore Claims 1, 10, and 19 are directed to an abstract idea without practical application.
Dependent Claim 11 recites an additional element. Claim 11 recites the previously recited processor and specifies the processors assign an intervention flag based on the entity data object and the prediction indicator determined for the entity. However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These limitations amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered individually or as an ordered combination, do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the processors, machine learning mode, and graphical user interface of Claim 1, the processors, machine learning mode, graphical user interface, and memory of Claim 10, and the processors, machine learning mode, graphical user interface, and non-transitory computer-readable medium of Claim 19 amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such, Claims 1, 10, and 19 are not patent eligible.
Dependent Claim 11 recites a previously cited additional element, which us not eligible for the reasons stated above, and further narrows the abstract idea. Claim 11 recites the previously recited processor and specifies the processors assign an intervention flag based on the entity data object and the prediction indicator determined for the entity.
Dependent Claims 2-9, 12-18, and 20 do not recite any additional elements and solely narrow the abstract idea. Claim 2 recites assigning an intervention flag based on the entity data object and the prediction indicator determined for the entity. Claims 3 and 12 recite the data sets includes a determinate data set, and assigning the intervention flag is further based on the determinate data set. Claims 4 and 13 recite the intervention flag includes instruction data which is associated with one or more management pathways for the respective entity. Claims 5 and 14 recite the prediction indicator is a numeric score. Claims 6 and 15 recite what the event data set comprises. Claims 7 and 16 recite the re-utilization offset data object is based on a reduction of total resource utilization associated with a management pathway. Claims 8, 17, and 20 recite the re-utilization offset data object is further based on a likelihood that an implementation of the management pathway avoids a utilization of one or more resources. Claim 9 recites the entity data object is generated for each member during a specific phase of an admission cycle.
Hence, Claims 2-9, 11-18, and 20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C 101 as being directed to non-statutory subject matter.
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.
Claims 1-2, 4-11, and 13-20 are rejected under 35 USC 103 as being unpatentable over Farooq et al. (US 20210090695 A1) in view of Snow et al. (US 20170169173 A1).
Regarding Claim 1, Farooq discloses the following limitations:
A computer-implemented method, the method comprising: (Farooq discloses a method is provided for predicting hospital readmission [0006].)
receiving, by one or more processors, a first data object, the first data object including: (Farooq discloses the different data sources have a same or different format. The mining is configured for the formats. For example, one, more, or all of the data sources are of structured data [0027]. Any technique may be used for mining the patient record, such as structured data based searching [0093]. The Examiner interprets the structured data as a data object.)
an entity data set containing a plurality of entities; (Farooq discloses for generating the predictor from data of previous patients and for applying the predictor for a current patient, patient data is obtained from Electronic Medical Records (EMRs), such as patient information databases, Radiology Information Systems (RIS), Pharmacological Records, or other form of medical data storage or representation [0015]. The Examiner interprets the patient as an entity (see ¶ 0101 of Applicant's specification which states an entity is typically a member, such as a patient).)
a first data set including request data associated with the plurality of entities; (Farooq discloses a user may be requested to enter additional information to help improve readmissions rates in general, such as the user reconciling different prescriptions, scheduling a follow-up, resolving discrepancies in the electronic medical record, resolving a lack of adherence to a guideline, completing documentation in the electronic medical record, or arranging for a clinical action [0056].)
an event data set; (Farooq discloses a nurse or administrator enters data for the medical record of a patient indicating discharge. The entry may be doctor instructions to discharge, may be that the patient is being discharged, may be scheduling of a discharge, or may be another discharge related entry. As another example, a new data entry is provided in the electronic medical record of the patient. In another example, an assistant enters data showing admission or other key trigger event (e.g., completion of surgery, assignment of the patient to another care group, or a change in patient status) [0021]. The Examiner interprets a patient discharge and surgery information as event data.)
generating, by the one or more processors, based on at least one of the entity data set, the first data set, or the event data set, an entity data object for each of the plurality of entities; (Farooq discloses mining from structured and unstructured patient records. FIG. 3 illustrates an exemplary data mining system implemented by the processor 102 for mining a patient record to create high-quality structured clinical information [0093]. The Examiner interprets the patient record as an entity data set and structured clinical information of an entity data object.)
applying, by the one or more processors, a machine-learning model to the entity data… generated for the plurality of entities, (Farooq discloses the tasks for predicting the risk of readmission of a patient are automatically performed using this combination. Deviations and discrepancies may be identified, and mitigations to possibly prevent the readmission may be output. The risk of readmission may be specific to a given medical entity. Any medical entity, such as a hospital, group of hospitals, group of physicians, region group (e.g., hospitals in a city, county, or state), office, insurance group, or other collection of medical professionals associated with patients, may contribute data to mitigation of risk of readmission [0016-17]. The probability of readmission is predicted by applying the predictor. The predictor is a classifier or model. In one embodiment, the predictor is a machine-trained classifier. Any machine training may be used, such as training a statistical model (e.g., Bayesian network) [0034].)
the machine-learning model trained to identify a correlation between the entity data object for each of the plurality of entities and a probability of re-utilization of one or more resources; (Farooq teaches the probability of readmission is predicted by applying the predictor. The predictor is a classifier or model. In one embodiment, the predictor is a machine-trained classifier [0034]. The predictor of the readmission is applied to an electronic medical record of the patient in response to the triggering. The predictor is based on readmission data of the medical entity. A probability of readmission of the patient is predicted based on applying the predictor to the electronic medical record of the patient at discharge. An output is provided as a function of the probability [0006]. The Examiner interprets the use of medical record data in determining a probability of readmission of a patient as a correlation between the medical record data and the probability.)
determining, by the one or more processors, based on the application of the machine-learning model to the entity data objects, a prediction indicator for each entity of the plurality of entities; (Farooq teaches a feature vector used for predicting the probability is populated. By mining, the values for variables are obtained. The feature vector is a list or group of variables used to predict the likelihood of readmission [0031].)
Farooq does not teach determining a re-utilization offset data object which is met by Snow:
and a plurality of data sets associated with one or more performance metrics; (Snow teaches the network referral advisor logic may evaluate individual physicians for savings and performance metrics [0071]. The variance between expected and actual costs for each attributed physician and the variance between expected and actual costs for each primary care physician are calculated. A relative performance measure for each attributed physician may then be calculated [0088].)
generating, by the one or more processors, a re-utilization offset data object for each of the plurality of entities, the re-utilization offset data object based on the prediction indicator determined for the entity; (Snow teaches prescriptive opportunity scripts may include executable instructions generated by the data processing server that are associated with corrective actions that may be applied to outliers… Corrective actions may include for example, terminating physicians that are too expensive, prescribe less and lesser expensive procedures, diagnostics,… and advise others within a network not to refer to more expensive physicians, and any other actions that may be taken to bring the outliers “inline” or reduce the variance in care and cost [0079]. Generating prescriptive opportunity scripts may further include identifying and monitoring savings or improvement opportunities by collecting a set of statistically significant variations or deviations, identifying the participant physicians involved in or who can affect the variations by recording the identity of the attributed physician for the episode, and recording the identity of the primary care physician for the episode… Potential savings associated with eliminating each variation are then identified. The savings amount may be adjusted to reflect the terms of the risk contract and the ease of execution associated with its elimination [0080]. The Examiner interprets the potential savings amount as a function of eliminating a variation as a re-utilization offset data object, consistent with ¶ 0113 of Applicant's specification which states the re-utilization offset data object provides a quantified measure that represents potential monetary savings if a healthcare intervention is executed for the entity.)
and causing, by the one or more processors, one or more of the re-utilization offset data …generated for the plurality of entities to be displayed on a Graphical User Interface (GUI). (Snow teaches the devices may also comprise a graphical user interface (GUI) or a browser application provided on a display (e.g., monitor screen, LCD or LED display, projector, etc.) [0044]. Analytic output data generated from analytic models engine 204 may be used by performance monitor 202 to generate data for display of comparisons, distributions, risk, expected costs, and an optimal intersection between cost and quality to healthcare manager devices 106 [0053].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for obtaining a variety of data sets, generating a data object, and using a machine learning model to determine a correlation between data objects and a probability of re-utilization of one or more resources as disclosed by Farooq to incorporate the use of performance metrics and determining a re-utilization offset data object and displaying it on a GUI as taught by Snow. This modification would create a system and method which can identify opportunities for cost savings and other improvements in financial, operational and clinical performance (see Snow, ¶ 0004).
Regarding Claim 10, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Farooq further discloses:
A system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: (Farooq discloses a system is provided for predicting hospital readmission. At least one memory is operable to store data for a plurality of readmitted patients of a first medical entity. A first processor is configured to: identify variables contributing to …for the plurality of the readmitted patients…[0007].)
Regarding Claim 19, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Farooq further discloses:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: (Farooq discloses a non-transitory computer readable storage medium has stored therein data representing instructions executable by a programmed processor for predicting hospital readmission [0008].)
Regarding Claim 2, Farooq and Snow teach the limitations as seen in the rejection of Claim 1 above. Farooq further discloses:
for each entity, assigning an intervention flag based on the entity data object and the prediction indicator determined for the entity. (Farooq discloses the probability of readmission and/or variables associated with the probability of readmission for a particular patient may be used to determine a mitigation plan. The mitigation plan includes instructions, prescriptions, education materials, schedules, clinical actions, or other information that may reduce the risk of readmission. The next recommended clinical actions or reminders for the next recommended clinical actions may be output so that health care personnel are better able to follow the recommendations [0061]. The Examiner interprets the mitigation plan as an intervention flag.)
Regarding Claim 11, this claim recites limitations that are substantially similar to those recited in Claim 2 above; thus, the same rejection applies.
Regarding Claim 4, Farooq and Snow teach the limitations as seen in the rejection of Claim 2 above. Farooq further discloses:
the intervention flag includes instruction data, the instruction data being associated with one or more management pathways for the respective entity. (Farooq discloses the probability of readmission and/or variables associated with the probability of readmission for a particular patient may be used to determine a mitigation plan. The mitigation plan includes instructions, prescriptions, education materials, schedules, clinical actions, or other information that may reduce the risk of readmission. The next recommended clinical actions or reminders for the next recommended clinical actions may be output so that health care personnel are better able to follow the recommendations [0061]. The Examiner interprets the mitigation plan as an intervention flag.)
Regarding Claim 13, this claim recites limitations that are substantially similar to those recited in Claim 4 above; thus, the same rejection applies.
Regarding Claim 5, Farooq and Snow teach the limitations as seen in the rejection of Claim 1 above. Farooq further discloses:
the prediction indicator is a numeric score, the score indicative of a likelihood the respective entity re-utilizes a resource during a pre-determined time period. (Farooq discloses a feature vector used for predicting the probability is populated. By mining, the values for variables are obtained. The feature vector is a list or group of variables used to predict the likelihood of readmission [0031]. The Examiner interprets readmission of a re-utilization of a resource. The training data is manually acquired or mining is used to determine the values of variables in the training data. The training may be based on various criteria, such as readmission within a time period [0036].)
Regarding Claim 14, this claim recites limitations that are substantially similar to those recited in Claim 5 above; thus, the same rejection applies.
Regarding Claim 6, Farooq and Snow teach the limitations as seen in the rejection of Claim 1 above. Farooq further discloses:
the event data set comprises one or more of an episode treatment groupers array, a service categories array, or a data records array associated with admissions, discharges, and transfers. (Farooq discloses an indication of discharge of, admission of, or new data for a patient from a medical entity is received…a nurse …enters data for the medical record of a patient indicating discharge. The entry may be doctor instructions to discharge, may be that the patient is being discharged, may be scheduling of a discharge, or may be another discharge related entry. As another example, a new data entry is provided in the electronic medical record of the patient. In another example, an assistant enters data showing admission or other key trigger event (e.g., completion of surgery, assignment of the patient to another care group, or a change in patient status) [0021]. The Examiner interprets a patient admission, discharge, and assignment of the patient to another care group as the event data associated with admissions, discharges, and transfers, respectively.)
Regarding Claim 15, this claim recites limitations that are substantially similar to those recited in Claim 6 above; thus, the same rejection applies.
Regarding Claim 7, Farooq and Snow teach the limitations as seen in the rejection of Claim 1 above. Farooq further discloses:
the re-utilization offset data object generated for each entity is further based on a reduction of total resource utilization associated with a management pathway. (Farooq discloses given the hospital specific predictor, appropriate mitigation may be provided in response to the prediction. Alerts and associated workflow actions may be output to reduce the risk of readmission for the patient [0018]. The recipient of the alert may examine why the probability is beyond the threshold, determine changes in workflow to reduce the risk of readmission for other patients, and/or take actions to reduce the risk for the patient for which the alert was generated [0053].)
Regarding Claim 16, this claim recites limitations that are substantially similar to those recited in Claim 7 above; thus, the same rejection applies.
Regarding Claim 8, Farooq and Snow teach the limitations as seen in the rejection of Claim 7 above. Farooq further discloses:
the re-utilization offset data object is further based on a likelihood that an implementation of the management pathway avoids a utilization of one or more resources. (Farooq discloses readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted at the time of discharge and used to generate a workflow action item to reduce the probability… [0005].)
Regarding Claims 17 and 20, these claims recite limitations that are substantially similar to those recited in Claim 8 above; thus, the same rejection applies.
Regarding Claim 9, Farooq and Snow teach the limitations as seen in the rejection of Claim 1 above. Farooq further discloses:
the entity data object is generated for each member… (Farooq discloses for generating the predictor from data of previous patients and for applying the predictor for a current patient, patient data is obtained from Electronic Medical Records (EMRs), such as patient information databases, Radiology Information Systems (RIS), Pharmacological Records, or other form of medical data storage or representation [0015]. The Examiner interprets the patient as an entity (see ¶ 0101 of Applicant's specification which states an entity is typically a member, such as a patient).)
…data…is generated for each member during a specific phase of an admission cycle, the specific phase selected from a group consisting of: admission, transfer, and discharge, (Farooq discloses a nurse or administrator enters data for the medical record of a patient indicating discharge. The entry may be doctor instructions to discharge, may be that the patient is being discharged, may be scheduling of a discharge, or may be another discharge related entry. As another example, a new data entry is provided in the electronic medical record of the patient. In another example, an assistant enters data showing admission or other key trigger event (e.g., completion of surgery, assignment of the patient to another care group, or a change in patient status) [0021]. The Examiner interprets a patient discharge as event data.)
and wherein the entity data object is updated with new data received about the member at each respective phase. (Farooq discloses as more data (e.g., new labs results, new medications, new procedures, existing history etc.) is gathered, the risk may be updated continuously for the care provider to monitor [0020]. The prediction may be made at the time of discharge, such as the day of discharge. The prediction may be updated, such as made before discharge and updated after discharge based on any data entered after the original prediction [0045].)
Although Farooq does not disclose the data object being updated as a result of the new phase, it does disclose gathering data from each of the three claimed phases and updating data when new data is collected. This configuration of updating datasets as new data is input still provides the same advantage of ensuring a prediction’s accuracy stays updated with evolving healthcare data trends (see Applicant’s specification, ¶ 0103). Since each individual element and its function are shown in the prior art, albeit in different embodiments, this practice of updating datasets for each stage of admission is well known in the art and would be obvious to try. Therefore, it would have been obvious to try, by one of ordinary skill in the art at the time the invention was made, to update the dataset during each respective phase since there are a finite number of identified, predictable potential solutions (i.e., times to update datasets) to the recognized need (identifying re-utilized resources) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
Regarding Claim 18, this claim recites limitations that are substantially similar to those recited in Claim 9 above; thus, the same rejection applies.
Claims 3 and 12 are rejected under 35 USC 103 as being unpatentable over Farooq et al. (US 20210090695 A1) in view of Snow et al. (US 20170169173 A1), further in view of Adams et al. (US 20110137672 A1).
Regarding Claim 3, Farooq and Snow teach the limitations as seen in the rejection of Claim 2 above. Farooq further discloses:
…and wherein assigning the intervention flag is further based on the… data set. (Farooq discloses the probability of readmission and/or variables associated with the probability of readmission for a particular patient may be used to determine a mitigation plan. The mitigation plan includes instructions, prescriptions, education materials, schedules, clinical actions, or other information that may reduce the risk of readmission. The next recommended clinical actions or reminders for the next recommended clinical actions may be output so that health care personnel are better able to follow the recommendations [0061]. The Examiner interprets the mitigation plan as an intervention flag.)
Farooq does not disclose the use of performance metrics which is met by Snow:
the plurality of data sets associated with the one or more performance metrics includes… (Snow teaches the network referral advisor logic may evaluate individual physicians for savings and performance metrics [0071]. The variance between expected and actual costs for each attributed physician and the variance between expected and actual costs for each primary care physician are calculated. A relative performance measure for each attributed physician may then be calculated [0088].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for obtaining a variety of data sets, generating a data object, and using a machine learning model to determine a correlation between data objects and a probability of re-utilization of one or more resources as disclosed by Farooq to incorporate the use of performance metrics as taught by Snow. This modification would create a system and method which can identify opportunities for cost savings and other improvements in financial, operational and clinical performance (see Snow, ¶ 0004).
Farooq and Snow do not teach the data set being disparate data which is met by Adams:
…a determinate data set,… (Adams teaches the historical claim transaction data 104 or the current claim transaction data 106 might include, according to some embodiments, determinate …data [0024]. The determinate data may come from one or more determinate data sources 108 that are included in the computer system 100 and are coupled to the data storage module 102 [0026].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for obtaining a variety of data sets, generating a data object, and using a machine learning model to determine a correlation between data objects and a probability of re-utilization of one or more resources as disclosed by Farooq to incorporate the data set being a determinate data set as taught by Adams. This modification would create a system and method which is capable of providing improved healthcare intervention (see Adams, ¶ 0009).
Regarding Claim 12, this claim recites limitations that are substantially similar to those recited in Claim 3 above; thus, the same rejection applies.
Relevant Art Not Currently Being Applied
The following references are not currently being applied but are considered pertinent to Applicant’s disclosure:
Amarasingham et al. (US 20150213224 A1) teaches holistic hospital patient care and management system which receives patient data; at least one predictive model configured to identify medical conditions of the patients; a risk logic module configured to determine at least one risk score associated with each patient.
Pankoke et al. (US 20210035679 A1) teaches a system which identifies factors related to negative outcomes for reducing the likelihood of negative health outcomes such as readmission rates and emergency department utilization.
Spurlock et al. (US 20190108912 A1) teaches a system which utilizes machine learning to detect disease and provide early intervention by building correlations between associations and future disease states.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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